Institutions, Governance, and the Economic Performance of Protected Areas in Southern Africa

INSTITUTIONS, GOVERNANCE, AND THE ECONOMIC PERFORMANCE OF PROTECTED AREAS IN SOUTHERN AFRICA

ALEXANDER CHIDAKEL

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2018

ACKNOWLEDGMENTS

Two classes were instrumental in narrowing the broad ideas I arrived with at UF.

Principles of Community Conservation, taught by my adviser Dr. Brian Child, illuminated the depth of science underneath the facile rhetoric of community based natural resource management. Through it, my introduction and re-introduction to theories political, social, and economic imparted essential frameworks for understanding and describing the social-ecological systems at the heart of this research. Protected Area Management, also taught by Brian Child, inspired the philosophical questions at root of the research.

Namely, “who are parks for?” and “what is their value?”. My committee co-chair Dr.

Grenville Barnes’ course on land tenure and administration was also influential by illuminating the complexities and risk inherent in the formalization of predominantly informal tenure systems.

In South Africa, I am grateful for the data and institutional support provided by

Louise Swemmer of South African National Parks as well as the Mpumalanga Tourism and Parks Agency. Additionally, much of the preliminary field work was based out of the

Southern African Wildlife College in South Africa and there I thank Sandy du Plessis, who provided logistical support and valuable contacts, and Thabisile Sibuye, Collen

Mkansi, Freddie Nokeri, Rejoice Ndlovu, and Thomas Ndhlovu who helped with conducting surveys inside Kruger National Park in their role as environmental monitors.

Most importantly, the research would not have been possible without the cooperation of the reserve wardens, executive committee chairmen, and lodge owners and managers of the Greater Kruger Area. Given the size of the study area, gaps in the initial dataset were inevitable. Thankfully though, through collaboration with Candice Eb of the Global

Environment Facility (GEF), who conducted an extensive survey of reserve managers, many of these gaps were filled.

In Zambia, institutional support and critical data were provided by the Department of National Parks and Wildlife. The support of the Luangwa Safari Association, on behalf of the community of tourism operators in the valley, was also invaluable.

Additionally, contacts in the Wildlife Producers Association of Zambia contributed important background information and helped to encourage the cooperation of the association’s members. In terms of data collection, significant progress was made during an intense two week GEF-sponsored and Brian Child-organized workshop at

Mfuwe in June, 2016, where Usman Iftikhar, Thiago Beraldo, Candice Eb, Grant

Simuchimba, and others helped in designing and initiating surveys related to the local economy. Thanks to the dedication of William Mwembela, of Malama, one of these surveys—of local non-tourism businesses—was greatly expanded following the workshop. I also thank the community resource boards of Kakumbi, Malama, Mkhanya,

Nsefu, Jumbe, Mwanya, Msoro, Luembe, and Nyalugwe for the opportunity to meet with representatives and their general cooperation.

Finally, I thank Dr. Christa Court of the Institute for Food and Agricultural

Sciences at UF for reviewing the methodology and technical procedures I employed in the modeling of economic multipliers. This analysis was the most complicated and tedious part of the research.

Financial support for field research was provided primarily by the GEF, The

Norwegian Programme for Capacity Development in Higher Education and Research for Development (NORHED), and the College of Agriculture and Life Sciences at UF.

The Center for African Studies at UF also contributed funding, for field research during the preliminary stage, and for two years of campus-based studies.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 3

LIST OF TABLES ...... 9

LIST OF FIGURES ...... 11

LIST OF OBJECTS ...... 13

LIST OF ABBREVIATIONS ...... 14

ABSTRACT ...... 16

CHAPTER

1 INTRODUCTION ...... 18

The Dependent Variables: Value and its Distribution ...... 21 Goals ...... 22 Contributions ...... 22 Structure of the Dissertation ...... 24

2 LITERATURE REVIEW ...... 25

The Comparative Economic Advantage of Wildlife-based Land Uses ...... 25 The Conservation Value of Game Ranching ...... 26 New Institutional Economics and a Theory of Economic Development ...... 32 Institutional Change and Analysis ...... 33 The Formal Institutional Environment: Property Rights and Markets ...... 37 Structures of Governance ...... 40 The normative logic of structure ...... 45 The Study Area and the Institutional Context of PAs ...... 52 The Economic Value of Wildlife in the Study Region ...... 55

3 RESEARCH METHODS ...... 62

Study Design ...... 62 Methods ...... 63 Methods Used in Zambia ...... 66 Methods Used in South Africa ...... 73

4 A MIXED METHODS APPROACH TO ASSESSING THE SOCIO-ECONOMIC IMPACT OF PROTECTED AREAS: PARKS, COMMUNITIES AND GAME RANCHES IN SOUTH LUANGWA, ZAMBIA...... 80

Introduction ...... 80 The Formal Institutional Environment for Wildlife and Land in Zambia ...... 80 Protected Areas in the Study Region ...... 83 Results: South Luangwa National Park...... 88 Park Visitation ...... 88 Economic Impacts of Tourism ...... 88 The Local Business Economy ...... 95 Results: The GMAs and Game Ranches ...... 97 Hunting Activity in the Lower Luangwa Valley ...... 97 Hunter Spending Associated with the GMAs and Game Ranches ...... 97 Meat Production ...... 98 Direct Impacts of Hunter Spending ...... 99 Government remittances to CRBs and Impacts of CRB Spending ...... 100 Governance of game management areas ...... 102 Discussion ...... 103 Putting the Figures into Context: The Park as an Engine for Economic Development ...... 103 The GMA Wildlife Economy ...... 108 Conclusion ...... 112

5 THE ECONOMIC IMPACT OF KRUGER NATIONAL PARK AND THE SURROUNDING RESERVES ...... 127

Introduction ...... 127 Study Objectives ...... 127 Study Area ...... 128 The Formal Institutional Environment for Wildlife and Land in South Africa ... 128 Protected Areas in the Study Region ...... 131 Human Populations in the Impact Zones ...... 136 Methods ...... 137 Results ...... 138 Tourism Infrastructure ...... 138 Visitation to the Greater Kruger Area ...... 139 Direct Spending of GKNP Tourism ...... 139 Direct Economic Impacts of GKNP Tourist Spending ...... 141 Local Indirect Spending and Procurement ...... 144 Social Responsibility and Investment by Tourism and Reserve-Level Entities of the Contiguous Reserves ...... 145 Reserve Business Models and the Financial Viability of Tourism ...... 147 Multiplier Impacts of GKNP Tourist Spending ...... 153 Discussion ...... 157 Return on Investment ...... 157 Governance for Sustainability ...... 159 Conclusion ...... 164

6 CONCLUSION ...... 177

APPENDIX

A EXPLANATION OF SAM METHODOLOGY ...... 189

B A HOW-TO GUIDE FOR IMPROVING SOCIO-ECONOMIC ANALYSIS OF PARK IMPACTS: AN APPROACH FROM THE BOTTOM-UP ...... 191

Background ...... 191 A Guide to Conducting a Business Survey ...... 192 Why Conduct a Business Survey? ...... 192 What Businesses Should be Included in a Survey? ...... 193 What to Consider before Undertaking a Business Survey ...... 193 The Elements of a Business Survey ...... 194 Data Management ...... 199 Making Sense of the Data ...... 202 Surveys to Improve Multiplier Analysis ...... 203 Presenting and Summarizing the Data ...... 206

C RESEARCH INSTRUMENTS ...... 211

Modified Visitor Spending Questionnaire for South Luangwa NP ...... 211 Modified Tourism Operator Spending Questionnaire for South Luangwa NP ...... 213 Local Business Questionnaire (for Tiers 2 and 3) ...... 217 Tourism Operator Employee Spending Survey (Zambia & South Africa) ...... 218 Visitor Spending Questionnaire for the GKNP, South Africa ...... 219 Tourism Operator Spending Questionnaire for the GKNP, South Africa ...... 220

D LUANGWA VALLEY SUPPLEMENTARY DATA ...... 224

E HUNTING-RELATED ANIMAL AND LEASE FEES IN ZAMBIA ...... 232

DNPW 2016 Resident and Non-resident Hunting License Fees ...... 232 Concession Lease Fees (ZAWA, 2015) ...... 233

F GKNP SUPPLEMENTARY DATA ...... 234

LIST OF REFERENCES ...... 237

BIOGRAPHICAL SKETCH ...... 250

LIST OF TABLES

Table page

2-1 A natural resource property rights typology. Source: Schlager & Ostrom (1992) ...... 58

2-2 The IUCN’s governance classification system for PAs...... 59

2-3 A PA governance classification system recognizing the nature of tenure...... 59

2-4 A PA governance classification system recognizing the major source of income...... 59

3-1 Summary of surveys conducted in Zambia...... 78

3-2 Summary of surveys conducted in South Africa...... 78

3-3 South African economic multipliers ...... 79

4-1 Breakdown of allocation of government fees from trophy hunting in GMAs ..... 114

4-2 GMAs and game ranches in the study area ...... 115

4-3 Total trip spending per tourist and total overall spend, by spending category. . 115

4-4 Number of local employees and income in 2015...... 116

4-5 Effects of spending by DNPW of $2.66m on SLNP management in 2015 ...... 118

4-6 Direct hunting related fees for study area GMAs and game ranches ...... 121

4-7 Indicators of anti-poaching enforcement intensity of the government and communities...... 123

4-8 Park tourism multipliers at the local and national level...... 123

5-1 Description of PAs in the GKNP ...... 165

5-2 Tourism infrastructure and visitation of PAs in the GKNP...... 168

5-3 Spending of visitors to KNP...... 170

5-4 Total expenditures of visitors to the private and provincial reserves ...... 171

5-5 Employment and wages in the GKNP ...... 171

5-6 Tourism employee characteristics, income, and spending ...... 172

5-7 Tax contributions arising from GKNP tourism ...... 173

5-8 Direct spending and multiplier impacts of GKNP tourism...... 175

D-1 Trip profiles, by tourist segment...... 227

D-2 Bed-nights sold and total spend by tourist segment...... 228

D-3 Segment sample sizes of the employee spending survey and percent of local tourism employees who migrated to Mfuwe...... 228

D-4 Highest level of education reached by local tourism employees, by skill level. 228

D-5 Indirect effects by economic sector...... 228

D-6 Charitable organizations connected to SLNP tourism ...... 229

D-7 Origins of the owners of businesses in the Mfuwe area...... 230

D-8 Direct + Indirect (type 1) multipliers of SLNP tourism in relation to other economic sectors of the Zambian economy...... 231

F-1 Demographics and travel patterns of visitors to KNP and the private reserves...... 234

F-2 Jobs per bed and per tourism revenue in the contiguous reserves ...... 235

F-3 Wage income to tourism employees by education level ...... 235

F-4 National level total income and GDP multipliers comparison with likely alternative industries ...... 236

LIST OF FIGURES

Figure page

2-1 Theoretical framework for understanding protected area performance as an outcome of institutional alignment...... 58

2-2 Governance, resource tenure, and scale of PAs in the South African and Zambian study areas...... 60

2-3 The formal institutional environment of property rights, taxes, and regulations for land and wildlife in Zambia and South Africa...... 61

4-1 Map of South Luangwa National Park and surrounding game management areas ...... 114

4-2 Total local jobs from tourism...... 117

4-3 Total local and national level value added and income ...... 118

4-4 Procurement from Mfuwe-area suppliers by tourism businesses...... 119

4-5 Tourism related charitable contributions made in 2015 to local causes...... 119

4-6 Map of the center of the local tourism economy...... 120

4-7 The first year of operation for businesses along the road from Mfuwe to the airport...... 120

4-8 Average income and expenditures of active CRBs...... 122

4-9 DNPW income and expenditures for SLNP in 2015...... 122

4-10 SLNP park budget in relation to economic contributions at the local and national level...... 124

4-11 The GMA wildlife economy in relation to the local national park economy ...... 125

4-12 The pattern of earnings and investment in the Luangwa Valley...... 126

5-1 PA types in the GKNP, human population density, and the local impact areas...... 167

5-2 Tourist lodges and camps, bed density, and financial returns in the GKNP.. ... 169

5-3 Total management level income, spending, and spending trends across the private reserves ...... 173

5-4 Index of per-area revenue generation potential from non-consumptive tourism ...... 174

5-5 Total GDP contributions and impacts of the GKNP in relation to spend on (non-tourism) PA management and visitor spending...... 175

5-6 Comparison of the dual economies of the GKNP...... 176

6-1 Comparative economic impacts and contributions of the four sub-system PA networks in the two study regions...... 186

6-2 Direct personal and community income per km2 from PA types in the two study regions...... 187

6-3 Institutional and governance characteristics of PA types in the two study regions...... 188

A-1 The basic structure of the social accounting matrices used in this study...... 190

B-1 Sample graph illustrating the growth in number of local businesses in parallel with the growth in park visits...... 207

B-2 Sample graph of total employee wages across business types...... 207

B-3 A color-coded Conceptual representation of the flow of tourism spending through the local economy ...... 208

B-4 A color-coded spreadsheet capturing measured direct and indirect effects (example data)...... 209

B-5 Using an inverted pyramid to describe a park economy and its vulnerabilities. 210

D-1 South Luangwa National Park tourism outlay...... 225

D-2 Number of park visitors in 2015, by visitor segment...... 226

D-3 Number of daily entries into SLNP in 2015, by visitor origins...... 226

D-4 Nationalities of respondent groups...... 226

D-5 Historical commercial and donor income to DNPW and management expenditures...... 230

F-1 Average bednight rate (per-person, sharing) against the number of beds for each lodge and camp in the GKNP...... 234

F-2 Focal areas of operators with formal social commitments ...... 235

LIST OF OBJECTS

Object page

B-1 Excel workbook containing sample data entry and analysis forms (.xlsx file 23KB) ...... 195

D-1 Inventory of SLNP accommodation (.xlsx file, 13KB) ...... 231

F-1 Inventory of tourism accommodation in the GKNP (.xlsx file, 26KB) ...... 234

LIST OF ABBREVIATIONS

APNR Associated Network of Private Nature Reserves bn billion

CBNRM community based natural resource management

CBO community based organization

CPA communal property association

CP contractual park

DEA Department of Environmental Affairs (South Africa)

DNPW Department of National Parks and Wildlife (Zambia)

EIA economic impact analysis

GEF Global Environment Facility

GKNP Greater Kruger National Park

GMA game management area

GMP general management plan

GRZ Government of the Republic of Zambia ha hectare

I/O input/output kg kilogram km kilometer

Limpopo Department of Economic Development, Environment, and LEDET Tourism m million

MCP Makuleke Contractual Park

MTPA Mpumalanga Tourism and Parks Agency

NIE new institutional economics

NP national park

NR nature reserve

PA protected area

PNR private nature reserve p.p.n. per-person/night

SAM social accounting matrix

SANParks South African National Parks (National Park Management Authority)

SCT structural contingency theory

SLNP South Luangwa National Park

TCE transaction cost economics

UNDP United Nations Development Programme

UNEP United Nations Environment Programme

VA value added

VAT value added tax

ZAWA Zambia Wildlife Authority

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

INSTITUTIONS, GOVERNANCE, AND THE ECONOMIC PERFORMANCE OF PROTECTED AREAS IN SOUTHERN AFRICA

Alexander Chidakel

December 2018

Chair: Brian Child Cochair: Grenville Barnes Major: Interdisciplinary Ecology

Though wildlife has been demonstrated to hold an economic comparative advantage in dryland areas of southern Africa, public and private investment in this land use varies significantly. Variation exists both between countries with different institutional regimes for wildlife and land, and between protected areas (PAs) under different governance structures within countries, presenting an opportunity to test predicted relationships with economic efficiency and distributional equity. Economic impact analyses (EIA), which measure value from an activity in terms of income, jobs, and value added, are becoming increasingly common of national parks (NPs), though they are not often aligned in scale with local areas, and their application to non-statutory

PAs, where the stakes for rural communities are arguably higher, is rare. In this research, a newly developed approach to EIA formed the basis of a cross-sectional comparison of the local economic value of public, private, and communal protected areas of South Luangwa NP and surrounding wildlife areas in the institutionally centralized country of Zambia, and of the Greater Kruger National Park (GKNP) system of the institutionally devolved country of South Africa.

In Zambia, the ability of SLNP to retain and re-invest revenue in park operations is largely responsible for enabling the growth of a tourism economy that generates nearly $40m in national GDP, including $3.6m in income for local residents. The sustainability of the Luangwa Valley wildlife economy is ultimately undermined, however, by the lack of alignment between the park and resource institutions which centralize ownership of wildlife and prevent integration with economic activities in the surrounding landscape.

In South Africa, the economic growth in the GKNP is largely driven not by the NP itself, but by the neighboring private preserves. The system is an example of how statutory PAs embedded in landscapes where formal institutions devolve resource rights to local stakeholders can serve as catalysts for the growth of a large and vibrant wildlife economy in impoverished areas. Despite the positive effect of devolved institutions on the economic performance of non-statutory PAs, structures for governing

PAs in both countries undermine the current and future ability of wildlife to create value for society.

17 CHAPTER 1 INTRODUCTION

Long the cornerstones of conservation, PAs safeguard biodiversity and play a critical role in the provisioning of ecosystem services (United Nations Environment

Programme [UNEP], 2012). Yet as the geographies of PAs often overlap with impoverished rural communities, the discourses of conservation and development have merged, drawing critical attention to the impacts of PAs on human well-being and vice- versa (Naughton-Treves et al., 2005; West et al., 2006). While the relationship between parks and people has long been debated, evidence remains mixed on the overall value of PAs to local human populations and the circumstances under which social value is enhanced. However, the literature on the economic impact of PAs, with a few exceptions (Stynes et al., 2000; Lewis et al., 2002; Rasker et al., 2013), is very thin or lacks internal validity. For example, a recent meta-study (Pullin et al., 2014) commissioned by the Global Environment Facility (GEF) concludes that the economic impact of PAs on nearby communities is inconclusive, primarily because of the lack of rigor of study designs and methods. A literature review demonstrates, however, that wildlife is a highly competitive land use in the drylands of southern Africa provided institutional barriers such as weak property rights, market restrictions, and bureaucratic costs and fees are removed.

The performance of PAs in terms of conservation value has also been brought into question. As globally aggregated data shows, these values are undermined by the poor and widely varying performance of PA management, with particularly low performance in Africa (Leverington et al., 2010). The proximal factors of management performance that are correlated with biodiversity outcomes have been well studied and

are becoming better known (Dudley et al., 2004; Zimsky et al., 2010). In Africa these factors include high costs of management, lack of funding, and the high pressure on biological resources (Dudley et al., 2004; Zimsky et al., 2010) to which declines in large mammal populations have been attributed (Craigie et al., 2010). Protected areas with a lack of roads, poor anti-poaching enforcement, and diminished wildlife populations certainly can be understood to have a lower potential to generate revenue from tourism and provide jobs, however, a theoretical framework for more broadly understanding performance, and one that can explain why poor management systems persist despite awareness of their deficiencies, is still underdeveloped (James, 2001). For example, while application of a socio-ecological systems framework (Armitage, 2005; Lebel et al.,

2006) and associated subfields including ecological economics and common property theory (Agrawal, 2001; Ostrom, 1990) have yielded important insights into the sustainable management of the natural resources upon which local communities most directly depend--such as fisheries, water, and forests--the cultural services and recreational experiences produced by tourism-based PAs in general, and wildlife- tourism-based PAs in particular, have not received as much attention from this line of inquiry. Yet these less tangible qualities of PAs and wildlife are capable, just the same, of generating substantial economic value and strong behavioral incentives, recommending a research approach grounded in new institutional economics (NIE).

According to theory developed under the NIE perspective (North, 1990; Platteau,

2009; Rodrik, 2004), wealth is created when institutions such as property rights and markets allow for greater specialization, exchange, and expansion of production chains.

Property rights also strengthen the retention of benefits by landholders (Alchian &

Demsetz, 1973). Development seen through this lens is the evolution of low-value local systems based on personal exchange to complex high-value global systems based on ever increasing impersonal exchange. As a consequence, societies or resource use regimes which lack the requisite institutions are forced to keep up with global economic growth by rapidly drawing down natural capital (Daly, 1997; Smil, 2011), while societies that are institutionally robust are able to produce “more from less” by adding increasing value to existing resources. Neoliberal prescriptions for conservation and development that are informed by the NIE school of thought have, however, been criticized for their focus on efficiency to the neglect of equity concerns in a world of mounting wealth disparity (Igoe & Brockington, 2007). Therefore it is important not only to assess how institutions affect economic efficiency, but also to assess the distributional equity of capital, income, and benefit streams created by wildlife and PAs and to evaluate outcomes against livelihood data, where available.

Research quantifying the magnitude of net livelihood improvements brought about by the presence of PAs typically requires a rigorous quasi-experimental approach and the existence of suitable counterfactual areas to which comparisons may be made

(Stynes et al., 2000). Conditions are often not permitting of this type of research for a given PA and unless counterfactual comparisons are highly controlled, the mechanism by which livelihoods are affected may not be revealed. Economic impact evaluations, on the other hand, quantify the monetary benefits and jobs flowing from a PA but do not, strictly speaking, indicate how these measures would be different in the absence of the

PA. Although evaluations of the economic impacts of PAs cannot substitute as a method for answering whether livelihoods have been improved, the evaluation of

economic impacts sheds light on an obvious potential mechanism for livelihood improvement, through PA-associated tourism value chains. It is thus surprising that direct studies that trace PA-related expenditure, value-chains, and impacts have rarely been tested, except in the USA (Ibid.). This highlights inadequacy of theory and methods in understanding the economic relationships between PAs, economies, and local people. It also highlights the practical need for developing generalizable methods to measure the economic value of PAs. The monitoring of economic value will help to situate performance in an institutional framework, allowing performance to be seen as an outcome not of investment alone, but also of the behavior of stakeholders who are guided by the incentives of a much broader institutional landscape (James, 2001).

The Dependent Variables: Value and its Distribution

This research primarily considers the magnitude and distribution of local economic impacts from direct use, non-consumptive and consumptive wildlife-based tourism in two separate PA landscapes in Zambia and South Africa. The impacts of spending by the park and reserve management authorities are considered secondarily.

Both sets of impacts arise from the introduction of new money into the region but are treated separately on the basis that the former is a result of market forces and the latter an outcome of the policy process and obligations under national statutes and international conventions. The specific impacts considered are the resulting jobs, personal income, tax contribution, and profit generated by the study area PAs. Though the PAs in the two study regions undoubtedly embody significant indirect use value through the provisioning of ecosystem services as well as non-use value derived from the benefit people experience from the knowledge of the PAs’ existence, these sets of values are both difficult to measure and not directly attributable to any particular policy

decision. They are therefore not considered. Another form of value not considered is consumer surplus, which is also difficult to measure and, more importantly, is captured largely by foreign tourists, making it a less relevant indicator to policy-makers.

Goals

The goals of the research are:

1. to develop methods by which to evaluate the economic impacts of a PA at a management-level scale in a data deficient setting and test their practicality (see goal 2a below)

2. To determine the degree to which the formal institutional environment and structures of PA governance, as independent variables, prevent the economic value of wildlife-based land uses from being reflected in financial prices and therefore in land use and economic outcomes. A major set of objectives relating to this goal is the measurement of the economic impacts of consumptive and non-consumptive tourism centered around two contrasting PA systems. Specifically:

a. the system of PAs of the Lower Luangwa Valley in Zambia, encompassing South Luangwa National Park, jointly-governed game management areas, and private game ranches. This study uses the methods developed for goal 1.

b. The Greater Kruger National Park in South Africa, which encompasses Kruger National Park, a network of private reserves, a contractual park, and several provincial reserves. This study uses standard methods.

Contributions

This study contributes to the explanatory power of a new institutional economics framework for protected area performance mainly by developing a new approach to research on the socio-economic impact of protected areas and by using entirely empirical data, collected in the “real world”, with minimal reliance on modeling. As stated, existing studies on the socio-economic impacts of protected areas tend to be plagued by methodological issues which undermine their reliability (Pullin et al, 2014).

Many lack baseline assessments, proper comparison groups, or do not provide enough

detail for replication (Ibid.). Studies that are methodologically sound commonly employ matched pair evaluations that compare the impacts of protected areas on households near a protected area boundary to similar households far from a boundary

(Bandyopadhyay, 2009; Foerster et al, 2011), but methodological rigor does not imply that such studies are grounded in theory. Without a theoretical foundation it may not be clear why household welfare is found to co-vary with proximity to a protected area. This study aims to contribute an explanation by directly measuring and tracking the value created by a protected area, taking not the absence of a protected area or the distance to a protected area as its counterfactual, but rather the condition of a set of collateral institutions obtaining in the broader landscape that have the potential to leverage the value of protected areas. Where these institutions are weak, value added to state-parks by complementary economic activities on surrounding land is expected to be low.

Conversely, where institutions are strong value is expected to be high, though because of differences in the structure of local and national economies, leakage of value out of the local region may significant. Where the value goes, therefore, is just as important, in terms of explanatory power, as a measure of the ultimate benefit at the household level.

Whereas other studies have employed models to predict the long-term value of private protected areas (Lindsey et al, 2013), this study contributes empirical data to the question of present day value. Economic modeling was necessary to estimate the multiplier effects of spending, but steps were taken to minimize the variance introduced by multiplier analysis (Stynes et al., 2000). This data is also complemented by an analysis of the financial incentives to adopt wildlife-based land uses that landholders face in Zambia and South Africa. By coupling economic and financial analyses potential

explanations can be offered for the relative economic underperformance of land use surrounding parks in one country, vis-a-vis another, especially if disincentives owe to specific national level policies that impose explicit or implicit taxes on wildlife use.

Structure of the Dissertation

This dissertation is organized into seven chapters. Chapter 2 provides a review of the literature on the wildlife ranching industry in Africa, new institutional economics, and briefly describes the setting of the research. Chapter 3 describes the design of the study and the methods used. Chapters 4 and 5 present the results of the economic studies of the South Luangwa system of Zambia and the Greater Kruger system of

South Africa, respectively. Chapter 6 concludes.

CHAPTER 2 LITERATURE REVIEW

The Comparative Economic Advantage of Wildlife-based Land Uses

Wildlife-based land uses in the dry-land regions of South Africa have both a theoretical and empirically demonstrated comparative economic advantage over competing and less sustainable land uses, such as cattle ranching (Child et al., 2012;

Barnes, 2001; Bond et al., 2004; Milton et al., 1996; Muir-Leresche & Nelson, 2000).

The advantage consists in the value added primarily by service-based (as opposed to extractive) industries built around wildlife. The realization that value could be added to wildlife when it was made the object of experience followed unsuccessful attempts to profit from meat production in the face of structural constraints and market impediments that favored beef (Prins, 2000). This value comes not from a demand for dietary alternatives, but rather the demand and willingness to pay for the exclusive experience of the hunting or viewing of Africa’s famed megafauna, which is met by the synergistic provision of accommodation and guiding services (Child et al., 2012). Once detached from the biophysical limitations of the ecosystem, this economic activity is not only free to acquire monetary value orders of magnitude higher, but between high-end and low- end lodging, photographic and hunting safaris around the “big 5” and rarer species, it enables economic niche separation and greater market specialization (Child et al., 2012;

Muir-Leresche & Nelson, 2000). Whereas the economics of meat production require harvesting rates of up to 20%, and thus herd sizes able to sustain such high annual yields, offtake from hunting may range from only 2-3% (Child et al., 2012; Bos et al.,

2000). The light skimming of high value trophy males allows small populations, able to grow at rates up to 30%, to immediately be put to production without a buildup period.

Farmers—commercial or subsistence--and ranchers primarily dependent on cattle therefore need not possess any great patience or capital to make the transition and depressed wildlife populations that have suffered from decades of persecution and competition are allowed, simultaneously, to rapidly recover. The demonstrated compatibility between wildlife and cattle on the same land is critical in explaining this transition by ranchers from beef production into the safari hunting industry in South

Africa (Child et al., 2012; Muir-Leresche & Nelson, 2000). As marginal ranches strived for economic buoyancy, what wildlife remained was not considered a threat but rather a more viable asset representing an income supplement. Eventually, with greater experience, infrastructure, word-of-mouth, and most importantly, increasing wildlife numbers, ranchers were able to realize more significant and steadier income streams from hunting. In addition, the hunting product was commonly improved by diversifying the species assemblage with introductions of highly prized game, including elephants and predators. The reorientation of the production system was perhaps most marked by new management practices that defragmented habitat by removing or modifying fences

(Kreuter et al., 2010) to allow greater movement by wildlife species dependent on spatially variable patterns of rainfall.

The Conservation Value of Game Ranching

In South Africa as many as 9,000 game ranches cover an area of approximately

200,000 sq.km., representing 17% of total land in the country. Land area under formal statutory protection, in comparison, stands at 5% (Cousins et al, 2008). Given the political infeasibility of expansion of the formal PA network in a climate of pro-black land reform the marketing of wildlife on private land represents a potential avenue for ensuring that the threat of habitat conversion is abated and that national conservation

goals of endangered species protection are met (Ibid.). Noted conservation successes of game ranching in South Africa include the growth in population of species formerly on the brink of extinction such as the Southern white rhino (Ceratotherium simum), bontebok (Damaliscus pygargus), cape mountain zebra (Equus zebra zebra), and black wildebeest (Connochaetes gnou). Other benefits include the representation of plant communities not covered by formal PAs, enhanced landscape connectivity, and the natural recolonization of recovered habitats by species such as the martial eagle

(Polemaetus bellicosus) (Ibid.).

Profit motives of game ranchers and the increased exposure of conservation to market forces in an increasingly competitive industry have, however, led to concerns about specific practices. The preferences of hunters for color variants and certain trophy characteristics are met by the market through the intensification of breeding programs which tend to increase the expression of recessive alleles, reduce genetic diversity, and go in and out of fashion according to market demand (Ibid.). Where hunting is the main economic activity, predators such as the wild dog (Lycaon pictus), jackal (Canis mesomelas), and caracal (Caracal caracal) may be persecuted in order to protect valuable trophy animals (Lindsey et al, 2005). Where photographic safaris are offered, there is a bias towards stocking more charismatic species such as the “big 5”, even where they may not have naturally occurred (Langholz & Kerley, 2006). The ability to respond to the forces of demand is supported by a specialized capture and translocation industry. However, private producers may not always take account of the ecological consequences of translocation. The introduction of extra-limital species increases the risk of hybridization (e.g. the black and blue wildebeest (Connochaetes

taurinus)) and disease as well as the displacement of natives (e.g. bushbuck

(Tragelaphus scriptus) by nyala (Tragelaphus angasii)) (Ibid.; Cousins et al, 2008).

What game ranch owners may not be doing is also a concern. Management activities tend to focus on facilities and tourist infrastructure, and not necessarily habitat quality or species diversity (Cousins et al, 2008). Because of the high costs of invasive weed removal, managers typically consider it an obligation of the government to provide incentives for this activity (Ibid.; Downsborough et al, 2011). Stewardship programs exist, but are concentrated outside the veld, in more biodiverse regions (Cousins et al,

2008; Downsborough et al, 2011, Pasquini et al, 2010; Snijders, 2012). In addition to cost, a lack of technical expertise and scientific understanding explains why few ranchers institute ecological monitoring. Where ranches are intensively managed in high demand areas ecological monitoring may be outsourced (Mike Peel, pers. communication, June 2014) but in general there is little capacity by civil society or provincial level governmental organizations to provide local support and training for ecological management to private ranch owners (Cousins et al, 2010). Government intervention has taken the form, rather, of national level legislation regulating the use and translocation of endangered and invasive species (Ibid., Downsborough, 2011).

The smaller the ranch the greater the management needs (Child et al, 2013).

Elephants (Loxodonta africana) and lions (Panthera leo) are known to breed faster on small reserves. Hunting may be used to limit reproductive rates, but the complex social dynamics of lion populations and the threat of inbreeding may require both the use of contraceptives and the translocation of young males to manage populations (Kettles &

Slotow, 2009). Small reserves, because of reduced spatial variability of rainfall, may

also require the provision of supplementary feed in the event of a drought and a greater density of water infrastructure (Child et al, 2013).

Conservancy establishment increases the scale of management and brings it closer to satisfying the range requirements of mobile species (Lindsey et al, 2012). The need for costly intervention is therefore reduced. Higher populations can be sustainably supported and the increased access to wildlife enhances opportunities for ecotourism.

As a service-based industry that delivers an experience, game ranching oriented towards ecotourism is less dependent than livestock ranching on the ecological primary productivity of the land (Child et al., 2012). The implication of this reduced dependency is that financial profits can be enhanced without increasing the rate at which natural capital stocks are drawn down. The scale of management of a conservancy can be increased further by becoming integrated with other conservancies or adjoining public parks. In the latter case, conservancies may benefit from the branding power of a national park and from the natural movement of game between the two areas to an extent that reduces the need to manage intensively. Likewise, the national park may benefit from the buffering effect of adjoining conservancies. It is such connected landscapes, in which statutory and non-statutory PAs are integrated, that is the focus of this research.

In area outside of Kruger NP, individual ranches began to realize the cost- savings of conservancy formation as early as the 1950s--a period shortly following the conversion of cattle ranches to game ranches (Kreuter et al, 2010). Conservancies may be considered a form of community based natural resource management among private landholders. This form of management entails the dropping of fences that formerly

separated individual properties--though an external boundary fence remains. Members delegate certain responsibilities upwards to a management authority but also have duties of their own and usually fund the authority by paying a conservation levy. The advantage is that with greater access to wildlife populations, properties can diversify their activities and tap into the higher value market for tourism. Yet until 1992 a significant barrier blocked access to KNP’s wildlife. Historically, wildebeest and zebra

(Equus quagga burchellii) would migrate west from Kruger NP to the Drakensburg

Mountains for dry season grazing, then return in the summer when grass in the park had recovered (Peel et al, 2009). A veterinary fence established on the western boundary of Kruger in 1960 blocked the migratory route. Large die-offs of wildebeest and zebra were recorded two years later (Ibid.). Outside of the park, fenced off cattle operations also obstructed migration and the land was overgrazed by sedentary populations. By the early 1990’s, however, a wave of land-use transformation had swept over the area to the west of Kruger and the boundary fence came down (Ibid.). Today, five of the largest conservancies in Limpopo and Mpumalanga Provinces, representing

180,000 ha, are connected to Kruger NP. Each conservancy operates under its own management plan, but all are guided under the unitary philosophy of an organization at a higher level—the network of Associated Private Nature Reserves (APNR; Ibid.).

Though managers within the APNR are sensitive to ecological dynamics, path dependencies and limitations on authority complicate their ability to pursue conservation visions. As few of the properties outside of Kruger have natural perennial sources of water, water points were constructed in the days of cattle ranching (Ibid.; Child et al,

2013). When the Kruger boundary fence was removed large numbers of impala

(Aepyceros melampus), zebra, blue wildebeest, and warthog (Phacochoerus africanus) were thus able to migrate to the conservancies from the park and remain throughout the year. Blue wildebeest and impala, specifically, were able to take advantage of changes in climax vegetation brought about by an influx of elephants and buffalo (Syncerus caffer), further altering communities and driving out less water dependent species such as roan (Hippotragus equinus), sable (Hippotragus niger), and tsessebe (Damaliscus lunatus). Today, largely because of the densities of elephant, rhino, and hippo

(Hippopotamus amphibius), the stocking rate of the APNR is believed to be over carrying capacity (Peel et al, 2009).

Though questions remain regarding the exact nature of the relationship between market and conservation values, and though through game ranching the practice of conservation has become increasingly exposed to the forces of the market, the practice of conservation on private lands is itself an outcome of the market. In other words, appropriate counterfactual conditions to which the conservation value of private game ranching should be compared are non-conservation land uses. Furthermore, and where private game ranches are integrated with public parks, it is only by recognizing the integrated whole that an accounting of the contributions of private land to conservation will properly weigh the externalities and synergies between these two PA types. In this study, the question of trade-offs between market and conservation values is largely avoided by confining analysis to landscapes in which the mosaic of PAs is fully contiguous and extensive enough to limit the need or advantage of intensive forms of management.

New Institutional Economics and a Theory of Economic Development

North describes NIE as an attempt to build on neo-classical theories of efficiency in the allocation of resources (North, 1990). Whereas the neo-classical approach assumes actors have perfect knowledge and attributes development to capital accumulation, an NIE perspective substitutes instrumental rationality with bounded rationality and expands the scope of analysis to attempt to account for the ultimate source of capital (De Soto, 2002). Institutional systems are defined as sets of incentives and disincentives that structure and constrain behavior (Nye, 2008). Institutions include both formal rules and informal norms. Examples of formal rules include constitutions, laws, regulations, and contracts. Informal norms are less precise or explicit but are far more prevalent. These norms include customs, conventions, and values. Separate from the rules and norms are the enforcement characteristics that pertain to them.

Enforcement characteristics determine the likelihood of detecting violations and the consequences for violators.

Together, the rules, norms, and enforcement characteristics of a society serve to reduce the uncertainty faced by actors who operate with limited—as opposed to perfect—knowledge (North, 2003). When institutions are scarce the costs of investing in knowledge acquisition prohibit all but the simplest transactions—those which personal memory of repeated dealings alone may serve. According to the evolutionary theory of

NIE (Platteau, 2009), the story of development is then one of institutional evolution and enrichment leading to an expanded realm of economic possibilities. Economic systems based on fragmented and recurring personal exchange may progress through time to economies based on increasingly complex and specialized interdependencies between agents who may engage in one-off, impersonal, and anonymous transactions (De Soto,

2002; North, 2003). The formal institutional infrastructure of rules supports this growth by working to guarantee anonymous transactions. The informal institutional infrastructure of customs and traditions supports growth by providing stability where coordination between anonymous agents would be hindered by price fluctuations

(North, 1996).

A modern illustration contrasting the institutional settings that characterize two very different stages of economic development is drawn by de Soto in his discussion of the origins of capital (De Soto, 2002). In arguing that the poor functioning of capital markets in developing countries is attributable to a lack of the institution of property, de

Soto contributes the key insight that property, or for example the formal ownership of land enshrined by title deed, unlocks opportunities for trade. This feature of property rests both on the ability of a property registration system to render essential and unambiguous information about the assets and credit of potential trade partners in an anonymous market, and on the possibility of property seizure by the state or other third party enforcer to incentivize compliance with agreements.

Institutional Change and Analysis

Though the stability provided by institutions is necessary for complex exchange, stability does not necessarily imply optimal performance. Two dimensions of performance recognized by North (1996) are allocative efficiency—that resources are allocated to their most efficient use at any moment in time—and adaptive efficiency— the ability of a society to learn from its failures and innovate new solutions. An understanding of the conditions for, and processes of societal change that are thought to affect performance is aided by Williamson’s framework for social analysis

(Williamson, 2000) (Figure 2-1).

Four linearly connected levels of analysis describe how a society evolves over time (Ibid.). The first level consists of the informal and socially embedded norms that define a culture. The institutions at this level are thought to derive slowly and without deliberation but may be sensitive to changes in relative prices. The second level consists of the formal institutional environment of rules and the legal system for their enforcement. Of special consideration at this level are property rights (Alchian &

Demsetz, 1973), though the institutional environment also includes the legal infrastructure for markets. The structures at the second level are partially constrained by the cultural parameters of the first level, but they are also subject to deliberate alteration at a rate approximately an order of magnitude faster than the first level. Property rights theory is primarily concerned with this level of analysis and the implications for performance of the different ways that property rights may be distributed (Ibid.).

Although a property right itself is a static element, property rights are used dynamically and at cost when parties contract with each other. Because the cost of transacting varies under different forms of governance (Williamson, 1991), a third level of analysis is invoked. This 3rd level of analysis includes the structures of governance that create order and maximize the gains of transactions by managing contracts and settling disputes. Change at this level occurs on an order of magnitude faster yet. Finally, the fourth level of analysis is characterized by a continuously changing neo-classical production function that takes as given the parameters of the lower levels to determine the most efficient allocation of resources at a moment in time. The 2nd and 3rd levels are the focus of the proposed research.

As indicated in Figure 2-1, formal institutions evolve from an interplay with informal norms and values, but their origins are not necessarily domestic. Laws and policies are often adopted by, inherited from, or imposed on one jurisdiction by another.

For example, under British colonial rule, game reserves and hunting restrictions were established throughout southern Africa. These policies were inspired by Victorian era notions of hunting as sport and directly clashed with native culture. However, following independence, when newly formed countries had the opportunity to shed unpopular colonial wildlife policies, many policies were retained and used to the advantage of political elites and their followers (Gibson, 1999). In the modern era of globalization, multilateral and even unilateral rules of trade devised in wholly separate jurisdictions often impose constraints on a country’s economic opportunities1.

Notwithstanding the influence of geopolitics, the agent of change at the national level is the “institutional entrepreneur” (North, 1996) who pursues wealth maximizing opportunities across the economic landscape sculpted by the set of institutions and biophysical constraints (Westley et al., 2011). Change may happen passively, as first level institutions shift under movement across this landscape, or may be brought about directly as agents invest in the alteration of formal rules when the cost of doing so is less than the benefit (Platteau, 2009; North, 1996). An evolutionary process akin to

Darwinian selection proceeds at multiple scales to eliminate maladapted institutions

(Platteau, 2009). However, because of the significant startup costs of institutions and their enforcement mechanisms the nature of change is path dependent and the rate of

1 For example, trade restrictions under the Convention on International Trade in Endangered Species and import restrictions on trophy animals under US Fish and Wildlife Service policy.

change is limited by the inertia of the institutional system (Ibid.; James, 2001). Because of differential bargaining power between entrepreneurial agents, investment in changing the institutional constraints may result in negative-sum-games (Platteau, 2009; James,

2001). The combination of path dependency and inertia, as well as the differential bargaining power of agents, present two major challenges to performance, especially in the context of PAs.

First, the rapid growth of human populations and the demand placed on natural resources has frequently outpaced the rate at which institutions have evolved to respond to growing scarcity (Westley et al., 2011). With the transition from an “empty” to a “full” world (Daly, 1997; Smil, 2011), incumbent institutions suitable for simple and biologically productive agricultural landscapes have spread, along with human populations, to new, agriculturally marginal, and highly complex wild landscapes (Child et al., 2012). The institutional mismatch has resulted in the ecological reduction of these systems and the worsening of environmental problems as resources continue to be allocated according to the old rules of the game (Westley et al., 2011). Second, as the groups that make up a society are not homogenous they are subjected to different sets of informal institutions and are guided to different wealth maximizing positions on the economic landscape of opportunity. Thus, when it serves the interests of one group to modify the formal rules, the change may not align with the informal constraints of another group and may undermine the latter group’s position (Platteau, 2009; James,

2001; North, 1996). Laws that centralize ownership of wildlife and prohibit hunting, for example, may grant patronage opportunities to politicians (Gibson, 1999) at the same time as they threaten to limit the livelihood opportunities of poor populations surrounding

parks. The greater the misalignment between formal and informal institutions, the greater the costs of enforcement (James, 2001; Leader-Williams & Albon, 1988; Milner-

Gulland & Leader-Williams, 1992). A theoretical approach for understanding misalignment of the formal institutional environment with environmental characteristics in the context of wildlife and PAs is the focus of this research.

The Formal Institutional Environment: Property Rights and Markets

Property rights theorists have contributed to an understanding of social problems by tracing the consequences that flow from different systems of property rights (Alchian

& Demsetz, 1973). The social problem most amenable to this analysis is popularly termed the “Tragedy of the Commons” (Hardin, 1968). The commons problem pertains to resources or goods that are both rivalrous—meaning consumption by one person prevents simultaneous consumption by another person—and non-excludable—that is, it is impossible to prevent access to the resource by non-paying consumers. Wildlife is a prime example of a “common pool resource” in Africa. Though wildlife is typically a state-owned resource, the right to exclude people from “taking” wildlife may be costly and rarely exercised (Leader-Williams & Albon, 1988). Individual users may then free- ride, capturing all of the benefits of using the resource while externalizing the opportunity costs of diminished wildlife populations which become shared across the user base. Alchian and Demsetz (1973) observe that this problem arises from an instability in a property rights regime that assigns communal rights to a resource in one form (e.g. a live animal) and private rights—establishing excludability--in another form (a dead animal). Equilibrium will tend to favor the form to which private rights obtain. If the conversion comes at a cost, the net social consequences will be negative. One solution to the commons problem is to obviate the need for conversion by assigning private

rights to the resource in both its forms (Alchian & Demsetz, 1973). It is important to note, however, that private rights do not necessarily imply private, individual rights. Private rights to wildlife on communal land, for example, have been granted to communities in parts of Southern Africa (Hulme & Murphree, 2001) through such entities as communal property associations and community trusts.

Alternative and more traditional responses to negative externalities include regulation and the assignment of liability. However, in his essay titled “The Problem of

Social Cost”, Coase (1960) explains how a rights-based approach can lead to social- cost outcomes equivalent to the regulatory approach regardless of the way that property rights are originally assigned. This possibility rests on the assumption of the transferability of rights which itself depends on symmetrical knowledge of the values involved and a low-cost system for trading property rights. Though costless markets for property rights are purely theoretical, public property systems based on regulation rest on assumptions of their own. Namely, that government officials are held accountable and that voters are well informed (Stroup & Baden, 1983). Publicly owned and allocated resources, such as wildlife, may however be susceptible to the lobbying efforts of powerful groups or individuals. The Zambian government, for example, had been notorious for issuing to politicians “special hunting licenses” not subject to standard quotas for the purposes of nourishing patronage networks in rural constituencies

(Gibson, 1999). The opaque process by which the Zambia’s Department of National

Parks and Wildlife currently sells hunting concessions also undermines the ability of communities to judge the value of their wildlife resource and presents a significant market failure (Simasiku, 2008). Thus equity concerns may invite an argument for public

ownership but public allocation of resources can be problematic. Market based systems, by contrast, are believed to overcome the information asymmetry problem and address intra-generational equity because under fair conditions they allow for a full consideration of alternative activities whose opportunity costs become known through a process of open bidding (Stroup & Baden, 1983). However, market conditions and access to information may be far from ideal in certain settings in southern Africa. Market systems, furthermore, are at the center of active debate over the winners and losers of “the commodification of nature” (Igoe & Brockington, 2007). The effectiveness of markets and property rights in reducing poverty is thus an important question in need of more empirical information. It is a question that must also be asked with an appreciation of the nuance in the types of rights pertaining to property and by whom they may be held.

The distribution of rights in a property regime may exist along a continuum from fully public to fully private at the individual level (Figure 2). Identifying an optimal distribution can be difficult because of the complexity of the resource and the spatial and temporal scale of its dynamics (Murphree, 2000). According to common property theory (Ostrom, 1990), scale issues are best addressed by assigning multiple rights to a given resource in nested fashion at different levels of governance. A classification scheme adapted from Schlager & Ostrom (1992) is useful for understanding the multiplicity of property rights in a natural resource context. At the most basic level, operational rules and the corresponding rights constrain the activities of resource users.

These activities include accessing and withdrawing resources. Operational rules are created by collective choice actions. At a higher level collective choice rules and the corresponding rights specify who can take collective choice actions and the procedures

for doing so. The most important collective choice rights are the right to manage (to determine how resources may be withdrawn), the right to exclude (to determine who has access), and the right to alienate or sell the authority to manage and exclude. Also, and though not explicitly considered by Schlager & Ostrom, the right to manage the benefits flowing from a resource may be just as important as managing the resource itself. Actors may then be classified according to which rights they possess. Users possess only access and withdrawal rights, while owners possess these rights in addition to the rights for management, exclusion, and alienation. Proprietors, however, are more frequently represented in common property systems and they possess all but alienation rights. Therefore, in order to investigate the relationship between the distribution of property rights and economic performance it is necessary to not only describe the scale of the resource, but the level at which each of the resource rights are held.

Structures of Governance

Protected area governance may be understood as “the interactions among structures, processes and traditions that determine how power and responsibilities are exercised, how decisions are taken and how citizens or other stakeholders have their say” (Graham, Amos, & Plumptre, 2003, p. 2). That is, it is the process by which a constituted authority attempts to understand a system and to create and pursue a set of objectives through the allocation of resources and power. It is a process distinct from management in that it concerns “who decides what the objectives are, what to do to pursue them and with what means; how those decisions are taken; who holds power, authority and responsibility; [and] who is (or should be) held accountable” (Worboys et al., 2015, p. 171). Management, by contrast, encompasses the actions undertaken in

pursuit of the objectives and the technical and comparatively constrained decisions that support those actions.

Governance of PAs is typically studied by evaluating structures and processes against a set of normative criteria, such as that contained in the International Union for the Conservation of Nature’s (IUCN) “principles of good governance”2. However, if the adherence to these principles, or “governance quality”, is the dependent variable in evaluations of governance, it is less clear what the independent variable(s) should be.

Several typologies of PA governance have been proposed in order to answer this question, with different emphases and levels of precision. A brief review of these typologies will highlight a common point of confusion and make clear the deficiencies of each, as well as the way in which a framework more grounded in theory should bring important advantages in terms of resolving power when relationships between governance type and economic performance are investigated.

The need to better understand the diversity of PA governance arrangements was first given priority at the fifth World Parks Congress in 2003, and in 2008 the IUCN formally adopted a classification scheme of its own, to go along with its system for classifying parks by management category (Dudley, 2008). The purpose was to make consistent, and to simplify evaluations of governance quality through a common language describing the variation in the structural and procedural aspects of governance. Though the IUCN’s scheme may prove useful for this purpose, especially when applied at a global scale, it is an over-simplistic product of compromise between

2 The principles include legitimacy and voice, direction, performance, and accountability, as well as a number of specific ideals that clarify these broad concepts.

reporting and research considerations. As the present study is not principally concerned with governance quality, per se, what is proposed in this chapter is not an alternative governance typology, but rather a way of thinking about governance structures (and resource tenure) that is more amenable to inquiries driven specifically by economic theory.

Of the three typologies in Tables 2-2, 2-3, and 2-4, only that adopted by the

IUCN (Table 2-2) is in wide use. However, the alternatives are important to consider for three reasons. Firstly, they demonstrate a common misunderstanding in regards to the construct of governance by disaggregating it into tenurial and “management” components. While land or resource tenure is an important predicate upon which the form of governance will often depend (Lausche, 2011), tenure is more appropriately described as a condition separate from governance on account of its different nature, as a set of rights from which derivative contractual structures and processes of governance partly emerge. Notwithstanding the misuse of terms, the alternative typologies are important to consider, secondly, because they do bring attention to the question of tenure, which is arguably as critical a determining factor as the type of governance is for various dimensions of PA performance, including governance quality--and it is governance quality which the IUCN and PA specialists seek to better understand through enhanced classification. What the inclusion of tenure status (Table 2-3) may reveal, for example, are situations in which governance quality under joint-governance is lower than otherwise might be expected, because the community governing partner lacks tenure to the land or resources. Thirdly, the three typologies seen together are suggestive, either explicitly or implicitly, of deficiencies in each.

As Paterson (2010) argues, the IUCN’s governance categories permit a large degree of overlap, especially where governance is shared. For example, partners in

“shared governance” may include not only the government itself and a community, but also a private entity such as a legal trust through which the community is represented.

Private for-profit organizations may also hold concessions in the same such park. The

IUCN suggests that overlap in the typology can be resolved by including descriptions of the respective responsibilities of overlapping partners, even though this practice would seem to undermine the goal of developing a common language and simple classification scheme. Paterson’s solution is to append to the question of who is responsible for governance the questions of the basis of governance and the form that governance takes (Table 2-3). With respect to form, Paterson follows the IUCN’s terminological distinction between collaborative governance (or co-governance), “in which decision-making authority and responsibility rest with one agency but the agency is required, by law or policy, to inform or consult other rightsholders and stakeholders, at the time of planning or implementing initiatives” and joint governance, in which “the representatives of various interests or constituencies sit on a governance body with decision-making authority and responsibility, and take decisions together“ (Borrini-

Feyerabend et al., 2013, p. 32). It should be noted, however, that the term

“management” is used rather confusingly in place of governance in Paterson’s typology.

Eagles’ typology (Table 2-4) does not explicitly include shared governance as a category or attribute and further obfuscates the matter by treating individuals and communities alike as private entities, but by asking the source of income it implicates a much-overlooked aspect of governance, that of the structures and processes internal to

governing bodies. Whereas most of the literature on PA governance describes the external relationships between partners in governance, or, at most, the internal dynamics within community governance systems, the study of PA agencies from a corporate governance or management-science perspective has been much less common. Often, it is the corporate structure and administrative policies of a PA authority which determine the funding that a PA receives and, ultimately, its level of performance.

Structural contingency theory (SCT) provides the constructs for describing the internal structure of an agency such as a PA authority. More accurately described as a set of theories, SCT grew out of organizational sociology and has been applied in the study of business management to understand the relationship between the performance of a business, its structure, and the environment in which it sits (Donaldson, 2001). The four primary elements of an organization’s structure consist of the degree of centralization and formalization, the number of hierarchical levels, and the organization’s logic of departmentalization.

A centralized structure means that authority is concentrated within upper levels of management, whereas a decentralized organization empowers lower level managers or employees with authority and decision-making responsibility. Formalization refers to the degree to which procedures, operations, and employee responsibilities are constrained by formal policies and written rules. The number of levels in an organization’s hierarchy, controlling for absolute size, is a measure not only of the distance between top management and bottom level employees, but also of the ratio of managers to the employees they supervise. Organizations with many levels are referred to as “tall” and their employees can be closely monitored, while those with fewer levels are described

as “flat” and their employees have greater latitude in performing tasks on account of the greater number of departments or employees that each manager is responsible for.

Finally, organizations may be divided into departments on the basis of their function

(e.g. procurement, marketing, human resources, research, etc.), or on the basis of the products or services they provide. In the former case, a park agency might limit the responsibilities of park-level managers to day-to-day tasks while the other functions are performed in separate, specialized, and remote offices at a system-wide scale. In the latter case, the managers and employees at each park might be given the scope to perform multiple functions themselves.

Though each of these attribute types lie on their own spectrum, there are strong co-linearities between them. As a result, the collective attributes of an organization are often described by their position along a single continuous spectrum, going from so- called “mechanistic” structures that are centralized, formalized, tall, and functionally articulated, to “organic” structures that are decentralized, flexible, flat, and more autonomous at the level of production.

The normative logic of structure

Decentralization has become a dominant theme in natural resource governance

(Birner & Wittmer, 2004; Mburu et al, 2003) and an increasing trend in the governance of PAs--especially in Southern Africa (Hulme & Murphree, 2001). Common structures now range from full state-governance, to hybrid forms combining local institutions with state capacity (Birner & Wittmer, 2004), to various forms of collective governance that virtually exclude state involvement (Kreuter et al, 2010). Shifts in governance structures usually stem from cost-saving motivations or concerns about the equitable distribution of benefits. A large body of empirical evidence has tended to validate the logic of

decentralization on the whole (Agrawal & Chhatre, 2007; Lutz & Caldecott, 1996), but many questions remain about possible tradeoffs in the costs and benefits of different arrangements for decentralized governance (Pannell et al, 2013; Roggero, 2013). For example, shared governance is thought to reduce transaction costs of enforcement at the expense of increased planning costs (Roggero, 2013), but if relations between the state and a community are characterized by power inequities, if community capacity is undermined by opaque forms of communal governance, and if managers fail to address uncertainty because of a poor understanding of resource dynamics, then execution and quality of all necessary transactions cannot be assumed (Pennell et al, 2013). The internal organization of the state agencies with primary responsibility for the management of PAs is also characterized by a general trend of restructuring. Many wholly state-run agencies were partially privatized, beginning mostly in the 1990s.

Some, such as South African National Parks (SANParks), have experienced much success. Yet the performance of parastatal park and wildlife agencies has not been consistent. For example, the Zambian government in 2015 ended a 16 year experiment with the Zambia Wildlife Authority (ZAWA) by fully re-absorbing it into the Ministry of

Tourism and Arts. The parastatal had reportedly accumulated a debt orders of magnitude above annual profits, suggesting systemic roots to its mis-management.

This variance in both the external and internal structures of PA governance is predicted by the theory of transaction cost economics (TCE) and by SCT to have implications for the performance of PAs and PA authorities. These theories provide the normative logic for delineating the efficient boundaries of the state (TCE), and for measuring the degree of “fit” between the architecture of a PA agency and the

environment in which it operates (SCT). Though the literature on the local governance of natural resources is extensive and draws from many academic disciplines, few studies have sought to develop insights from the application of the above theories.

While the present study does not employ the methods of TCE or SCT, the results are nevertheless partly interpreted from the perspectives of these theories, which are elaborated below. As both theories have their origins in the study of private business, their focus on efficiency to the exclusion of distributional equity raises a question about their applicability to analyses of public sector institutions for which the idea of performance is complicated by the multiple constituencies and values they serve.

However, while it is possible for a PA to efficiently generate financial value that accrues only to the already wealthy, an ineffective and inefficiently run PA will neither generate much value nor be likely to ensure its equitable distribution. Even if, hypothetically speaking, land use alternatives to the protection of wildlife better contributed to pro-poor development, the more appropriate counterfactual against which outcomes should be evaluated is arguably an alternative property rights regime, not an alternative governance structure.

Transaction cost economics. Different governance arrangements will variably affect the costs associated with transactions and carry implications for both the transferability of property rights, the creation and flow of information, and the overall efficiency of an economic system (Williamson, 1991). The study of transaction costs grew out of analysis of private industry but in an adapted form TCE has been used to test hypotheses about public sector organization. The theory has also been used to evaluate structures in the management of natural resources (Adhikari & Lovett, 2006;

Birner & Wittmer, 2004; Kuperan et al, 1998; Mburu et al, 2003; Wang & Van Kooten,

1999). Transaction costs are defined as those arising from efforts to “define, establish, maintain, and transfer property rights” (Marshall, 2013). These costs are separate from those arising from activities associated with production, such as the management of a tourist lodge. Various typologies of transaction costs exist, though a common distinction is between three categories: search and information costs; bargaining, decision-making, or contracting costs; and monitoring, enforcement, and compliance costs (Mburu et al,

2003).

A number of transaction attributes and contextual factors are thought to be important in determining the level of cost of a transaction. In addition to the three standard transaction attributes of uncertainty (e.g. the level of understanding of cause- effect relationships in natural resource management), the level of specificity of the site or tasks involved (which complicates the contracting process), and the frequency of transactions, Birner and Wittmer (2004) propose the levels of care and contest intensity as two other salient factors in transactions pertaining to PA management. Transactions that are care-intensive are defined as “activities that are difficult to monitor because they involve carefulness, watchfulness, and diligence and, therefore, leave ample room for shirking—or even sabotage” (ibid., p. 673). Monitoring against poaching is often care- intensive, and in this sense is distinguished from effort-intensive activities such as the harvesting of resources, which entails production costs. Contest-intensity relates to transactions involving resources for which there are competing claimants. Because PAs impose restrictions on resource use, in developing countries where natural resources are scarce, contest-intensity may be very high.

Importantly, Birner and Wittmer argue that shared forms of governance between the state and local communities should have significant cost-reducing advantages over the traditional unitary state-governance model when the combination of the five transaction attributes describe a setting with high care-intensity and where there are significant threats to the resource and monitoring challenges. The cost-savings are expected to come primarily from lower implementation costs due to greater compliance with decisions on resource use restrictions. Decisions formed through shared governance may be seen by local resource users or residents as having a greater legitimacy, especially if decisions reflect a fair balance of power and if effective conflict mediation mechanisms are in place. When the devolution of rights to resources or benefits is coupled with shared governance, the greater incentive for locals to comply can also translate into reduced costs, as can the social pressure for compliance from one’s neighbors or peers.

However, the attributes that would favor shared governance must be seen in the context of environmental factors. These factors, including the characteristics of the resources in question, the relationship between the resources and the community, the capacities of both the community and the state, and the relations between a community and the state, often present challenges for the devolution of governance. Because PAs embody public goods, in that the resources and services they provide are typically rivalrous and non-excludable, the larger their scale (spatial or temporal) the more appropriate it may become for the state or other non-local institutions to take a major role in governance. In landscapes composed of multiple adjoining PAs, especially, some degree of centralization may be justified by considerations of scale economies

and security. Also, if there is physical separation between a PA and the location of the community or a low dependency by the community on its resources, the likelihood of successful local governance is reduced—and the capacity of rural communities in least developed countries (LDCs) to perform the functions of governance is often limited to begin with. As well, low literacy rates, heterogenous populations, and low levels of trust, among other common characteristics, undermine efforts to organize communities and channel the power of collective action. On the other hand, the capacity of the state in

LDCs is also often limited. Bureaucracies based on patronage rather than merit are poorly prepared to meet their statutory obligations. Though this reality underlies the logic of decentralization, an ineffective state institution may not be able to support capacity building of its community partners or hold community governance institutions accountable for their performance. Finally, these challenges to shared governance may be exacerbated when historical relations between the state and community are characterized by conflict and exclusion.

Structural contingency theory. The main postulate of SCT “is that organizational effectiveness results from fitting characteristics of the organization, such as its structure, to contingencies that reflect the situation of the organization”

(Donaldson, 2001, p. 1). The structural characteristics recognized by SCT have been described above. Contingencies include an organization’s size, the environment that surrounds it, and its organizational strategy.

Size is a straightforward measure of the number of employees working in an organization. The larger an organization, the more that the needs of efficiency will be served by a bureaucratic structure and by the formalization of rules. The smaller an

organization, the more it can be centralized, with decisions of managers replacing rule- bound procedures. Although size is taken as an exogenous variable in SCT, regional best practices suggest the appropriate number of staff for managing a park.

Comparisons of performance may then be made between different organizations on the basis of both the degree to which their size departs from best practices and the degree to which their structure fits their size.

The rate of market and technological change defines an organization’s environment. Because stable environments foster work that is predictable, the certainty regarding the tasks, tools, and knowledge required to perform a job in them is high and the need for innovation is low. Mechanistic structures are expected to achieve higher performance in such environments. Conversely, dynamic environments create task uncertainty and demand an organic structure, capable of processing new information through front-line staff and innovating solutions from the bottom-up. In this study, the market in which PA agencies operate and the nature of work-related tasks are highly similar throughout the region in which data was collected. The market for wildlife tourism in southern Africa ranges from budget accommodation for domestic visitors to five-star luxury lodges inclusive of safari activities. It is a growing market with tourist numbers expected to increase consistently over the coming decades. At the same time it has become more competitive, placing greater pressure on businesses to upgrade and innovate through, for example, designing travel circuits or offering different types of safaris, including ones led on foot. Though tourism is mostly outsourced by public PA agencies, the range of tourism options available for a particular PA is limited by the physical, infrastructural, and institutional environment that an agency is able to create

for potential concession-holders. Aside from the responsibility to create these enabling conditions for private enterprise, the major front-line tasks of PA agencies are conducting anti-poaching patrols, ecological monitoring, and maintaining community relations. These activities are needed as a direct consequence of the highly dynamic nature of the ecological and socio-economic environments in which PAs are embedded.

In light of both the consumer market and management tasks that PA agencies face, it is expected that agencies with a more organic structure will achieve higher performance.

The strategic decision of an organization to specialize on a single product or service or to diversify with multiple products and services also has implications for its optimal structure, independent of the environment. While departmentalization by function may serve an organization with an undiversified strategy, where there is a need to tailor products and services to different regions, a structure in which functions are replicated in each division is more suited. Divisional structures also allow for local innovation. Because most of the value and the costs that PAs generate are localized at the individual PA level, it is expected that PA agencies departmentalized by function will experience significant challenges to performance.

The Study Area and the Institutional Context of PAs

The research was based in and around South Luangwa National Park in Zambia and Kruger National Park in South Africa. These settings represent ideal comparison groups for evaluating institutional effects because of their similar physical environments and contrasting institutional environments. Protected areas in the study region are lands managed in a natural state and dedicated to the production of wildlife. They include government-run public parks, private game ranches, game ranch collectives (known as conservancies (Kreuter et al., 2010)), and areas co-managed between communities and

national or provincial level government authorities. That is, they vary with respect to how land and wildlife within the property is owned, with respect to the contractual arrangements between communities, the private sector, and the government through which wildlife use decision-making is structured, and with respect to the organizational structure of PA agencies.

Details of the formal institutional environment for wildlife and land and of the governance structures for PAs in Zambia and South Africa are elaborated in subsequent chapters, but the distinguishing features most relevant to the design of the study are given brief treatment below. Figure 2-2 illustrates how these features define the comparative groups of the study in terms of the degree of power decentralization and the degree of property rights devolution that they confer. Figure 2-3 elaborates on property rights distribution for both countries using the typology of Schlager & Ostrom

(1992).

Zambia. In Zambia, all land is owned by the state, though customary tenure governs access in communal areas and land elsewhere may be leased for up to 99 years. Leases may be held by individuals, but may also be held by a legal entity, such as a trust, on behalf of a community. Land tenure can be strengthened for communities in this manner (Figure 2-3), though possessing titled land does not confer any additional rights to wildlife. By default, all wildlife is owned by the state, though withdrawal rights in the form of game-capture or hunting licenses may be purchased by individuals or conservancies, at roughly market rates, conferring to them “user” status.

Several models for the governance of wildlife-based PAs exist in Zambia. With reference to Figure 2-2, they consist of:

A. National parks, owned by the government, and for which budgeting is usually done centrally. South Luangwa NP, however, is an exception and operates under a revenue retention scheme.

B. Game management areas (GMAs) are on communal land and are entitled only to partial benefits from wildlife through remittances by the government from the sale of trophy hunting licenses and hunting concessions. They are also jointly governed between the community, state, and private concessionaires. This latter arrangement elevates GMAs to claimant status—they cannot exclude resident hunters and spending of the benefits on social goods makes exclusion of beneficiaries impossible.

C. Community game ranches in Zambia are on leasehold land, are not entitled to the same wildlife benefits as GMAs, and are jointly governed between the community and a private partner, without the state.

D. Private game ranches in Zambia are on leasehold land and, unless fenced, lack ownership of wildlife. Owners of game ranch properties may, however, purchase use rights to wildlife.

South Africa. In South Africa, owners of suitably fenced land may be considered proprietors of wildlife in that they automatically possess rights of management3 and exclusion (Figure 2-3). These rights have enabled a large and thriving private protected area estate to exist. Again, with reference to Figure 2-2, the range of governance models in the South African study area consists of:

E. a contractual park and several provincial parks which are at present, or in the near future expected to become, the property, either wholly or partially, of communities as a result of the restoration of land rights lost during the apartheid era. They are jointly governed between the land-owning communities and either national or provincial park authorities.

F. Private game ranches in South Africa own wildlife and are individually governed.

G. Conservancies are groups of private game ranches formed where tourism- based economies of scale favor collectivization. Conservancies jointly own

3 In the Greater Kruger National Park, because of the lack of fences between the state-owned national park, the private reserves, and the provincial reserves, wildlife management rights—namely quota authorization--are technically shared with the local provincial governments which act as partial claimants.

their wildlife and are jointly governed between their private, plot-owning members.

H. Private reserves restituted to communities are owned by communities and jointly governed between the community and a private partner.

The Economic Value of Wildlife in the Study Region

Numerous studies have measured direct expenditures related to wildlife in

Southern Africa (Barnes & De Jager, 1996; Barnes, 2001; Lindsey, 2008; Van der

Merwe & Saayman, 2003; Langholz & Kerley, 2006). However, studies that evaluate total economic impact (inclusive of backwards linkages of tourism businesses to their suppliers and spending of wages by employees) of specific PAs are less common.

In Zambia, Pope (2005) authored a study of the direct financial and economic contributions to the local area and park authority of consumptive and non-consumptive tourism from the lower Luangwa Valley. He estimated that the valley generated nearly

$1m in direct income to local residents, however the estimation of multiplier impacts was not attempted and tourism has grown considerably since that study. The World

Bank (2007) evaluated the economic impact of nature-based tourism in Zambia at a sector-wide level and estimated a contribution to value added of $403m (6.5% of GDP), though value captured locally at a park level was not a focus of the research. Another sector-wide study (Bandyopadhyay, 2009) examined the impact of consumptive tourism on the welfare of communities in GMAs and found that the gains from wildlife use, though substantial for certain GMAs, were not distributed evenly amongst communities.

At a park level, a study (PMTC, 2010) of the potential economic rates of return (ERR) on investment for a Kafue NP tourism development project estimated a best-case scenario of nearly $140m in value added, representing a 93% ERR, though the park remains under-developed to this day. Comparing the performance of different Zambian

PA models to each other and to regional averages, Lindsey et al. (2013 & 2014) found that gross earnings in GMAs with active concessions, at $291/km2, were below the average of all but one other southern African country and well below the $878/km2 of unfenced ranches in Zambia. In a highly critical analysis of Zambian wildlife policy they cited the lack of devolution of wildlife rights, among other factors, for the failings of the sector.

Within the South African portion of the study region, several previous studies have investigated the economic value of tourism at Kruger NP specifically. Using input- output analysis on visitor spending, weighted by the proportional contribution of

Mpumalanga Province to various economic sectors, Saayman & Saayman (2006) estimated $2.6m of indirect expenditure from park tourism remained in the province in

2002, though only data on spending within the park and only spending by overnight visitors was collected. In a more thorough study, Saayman et al. (2012) sampled day groups, chalet groups, and camping groups and employed a social accounting matrix in addition to input-output analysis to estimate a total direct impact to the region of $100m, and total impact (including indirect and induced impacts) of $219m in 2009. A more inclusive accounting of the economic value of Kruger in 2000 (Turpie & Joubert, 2001) came to an estimate of direct on-site spending of $19.1m, but attributed an additional

$18.4m of spending to the park based on a discounting of spending off-site according to the percentage-wise importance of the park to a visitor’s whole trip. A consumer surplus value of approximately $141m was also estimated. However, a significant amount of off- site spending and consumer surplus value is not captured locally or nationally and is of little relevance to the local region.

Several studies of the economic impact of hunting in South Africa as a whole

(Van der Merwe et al, 2003; Van der Merwe et al, 2014), and in one of the provinces surrounding Kruger NP, in particular, show considerable value from this activity. Van der

Merwe et al. (2014) estimated a direct impact of $83.6m and a total impact of $150m from biltong and trophy hunting in Limpopo Province in 2009. However, a literature review yielded no studies which have attempted to estimate the local economic impact of wildlife reserves and conservancies that may incorporate hunting, but that specialize in ecotourism. Nor have any studies attempted to evaluate the economic impacts at the level of a single connected system, encompassing a diversity of PA types (from public to co-managed to private), management intensities, and objectives.

Figure 2-1. Theoretical framework for understanding protected area performance as an outcome of institutional alignment. Adapted from Williamson’s (2000) framework for social analysis.

Table 2-1. A natural resource property rights typology. Source: Schlager & Ostrom (1992) Rights Classification of rights-holders Owner Proprietor Claimant Authorized user Access & withdrawal X X X X Management X X X Exclusion X X Alienation X

58 Table 2-2. The IUCN’s governance classification system for PAs. Governance by gov't. Shared governance Private governance Indigenous or community governance Examples • national agency • transboundary • individual landowners • indigenous protected areas • sub-national agency • collaborative (pluralist • non-profit organization • community protected areas • delegated mgmt. (e.g. influence) • for-profit organization NGO) • joint (shared power)

Note: Adapted from Dudley (2008).

Table 2-3. A PA governance classification system recognizing the nature of tenure. Tenure Management Examples Who (single or How (basis) How (form) Who (single or How (basis)? How (form)? multiple) multiple)?

• Gov’t. • Statute • Full title • Gov’t. • Statute • Co-mgmt. • Community • Custom • Limited title • Community • Custom • Joint-mgmt. • Individual • Contract • Individual • Contract • Transboundary mgmt.

Note: Adapted from Paterson (2010).

Table 2-4. A PA governance classification system recognizing the major source of income. Ownership entity Income source Management body Examples • Public (govt.) • Societal taxes • Government agency • Private (non-profit) • User fees • Parastatal • Private (for-profit) • Donations • Non-profit corporation • For-profit corporation Note: Adapted from Eagles (2007).

Figure 2-2. Governance, resource tenure, and scale of PAs in the South African and Zambian study areas.

Figure 2-3. The formal institutional environment of property rights, taxes, and regulations for land and wildlife in Zambia and South Africa. Rights-holders are classified with respect to the wildlife resource only.

CHAPTER 3 RESEARCH METHODS

Study Design

A two-level explanatory cross-sectional analysis spanning PA landscapes in

Zambia and South Africa forms the logical structure of this research. The explanatory cross-sectional design is commonly used to identify relationships between one or more predictor variables and one or more outcomes, either within a single population or across multiple populations (De Vaus, 2001). Comparison groups are a representative sample of respective populations, each sharing distinct predictor variable characteristics. Although a recognized weakness of cross-sectional studies is their low explanatory power when there are few comparison groups or when the testing of alternative hypotheses cannot be accommodated (Ibid.), the number of comparison groups in this research was relatively limited and care was taken to identify potential alternative explanations of results and to control for confounding factors.

At the first level of this study there are two sets of comparison groups, each consisting of PAs under different governance models in the respective countries. In

Zambia the comparison groups are South Luangwa National Park, the GMAs of the lower Luangwa Valley that buffer the park, and game ranches found in the open area to the south of the Luangwa River. In South Africa, the comparison groups are

Kruger National Park, the contiguous private reserves, the contiguous provincial reserves, and Makuleke Contractual Park. At the second level the comparison groups are the collective first-level PA systems in both Zambia and South Africa.

Although the Kafue PA system in Zambia and the Limpopo PA system in

Mozambique fit into the logic of the research, logistical constraints prevented data collection from these areas.

These settings offer ideal comparison groups because a) the Luangwa and

Kruger systems differ substantially with respect to one of the primary variables hypothesized to predict the magnitude and distribution of PA-generated value—the formal institutions relating to wildlife as property—yet have similar biophysical characteristics, including habitat, faunal assemblages, and low rainfall, and b) the PA types within the two systems differ with respect to the contractual arrangements structuring wildlife-use decision-making between communities, the private sector, and the government (Figure 2-2).

Methods

For estimating the value generated by PAs, the principle methodological framework followed is that of an economic impact analysis (EIA). This form of analysis quantitatively describes the interrelationships between sectors of an economy and is used to estimate the changes that occur due to the introduction of money into a system as payment for goods and services. Because producers render their goods and services by combining their own resources and skills with inputs they purchase from other economic sectors, a market transaction at the consumer end sets in motion a wave of transactions that ripples throughout the economy, affecting wholesalers, manufacturers, farmers, employees, etc. At each step in the chain, value is added to a product in its intermediate form in exchange for a portion of the final consumer price. Whereas a financial analysis of a particular activity accounts only for the value added by the final seller and evaluates this against costs, an economic analysis accounts for value that accrues to all local economic actors, which then can be compared with the potential value from alternative economic activities. In short, the difference between financial and economic analyses is the difference between the measurement of the private returns from an activity and the

activity’s social returns. In a financial analysis, the key indicator of performance is profit. In EIA, the key indicators, or “effects”, are jobs, personal income (wages and salaries), taxes, and profit—the sum of the latter three is termed value added. The value of primary interest from both an academic and policy standpoint is local personal income. This measure can be understood both as an aggregate value and in terms of the level of change in income from an additional unit of currency spent in the local area. Gross sales (“direct spending”) is also considered, but due to a high level of leakage is not emphasized. The meaning of these measures is expanded on in the results sections of subsequent chapters.

Economic effects are measured at three different levels for any defined region. Direct effects arise from first level, consumer spending. In the context of PAs, this is spending on businesses that sell directly to visitors (e.g., lodges, campgrounds, restaurants, grocery stores, etc.), as well as what is captured by local first-level producers (Stynes, 2001). This first level of effects was estimated in this study by developing average spending profiles for different tourist segments and then multiplying these values by the number of recorded visitors in each segment.

Indirect effects arise from all subsequent business-to-business transactions, such as spending by tourism lodges on food and supplies, and spending by supplier businesses on their own supplies. Induced effects arise from the spending by those who earn personal income through a direct or indirect effect. For example, when an employee of a tourist lodge or business that supplies a tourist lodge spends their salary in order to satisfy household needs, this spending supports additional jobs in non-tourism businesses and leads to additional rounds of local spending across a broad range of economic sectors (Cullinane et al., 2014). The sum of direct, indirect,

and induced effects are called the total effects of consumer spending (Crompton,

2010).

The interactions between consumers and producers that yield the effects are commonly captured by economic input-output (I/O) models and expanded I/O models known as social accounting matrices (SAMs) which additionally capture household level transactions (Miller & Blair, 2009). From these models, regional economic multipliers--ratios by which direct effects are multiplied to give secondary or total effects—can be extracted (Camargo et al., 2008). An extraction method

(described in more detail in Appendix A) in which the economy is assumed to be unconstrained was used. An unconstrained economy is able to completely absorb extra demand by drawing on additional domestic factors, with no increase in consumer, producer, or factor prices, while the structural relationship between sectors of the economy remains constant (Breisinger et al., 2009). In reality, the ability of sectors to absorb extra demand is constrained and both prices and imports may increase in response. However, constrained models add complexity to the analysis and given the small scale of the changes being modeled, relative to the national economy, modification of the SAMs into constrained models was not considered justified.

The use of economic models in the study of PAs in developing countries, however, is challenged by the fact that such models, being costly to produce, may not exist for the particular country or sub-national region of interest. Even where a model does exist, it may not recognize tourism, let alone park tourism, as a distinct economic sector. These challenges were encountered in the analysis of the PA impacts in both Zambia and South Africa and the different approaches used in addressing them are discussed for each country, below.

Methods Used in Zambia

Economic impacts were assessed at two scales in Zambia, the local and the national. The GMAs adjacent to SLNP and the GMAs and unfenced game ranches extending south of SLNP along the wildlife corridor formed by the Luangwa River were considered to comprise the local area for the purposes of this study. Where a

GMA bordered a second national park but was divided into two hunting blocks, only the hunting block bordering SLNP was included. The logic of this scope was to ensure geographic correspondence between measured economic values and the distribution and extent of real or perceived “costs” of the park, including associated land or resource use restrictions.

As no model of the local economy existed, a bottom-up sampling approach was innovated. The data collected in this manner, from tourism and non-tourism businesses, employees, and charities, was mapped along a value chain (Figure 3-1) and allowed for the summing of impacts (Figure 3-2). From these impacts implicit local scale multipliers were then calculated. The lessons from this approach served as the basis for a “how-to” guide, to be published by the GEF, for estimating the local economic value of a park. Though methods for estimating the value of park tourism already exist, the guide has several purposes not fulfilled by any of the existing literature1.

At the national scale a base-year 2007 SAM of the Zambian economy

(Chikuba et al., 2013; IFPRI, 2014) was used for estimating multipliers and impacts.

1 First, the guide simplifies explanations of concepts, as well as data collection and analysis procedures to a level that can be readily understood and implemented by PA managers from developing countries. Second, it is geared towards a scale and level of detail that is most relevant for managers of individual parks. Third, through examples from the Zambian case study, it demonstrates how research results can be presented for maximum clarity and impact, and how arguments can be built around the results to potentially influence funding support and policy alignment for PA management.

The general procedure for extracting multipliers and impact values from SAMs are described in Appendix A. Specific procedures applied to the Zambian SAM are described in the data analysis section, below.

Surveys and sampling

Non-consumptive tourism. In all, seven separate surveys were conducted

(Table 3-1). Local direct effects were measured with three separate surveys including, firstly, a survey of park tourists which was carried out between September

17th, 2015 and November 11th, 2015. Tourist respondents were selected by interval sampling at the local airport, lodges, and campgrounds and segmented by market type to reflect different patterns of spending and minimize sample variance. Market segments included overlanders (i.e. large tour groups), campers, lower-end (< $300 per-person-per-night [p.p.n.], sharing), mid-range ($300 -- $500 p.p.n., sharing), and high-end (> $500 p.p.n., sharing) travelers. No park visitors encountered were local residents, nor were non-local but strictly day-visitors encountered, though the former category would have been excluded and the latter category is not thought to be significant in number. Survey questions (Appendix C) pertained to actual or anticipated amounts spent on domestic transport to and from the park, and on accommodation and activities at the park. These amounts represent local direct sales and were adjusted to take account of commission to foreign or national travel agencies before averaging them on a p.p.n. basis across each sample segment.

Secondly, to estimate direct personal income and jobs from tourism, a survey of tourism businesses was conducted in person and through email after contacting all such businesses in the local area and requesting their participation. This survey was initiated in October, 2015, and concluded in July, 2016. Only values pertaining to the

2015 season were requested. Survey responses prior to the end of the season were

based on projections for the final months of the season, though through subsequent follow-ups some values were adjusted. Additionally, a survey of local tourism employees (Appendix C) was used to corroborate personal income estimations while generating additional data on employee households and expenditures.

To measure local indirect effects, and because of both the absence of scale- appropriate secondary data modelling the contributions of tourism to other sectors of the Zambian economy and the lack of confidence in generic coefficients that could be used to estimate indirect effects by way of multiplication of direct spending, original data was obtained on tourism supply chains. This data, which included total expenditures, local expenditures, wages, employment, and estimated proportion of income related to tourism for the 2015 calendar year was obtained from the aforementioned tourism business survey and a survey of local non-tourism businesses identified in the tourism business survey as being their main local suppliers. Because virtually all wholesale purchases by local suppliers were non- local it was not necessary nor practical to sample beyond first level suppliers.

Local induced effects were estimated with data from the tourism employee survey which captured the proportion of income from tourism that is spent in the local area. A percentage of what lodge employees spend is also captured as local wages for shop employees, who, because of time constraints for sampling, were not interviewed about their own spending, but are assumed to spend a similar proportion of their income locally.

A census of all businesses identified along the road between Mfuwe and

Mfuwe Airport served to estimate the size of the local business economy, its structure, rate of growth, wages paid to shop employees, and other attributes. The

local business survey also provided some corroboration for estimates of induced effects.

Data on the income and spending of DNPW on SLNP management operations was obtained directly from DNPW and covered the period from 1992 to

2015. Some data, however, was missing for the period between 1998 and 2008.

Several non-governmental organizations (NGOs) also contribute resources and expertise towards park management and income and spending records were obtained from them as well. Lastly, DNPW also shared records on park visitation for the year 2015, disaggregated by the lodge at which visitors stayed. This information was used to extrapolate average p.p.n spending for each market segment to the entire population of park visitors, as well as to extrapolate lodge turnover to non- responding tourist lodges based on their p.p.n. rates.

Consumptive-tourism. The consumptive-tourism economy involves resident and non-resident trophy-hunters, DNPW, hunting outfitters and game ranch operators, and CRBs. Unlike non-consumptive tourism, hunters and hunting outfitters are dispersed across a wide area and tend to be much more guarded of their privacy, making it difficult to conduct in-person surveys. Instead, sampling was attempted by advertising a web-based survey on a forum of a popular hunting website and by requesting that hunting outfitters forward a link for the survey to their clients. This approach yielded a low response rate (<10 respondents), similar to other recent research (Southwick, 2015) on hunter spending in Zambia. For that reason, the approach was abandoned.

The alternative approach for estimating hunting revenues and benefits that was eventually adopted made use of publicly available prices (daily rates, trophy fees, and other fees) listed on the websites of hunting outfitters and concession

leaseholders in the local area (n = 6), the DNPW-approved off-take quotas and records of licenses sold for all local GMA hunting blocks (n = 8) and game ranches

(n = 5) for 2015 and 2016, the DNPW fee schedule for hunting licenses, the published concession fees paid by leaseholders to DNPW (ZAWA, 2015), and records kept at SLNP headquarters of actual hunts and off-take for three hunting blocks. The latter source of data was useful for estimating total daily rates in respect of the number of classical and mini safaris recorded for those three blocks. The

DNPW fee schedule discriminates on the basis of residency status (Appendix E) but for the purposes of estimating government fees, all trophy hunters were assumed to be foreign. The degree to which this resulted in an over-estimate of government revenue is assumed to be minimal. Trophy hunters may also spend money on non- hunting related travel and activities within the country, but for which an opportunity is provided because of the hunting trip. In lieu of actual data from respondents, this last category was estimated at $416 per hunter based on results from a similar study in

Zimbabwe (Southwick, 2015). For resident hunters, only government fees were estimated.

The number of trophy hunters in 2016 was counted from the records of three of six active GMA hunting concessions and one of five game ranches. An estimate of the total number of trophy hunters for the entire local area and the associated daily rate fees they paid to outfitters was then extrapolated on the basis of the trophy licenses sold for each hunting block and the minimum number of hunters required to satisfy typical “bags”2 (i.e. combinations of species from the list of species permits

2 For example, it was assumed that each license issued for a leopard or lion represented a separate “classical” safari of 14 days (leopard) or 16 days (lion), and that each classical safari included licenses for buffalo and five plains game. The remainder from the total licenses issued to a block after deducting the above was assumed to be allocated to mini safaris (seven days), six licenses each. This procedure was used to estimate the number of clients and number of days at site in order to estimate the daily rate total for three of the six GMA hunting blocks and four of the five game ranches

issued). In estimating the amount of meat produced by hunting, dressing percentages and average body masses for hunted species were obtained from

Bothma (2002). The dressing percentage was applied to the mean male body mass for trophy animals, and to the average of male and female masses for animals hunted for meat. Records of the local distribution of meat produced through trophy- hunting were not available, but outfitters have previously been observed (White &

Belant, 2015) to be largely in compliance with a general condition of concession leases stipulating that at least 50% of the meat from trophy animals must be distributed to local communities. All outfitters in the study area were assumed to be in compliance with this rule. A value of $3.8/kg was assumed for game meat, following White & Belant (2015) and accounting for inflation and movement in the exchange rate.

The income and expenditures of CRBs were obtained by reviewing the financial accounts of all but two CRBs (Nabwalya and Chisomo) in the local area (n =

9). The process entailed collating line-item records of receipts and expenditures, going as far back as 2010, according to the source of funds and the category of spending. This level of scrutiny was necessary to ensure consistency when summarizing accounts of multiple CRBs. Records of animal licenses sold and published lease payments were used to extrapolate financial data to the remaining two CRBs. When there was an opportunity to meet with a majority of representatives of a CRB (n = 8), informal group discussions were used to elicit subjective information on the CRB’s history, challenges, strategies, operations, and practices, as well as meta-information on the level of knowledge demonstrated by

from which the number of clients and lengths of hunts were not available. From the published daily rate prices of six outfitters, the average prices were $1,200, $1,800, and $2,500 per day for mini (seven to ten day), classical (14 day and 21 day) safaris, respectively.

representatives concerning specific details of transactions related to hunting. After meeting with CRBs, interviews were conducted with a sample of wildlife-scouts under their employ (n = 29), as part of the employee survey described above.

Data analysis

Analysis was complicated by a largely fluctuating ZMW-USD exchange rate, especially during the tourism peak season when the value of the kwacha halved in just over two months. On the spot purchases made by tourists in kwacha were converted based on an average exchange rate +/- two weeks from the date of the purchase. For other values reported in kwacha, an exchange rate of 8.64 kwacha to the dollar was used, representing the average of weekly exchange rates for 2015.

Because of this source of error, as well as the use of multiple extrapolation procedures and associated assumptions, all values reported should be considered accurate only to within 10-20% with direct effects estimates being more accurate than those for indirect effects.

The SAM contains 116 accounts in total, 88 of which are industries and commodities. Tourism, however, is not among the industries represented. The absence of a tourism satellite account necessitated an analysis-by-parts approach for multiplier estimation in which the sector-specific expenditures of local-region tourism businesses, as extrapolated from the surveyed sample of tourism businesses, were first margined (to trade and transportation accounts) and allocated to the corresponding producer accounts of the model. Then, the resulting “type I” effects (i.e. the sum of direct and indirect effects) were interpreted, in reality, as just the indirect effects and added to the separately calculated true direct effects to arrive at the true “type I” effects. A similar process was followed to arrive at the true “type

II” effects (the sum of direct, indirect, and induced effects).

National level direct spending and tax contributions were measured from the tourist spending and tourism business spending surveys. National level spending categories for tourists included transportation costs (airfare, bus fare, taxi, rental vehicle, and fuel), and non-local groceries or shopping related to the park trip. This spending is considered as national “leakage”. International leakage was not directly measured but was conservatively estimated from reported sales to international tour operators and average commission percentages. National tax contribution included value added tax, corporate tax, “pay as you earn”, and import duties on equipment.

Methods Used in South Africa

Economic impacts of wildlife-based tourism in the GKNP were assessed at three scales. The part of South Africa extending 50km from the boundary of the

GKNP was considered as the local area. The lack of an economic model for this region, however, meant that only the direct spending of tourists and the first round of indirect spending by tourism businesses in this area was estimated, but not the local downstream impacts of this spending. An intermediate scale was defined as the combined area encompassed by the two provinces in which GKNP is located,

Limpopo and Mpumalanga. Impacts were also assessed at the national scale. Use was made of intermediate and national scale SAMs in estimating aggregate impacts and the economic multiplier values of tourist spending.

Surveys and sampling

Non-consumptive tourism. Six separate surveys of tourism-related spending were conducted (Table 3-2), including two separate surveys of tourists

(Appendix C). One tourist survey, conducted between February 3rd, 2016 and March

23rd, 2016, targeted visitors to the private reserves and the private concessions of the provincial reserves. As in Zambia, these tourist respondents were selected by

interval sampling at a local airport (Eastgate Airport near Hoedspruit) and segmented by the average p.p.n. rate (for shared rooms) of the lodge at which they stayed. The second tourist survey targeted visitors to Kruger National Park. The majority of responses were collected online (Qualtrics, Provo, Utah) after SANParks twice forwarded a link for the survey to overnight visitors who had stayed at the park between December 1st, 2016 and January 14th, 2017 and had entered their e-mail address, as standard, when purchasing admission. Day visitors, many of whom were foreign, and other overnight visitors were interviewed in-person at Skukuza, Satara, and Lower Sabie rest camps on four separate days in November, 2016. A survey of the spending of tourist lodge operators was conducted between March 11, 2016 and

December 3, 2016. Most respondent businesses were located within the GKNP, though several were in reserves adjacent to, but not connected with the GKNP, and were included nonetheless because they are similar in all relevant respects to GKNP tourism businesses. The sample of businesses was segmented according to the same logic of the tourist survey. Effort was made to interview all respondents in- person in order to improve the quality of responses and the level of trust they placed in the confidentiality of information shared, though this was not possible for four respondents who opted to e-mail responses or be interviewed over the phone. A survey of employees yielded information regarding income, skill levels, personal background, and household characteristics of those employed at tourist lodges within the private reserves. At the reserve management level, in addition to obtaining data on income and spending, key informant interviews with wardens shed light on governance issues at the scale of individual reserves and at the scale of the GKNP system. This management level data was augmented with interview data collected contemporaneously by a collaborator conducting similar research.

Lastly, a web-based census of all tourism operators in the GKNP and adjacent reserves was performed to collect camp-specific information on their exact location, bed-night rates, number of beds, type of services offered (self-catered, full- service, etc.), and mention of community-support. The compiled list of operators and their camps served as the sampling frame and was also used in extrapolating values.

Data analysis

Spending averages of visitors on a p.p.n. basis were calculated from survey data and extrapolated to the entire population of visitors using separate methods for

KNP and the contiguous reserves of the GKNP. For KNP, extrapolation was on the basis of visitation records provided by SANParks. For the private concessions and the lodges of the contiguous reserves, extrapolation of visitor spending was performed by multiplying average per-segment occupancy rates by the bed capacity and p.p.n.3 prices of lodges, as obtained from the web-based census (Object F-1).

The estimate of turnover to non-responding tourist lodges was calculated in this same manner, after deducting VAT (which is included in the bed-night rate) and accounting for average per-segment sales commission paid to travel agencies. This method of estimating lodge turnover was cross-checked against the actual income reported by lodge-survey respondents. Because the actual turnover reported by respondents was 90.4% of the amount estimated by the method above, the initial turnover estimate for non-respondents was adjusted downward by this percentage.

3 Because rates commonly vary at individual lodges depending on the type of unit offered and the number of guests reserving a unit, the average p.p.n. rate for each lodge was calculated as an average of the p.p.n. rates for each unit type (assuming full occupancy of a unit—i.e. “sharing”), weighted by the number of beds for each unit type.

An exchange rate of 14.05 rands to the dollar was used for all analysis and reporting. This rate represents the average for the period April 1, 2016 to March 31,

2017—the fiscal period of SANParks.

As with Zambia, an analysis-by-parts approach was used to integrate the different spending surveys into the multiplier analysis. Trip-related spending reported by visitors that was not received by a tourism operator or management entity (e.g. travel costs, groceries, gratuities, etc.) was counted in one part of the analysis. Both revenue received and subsequent expenditures by tourism operators and management authorities, as estimated from above, was counted in separate multiplier analyses.

Multiplier extraction at the national level made use of a base-year 2012 national SAM (Seventer, 2016) containing 62 activity and 104 commodity accounts.

Only multipliers from the 10 most relevant activity accounts (after aggregating related sectors) were used in the calculation of multiplier effects. At the intermediate scale of the study (i.e. the area encompassing the two provinces of Limpopo and

Mpumalanga), the most recent available SAM has a base-year of 2000 (PROVIDE,

2006), which was considered too dated, especially in light of post-apartheid structural transformations to the economy. However, a contemporaneous national

SAM developed by the same project was also available and was used in conjunction with the 2012 national SAM to update the multipliers derived from the Limpopo-

Mpumalanga SAM. Specifically, the multipliers derived from the latter SAM were divided by the multipliers from the 2000 national SAM (aggregating activities where necessary) to obtain ratios of regional-to-national multipliers. After aggregating the ratios to align with the set of aggregated multipliers of the 2012 national SAM, they were finally multiplied by the latter set of multipliers to produce estimates of

multipliers applicable to the provincial region for the year 2012. The validity of this procedure rests on the assumption that despite economy-wide transformation, the relative structural relationship between the region containing Limpopo and

Mpumalanga Provinces and the nation as a whole remained constant. Results should be interpreted bearing this assumption in mind. Job multipliers were not available in any of the SAMs but type I job multipliers were found in a report by

Tregenna (2010) and adjusted, accounting for inflation. Type II job multipliers were estimated by multiplying type I multipliers by 1.5—a relationship which is typical in the US (Stynes et al., 2000). All multipliers used in the analysis are reported in Table

3-3.

To put economic values into context, national and provincial GDP data was obtained from Statistics South Africa (2018a). Tourism-specific GDP data was also obtained from Statistics South Africa (2018c).

Table 3-1. Summary of surveys conducted in Zambia. Number of Survey Description Respondents Tourist survey 151 groups (8 Representing 380 individuals (including non-responses) overlanders, campers, lower end, mid- range, and high-end travelers) Tourism business 13 (7 non- 5 high-end operations, 3 mid-range, 4 survey responses) lower-end, 2 souvenir shops 1 game drive provider Operations supplying data represent appx. 55% of total park occupancy for 2015. Supplier survey 7 Sellers of beverages, hardware, food, produce, etc. Local business 189 Of an estimated 280 local businesses survey Employee survey 161 (1 non- Managers, skilled, and unskilled workers, response) souvenir shop employees, freelance craftsmen, scouts (DNPW and CRB) Community resource 9 Current and historic income sources and boards expenditures Conservation NGOs 2 Income from local donations and expenditures

Table 3-2. Summary of surveys conducted in South Africa. Survey Number of Respondents Description Tourist surveys Kruger National 650 groups Online = 476 (non-responses Park (representing 2,319 unknown), in-person = 174 individuals) (18 non-responses) Contiguous 154 groups 18 lower end (< R1,500), 50 reserves (representing 520 mid-range (R1,501 – 5,000), individuals) 86 high-end (> R5,001), 14 non-responses Tourism business 34 independent < R1,500 = 9 survey operators (680 beds R1,501 – 5,000 = 12 total) > R5,001 = 13 59 non-responses Employee survey 98 Unskilled & semi-skilled = 62 Skilled = 36 Reserve manager 8 survey Tourism operator 123 Inclusive of operators directly website survey outside the GKNP (see Object F-1 for inventory of GKNP accommodation, including coordinates of all lodges, number of beds, and rates)

Table 3-3. South African economic multipliers Limpopo-Mpumalanga multipliers Textiles, Retail & clothing, Chemical, wholesale trade Business wood & rubber, Vehicles Utilities & accomm. Transport & Food & paper electrical & large (electricity Const- (incl. services & financial Type I (direct + indirect) beverages products Petrol products machinery & water) ruction restaurants) communication services Output 1.25 0.71 0.23 1.01 0.10 1.44 1.45 1.42 0.99 1.30 Value added 0.52 0.28 0.18 0.33 0.06 0.85 0.63 0.74 0.45 0.62 Income 0.23 0.14 0.09 0.15 0.02 0.36 0.35 0.37 0.16 0.22 Jobs (per R1m of spending) 1.70 1.40 0.30 1.00 0.20 1.10 2.60 3.50 0.80 1.20 Type II (direct + indirect + induced) Output 1.50 0.87 0.33 1.19 0.13 1.91 1.84 1.84 1.19 1.52 Value added 0.67 0.37 0.24 0.43 0.08 1.13 0.84 0.98 0.57 0.79 Income 0.35 0.21 0.14 0.22 0.03 0.58 0.53 0.56 0.25 0.33 National multipliers Type I (direct + indirect) Output 1.72 1.58 1.24 1.36 1.35 1.62 1.99 1.66 1.60 1.71 Value added 0.81 0.72 0.69 0.57 0.55 0.92 0.82 0.87 0.79 0.90 Income 0.41 0.38 0.28 0.31 0.29 0.44 0.46 0.50 0.40 0.50 Jobs (per R1m of spending) 3.00 3.73 1.00 2.00 2.67 1.33 3.33 4.67 2.00 2.67 Type II (direct + indirect + induced) Output 2.56 2.37 1.83 2.00 1.93 2.59 2.94 2.69 2.46 2.73 Value added 1.38 1.25 1.09 0.99 0.94 1.60 1.46 1.57 1.38 1.59 Income 0.79 0.73 0.55 0.59 0.54 0.92 0.88 0.96 0.81 0.96

CHAPTER 4 A MIXED METHODS APPROACH TO ASSESSING THE SOCIO-ECONOMIC IMPACT OF PROTECTED AREAS: PARKS, COMMUNITIES AND GAME RANCHES IN SOUTH LUANGWA, ZAMBIA.

Introduction

After elaborating on the institutional environment for PAs in Zambia and describing the PA types found in the study area, this chapter presents the findings of a financial and economic analysis of SLNP and an economic analysis of GMAs and private game ranches surrounding SLNP. Results are then interpreted in light of the framework described in chapter 2 and the implications of landscape-level economic asymmetries for the sustainability of the system are discussed.

The Formal Institutional Environment for Wildlife and Land in Zambia

Land. While all land in Zambia is vested in the president, around 60% (Honig

& Mulenga, 2016) of the country is classified as customary land where traditional leaders (chiefs), and the headmen to which they delegate authority, act as custodians and administrate according to informal norms and customary rights. All other land is classified as state land where leases of up to 99 years are possible.

Land leased in this manner from the state is most often held by individuals or companies and only in rare cases by communities. Though state land is mainly found in and around urban areas and at independence covered only up to 10% of the country, through the alienation of customary land its coverage has since grown to as much as 40%--the exact percentage is not known and depends on whether state- administered PAs are included or excluded from the customary total (Ibid.). The inadequacy of the existing Land Act (of 1995) to safeguard the land and agricultural livelihoods of rural communities is recognized by the government, which has been drafting revisions, but thus far the process of reform has suffered from inertia

(Munshifwa, 2018). Apart from tenure insecurity on customary land, another

disadvantage is the difficulty in obtaining financial credit in the absence of collateral, though out-grower schemes and the flexibility of the customary tenure system in what are relatively sparsely populated areas help to balance these costs. For example, in the Luangwa Valley, cotton growers on customary land typically obtain physical capital for farming through collateral-free loans, binding farmers contractually to specific cotton buyers, but shouldering the risk of default on distributors (Tschirley et al., 2007). More generally, by preserving shared rights and promoting reciprocal relations, customary systems of agricultural land administration are capable of balancing the production interests of individuals with the need for resilience of the social group (Delville, 2007, p.37).

In the context of PAs, customary land has historically been alienated for the purpose of setting up tourism lodges on small plots near national parks and to create game ranches. The degree to which these conversions reflected the collective will of the communities and occurred on equitable terms would largely depend on whatever informal accountability structures existed between a chief and their subjects and the ability or desire of the chief to negotiate a fair value. Often, customary land has been alienated on the basis of bribes (Manning, 2011). However, attention to the issue of land alienation has increased and more recently communities have become aware of the prospect of collective title based on the conservancy model. A community conservancy, such as Kaindu near Kafue NP, is a property whose title is held in trust by a legal entity constituted of elected representatives from the community, and on which hunting is conducted by a private-sector partner in accordance with the laws that apply to game ranches (discussed below) and under the conditions of a lease signed with the community. Though no community conservancies exist in the

Luangwa study area, the possibility has, and continues to be explored by

communities and conservation NGOs, and this idea is returned to in the discussion section.

Wildlife. As with land, all wildlife is vested in the president. Only game enclosed by a fence may be purchased outright by the leaseholder of a property, granting to them provisional ownership of the wildlife contained therein. However, such fenced properties tend to be small, especially in comparison to communal areas, and as they are uncommon in the vicinity of national parks, in general, and

SLNP in particular, they are not considered in this study. In communal areas, which constitute the majority of land neighboring SLNP, a provision of statutory law confers ownership, not of wildlife itself, but of a portion of the proceeds from the sale of trophy hunting licenses and from hunting concession lease fees to qualifying communities which have attained the status of GMAs (discussed below). The provision does not pertain to leasehold land even where the lease is held by a community based organization (CBO), nor does it pertain to communities whose land has not been gazetted as a GMA. This property rights regime, most recently codified in the Zambia Wildlife Act, 2015 (Government of the Republic of Zambia [GRZ],

2015) and supporting statutory instruments, has existed since the late 1990s (ZAWA,

1998). Prior to 1998, community entitlements to wildlife benefits were either non- existent or were guaranteed only by administrative policy. However, the enhancement of tenure security provided by the act is modest in that ownership still vests in the government, and its dispensation has been criticized for failing to create an investment environment in which the natural comparative economic advantage of wildlife can become realized (Lindsey et al., 2013).

Protected Areas in the Study Region

The protected areas of the study region are mapped in Figure 4-1 and described below. They consist of South Luangwa National Park, six game management areas, and five game ranches totaling roughly 27,000km2 .

The national park

Background. South Luangwa National Park, established in 1971, is Zambia’s most popular wildlife safari destination and is the country’s second largest national park, at 9,050km2. The greater Luangwa system includes SLNP as well as three other national parks (North Luangwa, Luambe, and Lupande) and 6 GMAs, though tourism in this system is minimal outside of SLNP. The park is accessible by a paved road from the nearest town, Chipata, 160km away, and is approximately 600km from the capital city, Lusaka. An international airport also services the park, and is located

30km from the main park gate.

The surrounding GMAs serve as buffer zones in which human settlement is permitted and where wildlife is cooperatively managed through partnerships between communities, the private sector, and the Department of National Parks and Wildlife

(DNPW). The local population is largest and most dense near the park gate, in the community of Mfuwe, and approximately 100,000 people live within 80km of Mfuwe in the districts of Mambwe and Lundazi. Agricultural activities in this area include subsistence farming of maize and cash cropping of cotton. Because of cultural preferences and the presence of tsetse flies, cattle is not kept in this part of Zambia.

Though commercial tourism at SLNP has existed since at least the 1960s, a lack of infrastructure, poor transportation in Zambia, and wildlife populations diminished by poaching limited the scope of expansion of tourism opportunities until the mid-1980s when funding from the Norwegian government allowed for enhanced

wildlife protection. Major donor funding continued to support park operations through

2005. With an enlarged internal road network and a more commercial outlook, tourism began to grow significantly beginning in the late 1990s. The park at present receives approximately 20,000 annual visitors who stay at a total of 45 lodges and camps operated by 19 different companies (Figure D-1 & Object D-1). Most beds are concentrated near the main gate. The more distant camps are small and only seasonally, operated, becoming inaccessible during the 6 month rainy season.

Governance of SLNP. Prior to 2015, the management of wildlife resources and national parks was the responsibility of a parastatal agency, the Zambia Wildlife

Authority (ZAWA). Its origins were in the late 1990s, at a time when other park authorities on the continent were also becoming partially privatized in order to improve their financial performance (Child et al., 2004). If the idea was to turn

Zambia’s parks into centers of profit, however, a much needed injection of capital funds did not accompany the weaning of the PA authority from the central treasury.

Nor did political interference cease. In the period of its existence, a high turnover of directors hampered the development of a long-term vision, and the short-term outlook of the agency led to an over-dependence on the more extractive sources of revenue (i.e. hunting in GMAs) to support the national parks (Lindsey et al., 2014).

By 2014 ZAWA had accumulated over $1m in debt (Chanda, 2015). In 2015, the

Zambia Wildlife Act No. 14 provided for the re-absorption of ZAWA back into the government, as the DNPW.

Though most national parks in the ZAWA era were centrally funded, SLNP operated under a revenue retention scheme. At the time of this study, the scheme was still in place but is secured only through administrative policy and not by statute.

This fiscal devolution to the South Luangwa Management Unit (SLAMU) is perhaps

the defining feature of the park’s governance structure as it enables both the institution of performance-based management systems at the level of operations and an adequately sized staff force. Though the average density of DNPW scouts at

SLNP (66km2/scout) is fewer than the 50km2/scout standard recommended by the

IUCN for similar parks (Henson et al., 2016), a large contingent of administrative and technical staff, and not a lack of funds, is mostly responsible—the park houses its own ecologist and is largely functionally autonomous. Where other powers lie is less clear. Tourism block concessions within national parks, for example, are awarded by the wildlife management licensing committee and other decisions relating to long- term planning are necessarily made with the DNPW director’s approval. In terms of outward-facing governance structures, SLNP is a government park, though a community office interfaces with local residents.

Game management areas

Established by law in the early 1970s, GMAs have been maintained through subsequent parliamentary acts, including the Wildlife Act of 2015. They are defined as areas of customary land neighboring national parks where settlement is permitted but where certain customary land and resource use rights are constrained, in the interest of wildlife management, by a locally drafted and DNPW-approved general management plan (GMP). In practice, only a fraction of GMAs have ratified GMPs-- including just three out of six in the local study area--and this PA category is mostly distinguished from others only by the manner in which hunting is governed. In GMAs, communities and the government are sanctioned by the Wildlife Act as de jure joint partners in wildlife governance, with local interests represented through democratically elected bodies known as community resource boards (CRBs). Each chiefdom in a GMA has the ability to form a CRB, which usually consists of around

10 board members. Each board member is typically the head of a village action group (VAG) in their respective villages, which is the lowest level of organization for natural resource governance. The GMAs and CRBs of the study area are listed in

Table 4-2 and appear in Figure 4-1. A ratings system classifies GMAs a prime, secondary, understocked, and depleted, according to their potential in generating revenue. All but depleted GMAs have sufficient abundance of wildlife populations to support safari hunting.

Key rights that CRBs possess include the right to negotiate concession leases with prospective hunting outfitters, the right to propose trophy hunting quotas, and most importantly, the right to a portion of the financial returns from the sale, by

DNPW, of animal licenses to trophy hunters and from the lease fees for hunting concessions. A breakdown of the allocation of these fees is provided in Table 4-1.

Whether these benefits constitute true entitlements or constraints, however, is a matter of perspective. Ultimately, the terms of a concession lease reflect the nature of the process for tendering a concession and collecting bids, which is out of the control of communities. Quotas, too, are often subject to revision after community proposals have been issued, and because of the structure of remittances, only 33% of trophy hunting-related government fees are returned to communities on average.

Community resource boards also lack legal personality and cannot enter into contracts outside of those sanctioned under the Wildlife Act. Some CRBs operate small-scale campsites in remote areas of the GMAs, but without facilities to attract high-paying guests the revenue derived from non-consumptive tourism is minimal.

Game ranches

Though GMAs surround many of the national parks, where wildlife populations were historically thin no claims by the wildlife authority were ever made.

The absence of jurisdiction by DNPW (or its progenitors) over the land of so-called

“open areas” means that neither hunting outfitters nor communities are bound by the stipulations concerning co-governance set forth by the Wildlife Act. On titled, leasehold land in open areas private game ranches may form. The ranches may be fenced or unfenced, but as mentioned, fenced game ranches are not considered in this study. The game ranches in the study area are shown in Figure 4-1 and listed in

Table 4-2.

Government license fees for animals hunted still apply to trophy hunting on such ranches, but the absence of DNPW jurisdiction over the land means that there is no concession lease to pay. However, where wildlife populations have rebounded in open areas under customary tenure and community conservancies have formed, the lack of DNPW jurisdiction also means that communities are not entitled to any portion of fees resulting from trophy hunting on their land. And whereas the DNPW is significantly involved in the management of wildlife in GMAs, including through contributions of personnel, salaries, and equipment towards anti-poaching enforcement, DNPW’s role with respect to private and community game ranches is relegated to approving hunting quota requests and ensuring compliance with regulations. Whether this tradeoff works to the favor community conservancies, which are then allowed to engage directly with private operators through joint venture partnerships and charge their own concession lease, remains to be seen.

Results: South Luangwa National Park

Park Visitation

Visitation to the park in 2015 is estimated at 21,214 people1 (Figures D-2 & D-

3). Total gate-entries attributed to lodges (62,459) declined by 9.5% from the previous year. The recent decline is attributed by some lodge managers to concerns by western travelers over the safety of travelling to Africa in light of the ebola epidemic and incidents of terrorism on the continent. Visitors responding to the survey represented 22 different countries (Figure D-4) and trip profiles varied across market segments (Table D-1).

Economic Impacts of Tourism

Local impacts

Direct spending. Total local direct sales (tourist spending) related to SLNP amounted to $25.7m in 2015 (Table 4-3 & Table D-2). An additional $3.4m of park related sales to tourists were made in Zambia, but outside the local area, and consisted mainly of purchases of domestic flights, commission to national tour operators, fuel, bus travel, and groceries. However, much of the value of local and national sales at retail stores rapidly leaks out of the economy2. More meaningful measures of economic impact are personal income and value added.

1 This estimate does not include a set of Zambian citizens, of unknown size, for whom no local accommodation is recorded and whose origins with respect to the local area are unknown, but who account for 7,107 gate-entries.

2 Gross sales is reported here only as a gauge of the scale of tourism activity at the park and as a value to which other measures can be related. Likewise, output, though a more conventional measure of economic value because it excludes non-local cost-of-sale, is also a misleading measure (Crompton et al., 2016) because, by definition, the direct output of service sector industries is 100% of gross sales, and in a service sector dominated setting such as SLNP, direct output is almost equivalent to direct sales, less leakage from purchases by tourists at retail stores. That said, local direct output was estimated at $25m.

Direct local income and jobs. In 2015 there were 1,097 local3 jobs directly created from tourism (Table 4-4) earning a combined K28.1m ($3.3m) in income4.

Another 97 positions were occupied by foreigners or those with residency status in

Zambia (DNPW unpublished data). The job count is inclusive of part-time and seasonal positions. Eight operators reported a total of 8,770 person-days of work hired between them (average = 1,096.3), although the total number of person-days of contracted casual work is not known. Additionally, independent crafts artisans, who earn on average approximately 10,500 Kwacha per year (n=3), may number around 10 (pers. communication, independent craft artisan). Non-wage benefits for local employees, including pension contributions and the value of food, uniforms, etc., was approximately K4.1m ($0.5m).

From the survey of tourism employees, the proportion of local employees who were born and raised in the Mfuwe area varied across skill types, but overall only a quarter of workers migrated from elsewhere (Table D-3) and less than 5% of

Zambian employees maintain a residence outside of the local area. The equitability of employment opportunities for locals in ecotourism has been raised as a concern elsewhere in Africa (Spenceley & Goodwin, 2007), but the employee survey did not indicate access to tourism jobs as being a problem locally. Of unskilled workers,

94.4% (n=71) reported no formal employment history prior to entering the local tourism industry and 69.0% lacked a secondary school diploma (Table D-4).

3 In keeping with a conservative construct of “local”, what is reported represents only the income that accrues to employees whose sole residence is in one of the GMAs bordering the park.

4 The measure of local personal income reported here includes wages, service charge, and tips received by local employees of tourism businesses (lodges and souvenir shops) and local shops that make direct sales to tourists. The measure excludes income received by freelance guides, casual workers, local taxi drivers, and independent craft artisans. The aggregate amount received by such workers could not be ascertained. The measure also excludes certain non-wage benefits, such as pension contributions, which do not immediately enter the local economy.

Additionally, 56.8% of all workers (n=111) reported that they acquired their initial job in the industry without advantage of access to vacancy information through social connections with existing staff. Instead, casual work was a commonly reported gateway into the industry.

The impact of wages is experienced most acutely at employee households where there are on average 4.9 dependents supported for every employment position created. Extrapolating by the total count of employees at lodges and souvenir shops, there are 5,356 such dependents in total5. A tourism income is the sole regular source of income for 59.8% of employee households (representing

83.0% of all income earned by tourism employees), and 86.7% of employees are the sole members of their household with a formal job.

Approximately one fifth (21.9%) of income is sent as remittances to relatives living both within and outside of the local area, not including household dependents.

Though average money saved as a percentage of income was just 6.0% (not accounting for debt) with only 37.4% of respondents reported being able to save at all, the high degree of household dependency and sharing of income are likely responsible for the low rate of savings.

Total local income and jobs. Total local income (the sum of direct, indirect, and induced effects) from tourism in 2015 was K31.5m ($3.7m), which accrued to a total of 1,473 local employees (Figure 4-2). This additional income and employment is generated from tourism in two ways. Mfuwe area shops make sales to tourism businesses. Jobs in these shops and the wage income earned by employees, weighted by the proportion of turnover that comes from tourism-related sales,

5 This estimate was derived by taking the average number of dependents per household (5.6, n=97) and then accounting for secondary income sources and households with multiple members who are formally employed

constitute indirect effects. Indirect effects were estimated at K0.76m ($88,000) in income and 48 jobs. Mfuwe area shops also make sales to wage-spending tourism employees and a certain proportion of these sales is subsequently translated into wages for shop employees allowing the cycle of spending to continue until the money leaves the local area. The latter process is an induced effect of tourism.

Induced income and jobs were estimated at K2.6m ($0.3m) and 327, respectively.

Local value added. Approximately $14.8m in value added6 was generated locally through the total effects of tourism, including personal income, park fees, direct taxes (mostly VAT), and downstream economic activity in the Mfuwe area. The relative contribution of each local value added component is illustrated in Figure 4-3.

Multiplier effects are minimal in the local area because few goods are produced locally. The tax contribution from direct spending alone, at $2.5m and consisting of

VAT, employment-related taxes, and corporate tax was similar in magnitude to the total collected by DNPW in park fees ($2.9m). Average net profit after tax as a percentage of annual turnover, and weighted by turnover, was 9.4%, as reported by seven lodge operators.

National impacts

Tourism at SLNP generates economic impacts on the national economy that are even larger, in absolute terms, than those experienced locally, including $23.4m in value added, of which $17.2m is personal income. Multiple spending pathways are responsible and the contribution from each is, again, illustrated through Figure 4-3.

6 The final price of a product, such as ecotourism, includes the value of goods and services that were produced or provided from outside of a particular region, such as food and beverages, fuel, etc. The value added component of the final price is only the portion that is attributable to business activity within the region, and therefore constitutes the contribution to the region’s gross product. It is similar to output, though is considered more meaningful as it is not confounded by definitional caveats or the double-counting problem inherent in measures of output (Jeong & Crompton, 2015). Specifically, value added is the final price paid, less costs of non-labor inputs. Here, it is calculated as the sum of profit, wages and salaries (including of non-local employees), taxes, and park fees (Figure 4-3).

Directly, tourists spend money on national air carriers and bus lines to travel to the park, often facilitated by Zambian tour operators. As mentioned previously, this spending amounted to $3.4m. Indirectly, local tourism businesses are connected to businesses elsewhere in Zambia through the supply chain and wages spent by supply chain workers as well as local tourism employees circulate nationally. Though the Zambian SAM (Chikuba et al., 2013; IFPRI, 2014) allowed for estimation of these indirect and induced effects, sufficient employment data for Zambia does not exist to be able to convert the income estimate to the number of jobs created at the national level.

After the tourism and transportation sectors, the sector of the Zambian economy that benefits the most from SLNP tourism is trade, which includes retail and wholesale businesses. The non-local indirect effects of tourism generated $7.1m of sales in this sector, translating into $3.9m of value added (Table D-5). Indirect effects across all sectors totaled $8.7m and $4.7m for value added and income, respectively. Induced effects, which are distributed relatively evenly across the national economy, totaled $14.7m in value added and $9.4m in personal income.

Regional and international impacts

Approximately $4.5m7 in sales accrued to international tour operators in 2015, paid by visitors on packaged trips and estimated on informed assumptions of percent commission paid by the lodges. Out of a total amount paid by visitors of $33.6m, this form of leakage was roughly 13%. A reliance on overseas tour operators and international air carriers for marketing the park and delivering tourists is, however, unavoidable. Additional leakage occurs when goods must be imported to Zambia, by

7 Not including international airfare, as survey respondents visiting from other countries were often on multi-destination trips.

both tourism businesses and other businesses in the supply chain. In all, tourism businesses at SLNP imported an estimated $850,000 of goods in 2015 and paid approximately $200,000 in import duties. Chief among the categories of goods imported by lodges was vehicles and equipment (e.g. Land Cruisers, generators, refrigerators, etc.), totaling $300,000 to $400,000. A further $1.3m of goods is estimated to have been imported by other businesses in the supply chain. A principal trading partner and beneficiary of this activity is South Africa, which accounted for about 33% of all Zambian imports in 2014 (Observatory of Economic Complexity,

2017).

Economic impacts of park management spending

An additional $1.9m in personal income, not included in the above analysis, is earned by local DNPW park employees, and through DNPW’s overall spending on the park, an additional $5.2m is contributed as value added to the national economy

(Table 4-5). These impacts are important, but national parks are supported by the government partly out of the recognition of national and international mandates to safeguard biodiversity, natural resources, and ecological processes. To what extent these obligations would be honored in the absence of tourism, and result in management spending, is unknown, but the possibility confounds the attribution of these impacts to tourism.

Local procurement

Between 20-30% of procurement is from the Mfuwe area when considering only the types of goods that are available for purchase locally. These goods mostly consist of fuel, rations for staff, processed foods, meat and dairy products, bottled beverages, hardware, fruits and vegetables, furniture, and natural materials for

construction. Excluding fuel8, sales by local retailers, wholesalers, and producers totaled $1.0m (Figure 4-4), though much of this value is cost-of-sale. Sales of goods actually produced locally totaled $100,000 and included fruits, vegetables, furniture, and natural building materials. According to lodge managers, the limiting factors on local procurement are the international standards the tourism industry must meet for quality, variety, consistency, and dependability. These standards are more easily met by businesses in Chipata, as well as by a third-party purchaser which makes bi- weekly deliveries from Lusaka, thereby saving time.

Charitable contributions

Total charitable contributions for 2015 were estimated at $1.5m (Figure 4-5), supporting approximately 110 jobs, of which 70 are in resource protection. These funds for social development and conservation are generated by the park in several ways. A set of tourism operators contribute a portion of total revenue either to community development needs they have identified and strive to meet themselves, or donate a portion of their revenue to local non-governmental organizations (NGOs) engaged in such work. For example, the Luangwa Conservation and Community

Fund is a mechanism initiated in 2009 through which a half-dozen operators participate by adding an amount ranging from $1 to $10 to the bed-night rate, which is then allocated evenly to education and conservation efforts run by local NGOs.

One tourism operator funds community development not out of its revenue, but out of the philanthropic arm of its parent company, while two other operators have established their own registered charities. Tourists who visit the park also make independent contributions to local NGOs either while on safari or after they have

8 Fuel sales are omitted for the sake of greater resolution at the lower end of the scale, and because fuel sales do not represent local entrepreneurial activity.

returned home (donations made from abroad may sometimes qualify for matched funds from foreign governments). Other charitable contributions attributable to the park are made through corporate sponsorships or by overseas organizations and individuals in support of local projects and NGOs. The park is arguably responsible for a large part of the latter set of contributions in the way that it draws donor attention to local needs and lends associational prestige to local NGOs and the overseas donors by which they are partially supported. The local NGOs most closely related to the park and which have the greatest impacts on the community are described in Appendix D (Table D-6).

The true value of local social investment, however, defies distillation into strict monetary terms and the absence of certain investment categories (e.g. health) does not reflect lack of attention to these needs, but rather the difficulty of putting a value to cross-cutting services that depend on knowledge and skill transfer and facilitation.

For example, the Luangwa Safari Association funds a full-time doctor to provide emergency medical care in the Valley on a rotational basis, but serving doctors also work at Kakumbi Rural Health Clinic where they volunteer a majority of their working hours.

The Local Business Economy

Growth of businesses along the road between Mfuwe and the airport (Figure

4-6) has roughly mirrored growth in tourism over the past two decades (Figure 4-7).

This growth has accelerated in recent years as a result of the paving of the road between Mfuwe and Chipata in 2010, and is characterized by both an increase in the number of shops and services as well as an upgrading of the quality of goods available. These businesses are both formal and informal, and are made up of service providers (barber and repair shops, grinding mills, etc.), which represent

34.1% of all businesses, and retail and wholesale stores or stalls which make up the remaining 65.9%. About a third of business owners originate from outside the local area (Table D-7). Among reasons for becoming established in the local area, many owners explicitly cited proximity to the lodges, but many more simply cited high demand, which, undoubtedly, is also linked to tourism. It is more instructive to consider the total annual turnover of local businesses and the portion that owners attribute to tourism.

The total annual turnover of the local business economy in 2015 is estimated at K38.7m ($4.5m), divided between 280 shops. Again, the fuel station was excluded from this estimate, as were the businesses on the grounds of the airport and businesses in the Mambwe Boma. Approximately 57% of annual turnover, or $2.6m, is attributed by respondents to demand from tourism lodges and lodge employees.

Though some respondents found it difficult to quantify this impact and often defaulted to a response of “50%”, estimation was facilitated by asking them to then report the average peak season turnover, average low season turnover, and then to re-visit their initial guess. Almost all products sold are procured from retailers or wholesalers in Chipata, Lusaka, or Malawi, meaning only the price mark-up is what is retained locally and this was estimated at 19% (n=70), or roughly $0.9m.

Between 610 and 680 people are employed, earning on average between

K446 ($52; junior staff) and K607 ($70; proprietors) per month. Tax contributions were not solicited, but 68% of businesses reported being VAT registered. Across all businesses, between K80,000 and K140,000 ($9,300-16,300) is paid to the district council each month.

Results: The GMAs and Game Ranches

As trophy hunting in Zambia’s GMAs was effectively banned from the end of

2012 through the 2014 season, as it was slow to resume in 2015, and as the ban on lion hunting was not lifted until 2016, the following results are from the 2016 season only.

Hunting Activity in the Lower Luangwa Valley

The total number of trophy hunters for the five ranches was estimated at around 30, and the total for the six active GMA concessions was approximately 90.

No trophy hunting took place at all in three GMA hunting blocks where there was no concession holder. It was not known how many local residents or Zambian citizens also hunted in the GMAs, but there was no resident hunting activity in just one of the nine GMA hunting blocks. The game ranches also occasionally host non- consumptive tourists, but their impacts were considered negligible and are not considered here.

Hunter Spending Associated with the GMAs and Game Ranches

Trophy hunters spent a total of $4.0m in the study area (Table 4-6). As with non-consumptive tourism, the majority of this spending is for the less tangible services provided on-site--as opposed to access to, or harvesting of wildlife--and considering just the local value added amount of $3.54m, the single largest component (53.4%) of this value was the service provided by hunting guides, captured as the “daily rate”. However, outfitters were able to capture an additional

$0.60-$0.80m as the difference between animal fees charged by the outfitter and what is paid by the outfitter, on behalf of the client, to the government, for an animal license. As a result, only 50.4% of resource fees ($0.88m) are captured by the government, meaning that animal licenses are undervalued by half compared to the

market rate for trophy-hunted species. This fee structure constitutes an apparent leakage of value9 at the direct spending level, not only for the government, but for communities as well.

In terms of individual areas, the highest-value GMA hunting block produced a total of $195,000 in trophy animals sold, and the highest-value game ranch produced

$106,000 in trophy animals sold. Per area, the highest values were $121/km2 and

$740/km2 for GMA hunting blocks and game ranches10, respectively.

Resident hunting in the GMAs brought in approximately $54,000 to DNPW which, as discussed below, represents a significant loss of value to the government and to local communities. No data was available on hunting for meat in the game ranches, though it is believed to be considerable (Lindsey et al., 2013).

Meat Production

The meat from animals hunted for trophies and by residents also has value, though it is not necessarily sold. At least 51,000kg of meat from trophy animals was distributed freely within the GMAs, representing a value of $194,000. The average amount produced among the four prime (sub-)GMA blocks was 10,100kg, which is considerably higher than the 6,187kg national average calculated by White & Belant

(2015) for prime GMAs (as whole units). An assumption of that study was a 60% utilization of quota. The actual utilization of quotas in this study was on average

72.6%, but it is unclear if the absolute size of quotas has changed appreciably. From game ranches, meat distribution data was only available for two properties and as

9 Even if concession leases paid at the indirect level recover, for the government, some of the discounted value of resources sold, it would imply the inefficient use of a fixed rent to recover variable fees and would reduce community remittances (which are 45% of animal license fees and only 15% of concession fees).

10 Comparisons of per-area production between game ranches and GMAs are misleading because game ranches have a higher ratio of river frontage (i.e. optimal habitat) to overall area.

ranch owners are free to do as they wish with meat, no attempt to extrapolate to the whole group is here made. An additional 48,600kg of meat was produced by the

GMAs through resident hunting. The equivalent value of this meat on the local bushmeat market, net of government fees, was about $131,000, indicating that resident licenses are priced at only 28.2% of the market value. Considering that few local residents can afford hunting licenses, rifles, and ammunition, much of the net value of meat from resident hunting is likely to the benefit of non-locals, either through consumption or sale.

Direct Impacts of Hunter Spending

Fixed fees. Out of the revenue to the hunting outfitter additional payments are then made to the government, to company employees, and to the CRB (Table 4-6).

The hunting-related government fees at this level include an annual concession lease payment (Appendix E) and payments for a professional hunter’s license, a safari outfitter’s license ($1,500-$3,500), a tourism enterprise license, and various other hunting and employment related taxes11. In total, these fixed fees comprise over 20% of GMA outfitter revenue. Beyond this observation, however, the financial performance of hunting outfitters in both the GMAs and game ranches given the prevailing cost structure could not be determined due to insufficient data.

Jobs. The seven combined GMA outfitters provide at least 100 local permanent staff positions and the five game ranches provided at least another 100 local permanent staff positions12. Casual workers, who may labor for only two months of the year in setting up and taking down camps in the GMA, probably

11 Apart from the published lease payments and hunting licenses, an estimate of total tax contribution could not be made, though all other taxes are considered minor in comparison

12 Based on extrapolations from 3 GMA outfitters and one game ranch.

comprise another 150-200 workers. Assuming a salary of around $100/month per staff, and accounting for reduced payrolls in the off-season, local wages from hunting likely totaled at least $150,000.

Support of CRBs and communities. Direct payments from the outfitter to the community take the form of an annual “obligatory commitment”, as outlined in the concession lease and paid to the CRB (Appendix E), salary support for 50-55 CRB scouts, and ad-hoc support for infrastructure development, fire management, and ecological monitoring. The total income to CRBs from GMA operators was approximately $0.24m. While it is not known what percentage of outfitter gross margin this represents, it is 12.6% of their combined daily rate and trophy fee income. Fees paid to communities by game ranch proprietors are not required by the government, but community support is typically on an in-kind and as-needed basis or in the form of a lease payment when access for hunting on communal land in open areas is granted to them by chiefs. Examples of recent community support by game ranches include the separate construction of three classroom blocks, and a clinic.

Government remittances to CRBs and Impacts of CRB Spending

Overall remittances. As described, the study area CRBs derive income from the government’s remittance of animal license and concession fees (61.1%), from the hunting outfitter (37.2%), and through the sale of non-wildlife resources, manual labor, or other donations (1.7%). Combining government remittances with these other sources of income, including salaries received from hunting outfitters, gross income to the GMAs from activities in 2016 was approximately $520,000 ($31/km2).

Delinquency on the part of the government is common though, and no remittances were made to the CRBs in 2015. Payment of what was owed from hunting activities in 2015 was only completed in late 2016, combined with 2016’s remittances. For that

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reason, the actual 2016 income to CRBs, from the government, amounted to

$413,000, including $170,000 in scout salaries deposited directly into scouts’ personal bank accounts13. Accounting pro rata for the revenue earned from 2016 hunting activities only, CRBs took in $280,000 from DNPW (including scout salaries).

Not all CRBs that were owed hunting revenues received payment full payment, however. A grievance expressed by several CRB chairmen is that DNPW was withholding their remittances by treating scout salaries paid during the GMA hunting moratorium of 2013-2014 as loaned amounts and counting hunting funds generated subsequently against this credit. Because of this alleged practice, three CRBs received nothing from DNPW in 2016, save for continued support of scouts.

Scout salaries. Approximately 120 CRB scouts are supported by funds from

DNPW (not including the 50-55 CRB scouts supported by the outfitters) at a rate of about $60 a month, post-deductions. Delinquency in the payment of wages is common here too. At the time scouts were surveyed (August 2016), the average length of time since they had received their last pay check was 5.3 months and the average arrears owed from the previous year was 1.9 months (n = 30). Despite the low and uncertain pay, the scouts continue to conduct patrols, though on account of shortages in rations some CRBs no longer conduct multi-day patrols and even day patrols are shortened to several hours.

Spending. Second to the funding of scouts, administrative costs swallow much of CRB income, leaving little left over for community projects. Among CRBs with active hunting concessions, the largest project budget was in 2012 when

$8,750, on average, was spent in this category per CRB (Figure 4-8). No funding at

13 Prior to 2013, CRBs were responsible for paying the salaries of scouts out of general funds deposited by ZAWA into CRB bank accounts. Though the payment of scout salaries is no longer recorded by CRBs, this estimate of total salaries is based on the survey of CRB scouts.

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all was available for either administration or projects during the hunting moratorium, and between 2015 and 2016 the average spend on projects was only $1,201. Even when money is available for projects, however, the most common use of these funds is the construction or renovation of CRB offices. More dubious allocations of “project funds”, such as the purchase of bicycles for CRB board members, were recorded in this study as administrative costs instead. Filtering financial ledgers in this alternative manner yielded an estimate of project spend during the pre-ban years at 18.1% of the average total budget—a substantial deviation from the 35% target set forth in

DNPW guidelines for CRBs. Following the ban, projects accounted for only 6.2% of total spending, largely due to the delinquency of DNPW remittances. The lateness of payments, the uncertainty in their timing and amount, and the insufficiency of the amount itself are cited by CRB representatives as major challenges for the long-term planning of community development initiatives and to community governance processes more generally.

Governance of game management areas

Resource governance is also hampered by a lack of transparency by DNPW in matters of marketing concessions and setting annual quotas. Informal group discussions with CRB members including chairmen, executive officers, and finance committee members, revealed a general lack of knowledge around the bidding process and an inability to characterize the competitiveness of proposals put forth by hunting outfitters for tendered concessions. One CRB could collectively report the number of proposals put forth, but was unaware of the pledge amounts offered by alternative bidders. The leadership of another CRB, which had long failed to attract a concessionaire, could not report who was in attendance on their behalf at the last meeting of bidders, nor the details of the presentations. Though the degree of

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transparency varies between CRBs, and some were well represented in Lusaka at the time of bidding, no on-location formal presentations were made to any community by any outfitter. This physical disjuncture at a time of critical decision- making was felt by interviewees to shift the balance of power not just to DNPW and prospective concession-holders, but to the office of the president and other government offices with influence over the process.

Though CRBs must sign off on an initial quota proposal to which they, the local DNPW office, and the hunting outfitter all contribute their judgement, the quota is sometimes later adjusted by the DNPW main office in Chilanga before final approval in May. Furthermore, CRBs do not keep records of annual quotas or actual harvests and report forms filed on individual hunts were only available at one out of the seven CRBs visited with active concessions—the others stating such reports are normally kept by DNPW. Without such records, an understanding of what CRBs are owed in hunting fees is not only made difficult, but becomes subject to rumor.

Discussion

Putting the Figures into Context: The Park as an Engine for Economic Development

The aim of this study was to evaluate the contributions of tourism at SLNP to the local and national economies. To attach meaning to the measures of income and value added discussed above and to determine the economic competitiveness of park tourism, the measures should be considered in light of opportunity costs: namely, the level of public investment that make these impacts possible, as well the value of potential alternative economic activities and land uses.

Current investment by DNPW and the financial viability of the park

Revenue to DNPW’s South Luangwa Area Management Unit of $2.91m

($321.14 / km2) exceeded management costs of $2.66 ($293.38 / km2) by 9.5%

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(Figure 4-9) and park revenue has exceeded management costs since at least

201114. The increasing profitability of the park, viewed alongside the rise in park visitation, strongly suggests an economy of scale in park management. In an environment of growth, an economy of scale will continue to increase profitability for two reasons: overhead costs do not increase proportionately with increasing visitation, and an expanded tourist presence raises the risks of detection for illegal hunters, increasing the efficiency of anti-poaching expenses. However, the corollary to this economy of scale is that a lapse in appropriate investment, if followed by a reduction of wildlife stock and a diminished product, may raise relative costs of management such that significant donor funding would again be required to reach present performance levels, all the while putting at risk the tourism economy and park-dependent livelihoods in the local area.

While the sufficiency of revenue and efficiency of spending are assessments that lie beyond the scope of this analysis, management inputs for SLNP (Table 4-7) fall below regional standards of 50 km2 per scout and an operational budget of $150 per km2. However, DNPW leans to a significant extent on the private sector, with which it shares management responsibilities of resource protection, research, monitoring, and road construction and maintenance. In 2015 an additional $750,000 was spent on park-related conservation activities by two NGOs that operate both inside the park and in the surrounding GMAs. A combined $90,000 of income to both

NGOs was generated directly from tourism to the park, though the majority of their revenue was from overseas donations demonstrating how management partnerships are critical for capturing and converting the latent and global level “existence value” of the park and its wildlife. On the other hand, however, contributions of private

14 Expenditure data was not available for a nine year period between 1998 and 2008 (Figure D-5).

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tourism concessionaires, who spent a total of $85,200 on the grading of existing public roads in the park and the construction of new public roads, may not represent additional value if these responsibilities are expected to lie with DNPW. The continued onus may undermine relationships between DNPW and concessionaires if these roles and responsibilities are not clearly assigned in concession agreements and/or adhered to.

South Luangwa National Park is one of only two parks in Zambia to generate a revenue surplus (the other being Mosi-oa-Tunya NP), yet it would be imprudent at this point to draw from this surplus in the service of other DNPW assets. Ultimately, financial viability depends not on budget margins, but on the extent to which the management regime afforded by current spending levels is adequate to the threats facing the park. On this question, the evidence is ambiguous. Wide variances in wildlife population estimates from aerial surveys have made trends difficult to discern for many species. Of the more charismatic species important for attracting tourists, elephants, lions, and leopards may be in decline, though for different reasons. Data from ground-based patrols from 2010-2013 suggested both a declining rate of growth for elephant populations and a high level of elephant poaching (Nyirenda et al., 2015), though a 2015 aerial survey indicated that the elephant population for the

Luangwa system as a whole may be stable (DNPW, 2016). The population of lions had been in decline prior to the suspension of hunting in 2012, though as the primary cause of mortality was safari hunting (Rosenblatt, 2014), the trend most likely implicated wildlife policy most directly, and not necessarily management effectiveness. Leopard population trends are not well known, but the fact that leopard density is higher in the park than in the GMAs is attributed to a higher poaching intensity for prey animals outside the park (Rosenblatt et al, 2016; Watson

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et al, 2013) which was not found to be in decline (Becker et al, 2013). Prey and predator populations in general, and across the park and GMAs as a whole, are well below carrying capacity as a result of bushmeat poaching (Lindsey et al, 2014).

Human encroachment on natural habitat in Lupande and other GMAs, which occurs at a rate of 0.75% a year (Watson et al, 2015), has exacerbated anthropogenic pressure on the wildlife resource, but is effectively checked by the park boundary.

Combined, these trends threaten to undermine the quality of the tourism product, to say nothing of their significance to conservation values. While the ultimate causes may have more to do with land and resource governance in the GMAs than park management effectiveness, per se, they signal a need to reinforce resource protection at a level beyond that which the modest, yet growing, revenue surplus can presently afford.

The opportunity costs of investment in SLNP tourism

The opportunity costs of investment in SLNP include the value that is foregone as a result of the curtailment of alternative activities. Compared with the

Zambian economy as a whole, SLNP tourism generates more value added through direct spending and backwards linkages to the supply chain (Table 4-8) than any other sector, per Kwacha spent by the final consumer (Table D-8)15. As a service- based industry with high employment, and one which can afford to pay direct and indirect taxes as well as park admission and other fees, this may not be surprising. In terms of direct and indirect income, SLNP tourism ranks 7th among all sectors. The rank is lower for total effects (not shown), though this is a reflection of the way that wages are spent and not of the structure of the tourism industry or its supply chain.

15 Though opportunity costs arising out of the non-market, subsistence activities that occur in the vicinity of the park are better captured under a livelihoods framework, the focus here is on the opportunity costs to the national level economy and were calculated using the SAM.

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Comparisons on the basis of these multiplier values are not to be interpreted in the traditional sense of a cost/benefit analysis on government investment. Multiplier values represent the relationship between consumer spending—not government spending—and subsequent effects. Yet they point up the comparative advantage of an economy with a large tourism sector.

These values may be placed in greater context by considering the historical and projected trajectory of the wider tourism sector in Zambia. By any of several metrics, tourism peaked in 2005 after several years of rapid growth. In that year tourism directly contributed $0.59bn to Zambian GDP, or 5.1% of total GDP, and directly provided 99,000 jobs (WTTC 2016). After several years of stability the sector was hit hard by the global financial crisis and it wasn’t until 2014 that contributions to

GDP surpassed 2005 levels. In 2015, the direct contribution to GDP stood at

$0.76bn, with 108,000 direct jobs. Despite the slow recovery, Zambia is projected to rank as one of the top countries in the world for growth in tourism over the next 10 years. An estimated annualized growth in direct GDP contributions from tourism of

8.2% between 2016 and 2026 places Zambia second in the world, behind only

Namibia. In terms of growth in direct employment during that period, Zambia is first in the world with a growth rate of 6.6%, translating to an estimated 188,200 direct jobs in the sector by the year 2026. These projections rest on global trends of the demand for tourism, which are expected to be especially high in Africa. For Zambia, foreign tourism spending is expected to increase more than 1.5 times from its level in

2015 ($0.8b) to $1.3b in 2026, a rate that places 2nd globally (Ibid.). The ability of

Zambia to capitalize on this trend efficiently and effectively will, of course, depend on the government’s provision of suitable public infrastructure and adoption of policies conducive to growth in the sector. More specifically, as regards protected areas, it

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will require appropriate levels of investment to safeguard wild resources, and policies that leverage tourism’s footprint, both within and surrounding parks, to reduce resource protection costs. As discussed below, even in the short term, investments by DNPW will have multiplier effects throughout the economy.

Economic return on investment in the park

By accounting for economic values, as depicted in Figure 4-10. It is clear to see that the combined local and national income and tax contributions generated by

SLNP pay for the park’s management costs many times over. Tourism-generated direct and indirect tax revenue alone was 2.3 times the value of park management expenditures in 2015. Total personal income at the local and national level, together with tax revenue, was roughly 10 times the value of park management expenditures16.

The GMA Wildlife Economy

The size of the hunting-based economy in GMAs is comparatively much smaller than the tourism economy of the park itself (Figure 4-11), despite the fact that the GMAs occupy nearly twice the area. Against unknown costs of human- wildlife conflict and foregone land-use options, the benefits to communities from hunting in 2016 amounted to only around $31/km2 ($520,000) for the entire study area, and around $62/km2 for the four most active hunting blocks from which community income data was obtained17. Both averages are higher than the national average reported by Lindsey et al. (2014; $11.9/km2), yet far below average per-area

16 In terms of the effects brought about per kwacha invested by DNPW (as opposed to consumer spending), it is unconventional to apply a multiplier framework because the relationship between spending by DNPW on SLNP and the park visitation and tourist spending that follow is not expected to be proportional. The ratios reported are economic returns on investment, not multiplier effects.

17 Including the obligatory commitment of hunting outfitters and wages paid to camp staff.

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spending by hunters in the study area ($285/km2). Total spending by hunters, in turn, is far below hunting revenue from other countries in the same region, when controlling for area—as low as 30% of revenue in Zimbabwe, and between 70% and

80% of revenue in Tanzania and Namibia, respectively (Ibid.). From Figure 4-11 it is clear that returns to local populations could be more than tripled to $1.86m

($111/km2) by devolving full wildlife ownership to communities. Ownership would allow resource fees to reflect their full market value and for communities to enter into bilateral concession leases with hunting outfitters. Revenue gains could then be used to enter into separate, performance-based contracts with DNPW to provide resource protection services, or for training of additional CRB scouts.

Long term growth of the GMA wildlife economy will only be possible if wildlife populations are afforded greater protection and if economic returns serve as an adequate incentive to develop and enforce GMPs that restrict development in areas sensitive to wildlife populations. At present, wildlife populations in the Luangwa

Valley GMAs are thought to be well below theoretical carrying capacity (Munyamadzi

– 20, Lupande – 10%, Luembe – 2%, West Petauke – 2%; Ibid.). The area impacted by humans in the valley has also grown at an accelerating rate. For the Luangwa

Valley GMAs as a whole, the human footprint grew by 143% between the mid-80s and 2010, and in Lupande GMA, where the non-consumptive tourism economy is concentrated, by 171% (Watson et al., 2015). If the income to CRBs from hunting under a devolved institutional and governance regime made more funds available for resource protection and if this led to growth in wildlife populations, a positive feedback loop may lead to further increases in income and wildlife, possibly setting the ground for the introduction of non-consumptive tourism to previously understocked areas. The direct coupling, in this manner, between the resource and

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the financial performance of community-governance institutions is what distinguishes the consumptive from the non-consumptive, tourism-based wildlife economy. The tourism economy may lag years behind trends in wildlife numbers and, as a consequence of being less sensitive to the gradual loss of marginal habitat to a growing human population attracted to park-related opportunities, may itself induce pressure on wildlife populations.

Bottom-up demand for institutional reform, however, is unlikely to cross a threshold for change in the current political-economic context. Figure 4-12 illustrates the asymmetric pattern of extraction and investment between the three PA types in the study area. The national park, as a profit center, is funded at a relatively high level, allowing it to generate a revenue surplus that could potentially be used to subsidize other DNPW assets. Meanwhile, the GMAs and game ranches are highly taxed while receiving little to no investment. This pattern contributes to the low returns to communities from hunting and explains the short-term incentive structure of DNPW’s funding model. Though the Lower Luangwa Valley encompasses 10

CRBs (or CRB equivalents) representing over 100,000 people in matters of wildlife governance, there was, at the time of the study, little to no communication or organization between the CRBs of different GMAs. Given the number of different communities, without collective action at a system-wide scale, the intensity of political bargaining over the distribution of revenues and the likelihood of institutional change will likely remain low (Liebcap, 1989).

A communal conservancy is an alternative community model for wildlife that requires no statutory reform to implement. Under the conservancy model, because title to community land is held by a CBO, communities are empowered to enter into direct bilateral contracts with a hunting outfitter, precluding DNPW’s privilege to

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charge concession fees. Though a community may negotiate with a hunting outfitter the terms of its own concession fee, returns to the community from animal license fees are sacrificed. For the Kaindu Conservancy Game Ranch, managed by the

Kaindu Natural Resources Trust near Kafue National Park, this arrangement has meant, in practice, a return to communities of 121% of the value of animal license fees18, compared with 86% in the GMAs of the Lower Luangwa Valley. If the concession leases in the Luangwa study area were instead allocated to the communities, returns would increase to 150% of the value of animal license fees, or from roughly $520,000 to $900,000.

While the establishment of a community conservancy on open land may involve navigation of the often thorny internal politics of communities and the need to placate customary leaders, in GMAs the challenge is multiplied by jurisdictional turf wars between powerful government departments fighting to maintain status quo hierarchies and access to valuable resources. A recently proposed conservancy in

Lupande GMA had the backing of the World Bank, but fell through when the transaction costs of overcoming the competitive dispositions of DNPW, the

Department of Forestry, customary leaders, and other stakeholders proved prohibitive (A. Coley, pers. comm., June, 2016). The failure of the entrenched authorities to come to an agreement to grant land and resource entitlements to

Lupande communities is a microcosm of the more general pattern observed by

Nelson & Agrawal (2008) at the sector level across Africa. Resource value must necessarily underpin the effectiveness of community based natural resource management (CBNRM), but paradoxically, valuable resources create incentives for

18 From the 2015 season. Including voluntary donations from hunting clients the returns were 143% of the value of animal license fees.

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state actors to retain ownership or power (Murphree, 2004). Conversely, conservancy establishment may be a more practical endeavor where the resource value is currently low. For example, the aforementioned Kaindu Conservancy was alienated from customary land in an open area, and at the time the first lease was signed with a hunting outfitter, in 2009, the ranch was described as depleted of wildlife (G. Kabinda, pers. comm., January, 2017). Under augmented protection, wildlife populations soon rebounded and the first hunting season was in 2012.

Several GMA hunting blocks and at least one open area in the Luangwa Valley may present similar opportunities should the communities be so inclined to pursue them.

A promising development at the sector level was the recent formation of the

National CRB Association. The association’s responsibilities include the support of

CBNRM development activities and capacity building of CRBs nation-wide, though at this incipient stage it has access to few resources (I. Banda, pers. comm.,

December, 2016). If external funding of wildlife management in Zambia is to avoid stirring the pot in costly and complicated contested settings and is to prioritize rather on “investing in local capacity to negotiate for resource rights, improvements in information sharing about how proceeds from tourist hunting are spent, and more transparent processes for allocating hunting rights and concessions” (Nelson &

Agrawal, 2008), then the National CRB Association may become an important vehicle for communities to drive bottom-up reform.

Conclusion

South Luangwa National Park has grown to become one of southern Africa’s premier national parks and one of Zambia’s top tourism destinations. The ability of

SLNP to retain and re-invest revenue in park operations is largely responsible for the quality of the tourism product available, which is the foundation of a tourism

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economy that generates nearly $15m in GDP at the local level, including $3.6m in personal income to local residents. By factoring in the downstream and induced effects of tourist spending, SLNP can be appraised at a value of $38.2m in GDP.

This park tourism economy rests precariously, though, on a budget of $3m for management operations and on road infrastructure that supports tourism for only 6 months of the year. To shore up the base, to ensure capacity for continued growth, and to generate greater returns to the local and national economy, it is important that the devolved fiscal governance of SLNP is retained into the future and that a one-off funding allotment be made available for much needed capital improvements.

The sustainability of the Luangwa Valley wildlife economy is ultimately undermined, however, by the lack of alignment between the park and resource institutions which centralize ownership of wildlife and prevent integration with economic activities in the surrounding landscape. Park tourism has attracted significant, but unmanaged growth of human settlement in the GMAs. Though consumptive use of wildlife is tightly coupled to the state of the resource, weak wildlife institutions in Zambia mean that community benefits from wildlife use in the

GMAs are too few to provide an incentive for organizing around resource and land- use planning ahead of further economic growth in the GMAs. This divergence between the growth-promoting institutions of park tourism and the growth-limiting institutions of the GMAs may erode resiliency and lead to increased conflict between people, wildlife, and the government.

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Figure 4-1. Map of South Luangwa National Park and surrounding game management areas

Table 4-1. Breakdown of allocation of government fees from trophy hunting in GMAs Entity (CRB Animal license Concession lease Guidelines for function) fees fees CRB spending DNPW 50% 80% Chiefs 5% 5% CRBs 45% 15% (Wildlife protection) 45% (Community projects) 35% (Administration) 20%

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Table 4-2. GMAs and game ranches in the study area Concession GMA status at time concession Size Concession of study (2015 PA type Name name CRBs (km2) rating & '16) GMAs West Nyalugwe Nyalugwe 971 Secondary Not active Petauke Luembe Luembe & 3,359 Prime Active Mwape Chisomo Chisomo Chisomo 3,638 Understocked Active Sandwe Sandwe None at time 1,293 Secondary Not active of study Lupande Lower Malama 1,453 Prime Active Lupande Kakumbi Upper Mkhanya 1,158 Prime Active only in Lupande Nsefu 2016 Jumbe Msoro Msoro 1,613 Secondary Not active Lumimba Mwanya Mwanya 1,401 Prime Active Munyamadzi Nyampala Nyampala 1,837 Prime Active Sub-total: 16,723 ------Game Munyamadzi Luembe 115 ranches Kazumba Luembe 203 Nkalamu N/a Luembe 138 N/a Nyamvu Mwape 116 Nyakolwe Mwape 606 Sub-total: 1,178

Table 4-3. Total trip spending per tourist and total overall spend, by spending category. Trip spend Total local Local spending category per tourist spend Lodge related fees (all- inclusive packages, accommodation, meals, drinks, and activities) $ 1,018.57 $ 21,607,986 Groceries $ 0.61 $ 12,973 Local transport $ 0.71 $ 14,964 Park fees $ 108.39 $ 2,299,415 Souvenirs $ 11.61 $ 246,361 Tips $ 43.43 $ 921,270 Other local expenses $ 0.48 $ 10,083

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Table 4-4. Number of local employees and income in 2015. Skill level Number Average Average Total annual of local monthly monthly income weighted jobs post- income from by average directly deductions wage plus number of created income from service months paid per from wage only charge & year by skill level tourism tips Unskilled 675 $117.04 $166.08 Lodges and $3,159,512 semi- skilled Skilled 275 $425.98 $530.14 Managers 60 $657.72 $758.21 Souvenir Unskilled 83 - - $90,765 shops and semi- skilled General Unskilled 4 - - $4,003 shops Total 1,097 $3,254,280 Note: For the purposes of the study, skilled workers were considered to be employees who perform a job for which a specific qualification is traditionally required (e.g. guides, head chefs, etc.).

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Figure 4-2. Total local jobs from tourism.

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Figure 4-3. Total local and national level value added and income

Table 4-5. Effects of spending by DNPW of $2.66m on SLNP management in 2015 Indirect Direct Effects Effects Induced Effects Total Effects Value Added $2,137,121 $312,000 $2,733,000 $5,182,000 Personal Income $1,885,889 $147,000 $1,853,000 $3,886,000

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Figure 4-4. Procurement from Mfuwe-area suppliers by tourism businesses.

Figure 4-5. Tourism related charitable contributions made in 2015 to local causes.

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Figure 4-6. Map of the center of the local tourism economy.

Figure 4-7. The first year of operation for businesses along the road from Mfuwe to the airport.

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Table 4-6. Direct hunting related fees for study area GMAs and game ranches GMAs Game ranches Trophy Resident Trophy Class of fees Specific fee hunting hunting hunting Outfitter fees (paid by client) Daily rate $1.34m $0.55m Trophy fees $0.60m $0.16m Other hunting-related fees $0.32m $0.09m Government fees Paid by client (animal licenses, rights, and GMA permits) $0.60m $0.05m $0.28m Paid by outfitter (commercial licenses and lease fee) $0.48m ~$0.02m Total govt. fees per km2 $68.2 $241.4 Community fees Obligatory commitment & other support $0.14m $0.05m Employment (camp staff and CRB scouts) ~$0.10m ~$0.08m Total community fees per km2 $14.60 $106.10 Value of resources sold $1.20m $0.18m $0.45m (per km2) ($72/km2) ($11/km2) ($381/km2) Added value (per $2.54m $0.18m $1.00m km2) ($152/km2) ($11/km2) ($849/km2) Non-hunting related fees (paid by client) $0.04m $0.01m Total client spending $2.89m $1.11m Note: Community fees shown here are only direct-level fees and do not include government remittances. Value of resources sold includes trophy fees and government fees paid by clients for trophy hunting and the market value of meat for resident hunting. Added value includes value of resources sold and the daily rate.

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Figure 4-8. Average income and expenditures of active CRBs.

Note: Income is accounted to the year funds were received, not the year the funds were generated from hunting

Figure 4-9. DNPW income and expenditures for SLNP in 2015.

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Table 4-7. Indicators of anti-poaching enforcement intensity of the government and communities. Employer Number of scouts Km2 per scout Operational spending per km2 DNPW 138 66 $85 Community 120 139 nil resource boards

Table 4-8. Park tourism multipliers at the local and national level. Economic Income Jobs Value Meaning Level Multipliers Multipliers Added Multipliers Local Direct: 0.13 Direct: Direct: For every dollar in locally Total (direct 43.87 0.56 captured sales, $0.15 in + indirect + Total Total income, and $0.60 in induced): (direct + (direct + added value is generated 0.15 indirect + indirect + locally. For every million induced): induced): dollars in captured sales, 58.91 0.60 58.91 local jobs are generated. Local & Direct + n/a Direct + For every dollar in National Indirect: Indirect: nationally captured sales, 0.45 0.93 $0.73 in income, and Total: .73 Total: 1.33 $1.33 in added value is generated nationally through direct, indirect, and induced effects.

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Figure 4-10. SLNP park budget in relation to economic contributions at the local and national level.

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Figure 4-11. The GMA wildlife economy in relation to the local national park economy. Enlarged for detail.

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Figure 4-12. The pattern of earnings and investment in the Luangwa Valley.

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CHAPTER 5 THE ECONOMIC IMPACT OF KRUGER NATIONAL PARK AND THE SURROUNDING RESERVES

Introduction

This chapter begins with a discussion of the legal frameworks for wildlife and PAs in South Africa before describing the PAs that constitute the Greater Kruger National

Park (GKNP), the manner in which they are governed, and their physical setting and infrastructure for tourism. The chapter then proceeds with the results of financial and economic impact analyses of non-consumptive tourism to Kruger National Park and to the reserves contiguous with the national park. The sections are organized at the first level by the type of measure considered, rather than the PA category. Where it is logical to do so, comparisons are made between PA categories (Figure 2-2), but the economic impact estimates for the contiguous reserves are aggregated together. The chapter concludes with a discussion of weaknesses and potential fault lines across the PA system as the wildlife economy continues to grow.

Study Objectives

The objectives of this study are to:

• measure and compare the regional and national economic impacts of non- consumptive tourism for two sub-systems of the GKNP, including a network of mostly private reserves and the national park at the core of the GKNP,

• determine the financial viability of PA business models within the GKNP,

• and explain these economic and financial results in terms of how PAs within the system are governed.

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Study Area

The Formal Institutional Environment for Wildlife and Land in South Africa

Wildlife. Wildlife in South Africa possesses qualities of res nullius (“a thing belonging to no one”) in that, by default, neither an individual nor the government can claim ownership over a wild animal. However, at both the provincial and national levels wildlife is a regulated resource with legislation controlling the right to hunt, capture, or translocate wild animals. Chief among the set of national laws is the Game Theft Act of

1991, which grants ownership of wild animals to a person who has sufficiently fenced their land so as to confine the game. Standards of enclosure vary by species and are determined by the provincial government, which is the authority that grants certification.

Properties that qualify for certificates of enclosure are then exempted from most hunting regulations, such as the requirement to obtain permits for each hunt. Wildlife on unfenced land, by contrast, retains its res nullius status and is protected by the full set of wildlife regulations of the province. Though permits are necessary to hunt wildlife on unfenced land, they are sold at nominal rates reflecting only the cost of processing and the fact that there is no owner to compensate. Certain species of rare or endangered wildlife are afforded additional protection by both provincial and national regulations and, even on enclosed land where they have been claimed by an owner, a permit is still necessary to hunt such animals. This overlapping regulatory authority is what allowed the Department of Environmental Affairs (DEA) to impose a national moratorium on leopard (Panthera pardus) hunting in 2016 with an extension in 2017, citing a lack of knowledge of the species’ population status (DEA, 2017). Overall, this relatively devolved institutional environment, together with the reduction of agricultural subsidies, has been credited for the rapid rise of the game farming industry (Bothma et al., 2009).

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Land. With respect to real property, the South African constitution permits of outright, or “freehold”, ownership of land, subject to ordinary limits and special statutory provisions. The most important such provision in rural areas of the former Bantustans is the Restitution of Land Rights Act 22 of 1994. This act provides for a judicial process through which a person, having been dispossessed of their land holdings by racially motivated apartheid era policies not prior to the date of June 19, 1913, may file a claim evidencing their previous ties to the land and receive restitution. Restitution may be in the form of rights in the land formerly lost, rights to alternative land, or financial compensation. Where actual rights are granted, the Expropriation Act requires the government to compensate the current owner at fair value. In practice, the government has adopted a “willing buyer-willing seller” policy, giving leverage to current landowners in negotiations over compensation. Protracted negotiations between the government and landowners, along with numerous legal challenges against the constitutionality of subsequent amendments--which have mirrored power struggles between tribal leaders and ordinary citizens over communal land--as well as the need to adjudicate competing claims to the same properties, have all combined to delay the fulfillment of the

Restitution Act’s mandate (Bennett et al., 2013; Weinberg, 2015).

The present challenges belie the Act’s auspicious beginnings. An early land restitution “success” story involves the Makuleke people who were forced from their homeland in the northern part of Kruger National Park in 1969 and relocated outside of the park. Their claim was settled in 1998, with rights to their former land entrusted to a communal property association (CPA) of democratically elected leaders acting on behalf of the community (Environmental Resources Management [ERM], 2012). As the

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land encompassed 24,000 ha of the Pafuri section of the national park, an agreement was negotiated with SANParks whereby the land would remain as a PA, but only after it was re-proclaimed as a contractual park. A contractual park in South Africa is a special arrangement, upheld through lease, in which a landowner grants a PA authority management rights to their land. A common stipulation of contractual parks, especially when communities are involved, is the creation of a joint governance board (though referred to as a “joint management board”). In the Makuleke case, the board consists of

3 members of the CPA and 3 representatives from SANParks.

Despite the celebrated status of the Makuleke Contractual Park (MCP) in the popular media, or perhaps due in part to it, the scope for the restitution of land that falls within a PA is now considerably narrower. Faced with the reality that 139 statutory PAs in the country were under land claims, with over a dozen claims filed for KNP alone, and that 90% of claimants preferred land restoration as the means of restitution (Cundill et al., 2013), a memorandum of agreement was concluded in 2007 between the ministers of what is now the Department of Rural Development and Land Reform and the

Department of Environmental Affairs that declared the protected status of conservation areas under land claim to be a non-negotiable condition of any settlement agreement

(Paterson, 2010b). Shared governance (without the advantage of a terminable lease agreement) was instead encouraged as an option for such areas, should the land be restored (Ibid.). Significantly, this decision also appears to pertain to private nature reserves that are formally proclaimed. A year later, the national level cabinet declared that for KNP, specifically, land restoration would be excluded as an option, leaving only the possibility of equitable redress in the form of financial compensation or the awarding

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of alternative land. Both decisions were made in recognition of the national and international importance of South Africa’s PAs and the need to strike a balance between the rights of claimants and the interests of society as a whole (SANParks, 2018).

Protected Areas in the Study Region

Geographic context

Kruger National Park (KNP), proclaimed in 1926, is South Africa’s largest national park at 19,181 km2 and attracts almost 2 million visitors yearly. The park also lies at the core of a contiguous network of wildlife reserves both in South Africa and in the neighboring countries of Mozambique and Zimbabwe. The South African network of reserves is known collectively as the “Greater Kruger National Park” (GKNP), which encompasses a total of 22,686 km2 (Table 5-1 & Figure 5-1 A). The GKNP is a mosaic of protected areas, including in addition to the national park itself, three provincial reserves, two contractual parks, and a constellation of private reserves. Together with several national parks and reserves in Mozambique and Zimbabwe, the entire transboundary network is known as the Great Limpopo Transfrontier Conservation Area

(GLTFCA), which encompasses nearly 100,000 km2. This chapter focuses exclusively on the South African portion of this network in order to hold constant the national level formal institutional environment in comparisons between sub-units of the system.

The GKNP falls across low-lying savannas, mostly under 800m and is in a tropical to sub-tropical climatic region. Rainfall is distributed unevenly from the drier north, where average annual rainfall is 350mm to the wetter south (750mm).

The national park

Governance. National parks in South Africa are managed by the parastatal agency South African National Parks (SANParks), under the Department of

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Environmental Affairs and Tourism. Specifically, SANParks is a schedule 3A public entity, which means that it relies partially on funding from the central treasury. This status gives SANParks less autonomy than other parastatal organizations, and doesn’t guarantee that profit at the corporate level will be retained, but in practice the treasury provides significant investment funds for the majority of SANParks’ capital infrastructure needs, allowing SANParks’ profit to be re-invested into operational management of the national park system.

At the level of KNP, no revenue generated by the park is retained. Instead, a budget is annually approved by SANParks’ corporate office. The centralization of finances is critical to the ability to support other parks within the national system as KNP is one of only a few national parks that generate a profit. In addition to its profitability and its size, KNP is also exceptional in terms of its functional self-reliance. The set of functions core to SANParks’ mission, being conservation, “people and conservation”, and tourism and marketing, are performed centrally for all parks but Kruger, which reproduces these functions locally. The park also has its own managing executive while there is only one managing executive for all other parks in the national system. Thus, while decision-making authority within SANParks corporate structure is centralized for the most part, for KNP it is highly decentralized to a relatively flat hierarchical structure.

High-level objectives outlined in a corporate policy framework are adapted to the park’s context through lower level planning processes organized around seven different thematic programs, each seeking input into decision-making from a range of public and private stakeholders (SANParks, 2018). Though the reserves contiguous with KNP are among these stakeholders, the form of shared governance embodied in these

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interactions, at this stage, is only controlling of management operations within KNP; actionable cooperative agreements between the contiguous reserves and KNP are not yet finalized.

Commercial operations at the park are a combination of high-end private concessions on leased land and public facilities managed by SANParks. Within the public camps, certain services are also outsourced through a concession model.

Concessionaires pay a percentage of gross revenue to SANParks.

As discussed above, one property that SANParks manages and jointly governs is

MCP. Stipulations of the lease between the Makuleke CPA and SANParks are that the former is entitled to 10% of the tourism revenue brought in by commercial concession holders in the contractual park and that until the costs of managing the park can be afforded by these returns, SANParks is solely responsible for management operations.

The original lease agreement is set to expire after 50 years, though the CPA has the option to void it after a mid-term review (ERM, 2012). A second contractual park (pre- dating MCP), known as Kempiana, lies to the west of the Orpen gate, but as it is not owned by a community and as lease fees do not flow to SANParks, it is treated, for the purposes of this study, as a private reserve aggregated together with Timbavati PNR.

The private reserves

The background to the private reserves adjacent to KNP was discussed in chapter 2. The reserves are listed in Table 5-1.

Governance. With the exception of MalaMala and Mjejane Game Reserves which lie on land restored or soon-to-be restored to communities, the members of private reserves are land-owning individuals. These reserves are organized at several levels. At the lowest level are individually-owned parcels, some with tourism operations,

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some vacant, and others with residential units. Conservancies, as discussed, consist of collections of these properties. However, some conservancies consist of a collection of conservancies and have adopted a federated structure. At the highest level, the

Associated Private Nature Reserves (APNR) is a loose federation of conservancies that is united mostly by a shared philosophy of conservation and tourism enterprise rather than a binding legal instrument, but it also periodically interacts with SANParks and the provincial authorities to set hunting guidelines and quotas. Most, but not all conservancies of the GKNP belong to the APNR.

In the highly nested organizational structure of the private reserves, the locus of decision-making power, as regards actions that impact on conservation and tourism, is the first-level conservancy. Associations of members at this level are legal entities with constitutions outlining the rights of members, the limits of their rights, and which provide for executive committees to approve requests related to tourism development, hunting, utilization of property, and other matters. It is partly through this executive authority that the idiosyncrasies of the conservancies—some of which allow high-density tourism; others of which strictly enforce limits to commercial growth—take shape, as reflections of member preferences. While conflict resolution mechanisms exist for each conservancy, the recourse of last resort for members is to “fence-out” (i.e. to leave the conservancy, which would entail erection of a game-proof fence around the property).

Conservancies are also empowered to generate revenue by imposing levies on members and charging fees for hunting. Although by law, only the provincial authorities can issue actual hunting permits, conservancy associations reserve the right to grant permission to hunt to their executive committees. In the conservancies that practice

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hunting, quota allocations are proposed after a process in which representatives of the private reserves, SANParks, and the provincial authorities review ecological monitoring data across the entire system and determine sustainable offtake levels. Permission to hunt, whether for sport or for meat, may then be given for requests that are in accordance with conservancy-specific quota allocations. All revenue from animal fees is collected by the association.

Associations, as non-profit entities, use revenue to fund management. With a few exceptions, to each conservancy is attached a management authority, employing a warden, a contingent of wildlife rangers, and other staff. The management authority is responsible for maintaining the security of the conservancy, ecological monitoring, ecological interventions, research, and supervising hunting activities. Because of their degree of autonomy, the management authorities of the private reserves are known for their innovation and pioneering application of new technologies to the escalating security challenges they face. Examples include an all-female anti-poaching force

(Barbee, 2015) and an advanced reserve-wide electronic tracking system (Caboz,

2018).

The provincial reserves

Governance. Two of the provincial nature reserves of the GKNP (Makuya and

Letaba Ranch) are located in Limpopo Province and the third (Manyeleti) is in

Mpumalanga Province. Though their respective parent departments—Limpopo

Department: Economic Development, Environment and Tourism (LEDET), and

Mpumalanga Tourism and Parks Authority (MTPA)—have contrasting structures, they share common governance characteristics. In Limpopo, LEDET and its tourism arm, the

Limpopo Tourism Agency (LTA), are both fully government entities. In Mpumalanga,

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MTPA is a parastatal agency. However, the key shared attribute among the three reserves is the inability to retain revenues, which stems from two factors. First, budgeting is centralized to the respective parent departments, which, in turn, are largely dependent on external grants to fund PAs. Second, as the land of each reserve is either wholly or partially claimed by nearby communities, revenue streams from hunting and most non-consumptive tourism operations are claimed by communities. In theory, joint governance arrangements at each reserve should allow for the possibility of re-investing a portion of community benefits into reserve operations, but the lack of efficient mechanisms to resolve perennial disputes between competing claimants and tribal authorities has cast uncertainty over planning efforts and brought deadlock, at times, to commercial operations (SAWC, 2017).

The organizational hierarchy at each provincial reserve is also highly centralized.

Most administrative functions are performed by department offices at provincial headquarters. As a result, staff contingents at the reserves are thin, consisting mainly of rangers. While the density of rangers (10km2/ranger) is on par with the private reserves

(12.5km2/ranger), wage bills as high as 95% of allocated budgets reduce funding for operations (de Koning & De Beer, 2017). Ranger effectiveness is further limited by the advanced age of many rangers (NCC, 2017a & 2017b) and bureaucratic procedures would encumber efforts to re-constitute ranger forces with younger members.

Human Populations in the Impact Zones

There exist three impact zones accounted for in this study: the local area, the local provinces, and the country of South Africa as a whole. As discussed in chapter 3, the “local area” is comprised of the region, within South Africa, contained by a 50km buffer from the boundary of the GKNP (Figure 5-1 A). This area is home to

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approximately 2.7m people. The GKNP is located within Limpopo and Mpumalanga provinces, which are combined and considered as a collective whole for the purpose of multiplier analysis. The area encompassing the two provinces is referred to as the

“provincial level” in this study. It is a region of over 10 million people. Impacts are also considered at the national level (Figure 5-1 B).

The average unemployment rate for the seven municipalities within the local area, weighted by population, is 40.8% and well above the national average of 26.7%

(Statistics South Africa, 2018b). Settlements in these municipalities have roots in the

“homeland” system of the Apartheid government which confined black people to densely populated marginal areas. The migration, in the 1980s, of refugees from nearby war-torn Mozambique has contributed to the poor state of the economy. Though the rearing of livestock on communal land is common in the villages outside the boundaries of the GKNP, people rely on a diverse set of income streams, including social security payments and government transfers, remittances from seasonal migrants. Cash income is also supplemented by the collection of fuelwood and food from communal areas

(Hunter et al., 2014).

Methods

Methods used in South Africa are described in detail in chapter 3. Table 3-2 summarizes the surveys from which most data was drawn. Supplementary data included quantitative information shared by a collaborator on the finances of reserve management authorities and their qualitative notes from interviews with two provincial reserve managers.

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Results

Tourism Infrastructure

Across the entire GKNP there are nearly 8,000 beds and around 700 campsites available for tourists (Table 5-2). Accommodation varies from campsites with ablution facilities (in the public PAs), to rental homes (mainly in Mjejane PNR), to shareblocks, to five-star lodges. Public facilities tend towards a high-volume and low-value sales strategy while private camps, both large and small, tend towards luxury (Figure F-2).

Figure 5-2 depicts the locations of commercial lodges and the density of commercial beds in the region. Tourism is concentrated around the southern end of the park and in the private reserves to the west. Within the private reserves, and within the APNR specifically, the two areas with the highest bed density are shareblock properties1. Low- density private residences (not mapped) are scattered throughout the private reserves mostly in areas appearing vacant (Figure 5-2 B).

The greater road density of the private reserves (Table 5-2) reflects not only the greater commercial aspect of these PAs, but the legacy of cattle farming as well. A measure of road density alone, however, suggests a greater degree of parity between reserve categories, in terms of accessibility, than exists in reality. Almost all roads in the

GKNP, save for the main thoroughfares of the national park and an access road through

Timbavati PNR, are unpaved, being either of gravel or dirt, and their condition varies widely. Generally speaking, accessibility is poorest in Makuya NR and Letaba Ranch

1 Shareblocks in the study area are tourism developments, each consisting of a cluster of chalets owned collectively by share-holding members. Though these properties may be considered only semi- commercial, in that visiting members are part owners of the enterprise, the main reason that members visit the area is to partake in recreational activities for which money spent is mostly brought from outside of the local provinces. For that reason, they are included in this study and not considered residential properties.

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NR, where self-drive opportunities and even management activities are limited by the need for high-clearance 4x4 vehicles on many of the internal roads.

Visitation to the Greater Kruger Area

Between April 1, 2016 and March 31, 2017 (the period by which SANParks aggregates annual visitation records) there were almost 3.5 million visitor-nights in the

GKNP (Table 5-2). Visitation was highest to KNP (82.2%), followed by the private reserves (16.8%) and the provincial reserves (1.0%)2. The demographics and travel patterns of visitors also varied between reserve type (Table F-1). In general, KNP attracts a greater proportion of domestic visitors who use ground transportation to travel to the park, whereas visitors to the private reserves are mostly foreigners, are commonly on a trip booked as a package through a tour operator, and fly into the local area.

Direct Spending of GKNP Tourism

Direct non-consumptive tourist spending

Overall, trip-related spending for KNP visitors3 between April, 2016 and March,

2017 was $167m at the national level, of which $144m was received in the local provinces (Table 5-3). This total does not include conservation and other entry-related fees, which were roughly $10m-$20m, but does include spending on accommodation and other services outside of the park if it was trip-related. Extra-local spending is mostly transport and fuel-related.

2 Visitation to lodges within the private and provincial reserves was estimated by extrapolating occupancy rates, as reported by 25% of all lodge operators in the study region. The overall average occupancy rate for lodges in these reserves (excluding shareblocks) was 60.7%.

3 Included is the spending of visitors to Makuleke CP on account of its free admission to KNP day-visitors.

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Visitors to the private reserves of the GKNP spent $228 in total, of which $191m was received in the local provinces (Table 5-4). This amount does not include gate fees, which could not be disaggregated from other reserve-level income, nor be reliably extrapolated from visitation estimates. However, the income to reserves, of which gate fees are a part, is discussed in the section on business models and financial viability below.

Visitors to the provincial reserves of the GKNP spent just over $11m, of which roughly $9m was received in the local provinces (Table 5-4). Again, gate fees could not be estimated.

The areas in the GKNP where lodge and camp-based spending are concentrated are depicted as a heat map in Figure 5-2, panel C.

Direct consumptive tourist spending

The trophy fees paid by clients to those reserves contiguous with KNP that practice sustainable use was estimated to be no less than $3.5m during the 2016-2017 season, based on rough percentages by reserve managers of the degree of reliance on hunting revenues. Other spending, including daily-rate fees to the outfitter, taxidermy, and transport costs are not known. In the absence of direct survey data from trophy hunters it was not possible to estimate the total hunting-related spending of this segment of tourists.

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Direct Economic Impacts of GKNP Tourist Spending

Jobs and wages

In the 2016 season there was a total of 9,091 jobs4 (0.40/km2) within the PAs of the GKNP accruing approximately $61m ($2,653/km2) in personal income (Table 5-5).

Including additional jobs created outside of the GKNP in hotel accommodation and transport5, tourism supported another 800-900 jobs, generating an additional $8m in personal income.

The origins of employees varies by skill level (Table 5-6). Employees in higher skilled positions are less likely to originate locally, but for the contiguous reserves 80.3% of employee respondents are local, coming from the area within 50km from the GKNP boundary. This estimate is similar to that reported by SANParks for its own KNP employees (Swemmer & Mmethi, 2016), 80% of whom are local to the park and 90% of whom come from one of the local provinces.

For the contiguous reserves it was possible to disaggregate employment data more granularly for tourism jobs specifically (as opposed to jobs in reserve management). By comparing the number of skilled and unskilled jobs per bed in each lodge segment, it is clear that higher-end lodges generate greater employment per volume of tourists (Table F-2) and that this pattern holds even when accounting for the volume of money spent by tourists. From the employee survey, a lack of educational experience is revealed, especially by unskilled employees, 78% of whom lack a

4 This job count does not include temporary workers, nor does it include jobs provided through public works programs because they are not tied to tourist spending.

5 Strictly speaking, these additional jobs outside of the GKNP were not all created by tourism, but are partially supported by tourism.

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secondary school diploma. Though a lack of formal education does not pose a barrier to entry, it does impede advancement in the industry unless employers are willing to provide or pay for training (anonymous lodge manager, pers. communication).

The employee survey also revealed differences in sources of income between skill levels (Table 5-6). For example, though the industry norm is for gratuities to be pooled and distributed equitably amongst staff, skilled positions receive 27% of their overall wage income from tips, which is triple and almost double the percentage of unskilled and semi-skilled employees, respectively. The disparity likely reflects the greater opportunities that safari guides have to socialize with guests and receive tips directly. How employees manage their money varied by skill level as well. Though savings by employees in the lowest skilled positions are only 7% of their income (not accounting for debt) and the portion spent on food is almost half, the national average household savings rate in 2017 was just 0.2% (SARB, 2017), remittances of these employees were between 9% and 15%, and they tended to have a greater number of household dependents than skilled employees. For employees of the contiguous reserves alone, their income supported an estimated 18,800 household dependents.

Thus the sufficiency or insufficiency of a tourism wage must be considered in light of the social safety net into which employees invest. The analysis of returns to labor from tourism in the contiguous reserves can also borrow from national SAM data to shed light on the equitability of worker payments. While nationally, those with less than a high school diploma earn about 15% of the total returns from labor (Seventer et al., 2017), those with a similar education level in the reserves earn about 24% of total reserve wages (Table F-3). Including returns to capital (profit) would render a less equitable

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distribution of payments, but in terms of the remuneration of an under-educated population, it is a relatively progressive share.

Tax contribution, profit, and GDP

Taxes. The non-discounted tax contribution of commercial operations throughout the GKNP amounted to roughly $54m6, with an additional $14m contributed through trip-related spending outside of the reserves (Table 5-7). This tax revenue to the national government arises from value added tax (VAT) on goods and services purchased by tourists, income tax (pay as you earn [PAYE]) for tourism employees, contributions to the unemployment insurance fund (UIF), and corporate tax on company profits. Reserve management authorities are exempt from the latter tax. Municipal taxes apply to property owners within the reserves but are minimal in comparison to national tax levies.

Profit. Profits arising from tourism spending mainly accrue to businesses in the hospitality and retail industries, not PA management authorities. The exception among

PA authorities is SANParks, which earned a surplus of $6.1m in the ‘16/’17 season through rental and lease fees from concessionaires, as well as from tourism facilities they operate directly (SANParks, 2017). For tourism lodges throughout the GKNP, including private concessions within KNP, the average post-tax profit as a percentage of turnover was 15.3%, yielding a total absolute amount of $28m. Altogether, and including trip-related spending on transport, groceries, and other miscellaneous items, total profit from GKNP tourism was roughly $35m.

6 For the purposes of estimating VAT contributions, the 14% rate on most goods and services was assumed for tourist spending (technically, gross tourist spending was multiplied by 12.28% to backwards- calculate VAT). Employee taxes were on average 9% of wages and corporate tax was estimated at 21.4% of profits.

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GDP. Gross domestic product is the sum of income, profit, and taxes. The direct contribution of the GKNP towards national GDP for the year of the study was $171m.

This value, which does not include GDP generated through backwards linkages or induced effects, represents 1.9% of the entire tourism sector’s contribution in 2016,

0.35% of the GDP of the Limpopo-Mpumalanga region7, and 0.06% of national GDP.

Local Indirect Spending and Procurement

Operational expenditures of the tourism businesses and management authorities of KNP and the contiguous reserves amounted to approximately $93m. Of this spending, at least 60% is received locally, within 50km of the GKNP. Among private tourism businesses, local spending is even higher, at 70% (or 78% when excluding fuel purchases). Only financial and business services are commonly procured from outside the local area or provinces. That tourism operators are able to achieve higher local procurement than public management authorities is likely due both to the smaller size of their orders as well as the procurement protocols used by the public authorities.

Suppliers of KNP and the provincial reserves must be registered in the respective central databases used by each authority to tender bids and select vendors for all regional PAs under their management. These databases do not distinguish between local and non-local businesses.

The nature of supply-chain businesses was also a matter of inquiry. Despite lacking an official policy on local procurement, SANParks does have informal commitments to support local small, medium, and micro-sized enterprises (SMMEs) and

7 GDP contributions to the provincial-level region are calculated after subtracting non-local spending (transport, fuel, etc.), which yields a gross regional product contribution of $151m.

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a formal commitment to procure from Black Economic Empowerment (BEE) companies.

The former category of businesses is supported by hosting locally owned souvenir shops and kiosks at KNP’s major gates, which employ 16 shop-workers and support

400 crafts-makers, by training local construction contractors, and by using SMMEs to provide event services (Swemmer & Mmethi, 2016). Among the private reserves, many tourism operator respondents expressed confusion with regards to what constituted an

SMME and those respondents located near Hoedspruit and Phalaborwa were generally unaware of opportunities to support SMMEs even when they desired to do so. Lodges closer to Bushbuckridge Municipality were the exception. There, a larger local population and the work of two NGOs--partly funded by SANParks and Sabi Sands

PNR--to identify, train, and mentor local entrepreneurs has created more opportunities for lodges to procure directly and indirectly from nearby communities. The SMMEs with ties to the tourism industry tend to be service-based, as it remains difficult to attain the quality standards for produce and crafts-work that higher-end lodges demand.

Social Responsibility and Investment by Tourism and Reserve-Level Entities of the Contiguous Reserves8

Through both financial support and the organization of development initiatives, tourism operators and reserve management authorities of the contiguous reserves invest on a continual basis in the well-being of surrounding communities. A dozen trusts or social development organizations in the area immediately outside of the GKNP have direct ties to tourism, including those established by four out of the five private conservancies, four established by tourism operators, and another four to which other

8 The social commitments of SANParks in the Kruger area are well documented by Swemmer & Mmethi (2016).

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tourism operators contribute (GKEPF, 2018). Funding comes either from donations out of the income to the reserve management authority, out of the profit of tourism operators, from donations by guests, or from external donors. It was not possible to determine the total value of donations or spending from throughout the contiguous reserves, but average reported donations from the lodge survey were 1-2% of annual expenditures. However, the level of commitment varies by the market segment that lodges serve, so the true percentage may be higher. Out of 118 operator websites reviewed, 44.1% of operators with room rates above the median ($199.3) indicated a formal community development commitment, whereas only 5.1% of operators below the median indicated a commitment9. The difference is likely due to larger public profiles and absolute profit levels of higher-end lodge businesses, imparting greater pressure for

CSR activities and a greater ability to deliver them. Of those operators indicating a formal commitment, the most popular focal area was the support of children, through building, running, or donating to orphanages and creches. Other common focal areas were conservation, child and adult education, local enterprise development, environmental education, and health and nutrition (Figure F-2). It is unclear, however, if this distribution pattern, which is based on the number of operators supporting each cause, reflects the actual allocation of resources. It is also unclear if it reflects the priority needs of communities, though the NGOs in the area often partner together to achieve greater scale and coordination, and the Greater Kruger Environmental

Protection Foundation is an incipient coordinating body at the system level.

9 Six out of 34 operators interviewed reported a commitment in their responses while no indication of a commitment was found on their website, suggesting an under-estimate of the number of lodges with a formal commitment.

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Reserve Business Models and the Financial Viability of Tourism

The GKNP system at large is driven by non-consumptive tourism, which, combined with donations, accounts for approximately 80% of all reserve-management income10. However, within the private reserves consumptive tourism provides 60% of management income (Figure 5-3) and the dependency on hunting is as high as 80% among just the four reserves that practice sustainable use. The provincial reserves, which face several structural impediments to development, are the only PA category unable to self-finance management, relying almost entirely on public funds. Common to all PA types are rapidly escalating security costs which have reduced the budget for, and time spent on conservation activities.

It is instructive to consider the viability of each PA category from a system-wide perspective on the potential to indirectly generate revenue for management operations from non-consumptive tourism. An index (Figure 5-4) roughly illustrates the relative degree of commercial activity, as measured from bednight rates and tourist volumes, standardized by area, for PAs across the GKNP. Large disparities have necessitated different funding approaches (discussed below).

10 Incomplete reporting prevents a precise accounting of reserve-level income and expenditures in the GKNP, though it was possible to approximate values. Of the six private reserves with management operations, financial data was only forthcoming from three (representing about 55% of the total land area they share), and of the three provincial reserves, data was only fully available from one. Extrapolation to the remaining reserves was done on the basis of area, though adjusted where informal guesses by key informants or data from similar reserves outside of the system were available.

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The national park

As mentioned above, KNP generated a profit for SANParks of $6.1 (8.6%) off of

$70.6m11 in revenue, almost entirely from exchange transactions in tourism accommodation and activities, conservation (entrance) fees, retail profit, and concession lease fees (SANParks, 2017). This represented a decline in profit percentage from the previous year of 0.6%, but an increase in revenue of $6.7m and in absolute profit of $0.2m. Profit is achieved, despite the low degree of commercialization

(Figure 5-4), because visitors are charged directly for the costs of PA management through a separate conservation fee, which constitutes a significant fraction of overall revenue (approximately 15-25%). Revenue in excess of the budget is allocated to other

SANParks PAs, but funding for capital improvements was provided by the DEA, which also awarded $19.2m in tenders for the construction of a 250-bed lodge at Skukuza.

Managers, however, expect that this support from the national government will decline in the future.

Makuleke Contractual Park, though separately governed from KNP, is managed by SANParks as if it was a section of the national park. Data on the costs of management in this section was not forthcoming and the costs are possibly difficult to disaggregate from the larger region in which MCP sits, but revenue generated for management by visitors to MCP is minimal. The situation owes to the fact that there is no separate entrance fee for day-visitors into MCP and per the lease agreement for the contractual park, concessionaires pay a fee not to SANParks, but only to the Makuleke

11 Profit is calculated against actual expenditures. In terms of budgeted needs, however, SANParks expects a shortfall of $17.6m for the 2018/2019 season (SANParks, 2018).

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CPA (10% of revenue), which, in turn, is only required to contribute funds towards park management if the revenue earned is at least 50% of management costs. Accounts of the CPA indicate that revenue from concessionaire fees to the CPA has historically been low, fluctuating around $120,000 from 2007 to 2015 (Maluleke, 2018) and thus has covered only the administrative expenses of the CPA. Low occupancy rates may be attributed to the low density of “big 5” game species and the relative remoteness of

MCP. Though transit time by road from Johannesburg to MCP is only about an hour longer than to alternative lodges near Skukuza or Hoedspruit, the 6.5 hour trip exceeds a half-day of travelling and therefore is thought to represent a psychological hurdle for tourists arriving by road. A factor contributing to the low sightings of the “big 5” is the lack of, or poor condition of dirt roads. A total of 135km of game viewing roads exists in the entire MCP with an additional 23km of transit road. This equates to 5.1km of road per 1,000ha, and 12.2km per game drive vehicle, both well below the norms of private reserves (Environmental Resources Management , 2012). The dilemma presented to the CPA, which must soon decide on whether to renew its lease with SANParks, is further considered in the discussion.

The private reserves

The total amount spent by the management authorities of the private reserves in

2016 was approximately $6.3m12 ($2,522/km2; Figure 5-2) and can be compared to the total commercial revenue generated by the private reserves through tourism and hunting of roughly $158m (representing only 4.0% of revenue). This spending is

12 This spending cannot be compared to the above reported income and spending by SANParks on KNP since SANPark’s KNP budget includes expenses on the tourism operations it is responsible for, whereas the management authorities of the private reserves are only responsible for conservation, security, and community relations (the corresponding conservation and security budget for KNP is not disclosable).

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assumed to be equal with the income to the reserves, as profit is not a goal at the management level. Funds are raised through a diverse array of mechanisms, though it is not possible from the data obtained to disaggregate the sources beyond what is generated by hunting and what is raised by other means.

Hunting, as mentioned, accounts for roughly 60% of management costs when averaged across all private reserves. In addition to trophy hunting, some reserves sell hunting rights for non-commercial quality game to property owners for the purpose of consumption, but a veterinary cordon prohibiting the export of meat outside of a small area around the GKNP, as well as the importance of maintaining the Kruger brand prevent a commercial market for game meat from developing as a supplementary income source. One funding mechanism shared by all reserves is a levy on members.

Such levies may be applicable only to tourism operators, or alternatively to all property owners in the reserve. The collective total is usually based on budgeted needs, but individually the levies may be rated according to area of land owned, number of beds, number of guests, or a percentage of property value in the case of sales. Gate fees are also commonly charged to guests upon entry, but are less significant and typically only used to cover the costs of gate operations.

The balance between levy-based revenue and hunting revenue can be a matter of purposive choice, as in the case of individual sections of federated reserves which decide on their own how they will cover management costs, or may be an outcome of collective resistance to the imposition of, or increase in member levies or growth in tourism. Reserves in which a large share of the properties are non-commercial unsurprisingly lean on hunting revenue. Thus, while the private reserves are highly

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commercialized, generating orders of magnitude more revenue than the national park on a per-km2 basis (Figure 5-4), less than 2% of tourism revenue is made available for reserve management.

Managers recognize the risks of relying on hunting. The market is susceptible to global shocks (e.g. the ensuing outcry after the killing of “Cecil the lion”) and unpredictable regulatory actions taken at the national and international level, including a ban imposed by South Africa on the hunting of leopards and bans imposed by the US on the import of elephant and lion trophies. Yet despite pressure to commercialize further, a strong preference by reserve members and executive committees to limit the growth of tourism operations, as well as slow turnover times in the ownership of non- commercial properties and the time required to upgrade existing lodges all inhibit a faster transformation of reserve business models. As a result, donations from 3rd party organizations are becoming an increasingly important source of funding, especially in the last 5 years as security costs have multiplied several times over.

Costs related to enhancing the outer fences of the GKNP, anti-poaching patrols, and other security measures now constitute about 60% of management costs for the private reserves (Figure 5-3). Conservation spending has been reduced to around 30% of costs. Previously, these categories bore the opposite relationship to each other. The remainder of management budgets is allocated to social investment.

The provincial reserves

As with KNP, no revenue from activities in the provincial reserves is retained at the PA level. Furthermore, managers have limited knowledge of the income generated by reserve activities and, for two of the three reserves, where supplies must be requested on an ad-hoc basis from provincial headquarters in lieu of an annual budget,

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managers had no records of the non-wage operational costs of running the reserves.

However, funds for operations at all three reserves were minimal, with wages constituting around 90% of overall costs. This translates to a total expenditure amount of at least $1.5m ($2,064/km2). The revenue generated from commercial activities in the reserves is roughly $7m. However, less than $0.2m is actually available to the provincial authorities (Figure 5-4). In the LEDET-run reserves Makuya and Letaba Ranch, the government is entitled to a share of the proceeds from the three lodges, which are otherwise split with the communities and private operators, but the lodges are at the low-end of the market. Hunting proceeds in these reserves are also minimal and go to the communities, making them unavailable for funding management. In the MTPA-run

Manyeleti Nature Reserve, the private concessions generate a majority of the revenue, but the community owners, not MTPA, are entitled to the concession fees. There is also no commercial hunting in Manyeleti.

Though the collective wage bill of the provincial reserves, at roughly $1,850/km2 is three times that of the private reserves (~$640/km2), the average number of rangers per km2 is only slightly higher (0.10 compared to 0.08) for the provincial reserves. The inflated wage bill is likely due to high average salaries for the government jobs provided by the provinces in the reserves, which at $11,000 is over 150% of the average ranger salary in Kruger NP (~$6,400). With the high cost structure for labor, the vacancies— some as large as 28% and mostly in ranger positions—are unlikely to be filled. Because staff of these reserves tend to be older, as well, the vacancy rate is likely to soon increase.

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The low level of the income streams to which the provincial authorities have a claim, the small funding allocations of the central offices which are then swallowed up by salaries leaving little for operational support, and the poor infrastructure of the provincial reserves explain why other reserve managers consider them to be the GKNP network’s weakest link. The implications of the performance of the provincial reserves on the viability of the larger system is discussed further at the end of the chapter.

Multiplier Impacts of GKNP Tourist Spending

Discounting. In order to account for the multiplier impacts of GKNP visitor spending, only the portion of money actually introduced into the respective impact region is considered and excluded is the spending by the region’s residents on the assumption that some portion of their spending is attributable to a demand for general recreation, not necessarily for visiting the GKNP, and would have entered the economy regardless of the opportunity to visit the GKNP13. That is, values expressed as impacts account for the “additionality” of the GKNP on provincial and national level economic multiplier effects. After discounting local residents, the total amount of money introduced into the two local provinces by visitors to KNP was estimated at $121.8m (Table 5-8).

To consider impacts to the country as a whole of KNP visitation, the spending by South

African residents was discounted, leaving a remainder of $92.7m. For spending related to the contiguous reserves (both private and provincial), no discounting was performed at the provincial level on the spending of $190.5m on the educated assumption that only a minimal amount is spent by residents of the local provinces in visiting these reserves.

13 Though survey respondents included residents who were asked to report the likelihood that they would not have spent an equivalent amount of money elsewhere in the region, responses to this question were not always reliable. The most conservative approach, of fully discounting residents’ spending, was therefore chosen instead of using alternative discount factors.

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At the national level, discounting produced an estimate of $159.7m as the amount introduced to the country by visitors to the contiguous reserves.

Total impacts. Through backwards linkages of the supply chain and through the induced spending of wage-earners, the impacts of the GKNP on the national economy were over 21,000 jobs, approximately $226m of personal income, $60m in taxes, and

$79m in profit (Table 5-8). In other words, the value of GDP arising from additional money ($252m) brought into South Africa by non-resident tourists for spending at the

GKNP was $364m (Figure 5-5), or approximately 0.1% of national GDP. The mostly private contiguous reserves are responsible for 67.8% of this impact to GDP.

Comparisons may also be made with likely alternative land uses in the local region on the basis of total income and GDP impact multipliers (Table F-4). On the whole, the GKNP achieves a higher total impact ratio than commercial livestock, but not mineral ore mining. However, the more labor intensive contiguous reserves outcompete both sectors in terms of both ratios.

Total contributions. By including the spending of South African residents who visited the GKNP, the GDP estimate no longer represents the change in GDP that

GKNP tourism is responsible for, but it is useful for comparisons with conventional GDP estimates of other sectors. The total national level GDP contribution of GKNP tourism was $641m, or 0.2% of the national economy14. The mostly private contiguous reserves are responsible for 56.2% of this contribution to GDP.

14 The proper interpretation of this figure is that 0.2% of South African economic production was related to GKNP tourism, though not necessarily caused by it.

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The contribution of the GKNP to provincial-level GRP was approximately $335m.

The contiguous reserves are responsible for approximately 63% of this contribution to

GRP.

Comparisons with previous studies. Few other studies of the economic impacts of tourism in the Kruger area are generally comparable and none are directly comparable to the present study. From Saayman et al.’s (2012) estimate of spending by

KNP visitors during the 2008/2009 financial year ($82m) and Turpie & Joubert’s (2001) estimate in 1999/2000 ($19.1m), local spending by national park visitors appears to have multiplied by at least 144% in the last 8 years and by 754% in the last 17 years to its present value of $144m15.

Only Saayman et al. (2012) have applied multiplier analysis to estimate the total effects of visitor spending on the economy. However, the scope of spending considered by that study was more limited and several inconsistencies bring into question the validity of some of the results. Spending by day visitors on accommodation outside of the park is a significant source of impacts (16% of total provincial-level spending in the present study), but was not included in this previous impact study. Nor was non-local spending on transportation, fuel, and commissions considered. Except for the lease fees paid by the private concessions to SANParks, the spending of concession visitors was also excluded. These last two categories were significant in the present study (16% and 23%, respectively, as percentages of provincial-level spending). Although results

15 A current measure of direct, on-site spending more consistent with the scope of Saayman et al.’s study is $118m.

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were described as economic “impacts”, the spending of local residents was not excluded and the results are properly interpreted as economic contributions.

More importantly, the study also appears to conflate output, or “production”, with

GDP, though as output includes the value of intermediate inputs, much of output is actually leakage from an economy. It is also not possible for direct output ($97m as estimated for the Limpopo-Mpumalanga region) to exceed visitor spending ($82m).

Their estimate of the regional-level contribution to GDP from total effects ($220) therefore appears to actually be an estimate of regional-level total effects output. This production estimate when reported as a percentage of regional-level GDP (“1.5%”), also appears to be based, incorrectly, on a value of 6.8% for the total GDP contribution of the economies of both provinces to the country as a whole, when they intended to use a value of 13.6%, which would have yielded an estimate of 0.75% for output as a percentage of regional GDP (as compared to $130m or 0.30% in the present study).

The estimate for total income effects ($116m) also appears to have been incorrectly calculated, by applying the total income multiplier to total output, instead of direct output. Applied to direct output, their estimate for total income should have been $54m.

A comparable estimate from the present study of total regional-level income resulting from spending by KNP visitors is $67m. Finally, it appears that income distribution and employment estimates were erroneously based on a misuse of sector-specific output multipliers, which are properly interpreted as yielding economy-wide output estimates from sector-specific spending, not sector-specific output estimates. The compounding effect of these inconsistencies renders their results unreliable and likely explains the much lower income and GDP values of the present study by comparison.

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Discussion

Return on Investment

The investment of approximately $45m by SANParks into KNP for non-tourism management16 expenses and the investment into the same of nearly $8m by the private and provincial reserves of the contiguous area collectively enables a return of $641m in total GDP contributions (Figures 5-5 & 5-6), for a ratio of over 12 to 1. Even prior to the multiplier effect, out of the $171m in direct GDP contributions, the $68m in direct and indirect taxes (Table 5-7) arising from GKNP tourism is more than enough to cover the combined costs of management for the entire system. Once multiplier effects are considered, even the impact-only portion--that which may be considered additional to what otherwise would have been contributed—yielded a tax sum in excess of KNP’s non-tourism budget, at $48m. Finally, the tax contribution at the level of total effects, being in excess of $80m, means that the funding provided by SANParks to manage the core of this economic engine pays for itself more than 1.75 times over in government revenue.

In terms of jobs, for every visible employment position in the GKNP, another 0.63 jobs are supported within the local region and 1.39 jobs nationally17. The income that accrues to these 21,627 jobs at the national level ($226.5m) works out to $10,473 a year. This salary is below the 2016 national average of $15,802 (Statistics South Africa,

16 The budget for non-tourism management was estimated from the breakdown of projected costs for the 2018/2019 year (SANParks, 2018) and the actual aggregated expenditure total for the 2016/2017 year (SANParks, 2017). Tourism-related expenses of SANParks are excluded to allow for a direct comparison with the management expenses of the private reserves. Including tourism spending by SANParks, the total budget for KNP is roughly $63m.

17 The number of jobs to which economic activity in the GKNP contributes, as opposed to supports from “additional” spending, is likely greater than 2.0 for every direct job.

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2017), but many of these jobs are supported in regions of high unemployment.

Accounting for the roughly 21,000 dependents of the 9,091 directly employed alone, over 1% of the population of 2.7m people living within 50km of the GKNP is supported by the $68m of direct wages.

From Figures 5-2, C, and 5-6, it is clear that the private reserves are responsible for the majority of these economic effects. On just 12% of the total land area they generate 56% of total GDP contributions and 68% of total GDP impacts18. Yet their relationship with the national park is a symbiotic one and demonstrative of the economic complementarity that public-private PA mosaics can achieve. The commercial success of the private reserves owes much to the scale and branding power of KNP. The national park, in ensuring equitable and affordable access to nature, is also key in building a popular constituency, not only for KNP, but for South African PAs in general

(Dlamini, 2012). While KNP anchors the system politically, projects a widely recognized brand internationally, and, through SANParks, is responsible for managing critical non- market ecosystem services, positive externalities also flow the other way as well. The high-end, high-density tourism outlay of the private reserves is more effective at using the market to capture the global demand for the GKNP and convert this value to local economic benefits. Through the addition of 2,786km2 to the scale of the system, costs of ecological management are also reduced while ecological resilience is enhanced.

18 The 14% difference being due to the mostly foreign market that the private reserves cater to and the foreign exchange that is brought in as a result.

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Governance for Sustainability

As global demand for nature-based tourism rises and visitation increases, the scale of the GKNP economy will also grow. Opportunities for growth include the incorporation of currently fenced-out neighboring reserves, the inclusion of neighboring communal land, the provision of additional tourism infrastructure and accommodation within the present system, and upgrading of current accommodation. The former two options would be favorable, regardless, from an ecological and cost-savings perspective, but socio-ecological resilience requires that the scale of institutions matches the scale of the underlying resources (Cumming et al., 2015). The sustainability and equitability of the value generated in the process of scaling up the cultural and economic resource that is the GKNP will largely depend on how the system and its constituent reserves are governed. Though South Africa’s legislative and regulatory formula for land and resource tenure has proven successful for encouraging the adoption of economically competitive land uses such as wildlife-based tourism, in the underperformance of the GKNP’s provincial reserves and contractual park can be seen serious mismatches in the scale of governance institutions where communities have a large stake in the economic outcomes. At the system scale as well, the presence of only thin governance institutions between PAs raises questions about collective agency in response to large scale threats to the economic engines that are protected areas.

The provincial reserves. Contributing to the low performance of the provincial reserves, which have failed to become financially self-sustaining, is the centralized structure of the governing entities to which they belong. The two reserves of Limpopo

Province, in particular, are undercapitalized and receive minimal support for

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management operations. Lacking road and other infrastructure on par with competing reserves, tourism is marketed only towards the low-end. Other governance issues compound the lack of investment. Perhaps no institution in South Africa exists that is capable of competently and efficiently resolving the complicated disputes over reserve land claimed by multiple traditional authorities, but the facilitation of resolution in these conflicts over land rights is not even within the remit of a PA agency. In this regard, PA governance and joint-governance must be understood against the background of a larger political environment in which these processes are embedded (Nelson & Agrawal,

2008). Until these disputes are settled, LEDET’s reserves in the GKNP will remain less attractive to private investors. Another challenge related to land claims is that they entail claims on derivative revenues from commercial activities undertaken on the land. None of the concessionaire fees from Manyeleti NR and none of the trophy fees from hunting in Makuya and Letaba Ranch NRs currently feed back into management (Figure 5-4), going instead to community claimants. Ultimately it is at the discretion of the claimants whether any of the revenue will be re-invested into the reserves, though given the unmet need for major capital improvements it is unlikely that future returns on revenue re-invested would make up for income foregone in the present.

Short of a major commitment by LEDET to support Makuya and Letaba Ranch

NRs, the viability of these reserves and the community benefits that flow from them may possibly be enhanced by converting them into contract parks, leased to SANParks. This arrangement could potentially increase the area accessible to tourists in these comparatively small reserves and carry the cost-saving advantage of the economy-of- scale reached by SANParks. The economy-of-scale lies not only in park management

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operations, but also in the balancing of management costs with the need to honor residual claims on commercial income from community-owned land. For the MTPA- managed Manyeleti NR, centralized budgeting precludes incentives for performance- based management and cost savings at the reserve level. Though the community claims on concession leases means that the reserve may never become fully self- sustaining even if costs were to be reduced, the economic benefits of commercial activities at the reserve, including VAT contributions of nearly $1m, presents a compelling case for increased funding support from the national government.

Makuleke Contractual Park. The low occupancy rates of commercial lodges and low absolute and per-capita returns from concessions to the Makuleke community are countervailing realities to the notion of jointly-governed contractual parks as sustainable, positive-sum solutions to conservation and social justice problems. The physical obstacles to tourism growth, discussed above, are compounded by a problematic institutional setting for park governance in which decision-making processes are commonly attended by conflict and mistrust (anonymous CPA member, personal communication, June, 2014). Because of the lack of revenue generated by

MCP and the lack of technical knowledge of ecology and conservation within the CPA, the partnership is viewed by both the CPA and concessionaires as unequal and favoring the interests of SANParks (anonymous lodge manager, personal communication, June,

2014). SANParks, as the implementing agent for management, is therefore able to exercise greater discretion in setting conservation objectives. Furthermore, and owing to a lack of capacity, the CPA is neither able to set clear guidelines for these conservation objectives, nor monitor the performance of SANParks in meeting the objectives. As a

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result, it is believed by both the CPA and concessionaires that current management policies, such as those limiting the road network, unnecessarily sacrifice economic value for conservation value.

The unequal distribution of responsibilities is also cited by the CPA as a factor undermining transparency. The contractual park is managed not as a discrete entity by

SANParks, but rather as a sub-section of the Pafuri unit within KNP, thus obscuring an accounting of the resources invested therein. This management structure has led to suspicions on the part of the CPA and concessionaires of a divestment of resources away from MCP for allocation at the southern end of the national park.

Despite the logistical difficulties inherent in MCP, lodge operators are optimistic about the potential for growth in tourism and consider the remoteness of the region an advantageous tradeoff for exclusivity. The principle physical challenges are not viewed as insurmountable but are thought to require an improved and equitable relationship between the three stakeholder groups, and one built on capacity, trust, and a shared vision. As Adger et al. (2005) point out, the cross-scale linkages of joint-governance networks do not guarantee enhanced system resilience because inequities in power often determine the winners and losers from such interactions. If the transaction costs of obtaining knowledge are prohibitive for the less powerful, the unequal relationship can become locked-in. In the context of MCP, it is critical that all partners in the joint- governance of the park are provided with complete information on the costs of management. As the CPA will soon have the option of terminating the lease with

SANParks and assuming management responsibilities itself, it would be imprudent to act without a full understanding of the burden this option would entail. A more equal

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balance of knowledge would also benefit SANParks, as an argument for restructuring the lease and the commercial model on which MCP is based, if developed from a sound assessment of costs and benefits, would likely achieve greater buy-in from the CPA.

The GKNP system. The development of governance institutions at the system scale has been driven mainly by ecological and security concerns, though even with respect to these issues there is little ability to enforce consistent policies across the different reserves. For example, despite the joint process by which SANParks, the provincial authorities, and the APNR set a system-wide hunting quota (with specific allocations to individual reserves), the power to request hunting permits and the authority to issue them remains, respectively, with the management authorities of the individual reserves and the provincial governments. Poor information sharing between these partner institutions subjects all of them to risks stemming from non-compliance with protocols. Likewise, extension of the western boundary fence carries security risks for the whole system—as demonstrated by the preference of poachers to traverse the relatively under-patrolled provincial reserves in order to gain entry into the GKNP--but extension decisions are the responsibility of individual reserves along the perimeter and are not guided by a protocol. Less acutely, underdeveloped mechanisms to coordinate the social investment by GKNP members risks duplication of efforts and neglect of under-served needs. Institutions to address the latter issue would need to coordinate, as well, with local municipalities whose responsibilities some of these efforts may overlap with. Recently, and in recognition of the need for strengthening system-scale governance, $800,000 has been budgeted by SANParks for the development of cooperative agreements between management entities within the GKNP and the broader

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GLTFCA. If appropriate incentives are developed, it may be possible to match the rate of growth of the wildlife economy with the growth of commensurate governance institutions.

Conclusion

South Africa’s Kruger National Park is an example not only of the financial performance that can be reached in PA management through a decentralized, flat, and organic agency structure, but also of how statutory PAs embedded in landscapes where formal institutions devolve resource rights to local stakeholders can serve as catalysts for the growth of a large and vibrant wildlife economy in highly impoverished areas. The local economic benefits of the system include over 9,000 jobs and $60m in personal income but the less apparent downstream benefits are much larger. Tourism activity in the GKNP ultimately contributes $641m to the national economy by way of the linkages with other industry sectors. Though the scale of wildlife tourism in this system continues to grow on its own, other pressures, including from population growth and from rising security threats also mount. To ensure continued economic performance it is therefore important that budgeting needs are met, where needed, by public funding, and that the socio-ecological resilience of the system is enhanced through the scaling-up of PA governance institutions and through greater transparency in joint-governance partnerships.

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Table 5-1. Description of PAs in the GKNP Protected Area Size Tourism Commercial Type Name Manager Owners (km2) Operators Commercial Tourism Capacity Hunting National Park 8 (1 public 8,461 beds (4,243 public beds, operator, 7 3,840 campsite guests, 104 private wilderness and 4x4 trail concession guests, 274 private Kruger SANParks Govt. of South Africa 19,181 holders) concession beds) No Provincial Mpumalanga Provincial Mpumalanga Govt. (pending 128 public beds + 120 Provincial finalization of land 5 (1 public, 4 campsite guests + 94 private Manyeleti Govt. claim settlement) 231 private) concession beds No Mthimkhulu Tribal Limpopo Authority, Majeje Tribal Letaba Provincial Authority, Limpopo Ranch Govt. Govt. 359 1 26 beds Yes Limpopo Provincial Makuya Tribal Council Makuya Govt. and Royal Family 137 2 26 beds, 78 campsite guests Yes Contract Park Makuleke Communal Property Association Makuleke SANParks (CPA) 231 3 130 beds No Private Reserve Associated Private Nature Reserves (APNR) Own Timbavati management PNR authority 46 692 14 299 beds Yes Own Klaserie management PNR authority 120 588 9 168 beds Yes

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Table 5-1. Continued Protected Area Size Tourism Commercial Type Name Manager Owners (km2) Operators Commercial Tourism Capacity Hunting Federated management structure among 10 Private member 43 (and 2 686 beds (and ~300 shareblock Reserve Balule PNR sections > 100 503 shareblocks) beds) Yes Federated management structure among 3 Umbabat member 3 PNR sections 20 198 shareblocks ~600 beds Yes Own Thornybush management 11 (including one PNR authority community) 142 4 221 beds No Own Sabi Sand management Wildtuin authority 38 493 21 668 beds No MalaMala Own Game management Reserve authority N'wandlamharhi CPA 136 1 70 beds No Mjejane Game Mjejane Trust Reserve SANParks (Lugelane community) 34 9 107 beds No Note: Reported sizes are not official. Size estimates are based on GIS shapefiles. Timbavati PNR is inclusive of adjoining private properties managed by SANParks.

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Figure 5-1. PA types in the GKNP, human population density, and the local impact areas. The local boundary in A) extends from 50km the GKNP and includes 2.7m people. In B) the local area boundary encompasses the two local provinces (10m people), as seen in the context of the entire country of South Africa.

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Table 5-2. Tourism infrastructure and visitation of PAs in the GKNP. Reserve type Provincial Private National Park (Kruger) Reserves Reserves Makuleke Public Contractual facilities Prvt. conc. Park Infrastructure Area in km2 19,181 530 231 (1.0%) 727 (3.2%) 2,786 (% of GKNP) (83.7%) (12.1%) Structured 4,243 274 130 (prvt. 274 (114 in 3,523 (~900 beds conc.) prvt. conc.) in shareblocks) Beds/km2 0.24 0.52 0.56 0.38 1.26 Campsites 657 0 0 33 0 (including 4x4 trail) Road 0.47 - 0.68 1.24 1.81 density (km/km2) Average rate $55 $644 $308 $171 $498 p.p.n. sharing Visitation - Foreigners Chalet b-na 381,360 44,404 6,293 23,605 316,478 Camping b-n 48,619 N/a N/a N/a N/a Day visits 383,722 N/a Unknown Minimal N/a Shareblock N/a N/a N/a N/a Minimal b-n - SA residents Chalet b-n 457,083 18,602 7,939 10,207 128,158 (non-local) Camping b-n 220,212 N/a N/a N/a N/a Day visits 425,469 N/a Unknown Unknown N/a Shareblock N/a N/a N/a N/a 140,351 b-n - SA residents Chalet b-n 108,195 Minimal Minimal Minimal Minimal (local) Camping b-n 141,231 N/a N/a N/a N/a Day visits 615,445 N/a Unknown Unknown N/a Shareblock N/a N/a N/a N/a Minimal b-n aBednights

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Figure 5-2. Tourist lodges and camps, bed density, and financial returns in the GKNP. Panel A) indicates the location of lodges, B) the density of beds, excluding campgrounds, and C) an index of total turnover to tourist lodges and camps.

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Table 5-3. Spending of visitors to KNP. Absolute spending in local area (and at national level) by visitor type and origin, in millions

Day visitors Camping visitors Chalet visitors Concession visitors Resident Resident Resident non- non- non- Foreign and Foreigners locals Locals Foreigners locals Locals Foreigners locals Locals resident Accommod ation $19.54 $3.43 - $0.70 $2.56 $1.67 $16.55 $19.77 $6.22 $30.61 + ($0.76) Restaurant $1.32 $1.93 $2.04 $0.19 $0.58 $0.33 $2.09 $2.56 $0.72 - $0.37 + $1.02 + Groceries $0.15 $1.17 - $0.21 ($0.74) $0.56 $1.51 ($2.04) $0.76 $0.01 Transfers $0.83 $0.90 - $0.11 $0.21 $0.79 $3.85 $0.65 $0.85 ($5.46) Activities $3.50 - - $0.10 $0.19 $0.15 $1.64 $0.90 $0.16 - Souvenirs $0.39 $0.30 $0.80 $0.01 $0.24 $0.13 $1.23 $0.82 $0.21 $0.33 Misc. $0.01 $0.09 - $0.23 $0.42 $0.24 $1.14 $0.77 $0.28 $0.01 Tips $0.36 $0.28 - $0.03 $0.06 $0.03 $0.37 $0.36 $0.09 $1.69 Fuel ($1.02) ($3.12) ($3.80) ($0.39) ($1.49) ($0.79) ($2.48) ($3.59) ($0.53) ($0.14) $26.12 + $8.09 + $2.84 + $1.57 + $4.64 + $3.91 + $28.37 + $26.85 + $9.30 + Total ($1.02) ($3.12) ($3.80) ($0.39) ($2.43) ($0.79) ($2.48) ($5.63) ($0.53) $32.65 + ($6.36) Person- days or 383,722 425,469 615,445 48,619 220,212 141,231 381,360 457,083 108,195 80,238 bed-nights

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Table 5-4. Total expenditures of visitors to the private and provincial reserves Absolute spending in local area (and at national level, outside local area) by reserve type, in millions Private reserves Provincial reserves Lodge $172.11 + ($7.23) $8.44 + ($1.02) Groceries $0.23 + ($0.23) $0.01 Transfers ($25.79) ($1.30) Misc. $0.09 + ($0.01) $0.01 Tips $9.17 + ($0.43) $0.46 + ($0.02) Fuel ($1.36) ($0.07) Total $181.62 + ($35.05) $8.92 + ($2.41) Bed-nights 584,987 40 – 60,000

Table 5-5. Employment and wages in the GKNP Jobs Wages (millions) Unskilled & Managers Skilled semi-skilled Total jobs KNP SANParks 107 234 1,902 2,243 $20.3 Concessions 1,163 ~$3.4 Makuleke ~140 ~$0.7 CP Private Tourism 508 810 3,679 4,997 ~$33 reserves Reserve management 402 $1.5 Provincial LEDET & 6 8 32 46 - reserves MTPA tourism Concessions 11 13 76 100 $0.6 Reserve management 97 $1.5 Total 9,091 $61

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Table 5-6. Tourism employee characteristics, income, and spending Tips as Proportion with proportion Avg. Post- Proportion listed of total Proportion deduction No. Of from local educational cash Proportion spent on Proportion Household monthly wage respondents area experience income saved food remitted dependents plus tips Unskilled 25 .94 0.78 (less than 0.09 0.07 0.44 0.09 4.8 $ 262 secondary) Semi-skilled 37 .94 0.23 (less than 0.14 0.11 0.39 0.15 6.0 $ 297 secondary) Skilled 36 .56 0.80 (tertiary) 0.27 0.25 0.18 0.19 2.3 $ 897 Managers - .20 ------$ 1,729

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Table 5-7. Tax contributions arising from GKNP tourism Tax type Amount paid VAT $56m Employment taxes $5m Corporate tax $7m Total $68m

Figure 5-3. Total management level income, spending, and spending trends across the private reserves

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Figure 5-4. Index of per-area revenue generation potential from non-consumptive tourism. Calculated as the average bednight rate multiplied by bed capacity and divided by area.

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Table 5-8. Direct spending and multiplier impacts of GKNP tourism. GKNP Kruger NP Contiguous reserves Direct + Direct + Impact Direct Indirect Total Direct Indirect Total Total region Indicator effects effects effects effects effects effects effects Provincial Spend $121.8m $190.5m $312.3m Income $30.9m $44.9m $63.2m $49.2m $70.4m $99.2m $162.4m Taxes $11.6m $15.6m $18.3m $34.3m $37.5m $51.3m $69.6 Profit $8.4m $14.4m $23.9m $28.7m $32.4m $59.2m $83.1m Jobs 3,704 4,680 6,696 5,282 6,651 8,102 14,798

National Spend $92.7m $159.7m $245.6m Income $19.9m $39.1m $76.2m $37.7m $74.8m $150.3m $226.5m Taxes $9.5m $10.7m $14.4m $21.7m $26.2m $33.7m $48.1m Profit $10.2m $16.9m $26.5m $18.1m $43.5m $62.7m $89.2m Jobs 1,744 4,585 9,176 5,288 8,602 12,451 21,627 GDP $39.6m $66.7m $117.1m $77.5m $144.5m $246.7m $363.8m

Figure 5-5. Total GDP contributions and impacts of the GKNP in relation to spend on (non-tourism) PA management and visitor spending.

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Figure 5-6. Comparison of the dual economies of the GKNP.

Note: National level GDP impact estimates are exclusive of the provincial level region. Also, gross regional product at the provincial level is inclusive of spending by non-local South African residents, whose spending is excluded from national level GDP estimations, and for that reason the levels of the pyramids are not additive.

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CHAPTER 6 CONCLUSION

The goals of this research were to account for the institutional and governance characteristics that promote or constrain the economic value generated by PA systems in southern Africa and to develop scale-appropriate methods for measuring the economic value of a PA system in the absence of existing methods. Both goals contribute to the identified need to develop an evidence base for the socio-economic impacts of PAs on human populations (Ferraro & Pressey, 2015; Pullin et al., 2014). An important supporting objective was the production of empirical data on the impacts of two large, commercially successful national parks and a high-value network of private nature reserves.

For PAs that rely on tourism, the major source of positive impacts is expected to be personal income stemming from market transactions involving tourists. Under the socio-ecological-systems framework and resilience paradigm, these economic impacts can be understood as potential top-down drivers of landscape change (Cumming et al.,

2015). The economic drivers can be slow and insidious, as in the case of an expanding business economy feeding off of tourism money, fast and volatile, as when irrational fear over a distant and unrelated disease epidemic causes tourist arrivals to drop, or abrupt, as in the case of a ban on hunting. The drivers can also be modulated by the distribution of the rights to, and benefits from resources, and by the structure for assigning roles and responsibilities between stakeholders in the governance of the system. Yet even where PA authorities track wildlife populations, ecological parameters, and other indicators of management performance, local economic impacts are rarely monitored. Local, informal knowledge of such impacts may serve for purposes of

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planning and management when fiscal governance and a high degree of responsibility are devolved to individual PAs, but authority over statutory PAs is much more commonly centralized at a network level. The failure, then, to track and report impacts following a standardized approach limits the scope of adaptive management at the PA level and leaves officials at the policy level with an incomplete picture of economic performance. Both outcomes are likely to reduce system resilience.

Recognizing that PA managers from developing countries often have scarce resources at their disposal and may not fully understand nor feel comfortable with economic concepts and methods, the bottom-up approach to EIA developed for SLNP was designed for the non-expert and the “How-to” guide (Appendix B) supplies the methodological framework and tools to carry out impact analysis at a user-defined scale. The ultimate test of the feasibility of this approach will be its actual adoption by

PA managers, but the results from the SLNP case study have already been used to help ground-truth a less thorough spreadsheet model (Souza et al., 2018) which can be applied to similar PA settings when high precision is not necessary.

Both the bottom-up approach and the model-based approach, when applied purposively, as in this study, or if adopted widely, have the potential to shed light on factors contributing to, or undermining PA economic performance when comparisons of results are made between PAs and PA systems. However, and in contrast with study designs that aim to determine the net impacts of PAs on the well-being of local populations through carefully selected matched control groups (Ferraro & Pressey,

2015), EIA can only quantify gross impacts, and from only a single pathway (tourist

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spending)1. That is, the counterfactual condition of this cross-sectional study was not the absence of a PA, but the context in which PAs are situated, and the explanatory power of results rests not on statistical analysis, but on the ability to identify causal mechanisms and rule out alternative explanations. This approach complements the highly rigorous research program contemplated by Pullin et al. (2014) to evaluate the net socio-economic impacts of PAs because the narrow focus on economic value from market transactions and the specific framing of governance and institutions around this value (Figure 2-2) provides greater resolution and analytical depth (i.e. elucidating root institutional causes) on what is often the most significant positive-impact pathway. For instance, a PA network may be revealed through matched comparisons to have little to no impact on local populations, but an EIA may point to areas of spending leakage and cross-sectional comparisons of EIAs may suggest important structural differences in the corporate governance models of PA agencies which underpin differences in economic performance.

From this study, a side-by side comparison of the two PA systems and the four sub-system landscapes (Figure 6-1) offers a compelling example of not only the divergence between the economic impact of a national park and its surrounding wildlife areas, but how centralized institutions of property can actually invert the economic relationship between a public park and its buffer areas. The degree and direction of the divergence in the two systems is all-the-more striking considering the physical size of

1 To account for other impact pathways as well as the costs imposed by PAs on local populations, EIA can be integrated, qualitatively, with analyses of livelihoods and governance, though a counterfactual population would still be needed to confirm attribution of effects to the PA.

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the buffer areas relative to the two national parks—GMAs are 185% of the size of SLNP while the contiguous reserves of the GKNP are just 18% of the size of KNP.

There are a number of factors that account for the absolute disparity between the two PA systems2, to be sure, but the difference in absolute magnitude of the economic impacts in the two countries is not what lends salience to the comparison. Both PA systems contain at their core commercially successful public parks with firm statutory grounding which are surrounded by wildlife reserves where land use decisions are exposed to market forces. In South Africa, where landowners retain full returns from natural resources, the wildlife economy on private land has outgrown the economy of the national park. In Zambia, centralized ownership of natural resources and high taxation of resource-use mediates market forces to effectively limit the size of the wildlife economy outside of the national park. The most meaningful non-institutional factor in the comparison is perhaps the presence of significant human populations within the unfenced Zambian GMAs, whereas the fenced, private reserves of the GKNP have a low human footprint. However, large expanses of prime, undisturbed habitat still exist in the GMAs and the commercial success of unfenced conservancies elsewhere in

Africa (e.g. Namibia [Naidoo et al., 2016] and in Botswana prior to a hunting ban

[Mbaiwa, 2018]) suggests that these differences are less important than the institutional factors.

Comparisons between PA types within the two systems is aided by Figure 6-2, showing direct personal income per area, and Figure 6-3, which describes the

2 Tourism is disadvantaged in Zambia vis-à-vis South Africa because Zambia is more remote, landlocked, lacks the branding power of the Kruger system, has lower domestic demand for tourism, lacks the “big 5” safari species, and is possibly perceived as offering a less stable investment environment.

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institutional and governance characteristics of each PA category and color-codes them according to their degree of congruency or incongruency with the normative logic of property rights and governance theories. The institutional context of the private reserves of the GKNP, being most aligned with the theoretically optimal configuration of rights and governance attributes, enables the highest degree of performance among all PA categories. Though the high efficiency at which the reserves operate partially exceeds the capacity of the local labor market to supply higher-skilled workers, the local income generated, alone, is greater than other PAs.

Income generated from Makuleke CP is largely from employment positions with concessionaires, supplemented by lease fees paid to the CPA. For that reason, it ranks ahead of the mostly public KNP in terms of income per km2. However, a more apt comparison is with the private reserves, against which it falls far behind. The potential of

MCP to generate higher personal and community income streams has yet to be proven

(or disproven) but the balance of power in the joint governance relationship favors the preferences of SANParks--through no fault of its own--in its application of the precautionary principle to tourism development in what is the most biodiverse region of

KNP. Lacking expertise and access to technical information on SANParks’ management of MCP—which, due to the structure of SANParks’ management operations and accounting practices, may not yet exist in an appropriately disaggregated form--the CPA representatives on the joint governance board face steep transaction costs in attempting to monitor and evaluate the performance of the concessionaires and

SANParks, as well as the overall economic performance of MCP. For this reason, essential transactions are not carried out and a high level of uncertainty has raised the

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risks ahead of an opportunity to adapt the business model of the CP. Likewise, the three provincial reserves also face challenges stemming from the restoration of land rights— only Manyeleti NR generates significant income. These challenges complicate the marketing of concessions and are compounded by a high degree of centralization of revenue and power within the structures of the provincial PA authorities.

The economic performance of KNP is limited only by the conservation priorities of SANParks and by the need of a national park to balance commercial competitiveness with provision of equitable access to the public. While the sale of concessions within the park has been open and free, no new concessions have been sold since 2002.

Transaction costs of governing KNP are high, partly due to the bureaucratic structure of

SANParks, but transactions support high quality management. At SLNP, a similarly devolved governance structure is principally responsible for the financial viability of the park, but in contrast with KNP’s commercial aspect, SLNP continues to expand opportunities for investment with an open and competitive concession bidding process.

Of all the PA types considered, wildlife production is most constrained in

Zambia’s GMAs. There, lack of title to land permits the pre-emption of resource governance by independent CBOs with narrowly defined, inflexible, and incapacitated

CRBs. Without legal personality, and sanctioned only through the Wildlife Act, CRBs are beholden to DNPW and automatically forfeit both leverage over the marketing of community concessions and the ability to exclude outsiders from settling or hunting on the land. The centralization of wildlife ownership not only allows wildlife-use to be heavily taxed, but disempowers communities which are otherwise prepared to invest,

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alongside hunting outfitters, in the management of the resource. Private game ranches, too, face these taxes, but are less constrained in marketing wildlife.

The empirical contributions of this research include estimates of economic value from a diverse range of PAs at varying levels of performance. The impact analysis for

SLNP was the first such analysis for any NP in Zambia and provides a gauge of the level of economic performance ($38.2m in GDP) that can be achieved with a low to moderate level of management funding ($2.7m). The results from the local level of analysis ($14.8m in GDP and $6.8 in personal income) will help to establish rules of thumb for similar parks when extensive surveying is not possible--as a proportion of locally captured spending, total local value added and income were 0.59 and 0.27, respectively, and though local indirect and induced effects were minimal in terms of local direct spending, an additional 375 jobs (almost a third of direct level jobs) were supported from local downstream effects of tourist spending.

On communal land outside of SLNP, this study’s finding of low wildlife benefits for communities ($31/km2) aligns with previous assessments of GMAs (Bandyopadhyay

& Tembo, 2009; Lindsey et al., 2014), but the detailed accounting of monetary flows from a defined system also provides a useful baseline for future evaluations and a set of values to which alternative models of community-wildlife governance can be compared.

For example, returns to the 17 oldest communal conservancies in Namibia in 2013

($80/km2 [Naidoo et al., 2016]) were more than double the average returns to the

Luangwa Valley GMAs. Unfortunately, insufficient data precluded similar depth of analysis for the private game ranches of the Luangwa Valley. In South Africa, an impact analysis of KNP produced an estimate of the national value of South Africa’s largest NP

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($281m in GDP contributions) and updated an estimate of its local value to the neighboring provinces ($105m in GRP impact). For the first time the total impacts of a mostly private network of reserves were also analyzed. On what is likely the highest value private wildlife land in the world, the network of reserves neighboring KNP contributed $360m to national GDP.

The implications of this research extend well beyond the two case study landscapes in Zambia and South Africa. Laws and rules pertaining to wildlife as well as resource governance regimes for communal areas are widely varying throughout the continent. They are also constantly evolving, spurring the development of innovative legal instruments intended to improve local benefits from wild resources. Powerful rights-holders often work, however, to maintain the status quo, and de jure reform may fail to translate into substantial improvements in governing capacity or economic returns from these resources. Low-performance of PAs may sometimes be accounted for by environmental characteristics or lack of physical access to markets, but where comparisons of management units in close proximity to each other reveals high variation in performance, institutional factors are more likely implicated. Other opportunities for identifying these institutional constraints by studying patterns of economic performance across mosaics of different PA types are numerous in southern

Africa alone, and such studies can easily be aligned with efforts to build up the empirical data on the impacts of government-run PAs. Yet where the institutional constraints are already known—particularly for community-managed lands—innovation may be stymied by the high ex ante transaction costs of negotiating new sets of rights between stakeholders. Therefore, it will also be important to develop an understanding of the

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social, economic, and ecological conditions of landscapes that give investment in institutional reform the greatest chance of success.

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Figure 6-1. Comparative economic impacts and contributions of the four sub-system PA networks in the two study regions.

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Figure 6-2. Direct personal and community income per km2 from PA types in the two study regions.

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Figure 6-3. Institutional and governance characteristics of PA types in the two study regions. Shading intensity indicates degree of congruence (blue) or incongruence (red) with the normative logic of property rights and governance theories in the context of PAs.

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APPENDIX A EXPLANATION OF SAM METHODOLOGY

The SAMs used in this study (Chikuba et al., 2013; IFPRI, 2014; Seventer et al.,

2012; PROVIDE, 2006) took the basic form shown in Figure A-1. Each cell of the matrix represents a payment made by the column account to the row account. Expressed in the local currency, the SAM shows how much output was required from the sectors in each row to produce the total output of each column sector in a single year. What is of importance in an EIA is to find how much total output (inclusive of indirect and induced effects) is required for a given amount of direct output (i.e. exogenous, consumer, or

“final” demand). A SAM does not immediately indicate these multipliers because of the interdependencies between “activity” (i.e. industry) sectors. Multipliers can be derived through matrix algebra starting from Equation A-1, where X is a matrix containing the total outputs of each activity sector, M is a coefficient matrix in which each element of the SAM is standardized by its respective column total (and represents the proportion of output that is consumed in the production process alone), and E is a matrix containing the final demand for each commodity sector. This equation is read logically as saying that of an economy’s total output, what is left over for end-consumers to purchase is net of intermediate outputs consumed in production. To solve for output matrix X, it is factored out (Equation A-2, where I is the identity matrix), before multiplying both sides by the inverse of (I – M) to yield Equation A-3. If the diagonal elements of the commodity-commodity section of final demand (E) are set to 1 and all other elements set to 0, then the output matrix (X) will represent a set of multipliers (output, income, and other value added components) for each sector of the economy. If the final demand

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matrix is instead populated with values for final demand as obtained through a survey, then the output matrix will represent actual total outputs given that demand profile.

Because EIAs are intended to model the impacts on elastic sectors of the economy only, the exogenous demand columns of the coefficient matrix (M) were set to

0. These columns include demand from savings and investments, government transfers, and tax revenue, all of which are assumed not to dynamically interact with output in the short term. To model direct and indirect effects only, induced effects were excluded by setting the household demand columns of the coefficient matrix to 0.

Figure A-1. The basic structure of the social accounting matrices used in this study. Adapted from Breisinger et al. (2009).

X – MX = E (A-1)

X(I – M) = E (A-2)

X = (I – M)-1 * E (A-3)

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APPENDIX B A HOW-TO GUIDE FOR IMPROVING SOCIO-ECONOMIC ANALYSIS OF PARK IMPACTS: AN APPROACH FROM THE BOTTOM-UP

Background

Recognizing a dearth of empirical evidence on the socio-economic impact of PAs on local human populations (Pullin et al., 2014) and the inadequacy of existing PA management evaluation tools to assess socio-economic outcomes of supported projects, the GEF funded a workshop at SLNP in 2016 in order to develop simple evaluation protocols and instruction manuals for tracking these indicators of project success. This chapter presents one part of a manual on evaluating economic impacts, which is intended for use by managers of GEF projects and PA managers themselves.

The manual is titled Economic Impacts of Visitor Spending in Protected Areas in

Developing Countries: A Tourism Economic Model for Protected Areas (TEMPA). A major focus of the manual was the use of a spreadsheet tool (TEMPA) for estimating spending, wage income, value added, and jobs from tourism. However, the spreadsheet tool requires relatively few data inputs, describing tourists only, and its outputs are correspondingly general and highly approximated. The advantage of a more “bottom- up” approach through which data is collected from multiple tiers of a value chain is in greater accuracy and detail of results. Though the principle of conducting multiple spending surveys in iterative fashion to trace the flow of money out of an impact zone is not new (Vaughan et al., 2000) and has been used in research on park-economic impacts (Saayman et al., 2009), the approach outlined below simplifies and adapts the process to the context of management, in which an academic exercise yielding a few high level indicators may be seen as a costly and unnecessary allocation of scarce monitoring capacity unless accompanied by information rich enough to act on locally. It

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is an approach that was developed and tested in collaboration with park managers representing four different African countries who participated in the workshop, includes a broad range of business-related indicators, is capable of capturing historical trends, and requires no expertise or experience in economics research on the part of the user.

A Guide to Conducting a Business Survey

Why Conduct a Business Survey?

There are three major reasons for conducting a survey of businesses near a park. The first two reasons strengthen, complement, and cross-check the spreadsheet multiplier model (TEMPA) described in the main text. The third reason is to enrich the understanding of how local businesses have responded to the park.

1. To enhance the accuracy of the TEMPA spreadsheet model: A business survey can reduce reliance on the assumptions of the TEMPA model. In particular, the direct level of effects of tourist spending as revealed by a business survey can substitute for the default model parameters or results. The business survey can also improve the accuracy of tourist spending data entered in the model, especially for packaged tours where the tourists themselves are unsure of how exactly their money was spent locally.

2. To substitute for the TEMPA spreadsheet model: At the most detailed level of analysis, a business survey can be used to trace the path of tourism money from the time when it enters the local region until it leaves the local region, counting each transaction along the way. The TEMPA spreadsheet model implicitly assumes a default path of money. Constructing this path independently from surveys is time consuming and mentally demanding. However, substituting the spreadsheet model with survey-based analysis does increase the reliability of results. Practical challenges limit this method of analysis to the local direct and indirect level of effects, but for small local regions the induced effects are often minimal.

3. To reveal patterns of business growth in response to park tourism: Multiplier analysis, on which the TEMPA model is based, captures the quantitative magnitude of the park’s economic impact. However, it cannot tell the story of this impact and how it has changed over time. A business survey can paint a rich picture of the local context and help to create a narrative of how the supply of goods and services has historically evolved in response to the level of park tourism (Figure 4-1). The relationship might seem to be a common sense one, but the survey will translate this local knowledge into a form readily

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understandable by outsiders. It can also complement a presentation of multiplier effects by addressing common concerns about the distribution of benefits in a growing economy. Thus, a business survey can describe the makeup of the workforce in terms of employee origins and skill level, provide an overview of the local business economy and its entry opportunities or challenges, assess the level of social responsibility of tourism enterprises, and much more.

What Businesses Should be Included in a Survey?

There are, generally speaking, three tiers of businesses in a local tourism economy, just as there are three levels of multiplier effects:

1. Tourism businesses, which initially receive the tourism money, occupy the first tier

2. Supply chain businesses occupy the second.

3. The third tier is made up of all other businesses in the region.

The third tier is not directly linked to tourism, but benefits from the spending of wages by workers in the first two tiers with effects that may be equally as significant.

This tier, importantly, includes informal businesses as well, such as vegetable stalls and independent taxi drivers.

Tourism businesses must be included in a survey if the aim is to increase the reliability of estimated multiplier effects (reasons 1 & 2 above). Supply chain businesses may also be included to improve multiplier analysis, though this is not absolutely necessary (more discussion below). The main reason to survey tier three businesses relates to the value of demonstrating the more diffuse park-induced growth of the local economy (reason 1 above). Because fuel sales are typically high but have little local impact, fuel stations may be excluded from the business survey.

What to Consider before Undertaking a Business Survey

Responsiveness of tourism operators: Some of the most important questions in a business survey are of a financial and confidential nature. Unless operators can trust that the data they provide will remain confidential and/or anonymous, it is unlikely they

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will participate. For this reason it is suggested that the local community of tourism operators be contacted through an industry representative first, for a chance to get their feedback and support in undertaking the survey. If approached carefully, tourism business will see that an economic survey is in their interests, and may even become a primary “client” for the survey.

Time: A full survey of tier 1 businesses may take as much as an hour to complete per respondent and tourism operators are often very busy. Though an e-mail based survey may sound like a convenient and good idea, e-mails are easy to ignore and the response rate is likely to be higher when the survey is conducted over the phone or in person. These interviews will need to be scheduled and, depending on the sample size, it may take a month or longer to finish a survey of a park with twenty or thirty tourism businesses. Tier 3 businesses are likely to be much more numerous than the first two tiers and will likely call for a random sampling procedure. To count the number and types of local businesses, we suggest contacting local business leaders or organizations, such as a chamber of commerce, or undertaking a quick scoping trip by walking or driving through the region. For help determining sample sizes refer to https://www.surveymonkey.com/mp/sample-size-calculator/. Keep in mind that surveys of tier 3 businesses, fortunately, can take as little as 5-10 minutes per respondent.

How to segment tourism businesses: Like tourists, tourism businesses may need to be divided into various segments in order to extrapolate results to the entire population of tourism businesses. Some ways of segmenting businesses are obvious. Lodges, restaurants, and souvenir shops should all be sampled in separate segments. But even within business types, segmentation may be necessary across price ranges, from budget to luxury market businesses. If segmentation of the tourist survey was based on price, then segmenting the tourism business survey in the same way will simplify analysis.

How to segment general businesses (Tiers 2 and 3): Local businesses may vary in size (eg. annual sales or the number of employees), whether they sell goods or services (e.g. clothing retailers, hair dressers, etc.), or other factors. After a scoping trip you should have a sense of the distribution of businesses across these categories and segment accordingly. For example, a segment for small-scale services could be defined as service businesses that have no more than 2 workers. These smaller businesses are often operated by the relatively poor.

The Elements of a Business Survey

Beyond certain basic questions, the elements of a survey will vary according to the purpose of the survey and the tier of businesses targeted. The elements of a survey targeting businesses in Tiers 2 and Tiers 3 will be discussed first. The more detailed

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tourism business survey (Tier 3) and its elements are elaborated further below with reference to a sample survey questionnaire in Appendix C.

Businesses in tier 1 (tourism businesses):

Please refer to Appendix C for a sample tourism business survey and the associated spreadsheets 2a and 2b of Object B-1. Its sections are elaborated below.

Object B-1. Excel workbook containing sample data entry and analysis forms (.xlsx file 23KB)

Guest demographics. One of the most important pieces of information to the overall study is the number of park visitors. However, if this is unknown or unreliable, because a significant number of visitors enter the park unobserved through unmanned gates, then a better estimate of their number can be drawn from local places of accommodation. The number of bed-nights sold, combined with assumptions about the proportion of guests who came for the park, as opposed to other reasons, can be used to estimate visitor entries. At the very least, this provides a second set of data to cross- check park entry visitor statistics. Additional relevant information relates to the origins of guests (domestic or foreign, local or non-local), mode of transport to the park, and method of sale (e.g. direct or through a tour operator/travel agent). Though this information may also be collected through a tourist survey, it may be difficult to ensure that the sample of tourists is representative with respect to the above variables.

Employees. Relevant information includes the number of employees by origin and skill level. For example, the survey may reveal that most management positions are taken by non-locals or may demonstrate the opposite. How skill level is defined may vary, but it is generally based on the minimal educational qualifications for different jobs.

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Expenditures. Expenditures include fixed and variable operating costs. Capital expenditures, which include spending on construction or renovation or the purchase of major equipment or vehicles, are generally not included in economic impact studies. In other words, this section refers to all routine business expenses over the course of a year. The amount of wages paid is most important and ideally will be reported per skill level category. Other than wages, the total amount spent on supplies and services provided by other businesses or individuals within the local area is also important, as this spending represents part of the indirect effects. If respondents are uncomfortable in reporting actual spending amounts they may report instead the percentage of total spending on wages and local supplies, which can later be converted to an actual amount after multiplying it by average total spend in that sample segment of businesses.

Revenue. Businesses patronized by tourists may not exclusively serve the tourism market. If a hotel, for example, also receives income from people who stay in the local area but do not visit the park then not all of the income to the hotel, nor spending by the hotel, is attributable to the park. In this case, the percentage of income related to the park will probably be known by the proprietor, and can be used to adjust downwards the reported income and spending values.

The difference between income and expenditures (plus taxes) is profit and this reflects the financial competitiveness of businesses related to the park. The financial competitiveness of park-based industries, like any other industry, is affected by taxes and regulations as well as market accessibility, and this is why it may be important to measure. Of course, the sensitivity of this information makes it less likely that income

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will be disclosed. However, if the prices of the business are known and the number of customers/guests/clients can be approximated, then a reasonable estimate of income may be possible.

However, it may also be necessary to correct the listed price by the amount paid as commission to a travel agent. For example, if most bookings at a lodge are made through a travel agent, and the agent charges a 30% commission, then the lodge may only receive 70% of the listed price. When requesting information on the breakdown of sales methods (see ‘Guest Demographics’ above) it is useful to ask for typical commission rates for each sales method.

Taxes. Like income, taxes are also a sensitive subject and data may not be forthcoming. If respondents are reluctant to report taxes paid, it may suffice to inquire only about the types of taxes that apply and the rates, and then estimate tax amounts from assumed or reported income.

Local Suppliers. In order to understand how tourism businesses tie in with other businesses and support additional jobs in the area it is necessary to understand the extent of the local supply chain. A list of each tourism operator’s five main local suppliers of goods and services will give an idea of what is available in the local area, and of the scale and location of the supply chain. This information may be used to follow up with a sample of the supplying businesses and obtain values for the average price markup, the proportion of goods that are produced locally, and the number of employees or producers supported. Alternatively, tourism operators themselves may know the local price markup, the proportion of their purchases which is made locally,

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and may be able to estimate the number of employees at their supplying businesses, obviating the need to extend the business survey to the supplier level.

Social Responsibility. High profile businesses will often make financial commitments towards local causes or will facilitate donations by guests. The total amount contributed, as well as the nature of the projects funded and measures of success will communicate part of the social impact of the park on local communities.

Other Information. If interviews are conducted in-person, at the place of business, it may present an opportunity to conduct a survey of employees, provided employer permission is granted and employee consent is obtained. One of the main purposes of an employee survey is to characterize the beneficiaries of tourism-related employment, which includes not just workers themselves, but also their household members and relatives. An employee survey can also serve to cross-check data on wages as reported by business owners. Refer to Appendix C for an example employee questionnaire. It is a good idea to segment the sample according to the skill level of workers.

Finally, capital expenditures and startup costs are not as important as operational expenditures, but can be used, for example, to estimate the costs of expanding tourism at the park.

Local businesses in tier 2 and tier 3:

Please refer to Appendix C for a sample non-tourism business survey and the associated spreadsheets 3a and 3b of Object B-1. Its sections are elaborated below:

1. Name of the business, location, and segmentation: Name? By recording the name of the business it will be easier to avoid repeat-sampling, though this information should be recorded separately so that it is not associable with survey responses. Where is the business? GPS coordinates or a general description of the location will allow determination of the business’s distance from the park. What type

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of business is it (food, clothes, mobile phones, etc.)? Is it formal/informal? Is it large/medium/small (based on turnover or employment)?

2. Attribution to the park: When was the business started? What was the reason for establishing the business here? Are the founders local or non-local? Where did the funds for starting the business come from? What was the amount of these funds? Answers to these questions will help to determine to what degree the park is responsible for business growth, whether because of anticipated consumer demand or the availability of startup capital from tourism wages.

3. Dependence on park tourism: The amount of turnover (i.e. revenue) from tourism, and the percentage of total turnover that this represents for the business is perhaps the most important part of the survey. However, this can be confusing. If business owners are simply asked to estimate the portion of their turnover related to tourism, some respondents may include direct impacts (sales to tourists), indirect impacts (sales to tourism businesses), and even sales to individuals who work in tourism, while other respondents may only consider direct sales to tourists. To avoid this confusion, ensure that each type of tourism impact is asked in a separate question. Additionally, although the concept of tourism-related turnover is easy to understand, the estimation of this value may be difficult and respondents may default to a “50%” estimate even if it is not accurate. If tourism follows a seasonal trend, however, then a simple way to improve the accuracy of responses is to ask how monthly turnover varies between the high and low seasons for tourism. If the types of products sold to tourists or tourism businesses vary from the types sold to the local population, this may also be useful in determining dependency.

4. The extent of the supply chain: Where do the products come from? What percentage is locally made? This information can improve the accuracy of the spreadsheet model by adjusting the local production coefficients.

5. The value captured from sales: How many people are employed by the business? What is the average employee wage and annual business profit? Even if a respondent declines to answer questions related to finances, a close approximation of captured value can be made from the average price markup of products sold (i.e. the difference between what the business pays for merchandise and what it sells merchandise for). This is known as the retail or wholesale margin.

Data Management

Once the tourist survey and the business survey have been completed, both sets of data should be entered into a blank spreadsheet, keeping the different segments separate. This will make it easier to summarize and extrapolate from the data.

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Step 1: enter the raw data

The first step is to enter the raw data from your three surveys into spreadsheets.

As you will see from the examples, the data is not always perfect. Thus:

Spreadsheet 1a (Object B-1) is an example of raw data from tourism survey forms (Appendix C, “ Modified Visitor Spending Questionnaire for SLNP”)

Spreadsheet 1b (Object B-1) is an example of raw data from surveys of tier 1 tourism business (Appendix C, “Modified Tourism Operator Spending Questionnaire for South Luangwa NP”)

Spreadsheet 1c (Object B-1) is an example of raw data from surveys of tier 2 and 3 businesses (Appendix C, “Local Business Questionnaire”)

You should always save a read-only copy of the raw data on a separate spreadsheet that you never use for calculations, only for the safety of your data.

Step 2: Correct and standardize the data

Once you have these spreadsheets, the next step is to make a “clean” duplicate spreadsheet that standardizes all the data in terms of the currency you are using, most often USD. These will look exactly like spreadsheets 1, 2 and 3 of Object B-1 except that all the values will be standardized. Use your notes to correct and standardize all the raw data as accurately as possible.

If you collect data from every business, you can add up this data to assess the economic impact of the park. However, most often you will have run a sample survey and will need to calculate averages, and to then multiply up these averages by the number of units in the park. For example, if the average visitor fee is $350 per bednight in Segment 1 lodges, and there are 10,000 bednights, visitor spending on bednights will be $3,500,000.

Step 3: Calculating averages

The next step in the analysis is to calculate the average expenditure for:

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each tourist on the full range of items that tourist buy (Object B-1, Spreadsheet 1a), expenditure, employment, etc. per bed-night for tourism tier 1 operations (Object B-1, Spreadsheet 1b), wages, jobs, average revenue from tourism etc. from tier 2 and 3 businesses (Object B- 1, Spreadsheet 1c).

If possible, ask business owners for actual dollar values. However, where they are reluctant to provide these, your next step is to ask them to report their spending values as a percentage of their total expenditure. Here, you will have to correct the data by converting percentages to dollars on the basis of the average total expenditures for businesses in the same segment. For qualitative questions, this step means describing the frequency of responses (e.g. mode of transport to the park, communities where employees are drawn from, etc.).

This step sounds simple, but will likely take some thought. Despite your best efforts, the data you obtain will not be perfect. Some responses will be incomplete or may not match other data with which it should correspond. Use your judgement to select factors by which to extrapolate for missing data. For example, when it comes to places of accommodation, many values will vary within each segment depending on the number of beds at the business. For that reason, the number of beds is a good measure by which to standardize other variables, such as the number of workers at a lodge or the amount spent on local goods and services. One would then only need to count or estimate the total number of beds across that particular segment (including even businesses that were not surveyed) and multiply by averages for that segment (the next step).

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Making Sense of the Data

Describing the overall results of the survey entails taking averages, frequencies, total counts, and making extrapolations if necessary. The key to designing outputs that are relevant and compelling is to ensure that they help the set of decision-makers and/or stakeholders to answer the policy questions they want answered. For example, at the national level the policy question to address may be whether it makes social, economic, and financial sense to increase investments in protected areas so as to increase growth, employment, outputs and poverty reduction. At the local level, the policy question may be how to increase benefits to neighboring communities or how to reduce leakage in the value chain. The outputs should be kept simple and to the point.

Visual presentations (Figures 4-1 and 4-2) make the evidence easier for the audience to absorb and understand in stakeholder workshop settings. Figure 4-1 was compiled from the survey of level 2 and 3 businesses. It shows the growth rate of business over time outside South Luangwa National Park. Figure 4-2 shows the wages directly related to tourism from the surveys of all non-tourism business (Tiers 2 and 3).

Simple diagrams, charts, graphs, and maps that illustrate an important message are highly effective. They also have the advantage of being retained longer in the audience’s memory after the event.

Some possible points to emphasize include: the geographic distribution of business growth in relation to park and tourism features; the historical trend of business growth in relation to patterns of park visitation; the extent of the local value chain (are most goods imported to the region, or are they produced locally?); the reasons business owners decided to establish locally; the reasons for local infrastructure development

(e.g. is tourism responsible for bringing electricity and wireless networks to the region?);

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the specific types of social investment that tourism businesses have contributed to; the number of direct jobs in tourism vs. the number of additional jobs created as a result of tourism vs. the number of total jobs in the region; the total number of household dependents supported by tourism wages and remittances sent (as revealed by an employee survey), etc.

Surveys to Improve Multiplier Analysis

If the business survey is intended to be used to modify spreadsheet model results or parameters, refer to the section below. If the intent is wholly to substitute for the spreadsheet model, refer to the following section for a step-by-step procedure.

Modifying spreadsheet model parameters and/or results

As an optional step, the table at cell N73 on the main spreadsheet tab in the

TEMPA model allows for direct level effects and the local production parameters of the

TEMPA model to be substituted with direct level effects and production parameters as measured by a survey of businesses. It is not necessary to fill in the entire table. Any single value entered into the optional table will replace the corresponding value in the direct effects table. The optional table allows you to replace model results without overwriting the formulas in the result tables. Be careful to ensure that all cells in the optional table that are not meant to replace values in the direct effects table are blank.

Substituting for the TEMPA spreadsheet model

However, we can also use the data provided by these surveys to calculate everything in the TEMPA model from ground-based data, rather than from national-level input-output tables.

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Step 1: Describe money flows with a diagram. It is invaluable to compile a diagram to describe the flow of tourism dollars in the park economy. An example is provided in Figure 4-3.

Step 2: Compile a spreadsheet that reflects money flows. The second step is to compile a spreadsheet that reflects the money flows illustrated in Figure 4-3. This is not as hard as it sounds, and we provide an example in Figure 4-4. The spreadsheet is color coded to correspond with the diagram in Figure 4-3 depicting the pathways of park-tourism money in the local economy. Tourism businesses are those that primarily deal with tourists (e.g. places of accommodation). Non-tourism businesses are those that provide goods and services to general consumers, which may include tourists and tourism businesses (e.g. grocery stores, restaurants, hardware stores, etc.). Though job totals do not appear in the spreadsheet, counting jobs is straightforward for tourism businesses.

Step 3: Get average values for each segment. We have already described how we do this (above) to get spreadsheets 1b, 2b, and 2c (Object B-1).

Step 4: Get total values for each segment. Here we simply multiple average values by population count to get the total values of each segment. In the spreadsheet of Figure 4-4, first round spending is the amount of money spent by tourists. We have two sources where we can get this data. We know the total expenditure of tourists in lodges and in local business from the tourist expenditure survey (Object B-1, spreadsheet 1). We also know what it is from the lodge (tier 1) and business (tier 2 and

3) surveys (Object B-1, spreadsheets 2 and 3). Therefore we can cross-check the data from the tourism survey with data from the business survey.

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Step 5: 1st Round Spending. Using this data we know that total tourist expenditure on lodges was $114, of which $14 was sales tax. Tourism spending in local businesses was $30 plus $5 in taxes. When we add this up, we know that the total tourism expenditure was $149.

Note that for the first round of spending (spending by tourists), the calculations are the same as in the TEMPA model. The average per-bednight spending amounts for each segment (Object B-1, spreadsheet 1b) are simply multiplied by the total number of bednights recorded or estimated for the park that year.

Step 6: 2nd Round Spending. To get the second round of spending (spending by tourism businesses) we use the data from spreadsheet 2b. In this table we have wages, profit (after tax), tax, and locally purchased goods and services. We simply enter this data into the spreadsheet (Figure 4-4) as shown, notably $20 in wages, $10 in profit, $ 5 in taxes and $30 to purchase goods locally. We have also recorded that $35 is spent on goods that are non-local. This is called leakage because this money now leaves the local economy, and creates multipliers elsewhere. From the survey on local tier 2 and 3 businesses we enter data on wages ($6), profit ($2), Tax ($1), local expenditures ($7) and non-local expenditures ($14) in exactly the same way.

Step 7: 3rd Round Spending. The third round of spending (by local non-tourism businesses) is the average spending value for non-tourism businesses (as in spreadsheet 3b) multiplied by the number of businesses in each non-tourism business segment known to exist in the area. Estimates can, in theory, be made of the indirect effects of spending beyond the 3rd round, but because effects diminish rapidly with each subsequent round it may not be practical to include them.

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Step 8: Employment. We can compile a spreadsheet identical to Figure 4-4 for employment. For non-tourism businesses which receive direct and indirect spending, job totals need to be adjusted downwards by the proportion of revenue these businesses receive from non-tourism sources (if only 10% of a business’s revenue is from tourism, then only 10% of its jobs are attributable to tourism). This adjustment is made in spreadsheet 3b.

Presenting and Summarizing the Data

We can now use this data to present a summary of the socio-economic impact of the Park. An inverted pyramid (Figure 4-5) provides a powerful visual representation of the most important lessons to be learned from an economic analysis of parks.

• Level 1 information in the pyramid derives from a simple financial analysis of the park, namely its budget and the number of staff it employs.

• The local impacts of the park (level 2) can be calculated by TEMPA. However, the bottom-up methods just described provide a great deal of richness to this data.

• The national impacts of the park (level 3) are calculated by TEMPA and are consequently quite generic.

The inverted pyramid shows that the recurrent expenditure on the park is often returned many times over in the form of jobs, income, and tax revenue, at both local and national levels. Also, the narrower the investment base, the more unstable and liable to collapse is the larger park economy. The inverted pyramid conveys this key point.

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Figure B-1. Sample graph illustrating the growth in number of local businesses in parallel with the growth in park visits.

Figure B-2. Sample graph of total employee wages across business types. SSP = small- scale vegetable stalls, SSE = small-scale enterprises, SSS = small-scale services.

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Note: Data in the above graph is sample data and not from any case study.

Figure B-3. A color-coded Conceptual representation of the flow of tourism spending through the local economy

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Figure B-4. A color-coded spreadsheet capturing measured direct and indirect effects (example data).

Note: aTourists spend primarily at tourism businesses, but typically also spend money on groceries, local transport, and at restaurants. bTourists will not know how much they have paid in direct taxes (e.g. VAT or sales tax), but the tax amount can be estimated from known rates, or may be reported by tourism businesses. cIncome taxes paid in the 2nd round of spending, because they are based on profit, are part of direct value added.

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Figure B-5. Using an inverted pyramid to describe a park economy and its vulnerabilities.

Note: Microsoft Excel lacks the template to create this figure. It was created using PowerPoint by inserting three triangles and superimposing them on each other, starting with the smallest. The base triangle represents the annual park budget, the middle triangle the local level value added, and the largest triangle the additional value added non-locally. The relative sizes of the visible portion of the triangles should be in rough proportion to the relative monetary values of what they represent.

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APPENDIX C RESEARCH INSTRUMENTS

Modified Visitor Spending Questionnaire for South Luangwa NP

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Modified Tourism Operator Spending Questionnaire for South Luangwa NP

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Local Business Questionnaire (for Tiers 2 and 3)

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Tourism Operator Employee Spending Survey (Zambia & South Africa)

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Visitor Spending Questionnaire for the GKNP, South Africa

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Tourism Operator Spending Questionnaire for the GKNP, South Africa

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APPENDIX D LUANGWA VALLEY SUPPLEMENTARY DATA

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Figure D-1. South Luangwa National Park tourism outlay. The panels show the location of lodges and camps (A), the density of beds (B), and the relative degree of revenue generation from lodges and camps (C).

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Figure D-2. Number of park visitors in 2015, by visitor segment. Figure D-3. Number of daily entries into SLNP in 2015, by visitor origins.

Figure D-4. Nationalities of respondent groups.

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Table D-1. Trip profiles, by tourist segment. Number of Days in Days % % for whom % on a % arriving or departing by… (may add respondent Zambia at resident a wildlife packaged to more than 100%) groups park in safari is tour air private public (number of Zambia main ground ground individuals reason for reporting trip to expenses) Zambia High end 39 (87) 9.8 6.6 0 100 83.2 100 0 0 Mid-range 33 (77) 9.1 4.2 10.1 75.3 31.1 89.0 11.0 0 Low-end 32 (85) 6.9 3.2 22.2 65.2 18.1 37.0 51.9 26.0 Lodge 8.2 4.3 13.3 76.5 32.8 67.5 27.4 12.1 Weighted - Average Campers 34 (94) 9.1 4.0 11.8 57.6 0 0 85.3 14.7 Overlanders 13 (37) 4.8 2.3 0 25.0 100 0 100 0 Overall 8.0 4.1 11.9 69.4 38.9 53.2 41.0 11.3 weighted - average

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Table D-2. Bed-nights sold and total spend by tourist segment. Segments Total bed-nights Local spend per bed-night Local spend per year High end 26,265 $ 542.53 $ 14,249,550 Mid-range 20,921 $ 346.96 $ 7,258,750 Low end 24,871 $ 133.85 $ 3,328,983 Campers 9,913 $ 52.61 $ 521,522 Overlanders 4,607 $ 72.18 $ 332,533 Total 86,577 $ 25,691,340

Table D-3. Segment sample sizes of the employee spending survey and percent of local tourism employees who migrated to Mfuwe. Skill level Number of Originate non-locally (25.2% respondents overall weighted average) Unskilled 71 19.7% Skilled 32 68.8% Managers 8 50.0%

Table D-4. Highest level of education reached by local tourism employees, by skill level. Skill level < Grade 12 Grade 12 Trade College certificate Unskilled 69.0% 19.7% 11.3% 0.0% Skilled and managers 18.4% 34.2% 42.1% 5.3%

Table D-5. Indirect effects by economic sector. Sectors Value added Income Trade (retail and wholesale) $3,890,000 $2,520,000 Services (financial and business) $1,785,000 $728,000 Transport $866,000 $432,000 Food and Beverage $806,000 $434,000 Construction $451,000 $227,000 Petroleum $393,000 $126,000 Communications $314,000 $112,000 Hotels and Catering $237,000 $128,000

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Table D-6. Charitable organizations connected to SLNP tourism Organization Year Description Name initiated Project 2009 The largest local education NGO, Project Luangwa Luangwa supports education through the construction of schools, provision of materials, sponsorship of students and teachers in training, and fostering of clubs and new learning technologies. Special attention is given to unique obstacles to education faced by girls, as well as the needs of disabled students. Conservation 2000 Supports 65 village scouts through payment of salaries South and provision of equipment, rations, training, and Luangwa transport. Also assists anti-poaching patrols with aerial surveillance, rescue of wildlife caught in snares, interdiction of bushmeat smuggling, and mitigation of human-wildlife conflict through the promotion of chilli farming as an elephant deterrent. Charity Begins 1999 Started by The Bushcamp Company. Community at Home development activities include sponsoring several hundred primary and secondary school students, provision of over 2,000 meals a day at two area schools, the drilling of 48 boreholes over a two-year span, and support to a local clinic and drama groups. Conservation is supported by providing CSL and ZCP with funds and an aircraft to conduct research and anti-poaching activities, as well as through tree planting at local schools. Nsefu Wildlife 2014 Started by Zikomo Safari Lodge. Supports resource Conservation protection by providing salaries, housing, equipment, Foundation and a vehicle for game scouts. Has plans to expand into education and alternative income generating activities. Chipembele 1988 Supports conservation education by managing its own conservation education center and a separate student resource center, conducting outreach at area schools, sponsoring orphaned and vulnerable students, providing scholarships for conservation education through the tertiary level, and running a wildlife rescue and rehabilitation center, among other activities.

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Table D-7. Origins of the owners of businesses in the Mfuwe area. Origins % of business owners (n = 186) Local 66% Eastern 22% Province National 11% International 1%

Figure D-5. Historical commercial and donor income to DNPW and management expenditures. Source: DNPW unpublished data.

Note: A nine year gap in data exists between 1999 and 2008.

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Table D-8. Direct + Indirect (type 1) multipliers of SLNP tourism in relation to other economic sectors of the Zambian economy. Direct + indirect effects multipliers Value added Income (with rank) SLNP tourism 0.93 0.45 (7) Health & education 0.89 0.70 (1) Government 0.89 0.69 (2) Food agric. 0.85 0.61 (3) Trade, hotels 0.84 0.54 (5) Construction 0.82 0.41 (8) Livestock, forestry, & fishing 0.82 0.57 (4) Food processing 0.79 0.40 (10) Utilities 0.77 0.41 (9) Export agric. 0.76 0.47 (6) Wood & paper 0.67 0.32 (11) Petroleum & chemicals 0.66 0.21 (14) Mining 0.65 0.30 (12) Textiles & clothing 0.63 0.26 (13) Transport & communications 0.49 0.21 (15) Other manufacturing 0.34 0.17 (16) Services 0.28 0.15 (17)

Object D-1. Inventory of SLNP accommodation (.xlsx file, 13KB)

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APPENDIX E HUNTING-RELATED ANIMAL AND LEASE FEES IN ZAMBIA

DNPW 2016 Resident and Non-resident Hunting License Fees

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Concession Lease Fees (ZAWA, 2015)

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APPENDIX F GKNP SUPPLEMENTARY DATA

Object F-1. Inventory of tourism accommodation in the GKNP (.xlsx file, 26KB)

Figure F-1. Average bednight rate (per-person, sharing) against the number of beds for each lodge and camp in the GKNP (n=210). Axes have been log-transformed.

Table F-1. Demographics and travel patterns of visitors to KNP and the private reserves. Kruger NP Private reserves Avg. group sizes 3.57 3.38 Avg. length of visit to park (days) Day visitors 2.19 - Overnight visitors 3.45 3.80 Avg. length of stay in country (days; foreigners 14.45 10.80 only) Percent foreign 0.35 0.60 Proportion indicating visiting a game park was 0.85 0.72 major reason for their SA trip (foreigners only) Proportion visiting the GKNP for first time 0.87 0.78 (foreigners only) Proportion flying to local area 0.05 0.55 Proportion who purchased trip through tour - 0.54 operator

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Table F-2. Jobs per bed and per tourism revenue in the contiguous reserves Avg. non- Beds in contig. Avg. non- management jobs per Segments reserves management jobs tourism revenue (per per bed (std. dev.) R1m) < R 1,500 476 0.22 (0.19) 1.68 R 1,501 – 5,000 626 0.90 (0.58) 1.91 > R 5,001 1225 3.15 (0.80) 2.11

Table F-3. Wage income to tourism employees by education level Less than Secondary Tertiary secondary school school diploma degree Total wage income from tourism in contiguous reserves $ 7.59 m $ 6.82 m $ 17.4 m Tourism in Proportional contiguous wage reserves 0.24 0.21 0.55 distribution SA national average 0.15 0.25 0.60

Figure F-2. Focal areas of operators with formal social commitments

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Table F-4. National level total income and GDP multipliers comparison with likely alternative industries Sector Income multiplier GDP multiplier Livestock farming 0.84 1.45 Metal ore mining 0.98 1.58 Kruger NP tourism 0.87 1.33 Kruger contiguous reserves tourism 1.19 1.79 GKNP overall 0.94 1.5

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LIST OF REFERENCES

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Zimsky, M., Ferraro, P., Mupemo, F., Robinson, J., & Sekhran, N. 2010. Results of the GEF biodiversity portfolio monitoring and learning review mission, Zambia. Enhancing outcomes and impact through improved understanding of protected area management effectiveness. Global Environment Facility, Washington, D.C.

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BIOGRAPHICAL SKETCH

Alex received a Bachelor of Arts degree in Environmental, Population, and

Organismic Biology from the University of Colorado at Boulder. Between 2008 and 2010 he served in the Peace Corps in Zambia while also earning a Master of Science degree from Florida International University. He received his Ph.D. from the University of

Florida in the fall of 2018.

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