When Good Conservation Becomes Good Economics: Kenya’s Vanishing Herds

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WHEN GOOD
CONSERVATION
BECOMES GOOD
ECONOMICS
KENYA’S VANISHING HERDS
Richard Damania, Sébastien Desbureaux, Pasquale Lucio Scandizzo,
Mehdi Mikou, Deepali Gohil, Mohammed Said
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Public Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure Authorized

When Good Conservation
Becomes Good Economics
Kenya’s Vanishing Herds
Produced with support from
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iii
Table of Contents
List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
At a crossroads: Safari tourism under threat in Kenya. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
Following the tracks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
The trade-off between road construction and wildlife protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Building smart infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
The promise of conservancies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
The race between conservancies and construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Good conservation is good economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv
1. Vanishing Herds: Wildlife Dynamics and Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
A declining tourism asset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Declining natural assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3
2. Weighing the Impacts: Generating Scenarios and Simulating Trade-offs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Developing a regional ESAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
The CGE model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Rural poverty and tourism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Concluding comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3. Wildlife-Friendly Roads: Fable or Fact? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
New approaches to enhancing road access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Conventional approaches of increasing road access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Counting the costs of business as usual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
A greener scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Fine-tuning the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33
4. The Way Forward and Next Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Smart infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Realizing economic opportunities through conservancies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37
Appendix A. Conservancies—An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
The environmental promise of conservancies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
The economic significance of conservancies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43
Going further. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Appendix B. Road Extension and Wildlife Loss between 1980 and 2010: A Difference-in-Differences Approach. . . 48
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
The model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
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iv WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
List of Figures
Figure ES.1: Kenya has witnessed a dramatic collapse in wildlife since the 1980s. . . . . . . . . . . . . . . . . . . . vii
Figure ES.2: Wildlife is now found in fragmented habitats and has vanished across vast areas in the North . . . . viii
Figure ES.3: Roads lead to changes in land use, impacting wildlife most severely within a distance
of 20kilometers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Figure ES.4: Production frontier for GDP and loss of wildlife . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Figure ES.5: Factoring in wildlife constraints significantly reduces the impact of new roads on wildlife . . . . . . . xii
Figure ES.6: A map of Kenya’s conservancies and parks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Figure 1.1: Kenya has lost 68 percent of its wildlife in recent decades . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Figure 1.2: Wildlife trends in the 19 rangeland counties between 1977 and 2016 (percent). . . . . . . . . . . . . . . .2
Figure 1.3: Kenya’s wildlife populations have shrunk dramatically, becoming fragmented,
and almost vanishing in some counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Figure 1.4: Human populations have been expanding in areas with wildlife and around parks . . . . . . . . . . . . . 4
Figure 1.5: Kenya’s road network has increased by 50 percent in the last 40 years . . . . . . . . . . . . . . . . . . . 5
Figure 1.6: Roads lead to changes in land use, impacting wildlife most severely within a distance
of 20 kilometers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Figure B.1.1: Declining wildlife . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9
Figure B.1.2: Declining wildlife. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9
Figure 2.1: Mapping the CGE regions for Kenya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Figure 2.2: Forward multipliers for productive sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Figure 2.3: Backward multipliers for productive sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Figure 2.4: CGE model simulation for base-year production activities . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Figure 2.5: Multipliers as a function of investment size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Figure 2.6: Value added variation in regard to wildlife reduction and tourism elasticity . . . . . . . . . . . . . . . . 21
Figure 2.7: Relationships between wildlife reduction and value added, and value added growth . . . . . . . . . . . 21
Figure B2.1: Impact on environmental deterioration against GDP growth . . . . . . . . . . . . . . . . . . . . . . . . 22
Figure 2.8: Income elasticities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Figure 2.9: Income elasticities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Figure 3.1: Existing all-weather roads and tracks in Kenya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Figure 3.2: The costs of increasing Kenya’s RAI under the “business as usual” scenario . . . . . . . . . . . . . . . 30
Figure 3.3: Building new roads to increase Kenya’s RAI, starting with the densely populated western counties . . 30
Figure 3.4: Wildlife loss is constant for the first 1.5million people connected to the road network,
with losses sharply increasing thereafter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Figure 3.5: The costs of increasing Kenya’s RAI under the two scenarios . . . . . . . . . . . . . . . . . . . . . . . . 32
Figure 3.6: Factoring in wildlife constraints significantly reduces the impact of new roads on wildlife . . . . . . . . 33
Figure 3.7: Mapping elephant and wildebeest routes in Kenya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Figure 4.1: Map of parks and conservancies in Kenya (2018). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39
Figure 4.2: Wildlife generally increased in the older conservancies and decreased in areas where
conservancies were established after 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Figure A.1: The rapid growth of conservancies in Kenya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Figure A.2: Tourism income earned by conservancies (Ksh, 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Figure A.3: Proportion of conservancy income sources in 2017 (Ksh, millions) . . . . . . . . . . . . . . . . . . . . . 45
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TABLE OF CONTENTS v
Figure B.1: Kenya’s wildlife populations have shrunk dramatically since the 1980s, becoming fragmented,
and almost vanishing in some counties, such as in West Pokot, Baringo, Turkana, Machakos, Kwale,
andMandera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Figure B.2: Distance to roads and wildlife loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
List of Tables
Table ES.1: Regional impacts of investing inconservancies and construction . . . . . . . . . . . . . . . . . . . . . . xiv
Table 1.1: Quantifying the impact on wildlife of construction of a road (wildlife biomass) . . . . . . . . . . . . . . . . . 8
Table 2.1: CGE investment impact multipliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Table 2.2: Impacts of investment in conservancies (50% of current value = $142 million) . . . . . . . . . . . . . . . 17
Table 2.3: Impacts of investment in construction ($142 million) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Table 2.4: Impact on regional value added of an increase in infrastructure (+ 10%) and reduction in wildlife
in the South (percent from baseline) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Table 2.5: Impact on regional income distribution of an increase in infrastructure and reduction in wildlife
in the South (percent from baseline) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Table 2.6: Impact on regional value added of an increase in investment in road construction (+10%)
and greater reduction in wildlife in the South (percent from baseline) . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Table 2.7: Impact on income distribution of an increase in infrastructure and a greater reduction
in wildlife in the South (percent from baseline) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Table 2.8: Doubling the investment in conservancies: impact on value added . . . . . . . . . . . . . . . . . . . . . 23
Table 2.9: Doubling the investment in conservancies: impact on income distribution . . . . . . . . . . . . . . . . . 24
Table 2.10: Doubling investment in conservancies and wildlife conservation: impact on value added . . . . . . . . 24
Table 2.11: Doubling investment in conservancies and wildlife conservation: impact on income distribution. . . . .24
Table 2.12: Doubling investment and capital productivity in conservancies and wildlife conservation:
impact on value added . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Table 2.13: Doubling investment and capital productivity in conservancies and wildlife conservation:
impact on income distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25
Table 4.1: GDP multipliers for investments (in million USD) in conservancies. . . . . . . . . . . . . . . . . . . . . . .36
Table A.1: Typology of Kenyan conservancies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Table A.2: An overview of tourism facilities in Kenya’s conservancies . . . . . . . . . . . . . . . . . . . . . . . . . .44
Table B.1: Main model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Table B.2: Parsimonous model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
List of Boxes
Box 1.1: Building a Statistical Model to Quantify the Direct Impact of Roads on Wildlife . . . . . . . . . . . . . . . . . 8
Box 1.2: Population Density and Wildlife Loss. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9
Box 2.1: Key Assumptions of the CGE Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Box 2.2: Trade-offs between Economic Growth and Environmental Impact . . . . . . . . . . . . . . . . . . . . . . .22
Box 4.1: Smart Infrastructure and Spatial Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Box A.1: Some Key Figures on the Economics of Conservancies in Kenya . . . . . . . . . . . . . . . . . . . . . . . 43
Box A.2: Types of Benefit-Sharing Arrangements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
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vi vi
List of Acronyms
CGE Computable General Equilibrium
DRSRS Department of Resource Surveys and Remote Sensing
ESAM Environmentally-extended SAM
Ksh Kenya shilling
KWCA Kenya Wildlife Conservancies Association
KWS Kenya Wildlife Service
RAI Rural Access Index
SAM Social Accounting Matrix
SDGs Sustainable Development Goals
TLU Tropical Livestock Unit
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vii
Executive Summary
At a crossroads: Safari tourism
under threat in Kenya
It is no exaggeration to state that Kenya’s wildlife has
done much to shape the image and development for-
tunes of the country. At independence, the country was
reliant on agricultural exports for its foreign exchange
revenue and was exposed to the vagaries of commodity
price cycles. The vast and varied endowment of wildlife
catalyzed a new industry—nature-based tourism—that
provided an opportunity to diversify and boost export
revenues while playing to the country’s natural compara-
tive advantage.
Today tourism is among Kenya’s top sources of foreign
exchange, dominates the service sector, and contrib-
utes significantly to employment, especially in rural areas
where economic opportunities are limited. The typical
international tourist arrives on a package tour that may
include a safari, a visit to the beach, or both. It is safari
tourism, however, that generates the most employment
and economic activity across the country. A recent study
by Sanghi et al. (2017) found that despite a diversifying
economy, wildlife-based safari tourism is deeply inte-
grated into Kenya’s economic fabric in complex ways that
stimulate much employment in rural areas. Official statis-
tics of the sector’s contribution to the economy tend to
neglect the full panoply of backward and forward linkages
and their dynamic effects on poverty and rural growth.
But the wildlife that has lured travelers to Kenya by the
planeload is in dramatic decline (Figure ES.1). In the past
three decades, the country has lost more than half of its
wildlife (ungulate) biomass according to data from the
Directorate of Resources, Surveys and Remote Sensing
(DRSRS).
FIGURE ES.1: Kenya has witnessed a dramatic collapse in wildlife since the 1980s
1980
800
1,000
1,200
1,400
Total wildlife biomass (in 1,000 kg)
1,600
1,800
1985 1990
Decade
Wildlife Loss in Kenya, 1980–2000
1995
2000
Source: Authors based on DRSRS data.
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viii WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
Wild herds that once roamed freely across the borders
of Kenya and Tanzania have shrunk dramatically in num-
bers and vanished completely from much of the North
(Figure ES.2). Once connected habitats have been sev-
ered, with herds trapped into shrinking areas, jeopar-
dizing the long-term sustainability of many isolated and
unconnected populations.
Perhaps most troubling is that recent monitoring of
wildlife populations suggests that long-term declines of
many of the charismatic species that attract tourists—
lions, elephants, giraffes, impalas, and others—are
occurring at the same rates within the country’s national
parks as outside of these protected areas (Ogutu et al.
2016). Parks in Kenya were established in areas in which
large aggregations of animals were observed typically
during the dry seasons, but in their haste to establish
these protected areas, policy makers neglected the
migratory needs of wildlife, especially of the ungulate
herds. Dispersal is a fundamental biological process
that influences the distribution of biodiversity in every
ecosystem and determines whether a species will sur-
vive.1 The process of dispersing from a natal territory is
essential to avoid inbreeding and it strongly influences
individual fitness.
As a result, wildlife depends as much on adjacent land
for continued viability as it does on the protected areas.
Pressures around the parks are affecting wildlife within
the parks. The way in which land outside of protected
areas is utilized and managed will become a crucial
determinant of the industry’s future. Expanding tour-
ism to these areas remains among the most successful
approaches that have been piloted. However, the feasi-
bility of this approach depends upon economic incen-
tives and the opportunity costs of land.
1 Dispersal is a fundamental behavioral and ecological process. The distance
that individual animals disperse, and the number of dispersers, can be primary
determinants of where and whether species persist. Dispersal fundamentally
influences spatial population dynamics, including meta-population and meta-
community processes.
FIGURE ES.2: Wildlife is now found in fragmented habitats and has vanished across vast areas in the North
Source: Authors. Data from DRSRS and Ogutu et al. (2016).
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EXECUTIVE SUMMARY ix
This report uses a variety of approaches to investigate
the economic consequences of this decline. State-of-
the-art spatial econometric methods are used to identify
the causal drivers of the loss and quantify the impacts on
wildlife. A Computable General Equilibrium (CGE) model
is used to estimate the economic consequences of wild-
life loss and compare these consequences to alternative
development pathways. Finally, spatial algorithms are
developed to show how losses can be avoided and how
to create win-win solutions that maximize economic gains.
Following the tracks
Reasons for the decline in Kenya’s wildlife have been
widely documented, and they entail an interconnected
suite of pressures typically linked to habitat conversion—
factors such as population growth, the expansion of
arable agriculture, fencing, poaching, and intrusive infra-
structure. This report identifies with greater precision the
drivers of land conversion from natural habitats to other
uses, and examines the extent to which land conversion
leads to the extirpation of wildlife and the loss of tourism
incomes.
The analysis presented in Chapter 1 finds that roads are
typically accompanied by a change in land use pattern
from natural habitats to farms and settlements. On aver-
age, the extent of conversion is especially stark up within
a corridor of about 20 kilometers from the road. Thereaf-
ter, the conversion of natural habitats into cropland slowly
decreases and is almost negligible for settlements. An
obvious consequence of this change in land use is the
almost complete collapse of wildlife in areas around the
roads (Figure ES.3). The statistical model developed for
this report indicates that roads built over the last four
decades have caused an 80 percent decrease in wild-
life within a 20-kilometer radius. There are also predict-
able effects on migratory corridors, which have almost
all been diminished and degraded to varying degrees
(Ojwang et al. 2017).
FIGURE ES.3: Roads lead to changes in land use, impacting wildlife most severely within a distance of
20kilometers
.2
.4
.6
.8
1
1.2
Average area converted in cropland per cell (ha)
0 20 40 60 80 10
0
Distance to nearest road (km)
Natural habitat converted in croplands and distance to roads
Source: Authors based on ESA land use data and Michelin roads data.
Note: TLU stands for Tropical Livestock Units.
61641_Kenya_Wildlife_Tourism_new.indd 9 11/4/19 3:07 PM

x WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
FIGURE ES.3: Continued
0
.1
.2
.3
Average area converted in settlement per cell (ha)
0 20 40 60 80 10
0
Distance to nearest road (km)
Natural habitat converted in settlement and distance to roads
150
200
250
350
300
Total loss of wildlife biomass (in 1,000 TLU)
0 20 40 60 80 10
0
Distance to nearest road in km
Total loss of wildlife between 1980–2000s and distance from roads in 2000s
Source: Authors based on ESA land use data and Michelin roads data.
Note: TLU stands for Tropical Livestock Units.
61641_Kenya_Wildlife_Tourism_new.indd 10 11/4/19 3:07 PM

EXECUTIVE SUMMARY xi
The report then provides an assessment of the eco-
nomic consequences of this loss. Clearly, if the eco-
nomic benefits brought about by habitat conversion
outweigh the losses, it is arguable that the extirpation
of wildlife is a necessary, if regrettable, price to pay for
development. But if the loss of actual and potential tour-
ism income exceeds the benefits from land conversion,
greater care and caution would be warranted in both the
placement of intrusive developments and the extent of
land conversion.
The trade-off between road
construction and wildlife protection
To explore this issue in a rigorous manner, this report
employs a computable general equilibrium (CGE) model
that divides the economy into two regions—North and
South. The model tracks the contribution and linkages
between various economic activities and provides an
indication of the economic consequences of alternative
development strategies (Chapter 2).
The projections indicate that the two regions have differ-
ent economic structures. In general, land-based activi-
ties, manufacturing, trade, and transport are the sectors
that create the largest gains (production multiplier
effects) in the economy. Production multipliers, though
low in both regions of the country, tend to be compara-
tively larger in the more developed areas of the South.
Has the loss of wildlife generated economic gains
commensurate to the economic loss? A road through
rural areas brings multiple benefits through the expan-
sion of agriculture, access to markets, and myriad eco-
nomic opportunities that such market integration brings.
Accordingly, the CGE model finds that if the consequent
loss of wildlife is relatively modest and below around
30 percent (or alternatively, if the elasticity of tourism
with respect to wildlife is small), there is limited loss of
tourism and there is a net gain to the conversion of land.
If, on the other hand, the loss of wildlife is much larger,
there is a decline in overall regional GDP. With the
80percent loss of wildlife experienced in Kenya within
20 kilometers of a road, parts of the country would no
doubt fall into the latter category. In general, impacts of
wildlife tourism loss are much more severe in the North
where there are limited development opportunities.
This relationship is summarized in Figure ES.4, which
shows the production possibility frontier of the Kenyan
economy. If a road brings losses of wildlife that are below
a threshold (around 30 percent), it confers a net eco-
nomic benefit and an increase in GDP. But losses that are
much larger induce a net loss in GDP. Put simply, good
conservation has become good economics for Kenya.
FIGURE ES.4: Production frontier for GDP and loss
of wildlife
Value added ($, millions)
55,000
55,500
56,000
56,500
57,000
57,500
58,000
58,500
59,000
59,500
60,000
0% 20%40%
Reduction of wildlife
60%80% 100%
Source: Authors.
One implication of this finding is that the induced con-
version of habitats has come at a high cost to much of
the country. A second implication is that since the poten-
tial and often hidden benefits of habitats are significant,
development opportunities exist to harness the dual
benefits of both conservation and development. Finally,
these results also suggest that if the consequences
of construction were managed and controlled better
so that habitat conversion was prevented and wild-
life losses avoided, it might be possible to simultane-
ously obtain the benefits of infrastructure development
as well as those brought by tourism. This would likely
entail significant and different policy interventions. The
available data suggest that the declaration of protected
area status or conservancy status may slow, though not
prevent, the rate of land conversion for agriculture and
settlements. The report explores two sets of solutions to
maximize the benefits of infrastructure and of tourism:
a road network that pays attention to the externalities
that it generates, and a policy that expands the role of
conservancies.
61641_Kenya_Wildlife_Tourism_new.indd 11 11/4/19 3:07 PM

xii WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
Building smart infrastructure
The key to avoiding the economic costs identified in
this report is to find ways to maximize the benefits from
infrastructure and minimize the economic losses. About
30 percent of Kenya’s rural population is currently con-
nected to the national all-season road network. Increas-
ing the country’s Rural Access Index (RAI) will be key
to achieving the goals set in Kenya’s Vision 2030 and
under the Global Sustainable Development Goals.
Using state-of-the art algorithms, this report finds that
the judicious location of roads can connect much of
the country to centers of economic activity while avoid-
ing potential losses of wildlife. This is because much
of Kenya’s densely populated western counties require
rural roads, but they are also areas with low levels of
wildlife and tourism potential. Figure ES.5 illustrates
one such example and shows that with sophisticated
planning approaches, equivalent “connections” can be
made with much more limited losses to wildlife (com-
pare the green and orange lines) and at roughly the
same cost.
In sum, deploying smarter, greener approaches to
infrastructure also makes economic sense. Achieving
this equilibrium will call for more sophisticated plan-
ning approaches that recognize both the benefits as
well as the adverse impacts for both the economy and
wildlife.
The promise of conservancies
Conservancies can play an important role in diversifying
the tourism product and securing critical habitats while
generating economic activity. There are currently more
than 166 conservancies spread across Kenya’s 28coun-
ties (Figure ES.6). They cover an area larger than the coun-
try’s national parks, are home to more than 22 percent of
Kenya’s ungulate wildlife biomass, and have some of the
highest densities of wildlife in the country. In fact, 18 out
of the 20 zones with the highest density of wildlife are
in conservancies and not parks. Conservancies create
buffers around parks and maintain connectivity between
several ecosystems. In essence, conservancies are key
to the resilience of wildlife.
FIGURE ES.5: Factoring in wildlife constraints significantly reduces the impact of new roads on wildlife
Population with new access to all-weather roads
3,000,0002,500,0002,000,0001,500,0001,000,000500,000
25,000
0
50,000
75,000
100,000
125,000
150,000
175,000
200,000
Impacted wildlife (kg)
0
Current situation More wildlife friendly roads
Source: Authors.
61641_Kenya_Wildlife_Tourism_new.indd 12 11/4/19 3:07 PM

EXECUTIVE SUMMARY xiii
Tourism remains an important revenue stream for con-
servancies, accounting for an average of 83 percent
of commercial revenue. In many of the conservancies,
tourism facilities were established to create an exclu-
sive game viewing experience as an alternative to the
mass tourism strategies in neighboring national parks
and reserves. The game lodges in the conservancies
account for about 16 percent of the total bed-nights
spent in Kenyan game lodges, suggesting considerable
scope for expansion. In remote areas, conservancies
remain among the few ways in which communities can
boost and diversify income sources.
The race between conservancies
and construction
It is instructive to determine the economic benefits of alter-
native investment strategies in contexts when there are
limited resources available for expansion. Using the CGE
model, Table ES.1 provides an indication of the benefits
FIGURE ES.6: A map of Kenya’s conservancies and parks
Source: Authors.
61641_Kenya_Wildlife_Tourism_new.indd 13 11/4/19 3:07 PM

xiv WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
that accrue from investing in a road in each region of the
country, which is compared to investing in conservancies.
The table shows the payoffs from a road-building invest-
ment in the South. Each dollar invested in the South gen-
erates on average a GDP increase of $1.51 in the South
and $0.8 in the North. An equivalent investment in the
North has a similar multiplier effect, so that every dollar
invested in the North has a payback of $1.53, but this
time with a much smaller spillover to the South (.01).2
The North has historically lagged in economic terms.
The investment in tourism offers high payoffs with the
promise of igniting economic activity in ways that also
contribute to environmental sustainability in an arid area
with geographic constraints. Realizing this promise will
require enabling policies that provide access to conser-
vancies and share the benefits with the population.
A similar investment in conservancies generates signifi-
cantly higher multipliers—almost twice as high. This is a
consequence of the important role that wildlife plays in
the tourism value chain, with multiple direct and indirect
connections to employment-generating activities in sec-
tors that themselves have high multipliers, such as trans-
portation and lodging.
Good conservation is good
economics
A 70 percent decline in wildlife, within thirty years, is a
sobering statistic. As Kenya’s population grows, its infra-
structure needs expand, and climate change makes rain-
fall more erratic; the pressures on wildlife and natural
habitats will intensify in regions that are already under
environmental stress and will spread to other parts of the
country. The journey along the current policy path has
failed to halt the degradation of natural habitats, and it is
2 The magnitude of these multipliers is similar to global estimates.
unlikely to do so in the future when pressures expand and
competition for land, water, and other natural resources
intensifies. This suggests an urgent need for a careful
reassessment of pressures, policies, and future prospects.
Wildlife in Kenya, especially in the North of the country,
represents a lucrative economic asset whose contribution
has been underestimated and potential unrealized. Con-
verting habitats and dissecting wildlife migration corridors
diminishes populations, tourism appeal, and the earning
potential of natural assets in ways that are often irreparable
and irreversible. Given the significant and long-term impli-
cations of such decisions, a rigorous economic assess-
ment is necessary to guide choices. The CGE assessment
indicates that every dollar invested in conservation and
wildlife tourism could generate benefits that range from $3
to $20, with returns that increase with the level of invest-
ment (Chapter 4). Such increasing returns reflect the eco-
logical importance of connected natural habitats that are
more productive in terms of the ecosystem services that
they provide and are also more resilient to droughts and
other weather extremes. With the right infrastructure and
the enabling environment to further develop the conser-
vancy sector, there are significant opportunities to enhance
growth through the conservation of wildlife assets.
The evidence presented in this report suggests that
there are wide opportunities to stop the dramatic col-
lapse of wildlife populations and that investing in the
tourism sector yields significant benefits which are espe-
cially pro-poor. The most pressing need is for planners to
incorporate the tools developed in this report and else-
where in order to consider the long-term implications of
irreversible decisions and harness the full potential that
the country’s natural endowment offers.
REFERENCES
Ogutu, Joseph O., et al. (2016). “Extreme wildlife declines and
concurrent increase in livestock numbers in Kenya: What
are the causes?” PloS one 11.9.
Ojwang, Gordon O., et al. (2017). Wildlife Migratory Corridors
and Dispersal Areas: Kenya Rangelands and Coastal Ter-
restrial Ecosystems.
Sanghi, Apurva, Richard Damania, Farah Manji, and Maria
Paulina Mogollon. (2017). Standing out from the herd: An
economic assessment of tourism in Kenya (English). Wash-
ington, D.C., World Bank Group.
TABLE ES.1: Regional impacts of investing
inconservancies and construction
Invest in South Invest in North
Construction multiplier 1.51 0.88 0.1 1.53
Conservancy multiplier 3.02 0.22 1.75 4.41
Source: Authors.
61641_Kenya_Wildlife_Tourism_new.indd 14 11/4/19 3:07 PM

1
CHAPTER 1
VANISHING HERDS: WILDLIFE DYNAMICS AND DRIVERS
Wildlife, the principal asset of Kenya’s tourism indus-
try, is in rapid decline. To some this may be a neces-
sary, if regrettable, price to pay for development and an
expanding economy. For others, declining wildlife num-
bers are associated with costs, including lower tourism
revenues, which could have been avoided with a less
intrusive development trajectory. The aim of this report
is to explore these issues using a suite of rigorous eco-
nomic modeling approaches. The report combines sta-
tistical approaches to determine what has happened,
with macroeconomic modeling to answer counterfactual
questions regarding what might have happened with
alternative policies.
The overall analysis suggests that the economic impacts
of natural capital erosion have been significant, and they
have received less policy attention than seems war-
ranted since these issues are viewed as environmental
problems that drain public funds, rather than an eco-
nomic loss. The focus of this chapter is on tracking the
changes and dynamics of Kenya’s key tourism asset—its
wildlife. Subsequent chapters explore the economic con-
sequences of this loss and then turn to policy options. At
the outset several caveats must be noted. First, due to
insufficient data this report is narrowly focused on herds
of (charismatic) mammals and thus ignores other spe-
cies, as well as ecosystem productivity. In addition, the
investigation is restricted to the measurable and pecuni-
ary benefits generated by conservation through tourism.
Consideration of the wider benefits (such as watersheds)
conferred by ecosystems would suggest that the value
of Kenya's wildlands are much higher than is suggested
in this report.
A declining tourism asset
Globally, there is mounting evidence of catastrophic
declines in the number and range of wildlife populations
(Ceballos et al. 2017). Rapid human population growth, land
use changes, land fragmentation, infrastructure develop-
ment (Sala et al. 2000; EC 2001), poaching (WWF 2017),
climate change (Wiens 2016), and other factors are among
the long litany of reasons given for this rapid decline
(Dybas 2009; Daskin and Pringle 2018; WWF 2017).
In Kenya, wildlife has declined precipitously across the
country, and for certain species, this decline has been cat-
astrophic. Within three decades, Kenya has lost 68per-
cent of its wildlife (Figure 1.1). The declines were particularly
extreme with a wide cross-section of species that includes
ungulates and predators.3 As a consequence, in 2018,
3 To be precise the declines were: warthog (–87.7 percent), waterbuck
(–87.8percent), Grevy’s zebra (86.3 percent), impala (–84.1 percent), Coke
hartebeest (84 percent), topi (–82.1 percent), oryx (–78.4 percent), eland
(–77.7percent), Thomson’s gazelle (–75 percent), and lesser kudu (–72.4percent).
The declines were also severe for Grant’s gazelle (–69.6percent), gerenuk
(68.6percent), giraffe (–66.8 percent), and wildebeest (–64.2percent).
In comparison ostrich (–43.4 percent), elephant (–42.3 percent) buffalo
(–36.9percent), and Burchell’s zebra (29.5 percent) experienced moderate
declines. Similar downward trends were exhibited by the big cats and other
carnivores as their populations have also declined rapidly (Virani etal. 2011).
FIGURE 1.1: Kenya has lost 68 percent of its wildlife in
recent decades
1977 1986 1995 2004
2013
Year
500,000
1,000,000
1,500,000
2,000,000
2,500,000
Population estimates
Source: DRSRS, Ogutu et al. (2016).
61641_Kenya_Wildlife_Tourism_new.indd 1 11/4/19 3:07 PM

2 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
Kenya was ranked 5th in Africa in terms of the number of
threatened species within its country (IUCN 2018).
The losses have occurred across the entire country, with
some variation over the 19 counties. The highest decline
was observed in West Pokot, which has experienced
a total collapse, with 99 percent of its wildlife lost. The
smallest decline of wildlife was observed in Laikipia,
which experienced a 7 percent decrease in wildlife bio-
mass (Figure 1.2). The three other major tourist- dependent
counties of Narok, Kajiado, and Taita Taveta showed
varying trends: Narok, despite its high dependence on
wildlife-based tourism, has lost about 70 percent of its
wildlife; in Kajiado, the decline stands at 60 percent; and
Taita Taveta registered a moderate decrease of about
40percent. This suggests that the presence of buoyant
wildlife-based tourism in a county may not be sufficient to
counter the forces behind the decline in wildlife. This also
implies that there is a need for a deeper understanding of
the drivers of wildlife loss to counter the problem.
Data from the Department of Resource Surveys and
Remote Sensing (DRSRS) provide a more precise indica-
tion of trends and drivers of change. DRSRS has con-
ducted aerial surveys of wildlife in the rangelands of
Kenya since 1977, offering a uniquely rich database of
wildlife population trends at a fine spatial scale. Within
each grid, wildlife populations for 18 common species
are measured in terms of their biomass (calculated using
the Tropical Livestock Unit (TLU) where 250 kilograms is
equivalent to 1 TLU).4
The changes in the status of wildlife has been striking.
In the 1980s, around 53 percent of the (5 × 5 kilome-
ter) grids in the 19 counties were occupied by wildlife.
By the 2000s, this had fallen to 31 percent of grid cells.
Figure 1.3 provides a summary of these data and shows
extirpation over large areas of the country. The distribu-
tion map of the 2000s indicates that the wild herds that
once roamed freely across the country have shrunk dra-
matically in numbers and distribution, and have vanished
in counties such as West Pokot, Turkana, Baringo, Kilifi,
Lamu, Machakos, and Tana River. Once connected habi-
tats have been severed and isolated, with herds trapped
into shrinking areas, which affects their long-term sus-
tainability (Said et al. 2016).
Perhaps more troubling is that recent monitoring efforts of
key species suggest that the long-term decline of many
of the charismatic species that attract tourists— including
lions, elephants, giraffes, and impalas—are occurring at
4 Eighteen species were used in the analysis: buffalo (Syncerus caffer),
Burchell’s zebra (Equus burchelli), Coke hartebeest (Alcelaphus buselaphus),
eland (Taurotragus oryx), elephant (Loxodonta africana), gerenuk (Litocranius
walleri), giraffe (Giraffa cemelopardalis), Grant’s gazelle (Gazella granti), Grevy’s
zebra (Equus grevyi), impala (Aepyceros melampus), lesser kudu (Tragelaphus
imbermbis), oryx (Oryx gazelle beisa), ostrich (Struthio camelus), Thomson’s
gazelle (Gazella thomsoni), topi (Damaliscus lunatus korrigum), warthog
(Pharcoerus africanus), waterbuck (Kobus ellipsiprymnus), and wildebeest
(Connochaetes taurinus).
FIGURE 1.2: Wildlife trends in the 19 rangeland counties between 1977 and 2016 (percent)
–85
–56
–76
–60
–92
–50
–71
–7
–86 –87 –84
–60
–70
–79
–41
–87 –90
–66
–99
–100
–80
–60
–40
–20
0
Baringo
Garissa
Isiolo
Kajiado
Kilifi
Kitui
Kwale
Laikipia
Lamu
Machakos
Mandera
Marsabit
Narok
Samburu
Taita Taveta
Tana River
Turkana
Wajir
West Pokot
Source: Authors based on Ogutu et al. (2016).
61641_Kenya_Wildlife_Tourism_new.indd 2 11/4/19 3:07 PM

VANISHING HERDS: WILDLIFE DYNAMICS AND DRIVERS 3
comparable rates within and outside protected areas
(Scholte 2011). This is consistent with a growing body of
evidence in the conservation literature, which finds that
the creation of protected areas does not necessarily mean
that habitats and species are effectively protected (Andam
et al. 2008), and that stricter rules on land use do not nec-
essarily translate into less degradation (Ferraro et al. 2013).
Parks in Kenya were established in areas where large
aggregations of animals were observed, typically dur-
ing the dry seasons. However, in the process of estab-
lishing these protected areas, policy makers neglected
the migratory needs of wildlife, especially the ungulate
herds. Dispersal is a fundamental biological process
that influences the distribution of biodiversity in every
ecosystem and determines whether a species will sur-
vive. Among other things, the process of dispersing
from a natal territory is essential to avoid inbreeding and
strongly influences individual fitness. As a result, wildlife
depends as much on adjacent land as it does on the pro-
tected areas for continued viability. Between 60–80per-
cent of the wildlife in Kenya is found outside protected
areas (Grunblatt et al. 1996; Western et al. 2009).
Declining natural assets
Reasons for the decline of Kenya’s wildlife have been
widely documented and involve an interconnected suite
of pressures typically linked to habitat conversion. These
include population growth (Kenya’s population has grown
more than sixfold since 1961), the expansion of arable
agriculture, fencing, poaching, and intrusive infrastruc-
ture (Said et al. 2016) (Figure 1.4). This report expands
upon this literature by providing quantitative estimates
of some of the drivers of the loss of wildlife—something
that, to our knowledge, has been done for the first time.
PAVING THE WAY
Roads are a formidable engine for growth and poverty
reduction. They connect people to jobs, schools, markets,
and hospitals. In rural areas, they improve market access
for farmers, allowing them to sell their products at higher
prices, thus raising incomes. Roads boost the develop-
ment of commercial agriculture, aiding in the transition
from subsistence to market economies. New roads also
connect people to the rest of society, which creates a
shared existence and builds a larger identity. For these
reasons and more, increasing the rural road network is
central to the Sustainable Development Goals (SDGs).
Specifically, SDG indicator 9.1.1 encourages policy mak-
ers to increase the share of the rural population who live
within 2 kilometers of an all-season road that is motorable
all year round by the prevailing means of rural transport. In
the relatively dry environment of Kenya, paved as well as
improved roads can be considered as all-season roads.5
5 In countries with more wet conditions, it is often only paved roads that are
considered to be all-weather roads.
FIGURE 1.3: Kenya’s wildlife populations have shrunk dramatically, becoming fragmented, and almost vanishing
in some counties
Source: Authors based on DRSRS data.
61641_Kenya_Wildlife_Tourism_new.indd 3 11/4/19 3:07 PM

4 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
Indeed transport networks such as the railways have
played a key part in the development of Kenya.
Kenya’s road network has grown considerably over the
last decades. Michelin maps of East Africa, dating back
to 1978, were digitized to investigate the expansion and
effects of roads. In 1978, there were around 7,000 kilo-
meters of paved and improved roads (Figure 1.5), and
the entire North of the country only featured improved
gravel roads at this time. In the subsequent 40 years,
Kenya’s road network had increased by 50 percent to
cover around 11,000 kilometers of improved and paved
roads as of 2017. The network of roads has become
denser in the South but has also been extended in the
North to connect the major urban centers in the region,
an example being the recent paving of roads leading to
Marsabit and Turkana Counties.
Despite the significant and successful extension of Kenya’s
road network, the majority of the country’s rural popula-
tion continues to live more than 2 kilometers from an all-
season road. Population growth has far outpaced efforts
to connect the country’s centers of agglomeration. By
overlaying data from WorldPop, which provides estimates
of population density at a precise spatial scale of about
1kilometer, with the 2017 road network, an index of acces-
sibility to roads can be derived. This index is termed the
Rural Access Index (RAI) (Stevens etal. 2015). The results
show that only about 28 percent of the rural population in
Kenya lives within 2 kilometers from a road—an RAI that
is comparable to most developing countries. Significant
opportunities therefore exist to connect Kenya’s rural
population to the main network, and continued invest-
ments in road infrastructure may serve as a key lever to
reducie poverty and promote inclusive growth. The chal-
lenge for the country is to achieve this in ways that do not
diminish the economic value of its natural assets.
CONNECTIONS THAT DISCONNECT
While roads bring important benefits to people, there is a
growing body of evidence suggesting that they may also
generate significant environmental impacts, especially in
areas with rich biodiversity. A large and rapidly expand-
ing literature has documented the impact of roads on for-
est cover across countries as diverse as Brazil (Laurance
FIGURE 1.4: Human populations have been expanding in areas with wildlife and around parks
Source: Authors based on Michelin and WorldPop data.
61641_Kenya_Wildlife_Tourism_new.indd 4 11/4/19 3:07 PM

VANISHING HERDS: WILDLIFE DYNAMICS AND DRIVERS 5
et al. 2014); the Democratic Republic of Congo (DRC)
(Damania et al. 2018); India (Asher et al. 2018); Indone-
sia, Tanzania (Arcus Foundation 2018); and at a global
scale (Arcus Foundation 2018). Studies consistently find
that the extension of roads into forested areas catalyzes
deforestation or forest degradation, though the magni-
tude of impact differs considerably across countries.6 This
occurs not only through the direct clearing of vegetation
to open up the road, but mainly from the indirect threats
brought by people settling around the new roads, who
now benefit from easier access to markets, which leads
to the conversion of natural habitats into croplands. In the
6 Asher et al. (2018) find no effect of local roads on deforestation in India, but a
large impact of national roads on deforestation.
DRC, for example, a significant impact on deforestation
is seen up to 2 kilometers from roads, and in Western
Tanzania, the impact is seen even farther—deforestation
even increased 20 to 30kilometers away from the newly
built Ilagala–Rukoma– Kashagulu Road (Asher etal. 2018).
In general, the scale of habitat loss is determined by the
incentives unleashed to expand cropland into natural
habitats and the capacity to regulate these. There are
likely other effects, such as the spread of invasive spe-
cies, that are ignored in this report.
QUANTIFYING THE CAUSAL IMPACTS
In Kenya too, statistical analyses indicate that roads are
a key part of this dynamic and have predictable effects
FIGURE 1.5: Kenya’s road network has increased by 50 percent in the last 40 years
61641_Kenya_Wildlife_Tourism_new.indd 5 11/4/19 3:07 PM

6 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
on wildlife and on migratory corridors. Because land use
change is the primary driver of biodiversity loss, it is no
surprise that the rate of wildlife loss in Kenya between
the 1980s and the end of the 2000s was significantly
faster in areas in close proximity to roads. Almost all
wildlife corridors have been affected by land conversion,
though the extent varies (Ojwang et al. 2017).
Nonparametric statistical models known as LOWESS
regressions were used to investigate the causal links, if any,
between natural habitat loss in Kenya and the distance to
roads. The approach uses state-of-the-art statistical mod-
els to identify the causal impact of roads and isolate these
from the confounding effects (Ali etal. 2015). The Euclidean
distance between each grid cell and the nearest paved or
improved road was calculated for each decade from the
1980s to the 2000s. These distances were then catego-
rized into different bins depending on whether a cell was
less than 5, 10, 15, 20, or 50kilometers from a road.
Difference-in-differences models were used to estimate
the change in wildlife biomass inside cells that, over time,
came into closer proximity to a road (treatment) com-
pared to those cells that remained farther away from a
road (control). Simultaneity bias may be a significant threat
when studying the impact of roads on wildlife since wild-
life distribution and road placement are jointly determined.
For example, new roads may be targeted for regions
with expanding agricultural activity and land use, imply-
ing that these roads may be a response to activities that
are already causing forest cover reduction. Difference-
in-differences models combined with fixed effects are an
effective method to overcome this challenge (Asher et al.
2018). The approach presents what may be the first causal
estimates for Kenya by exploiting spatial location and
timing—comparing rates of biodiversity loss of a cell that
remains at a large distance from a road to one that was
once a large distance away but has been brought close
to the road. Time, cell, and other fixed effects control for
other factors to address omitted variable bias and other
problems. The full model is presented in Box 1.1.
The results, illustrated in Figure 1.6, show that the closer
a grid cell is to a road, the faster the conversion of natural
habitat to cropland and settlements, which consequently
has an impact on wildlife. Results from the statistical
model consistently suggest that cells located within a
20-kilometer distance to a road are associated with a
FIGURE 1.6: Roads lead to changes in land use, impacting wildlife most severely within a distance
of 20 kilometers
0.2
0.4
0.6
0.8
1.0
1.2
Average area converted in cropland per cell (ha)
0 20 40 60 80 10
0
Distance to nearest road (km)
Natural habitat converted in croplands and distance to roads
Source: Authors based on DRSRS, ESA, and Michelin data.
61641_Kenya_Wildlife_Tourism_new.indd 6 11/4/19 3:07 PM

VANISHING HERDS: WILDLIFE DYNAMICS AND DRIVERS 7
FIGURE 1.6: Continued
0
.1
.2
.3
Average area converted in settlement per cell (ha)
0 20 40 60 80 10
0
Distance to nearest road (km)
Natural habitat converted in settlement and distance to roads
150
200
250
350
300
Total loss of wildlife biomass (in 1,000 TLU)
0 20 40 60 80 10
0
Distance to nearest road in km
Total loss of wildlife between 1980–2000s and distance from roads in 2000s
Source: Authors based on DRSRS, ESA, and Michelin data.
61641_Kenya_Wildlife_Tourism_new.indd 7 11/4/19 3:07 PM

8 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
BOX 1.1: Building a Statistical Model to Quantify the Direct Impact of Roads on Wildlife
Data compiled by the Department of Resource Surveys and Remote Sensing (DRSRS) using aerial surveys in the rangelands
of Kenya since 1977 cover 19 rangeland counties. The approach used for estimating impacts follows best practices. Kenya
was divided into a grid of 10 × 10 kilometers, and for each cell, wildlife in the 1980s were identified in each pixel. Changes in
wildlife measured in TLU were then determined for the 1990s and 2000s. In addition, the average distance between each
grid cell and the nearest road between 1978 and 2010 was also determined.
A “difference-in-differences” specification is used to determine the impact of roads on wildlife loss. The model exploits the
expansion of the road network in Kenya in the 1980s–90s. Between 1978 and 1992, the average distance of a cell’s centroid
from a road went from 55 km to 44 km (10% decrease).
Cells that were originally (1980s) far from a road (50–100 km) are kept in the analysis. Among these cells, the model looks at
how the loss of wildlife differed between cells that became closer to a road (treatment groups, 5 km, 10 km, 15 km, 20 km,
and 50 km) and cells that remained far from a road (control group, >50 km).
Roads here include both paved and improved roads. Formally, the model is:
Wildlifei,t = β Cell Close from Roadi,t + γ Posti,t + ω Cell Close from Road ∗ Posti,t + η Protected Areai,t + i + µt + ∈i ,t
Where:
—Wildlifei,t: Total biomass of wildlife in cell i during decade t (t = 1980, 1990, 2000).
—Cell Close from Road (Treatment): Whether the cell has become 5, 10, 15, or 20 km closer to a road during the period.
—Post: Dummy variable for post 1980s decade (i.e., once most cells became close to a road).
— Roads of the 1980s = roads observed in 1978; roads in the 1990s = roads observed in 1992; roads in the 2000s = roads
observed in 2003.
—Additional controls: Dummy variable for the presence of a protected area in the cell, province × decade fixed effect.
—Clustered standard errors. Weights based on the area of each cell.
The methodology is further detailed in Appendix B.
TABLE 1.1: Quantifying the impact on wildlife of construction of a road (wildlife biomass)
Variables Less than 5 km Less than 10 km Less than 15 km Less than 20 km Less than 50 km
Treated × post –217.369*
(121.325)
–185.138**
(85.765)
–134.558*
(79.109)
–114.494*
(65.091)
–65.554
(44.057)
Post –358.628***
(101.365)
–389.410***
(100.153)
–345.288***
(110.082)
–326.846***
(105.581)
–530.919***
(143.272)
Observations 2,586 2,730 2,868 3,027 4,029
Number of cells 862 910 956 1,009 1,343
Treatment Road becomes <5 km Road becomes <10 km Road becomes <15 km Road becomes <20 km Road becomes <50 km
Control Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Note: * = p<0.05, ** = p<0.01, *** = p<0.001.
61641_Kenya_Wildlife_Tourism_new.indd 8 11/4/19 3:07 PM

VANISHING HERDS: WILDLIFE DYNAMICS AND DRIVERS 9
BOX 1.2: Population Density and Wildlife Loss
The question of how population growth impacts natural habitats and wildlife is at the heart of policy debates, at least since
Hardin’s (1968) seminal assessment of the Tragedy of the Commons. While the tragedy can be avoided (Boserup 1965;
Ostrom 1990), numerous studies have empirically established a correlation between population growth and environment
degradation. Population growth was found to be associated with losses of both natural habitats such as forests and savan-
nahs (Cropper and Griffiths 1994; Jha and Bawa 2006) and wildlife (Du Toit and Cumming 1999). Notably, these correlations
were observed in East Africa and Kenya (Du Toit and Cumming 1999; Ogutu et al. 2011; Ogutu et al. 2016; Veldhuis et al. 2019),
where demographic growth remains high today (2.4 percent annually in the Seregenti-Mara region (Veldhuis et al. 2019)).
Figures B.1.1 and B.1.2 illustrate the correlations that exist between population density and wildlife in Kenya using the data
compiled for this report. Figure B.1.1 shows the correlation between ungulate wildlife biomass between 2000 and 2010, and
total human population using 5 km × 5 km gridcells. It shows a negative correlation between ungulate wildlife density in the
2000s and human population. Indeed, in the 5 km × 5 km grid cells where human population is under 500 inhabitants, ungu-
late wildlife density is estimated at about 40 Tropical Livestock Units (TLU). However, in grid cells where human population
reaches 4,000 inhabitants, almost no wildlife is found any more. Figure B.1.2 illustrates a dynamic. It plots the relationship
between the rate of ungulate wildlife loss between the 1980s and the 2000s, and human population in the same grid. Once
again, it shows that the rate of wildlife loss is positively correlated with human population: the more a grid cell is populated,
the larger is the wildlife loss. The results in this section illustrate that for any given population density, the construction of a
road will hasten and intensify the decline in wildlife.
A long list of economic literature highlights that the development of infrastructure—particularly roads, is a leading determi-
nant of where population growth happens: people follow infrastructure since it offers economic opportunities. Therefore,
the current choices made regarding infrastructure construction will have long-lasting impacts on the demography of the
country, and consequently consequences on future wildlife trends. As demonstrated in the rest of this report, large room
exists to build infrastructures in key economic areas and protect wildlife at the same time.
FIGURE B .1.1 Declining wildlife
50
40
30
20
10
0
1,0000
2,000
Total population in 2010
Wildlife density and population
3,000 4,
000
Wildlife density in the 2000s (in TLU)
FIGURE B.1.2 Declining wildlife
.92
.90
.88
.86
.84
.82
1,0000
2,000
Total population
Wildlife loss and total population
3,000 4,
000
Wildlife loss 1980s–2000s (in %)
61641_Kenya_Wildlife_Tourism_new.indd 9 11/4/19 3:07 PM

10 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
significant decrease in wildlife following construction of
the road, and the closer a cell is to a road, the larger the
impact.
To be specific, the results in Table 1.1 suggest that a cell
that was once 50 kilometers away from a road and that
has been brought to within 5 kilometers of a road will
have lost an additional 217 Tropical Livestock Units
(TLUs) (or 54,250 kilograms of wildlife biomass) over a
decade compared to cells that remained 50 kilometers
from a road. Given that the average wildlife biomass in a
cell between 1980 and 2009 was 266 TLU, the impact of
roads has been significant. The estimates suggest that
in the first 5 kilometers from a road, wildlife loss is the
most severe, at 80 percent (217/266). Wildlife loss falls
to 69percent at a distance of 5 to 10 kilometers, 50 per-
cent at a distance of 10 to 15 kilometers, and 40 percent
at a 20-kilometer distance. Hence, even after 20 kilo-
meters from a road, the impact remains ecologically sig-
nificant though much smaller. Overall, and on average, a
road results in a decline of 76 percent of wildlife biomass
within a 20-kilometer radius.
Having identified the causal impact of roads on wildlife,
it is necessary to determine if the resulting gains have
outweighed the forgone losses of tourism revenue. An
economic model of Kenya is used to answer this ques-
tion in the next chapter, followed by a discussion of win-
win solutions to these problems.
REFERENCES
Ali, Rubaba, et al. (2015). Highways to success or byways to
waste: Estimating the economic benefits of roads in Africa.
Washington DC: The World Bank.
Andam, Kwaw S., et al. (2008). “Measuring the Effectiveness
of Protected Area Networks in Reducing Deforestation.”
Proceedings of the National Academy of Sciences 105.42.
Arcus Foundation. (2018). State of the Apes: Infrastructure
Development and Ape Conservation, (Cambridge: Cam-
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Asher, S., T. Garg, and P. M. Novosad. (2018). The Ecological
Impact of Transportation Infrastructure. Washington DC:
World Bank.
Boserup, Ester. (1965) The conditions of agricultural growth:
The economics of agrarian change under population
pressure.
Ceballos, G., P. R. Ehrlich, and R. Dirzo. (2017). “Biological Anni-
hilation via the Ongoing Sixth Mass Extinction Signaled by
Vertebrate Population Losses and Declines.” Proceedings
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Cropper, M., and Griffiths, C. (1994). The interaction of popula-
tion growth and environmental quality. The American Eco-
nomic Review, 84(2), 250–254.
Damania, R., et al. (2018). “The Road to Growth: Measuring
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Daskin, J. H., and R. M. Pringle. (2018). “Warfare and Wildlife
Declines in Africa Protected Areas.” Nature 553, 328–332.
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Dybas, C. L. (2009). “Infectious Diseases Subdue Serengeti
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Ogutu, J. O., Owen-Smith, N., Piepho, H. P., and Said, M. Y.
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Ojwang, Gordon O., et al. (2017). Wildlife Migratory Corridors
and Dispersal Areas: Kenya Rangelands and Coastal Ter-
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Ostrom, E. (1990). Governing the commons: The evolution of
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Said, M. Y., J. O. Ogutu, S. C. Kifugo, O. Makui, R. S. Reid, and
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61641_Kenya_Wildlife_Tourism_new.indd 11 11/4/19 3:07 PM

12
CHAPTER 2
WEIGHING THE IMPACTS: GENERATING SCENARIOS
AND SIMULATING TRADE-OFFS
Appendix B, Scandizzo and Ferrarese (2015), and Sanghi
et al. (2017).
The ESAM describes an economy with strong dualistic
features, where the South is vastly more developed than
the North, and where inter-sector linkages tend to rein-
force a pattern of concentration of economic activities
in the more advanced South. While the Southern value
chains have depth, especially in agriculture and other
land- and food-related activities, the region is still highly
dependent on imports in the manufacturing sectors (Fig-
ures 2. 2 and 2.3).
To gain a better understanding of the structure of the
economy, it is instructive to examine the multipliers in
The aim of this chapter is to examine the economic con-
sequences of the trade-offs that confront policy makers.
While roads are necessary for development, they bring
economic costs through the loss of tourism income. The
magnitude of gains and losses involved is unknown,
rendering policy choices difficult and questionable.
This chapter attempts to provide answers to these far-
reaching and difficult issues.
To do so, it relies on two analytical tools: a regional social
accounting matrix (SAM) extended to a set of environ-
mental accounts (environmentally-extended SAM or
ESAM), and a computable general equilibrium (CGE)
model. Consistent with the characteristics of a new gen-
eration of applied economic models (Perali and Scan-
dizzo, 2018), SAM and ESAM provide a way of linking
Kenya’s national accounts to investment scenarios and
policy changes in order to estimate impacts on growth,
jobs, incomes, exports, and other key economic and
social indicators, as well as environmental variables.
While the ESAM provides the data for the exercise, the
CGE is the engine (the model) that simulates impacts.
It remains one of the most rigorous quantitative meth-
ods for generating economically consistent scenarios to
evaluate the impact of economic and policy shocks. The
model used for this report is an extension of an earlier
model that was used to assess the economic impacts of
tourism in Kenya (see Sanghi et al. 2017).
Developing a regional ESAM
The ESAM estimated for Kenya divides the country into
two parts—the South and the North—as shown in Fig-
ure 2.1. It comprises 30 sectors for each region, and
several environmental sectors and factors, as well as
household types and institutional accounts (govern-
ment, capital formation, and rest of the world). Details
of the methodology and the estimates are contained in
FIGURE 2.1: Mapping the CGE regions for Kenya
Source: Method developed by the World Bank.
61641_Kenya_Wildlife_Tourism_new.indd 12 11/4/19 3:07 PM

WEIGHING THE IMPACTS: GENERATING SCENARIOS AND SIMULATING TRADEOFFS 13
FIGURE 2.2: Forward multipliers for productive sectors
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
Agriculture
Livestock
Forestry
Fishing
Wildlife
Parks
Conservancies
Water biodiversity
Mining
Food, beverage, and tobacco
All other manufacturing
Distribution water
Utilities
Construction
Trade
Accommodation and restaurant
Lodge
Transport
Information and communication
Financial and insurance activities
Real estate
Business and administrative
Other services
Public administration and defense
Health and social work
Education
Park tourism
Beach tourism
Cultural tourism
Business tourism
South North
Source: Elaboration of the Kenya SAM.
the model. In a CGE context, “forward” multipliers mea-
sure the degree to which a sector participates in an over-
all expansion (or contraction) of the economy, i.e., the
increase required in the supply of one sector to meet
a uniform increase of demand, spread over all sectors.
Conversely, “backward” multipliers measure the degree
to which a sector is capable of stimulating other sectors
through an increase in the demand for inputs. A back-
ward multiplier thus indicates the amount of output gen-
erated in an economy due to an exogenous increase in
the demand in a given sector.
The multipliers tend to be comparatively larger in the
more developed areas of the South, where forward mul-
tipliers are much larger in sectors such as agriculture,
trade, transport, manufacturing, and financial services.
Differences are smaller in natural resource–based sec-
tors and ecotourism, reflecting the comparative advan-
tage of the North. Backward multipliers are low in both
regions, suggesting that value chains still lack overall
depth and interconnectedness. However, backward mul-
tipliers in the South are without exception higher than in
the North.
The CGE model
While the SAM multipliers may give a first approxima-
tion of the indirect effects of investment and other policy
changes, they do not take into account the more com-
plex secondary impacts on employment and prices.
These effects are likely to be important when explor-
ing economic changes of significance, such as a large
investment or a policy shift. Box 2.1 provides an overview
of the key assumptions of the CGE model.
Figure 2.4 illustrates the ability of the model to track the
Kenyan economy. Model calibration achieves almost a
perfect fit, except for the category of “all other manufac-
turing activities,” which is a residual.
61641_Kenya_Wildlife_Tourism_new.indd 13 11/4/19 3:07 PM

14 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
FIGURE 2.3: Backward multipliers for productive sectors
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
Agriculture
Livestock
Forestry
Fishing
Wildlife
Parks
Conservancies
Water biodiversity
Mining
Food, beverage, and tobacco
All other manufacturing
Distribution water
Utilities
Construction
Trade
Accommodation and restaurant
Lodge
Transport
Information and communication
Financial and insurance activities
Real estate
Business and administrative
Other services
Public administration and defense
Health and social work
Education
Park tourism
Beach tourism
Cultural tourism
Business tourism
South North
Source: Elaboration of the Kenya SAM.
BOX 2.1: Key Assumptions of the CGE Model
The CGE is built on the assumption of a “small economy,” in the sense that the country cannot influence international prices
of imported and exported goods. Each sector produces a composite commodity that can either be exported or produced for
the domestic market. Each producer is assumed to maximize profits by producing one commodity, with labor, capital, land,
and ecosystem services as primary inputs, according to a constant elasticity of substitution (CES) production function. The
demand for intermediate inputs assumes fixed input-output coefficients, and the demand for primary factors is given by the
first order condition for profit maximization using value added prices. Production is either for the domestic market in each
region or for trade/exports with the other region or the international market according to a Constant Elasticity of Transfor-
mation (CET) function. Producers are assumed to maximize revenue from sales subject to the CET function. Export supply
represents the first order condition and is a function of the elasticity of transformation and the relative export price with
respect to domestic price. The allocation of imports and domestic production is determined according to the hypothesis
that domestic and internationally traded goods are imperfect substitutes that are combined in a composite good according
to a constant elasticity technology.
Aggregate domestic demand is divided into four components for both regions: consumption, intermediate demand, gov-
ernment, and investment, referring to both capital formation and natural capital formation. Following the SAM, four types
of households are considered for each region according to their income threshold, and who receives income from produc-
tion factors and enterprises, as well as who receives income in the form of remittances from abroad and transfers from the
61641_Kenya_Wildlife_Tourism_new.indd 14 11/4/19 3:07 PM

WEIGHING THE IMPACTS: GENERATING SCENARIOS AND SIMULATING TRADEOFFS 15
government. Households also pay taxes to the government and save a proportion of their incomes. Consumer expenditure
is a function of prices and incomes according to a Linear Expenditure System (LES) that in its simpler version reduces to fixed
expenditure shares and a Cobb-Douglas Utility function (Robinson et al. 1989). Households also spend their incomes to use
natural capital, which is added to the expenditure function as an exogenous variable.
Intermediate sector demand, including the exchange between the two regions, is given by fixed input-output coefficients.
Aggregate spending for government consumption is exogenously determined and defined in terms of fixed shares of aggre-
gate government spending for goods and services. Part of government spending is also natural capital, which is added to
the government expenditure function and exogenously determined. Sector capital investment is assumed to be allocated in
fixed proportions among various sectors and is exogenously determined.
The rest of the world includes foreign and out-of-state tourists and is set exogenously. For the balance of trade, we adopt
the hypothesis that this is set exogenously and the real exchange rate adjusts to achieve equilibrium. CET, Armington, and
export elasticity parameters were taken from literature such as Hinojosa-Ojeda and Robinson (1991), Hanson et al. (1989),
and Reinert and Shiells (1991).
Factors are assumed to be mobile across activities, available in fixed supply, and demanded by producers at market-clearing
prices. Factor incomes are distributed on the basis of fixed shares (derived from base-year data) and transferred to the
households. For the depletion of natural capital, an exogenous variable is added to the intermediate use of commodities in
the supply-demand equation of final goods.
POLICY SIMULATIONS
The CGE model can be used to determine the effects
of investment in different sectors by examining the mul-
tipliers and the overall impact on key economic vari-
ables of interest such as value added (GDP). Table 2.1
and Figure 2.5 show how the CGE multipliers vary with
the size of the investment involved. The table shows
the consequences of different levels of investment in
three sectors— construction which proxies investment
in roads, conservancies as an indicator of investment
in wildlife tourism, and greater wildlife protection (such
as anti-poaching patrols and habitat restoration and
regeneration).
TABLE 2.1: CGE investment impact multipliers
Investment ($, millions)
10 50 100 500 1,000
Sector Value added multipliers ($, millions)
Construction 1.96 1.97 1.98 2.06 2.17
Conservancies 4.28 4.41 4.57 6.55 13.61
Wildlife 4.26 4.39 4.57 6.75 16.54
Source: Elaboration of the Kenya CGE model.
Three critical features of the Kenyan economy become
evident. First, investment multipliers for construction
appear to be linear (i.e., they do not vary with the scale
of the investment). Second, the construction multipliers
are lower than the multipliers associated with conser-
vancies and wildlife conservation activities. This is due to
the greater complexity and connectivity of tourism value
chains and the complementarity of wildlife tourism with
other sectors of the economy. Third, in contrast to the
construction sector, conservation investments exhibit
scale effects and increase with the amount invested.
These effects emerge as a consequence of deeper link-
ages to other parts of the economy.
Table 2.2 shows the impact on value added of an invest-
ment in conservancies of the same magnitude in both
regions. Since the regions are so different, with the
South commanding most of the export trade compared
to the North, an investment in the South has a high
own- multiplier effect, but virtually no spillover effects to
the North. Investment in the North, on the other hand,
spills over into the South. Note too the high investment
multiplier (4.41) in the North, which reflects the fact that
investments in wildlife tourism in the North entail bet-
ter utilization of the endowments of land and natural
61641_Kenya_Wildlife_Tourism_new.indd 15 11/4/19 3:07 PM

16 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
FIGURE 2.4: CGE model simulation for base-year production activities
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
Agriculture
Livestock
Forestry
Fishing
Wildlife
Parks
Conservancies
Water biodiversity
Mining
Food, beverage, and tobacco
All other manufacturing
Distribution water
Utilities
Construction
Trade
Accommodation and restaurant
Lodge
Transport
Information and communication
Financial and insurance activities
Real estate
Business and administrative
Other services
Public administration and defense
Health and social work
Education
Park tourism
Beach tourism
Cultural tourism
Business tourism
South base case South simulation
0
500
1,000
1,500
2,000
2,500
Agriculture
Livestock
Forestry
Fishing
Wildlife
Parks
Conservancies
Water biodiversity
Mining
Food, beverage, and tobacco
All other manufacturing
Distribution water
Utilities
Construction
Trade
Accommodation and restaurant
Lodge
Transport
Information and communication
Financial and insurance activities
Real estate
Business and administrative
Other services
Public administration and defense
Health and social work
Education
Park tourism
Beach tourism
Cultural tourism
Business tourism
North base case North simulation
Source: SAM base year data and CGE simqqlations.
61641_Kenya_Wildlife_Tourism_new.indd 16 11/4/19 3:07 PM

WEIGHING THE IMPACTS: GENERATING SCENARIOS AND SIMULATING TRADEOFFS 17
resources, which are relatively abundant in this part of
the country.
Consider next an equivalent investment in roads in
both regions. Using the econometric estimates from
the previous chapter, it is assumed that this leads to
an expansion in agricultural activity and a reduction in
wildlife.
The absolute impact of investment in construction is
higher when it occurs in the North. In percentage terms,
however, unlike the case of conservancies, investment
in construction has more balanced results when carried
out in the South (Table 2.3). Even though the multipliers
are smaller, they are significant in both regions. The size
of the multiplier (about 1.5 in both cases) is similar to esti-
mates obtained in other countries.
These results suggest that investment in conservan-
cies and wildlife tourism display important scale effects.
Investments in conservation and construction both
appear to have higher potential in the North, where nat-
ural resources are more abundant and land is cheaper,
and where induced tourist activity may spill over to the
rest of the country through connections to the better
developed southern value chain and infrastructure.
TAB LE 2. 2: Impacts of investment in conservancies (50% of current value = $142 million)
Value added components
Investment in the South (Region A) Investment in the North (Region B)
Impact on Region A
($, millions)
Impact on Region B
($, millions)
Impact on Region A
($, millions)
Impact on Region B
($, millions)
Labor 100.12 8.73 96.49 102.95
Capital 120.26 13.34 114.53 175.50
Land 132.69 4.99 22.95 176.92
Other (eco) services 353.08 27.06 233.96 455.36
Total value added 706.15 54.13 467.92 910.73
Value added components
Investment in the South (Region A) Investment in the North (Region B)
Impact on Region A
(%)
Impact on Region B
(%)
Impact on Region A
(%)
Impact on Region B
(%)
Labor 0.52 0.51 0.50 6.00
Capital 0.38 0.53 0.36 6.92
Land 2.62 0.56 0.45 20.00
Other (eco) services 6.69 0.64 1.25 25.30
Total value added 0.75 0.54 0.44 10.78
Investment multiplier 3.02 0.22 1.75 4.41
Source: Kenya CGE model.
FIGURE 2.5: Multipliers as a function of investment size
0
5
10
15
20
10 50 100 500 1,000
Multiplier
Investment ($, millions)
Construction Conservancies Wildlife
Source: Elaboration of the Kenya CGE model.
61641_Kenya_Wildlife_Tourism_new.indd 17 11/4/19 3:07 PM

18 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
Trade-offs between road construction
and wildlife toursim
Having described the sector multipliers, this section turns
to the central policy question—the trade-offs involved
between road construction and investments in wildlife-
based tourism. To gain a clearer understanding of likely
effects and the sensitivity of results to key parameters,
the simulations are based on a wide range of alternative
scenarios. A variety of cases are considered regarding
the sensitivity of tourism to wildlife loss.
The simulations explore the impact of an increase in
investment in road construction on GDP and its com-
ponents, assuming different demand elasticities and
different rates of wildlife loss.7 Clearly, the greater the
sensitivity of tourist demand to wildlife loss, the greater
will be the decline in demand resulting from wildlife
declines. Likewise, a variety of cases are considered for
the loss of wildlife from road construction (15 and 30, to
7 The CGE model is calibrated using elasticities of substitution of CES
production functions ranging from 0.6 (agriculture) to 1 (industry). CET functions
for Armington hypothesis are also calibrated with a higher elasticity range
(0.5to 2) and elasticity of foreign tourism demand with respect to wildlife
ranging from 0.3 to 1.5. The model is run with a Keynesian closure with labor
supply perfectly elastic, capital mobile across each region, and wage as the
numeraire.
77 percent), depending on the amount and location of
agricultural and livestock expansion. Included are the
following three scenarios: (1) a 10 percent increase in
investment in road construction and a 15 percent reduc-
tion in wildlife in the South; (2) high levels of reduction
in wildlife in the South (30 to 77 percent) as a result of
higher levels of road construction; and (3) combining
conservation and infrastructure policies—capturing the
elusive win-wins.
SCENARIO 1: A 10 percent increase in investment
in road construction and a 15 percent reduction in
wildlife in the South
The combination of a 10 percent increase in road con-
struction and a 15 percent decrease in wildlife in the
South has a positive impact on agriculture and livestock
production but a generally negative impact on service
activities in both the North and the South, but especially
in the South. The fall in production is particularly large in
the tourism sector.
The impact on value added in the South (Table 2.4) mainly
occurs through land, whose demand rises because of the
expansion of agriculture and trade. In the North, on the
TABLE 2.3: Impacts of investment in construction ($142 million)
Value added components
Investment in the South (Region A) Investment in the North (Region B)
Impact on Region A
($, millions)
Impact on Region B
($, millions)
Impact on Region A
($, millions)
Impact on Region B
($, millions)
Labor 91.07 4.31 49.67 79.26
Capital 109.48 6.16 61.87 109.91
Land 11.41 2.02 9.82 15.88
Ecoservices 211.96 12.48 121.35 205.05
Total value added 423.93 24.97 242.70 410.11
Value added components
Investment in the South (Region A) Investment in the North (Region B)
Impact on Region A
(%)
Impact on Region B
(%)
Impact on Region A
(%)
Impact on Region B
(%)
Labor 0.47 0.25 0.26 4.62
Capital 0.35 0.24 0.20 4.33
Land 0.23 0.23 0.1 9 1.80
Other (eco)services 0.25 0.22 0.31 1.89
Total value added 0.38 0.24 0.22 3.75
Investment mutiplier 1.51 0.88 0.1 1.53
Source: Elaboration of the Kenya CGE model.
61641_Kenya_Wildlife_Tourism_new.indd 18 11/4/19 3:07 PM

WEIGHING THE IMPACTS: GENERATING SCENARIOS AND SIMULATING TRADEOFFS 19
TAB LE 2. 4: Impact on regional value added of an increase in infrastructure (+ 10%) and reduction in wildlife in the
South (percent from baseline)
South (Region A)
Wildlife (15%)
Tourism demand
elasticity = 1
Wildlife (15%)
Tourism demand
elasticity = 0.6
Wildlife (15%)
Tourism demand
elasticity = 0.3
Labor 3.71 3.81 3.90
Capital 3.41 3.49 3.57
Land 12.78 12.99 13.20
Ecoservices –1.10 –0.80 –0.49
Total value added 4.21 4.31 4.41
North (Region B)
Labor 4.60 4.71 4.82
Capital 5.80 5.92 6.03
Land 4.74 4.89 5.03
Ecoservices –3.36 –3.19 –3.03
Total value added 4.39 4.51 4.63
Source: Elaboration of the Kenya CGE model.
TABLE 2.5: Impact on regional income distribution of an increase in infrastructure and reduction in wildlife in the
South (percent from baseline)
South (Region A)
Wildlife (15%)
Tourism demand
elasticity = 1
Wildlife (15%)
Tourism demand
elasticity = 0.6
Wildlife (15%)
Tourism demand
elasticity = 0.3
Enterprises 3.41 3.49 3.57
Rural poor 5.37 5.49 5.61
Rural non-poor 5.30 5.42 5.54
Urban poor 3.46 3.55 3.64
Urban non-poor 3.44 3.53 3.62
North (Region B)
Enterprises 5.80 5.92 6.03
Rural poor 3.95 4.06 4.17
Rural non-poor 4.00 4.11 4.22
Urban poor 4.21 4.31 4.42
Urban non-poor 3.84 3.94 4.03
Source: Elaboration of the Kenya CGE model.
other hand, income increases across all factors of produc-
tion. In both regions, multipliers are high, indicating both
direct and indirect effects of the same orders of magni-
tude and large spillovers from backward linkages. The
results suggest an overall improvement across most sec-
tors of the economy, despite the loss of tourism income.
In conclusion, when the decline in wildlife and tour-
ism demand elasticity is moderate, there is an overall
increase in value added (GDP), with the decrease in the
tourism value chain being compensated by the increase
in value added in other parts of the economy. It is also
useful to note that the poor in both rural and urban areas
61641_Kenya_Wildlife_Tourism_new.indd 19 11/4/19 3:07 PM

20 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
benefit equitably in this scenario. In this case, it pays to
deplete the natural assets that attract tourists, since the
gains from other sources of income outweigh the losses.
SCENARIO 2: High levels of reduction in wildlife
in the South (30 to 77 percent) as a result of higher
levels of road construction
A second set of simulations investigates the same
investment in road construction but with higher impacts
on wildlife in the South, involving reductions of wild-
life biomass 30 to 77 percent in both regions. Elastici-
ties of tourism demand with respect to wildlife are also
assumed to be higher, ranging from 1 to 1.5.
In both regions there is a general boost to the economy in
the agricultural and construction sectors. But in all cases
considered, this is insufficient to compensate for the fall
in production that is catalyzed by the near collapse of the
wildlife tourism industry and its value chain. Moreover,
since these effects are the result of spillovers from the
South, the multiplier effects are similar in both regions,
with only a slight tendency for the North to compensate
with its larger and cheaper supply of land and labor.
In terms of value added however, differences emerge
across regions and scenarios (Table 2.6) where the
South benefits and the North contracts. In the first two
scenarios where wildlife is assumed to decrease 30per-
cent, total value added in the South increases. In the
North, however, land incomes fall in response to the
higher supply of more accessible and fertile lands in the
South, and value added is reduced. This is a scenario in
which more amenable conditions in the South “crowds
out” economic activity from the North.
The third and fourth columns explore scenarios with
higher rates of wildlife reduction (assumed to be 70 per-
cent according to recent trends in areas close to roads).
In this scenario both regional economies suffer, with neg-
ative value added changes being especially large in the
North. The losses accruing from the decline in tourism
revenue and the associated value chain outweigh any
gains that a road might bring. What is especially striking
is the magnitude of the loss in the North relative to the
South, reflecting the different comparative advantages
of the two regions.
The value added effects bring to light a central disconti-
nuity in the response of the economy, which is illustrated
in Figure 2.6. In this diagram, the size of the balls rep-
resent the assumed elasticity of tourism demand with
respect to wildlife, while the horizontal and vertical axes
measure the changes in value added and the reduction
TAB LE 2. 6: Impact on regional value added of an increase in investment in road construction (+10%)
and greater reduction in wildlife in the South (percent from baseline)
Wildlife (30%)
Tourism demand
elasticity = 1
Wildlife (30%)
Tourism demand
elasticity = 1.5
Wildlife (77%)
Tourism demand
elasticity = 1
Wildlife (77%)
Tourism demand
elasticity = 1.5
South (Region A)
Labor 4.13 3.76 –0.05 –0.52
Capital 4.50 4.17 1.21 0.80
Land 10.35 9.63 –5.69 –6.54
Ecoservices –9.18 –10.07 –34.63 –35.66
Total value added 4.62 4.23 –0.54 –1.02
North (Region B)
Labor 1.52 1.13 –8.83 –9.30
Capital 2.55 2.14 –8.82 –9.29
Land –5.28 –5.75 –30.15 –30.62
Ecoservices –15.73 –16.22 –44.78 –45.25
Total value added –1.08 –1.50 –16.26 –16.73
Source: Elaboration of the Kenya CGE model.
61641_Kenya_Wildlife_Tourism_new.indd 20 11/4/19 3:07 PM

WEIGHING THE IMPACTS: GENERATING SCENARIOS AND SIMULATING TRADEOFFS 21
in wildlife, respectively. The diagram shows that out-
comes cluster around two key points: (1) a moderate
level of reduction of wildlife with low tourism elasticity
and an associated increase in value added, and (2) a
high level of reduction of wildlife, with an associated fall
in value added.
To summarize, the simulations suggest that when the
decline in wildlife is modest (less than around 30 per-
cent), then the benefits of construction investments out-
weigh the losses brought by a decline in tourism and its
value chain. The North is more vulnerable to the adverse
impacts due to limited alternative forms of economic
activity. But when the decline is large (≈ 70%), there is a
fundamental shift in the balance of costs and benefits. In
this case the loss of income associated with the panoply
of wildlife tourism–related value chains, outweighs the
benefits from improved access from road investments.
While it may be objected that these are hypothetical
simulations, the estimates are based on observed mag-
nitudes, suggesting these results are a cause for policy
consideration. Box 2.2 provides a more detailed expla-
nation of these results in the context of a production pos-
sibility frontier.
Figure 2.7 shows the relationship between wildlife reduc-
tion and GDP emerging from these model solutions, with
a general equilibrium frontier exhibiting an inverted “U”
shape pattern. The figure represents a production pos-
sibility frontier. It shows that when construction induces
declines in wildlife that are relatively modest and less
than around 30 percent, then there is a net economic
gain with value added increasing. Beyond this thresh-
old, further declines in wildlife induce net losses of value
added. The current magnitude of wildlife loss across
much of the country suggests that Kenya is on the
FIGURE 2.6: Value added variation in regard to wildlife reduction and tourism elasticity
–90%
–80%
–70%
–60%
–50%
–40%
–30%
–20%
–10%
0%
–3.00%
–2.00% –1.00% 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00
%
Reduction in wildlife
Value added variation
–8
8
–7
–
Source: Kenya CGE model.
FIGURE 2.7: Relationships between wildlife reduction
and value added, and value added growth
55,000
55,500
56,000
56,500
57,000
57,500
58,000
58,500
59,000
59,500
60,000
0% 20% 40% 60% 80%
100%
Value added ($, millions)
Reduction of wildlife
–1.50%
–1.00%
–0.50%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
0% 20% 40% 60% 80% 100%
Value added growth
Reduction of wildlife
Source: Elaboration of the Kenya CGE model.
61641_Kenya_Wildlife_Tourism_new.indd 21 11/4/19 3:07 PM

22 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
BOX 2.2: Trade-offs between Economic Growth and Environmental Impact
The CGE model summarizes an economy-wide equilibrium outcome that is termed a “general equilibrium” (GE). The results
of the model can be used to define the outcome and trade-offs between economic growth and ecological effects. The curve
in Figure B.2.1 summarizes the outcomes of the simulations conducted in this exercise. It shows that at low levels of environ-
mental impact, growth rises with environmental deterioration but it then reaches a turning point and begins to decline after
around a 30 percent loss of wildlife.
FIGURE B2.1: Impact on environmental deterioration against GDP growth
0%
1%
2%
3%
4%
5%
6%
0% 10% 20% 30% 40% 50% 60% 70% 80%
90%
GDP growth
Environmental deterioration
This curve in fact represents an efficiency frontier, in the sense that it bounds a feasible set of growth rates and degrees of
environmental deterioration (ED). Points below the curve are both feasible and inefficient. In the first part, for example, a
combination of 3 percent GDP growth and 20 percent ED could be improved upon by increasing growth at the same level of
ED or by reducing ED and maintaining the same level of growth. In the second part of the curve, this would also be possible
by exploiting the two branches of the curve. For example, the combination of 1 percent growth and 60 percent ED could
be improved upon by increasing growth up to the declining branch with the same amount of ED, or by decreasing ED with
the same amount of growth by moving to the increasing branch of the curve. The latter case, however, would signal a much
larger inefficiency than the former one.
More generally, the non-monotonic relationship between economic and ecological outcomes, popularized as the Kutznets
curve, suggests that growth-depressing feedback may indefinitely prolong the negative relationship between develop-
ment, inequality, and a deteriorating environment. For example, the limits theory (Arrow et al. 2013) defines the economy-
environment relationship in terms of environmental damage hitting a threshold beyond which production is so badly affected
that the economy shrinks. The so-called new toxics view claims that emissions of existing pollutants are decreasing with
economic growth, but the new pollutants substituting for them increase with growth. In fact, consistent with the new toxics
hypothesis, the U.S. EPA claims that it receives premanufacturing notices to approve over 1,000 new chemicals each year.
declining portion of this frontier. This is a region where
good conservation becomes good economics.
The impact on income distribution reflects, to an extent,
the changes in value added, and is also highly asymmet-
ric across regions and income groups (Table 2.7). In spite
of the surge in agriculture in all scenarios, the rural poor
appear to be the population group most disadvantaged
by the negative effects on the tourism industry, espe-
cially in the North. This is unsurprising as the evidence
on conservancies presented in Chapter 5 suggests that
wildlife tourism provides employment to sections of the
labor market with low levels of human capital and few
fungible skills.
61641_Kenya_Wildlife_Tourism_new.indd 22 11/4/19 3:07 PM

WEIGHING THE IMPACTS: GENERATING SCENARIOS AND SIMULATING TRADEOFFS 23
TAB LE 2.7: Impact on income distribution of an increase in infrastructure and a greater reduction
in wildlife in the South (percent from baseline)
Wildlife (30%)
Tourism demand
elasticity = 1
Wildlife (30%)
Tourism demand
elasticity = 1.5
Wildlife (77%)
Tourism demand
elasticity = 1
Wildlife (77%)
Tourism demand
elasticity = 1.5
South (Region A)
Enterprises 4.50 4.17 1.21 0.80
Rural poor 5.24 4.80 –1.79 –2.33
Rural non-poor 5.25 4.81 –1.60 –2.14
Urban poor 4.07 3.71 0.07 –0.38
Urban non-poor 3.97 3.61 –0.22 –0.66
Government 4.51 4.15 –0.92 –1.37
North (Region B)
Enterprises 2.55 2.14 –8.82 –9.29
Rural poor –0.15 –0.53 –12.68 –13.11
Rural non-poor 0.00 –0.38 –12.36 –12.80
Urban poor 1.77 1.38 –7.45 –7.88
Urban non-poor 1.78 1.43 –6.58 –6.99
Source: Elaboration of the Kenya CGE model.
SCENARIO 3: Combining conservation and infra-
structure policies—capturing the elusive win-wins
A third set of simulations assesses the possible con-
sequences of win-win policies, i.e., policies aimed at
increasing (doubling) investment by targeting both envi-
ronmental preservation and efficiencies. For this purpose,
three components of possible investment policies were
analyzed: (i) expanding conservancies, (ii) preserving
wildlife, and (iii) increasing productivity through “smart”
infrastructure of the kind described in the next chapter.
As Tables 2.8 and 2.9 show, doubling the investment
in conservancies has an overall positive effect (invest-
ment multiplier = 1.9 in terms of total value added). Its
distribution is regionally unbalanced, however, with the
investment boosting overall economic activities in the
North, but with most benefits spilling over to the South.
Natural capital activities (maintenance and conservation)
increase in both regions.
When investments in conservancies are also com-
plemented with wildlife preservation,8 the results in
Tables2.10 and 2.11 suggest a synergic effect, with a high
beneficial impact (investment multiplier = 2.42), which
would favor a pattern of growth more balanced across
regions and income groups. The simulations indicate
8 Wildlife preservation includes all investment aimed at identifying, protecting,
and expanding key areas to help wildlife thrive, and in many cases, recover
from endangered and threatened status.
TAB LE 2. 8: Doubling the investment in conservancies: impact on value added
Value added
($, millions)
South (Region A) North (Region B) % change
Base case Simulation Base case Simulation Region A Region B
Labor 19,324.90 19,500.00 1,764.40 1,764.90 0.9 0.0
Capital 31,278.20 31,497.90 2,607.40 2,634.20 0.7 1.0
Land 5,163.20 5,292.00 895.40 911.30 2.5 1.8
Ecoservices 1,214.00 1,289.30 700.30 701.90 6.2 0.2
Source: Elaboration of the Kenya CGE model.
61641_Kenya_Wildlife_Tourism_new.indd 23 11/4/19 3:07 PM

24 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
that agriculture and livestock would contract (moder-
ately) in the South and expand in the North, where the
economy would grow in terms of both value added and
personal incomes.
Tables 2.12 and 2.13 show the results of the simulations of
a hypothetical scenario that involves combining “smart”
technologies with traditional conservation techniques
through productivity increases and resource allocation.
The Spatial Monitoring and Reporting Tool (SMART),
already in use in some conservancies in Kenya, is one
such example of a new technology. It is a protected area
management tool designed to measure, evaluate, and
improve the overall effectiveness of law enforcement
patrols.9 In this simulation, the model predicts synergis-
tic effects with more than proportional increases of the
multipliers. The impact on incomes is large and more
balanced across regions and income groups, with the
North and the poor reaping the largest benefits. In sum,
9 https://loisaba.com/smart-using-cutting-edge-technology-monitor-loisabas-
wildlife-populations/
TAB LE 2. 9: Doubling the investment in conservancies: impact on income distribution
Income
($, millions) 
South (Region A) North (Region B) % Change
Base case Simulation Base case Simulation Region A Region B
Enterprises 31,278.20 31,497.90 2,607.40 2,634.20 0.7 1.0
Rural poor 7,996.50 8,102.30 1,683.90 1,698.30 1.3 0.9
Rural non-poor 13,069.50 13,238.20 2,607.00 2,629.40 1.3 0.9
Urban poor 1,437.90 1,450.40 214.80 216.10 0.9 0.6
Urban non-poor 36,365.70 36,693.50 6,480.90 6,523.90 0.9 0.7
Investment in conservancies 285.4 570.8 0.1 0.2
Source: Elaboration of the Kenya CGE model.
TAB LE 2.10: Doubling investment in conservancies and wildlife conservation: impact on value added
Value Added
($, millions)
South (Region A) North (Region B) % Change
Base case Simulation Base case Simulation Region A Region B
Labor 19,324.90 20,884.90 1,764.40 2,503.30 8 .1 41.9
Capital 31,278.20 33,931.20 2,607.40 4,005.90 8.5 53.6
Land 5,163.20 7,722.90 895.40 2,218.30 49.6 147.7
Ecoservices 1,214.00 2,374.70 700.30 1,964.00 95.6 180.5
Source: Elaboration of the Kenya CGE model.
TABLE 2.11: Doubling investment in conservancies and wildlife conservation: impact on income distribution
Income ($, millions)
South (Region A) North (Region B) % Change
Base case Simulation Base case Simulation Region A Region B
Enterprises 31,278.20 33,931.30 2,607.40 4,005.90 8.5 53.6
Rural poor 7,996.50 9,615.10 1,683.90 2,772.90 20.2 64.7
Rural non-poor 13,069.50 15,642.00 2,607.00 4,261.90 19.7 63.5
Urban poor 1,437.90 1,580.70 214.80 303.30 9.9 41.2
Urban non-poor 36,365.70 40,401.30 6,480.90 8,903.90 11.1 37.4
Investment in conservancies 285.4 570.8 0.1 0.2
Investment in wildlife 1,598.8 3,197.5 529.7 1059.3
Source: Elaboration of the Kenya CGE model.
61641_Kenya_Wildlife_Tourism_new.indd 24 11/4/19 3:07 PM

WEIGHING THE IMPACTS: GENERATING SCENARIOS AND SIMULATING TRADEOFFS 25
“smart” investments in conservation could be a “win-
win” policy with huge gains for both regions, a healthy
balanced expansion of the economy, and larger benefits
for the rural poor.
Rural poverty and tourism
Rural poverty and the conservation of natural capital are
linked to each other in several ways. First, a majority of the
rural poor directly depend for their livelihoods on agricul-
ture, pastoralism, and other natural resource–dependent
livelihoods. Second, this dependence, while supporting
their subsistence status, is also risky, as it exposes them
to the vagaries of weather and the oscillations of market
prices. Third, because the population continues to grow
at very high rates, the pressure on land increases and
productivity (per person) tends to fall, making the plight of
pastoralists and small farmers facing a shrinking resource
base ever more dramatic. Because landholdings are sub-
divided across an increasing population, the expansion
of agriculture at the expense of traditional pastoralism
and ecological balance has also undermined the pro-
ductivity of natural capital. The overall effects of these
trends has resulted in a negative link between population
growth and agricultural expansion on the one hand, and
the productivity of renewable natural capital on the other.
On the positive side, a significant reduction of rural pov-
erty has occurred because the pattern of development
in Kenya has been sufficiently diversified to offer both
alternative and complementary economic opportunities
to the rural populations. In the past 20 years, Kenya has
developed a diversified industrial and service economy,
with a vibrant tourism industry, which is itself diversified
across the whole range of the country’s considerable
supply of alternative products, from beaches to land-
scapes rich in wildlife. Nature-based tourism thrives on
a value chain directly dependent on local agriculture,
agroindustry, and specialized services.
The development of tourism in Kenya is thus a part of the
transformation from quasi-subsistence into commercial
TAB LE 2.12: Doubling investment and capital productivity in conservancies and wildlife conservation: impact on
value added
Value Added
($, millions)
South (Region A) North (Region B) % Change
Base case Simulation Base case Simulation Region A Region B
Labor 19,324.90 22,029.30 1,764.40 3,102.80 14.0 75.9
Capital 31,278.20 36,626.40 2,607.40 5,476.40 17.1 110.0
Land 5,163.20 9,358.40 895.40 3,284.50 81.3 266.8
Ecoservices 1,214.00 3,142.90 700.30 2,993.00 158.9 327.4
Source: Elaboration of the Kenya CGE model.
TAB LE 2.13: Doubling investment and capital productivity in conservancies and wildlife conservation: impact on
income distribution
Income
($, millions)
South (Region A) North (Region B) % Change
Base case Simulation Base case Simulation Region A Region B
Enterprises 31,278.2 36,626.5 2,607.4 5,476.4 17.1 110.0
Rural poor 7,996.5 10,769.4 1,683.9 3,702.5 34.7 119.9
Rural non-poor 13,069.5 17,492.1 2,607 5,676.2 33.8 117.7
Urban poor 1,437.9 1,701.1 214.8 383.8 18.3 78.7
Urban non- poor 36,365.7 43,777 6,480.9 11,055.3 20.4 70.6
Investment in conservancies 285.4 570.8 0.1 0.2
Investment in wildlife 1,598.8 3,197.5 529.7 1,059.3
Source: Elaboration of the Kenya CGE model.
61641_Kenya_Wildlife_Tourism_new.indd 25 11/4/19 3:07 PM

26 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
agriculture and brings with it greater integration into the rest
of the economy. Increasing reliance on the market has sev-
eral dimensions, including the share of consumption that is
purchased in the market, expenditure for food as a share of
total expenditure, old and new sources of off-farm income,
debt, and the need for storage facilities. Tourism-related ser-
vices and employment provide a series of backward link-
ages that increase the flexibility of the farm household in
ordinary times, reduce its direct and exclusive dependence
on agricultural markets, and make the poor more resilient
to adverse shocks. The backward linkages of tourism to the
rural economy may thus improve income prospects and sta-
bility for all the rural population, including the rural poor.
The CGE captures the interdependence between the
rural economy and nature-based tourism, both through
the estimates of transactions across the value chains,
and through the regional and economy-wide multipli-
ers arising from backward and forward linkages. Gen-
eral equilibrium price effects are also estimated by the
model, which registers a rise in value added through
both higher factor employment and higher prices of land.
For example, Figure 2.8 shows how in the CGE base
solution, household income elasticities, with respect to
park tourism expenditure (i.e., the percentage increases
in incomes following a 100% increase in park tourism
expenditure), range from 3.4 percent to about 2.3per-
cent across income groups in the two regions. The elas-
ticities decline smoothly from their highest value for the
rural poor in the South to the urban poor in the North,
but their basic values are not very different across the
various income groups.
If all tourism-related activities (not just expenditures
on maintaining parks) are given a boost by increasing
investment in parks and conservancies, as shown in
Figure 2.8, income elasticities (percentage increases in
incomes in response to 100 percent increase in spend-
ing) rise significantly (ranging from 25 percent to 16 per-
cent) and the difference in response between rural and
urban and poor and non-poor groups is heightened. For
completeness Figure 2.8 and Figure 2.9 also show how
these elasticities vary between urban and rural areas.
Concluding comments
The CGE model developed for this study presents a
picture of the Kenyan economy, with stark differences
of factor supply and employment between the more
developed South and the less developed North. The
two regions are interdependent to an extent, especially
because most of the industrial and service value added
is produced in the South. These linkages result in invest-
ments in the North generating larger spillovers in the
South in absolute terms, following a pattern common to
many unequal regional economies. At the same time, for
activities that depend on open spaces and nature, dam-
age to wildlife and tourism value chains in the South tend
to negatively affect both regions. However, absolute
FIGURE 2.8: Income elasticities
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
Elasticities
Household income
Income elasticities with respect to park tourism
expenditure (ESAM base values from multipliers)
Rural poor South
Rural non-poor South
Urban poor South
Urban non-poor South
Rural poor North
Rural non-poor North
Urban poor North
Urban non-poor North
61641_Kenya_Wildlife_Tourism_new.indd 26 11/4/19 3:07 PM

WEIGHING THE IMPACTS: GENERATING SCENARIOS AND SIMULATING TRADEOFFS 27
effects are larger in the South, while relative damages
are proportionally higher for the nascent tourism activi-
ties in the North.
The present surge of infrastructure investment in Kenya
is thus likely to bring some benefits to the already devel-
oped regions, though this will come at a cost of increasing
congestion and aggravating inequalities and environmen-
tal damage. Where the damage is large, it could outweigh
the benefits. The reason is that the decline in wildlife
results in a drastic fall of nature-based tourism in both
regions, as well as a decline in many service sectors due
to the linkages. Perhaps of greater concern is that these
impacts are disproportionately felt by the rural poor and
in the North. Prospects of development in this region
thus appear to be vulnerable to investment choices in the
South because of the concentration of economic activities
in this more developed region and the widespread nega-
tive effects on the environment and tourism in the North.
In sum, if wildlife reduction is large (which it is now),
and/ or demand elasticities of tourism are high (which is
also probably true), higher investment in infrastructure
may lead Kenya into a development trap, where major
negative effects on wildlife, the environment, and tour-
ism ultimately hamper both its resources and its eco-
nomic growth. The empirical findings suggest that, at the
present, Kenya is moving closer to this trap, which it will
likely only escape by appropriately combining invest-
ment in both infrastructure and conservation policies.
REFERENCES
Arrow, K. J., P. Dasgupta, L. H. Goulder, K. Mumford, and
K. Oleson. (2013).“Sustainability and the Measurement
of Wealth: Further Reflections.” Environment and Devel-
opment Economics 18 (4).
Hanson, Kenneth, Sherman Robinson, and Stephen Tokarick.
(1989). Working paper no. 510 United States Adjustment
in the 1990s: A CGE Analysis of Alternative Trade Strat-
egies. California Agriculture Experiment Station, Univer-
sity of California, Berkeley.
Hinojosa-Ojeda, R., and S. Robinson. (1991). “Alternative sce-
narios of US-Mexico integration: A computable general
equilibrium approach.’ Working paper series, California
Agricultural Experiment Station, Department of Agricul-
tural and Resource Economics.
Perali, F., and Scandizzo, P. L., eds. (2018). The New Gen-
eration of Computable General Equilibrium Models,
Springer.
Reinert, K. A., and C.R. Shiells. (1991). Trade Substitution
Elasticities for Analysis of a North American Free Trade
Area. US International Trade Commission.
Robinson et al. (1989). Multisectoral models. In Handbook
of development economics, vol. II, ed. H. Chenery and
T. N. Srinivasan. Amsterdam: Elsevier Science Publishers.
Sanghi, Apurva, Richard Damania, Farah Manji, and Maria
Paulina Mogollon. (2017). Standing out from the herd:
An economic assessment of tourism in Kenya (English).
Washington, D.C., World Bank Group.
Scandizzo, Pasquale L., and Ferrarese, Cataldo. (2015).
“Social Accounting Matrix, a New Estimation Methodol-
ogy,” Journal of Policy Modeling 37 (1).
FIGURE 2.9: Income elasticities
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
Elasticities
Income groups
Income elasticities with respect to conservation
tourism activity level (ESAM base values from multipliers)
Rural poor South
Rural non-poor South
Urban poor South
Urban non-poor South
Rural poor North
Rural non-poor North
Urban poor North
Urban non-poor North
61641_Kenya_Wildlife_Tourism_new.indd 27 11/4/19 3:07 PM

28
CHAPTER 3
WILDLIFE-FRIENDLY ROADS: FABLE OR FACT?
The development of new roads in Kenya will be crucial to
spurring growth and human development, and promot-
ing shared prosperity. At the same time, as established
in previous chapters, the expansion of Kenya’s road
network ranks high in the list of factors contributing to
wildlife loss. As the CGE analysis has indicated, where
wildlife losses are substantial, the economic benefits
that a road brings may not outweigh the benefits for-
gone, especially in the more remote parts of the country
where economic opportunities are limited (and multipli-
ers are small). This problem would be overcome if it were
possible to construct a road with limited impact on wild-
life in ways that minimize losses and maximize benefits.
This chapter presents a new tool that can help identify
which roads should be developed based not only on
their economic potential, but also factoring in the pos-
sible negative impacts on wildlife. The results highlight
the existence of important margins to develop an eco-
nomically inclusive road network that at the same time
acknowledges externalities and is respectful to wildlife.
New approaches to enhancing
road access
Roads are key for economic development, and as pre-
viously highlighted, a staggering 70 percent of Kenya’s
rural population still lives more than 2 kilometers from
an all-season road. The SDGs promote the construc-
tion of all-season roads, defined as roads motorable all
year round by the prevailing means of rural transport. In
the relatively dry context of Kenya, in addition to tarmac
roads, paved and improved roads are also considered as
all-season roads. Indicator 9.1.1 of the SDGs encourages
policy makers to increase the share of the rural population
living within a 2-kilometer distance of an all-season road,
calculated as the Rural Access Index (RAI). Earlier studies
first measured RAI using household survey data (Roberts
et al. 2006), but advances in technology and the use of
GIS data have significantly expanded the scope of such
analyses, notably in data-poor contexts (Iimi et al. 2016).
The approach based on GIS data was refined and scaled
to 166 countries by Mikou et al. (2019), who also devel-
oped a tool to help predict which all-season roads should
be built by upgrading existing tracks in order to maximize
the RAI. Indeed, an algorithm using information pertain-
ing to where the population lives, where all-season roads
exist, and where other roads/tracks are located can lead
to prioritizing road improvements that connect the highest
number of people to the network at the lowest cost.
This chapter applies the method pioneered by Mikou
etal. (2019) to the Kenyan context and goes a step further
to take into account the externalities generated by the
road network. The method relies on data of human pop-
ulations, existing roads, and a set of possible new roads.
WorldPop data from 2015 provides gridded estimates of
population distributions at a 1-kilometer resolution, and
similar to the methodology outlined in Chapter1, data for
Kenya’s existing major roads are derived from Michelin
maps (2017). DeLorme data for Kenya is used to identify
paths and tracks that are potential candidates for new
roads (Figure 3.1). The DeLorme dataset is considered
to be comprehensive and up-to-date regarding transpor-
tation infrastructure, including roads, paths, and tracks,
but it is limited in terms of information on the quality of
surfacing. Michelin data are then used to more precisely
classify which segments correspond to existing all-
weather roads and which ones correspond to paths or
tracks. The latter are then used as candidate segments
for possible extensions of the road network.
Conventional approaches
ofincreasing road access
Conceptually, a road network is a mathematical graph.
This graph can be extended by converting a track
61641_Kenya_Wildlife_Tourism_new.indd 28 11/4/19 3:07 PM

WILDLIFEFRIENDLY ROADS: FABLE OR FACT? 29
connected to the current network into a road. An algo-
rithm determines all possible new graphs that would
be formed by the connection of one new track to the
existing graph. For each graph, the new RAI is calcu-
lated. By doing so, the algorithm determines which
track leads to the highest increase in the RAI, and
based on the length of each segment, it determines
the cost of converting this segment into a road. Here,
the construction cost of the road is a linear function of
the length of the new road (see Mikou et al. 2019 on
costs). The track that brings the maximum increase of
the RAI at the lowest cost is chosen and added to the
road network. A more complete mathematical graph is
consequently formed, and the procedure is repeated
until no gain in the RAI is possible. This method from
Mikou et al. is used to determine a set of priority roads.
It adopts what could be termed a “business as usual”
scenario in which the negative effects of roads on
wildlife are not internalized or considered in the con-
struction process.
According to available data, the share of Kenya’s rural
population living within 2 kilometers of an all-season
road is currently around 28 to 30 percent. The algorithm
developed here suggests that this RAI could be increased
to more than 50 percent simply by converting existing
tracks to roads. Figure 3.2 displays the marginal and total
cost of increasing the RAI, expressed as a percentage
of GDP. The cost of increasing the RAI is fairly constant
from the current level up to about 45percent of the popu-
lation. The figure indicates that for about 2.5percent of
current GDP, an additional 15percent of the rural popula-
tion could be connected to the road network. This addi-
tional 15 percent roughly represents 6 million new people
who primarily live in Kenya’s densely populated western
counties and around Nairobi (Figure 3.3).
FIGURE 3.1: Existing all-weather roads and tracks in Kenya
Source: Michelin maps (roads); DeLorme data (tracks).
61641_Kenya_Wildlife_Tourism_new.indd 29 11/4/19 3:07 PM

30 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
Progressively, more remote areas start to be con-
nected to the network. However, the cost of con-
necting each additional household sharply increases;
for instance, increasing the RAI from 45 percent to
46 percent would cost an additional 0.5 percent of
GDP. Even more so, bringing the RAI to 52 percent
(from 51 percent) would cost a further 2.5 percent of
GDP—which is the same cost as connecting the first
15 percent of the population to the network. This is
consistent with global trends observed by Mikou et al.
(2019) across Sub-Saharan Africa. However, thanks to
the higher GDP of Kenya compared to most other Afri-
can countries, the relative cost of increasing its RAI,
expressed in GDP, is lower.
FIGURE 3.2: The costs of increasing Kenya’s RAI under the “business as usual” scenario
RAI (%)
Marginal cost of increasing RAI
Cost (% of GDP)
30
0.0
0.5
1.0
1.5
2.0
35 40 45 50
Cumulative cost (% of GDP)
RAI (% )
0.0
30
35
40
45
50
2.5 7.55.0 12.510.0 15.0
17.5
Source: Michelin and DeLorme data; Method developed by the World Bank.
FIGURE 3.3: Building new roads to increase Kenya’s RAI, starting with the densely populated western counties
Source: Michelin and DeLorme data; Method developed by the World Bank.
61641_Kenya_Wildlife_Tourism_new.indd 30 11/4/19 3:07 PM

WILDLIFEFRIENDLY ROADS: FABLE OR FACT? 31
Counting the costs of business
asusual
What would the environmental cost of the “business as
usual” scenario be? Of primary interest, the western part
of Kenya,10 where many roads would be upgraded, falls
outside the rangelands and is home to limited wildlife.
This suggests that a large part of the rural population
could be connected to the road network at a low envi-
ronmental cost in terms of biodiversity loss.
To quantify the impact on wildlife when this conventional
“business as usual” approach is used, biomass data of
ungulate wildlife, derived from DRSRS, are overlapped
with roads. Using the estimates presented in Chapter 1,
it is assumed that the conversion of a track into a road
would lead to a decline of wildlife in a 20-kilometer buf-
fer around the newly built road. At each step of the algo-
rithm, wildlife loss in each extension is calculated.
10 This would include the following counties: Migori, Homa Bay, Kisii, Nyamira,
Bomet, Kericho, Kisumu, Nandi, Vihiga, Siaya, Busia, Bungoma, Trans-Nzoia,
Marakwet, Uasin Gishu, Nakuru, Nyandarua, Nyeri, Muranga, and Nairobi.
Figure 3.4 shows how much wildlife would be lost as the
RAI increases. The results suggest that the costs for wild-
life associated with extending the road network slowly
begin increasing and are followed by losses of wildlife
sharply increasing as more people are connected to the
network. Even among the first road segments built in
the rangelands, important wildlife areas are threatened.
Observe that the impact on wildlife is constant for the first
1.5 million people connected to the network, and that it
jumps very dramatically thereafter. The CGE analysis in
Chapter 2 warns that losses of this scale bring adverse
GDP consequences, especially in areas with limited
potential for growth and labor-intensive employment.
A greener scenario
However, even if the costs outweigh the benefits of such
policies outlined above, it is unlikely that this would pre-
vent the construction of roads in the rangelands. This
FIGURE 3.4: Wildlife loss is constant for the first 1.5million people connected to the road network, with losses
sharply increasing thereafter
Population with new access to all-weather roads
Impacted wildlife (kg)
0
0
500,000
50,000
1,000,000
100,000
1,500,000
150,000
2,000,000
200,000
2,500,000
250,000
3,000,000
Source: Authors.
61641_Kenya_Wildlife_Tourism_new.indd 31 11/4/19 3:07 PM

32 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
section demonstrates that a more careful extension of
the road network allows for as many people to be con-
nected to the network as in the “business as usual” sce-
nario, at a similar cost, but with moderate consequences
for wildlife.
To run the greener scenario, the original algorithm from
Mikou et al. (2019) was modified, allowing for the con-
sequences of road construction on wildlife to be con-
sidered. Thus far, the objective function of the algorithm
was to maximize the number of people connected to
the network at the lowest cost. In this section, an extra
parameter is added: simultaneously minimizing the
impact on wildlife. As is standard in statistical analysis,
human population data and wildlife biomass data were
normalized and scaled over the same support to ensure
that neither one was overweighed in the algorithm.11 In
doing so, the objective of this approach was to find areas
where roads could be built to maximize access and mini-
mize impact on wildlife.
11 Mathematically, the objective function of the algorithm is: maximizing people/
(km*wildlife impacted).
The results are promising. The first striking finding is that
both models (the original one as well as the model with
the added parameter on wildlife impact) attain the same
increase in the RAI at comparable cumulative costs (Fig-
ure 3.5). When focusing only on the rangeland counties
for which there is biodiversity data, the current RAI of
about 28 percent could be increased with both models
to approximately 38 percent. This holds for the model
that does not include a wildlife constraint (green line) as
well as the modified model that factors in a wildlife con-
straint (orange).
Of crucial importance, the model that includes the wild-
life constraint allows for a significant reduction in the loss
of wildlife from increased road access. Figure 3.6 com-
pares the environmental effectiveness of both models,
highlighting that the modified model (orange line) offers
solutions to connecting people to the road network while
having limited detrimental effects on wildlife.
Under the original model, wildlife is lost after approxi-
mately 500,000 people are connected to the road
network (green line), while wildlife loss in the modified
model only happens after 2 million people gain access
to these improved roads. Further, while wildlife loss in
FIGURE 3.5: The costs of increasing Kenya’s RAI under the two scenarios
Cumulative cost (% of GDP)
Normal RAI
RAI (%)
02 468101
21
4
30
0
32
34
36
38
RAI with wildlife constraint
61641_Kenya_Wildlife_Tourism_new.indd 32 11/4/19 3:07 PM

WILDLIFEFRIENDLY ROADS: FABLE OR FACT? 33
the first model skyrockets after 1.5 million people are
connected, under the modified model, this happens
after 2.5 million people gain access to the road network.
Hence, most people could be connected to the network
while avoiding negative impacts on wildlife.
Fine-tuning the model
The results presented above come with a few caveats.
More than definitive results, the value of this exercise
lies in its original approach—developing a tool that could
be used to inform decision making and to understand
the trade-offs between wildlife protection and economic
opportunities. The model developed could also be fur-
ther refined to provide more fine-tuned policy messages.
The protection of wildlife corridors has become a critical
aspect for wildlife protection in Kenya, as most are under
intense threat of conversion for other land use. Similar
to the way wildlife density data were introduced into the
model, data on wildlife routes could also be included.
Precise information on these routes is being gathered by
leading experts in Kenya and could become a valuable
source of information for this model (Figure3.7).12 In addi-
tion, though the model built in this instance was trained
to prioritize road improvement in order to connect the
highest number of people to the network, a similar model
could be adjusted to connect the area with the highest
agricultural potential to the network, or areas with the
highest poverty rates to the network. This would con-
stitute a fine-tuning of the model but would not change
the central message: huge opportunities exist to extend
Kenya’s road network and to protect wildlife at the same
time.
In sum, smarter, greener approaches to infrastructure
are also economically more beneficial. Achieving this
outcome is not impossible, and it requires policy mak-
ers to properly identify areas where roads should not be
constructed.
12 Among other refinements, we should note the possibility of varying the
functional form of the objective function of the algorithm—the size of the
buffers built around each road for which we assume an impact on wildlife (here
20kilometers). It could be 10 kilometers in a “more aggressive scenario” or
30kilometers in a “more conservative scenario.”
FIGURE 3.6: Factoring in wildlife constraints significantly reduces the impact of new roads on wildlife
Population with new access to all-weather roads
3,000,0002,500,0002,000,0001,500,0001,000,000500,000
25,000
0
50,000
75,000
100,000
125,000
150,000
175,000
200,000
Impacted wildlife (kg)
0
Current situation More wildlife friendly roads
61641_Kenya_Wildlife_Tourism_new.indd 33 11/4/19 3:07 PM

34 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
FIGURE 3.7: Mapping elephant and wildebeest routes in Kenya
Source: Ojwang et al. (2017).
61641_Kenya_Wildlife_Tourism_new.indd 34 11/4/19 3:07 PM

WILDLIFEFRIENDLY ROADS: FABLE OR FACT? 35
REFERENCES
Iimi, A., F. A. K. Ahmed, E. C. Anderson, A. S. Diehl, L. Maiyo,
T. Peralta Quiros, and K. S. Rao. (2016). New Rural Access
Index : Main Determinants and Correlation to Poverty.
Washington, D.C.The World Bank.
Mikou, M., J. Rozenberg, E. Koks, C. Fox, and T. Peralta Quiros.
(2019). Assessing Rural Accessibility and Rural Roads
Investment Needs Using Open Source Data. Washington,
D.C.The World Bank.
Ojwang, Gordon O., et al. (2017). Wildlife Migratory Corridors
and Dispersal Areas: Kenya Rangelands and Coastal Ter-
restrial Ecosystems.
Roberts, P., S. KC, and C. Rastogi. (2006). Rural Access Index:
A Key Development Indicator. Washington, D.C.The World
Bank.
61641_Kenya_Wildlife_Tourism_new.indd 35 11/4/19 3:07 PM

36
CHAPTER 4
THE WAY FORWARD AND NEXT STEPS
A 70 percent decline in wildlife, within thirty years, is a
sobering statistic. As Kenya’s population grows, its infra-
structure needs expand, and climate change makes
rainfall more erratic, and the pressures on wildlife and
natural habitats will intensify in regions that are already
under environmental stress and will spread to other parts
of the country. The journey along the current policy path
has failed to halt the degradation and fragmentation of
natural habitats, and it is unlikely to do so in the future
when pressures expand and competition for land, water,
and other natural resources intensifies. This suggests
an urgent need for a careful reassessment of pressures,
policies, and future prospects.
Wildlife in Kenya, especially in the North of the country, rep-
resents a lucrative economic asset whose contribution has
been underestimated and potential unrealized. The CGE
assessment indicates that every dollar invested in conser-
vation and wildlife tourism could generate benefits that
range from $3 to $20. For comparison, it is instructive to
note that in the United States and Brazil, $1 invested in pro-
tected areas generates approximately $6–$8 as a return
(do Val Simardi Beraldo Souza, 2017). Table 4.1 illustrates
that in Kenya the economic benefits from investments in
wildlife tourism rise with the amount that is invested. Such
increasing returns likely reflect the ecological importance
of connected natural habitats that are more productive in
terms of the ecosystem services that they provide and are
also more resilient to droughts and other weather extremes
(Haddad et al. (2015). In the remote and arid North of the
country there are few other investments that could yield a
comparable economic return.
TABLE 4.1: GDP multipliers for investments
(in million USD) in conservancies
10 50 100 500 1,000
Investment in
conservancies
North 3.13 3.16 3.19 3.52 4.02
South 5.43 5.63 5.89 9.07 20.2
Realizing this economic potential will call for a significant
shift in two key policy areas. First, it will require changes
in the way in which intrusive infrastructure is planned
and located to avoid the fragmentation and conversion
of natural habitats with economic potential. Second,
there is a need to create the enabling conditions to real-
ize the economic potential through investments in con-
servancies at scale. Neither approach will be sufficient
on its own and both will need to work in tandem: the first
to prevent the loss of economic opportunities by land
conversion, and the second to harness economic poten-
tial through investments. The remainder of this chapter
discusses critical elements of this approach.
Smart infrastructure
Where ecotourism potential exists, it is important that
infrastructure investments are done with consideration of
ecotourism’s impacts on these assets. The fact that much
remains to be built creates an opportunity to build “right.”
Getting infrastructure “right” is critical because infra-
structure choices have long-lived and difficult-to-reverse
impacts on land, wildlife, water, and future patterns of
development. Infrastructure decisions influence the type
and location of development and, as such, create sub-
stantial inertia in economic systems, with irreversible con-
sequences that need to be weighed against alternatives.
Recognition of these complex issues suggests the need
for a different approach to infrastructure needs with a
focus on “building right” rather than simply “building more.”
Building right typically brings benefits that accrue over the
longer term. The fact that infrastructure needs are so large
implies that there are wide opportunities to build right—
garnering benefits while minimizing or avoiding possible
negative impacts on the country’s comparative advantage.
The right infrastructure also offers substantial co- benefits
that could enhance the productivity and earning capacity
61641_Kenya_Wildlife_Tourism_new.indd 36 11/4/19 3:07 PM

THE WAY FORWARD AND NEXT STEPS 37
of the country’s natural capital. The trade-offs and syner-
gies from infrastructure and roads are considerable and
warrant closer examination in decision making. This is
especially important for remote parts of Kenya with a
limited natural comparative advantage for arable agri-
culture. Where appropriately managed, there are con-
siderable synergies between wildlife tourism and cattle
ranching, both of which offer climate resilient livelihood
opportunities in areas with limited economic potential.
As human population densities increase throughout
Africa, there will be a growing premium on places that
offer such experiences. Destroying this economic poten-
tial could be a short-sighted strategy.
Development of large strategic infrastructure to pro-
mote connectivity can be consistent with efforts to
conserve natural assets, which also contribute to eco-
nomic growth. As illustrated in Chapter 3, tools are
available that allow planners to predict the impacts of
their decision on wildlife—a key economic asset. The
same tools can be used to predict how to meet other
development objectives more effectively. Through
careful and strategic planning, spending on infra-
structure can be rendered more effective and more
conducive to growth and poverty reduction, and less
impactful on wildlife and the economic opportunities
that they bring (Box 4.1). The additional complexity
and cost of planning, such as in infrastructure, would
be justified by the vastly greater benefits that would
accrue to the country.
Realizing economic opportunities
through conservancies
Conservancies could play a crucial role in halting the col-
lapse of wildlife in Kenya by extending the areas under
protection around parks, reconnecting habitats, and
limiting overcrowding in parks. And more than that, con-
servancies offer levers to boost and diversify economic
BOX 4.1: Smart Infrastructure and Spatial Planning
The lack of spatial planning when combined with inadequate investment in infrastructure can create dynamics that are
unsustainable and non-inclusive. There are significant deficiencies with the piecemeal and project-by-project assessment
of each investment alternative in isolation.
One obvious consequence is that options which generate higher benefits may be overlooked since the focus is on a single
project.
Another and seldom recognized problem is that of “dynamic inconsistency”: where the first project unleashes conse-
quences for other projects. For instance, suppose that the first project diminishes environmental quality in a protected area.
This makes it more likely that another intrusive structure will “pass” a cost-benefit test. The first project therefore unleashed
a dynamic that leads to complete transformation of the landscape, which was not considered at the outset. This is termed
dynamic inconsistency and leads to poor decision making and economically unwarranted destruction of natural assets.
Against this background of escalating and suboptimal land conversion, two new concepts of spatial planning are advanc-
ing, both require prioritizing ecosystem services (forests, rural areas, watersheds, urbanized vast areas, etc.). One approach
uses physical measures in GIS models to avoid damage and build synergies with ecosystems, as illustrated in Chapter 3.
The other takes a more economic approach by adopting a set of values or shadow prices that make the land use scale hier-
archical and compatible with the functionality of potential ecological networks. This requires prioritizing ecosystem services
(forests, rural areas, watersheds, etc.) by adopting a set of values or shadow prices that make the land use scale hierarchical
and compatible with the functionality of potential ecological networks. Combined with higher capacity for project manage-
ment, implementing the new concept of infrastructure is a promising strategy to invest wisely and more effectively.
In sum the idea is to make aspirations for “smart” infrastructure into a reality by using tools to combine functional efficiency,
technology, and ecosystem conservation.
61641_Kenya_Wildlife_Tourism_new.indd 37 11/4/19 3:07 PM

38 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
activities in some of the most remote parts of the coun-
try. In places where ranching and agriculture are under
stress due to shifting weather patterns, land degradation,
or overstocking, conservancies offer more sustainable
livelihood options that will inevitably increase in value as
wildlife numbers and wilderness viewing opportunities
shrink across the globe. In sum a strategic expansion of
conservancies offers an opportunity to complement the
government’s current focus
More generally, conservancies represent projects that
offer a platform to integrate ecological and economic
functions, which contrasts with the segregated conven-
tional approaches of conservation and development.
By allowing an array of organizational forms based on
the coexistence of activities involving agriculture, live-
stock, conservation, and different forms of culture and
nature-based activities, conservancies widen the menu
of choices and offer a promising strategy to end the
chaotic process of landscape fragmentation and wildlife
extirpation.
There are currently around 160 conservancies in Kenya,
spread across 28 counties, under the umbrella of Kenya
Wildlife Conservancies Association (KWCA). These cover
around 11 percent of the country’s territory, with 3.7 mil-
lion hectares in the North and 2.1 million hectares in the
South (Figure 4.1). By comparison, the terrestrial national
parks and reserves cover 4.7 million hectares.
Conservancies significantly increase the share of wild-
life living in legally protected areas. Around 22 percent
of the total ungulate wildlife biomass is found in conser-
vancies. This represents a significant complement to the
38 percent of ungulate wildlife biomass found within
Kenya’s national parks. Perhaps of greater importance,
18 out of 20 zones with the highest wildlife density are
found in conservancies rather than in parks. For exam-
ple, Olare Orok, located next to the Maasai Mara, is the
conservancy with the highest density of wildlife biomass.
Key species such as the Grevy’s zebra are mostly found
in conservancies, while lion populations in the conser-
vancies of the Maasai Mara are among the highest on
the continent (Elliot and Gopalaswamy 2017; Ogotu et al.
2017). The data suggest that there is a lag in the recovery
of ungulate biomass in conservancies with the greatest
increase occurring in conservancies that were created in
the 1980s (Figure 4.2).
The contribution of conservancies to the tourism industry
remains modest—it accounts for a meager 1.3 percent of
earnings in the industry, suggesting considerable poten-
tial and scope for expansion in a specialized market that
caters to the high-value and low-volume tourists. A sur-
vey of 13 regional associations and 160 conservancies
registered under KWCA suggests that there are around
2,510 beds available in lodges within conservancies,
and most (97 percent) are found in the southern con-
servancies. The average conservancy in the sample has
28 beds, but with considerable variation ranging from
6in Machakos, with its conservancies being in the early
stages of development, to over 1,000 in Narok, which
abuts the overcrowded Maasai Mara.
Tourism is the most significant source of income for
conservancies, contributing an average of 83 percent
of commercial income with buoyant growth in recent
years.13 Cattle ranching has, over the past years, gained
prominence and offers a way to diversify income
sources. A key challenge is to keep livestock herds in
balance with wildlife numbers in cultural contexts where
livestock is more than an economic asset. Iconic animal
conservation programs (of species such as rhino, ele-
phant, Grevy’s zebra, chimpanzee) and other payment
for environmental service programs are also a significant
contributor to incomes, with conservancies earning an
average of Kenya shilling (Ksh) 12.8 million in 2016 and
Ksh 11 million in 2017 from conservation fees.
For communities who live within or near conservancies,
there are significant benefits. The survey indicates that
the 160 conservancies hired around 2,600 people, and
provide bursaries and educational support especially to
women, and are a significant source of income for food
and other provisions required by tourists. Income from
conservancies is the only drought-proof source of rev-
enue that is available to many of the poor and vulnerable
communities.
Despite these benefits, investments in conservancies
carry high risks and as such require patient capital. This
is because investors must gamble not only on the pros-
pects of attracting tourists to a new location, but must also
engage in a host of investments to build community sup-
port and fill crucial infrastructure gaps. This may suggest
13 NRT, 2018, State of Conservancies Report, 2017.
61641_Kenya_Wildlife_Tourism_new.indd 38 11/4/19 3:07 PM

THE WAY FORWARD AND NEXT STEPS 39
the need for innovative investment mechanisms such as
green bonds and risk guarantees to shift the risk-reward
balance, especially in areas that confer high ecological
benefits. Recognizing that the conservancies confer pub-
lic benefits, there is a case to be made for enabling policy
support—for example through investments in marketing
strategies aimed at both local and international travelers.
The presence of wildlife in conservancies has been the
single most important determinant of success, though this
is not sufficient to assure success. The establishment and
promotion of conservancies offers the most scalable ave-
nue in ensuring wildlife habitats are secured and migra-
tion corridors are established. Wildlife hot spot areas,
such as the Mara, Amboseli, and Laikipia regions, indicate
that high wildlife densities can lead to significant wildlife-
based tourism operations outside of national parks.
To further promote the development of tourism outside
of national parks and reserves, the national and county
FIGURE 4.1: Map of parks and conservancies in Kenya (2018)
Source: Authors.
61641_Kenya_Wildlife_Tourism_new.indd 39 11/4/19 3:07 PM

40 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
governments need to recognize the role conservancies
play as custodians of wildlife and in developing syner-
gistic livelihood enhancement programs. Integration of
conservancy management plans in the county develop-
ment plans acts as a first step to foster this recognition.
REFERENCES
do Val Simardi Beraldo Souza, T. T. (2017). Economic impacts of
tourism in protected areas of Brazil. Journal of Sustainable
Tourism, 1–15.
Elliot, Nicholas B., and Arjun M. Gopalaswamy. (2017). “Toward
accurate and precise estimates of lion density.” Conserva-
tion Biology 31.4: 934–943.
Haddad, N. M., Brudvig, L. A., Clobert, J., Davies, K. F., Gonzalez,
A., Holt, R. D., . . . and Cook, W. M. (2015). Habitat fragmenta-
tion and its lasting impact on Earth’s ecosystems.Science
Advances1(2), e1500052.
Ogutu, Joseph O., et al. (2017). “Wildlife population dynamics
in human-dominated landscapes under community-based
conservation: the example of Nakuru Wildlife Conservancy,
Kenya.” PloS one 12.1: e0169730.
Olare Orok Conservancy.” Master’s thesis, University of Oslo.
FIGURE 4.2: Wildlife generally increased in the older conservancies and decreased in areas where conservancies
were established after 1995
Year of establishment of conservancy
Percentage change in wildlife, 1980–2010
1970
–100
–50
0
50
100
150
200
250
300
350
400
450
500
550
600
1980 1990 2000 2010
600
400
200
Loss
Gain
Dyanmic
Size impact
Source: Authors using DRSRS data and conservancies data developed in this report.
61641_Kenya_Wildlife_Tourism_new.indd 40 11/4/19 3:07 PM

41
APPENDIX A
CONSERVANCIES—ANOVERVIEW
The history of conservancy development in Kenya was
founded upon conservation practices introduced by the
British colonialists in the 1800s and 1900s. These altered
the traditional land tenure system and enabled commer-
cial harvesting of wildlife, leading to significant declines
in wildlife numbers. The 1933 “London Convention” rep-
resented a turning point that marked the beginning of
the end to commercial wildlife harvesting, and it vested
authority to a central body for wildlife management. In
1946, the National Park Ordinance resulted in the estab-
lishment of Nairobi, Tsavo, Mount Kenya, and Aberdares
National Parks. Game hunting and an increase in human-
wildlife conflict in the 1950s and 1960s led to the cen-
tralization of wildlife management. Non-state protected
areas—as they were called before the term conservan-
cies was coined—emerged at this time, with the creation
of the Solio, Ol Jogi, Sangare, Sergoit, and Taita Hills pro-
tected areas for rhinos and other wildlife species.
Momentum for conservancies gained traction in the
2000s with the formation of regional conservation
groups such as The Northern Rangelands Trust (NRT)
and the South Rift Association of Landowners (SORALO).
The establishment of a national association in 2012—the
Kenyan Wildlife Conservancies Association (KWCA)—
helped to further promote the approach.14
There are currently more than 160 conservancies in
Kenya, spread across 28 counties, under the umbrella
of KWCA. The overwhelming majority of these conser-
vancies (137) are located in the country’s South, with
Kajiado and Taita Taveta counties being home to the
14 This was driven by the draft Wildlife Conservation and Management
Bill of 2011 and the Conservancy Regulations of 2012, which both explicitly
recommended devolution of rights to landholders and the institutionalization of
the wildlife industry in Kenya (Kenya Wildlife Conservancies Association, “Our
Story,” https://kwcakenya.com/about-us/our-story/). A study tour to the Namibian
Association of Community Based Natural Resource Management Support
Organization (NACSO), consultative meetings with over 600 stakeholders,
followed by a national consultative forum, enabled the endorsement and
registration of KWCA in December 2012 and April 2013, respectively.
largest number of conservancies, each hosting 25 con-
servancies, with Narok (16) and Nakuru (14) following suit.
The northern counties of Samburu, Isiolo, Marsabit, Tur-
kana, Garissa, and Mandera host a much smaller share
of Kenya’s conservancies (23), while 19 counties located
in the Central and Western regions of Kenya do not host
any conservancies at present
Of the 160 conservancies, 107 are currently operational,
44 are emerging, and 9 are proposed. As seen in Fig-
ure A.1, the three types of conservancies found in Kenya
include (i) community conservancies—those set up by a
community on community land for the purpose of liveli-
hood development and wildlife conservation; (ii) private
conservancies—those set up on private land by a private
individual or corporate body for the purpose of wildlife
conservation; and (iii) group conservancies—those which
include the creation of a single conservancy by private
landowners who pool land for the purpose of wildlife
conservation.
Community conservancies first appeared in Kenya in
the mid-1990s with support from nonprofits, neighbor-
ing private conservancies, and conservation-oriented
corporations as a way of incentivizing landowners and
communities to be custodians of wildlife. The success
of establishing Kimana in 1992, Namunyak and Koiyaki-
Lemek Wildlife Trust in 1995, and Il Ngwesi in 1996, all
of which offered direct economic benefits from wildlife-
related activities to landowners, catalyzed the growth
of the community conservation model (Figure A.1). The
establishment of group conservancies in the south-
ern counties in the 2000s was catalyzed by the need
to create wildlife dispersal areas and ensure con-
nectivity of subdivided lands outside the Maasai Mara
National Reserve and the Amboseli National Park. This
also created an opportunity to sell an exclusive wild-
life experience to visitors, promoting high-end, low-
impact safari-based tourism, an alternative to the mass
61641_Kenya_Wildlife_Tourism_new.indd 41 11/4/19 3:07 PM

42 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
tourism experience in the neighboring national parks.
Now classified as a conservancy, Sergoit Farm was the
first privately owned area set aside for the conservation
of rhinos outside of national parks and reserves in 1953.
This was followed by Ol Jogi in 1965 and Wangalla Ranch
in 1968. Following the hunting ban in the 1980s, other
private entities turned to a combination of ranching and
conservation, driving the growth of private conservan-
cies in the Taita Taveta, Laikipia, and Rift Lakes regions
up until the mid-2000s.
The majority of Kenya’s conservancies (51 percent) are on
community land, while 36 percent have been established
on private land, and 13 percent exist on group lands
(Table A.1). Because of the ability of wildlife and livestock
to coexist, coupled with the expanse of conservancies
and connectivity between them, communally owned pas-
toral lands host vast amounts of wildlife in Kenya. This
has, by default, led to community conservancies offering
significant conservancy potential and demonstrating the
largest growth in the conservancy movement.
The environmental promise
ofconservancies
Conservancies span more than 11 percent of Kenya’s ter-
ritory, over 5.8 million hectares, with the northern conser-
vancies covering 3.7 million hectares and the southern
conservancies covering 2.1 million hectares. By com-
parison, Kenya’s terrestrial national parks and reserves
cover 4.7 million hectares, spanning 16 counties. As the
country develops, conservancies can play a significant
role in securing a place for wildlife in Kenya’s future.
FIGURE A.1: The rapid growth of conservancies in Kenya
0
20
40
60
80
100
120
140
160 Conservancy type
Community
Group
Private
Number of conservancies
1965
1977
1984
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
1970
Source: KWCA Conservancy Database, 2018.
TABLE A.1: Typology of Kenyan conservancies
Conservancy type
Number and %
of conservancies
Area
(ha)
Area
(%)
Community conservancy 82 (51%) 6,100,000 76
Private conservancy 58 (36%) 1,200,000 15
Group conservancy 20 (13%) 723,000 9
Note: Analysis is based on a sample of 130 conservancies assessed
for this study.
61641_Kenya_Wildlife_Tourism_new.indd 42 11/4/19 3:07 PM

CONSERVANCIESANOVERVIEW 43
Conservancies significantly increase the share of wildlife
living in legally protected areas, A spatial assessment of
biodiversity indicates that 22 percent of the total ungulate
wildlife biomass is found in conservancies, according to
DRSRS data. This represents a significant complement to
the 38 percent of ungulate wildlife biomass found within
Kenya’s national parks. Perhaps of greater importance,
18 out of 20 zones with the highest wildlife density are
found in conservancies rather than in parks. Olare Orok,
located next to the Maasai Mara, is the conservancy with
the highest density of wildlife biomass. Key species such
as the Grevy’s zebra are mostly found in conservancies,
while lion populations in the conservancies of the Maa-
sai Mara are among the highest on the continent. These
figures highlight the crucial role conservancies can play
in protecting wildlife and helping landscapes thrive.
More significantly, a growing body of evidence suggests
that conservancies have been highly successful at pro-
tecting biodiversity. For instance, in Nakuru Wildlife Con-
servancy, Ogotu et al. (2017) found that populations of
monitored wildlife in the conservancy had stabilized for
some species and increased for most, in stark contrast
to the declines observed elsewhere, including in the
national parks.
The economic significance
ofconservancies
Kenya’s tourism sector generated Ksh 99.7 billion in 2016,
a figure that increased by 20.3 percent to Ksh 119.9bil-
lion in 2017 (KNBS 2018). According to the Kenya Wildlife
Service (KWS) Strategic Plan of 2012–2017, safari tour-
ism accounts for 75 percent of national tourism earnings
(Ksh 74.8 billion in 2016 and Ksh 90 billion in 2017). But
the share of tourism income earned by conservancies
amounted to a modest 1.3 percent, suggesting consider-
able potential and scope for expansion in a specialized
market that most likely caters to the high-value and low-
volume tourists.
Safari tourism—first established as hunting safaris and
progressing to ecotourism—has been one of the top rev-
enue earners for Kenya, with national parks historically
playing a crucial role. It has offered income-generating
prospects to pastoral households in the arid and semi-
arid regions of Kenya, which are areas of low agricultural
potential. Conservancies now offer key possibilities to
extend and differentiate Kenya’s tourism product. For the
first time, this report has collected data on the economic
contribution of conservancies through tourism (Box A1).
The 13 regional associations and 160 conservancies
registered under KWCA were surveyed in 2018 to col-
lect information on Kenya’s tourism infrastructure and
sources of income of conservancies in 2016 and 2017.
Twenty-five tour operators were also approached to
gather data on income paid to conservancies, bed-
nights, benefit sharing mechanisms, and philanthropic
activities supported within the conservancies.
BOX A.1: Some Key Figures on the
Economics of Conservancies in Kenya
• More than 930,000 members in conservancies
• 131 tourism facilities (~2,500 beds)
• 175,000 bed-nights in 2017, a 30% increase com-
pared to 2016; occupancy of 20%.
• 2,620 locals directly employed (20% women)
• Tourism operators paid more than Ksh 1.2 billion in
bed-nights to conservancies in 2017
Of the 160 conservancies documented in this study,
69host a total of 131 tourism facilities within their borders
(Table A.2). A total of 2,510 beds exist in lodges within
the conservancies mapped, with 97 percent found in the
southern conservancies. Of the total beds, 41 percent
are located in Narok County, 13 percent each in Kajiado
and Laikipia counties, 11 percent in Taita Taveta, and
9percent in Nakuru. The Mara conservancies (located in
Narok County) currently host the largest number of facili-
ties outside of national parks and reserves (37 percent).
It should be noted though that the scope for expansion
of tourism activity is constrained by a limit on “bed-
nights” (conservancies such as Olare Orok only allow a
single bed per 300 acres) (Bedelian 2014). These limits
are meant to assure an exclusive game viewing experi-
ence and build a differentiated market and product to
the high-volume tourism in the parks. Table A.2 provides
an overview of the scale of tourism operations in the
conservancies surveyed.
61641_Kenya_Wildlife_Tourism_new.indd 43 11/4/19 3:07 PM

44 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
TAB LE A.2: An overview of tourism facilities in Kenya’s
conservancies
County
No. of
conservancies
No. of
tourism
facilities
Average no.
of beds per
conservancy
Baringo 2 2 15
Elgeyo Marakwet 1 1 Under
construction
Kajiado 11 13 28
Laikipia 11 23 31
Lamu 1 1 21
Machakos 2 2 3
Meru 2 7 57
Nakuru 9 15 26
Narok 14 49 76
Nyeri 1 1 24
Samburu 4 6 16
Taita Taveta 7 7 39
Tana River 1 1 14
Trans Nzoia 1 1 32
Vihiga 1 1 20
West Pokot 1 1 12
Total 69 131 28
In general, conservancies that neighbor highly fre-
quented parks and reserves have higher average bed
densities, as they take advantage of other attractions
and better access. The average conservancy in the sam-
ple has 28 beds, with numbers ranging from 6in Macha-
kos, with its conservancies being in the early stages of
development, to over 1,000 in Narok, which abuts the
overcrowded Maasai Mara. The Amboseli and Laiki-
pia regions are also wildlife hot spots, with a proximity
to Mt. Kilimanjaro and Mt. Kenya adding an additional
attraction for visitors.
Conservancies target the high-value international tour-
ist, though there are a growing number of local visitors
with much regional variation. In Nakuru County, about
60 percent of visitors are local, and in Narok and Taita
Taveta the figure stands at 30 percent, which is close to
the national average. On the other hand, in Laikipia, Sam-
buru, Meru, and Kajiado, the percentage of local tourists
is much lower (at around 15 percent)—these being des-
tinations that are targeted to the international traveler.
TOURISM: THE PRIMARY SOURCE OF INCOME
FORCONSERVANCIES
Tourism is the most significant source of income for
conservancies, contributing an average of 83 percent
of commercial income (NRT 2018). There are signs that
income from tourism is growing rapidly in relative and
absolute terms. From 2016 to 2017, the 69 conservan-
cies with tourism facilities experienced an 18 percent
increase in their total income from tourism, earning a total
of Ksh1.15billion (Figure A.2). This amounted to an aver-
age of Ksh 26.2 million per conservancy (a minimum of
Ksh 20,000 and a maximum of Ksh 253 million). Growth
in income was highest in the northern conservancies,
who saw a 33 percent increase in tourism income, com-
pared to a 23 percent increase among conservancies in
the south.
Members of conservancies (i.e., the local households)
share the benefits from tourism either directly as reve-
nues from running tourism facilities or through a matrix
of profit-sharing structures, conservation fees, bed-night
fees, or lease-holding arrangements (Box A.2).
BOX A.2: Types of Benefit-Sharing
Arrangements
Bed-night fee: A proportional fee paid per occupied bed
to the conservancy.
Lease-holding fee: A set monthly or annual fee paid out
as rent for land or building infrastructure for an agreed-
upon period.
Conservation fee: An additional fee paid per visitor or
occupied bed as a payment for conservation services.
The bulk of income to conservancies (> 50 percent)
is generated by fees earned from tourism-related
benefit-sharing agreements, followed by livestock sales.
Noncommercial activities, animal conservation (12 per-
cent), and payments for ecosystem services (8 percent),
together with livelihood activities, account for the rest of
the income (Figure A.3).
The expansion of cattle ranching and improved beef
production has, over the past years, gained prominence
61641_Kenya_Wildlife_Tourism_new.indd 44 11/4/19 3:07 PM

CONSERVANCIESANOVERVIEW 45
within community and group conservancies, as the
need for wildlife-compatible opportunities arises.
However, this remains a challenge as grazing regimes
need to be established, and an equilibrium between
livestock and wildlife-carrying capacities needs to be
determined and managed, keeping in mind cultural
contexts of livestock being a measure of wealth within
these communities.
Iconic animal conservation programs (of species such
as rhino, elephant, Grevy’s zebra, chimpanzee) are also
a significant contributor to incomes, with conservan-
cies earning an average of Ksh 12.8 million in 2016 and
Ksh 11 million in 2017 from conservation fees paid by
visitors to animal sanctuaries. Iconic animal conservation
programs, which were the initial drivers of conservancy
development in the 1960s, continue to attract tourists.
FIGURE A.2: Tourism income earned by conservancies (Ksh, 2017)
SamburuNorth
South
Isiolo
Total
51,099,560
172,500
51,272,060
Narok
Laikipia
Meru
Nakuru
Kajiado
Taita Taveta
Tana River
Baringo
Nyeri
Vihiga
489,152,444
342,591,509
151,938,077
62,212,694
50,741,184
2,508,824
350,000
681,000
109,280
60,000
1,100,345,012
1,151,617,072
Total
Grand total
North South Grand total
Source: Conservancies surveyed in this study.
FIGURE A.3: Proportion of conservancy income sources in 2017 (Ksh, millions)
1172
492
323
214
136
124
18 17 8
Bednight and conservation fee
Tourism leasehold
Livestock sales
Payment for ecosystems
Iconic conservation
Farming
Grazing fee
Bead sales
Hay sales
Source: Conservancy survey.
61641_Kenya_Wildlife_Tourism_new.indd 45 11/4/19 3:07 PM

46 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
Payments for ecosystem services, particularly from car-
bon sequestration, have increasingly become an impor-
tant revenue source for conservancies. In 2016, southern
conservancies earned Ksh 30.4 million from carbon off-
sets (an average of Ksh 4.4 million per conservancy), and
this figure increased by 605 percent in 2017 to reach
Ksh214.4 million (an average of Ksh 21.4million per con-
servancy). This was mainly due to carbon-offset revenues
from the Chyulu Hills REDD+ project, a multi-partner ini-
tiative aimed at reducing emissions from deforestation
and degradation. As the international policy framework
around land-based climate change strategies continues
to mature, landscape-level conservation will offer oppor-
tunities to reap benefits from payments for ecosystem
services.
OTHER BENEFITS TO COMMUNITIES
Tourism facilities within the conservancies hired
2,111 employees (12 percent women) in 2016 and
2,619employees (17 percent women) in 2017. Most conser-
vancies are located in pastoral areas where gender ineq-
uity exists in terms of access to education and economic
opportunities, with traditional livelihood practices limiting
women’s opportunities outside the homestead. However,
as gender empowerment through bursary and education
support continues to be promoted through conservancy
management structures, this trend may change.
The facilities also provide alternative sources of income
to households through direct purchases of goods and
services, which amounted to around Ksh 36.5 million
in 2017, cultural activities such as visits to homesteads
(Ksh11.9 million in 2017), the purchase of livestock and
food (Ksh 36 million in 2017), and the purchase of bead-
work (Ksh 4 million). Tourism facilities have also invested
in roads, education, health, and water-related infrastruc-
ture in some of the most remote regions of the country. In
2017, 11 conservancies had invested about Ksh 28.6 mil-
lion in such activities, suggesting that the unaccounted
impact of tourism in the form of social initiatives may be
more significant than direct payments to conservancies
in the form of tourism operations.
PUTTING THE NUMBERS IN PERSPECTIVE
The presence of wildlife in conservancies has been the
single most important determinant of success, though
this is not sufficient to assure success. Critically, there
is a need for strong governance structures with trans-
parent and equitable benefit-sharing structures. Invest-
ments in conservancies carry high risks and as such
require patient capital. This is because investors must
gamble not only on the prospects of attracting tourists to
a new location, but also engage in a host of public good
investments to build community support and fill crucial
infrastructure gaps. This may suggest the need for inno-
vative investment mechanisms, such as green bonds
and risk guarantees, to shift the risk-reward balance,
especially in areas that confer high ecological benefits,
such as wildlife corridors.
Though tourism is the primary income-generating
source for most conservancies, accounting for almost
83 percent of income (NRT 2018), conservancies and
their regional associations are exploring ways to inno-
vate and create income from other sources. The Chyulu
Hills REDD+ project has demonstrated returns at scale
from conservation through payments from ecosystem
services. While cattle ranching also offers opportuni-
ties, it is more complex in the context of degraded land,
increasing population numbers, and the need to balance
livestock numbers with wildlife populations due to lim-
ited carrying capacity.
The southern tourist circuit in Kenya hosts a well-
maintained infrastructure and offers opportunities for tour-
ists to travel by road within a radius of one to five hours
from Nairobi. It also hosts high wildlife densities and ben-
efits from strong marketing. Such potential also exists in
destinations such as Laikipia and in the North more gener-
ally, which also host some of the highest wildlife numbers
in the country. This region, however, requires significant
investments in marketing strategies aimed at both local
and international travelers. Critically, as other chapters in
this report have highlighted, there is also a need for infra-
structure approaches that carry a lower negative footprint
in order to catalyze and enable the economic opportuni-
ties that Kenya’s natural assets bring.
Going further
Establishing stable or increasing wildlife population num-
bers is critical toward enhancing tourism income, with its
potential for addressing high poverty in rural areas. The
61641_Kenya_Wildlife_Tourism_new.indd 46 11/4/19 3:07 PM

CONSERVANCIESANOVERVIEW 47
establishment and promotion of conservancies offers
the most scalable avenue in ensuring wildlife habitats
are secure and rehabilitated, and migration corridors are
established. Wildlife hot spot areas, such as the Mara,
Amboseli, and Laikipia regions, indicate that high wildlife
densities can lead to significant wildlife-based tourism
operations outside of national parks.
In addition to this, the assessment of policies and pro-
grams across all sectors that impact wildlife numbers
should be established to ensure for wildlife-friendly
national development plans.
To further promote the development of tourism outside
of national parks and reserves, the national and county
governments need to recognize the role conservancies
play as custodians of wildlife and in developing syner-
gistic livelihood enhancement programs. Integration of
conservancy management plans in the county develop-
ment plans acts as a first step to foster this recognition.
Furthermore, financial support to strengthen proposed
and growing conservancies on their path to sustainabil-
ity will catalyze growth of the movement.
In line with Vision 2030, conservancies, which have
already paved the way for exclusive wildlife-based
tourism experiences, need to be incorporated into
the country’s parks and reserves plans to achieve
the national goal of the country becoming a premium
destination of high-end safari tourism. The triple bot-
tom line of conservation, livelihoods, and economic
sustainability provided by conservancies should be
marketed as a unique wildlife experience within this
portfolio. There is also a need to promote conservan-
cies through Kenya Tourism Board (KTB) and Ministry
of Tourism programs.
REFERENCES
Bedelian, Claire. (2014). Conservation, Tourism and Pastoral
Livelihoods: Wildlife Conservancies in the Maasai Mara,
Kenya.
Jenya National Bureau of Statistics (KNBS). (2018). Kenya Eco-
nomic Survey 2018.
Northern Rangelands Trust (NRT). (2018). “State of Conservan-
cies Report, 2017.”
Ogutu, Joseph O., et al. (2017). “Wildlife population dynamics
in human-dominated landscapes under community-based
conservation: the example of Nakuru Wildlife Conservancy,
Kenya.” PloS one 12.1: e0169730.
61641_Kenya_Wildlife_Tourism_new.indd 47 11/4/19 3:07 PM

48
APPENDIX B
ROAD EXTENSION AND WILDLIFE LOSS BETWEEN 1980 AND 2010:
A DIFFERENCE-IN-DIFFERENCES APPROACH
In this appendix, we detail the methodologies and the
results of the econometric model that estimate the
impact roads had on wildlife in Kenya between the
1980s and the 2000s. Results echo the findings of a sig-
nificant amount of literature including previous work by
the World Bank, that shows a negative effect of roads on
natural habitats—notably forests.
Data
WILDLIFE
Wildlife data come from the Department of Resource Sur-
veys and Remote Sensing (DRSRS) of Kenya based on aer-
ial surveys in the rangelands of Kenya since 1977. DRSRS
conducted a total of 359 surveys from 1977 to 2016 cover-
ing 19 rangeland counties. Each county is partitioned into
5 km × 5 km UTM grids. Each 5-km transect segment is
treated as an observation unit. Systematic transect lines
are flown through the center of each grid on a north-south
or east-west axis at a nominal height of 91–122 m (300 to
400 feet) aboveground. Widths of counting strips ranged
between 224–490 m during 1977–2016. Two rear-seat
observers count all wild and domestic animals the size of
Thomson’s gazelle (15 kg) and larger within each strip and
record all counts on tape recorders. Animals in large herds
of more than 10 are photographed and later counted under
a binocular microscope (in earlier years) or on a large digi-
tal screen (currently) in digital photos. Refer for details to
Norton-Griffiths (1978)15 and for survey parameter (survey
dates, aircraft settings, sampling fraction, and personnel
involved) to Ogutu et al. (2016). Population estimates (PE)
and their standard errors (SE) for each species are calcu-
lated from the sample fraction by treating each transect as
a sample unit using Jolly’s Method 2 (Jolly, 1969).16 For com-
putation limits, data were resampled at a 10 km afterwards.
15 Norton-Griffiths, M. (1978). Counting animals. Nairobi: Africa Wildlife
Leadership Foundation.
16 Jolly, G. M. (1969). Sampling methods for aerial censuses of wildlife
populations. East African Agricultural and Forestry Journal, 34, 46–49.
To analyze how wildlife population has changed over
time and spatially the data were aggregated into census
periods covering surveys conducted between 1977–1989
(1980s), 1990–1999 (1990s) and 2000 and 2016 (2000s).
For each grid, population estimates were calculated
based on biomass (calculated in terms of Tropical Live-
stock Units where 250 kg is equivalent to 1 TLU) for the
18 common wildlife species17 and were averaged for
each of the counting periods. The average over the time
period minimizes the influence of stochastic variation in
the count totals and the distribution of animals.
WILDLIFE DYNAMICS
In the 1980s, wildlife was present in 53 percent of grid cells.
In the 2000s, this number is of only 31 percent (Figure B.1).
The densities of wildlife in the 1980s were highest in the
southern rangelands, and the northern rangelands also had
substantial wildlife distributed across the northern range-
land counties. The highest wildlife densities in the 1980s
were observed in the counties of Narok, Kajiado, Taita,
Lamu, and Laikipia. The 2000s distribution map indicates
that the wild herds have shrunk in numbers and distribu-
tion, and have vanished rapidly in many counties including
West Pokot, Turkana, Baringo, Kilifi, Lamu, Machakos, and
Tana River (Said et al., 2016).18
ROADS
Kenya’s road network has grown considerably over
the last decades. We use Michelin maps of East Africa
17 Eighteen species are used in the analysis of the report: buffalo (Syncerus
caffer), Burchell’s zebra (Equus burchelli), Coke hartebeest (Alcelaphus
buselaphus), eland (Taurotragus oryx), elephant (Loxodonta africana), gerenuk
(Litocranius walleri), giraffe (Giraffa cemelopardalis), Grant’s gazelle (Gazella
granti); Grevy’s zebra (Equus grevyi), impala (Aepyceros melampus), lesser kudu
(Tragelaphus imbermbis), oryx (Oryx gazelle beisa), ostrich (Struthio camelus),
Thomson’s gazelle (Gazella thomsoni), topi (Damaliscus lunatus korrigum),
warthog (Pharcoerus africanus), waterbuck (Kobus ellipsiprymnus), and
wildebeest (Connochaetes taurinus).
18 Said, M. Y., Ogutu, J. O., Kifugo, S. C., Makui, O., Reid, R. S, and de Leeuw, J.
(2016). Effects of extreme land fragmentation on wildlife and livestock population
abundance and distribution. Journal for Nature Conservation, 34: 151–164.
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ROAD EXTENSION AND WILDLIFE LOSS BETWEEN 1980 AND 2010: A DIFFERENCEINDIFFERENCES APPROACH 49
to highlight these changes and study the impact of
road expansions on wildlife. For this study, all avail-
able Michelin maps for Kenya were digitized and trans-
formed into GIS files. In 1978, the maps recorded about
7,000kilometers of paved and improved roads, and the
entire north of the country only featured improved gravel
roads at the time. In the subsequent 40 years, Kenya’s
road network has increased by 50 percent to cover
around 11,000kilometers of improved and paved roads
as of 2017. The network of roads has become denser in
the South but has also been extended in the North to
connect the major urban center in the region, an exam-
ple being the recent paving of roads leading to Marsabit
and Turkana counties.
The model
A “difference-in-differences” specification is used to
determine the impact of roads on wildlife loss. It follows
best practices, followed by recent studies such as Asher,
Garg, and Novosad (The Economic Journal, forthcom-
ing). The model exploits the expansion of the road net-
work in Kenya in the 1980s–1990s.
The Euclidean distance between each grid cell and the
nearest paved or improved road was calculated for each
decade from the 1980s to the 2000s. These distances
were then categorized into different bins depending on
whether a cell was less than 5, 10, 15, 20, or 50 kilometers
from a road. Simultaneity bias may be a significant threat
when studying the impact of roads on wildlife since wild-
life distribution and road placement are jointly deter-
mined. Difference-in-differences models are an effective
method to overcome this challenge.
Cells that were originally (1980s) far from a road
(50–100 km) are kept in the analysis. Among these
cells, the model looks at how the loss of wildlife differed
between cells that became closer to a road (treatment
groups, 5km, 10 km, 51 km, 20 km, and 50 km to test for
the robustness of the estimates) and cells that remained
far from a road (control group, >50 km). Roads here include
both paved and improved roads. Formally, the model is:
Wildlifei,t = β Cell Close from Roadi,t + γ Posti,t + ω Cell
Close from Road ∗ Posti,t + µt × Province + PAi,t + ∈i,t
Where Wildlifei,t is the total biomass of wildlife in cell i
during decade t (t = 1980, 1990, 2000), Cell Close from
Road measure whether the cell has become 5, 10, 15,
20, or 50 km closer to a road during the period. Post
is a dummy variable for periods post 1980s (i.e., once
most cells became close to a road). The interactive term
Cell Close from Road ∗ Posti,t captures the difference-in-
difference impact of roads on wildlife. µt × Province is a
province specific time fixed effect. PAi,t is a time varying
variable that equals one if the cell belong to a Protected
Area during a given decade. Cells at the borders of Kenya
have a smaller area than cells which do not touch the
FIGURE B.1: Kenya’s wildlife populations have shrunk dramatically since the 1980s, becoming fragmented, and
almost vanishing in some counties, such as in West Pokot, Baringo, Turkana, Machakos, Kwale, andMandera
61641_Kenya_Wildlife_Tourism_new.indd 49 11/4/19 3:07 PM

50 WHEN GOOD CONSERVATION BECOMES GOOD ECONOMICSKenya’s Vanishing Herds
border. Therefore, observations are weighted regarding
the area of each cell. Finally, standard errors are clus-
tered at the cell level to account for heteroskedasticity.
The main results are presented in Table B.1. In addition
to showing the robustness of the results to different dis-
tance thresholds, we also show their robustness in the
standard parsimonious difference-in-difference model:
Wildlifei,t = β Cell Close from Roadi,t + γ Posti,t +
ω Cell Close from Road ∗ Posti,t + µt + ∈i,t
Results
NON-PARAMETRIC EVIDENCE ON ROADS
AND WILDLIFE
Figure B.2 plots a local smoothing regression (LOWESS)
between the total wildlife loss in Kenya between the
1980s and the end of the 2000s, and the Euclidean dis-
tance to the nearest road. Wildlife decreased at a faster
pace closer to roads. It highlights that wildlife loss was
higher close to roads (5 to 10 km). 100 km from a road,
results are no more significant.
DIFFERENCE-IN-DIFFERENCE ESTIMATES
Table B.1 presents results of the main model approach.
Results from the statistical model suggest that cells
located close to a road are associated with a significant
decrease in wildlife, following construction of the road.
The closer a cell is to a road, the larger the impact. Results
in Table B.1 reveal that a cell that was once 50kilome-
ters away from a road, and which subsequently had a
road built less than 5 kilometers away from it, lost an
additional 217 TLU (or 217 × 250 = 54,250kg) of wildlife
biomass over a decade compared to cells that remained
50 kilometers from a road. Given that the average wild-
life biomass in a cell between 1980 and 2009 was
266 TLU, the impact of roads has been significant: It
amounts to a 78 percent additional decrease of wildlife.
Twenty kilometers from a road, the impact, although two
times smaller, remains ecologically significant.
Table B.2 shows the results of the standard parsimoni-
ous difference-in-differences model in which results
remain robust.
FIGURE B.2: Distance to roads and wildlife loss
500
250
0
–250
050 100
Distance to nearest road (in km)
Wildlife lost (in 1,000 TLU)
150
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ROAD EXTENSION AND WILDLIFE LOSS BETWEEN 1980 AND 2010: A DIFFERENCEINDIFFERENCES APPROACH 51
TABLE B.1: Main model
(1) (2) (3) (4) (5)
Less than 5 km Less than 10 km Less than 15 km Less than 20 km Less than 50 km
Treated × post –217.369* –185.138** –134.558* –114.494* –65.554
(121.325) (85.765) (79.109) (65.091) (44.057)
Post –358.628*** –389.410*** –345.288*** –326.846*** –530.919***
(101.365) (100.153) (110.082) (105.581) (143.272)
Observations 2,586 2,730 2,868 3,027 4,029
Number of cells 862 910 956 1,009 1,343
Treatment Road becomes
<5 km
Road becomes
<10 km
Road becomes
<15 km
Road becomes
<20 km
Road becomes
<50 km
Control Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Note: * = p<0.05, ** = p<0.01, *** = p<0.001.
TABLE B.2: Parsimonous model
(1) (2) (3) (4) (5)
Less than 5 km Less than 10 km Less than 15 km Less than 20 km Less than 50 km
Treated × post –207.072* –177.400** –134.719 –122.465* –97.502*
(125.676) (88.791) (83.136) (70.353) (56.474)
Post –157.666*** –163.580*** –165.305*** –164.635*** –165.006***
(16.259) (16.427) (16.704) (16.633) (16.338)
Observations 2,586 2,730 2,868 3,027 4,029
Number of cells 862 910 956 1,009 1,343
Treatment Road becomes
<5 km
Road becomes
<10 km
Road becomes
<15 km
Road becomes
<20 km
Road becomes
<50 km
Control Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Road 50 to 100 km
from cell
Note: * = p<0.05, ** = p<0.01, *** = p<0.001.
REFERENCE
Ogutu, J. O., Piepho, H. P., Said, M. Y., Ojwang, G. O., Njino, L. W.,
Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife
declines and concurrent increase in livestock numbers in
Kenya: What are the causes? PloS one, 11(9), e0163249.
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