Stocking Rates and Varying Social-Ecological Conditions on the Rangelands of Inner Mongolia, China

David Crook Department of Geography McGill University, Montreal

Submitted August 2017

A Thesis Submitted to McGill University in Partial Fulfillment of the Requirements of the Degree of Master of Arts

Table of Contents Abstract ...... 2 Résumé ...... 3 Acknowledgements ...... 5 Preface ...... 7 Chapter 1: Introduction ...... 9 1.1 Policy Context ...... 9 1.2 Theoretical Background ...... 10 1.3 Thesis Outline and Fieldwork ...... 13 Chapter 2 : A Review of Literature Relating to Ovegrazing and Grazing Management Strategies ...... 15 2.1 Introduction ...... 15 2.2 The Concept of Overgrazing: Definitions and Debates ...... 16 2.3 Herd Maximization among Pastoralists ...... 17 2.4 Self-organization among Resource Users ...... 18 2.5 Livelihood Pressures and Ecological Degradation ...... 19 2.6 Land Tenure: Definitions and Evaluation ...... 20 2.7 Access to Credit and Environmental Sustainability ...... 22 2.8 Critical views of overgrazing discourse ...... 24 2.9 Conclusions ...... 26 Chapter 3: Effect of Environmental Shocks on Stocking Rates in Inner Mongolia ...... 28 3.1 Introduction ...... 28 3.2 Methods...... 32 3.3 Results ...... 40 3.4 Discussion ...... 45 3.5 Conclusion ...... 54 Appendix 1 – Stocking rates and environmental shock occurrence across counties ...... 56 Appendix 2 – Stata output of PCA for asset index ...... 57 Chapter 4: A choice model of incentive strategies for reducing overgrazing Inner Mongolia’s rangelands...... 59 4.1 Introduction ...... 59 4.2 Study Site and Experimental Design ...... 62 4.3 Results ...... 72 4.4 Discussion ...... 78 4.5 Conclusion ...... 81 Chapter 5 - Thesis Conclusions ...... 82 References ...... 85

Abstract

Overgrazing is a serious problem in northern China’s Inner Mongolia region. The government has attempted to reduce overgrazing by mandating maximum livestock stocking densities. These stocking limits are enforced using a combination of cash subsidies for herders who abide by them and fines for those who don’t. However, the success of these policies has been mixed, and overgrazing has continued. Herd sizes in Inner Mongolia are influenced by a variety of socio-economic, political and environmental conditions. Environmental shocks, lack of credit and insecure land tenure are all factors that push herders toward herd maximization as a short-term survival strategy rather than investing in long-term sustainable grazing.

In this thesis, I performed two analyses to gain insights into how herders’ stocking choices are influenced by environmental and policy conditions. First, I performed a difference- in-differences analysis to test for a relationship between stocking rates and the frequency of environmental shocks. I found that while droughts were associated with higher stocking rates, snowstorms were associated with lower stocking rates. For my second analysis, I performed a choice experiment to compare how variations in different policy incentives influence herders’ willingness to keep lower stocking rates. I found that lengthening land tenure contracts had the greatest effect on herders’ choices. These findings have important implications for future policy formulation in Inner Mongolia. The varying effects of different environmental shocks on stocking rates means that stocking regulations may need to vary depending on which type of shock predominates. Herders’ interest in land tenure reform suggests that longer land tenure contracts may make a major improvement in tackling overgrazing. Together, these findings demonstrate that mitigating overgrazing requires understanding and addressing herders’ vulnerabilities to both environmental and socio-economic uncertainties.

Résumé

Le surpâturage constitue un grave problème au sein des prairies de la Mongolie Intérieure en Chine septentrionale. Le gouvernement a tenté d’y réduire le surpâturage en imposant des taux d’occupation maximums du bétail sur les terres. Ces limites sont mises en œuvre par l’entremise d’une combinaison de subventions financières pour les éleveurs qui les respectent, et d’amendes pour ceux qui y dérogent. Cependant, le succès des politiques appliquées est mitigé et le surpâturage se poursuit. En Mongolie Intérieure, la taille des troupeaux est influencée par une grande variété de conditions socio-économiques, politiques et environnementales. Les chocs environnementaux, un manque d’accès au crédit et l’insécurité associée au régime foncier sont parmi les facteurs qui poussent les éleveurs à maximiser la taille de leurs troupeaux comme stratégie de survie, au lieu d’investir dans une stratégie de pâturage durable.

Dans ce mémoire, j’ai effectuée deux analyses pour mieux comprendre comment les choix des éleveurs concernant la taille de leurs troupeaux sont influencés par les conditions environnementales et les politiques gouvernementales. J’ai d’abord employé la méthode des doubles différences afin de déterminer s’il y a une relation entre la fréquence des chocs environnementaux et le taux d’occupation. J’ai constaté que les sècheresses sont associées à des taux d’occupation élevés, tandis que les tempêtes de neige sont associées à des taux d’occupation plus faibles. Pour ma deuxième analyse j’ai appliqué la méthode d’expérimentation des choix pour comparer les effets des différentes politiques incitatives sur la volonté des éleveurs de réduire leurs taux d’occupation. J’ai découvert que la prolongation des contrats fonciers est le type d’incitatif ayant le plus grand effet sur les choix des éleveurs. Ces constatations ont des implications importantes pour la formulation de futures politiques en Mongolie Intérieure. Les effets variables des différents types de chocs environnementaux sur le taux d’occupation

3 signifient qu’il sera peut-être nécessaire de faire varier les réglementations en fonction du type de choc prédominant dans chaque cas particulier. L’intérêt des éleveurs envers la réforme du régime foncier indique que l’allongement des contrats fonciers pourrait renforcer les efforts d’atténuation du surpâturage. Ces constatations indiquent que pour atténuer le surpâturage, il est nécessaire de mieux comprendre les vulnérabilités des éleveurs aux incertitudes environnementales ainsi que socio-économiques, et d’y répondre efficacement.

Acknowledgements

I would like to thank my supervisor, Professor Brian Robinson, for his guidance in writing this thesis. Brian has consistently given thorough and insightful feedback on my work while simultaneously fostering confidence and independence on my part. He has also provided much help logistically and worked hard to procure funding on my behalf. I also wish to thank

Professor Sébastien Breau for acting as my committee member and for providing helpful comments on my thesis draft. I owe thanks to my thesis examiner, Professor Oliver Coomes, for his comments on the initial submission of my thesis. I am grateful to Dr. Eli Fenichel of Yale

University for his help with my experimental design and statistical analysis, and to Dr. Arianne

Cease of Arizona State University for welcoming me into the Living With Locusts research team. I also thank the very friendly and hardworking members of McGill Geography’s administrative team, particularly for their help in navigating paperwork and procuring funding. I am grateful to Mr. Roger Warren and to the family and friends of Theo Hills for providing me with generous scholarships. I thank McGill’s Geography Department for the financial aid that it has provided me.

I am extremely grateful to Dr. Li Ping of the Grassland Research Institute of the Chinese

Academy of Agricultural Sciences, in Hohhot. Li Ping took the lead in planning and implementing my household surveys on the grasslands. This thesis would have been impossible without her hard work, generosity and expertise. I also wish to thank her and Brian for allowing me to use their impressive dataset from previous surveys. I wish to thank Dr. Hou Xiangyang,

Director General of the Grassland Research Institute for inviting me to collaborate with his institute. I also thank Dr. Li and Dr. Hou for sharing their own household dataset with me. I am grateful to my field assistants, Feng Ting and Jia Lu for their hard work and companionship. I

5 also thank the numerous interpreters, drivers, researchers and local government officials for their collaboration during the fieldwork and for making me feel welcome in their wonderful corner of the world. Of course, I must thank the herders for sharing their precious time with us and participating in our survey, and for welcoming us in to their homes to enjoy endless cups of salty milk tea and delicious jerky and cheese.

Thank you to Laurence Côté-Roy for helping me with the French version of my abstract, and Sai Ma for helping me to print and hand in my thesis for lying-in-state while I was away.

Thanks also to Sai and the rest of my labmates, Rachel Maynard, Lucy Lu, Holly Cronin and

Marina Smailes for your support and friendship. Thank you to my parents Marni and Carl and to my brothers Martin and Ted for your love and intellectual inspiration. Thanks to my grandmother Isabel for being a role model of commitment to revolutionary ideals and for inspiring me with your own fieldwork 76 years ago. Finally, thank you to Anna for the wisdom and joy that you bring me. Time after time I tell myself how lucky I am.

Preface

This thesis consists of five chapters. The first chapter is an introduction, while the second is a review of literature. The third and fourth chapters report the two research projects that I conducted during my degree. The fifth and final chapter is the conclusion of the thesis. I am the primary author of all the chapters of this thesis. However, co-authors in the writing of the third chapter include my supervisor Dr. Brian Robinson of McGill University, as well as Dr. Li Ping and Dr. Hou Xiangyang of the Grassland Research Institute of the Chinese Academy of

Agricultural Sciences in Hohhot, Inner Mongolia. Dr. Li and Dr. Hou collected the data and created the dataset upon which the research is based, and allowed Dr. Robinson and I to have access to the dataset. Co-authors in the writing of the fourth chapter include Dr. Robinson, Dr. Li and Dr. Hou, as well as Dr. Eli Fenichel of Yale University. Dr. Li played a major role in conducting the survey for the fourth chapter. The fourth chapter also draws partly upon the dataset shared by Dr. Li and Dr. Hou, while Dr. Fenichel contributed to the analysis of this chapter. I am the sole author of the first, second and fifth chapters.

In the first chapter, I introduce the subjects of pastoralism, overgrazing and related mitigation policies in China’s Inner Mongolia Autonomous Region. I also state my research questions and briefly describe the methodologies that I employ in subsequent chapters to answer these questions.

In the second chapter, I present a review of literature relevant to these issues. This includes theory and debates surrounding the concept of overgrazing, herd management and self- organization among pastoralists, and the relationship between livelihood vulnerabilities and ecological degradation. In addition, potential leverage points for mitigation are discussed,

7 particularly subsidies, land tenure and access to credit. Critical debates surrounding the theoretical basis and deployment of the overgrazing discourse are also presented.

In the third chapter, I describe my difference-in-differences analysis of the effects of environmental shocks on stocking rates on Inner Mongolia’s rangelands. I describe the survey dataset of approximately 1,000 herding households from throughout the region that I use for my analysis. I also describe variations in stocking rates and incidence of environmental shocks

(droughts, snowstorms and locust outbreaks) in Inner Mongolia. I then introduce the difference- in-differences methods and my application of it for the current analysis. The results of the analysis are then presented and discussed, including its contribution to knowledge and implications for future grazing policy formulation in Inner Mongolia.

In my fourth chapter I describe the choice experiment that I conducted in Central Inner

Mongolia to compare the potential of different incentive strategies for reducing grazing intensity.

I start with an overview of current policies in place to mitigate overgrazing in Inner Mongolia, and the observed degree of success of these policies. I then present different arguments in the literature as to reasons why current policies have limited success, and which alternative policy approaches may yield better results. After this I introduce the method of choice modeling. I then describe how the choice experiment was implemented for this analysis, including the experimental design and the fieldwork process. Finally, I present the results and discuss their implications for theoretical knowledge and policy design.

In the final chapter, I describe how and to what extent the research objectives for this thesis were met, and integrate the findings of the two chapters to summarize their overall implications for theoretical knowledge and policy.

Chapter 1: Introduction

1.1 Policy Context

Overgrazing has become a serious problem on China’s rangelands in recent decades

(Thwaites et al. 1998). Livestock numbers have increased significantly, leading to more intensive grazing (Thwaites et al. 1998). This increased intensity has contributed to widely-reported instances of rangeland degradation in the form of soil erosion, unfavorable shifts in plant species composition, and desertification in the most extreme cases (Tong et al. 2004). The threats posed by rangeland degradation to human well-being are severe and wide-ranging, as rangelands provide a variety of ecosystem services. Perhaps the greatest threat is to the livelihoods of the millions of herders in China who rely on rangelands to feed their flocks (Thwaites et al. 1998). In addition, overgrazing compromises rangeland biodiversity and ecosystem function. Furthermore, erosion and desertification resulting from overgrazing have negative effects on air and water quality in urban areas (Normile 2007).

The Inner Mongolia Autonomous Region accounts for over a fifth of the country’s rangelands and one third of the more than 3.3 million pastoral households in China (Squires and

Hua 2010; Chen et al. 2017). Also, with 90% of its rangelands categorized as degraded, it is one the regions with the highest degree of rangeland degradation, being surpassed only by Xinjiang

(Han et al. 2008; Chen et al. 2017). In response to the problem of overgrazing, the Chinese government has enacted grazing policies with the goal of maintaining stocking rates at ecologically sustainable levels (Hua and Squires 2015). For the above-mentioned reasons, Inner

Mongolia has been the site of the most intense rangeland intervention policies. These policies operate largely within a framework of semi-privatization of rangelands, and while the

9 privatization process is occurring throughout China’s rangelands it is most advanced in Inner

Mongolia, where over 90% of rangelands are leased to individual households (Lu 2017; Wang et al. 2010). Rangeland protection strategies in Inner Mongolia involve setting maximum allowed stocking rates for different areas, depending on their ecological conditions (Waldron, Longworth, and Brown 2008). In principle, these regulations are enforced by fining herders who surpass the proscribed stocking rate limit. In addition, herders receive cash subsidies, which are reduced if the proscribed stocking rate is exceeded (Kolås 2014). However, these policies seem to have had limited success with respect to degradation, as overgrazing has continued and herders often ignore regulations. Lack of enforcement is a major part of the problem, with fines often going unpaid. There is evidence that local officials are reluctant to enforce policies because they consider the policies to be unrealistic due to the socio-economic pressures that they exert on herders (Kolås 2014).

1.2 Theoretical Background

While the success of these policies has been mixed in terms of rangeland protection, they also exacerbate existing strains on herders’ livelihoods. These strains have had the unintended consequence of forcing herders to exploit their rangeland resources further, thereby leading to further rangeland degradation (Squires and Hua 2010). These difficulties suggest that it is necessary for experts and policy-makers to improve their understanding of the ecological and socio-economic contexts in which rangeland degradation is occurring. Socio-economic and ecological systems on rangelands are interconnected, unpredictable and include a combination of fast and slow variables (Squires and Hua 2010). Links between these two types of systems can be understood as constituting a larger social-ecological system, or SES (Ostrom 2009). The dynamics of the rangeland SES vary spatially and temporally. While there is a high level of

10 uniformity in policy formulation on China’s rangelands, there is a high degree of ecological and socio-economic heterogeneity in these regions, leading to vastly different outcomes from the same policies (Squires et al. 2010b).

The SES framework is composed of four main subsystems: the resource users, the governance system, the resource system and the resource units. The behavior of the resource users is influenced by various factors in the other three sub-systems. (McGinnis and Ostrom

2014). On rangelands, one of the key challenges of mitigating overgrazing is the need to understand how herders make decisions regarding herd management and use of rangeland resources, with a particularly important decision being how heavily they decide to stock their rangelands. These decisions are influenced both by rangeland governance institutions and by climate factors such as environmental shocks (Cease et al. 2015). In SES terms, for policymakers to bring about a desirable system outcome (i.e. sustainable stocking rates), it is necessary to understand how resource users’ use of the resource units (i.e. grass) are influenced by variables within both the governance system (i.e. policies) and the resource system (i.e. ecosystem and climate dynamics).

Knowledge on rangeland ecosystem function has developed significantly in recent decades, but rangeland policy in China has been slow to meet these developments. Although grazing policies are formulated around the belief that rangelands have stable livestock carrying capacities which must not be surpassed, there is now more evidence that rangeland SESs are characterized by unpredictable climate events that render the carrying capacity inherently unstable (Vetter 2004). While environmental shocks such as droughts and blizzards tend to inflict heavy mortality on herds, herders have traditionally adapted to these conditions by maximizing herds when conditions are good, thereby buffering them against future shocks

(Kirychuk and Fritz 2010). Environmental shocks therefore have both a direct negative effect on stocking rates by inflicting livestock mortality, and an indirect positive effect on stocking rates by encouraging a herd maximization strategy on the part of herders. However, the net effect of these two mechanisms may depend on local environmental and socio-economic conditions.

Researchers have reported a need for more data to assess the impact of extreme weather events on grazing systems (Qi et al. 2012). One potentially informative way to assess these impacts is to perform quantitative analyses of the effects of extreme weather events on rangeland stocking rates. Such knowledge may allow policy-makers to adjust grazing policies in accordance with the spatial and temporal variation in the occurrence of extreme weather events across China’s pastoral regions.

Currently, government policies for mitigating overgrazing are restricted mainly to monetary incentives for abiding by legally prescribed stocking rates. However, there are many other aspects of the governance system that are likely to influence herders’ stocking decisions.

Livestock represent a significant portion of herders’ assets, and a herder’s decision to keep more or less of her wealth in the form of livestock is partly reflective of the degree to which she feels safe keeping her wealth in the form of other types of assets (Levine 1999). Other types of assets generally include cash, land, buildings, machinery, off-farm business enterprises, or access to state benefits, among others. Therefore, government policies that affect the availability or stability of these forms of wealth may have a significant effect on herders’ stocking decisions.

Land tenure security and access to credit are both cited as important factors that government policies can act on to mitigate overgrazing (Xie et al. 2015; Banks 2001). However, as of yet these theories have not been tested empirically in the Inner Mongolian context. While conditions do not easily permit a quantitative analysis of the relationship between overgrazing and existing

12 land tenure or credit conditions, choice analysis techniques can be used to elicit herders’ responses to hypothetical conditions (Bennett and Birol 2010).

1.3 Thesis Outline and Fieldwork

This thesis seeks to answer two questions, with each research question focusing on how herders’ resource use changes in response to variations in the ecological and socioeconomic conditions, respectively. First, how is grazing intensity influenced by environmental shocks such as droughts, snowstorms and locust outbreaks? Second, how might herders’ willingness to reduce their grazing intensity be affected by variations in the type and amount of compensation that the government offers them? These questions were investigated through two separate analyses. Chapter 3 lays out the first analysis, which concerns the effect of environmental shocks, including droughts, snowstorms and locusts, on overgrazing on the rangelands of Inner

Mongolia. This analysis employs a difference-in-differences regression techniques. The second analysis, the subject of Chapter 4 and also based in Inner Mongolia, consists of a hypothetical choice experiment to test herders’ willingness to accept different types of compensation in return for restrictions in grazing intensity.

The first analysis is based on a secondary dataset previously collected by colleagues at the Chinese Academy of Agricultural Sciences. The second analysis is based on data that I collected in Inner Mongolia in the summers of both 2015 and 2016. In 2015 I spent three weeks in July in the Xilingol, Ordos and Ulanqab regions of Inner Mongolia, and visited the grasslands and interviewed herders primarily to understand the research context and inform the development of the thesis. During this time I accompanied Dr. Li from CAAS as she conducted household surveys as part of her panel dataset of herding households in Inner Mongolia. In 2016

I spent over three weeks in Xilingol during May and June, during which time I conducted choice experiment surveys. Dr. Li also participated in this fieldwork. With the help of local interpreters,

Dr. Li, myself and two research assistants visited nearly 200 herding households. We presented sets of hypothetical policy alternatives to the herders and asked them their preferences, after which we asked follow-up questions to better understand their responses.

This introduction to the thesis is followed by a review of literature relating to overgrazing mitigation strategies on China’s rangelands, which is then followed by a description of the two quantitative analyses. The literature review will comprise the second chapter of the thesis, which will follow this introduction. The two quantitative analyses will comprise the third and fourth chapters. The thesis will end with a concluding section that integrates findings from the literature review and the two analyses.

Chapter 2 : A Review of Literature Relating to Ovegrazing and Grazing Management Strategies

2.1 Introduction

In this literature review I give an overview of the major academic discussions that have occurred in recent decades on the subject of sustainable grazing on rangelands. I begin with a section on the debates surrounding the scientific basis of “overgrazing” as a concept. I then go on to discuss the importance that pastoral communities have traditionally placed on the size of their herds. Following this is an overview of debates regarding whether or not herders are capable of self-organizing to establish rules to protect rangelands from over-exploitation. After this I review literature on the relationship between poverty and environmental degradation. I then discuss in detail two factors that are widely cited as major leverage points for mitigating overgrazing: land tenure security and access to credit. Finally, I examine literature that looks more critically at the ethical complexities of how rangeland science has been deployed as an instrument of state power. In the concluding section I sum up the common themes of the different sections and highlight important lessons.

The works cited in this review draw mainly for three overlapping literatures: rangeland ecology, development economics and social anthropology. Discussions and debates surrounding the concept of overgrazing fall mainly within rangeland ecology. Poverty and environmental degradation, land tenure and access to credit are topics that fall largely within development economics, while social anthropology covers literature on why herders maximize their herds, the degree to which herders self-organize and critiques of overgrazing discourse. Much of the literature on herder self-organization can also be described as political economy, while many of

15 the critiques of overgrazing discourse can also be described as coming from a political ecology tradition.

2.2 The Concept of Overgrazing: Definitions and Debates

According to Reid, Fernández-Giménez, and Galvin (2014), much of the difficulty in crafting policies to conserve rangelands appears to be due to the historical confusion among ecologists and policymakers as to the actual effects of grazing on rangeland ecosystems. These authors do not dispute that overgrazing occurs, and other authors such as Fleischner (1994) explain the well-documented effects of overgrazing. Indeed, as Fleischner explains, the term

“overgrazing” is poorly defined, but grazing tends to cause damage to three major aspects of rangeland ecosystems: species composition and richness, ecosystem function (i.e. nutrient cycling and ecological succession), and ecosystem structure (i.e. soil structure and water availability). Rather, Reid, Fernández-Giménez, and Galvin (2014) argue that the relative impact of grazing on rangelands has been overstated. They cite the work of Ellis and Swift (1988), who first pointed out that while rangelands had been historically viewed as systems with a stable carrying capacity for herbivores, carrying capacities on rangelands are in fact inherently unstable owing to erratic precipitation. In such systems, rangeland vegetation is more heavily influenced by abiotic factors like precipitation than by grazing. Ellis and Swift (1988) developed the distinction between equilibrium and non-equilibrium theories to describe these differing understandings. Von Wehrden et al. (2012) find that non-equilibrium rangelands tend to be less vulnerable to overgrazing than equilibrium rangelands because they are characterized by droughts which keep herbivore numbers low by inducing periodic population collapses, provided alternate feed or water supplies are not present. Vetter (2004) writes that in recent years

16 rangelands have increasingly come to be seen as existing in a continuum between the extremes of equilibrium and non-equilibrium, depending on the variability in precipitiation. Reid,

Fernández-Giménez, and Galvin (2014) et al. also cite state-and-transition models as another challenge to equilibrium models. State-and-transition models, first articulated by Westoby,

Walker, and Noy-Meir (1989), see rangelands as having multiple stable states, with transitions between these states being induced by climate events or changes in management, including grazing patterns. Despite developments in rangeland theory, Reid, Fernández-Giménez, and

Galvin (2014) argue that outdated equilibrium-centric understandings have lingered on, and

Kerven (2004) finds that ongoing confusion continues to complicate efforts to formulate appropriate grazing management strategies.

2.3 Herd Maximization among Pastoralists

To understand the factors driving high stocking rates on rangelands, it is important to consider what motivates herders to have such high numbers of livestock. Pastoralists often prefer to maximize their herds (Kerven 2004; Levine 1999). Levine (1999) provides an informative overview of literature on this topic and demonstrates that scholars have attempted to explain this phenomenon in both cultural and strategic terms. According to the East African cattle complex described by Herskovits (1926), large herds of cattle are seen as a marker of social status, and expansion of the herd is driven more by this sentiment than by desire for economic gain. By contrast, according to Schneider (1981) East African pastoralists regard cattle as a form of currency, the accumulation of which enhances herders’ access to other goods. Hart and Sperling

(1987) regard large herds as a form of insurance against future disasters. Ferguson (1985) sees

17 livestock as representing a unique form of value and prestige that is not necessarily convertible into cash. Subsequent research has joined the cultural and strategic perspectives into one in which both elements play a part (Comaroff 1990). The work of Hart and Sperling (1987) suggests that natural disasters can exert both a negative effect on stocking rates through herd mortality and a positive effect by motivating herders to increase their herds. From this perspective, we can see that the relationship between stocking rates and fluctuating environmental conditions is complex and may vary widely from one situation to the next, an aspect which is particularly important for policy formulation.

2.4 Self-organization among Resource Users

While there is general agreement that herders will usually increase the size of their herds when conditions permit, scholarship has been divided over the question of whether or not pastoral communities have the capacity to self-impose limits on the size of their herds in situations where overgrazing is a concern. For many decades this topic was dominated by

Hardin’s Tragedy of the Commons (1968), written in response to concerns of the effect of population growth on the use of natural resources. According to Hardin (1968), in a situation defined by common access to pasture, each herder will rationally seek to maximize their own profit by continually increasing the size of their herd regardless of the condition of the pasture, inevitably leading to overgrazing and ruin at the collective level. This model became dominant in development discourse, and international organizations such as the United Nations and the

World Bank implemented large-scale programs to encourage privatization and commercialization of pastoral systems. However, these efforts often failed to achieve reductions

18 in overgrazing, leading to criticism of Hardin’s theory (Fratkin 1997). Social scientists such as

Ciriacy-Wantrup and Bishop (1975) and Schlager and Ostrom (1992) argued that Hardin’s argument falsely characterized common property systems as having no rules. They argued that in doing so he confused common access with open access, pointing out that traditional common property regimes do in fact have strict rules governing resource use, and are often more effective than private systems (Fratkin 1997; Sjaastad and Bromley 2000). Rather than seeing access regimes as either privatized or open-access, it is argued that there is a wide array of structures of governance for natural resources (McGinnis and Ostrom 2014). More recent frameworks for understanding social-ecological systems view self-organization among resource users as something that is entirely possible, must be fostered, and studied carefully (Ostrom 2009).

According to Ostrom (2009), the degree of uncertainty within resource systems has a major influence on the likelihood that resource users will self-organize to conserve a resource.

2.5 Livelihood Pressures and Ecological Degradation

In addition to literature that focuses specifically on grazing management in pastoral systems, there is a wealth of literature in the field of development economics that has also informed grazing management policy. The discussion regarding the relationship between poverty and environmental degradation is particularly relevant. Barbier (2010) states that from the late

1980s, it has been widely held that poverty and environmental degradation reinforce each other.

This view is exemplified by Dasgupta and Mäler (1995), who argue that poverty forces people to use marginal lands, degrading them and lowering yields, further exacerbating poverty.

Furthermore, addressing either of the two problems can help to mitigate the other, leading to

19 win-win scenarios. However, according to McShane et al. (2011), win-win scenarios for environmental and development goals are difficult to achieve, and tradeoffs are the norm.

Barrett, Travis, and Dasgupta (2011) reviewed literature on the relationship between biodiversity conservation and poverty traps, and found that most studies pointed toward “win-settle” outcomes at best, where it is more achievable to improve one aspect without causing damage to the other, but where improving both aspects is difficult. According to Reardon and Vosti (1995) the outcome of poverty-environment interactions is affected by high variation in both the type of poverty and the type of environmental problem. In particular, land users may enjoy a standard of living above the line of “welfare poverty”, but continue to suffer from “investment poverty”, in which they remain too poor to invest in techniques that would make their livelihoods more environmentally sustainable (Reardon and Vosti 1995). Overall, it appears that efforts to obtain dual improvements in livelihoods and the environment, while not impossible, are challenging, and policymakers may need to decide which is a more immediate priority in a given situation.

This problem translates easily to pastoral contexts, where governments have incentives both to increase production of pastoral livestock products and to reduce grazing intensity on pastures.

2.6 Land Tenure: Definitions and Evaluation

Secure tenure is often cited as a precondition for the successful management and use of ecosystem resources. However, attempts to go about strengthening tenure security are hindered by the difficulty of determining what precisely tenure security is, and how to measure it (Arnot,

Luckert, and Boxall 2011). Theorists have traditionally described tenure systems using broad categorizations such as “private” or “common” property regimes, but these categories are often inadequate for capturing the considerable variation in the bundles of rights and responsibilities of actors in different tenure systems (Schlager and Ostrom 1992). Furthermore, academics and

20 policymakers have often prescribed a reform toward private or common tenure systems to address social or environmental problems without adequately considering the particular economic, environmental, social or institutional conditions that exist at the place and time in question (Simbizi, Bennett, and Zevenbergen 2014; Sjaastad and Bromley 2000; Schlager and

Ostrom 1992). Such problems are not limited to places with weak governance institutions, and even occur in developed countries (Schlager and Ostrom 1992). There is a need to improve our understanding of the myriad components of tenure security, and what components are the strongest determinants of positive outcomes.

In order to compare the environmental outcomes of different land tenure systems, some studies have looked directly at existing tenure systems and applied quantitative frameworks for comparing outcomes obtained by different bundles of rights. Fisheries have been a particularly useful setting, due to the quota systems that are widespread in fisheries and allow for market valuations of tenure rights. Grainger and Costello (2011) compared fisheries in different countries and found that various proxies for quota security translated to higher market values for quota and lower discounting, implying that greater quota security may lead to more sustainable management of fisheries. However, in many contexts there is a difference between how land tenure systems exist theoretically (de jure) and how they function in practice (de facto) (Ho

2014). On paper, tenure systems can be ambiguous in their allocation of rights and responsibilities to various stakeholders, allowing for the functional form of the system to proceed in ways not envisioned by their theoretical basis. This ambiguity is sometimes intentional and is used as a means to avoid conflict (Ho 2001).

Ho (2014) has argued that while much of the literature on tenure security has privileged de jure tenure systems over their de facto operation, it may be time to focus more on de facto

21 land tenure systems, as this aspect may have more bearing on social outcomes. Ho argues further that the functionality of a land tenure system may depend less on its theoretical and legal constitution and more on the perceived social support for the system among land users, a thesis that Ho termed the “credibility thesis” (Ho 2014). Ho (2014) found that the overall credibility of

China’s rural land lease system is actually quite high, as most rural people across the country have favorable views toward China’s land lease system. However, a significant minority of rural lease holders are dissatisfied with the system, and these tend to be people coping with specific problems, the most important of which is land expropriation due to urban sprawl, which only affects the minority of China’s farmers who live in a peri-urban area (Ho 2014). Though Ho’s study did not compare farmers, who constitute the vast majority of China’s rural land-holders, with herders, who make up a small minority, there is reason to suspect that herders would view

China’s land lease system in much less favorable terms than farmers would. China’s land tenure system (the Household Contract Responsibility System, or HCRS) was designed with cropping systems in mind, and Wang et al. (2010) argue that the environmental problems on China’s rangelands are in large part due to the failure of policy-makers to make adequate adaptations before introducing the HCRS to pastoral areas (Wang et al. 2010). In addition, as is discussed in more detail in section 2.8 of this thesis, herders in Inner Mongolia are known to be skeptical of both the wisdom and motives behind a variety of grassland protection policies (Kolås 2014). It is therefore very likely that the pastoral land tenure system in Inner Mongolia suffers from a credibility problem.

2.7 Access to Credit and Environmental Sustainability

In addition to land tenure security, another factor that is often cited in discussions of sustainable land use is access to credit. According to Dasgupta (1993, p. 276), “the environment is affected by the extent to which the rural poor have access to credit, insurance and capital markets”. Barbier (2010) identifies access to the credit market as a key influence on the choices and tradeoffs that the rural poor can make in terms of their use of environmental resources.

Barbier, López, and Hochard (2015) state that “favorable credit market conditions could in principle transform a vicious cycle of poverty, environmental degradation and more poverty into a virtuous cycle of poverty reduction, increasing natural and man-made assets, further improvement in access to credit and income growth”. In a study of soil conservation among hillside farmers in the Philippines, Shively (2001) found increasing access to credit was particularly effective for encouraging adoption of soil conservation practices on small farms, which tend to have especially severe credit constraints. However, credit may have a negative effect on sustainable land use in some situations. Barbier, López, and Hochard (2015) point to a poverty-debt-environmental degradation trap, in which land users who are already severely in debt are likely to further degrade their natural resources if their access to credit is further increased.

There is strong evidence that access to credit is a key factor in enabling subsistence smallholders to adopt sustainable land use practices (Barbier, López, and Hochard 2015). As governments attempt to refine environmental policies in ways that strike a balance between preserving natural systems and securing rural livelihoods, there is growing interest in the role of credit programs as a tool of environmental policy (Cranford and Mourato 2014). Credit-based payment for ecosystem services (CB-PES), in which access to credit is provided on condition of sustainable land management, have shown promise in places such as in Ecuador where its

23 potential to combat tropical deforestation has been studied (Cranford and Mourato 2014).

Literature on the potential of credit access to counteract overgrazing has focused largely on the role of credit in facilitating transfers to sustainable technology. Hua and Michalk (2010) argue that lack of access to credit is one of the primary barriers for herders in Northwest China to invest in infrastructure to control grazing. Shi, Zhang, and Wang (2005) found that providing access to credit helped herders in Inner Mongolia, China to increase their income dramatically while shifting from traditional grazing to sedentary beef production, which eases grazing pressure. However, there is also evidence that improved access to credit for rural land-users does not guarantee more sustainable practices, and may even have a detrimental effect depending on the amount of debt that borrowers accumulate (Barbier, López, and Hochard 2015). While access to credit for pastoralists has increased in China (Wang and Richter 2011), such measures do not appear to have been coordinated with environmental regulations, and have not resulted in reductions in overgrazing. More research is needed on how exactly credit programs should be employed within rangeland conservation policy.

2.8 Critical views of overgrazing discourse

While addressing the challenges posed by overexploitation of rangeland resources, it is important for researchers and policymakers to note that even research on environmental sustainability is not necessarily politically neutral. Subsistence land users are vulnerable not only to poverty but also to oppressive policies, and environmental discourse can play into this dynamic. Political ecologists have criticized the way that overgrazing discourse has been used in some contexts, especially in China. Yeh (2009) applies Foucault’s (1991) theory of

Governmentality to many of China’s ecological protection and restoration programs. She argues that environmentalist discourses are being used as a tool to extend state power in China. Not only do these policies often involve depriving already marginalized people from accessing the resources on which they depend, but the justification for these actions is based on claims of environmental degradation that are not necessarily supported by sound scientific evidence (Yeh

2009). Kolås (2014) finds that this dynamic applies well to rangeland conservation policies in

Central Inner Mongolia. In this case, the discourse of overgrazing is seen as a political tool for giving legitimacy to government regulation and thereby extending state control into people’s livelihoods. The criteria by which areas of rangeland are identified as degraded are often based on vague or incomplete data. However, Kolås (2014) finds that herders are largely unconvinced by the government’s scientific arguments, and consequently policymakers are forced to rely on neoliberal governmentality strategies, i.e., economic incentives (Kolås 2014). Williams (2000) reports similar findings, noting that the Chinese government’s use of scientific knowledge construction is particularly important in ethnic minority areas such as the Mongolian steppe. In these cases, the colonial aspects of the relationship between government and locals means that the government is in particular need of these practices to strengthen its legitimacy. Furthermore, the international scientific community has historically accepted Chinese government narratives uncritically, which is arguably reflective of positivist and colonial attitudes in environmental science at the global scale (Williams 2000). Therefore, while researchers work with governments to design effective rangeland policies, they must also be aware of the ethical complexities of how scientific knowledge is deployed by governments in their dealings with herding populations.

2.9 Conclusions

From this review of literature we can see that sustainable rangeland use and management is still a heavily debated topic. Debates continue regarding the ecological, economic and anthropological aspects of herding. However, in all three aspects the discussion appears to have seen a notable shift over recent decades. Ecologically, it appears that rangeland dynamics can show equilibrium or non-equilibrium properties, but that the non-equilibrium view has gained ground lately. Economically, we see that the relationship between economic development and environmental sustainability can range from mutually enhancing to oppositional, though win-win scenarios are now widely seen as less common than “win-settle” scenarios. Anthropologically, it is clear that herders’s preferences for large herds is a function of both strategy and cultural values, and that it should not be assumed that herders cannot self-organize to manage rangelands sustainable. Finally, while government policy can be an effective tool for rangeland management, government intervention can also be influenced by political rather than environmental motives. Overall, debates surrounding rangelands appear to have embraced greater complexity over time. With this is mind, it is essential that governments update their rangeland policies to better reflect these complexities.

While there has been a large amount of research on the relationships that exist between rangeland SES components, there are two particular knowledge gaps that I wish to explore. First, regarding the discussion in Section 2.3, we see that environmental shocks could theoretically exert either a positive or a negative effect on stocking rates. This presents us with the question: does this relationship tend to be positive or negative in the contemporary Inner Mongolian context? What are the particular factors that influence this outcome in Inner Mongolia? An empirical, quantitative analysis could yield valuable insights on how opposing mechanisms such

26 as these play out in this case. Second, Sections 2.5 through 2.7 of the literature review present multiple livelihood factors that have all been cited as major determinants of the tendency of land managers to manage land sustainably. These factors include those that relate to herders’ access to financial resources, i.e., income and access to credit, and those related to access to natural resources, i.e., land tenure. Literature will often alternatively describe on or the other of these factors as the “key” to sustainable land management, although either of these views may be more applicable depending on the context. In Inner Mongolia, both have been cited as crucial, but it would be interesting to see whether one of these issues would be a more impactful avenue of intervention from a policy perspective. A quantitative comparison of herders’ perceptions of these factors would help to answer such questions. The following two chapters of this thesis will seek to address these knowledge gaps.

Chapter 3: Effect of Environmental Shocks on Stocking Rates in Inner Mongolia

3.1 Introduction

Overgrazing is widely recognized as a serious threat to rangeland ecosystems (Tong et al.

2004; Wu et al. 2015; Squires 2009). The problem is particularly acute on the rangelands of

China’s northern region of Inner Mongolia, where herd sizes have witnessed a marked increase in recent years (Wu et al. 2015). Factors that likely contribute to this process are population growth and economic development, meaning that more herding families make a living from the same area of rangeland, and herders increasingly raise livestock not only for their own consumption but to sell to markets (Thwaites et al. 1998). This ecological degradation has limited herders’ ability to make a secure living from the rangelands and has prompted government controls on grazing that further limit herders’ access to pasture. These factors, combined with rising living costs and often inadequate access to healthcare and education, mean that herders in Inner Mongolia continue to face livelihood insecurity at the same time that their rangelands are threatened (Li 2016).

A wide body of literature indicates that herders maximize herd size as a means of buffering against future asset shocks arising from extreme climate events. This tendency has been reported among pastoralists including herders on the Tibetan Plateau (Levine 1999), pastoralists on the East African savannah (Scoones 1992) and Saami reindeer herders in Norway

(Næss and Bårdsen 2013). In these cases herders assume that their livestock will suffer high mortality due to extreme environmental events, and the sustainability of such a grazing system would demand that the livestock mortality rate continue to be high in order to keep livestock

28 numbers down and prevent overgrazing. This putative equilibrium has in many cases been broken by economic and policy changes in pastoral areas that make it easier for herders to procure livestock feed grain, buffering their herds against environmental shocks and reducing the environmental constraints that historically prevented unsustainable increases in stocking rates

(Kerven 2004).

In Inner Mongolia, policies have been implemented in recent years to limit overgrazing by imposing maximum stocking rate limits which are enforced with the use of economic incentives, namely subsidies and fines (Hua and Squires 2015). However, these policies have had limited success. Among the known challenges to successful implementation of these policies are lack of enforcement (Kolås 2014) and insufficient monetary compensation (Xie et al. 2015). In addition to these challenges, policies are met with resistance from the herders themselves (Li

2016). While the government expects herders to accept subsidies in exchange for restrictions on grazing, herders often choose to engage in forms of indirect resistance, grazing in excess of government regulations while continuing to receive subsidies (Li 2016). Such practices are in line with Scott’s (1987) description of hidden, everyday resistance as practiced by subsistence rural populations in general. Li (2016) attributes this resistance to economic necessity, while

Levine (1999), writing about the Tibetan Plateau, finds herders are reluctant to shift their investments from their herd to the cash economy because financial and market institutions are inadequate and unreliable. Additionally, researchers such Williams (2000) and Kolås (2014) point to theoretical disagreements over what practices are best for rangeland health, and also to a desire to protect their Mongolian identity and traditional way of life from perceived erosion stemming from regulations imposed by mainly ethnic Han officials.

Herders’ doubts about stocking regulations have been supported to an extent in the literature. Researchers have described stocking rate limits as overly rigid and simplistic in several ways. First, regulations prevent herders from taking advantage of good conditions during wet years, even though doing so would provide them with a buffer that would enhance their ability to make do with lower stocking rates during dry years (Hou et al. 2014). Second, the set stocking rate limits are uniform across entire counties, when in fact the health of rangelands is more influenced by variations in grazing intensity at much finer scales of observation (Hou et al.

2014; Ren et al. 2015). Third, while the type of grazing system employed has a large impact on the ability of vegetation to grow and regrow, policymakers set stocking rate limits without consideration of the type of grazing system employed (Zhang et al. 2014).

Rangeland degradation and livelihood vulnerability reinforce each other via positive feedback mechanisms (Squires and Hua 2010, 14). Overgrazing and vulnerability are therefore highly inter-related and subject to many of the same drivers. Addressing the combined issues of overgrazing and herder vulnerability requires a greater understanding of various factors that can positively or negatively affect the vulnerability of herder livelihoods. These factors have been studied in pastoral systems worldwide and include, among others, precipitation variability

(Martin et al. 2014), environmental shocks (McPeak 2004), the relatively reliance on off-farm versus on-farm income sources (Linstädter et al. 2016), and government policies (Li 2016).

These factors interact within rangeland social-ecological systems in complex ways that can confound efforts to measure the effect of any single factor (Martin et al. 2016).

Environmental shocks, the most prominent of which include droughts, snowstorms and locust plagues, pose a risk to herders by potentially inducing significant losses to their herds, which are a major share of their assets. This is particularly problematic for herders because

30 wealth in assets such as livestock provide a crucial buffer during years when income is low or volatile (McPeak 2004). Studying the effect of environmental shocks on pastoral household production is therefore important for at least two reasons – first, for its effects on herder vulnerability and, second, for how these vulnerabilities in turn influence ecological outcomes on rangelands. While there has been significant research on the effects of shocks on herder behavior

(Dercon 1998), such studies have focused mainly on income shocks that reduce the productivity of the herd (e.g., low rainfall that reduces meat or milk production but does not kill livestock) rather than asset shocks which result in the loss of a major portion of the herd (e.g., serious drought or storm) (McPeak 2004). This discrepancy is important to address because herders are believed to manage their herds differently in response to income shocks as compared to asset shocks (McPeak 2004). Empirical studies of this kinds can also help to develop the literature on the relationship between poverty traps and ecosystem conservation, which despite a wealth of theories is still relatively lacking in empirical models (Barrett, Travis, and Dasgupta 2011).

Considering the role that herders’ vulnerability to environmental shocks plays in driving overgrazing, it is important to understand the relationship between shocks and stocking rates on a finer level. This relationship may vary depending on the type (e.g., droughts versus snowstorms), frequency and intensity of shock. Variations in shocks may lead to variations in degree and even direction of stocking rate changes, considering that in addition to motivating herd maximization, environmental shocks can also cause large-scale starvation and death of livestock, thereby suppressing stocking rates (Kerven 2004). Also, different types of shocks can have very different effects on livestock mortality (Begzsuren et al. 2004). Knowledge of these dynamics could help policymakers to understand why overgrazing is happening in certain areas, and to predict where overgrazing might likely occur, as well as to develop strategies to aid herding communities

31 whose livelihoods are vulnerable to such shocks. Such knowledge will become increasingly important with climate change as the patterns of environmental shocks change (Squires and Hua

2010). In addition, understanding how drivers of overgrazing may differ between places would allow policymakers to adjust policy responses accordingly. For example, overgrazing that occurs in a drought-prone area may need to be addressed using more robust subsidies to placate livelihood fears, whereas overgrazing in areas without major incidents of droughts could be met simply with stricter regulations and a smaller emphasis on compensation.

The aim of this study was to estimate the effect of different environmental shocks on stocking rates on Inner Mongolia’s rangelands. The study looked at three types of shocks that are known to occur in Inner Mongolia: droughts, snowstorms, and locust outbreaks. My methods constructed a counterfactual estimate of how shocks affect households relative to similar households that do not experience shocks through a difference-in-differences (DID) statistical approach. I used data from a novel household dataset that recorded changes in stocking rates between 2010 and 2015 and environmental shocks that occurred during this period. The next section describes the study area, dataset, and the methods used to analyze the data. In the third section, I present the results of the analysis. In the fourth section, the findings are discussed along with its implications for theoretical understanding and for grazing policy. Finally, I conclude with recommendations for further avenues for research and for policy.

3.2 Methods

3.2.1 Study area

The Inner Mongolia Autonomous Region (also known simply as Inner Mongolia) is a region in Northern China, to the south of the independent state of Mongolia. Inner Mongolia is

China’s third largest province-level administrative division with an area of nearly 1.1 million km2, or 12% of the country’s total area (Cheng and Falkenheim 2013). Inner Mongolia is dominated by four climatic zones from west to east: cold desert, cold semi-arid, humid continental and subarctic (Peel, Finlayson, and McMahon 2007). Annual precipitation is between

100 and 500 mm throughout most of Inner Mongolia, with precipitation increasing from west to east (Hijmans et al. 2005). Temperatures increase from the northeast to the southwest, with average July temperatures ranging from 16°C to 26°C and average January temperatures between from -28°C and -8°C (Hijmans et al. 2005). This analysis focused on 15 Inner

Mongolian counties dispersed throughout the western, central and eastern regions of the province

(Figure 1). Environmental conditions vary widely throughout the counties and many different types of rangeland are represented, including meadow steppe, typical steppe, sandy steppe, desert steppe and desert. These rangeland types show a wide range of temperature and precipitation conditions (Hu and Zhang 2001). The population density of the 15 survey counties range from

0.33 people/km2 to 11.97 people/km2, with an average of 4.73 people/km2 (xzqh.org 2017). The percentage of ethnic minority (mostly Mongolian) people range from 9.60% to 74.91% and average 40.66% (xzqh.org 2017).

3.2.2 Dataset The analysis was performed on a panel dataset of herding households across Inner

Mongolia, with data recorded in 2010 and 2015. The data were collected through surveys developed and compiled by Dr. Brian Robinson of McGill University and Dr. Li Ping and Dr.

Hou Xiangyang of the Chinese Academy of Agricultural Sciences Rangeland Research Institute in Hohhot, China. Data were collected to characterize household assets (mainly land, buildings, machinery, household appliances and livestock), production, consumption and sales of pastoral products, income and expenses, and characteristics of the household such as family size and gender and age composition, as well as the ethnicity and education level of the household head.

The data were collected in 18 randomly selected counties stratified across five grassland types, classified by plant species, soil conditions, and grass productivity (Wenqiang Ding et al., 2014): meadow, typical steppe, sandy rangeland, desert steppe and desert. Within each grassland type, three counties were randomly selected. Then in each county, three sumu (towns) were selected that represented the grassland type. In each sumu twenty households were randomly selected to interview. On average, each county had responses from 50-60 livestock operations. The original dataset includes 1,092 households from 15 counties in Inner Mongolia and three counties in

Xinjiang. However, because a DID analysis requires data on multiple years for a single subject, and the survey respondents in the Xinjiang counties only had data for one year, the three

Xinjiang counties were not included in the analysis.

Several teams of enumerators were hired to collect the data. Though this survey eventually yielded a dataset with nearly 800 variables, the actual number of survey questions was far smaller. Most of the eventual variables were derived from calculations based on the original variables that were asked of the respondents directly. The surveys each took about 30 minutes on average to administer.

Among households in the dataset from Inner Mongolia, rangeland holdings range from

100 mu (6.67 ha) to 79,665 mu (5,311 ha) with a median holding of 4,700 mu (313.33 ha). Mean household size in the dataset is 3.88 people, and 73.88% of households are ethnically Mongolian,

34 while 20.76% are Han, 4.91% are Daur and the remainder are principally Manchu, Evenk or

Kazakh. In 2015 the overall composition of herds among households in sheep unit equivalents

(explained in the following section) were 44.1% sheep, 14.4% goats, 18.5% beef cattle, 4.3% dairy cattle, 10.1% horses and 8.7% camels. Herd sizes in sheep-unit equivalents ranged from 56 to 6481.6, with a median of 316.

Figure 1: Map of counties of Inner Mongolia that were included in analysis. Rest of Inner Mongolia in dark grey, with rest of China in light grey. Data from (Hijmans, Garcia, and Wieczorek 2010). Map created by author.

3.2.3 Data Exclusions Though the original dataset included 1,092 households and three counties of The

Xinjiang Uighur Autonomous Region, the current study used only households in Inner Mongolia with data on stocking rates and environmental shocks for both 2010 and 2015. The households from Xinjiang, as mentioned in the previous section, only included data from 2010. While Li and

Hou had initially intended to collect data from Xinjiang in 2015 as well, they decided, based on their review of the 2010 data, that the socio-economic and policy conditions in Xinjiang were too different from Inner Mongolia to be reliably compared. This narrowed down the number of households in the study to 707. Furthermore, just over a third of these households were subject to a policy under which they were banned from grazing their land after 2010 due to overgrazing concerns. I excluded these households from the present study because I was interested in the effect of environmental shocks on stocking rates, and did not want estimations to be skewed by other factors that could drastically affect the stocking rate. Finally, I excluded herders who sold more than 1,000 kg of milk in 2015. Upon studying the dataset I found that herders with a large dairy operation were generally associated with very high stocking rates. These stocking rates suggested that the households in question had dairy operations which relied far more on feed than on pasture, to the point that they might not be accurately described as herders. Therefore, I exclude such households from the model, as they were not the intended study population. Ten households fit this description and were thus excluded. After applied these exclusions, 451 households (902 observations) remained. However, as will be described in more detail in the

Results section, a further 15 (30 observations) households were excluded from the DID model because they were missing observations for several critical variables.

3.2.4 Data Preparation

Analyses were performed using the statistical software Stata 14.2 (StataCorp 2015). The main variables used in the analysis were each household’s stocking rate and three variables recording the number of times each household had experienced droughts, snowstorms or locust outbreaks between 2010 and 2015. The stocking rate was measured in sheep units/ha, with one

36 sheep being the standard unit. A goat was considered equivalent to 0.8 sheep, a dairy cow equivalent to eight sheep, a horse or beef cow to seven sheep, and a camel to nine sheep. These ratios are based on a combination of the standard Animal Index used in China as described by

Hu and Zhang (2001) and a number of other indexes around the world as described by Chilonda and Otte (2006) and other researchers. In order to make sure that patterns in stocking rate changes were not masked by inherent regional differences in absolute stocking rates, another variable was generated to capture the proportional change in each households stocking rate, rather than only showing the gross change in stocking rate. A dummy variable was created for each of the three shock types to indicate which households had or had not experienced the given shock at least once between 2010 and 2015. Each household had two observations because their stocking rate recorded in both 2010 and 2015, so a dummy variable was generated to show which year the observation belonged to. I then tested whether the stocking rate variable was normally distributed, and upon finding that the distribution was not normal, I performed a log transformation on the stocking rate. This yielded a normal distribution that could be used for the

DID analysis. ANOVA tests were used to compare mean stocking rates and frequencies of environmental shocks across the 15 counties and five rangeland types. Significant differences were found between counties and rangeland types for both of these variables, which indicated that counties and rangeland types would need to be controlled for in the model using dummy variables. Dummy variables were the therefore created for each county and rangeland type.

To control for differences in socioeconomic status among herders, I created an assets index for each herder (Filmer and Pritchett 2001; Sahn and Stifel 2003). The asset index was based on the number of passenger vehicles and the number of motorcycles owned by each household. Livestock assets and other assets related to production (e.g. farm machinery) were

37 excluded from the index in order to avoid endogeneity issues. Though it would have been desirable to include household appliances (such as TVs, refrigerators, cellphones etc.), the dataset did not include figures on these types of assets for the year 2010. An assets index was generated using Principal Components Analysis (PCA). These methods were based partly on those employed by Filmer and Pritchett (2001) and Sahn and Stifel (2003). Details on the PCA can be seen in appendix 2 at the end of this chapter. A dependency ratio was also created to show the ratio of working- to non-working members of each household.

3.2.5 The Difference-in-Differences Method A DID model studies the impact of a given treatment on a variable of interest by comparing the variable in the treatment group pre- and post-treatment with the variable in the control group pre- and post-treatment. The control group shows how the variable would have changed over time in the absence of the treatment (Slaughter 2001). In the current study, the treatment group was defined as those herding households which reported experiencing at least one environmental shock between 2010 and 2015, while the control group was defined as those households who reported not experiencing any environmental shocks between those years. By comparing the change in stocking rate between 2010 and 2015 of treatment households with the change in stocking rate in control households, we can isolate the effect of the occurrence of environmental shocks on stocking rates, which is the treatment effect. The treatment effect is equal to the difference between changes in stocking rates in the treatment and control group between 2010 and 2015, when the treatment (i.e. environmental shocks) occurred. Therefore, the treatment effect is referred to as the difference-in-differences (DID).

The equation for a DID model can be described as follows (Slaughter 2001):

푗 푗 푗 푗 푦 푖푡 = 훽0 + 훽1푑푡 + 훽2푑 + 훽3푑 푡 + 훽4푥 … . +푒 푖푡

푗 where 푦 푖푡 is the outcome for an individual i at time t in treatment group j. 푑푡 is a dummy variable and is equal to 0 if the measurement is taken before the treatment and 1 if the measurement is taken after the treatment. 푑푗 is another dummy variable and is equal to 0 if the

푗 individual is in the control group and 1 if the individual is in the treatment group. 푑 푡 is a third dummy variable that is a 1 if the measurement is taken after the treatment and the individual is in the treatment group. 훽0 is the y-intercept, while 훽1 represents the effect of being in the post- treatment measurement period, 훽2 represents the effect of being in the treatment group, and 훽3 represents the effect of both being in the treatment group and being in the post-treatment measurement period. X and any subsequent variables represent covariates that can be included in the equation, while 훽4and any subsequent β terms represent the effects of those covariates.

푗 Finally, 푒 푖푡 represents the error term associated with the observation. In DID analyses, the effect of interest is 훽3, which represents the actual effect of the treatment once we control for the effects of the time period and being in the treatment group.

3.2.6 Difference-in-Differences Analysis

Applying the DID method to the current study, the two observation periods are 2010 (t =

0) and 2015 (t = 1). The two treatment groups are those households that did not experience any shocks between 2010 and 2015 (the control, or j = 0) and those that experienced at least one shock in this period (the treatment, or j = 1). Covariance variables included household size, the area of rangeland owned by the household, the household’s dependency ratio, number of household members under 16 and over 60, the highest level of education achieved in the

39 household, and the household’s asset index. In addition, the covariance variables included dummy variables for geographic location by county or rangeland type, depending on the model.

We used Stata’s diff command (Villa 2016) to perform a DID analysis on the effect of each of the three shock types on stocking rates. Stocking rates from 2010 were used as the first wave and 2015 stocking rates as the second. The model was run using both the natural log of stocking rates and the natural log of proportional stocking rates as dependent variables, because both of these variables were found to be non-normally distributed, meaning that log transformations would be necessary for a DID analysis.

3.3 Results

3.3.1 Descriptive Statistics

Table 1 presents the number of shocks households reportedly experienced, by category, between 2010 and 2015. Among the 451 households remaining after the data exclusions, the average stocking rate decreased by nearly half between 2010 and 2015, from 2.10 to 1.39 sheep units/ha. 72.73% of households experienced at least one environmental shock, with an average of

1.82 shocks. The most frequent type of shock to be experienced was a drought, with an average of 1.28 droughts per household. Snowstorms were experienced on average 0.48 times per household, followed by locust outbreaks at 0.06 per household. Table 1 shows that while a large majority (72.73%) of households experienced at least one shock, no individual shock type was experienced by a majority of the herders. Large minorities of herders experienced at least one drought (49.00%) or one snowstorm (39.91%), though very few households (3.55%) experienced

40 locust outbreaks. Of those households that experienced droughts, all experienced between one and five droughts. Of households that experienced snowstorms, nearly all experienced one or 2, with only a handful of households experiencing more. Of households that experienced locust outbreaks, only one household (0.22%) experienced more than 2.

Table 1: Number and type of shock experienced, 2010-2015

# Shocks % Households by shock type and frequency experienced by Any Droughts Snowstorms Locusts household shocks 0 27.27 51.00 60.09 96.45 1 31.49 15.74 35.48 2.22 2 13.08 10.42 3.55 1.11 3 10.20 9.09 0.22 4 4.21 4.21 5 9.53 9.53 0.44 0.22 6 2.66 7 0.89 8 0.22 9 0.22 12 0.22 15 0.22

Table 2 shows that there is a wide range (3-53) in the share of households in each county that are included in the analysis. There is also a wide range in the percentage of households experiencing droughts (0-100%) and snowstorms (0-100%), while the percentage of households experiencing locusts is less (0-20%). The 2010 stocking rate ranged from 0.74 to 21.33 sheep units/ha. The county with the second highest stocking rate averaged 8.70 sheep units/ha.

Appendix 1 illustrates the above-described spatial patterns generated in QGIS (QGIS

Development Team 2015). Discrepancies between counties are statistically significant in an

ANOVA test. I therefore account for these in the DID model to help control for regional variation.

Table 2: Shock and Stocking Rate Statistics by County

County Total % % % 2010 Households Households Households Households Stocking Experiencing Experiencing Experiencing Rate Drought Snowstorms Locusts (SU/ha) Alashan left banner 21 47.62 9.52 0.00 1.95 Alashan right banner 40 37.50 5.00 20.00 0.74 Chenbaerhu 3 33.33 0.00 0.00 8.70 Dongwuqi 50 2.00 74.00 0.00 2.15 Etuoke 37 62.16 2.70 0.00 1.17 Ewenke 3 33.33 66.67 0.00 21.33 Hangjin 44 70.45 13.64 0.00 2.29 Siziwang 16 75.00 93.75 0.00 1.58 Sunite left banner 46 97.83 69.57 0.00 0.97 Sunite right banner 45 100.00 13.33 0.00 0.64 Wulatehou 40 27.50 30.00 10.00 1.56 Wushen 9 33.33 0.00 0.00 3.09 Xianghuangqi 40 52.50 45.00 10.00 3.46 Xilinhaote 53 3.77 81.13 0.00 3.51 Xinzuoqi 4 0.00 100.00 0.00 5.59

3.3.2 Models of Absolute Changes in Stocking Rate

Table 3 shows the coefficients and significance levels of the treatment effect (_diff) and covariates of four DID models. The models in this table are based on absolute changes in stocking rates between 2010 and 2015. The models in Table 3 are all based on county-level controls. They each do have a corresponding model with rangeland type-level controls, but because the results are very similar between the two control methods, for the sake of simplicity only those with county controls are shown. It should be noted that while the intentional data exclusions left 451 households remaining, in the model only 872 observations are included,

42 representing 436 households. 30 observations were automatically excluded from the model because they were missing values for certain variables. These included 12 observations for the logged stocking rate, stemming from the fact that some herders had a stocking rate of 0 in 2015, indicating that they had stopped herding between 2010 and 2015. Thirteen observations were missing for education, while eight observations were missing for the asset index, because some observations were missing values for the assets from which the asset index was developed (e.g. number of motorcycles etc.). There was also one missing observation for area of rangeland owned. While these missing values add up to 34, there is some overlap; only 30 observations were dropped.

The treatment effect (represented in the table by the “_diff” column) represents the percentage difference that a given shock is associated with compared to those households that did not experience the given shock. The model controls for changes over time as well as for differences between treatment and control groups that do not stem from the treatment itself. The treatment effect is not significant for the overall shocks model, and neither is it for the locust model, but it is significant for droughts and snowstorms. Droughts are associated with an increase in stocking rates of 22.8%, while snowstorms are associated with a decrease of 28.3%.

Many covariates are significant in the model. Larger rangeland holdings are associated with a very small but significant decrease in stocking rates across all models, while wealthier households (as identified with the asset index) are associated with higher stocking rates. Though omitted from the table for the sake of simplicity, nearly all counties and most rangeland types have a significant effect on the models and large coefficients. However the signs vary, indicating a high and significant degree of variation in stocking rates between places.

Table 3: Difference-in-differences models based on absolute changes in stocking rate, with treatment effects in bold italics. Only county-level controls included.

Shock Type Any Shock Drought Snowstorm Locust Wave -0.168 -0.249*** -0.0158 -0.124* Treat 0.0493 -0.164* 0.152* 0.172 _diff 0.0483 0.228** -0.283** -0.139 Household size 0.0253 0.0234 0.0231 0.0240 Grass area -0.0000461*** -0.0000461*** -0.0000467*** -0.0000458*** Dep. Ratio -0.0368 -0.00773 -0.0385 -0.0464 Under 16 0.00646 0.00997 0.00310 0.00460 Above 60 -0.0251 -0.0197 -0.0216 -0.0268 Education 0.0354 0.0351 0.0352 0.0359 Asset index 0.0622** 0.0633** 0.0626** 0.0624** N 876 876 876 876

3.3.3 Models of Proportional Changes in Stocking Rate

Table 4 shows DID models for each environmental shock using counties and rangeland types as covariates, but the models are based on proportional rather than absolute changes in stocking rates. Again, only those models with county-level controls are shown, because the results of those models with rangeland-type controls are very similar. For the same reasons described in the previous section, 30 observations were dropped from the model. Again, the treatment effects are found to be large and significant for droughts and snowstorms in all models, though they are positive in the former and negative in the latter. As in the absolute change models, locust outbreaks are not found to have a significant effect on stocking rates, and neither are shocks overall. The proportional change models different greatly form the absolute change models in terms of the significance of covariates. Unlike in the previous models, most counties

(whose effects were again omitted for the sake of simplicity) did not exert a significant effect on the model.

Table 4: Difference-in-differences models based on proportional changes in stocking rate, with treatment effects in bold italics.

Shock Type Any Shock Drought Snowstorm Locust Wave -0.180* -0.251*** -0.0367 -0.0989* Treat -0.0425 -0.100 0.129 -0.0336 _diff 0.0913 0.262** -0.186* -0.322 hh_size 0.0399* 0.0375* 0.0386* 0.0408* gsld_own_mu -0.00000260 -0.00000250 -0.00000300 -0.00000311 Depratio -0.166 -0.124 -0.171 -0.170 under16 -0.0539 -0.0481 -0.0554 -0.0591 above60 -0.0449 -0.0369 -0.0411 -0.0468 Education -0.0446 -0.0459 -0.0465 -0.0459 Assetindex 0.0183 0.0178 0.0185 0.0176 N 877 877 877 877

3.4 Discussion

3.4.1 Impact of droughts and snowstorms on stocking rates

Frequent environmental shocks are known to have a significant impact on stocking rates

(Kerven 2004). Environmental shocks kill off large numbers of livestock, hindering herd size increases. Shocks during the winter, including both snowstorms and winter droughts (referred to collectively as dzud in Mongolian) are particularly deadly and are known to decimate herds throughout the Central Asian rangelands (Nadin, Opitz-Stapleton, and Xu 2015; Kerven 2004).

In Outer Mongolia, severe winter weather has killed over 20% of the country’s livestock on two separate occasions since 1999 (Fernández-Giménez, Batkhishig, and Batbuyan 2012). In Inner

Mongolia winter storms also cause high livestock mortality rates (Yang, Ci, and Zhang 2008; Ye et al. 2017). Mortality rates largely depend on whether or not herders have stored sufficient winter fodder (Kerven 2004). It is probable that events on such as scale would keep stocking

45 rates low in areas where they are frequent, and it is therefore not surprising that my study finds snowstorms to be associated with lower stocking rates.

Considering that droughts are also a major stress for herders, it may appear counter- intuitive that my study finds droughts to be associated with higher stocking rates. However there is also support in the literature for this outcome, as herders are known to keep extra livestock as insurance against drought (Squires et al. 2010a). The traditional style of nomadic grazing in Asia was to maximize the herd size when forage was available and consume excess animals in lean periods such as droughts and storms (Kirychuk and Fritz 2010). From this perspective it is understandable that herders who experience drought more frequently would be more likely to have large herds, as the above-described livelihood strategy would be more necessary.

However, this does not explain why herders who experience droughts would have larger herds while herders who experience snowstorms would have smaller herds, as the herd maximization strategy theoretically should apply to both kinds of shocks. One explanation could be that the two types of shocks cause very different livestock mortality rates. In Mongolia severe winter weather, whether blizzards or droughts, are the dominant cause of livestock mortality, rather than droughts during the growing season (Rao et al. 2015). In my study, droughts refer only to growing-season droughts. Though growing-season droughts can greatly exacerbate the effects of winter shocks if they directly precede them, alone they do not cause as high a degree of mortality (Rao et al. 2015). Studies on Chinese rangelands find that growing-season droughts are mainly associated with financial burdens for herders because they force them to buy more hay and feed, while it is winter droughts that commonly lead to widespread livestock mortality

(Nadin, Opitz-Stapleton, and Xu 2015). This difference in outcome between summer and winter climate shocks is probably related to the fact that in winter, the actual mechanism by which

46 livestock are killed is not starvation but freezing. While unavailability of grass or surface water

(due to deep snow or winter drought) is the key factor determining mortality, the immediate impact of these conditions is that livestock cannot eat to keep their metabolism high enough to maintain a sufficient body temperature, and they freeze to death (Kerven 2004).

Therefore, freezing temperatures during the winter incur a much higher mortality rate than starving during the summer (Kerven 2004). In the long term, stocking rates are probably influenced mainly by a combination herd maximization strategies and livestock mortality from natural disasters. The outcome of my study suggests that while in the case of droughts the maximization strategy outweighs the disaster mortality, in the case of snowstorms the disaster mortality outweighs the maximization strategy. Winter snowstorms have fast-acting and intense effects on livestock, and this may make adaptation difficult. Summer droughts, by contrast, are less intense and occur over a longer period of time, which may make it easier to develop adaptation strategies. This could potentially explain why the two disaster types ultimately have opposite effects on the stocking rate. Put differently, the diverging effects of summer droughts and winter disasters can be seen as ex-ante behavior (i.e., deliberate herd increase in anticipation of hardship) and ex-poste outcomes (i.e., high livestock mortality resulting from natural disasters), respectively.

3.4.2 Insignificance of locust outbreaks

The statistical insignificance of locust plagues was not surprising because locusts, despite being a common and persistent problem in Inner Mongolia (Cease et al. 2015; Kang et al. 2007) in the data that I have used they are not widespread nor of the same magnitude and intensity as droughts or snowstorms. In this study only about 3.5% of herders experienced locust outbreaks

47 between 2010 and 2015. However, they were very spatially clustered, with 10-20% of herders experiencing outbreaks in the few counties where they did occur. In addition, while no particularly large locust outbreaks occurred between 2010 and 2015 (as compared to larger outbreaks in the early 2000s, for example), larger outbreaks could lead to rates of occurrence closer to those of droughts and snowstorms. In this study the level of occurrence of locust outbreaks was so low that it may have not been worthwhile for herders to adjust their herd management practices to cope with locusts. On the other hand, the sample size of herders who had experienced locusts was probably too small to be statistically sound for the purposes of this analysis, as only 16 herders reported experiencing locust outbreaks. Future studies on the relationship between locust outbreaks and stocking rates may yield more meaningful results if they focus on those counties which were found to have a higher incidence of locusts. A more in- depth study of Alashan right banner, where 20% of herders reported experiencing locusts, might serve this purpose. Theoretically, if herders were chronically exposed to locust outbreaks to the same degree as their exposure to droughts and snowstorms, they would probably employ similar adaptation strategies, maximizing their herd size. The net effect of locust outbreaks on stocking rates would therefore depend on the livestock mortality or morbidity rates that they induce, though data on this is scarce. Field trials in Inner Mongolia have found that vegetation losses from locusts are under 3% (Qiu, Li, and Fan 1994). However, locusts are widely perceived by

Inner Mongolian herders as a major threat (Sternberg 2008).

3.4.3 Effect of spatial variations on model

The analysis showed that stocking rates vary greatly by county. This was not surprising considering the great range of environmental conditions that exist across Inner Mongolia, which

48 give rise to a range of carrying capacities (Yu, Ellis, and Epstein 2004). Furthermore, there was strong spatial segregation between the treatment group (i.e., those herders that did experience the shock in question) and the control group (those that did not), with treatment households (for both drought and snowstorm models) being concentrated in counties with low stocking rates. This gave rise to several concerns. The first of these was that comparing treatment and control groups across such large stocking rate differences might mask or distort the model results. A decrease in stocking of 0.4 sheep/ha between 2010 and 2014, for example, could be considered large if it occurred in a treatment area where stocking rates were only 0.8 sheep/ha in 2010, but small if it occurred in a control area where stocking rates were four sheep/ha 2010. This would be equivalent to a 50% decrease in the former case but only a 10% decrease in the latter. If treatment and control households had averaged this same change between 2010 and 2015, one might conclude that the treatment had no effect if looking at absolute changes, even though treatment households were decreasing their stocking rate far more in relative terms, which is more relevant from a theoretical standpoint. I decided that the best way to control for this scale mismatch was to measure changes in stocking rate in relative terms rather than absolute terms.

A second spatial concern was that the treatment and control households might be located in counties where socio-economic or policy conditions are fundamentally different. These differences could theoretically be more important drivers of differences in stocking rates than the degree of exposure to environmental shocks, but I would run the risk of erroneously assigning causality to the latter. Ultimately, I decided that including dummy variables for the counties in the model would be sufficient to control for these effects. Initially, I considered using a propensity score to match households by their county location. This would have caused the model to only compare treatment household with neighboring control households, to ensure that

49 the two groups truly derived from the same population. However, I decided that this would be too restrictive because it is logical that there would be spatial correlation in the occurrence of climatic events, and trying to find sufficient spatial common support might prove unrealistic.

Furthermore, it is likely that herders who do not experience a given environmental shock but who are in an area where most herders do experience the shock would make herd management decisions in line with most their neighbors, and vice versa. Because a major purpose of this study is to observe herd management decisions, it therefore might be more informative to compare trends between areas with a high shock incidence and low shock incidence, rather than comparing trends between herders in a single area. Considering this, I decided that it was acceptable to compare treatment and control populations that were largely spatially segregated from each other, and that it would be sufficient to control for spatial effects by adding county and rangeland type covariates to the models.

3.4.4 Limitations of Analysis

One potential limitation of this study is that the spatial analyses used were not particularly sophisticated. I used ANOVA to compare stocking rates and environmental shocks across counties, rather than applying a more informative and specialized technique such as

Moran’s I. While Moran’s I is the most typical test used for observing spatial patterns, for this analysis it was not used because there did not appear to be an appropriate level of spatial units in the data for applying Moran’s I. The two levels of observation for which I had spatial data were the household level and the county level. Calculating Moran’s I for hundreds of households included in this study may have been computationally cumbersome, and the number of counties

(15) was too small for a calculation of Moran’s I to be particularly interesting. Using ANOVA to

50 compare counties is a crude way to observe spatial patterns, but it seemed justifiable considering that spatial patterns were not the main focus of this analysis. The “bluntness” of counties as a unit of observation is also a potential limitation in and of itself. However, while counties are a rather large unit, a considerable amount of the variation in biophysical, cultural, socio-economic and political conditions does occur at the county level, such that county-level indicator helps isolate the effects of environmental sihocks. Nevertheless, a more sophisticated and finer- resolution analysis of spatial variation in stocking rates and environmental shocks in Inner

Mongolia would be valuable in future research.

Another possible limitation lies in the creation of the assets index for controlling for varying degrees of wealth among the herders. For herders, their livestock are obviously a major part of their assets. However, if I had included livestock numbers in the assets index, this would have potentially caused endogeneity issues because the left-hand side of the equation is the stocking rate, and stocking rates and total livestock numbers could likely be correlated. On the other hand, there might not necessarily be such a correlation, and excluding such a major component of herders’ assets from the index would be challenging to justify. As a compromise, I created two different assets indices, one including livestock and one excluding them. I then tested whether there was strong correlation. The correlation was found to be very high, at over

90%. Therefore, I determined that I should exclude livestock from the asset index. In doing so the index would not be losing very much information, and it would also avoid the endogeneity issue while preserving nearly all of the information that was carried by including livestock.

3.4.5 Policy Implications

The diverging effects of droughts and snowstorms on stocking rates may be useful for policymakers to keep in mind as they tackle the issues of overgrazing and livelihood vulnerability. From the perspective of tackling overgrazing, it appears that drought-prone areas are probably more susceptible to overgrazing than snowstorm-prone areas, while no clear patterns have been discerned for locust-prone areas. Furthermore, drought is likely to be an important factor in areas that have already been identified as suffering from overgrazing.

Snowstorms, on the other hand, not only do not appear to contribute to overgrazing, but could even lead to less overgrazed rangelands in comparison with areas where environmental shocks of any kind are rare. Viewing the situation from the perspective of livelihood vulnerability shifts the emphasis from droughts to snowstorms. While droughts are more problematic from an ecological management perspective, snowstorms are more problematic from a livelihoods perspective, as snowstorms incur high and sudden livestock mortality, making herders’ livelihoods much more insecure. Additionally, because snowstorms have a more extreme and sudden impact on livestock than droughts, it is probably harder for herders to practice adaptation strategies for snowstorms than for droughts. This means that policymakers will need to find ways that alleviate herders’ vulnerabilities during the winter. Supplementary winter feed will probably be a major part of such strategies. However, policymakers would need to find a way to increase herders’ access to winter feed without leading to unsustainable increases in stocking rates, as has often occurred in grazing systems in China and the former Soviet Union (Kerven 2004). Ideally, the government could find an effective way to provide adequate and timely feed supplies to prevent livestock from freezing to death, but not provide an over-abundance of feed that leads to long- term increases in herd sizes.

In confronting overgrazing, one piece of knowledge that would be particularly useful for policymakers is whether drought is part of the dynamic in a particular area. While some degree of restrictive or punitive measures may be necessary to manage drought-related overgrazing, the problem may also require more substantial means of addressing herders’ vulnerabilities to drought, even though these vulnerabilities are less than those associated with snowstorms. This could be accomplished by providing more subsidies, or through further developing livestock insurance schemes (Ye et al. 2017; Peng et al. 2011), while simultaneously making enforcement of stocking rate regulations more consistent. Index-based livestock insurance (IBLI) that uses rainfall, snowfall or temperatures as the index could be appropriate for the Inner Mongolian grazing context (Ye et al. 2017). Such a system would make payouts to herders only when these shocks occur, so that herders have the means to buy feed during times of hardship but not at other times of year, thereby mitigating livestock mortality risk but also preventing unsustainable herd expansion. Research shows that IBLI could be more effective than more conventional insurance schemes, though lack of data, fluctuations in feed prices and herders’ limited knowledge about insurance would all be challenging for implementation of IBLI (Ye et al.

2017). It may prove challenging to convince herders to trade excess livestock for financial incentives considering that herd-maximization is such a deep-rooted traditional survival strategy, and that herders may be skeptical of the reliability of financial institutions (Levine 1999). These findings are made all the more relevant by climate change, which is expected to lead to more frequent and prolonged droughts on Inner Mongolia’s rangelands (Angerer et al. 2008). As droughts increase in frequency, it is probable that herders will increasingly buffer against this threat by increasing their herd sizes, thereby further exacerbating overgrazing.

Overall, the challenges of protecting rangelands and herder livelihoods appear to be similar in drought-prone areas and in snowstorm-prone areas. However, from a policy perspective the emphasis would need to be more on preventing overgrazing in the case of droughts, while in the case of snowstorms the emphasis should be more on alleviating livelihood vulnerability. At the same time, factors that improve one of these factors can often exacerbate the other, because large herds provide security to herders at the same time that they put greater strain on rangelands. Overall, a balance needs to be struck between these goals, but the precise balance will be different depending on the nature, frequency and intensity of environmental shocks that occur over space and time.

3.5 Conclusion

As policymakers seek to tackle the problem of overgrazing across the diverse conditions that exist on Inner Mongolia’s rangelands, a nuanced understanding of the diverse and interacting factors that drive overgrazing will be an asset. This study has contributed to our understanding of pastoral grazing dynamics by demonstrating that different types of environmental events can lead to divergent outcomes in terms of stocking rates. Droughts seem to drive increases in stocking rates, while snowstorms have a negative effect on stocking rates.

This is despite the seemingly similar mechanisms by which droughts and snowstorms influence herd sizes – they both deprive livestock of food or water resources leading to livestock mortality and morbidity, and they both elicit a response from herders to maximize their herds when they have the chance. The best available explanation for this divergence relates to the fact that snowstorms inflict a much higher mortality rate on livestock than droughts do. In the case of droughts, the lower livestock mortality rate is overpowered by the herders’ maximization

54 response, leading to a net increase in the stocking rate. In the case of snowstorms, however, the higher livestock mortality rate overpowers the herders’ maximization strategy, leading to a net negative effect of snowstorms on stocking rates. Avenues for further research on this explanation may include comparisons of livestock mortality rates between drought-stricken and snowstorm- stricken households, in addition to measurements to compare the pace at which herders rebuild their flocks following each of these events, and the eventual herd sizes that are typically attained in each case. Such research would be able to confirm empirically whether or not the hypothesized dynamic is in fact occurring. Regardless, the knowledge that environmental shocks lead to significant and varying stocking rate outcomes will be useful for grazing management on

China’s rangelands. This will help grazing management policy to be flexible and appropriate to the diverse needs of rangelands and herders across complex and changing conditions.

Appendix 1 – Stocking rates and environmental shock occurrence across counties Data from (Hijmans, Garcia, and Wieczorek 2010). Maps created by the author.

Appendix 2 – Stata output of PCA for asset index

Principal components/correlation Number of obs = 458 Number of comp. = 8 Trace = 8

Principal components (eigenvectors) ------Variable | Comp1 Comp2 Comp3 Comp4 Comp5 Comp6 ------+------gsld_own_mu | 0.2060 0.5535 0.0220 -0.2816 -0.4727 -0.1315 house_area~l | 0.5042 -0.1777 0.0451 0.2806 0.0152 0.3712 shed_area_~l | 0.3819 -0.0647 -0.3580 0.5964 -0.2664 -0.4853 passcar | 0.4915 0.0503 0.3341 -0.1789 -0.3379 0.3522 moto | 0.3382 0.4066 0.2002 -0.1628 0.5220 -0.4844 tractor | 0.3863 -0.4587 0.1739 -0.2028 0.3632 -0.1003 threewheel~r | -0.1378 0.3786 0.5136 0.6213 0.2072 0.1976 truck_number | 0.1826 0.3691 -0.6509 -0.0239 0.3793 0.4490

------Variable | Comp7 Comp8 | Unexplained ------+------+------gsld_own_mu | -0.2923 0.4952 | 0 house_area~l | -0.6884 -0.1469 | 0 shed_area_~l | 0.2425 0.0284 | 0 passcar | 0.5397 -0.2876 | 0 moto | -0.0804 -0.3743 | 0 tractor | 0.1482 0.6365 | 0 threewheel~r | 0.1364 0.2954 | 0 truck_number | 0.2087 0.1312 | 0 ------

Chapter 4: A choice model of incentive strategies for reducing overgrazing Inner Mongolia’s rangelands

4.1 Introduction

Grasslands and other rangelands are important ecosystems in China, representing about

40% of the country’s area (Hua and Squires 2015). Over three million pastoral households make their living off of China’s rangelands (Squires and Hua 2010). Since the 1980s, overgrazing has frequently been cited as a large and growing problem. In recent decades, many regions have witnessed an increase in rangeland livestock numbers, which have been largely attributed to population increase and economic development. These factors are regarded as the major drivers of overgrazing, though privatization of rangelands and sedentarization of herders is also cited as an exacerbating factor (Thwaites et al. 1998). Rangeland degradation is a major threat to pastoral livelihoods (Thwaites et al. 1998). In addition, the soil erosion that accompanies rangeland degradation has a detrimental effect on air and water quality in urban areas (Normile 2007).

To protect rangeland ecosystems, the Chinese government has instituted various rangeland policies. The Grassland Law of 2002 decreed that local administrative authorities should set legal limits to stocking rates (Jiang 2002). In 2011, the Rangeland Ecological

Compensation Program was introduced in Inner Mongolia. This program provides subsidies with the expectation that they will not exceed locally determined stocking rate limits (Hua and Squires

2015). Herders who exceed the stocking rate limit still receive a portion of the subsidy, but a large portion is withheld (Kolås 2014). However, these rules are frequently not enforced, and in many regions a majority of herders continues to graze well in excess of the legal limit (Kolås

2014). Herders are often opposed to stocking rate limits, describing them as overly rigid and

59 insensitive to fluctuating local conditions (Kolås 2014). Levels of compensation provided tend to be insufficient to make up for the opportunity cost of reductions in herd size (Xie et al. 2015).

Local officials, reluctant to provoke opposition from herders, frequently under-count livestock numbers in order to avoid penalizing herders (Kolås 2014).

It is clear that policymakers have struggled to bring about adequate reductions in grazing intensity through cash subsidies alone. In light of this, researchers have called for diversifying incentives and compensation mechanisms for rangeland protection (Xie et al. 2015). Alternative policy tools may include improvements in access to credit, land tenure, or various components of the social safety net. Access to credit is cited as a key influence on the choices and tradeoffs that the rural poor can make in terms of their use of environmental resources (Barbier 2010). In

China, improvements in credit access have been cited as a promising future avenue for eco- compensation schemes (Xie et al. 2015). Credit-based payment for ecosystem services has been practiced in Latin America and found to be effective in protecting forest resources (Cranford and

Mourato 2014). Though rural credit programs have been implemented in pastoral areas of China, these are not tied to demands that herders reduce stocking rates (Wang and Richter 2011).

Another possible mechanism that rangeland protection policies could employ is reform of land tenure. Secure tenure has been cited as a necessity for sustainable ecosystem management on rangelands (Banks 2001) as well as in forests (Robinson, Holland, and Naughton-Treves

2014) and fisheries (Grainger and Costello 2011). In general, since the 1980s, rangelands in

China have been managed under the Household Contract Responsibility System (HCRS). Under this system, land owned by the government is leased to herding households on time-limited contracts, which usually last for 30 years (Ho 2000). The short-term nature of herders’ land rights has been cited as a disincentive for herders to manage their rangelands sustainably (Banks

2001). Government officials have indicated longer land contracts are a long-term goal of the government (Ho 2001). Furthermore, in certain contexts local officials have been given the right to offer longer land contracts (up to 70 years) to herders as an incentive for sustainable management practice (Nelson 2006). Though this type of incentive has not been applied on a larger scale, its existence may serve as a model for regional policies.

While the above-mentioned literature indicates that other incentives and compensation tools besides cash subsidies may be able to mitigate overgrazing in China, there is little empirical data on the actual effectiveness of such strategies. Furthermore, it is unclear what degree of impact these alternative policies could have. Such impacts may be highly dependent on the specific attitudes and decision-making processes of local herders. In Inner Mongolia, it appears that rangeland policies have been undermined by an inadequate understanding of herders’ motivations (Williams 2000). One way in which researchers can predict and compare people’s reactions to different policy tools is by means of a choice experiment. Choice experiments model people’s preferences between alternatives based on the attributes of the alternatives (Adamowicz et al. 1998). Choice experiments have been widely applied in environmental economics as a tool for estimating the value that people put on specific environmental protection measures (Bennett and Birol 2010). With this method, policymakers can estimate how much the general public would be willing to pay toward a given environmental policy, as well as the type and amount of compensation that resource managers would need to receive in order to be willing to implement certain measures (Bennett and Birol 2010).

This paper describes a discrete choice experiment that was performed in central Inner

Mongolia during the summer of 2016. The objective of this study was to compare the potential of various policy tools to help in the mitigation of overgrazing. The next section of the paper will

61 consist of a description of the study site and the methodology, along with summary of the theoretical framework of the study. This will be followed by a description of the modeling approach and the results obtained from the models. Finally the findings of this study will be discussed and compared with wider literature on grazing policy, and the implications for future grazing policy formulation in the region will be discussed.

4.2 Study Site and Experimental Design

4.2.1 Xilingol Prefecture, Inner Mongolia Autonomous Region

The prefecture of Xilingol is located in the center of China’s Inner Mongolia

Autonomous Region, a northern region bordering Mongolia. The landscape is mostly composed of steppe, and outside of urban centers the majority of the population is ethnically Mongolian and engages in the herding of Sheep, goats, cattle, horses and camels. Pasture was collectivized in the 1950s and decollectivized in the 1990s. Land use rights were then distributed to individual households in the form of 30-year leases in accordance with the new Household Contract

Responsibility System (HCRS), but land remains government-owned.

Starting in the 1980s, overgrazing began to be recognized as an increasing threat to rangelands. In 2005, the government introduced the Grass-Animal Balance Policy which, set limits on stocking rates in the prefecture such that all rangeland had a mandated ratio of land area to livestock numbers that ought not to be exceeded. In 2011, fines were introduced to help enforce these laws in combination with subsidies to make up for lost income. However, local officials have indicated that they, out of fear of the consequences for herders’ livelihoods, have

62 often been unwilling to enforce these limits. As a result, the majority of herders continue to graze in excess of the government’s mandated stocking rates.

Three Counties of Xilingol Prefecture were selected for this study – Xilinhot, East

Ujumqin and Xianghuang. These three counties were chosen because they are considered to have similar ecological conditions and similar restrictions on grazing. However the three counties vary widely in size and population density, leading to markedly different average land holdings and herd sizes. Xianghuang in particular is regarded as suffering from land degradation, and has therefore been assigned stricter stocking regulations. In practice, herders in all three counties graze far in excess of legal stocking rates. Table 1 summarizes these figures below using unpublished panel data collected by Li and Hou in 2015. For the most part, this survey included the same households that were interviewed for Li and Hou’s 2015 panel data, in which herders were selected randomly. However, in the field we sampled additional households in Xianghuang that were not in the original dataset.

Table 1: Summary of grazing statistics for the three Counties based on Li and Hou’s dataset (Notes: 15 mu = 1 hectare; 1 sheep unit equivalent = 1 sheep/goat = 0.2 cattle/horses/camels)

County Xilinhot East Ujumqin Xianghuang Average Rangeland 4145 9316 1794 Holding (mu) (15 mu = 1 ha) Average Herd Size 512 840 212 (sheep unit equivalents) Average Stocking 10.8 7.9 10.7 Rate (mu/sheep unit) Official Stocking 21 25 50 Rate limit (mu/sheep unit) Source: Li & Hou (2015) The Grass-animal Balance Policy subsidy is generally given to herding households based on their land holding, and in such cases the subsidy rate is 1.71 yuan per mu of rangeland.

However, in Xianghuang county a large number of households receive the subsidy on a per- person basis (3000 yuan/member of household) because their land holding is deemed so small that a land-based subsidy would be too small to make an impact. In Xilinhot and East Ujimqin, the fine for exceeding the legal stocking limit is equivalent to 40% of the household’s expected subsidy. Subsidies are therefore given in two stages: In the first stage, 60% of the subsidy is granted, after which an inspector comes to observe the household’s stocking rate. If the legal stocking rate is being exceeded, the household is given a few months to bring its stocking rate down to regulation levels in order to receive the remaining 40% of the subsidy. If this demand isn’t met, the government keeps the remaining 40%. In Xianghuang, no fine is administered to households that exceed the stocking rate. Officials in Xianghuang justify this on the grounds that the legal stocking rate limit is too strict and that herders are too poor to absorb such a loss.

4.2.2 Econometric Framework This study employed a choice experiment, in which survey participants were presented with a choice set, or a set of alternative versions of a good or policy from which they are asked to choose. Each alternative is described using several of its key attributes, each of which is set at different levels which may vary from one choice to the next. Attributes are those components of the good or policy which are deemed to be the most important dimensions from the perspective of the target population, and attribute levels may be quantitative or qualitative. Holmes and

Adamowicz (2003) give a detailed description of the process of establishing attributes and levels.

Choice experiments use random utility theory (RUT) to observe how participants navigate the tradeoffs that are embedded in the differences between alternatives in one choice set

(Holmes and Adamowicz 2003). RUT assumes that people choose the alternative that maximizes

64 their welfare, or utility. RUT states that the utility (U) that a person derives from an alternative is composed of two parts, a systematic component and an error component. The systematic component, V, is a vector of all of the observable attributes of both the person and the alternative in question. The error component consists all of the factors in the situation that are not observable, and is random, therefore adding a random component to the choice that the person makes (Louviere, Hensher, and Swait 2000).

In RUT, when a person makes a choice from a set of alternatives she is said to obtain utility U from alternative i according to the following formula:

푈푖 = 푣푖 + ℇ푖

Where 푣푖 represents the systematic component and ℇ푖 represents the error component. A person will choose alternative i if it has a greater utility than the other alternative(s), described as alternative j. Therefore, the probability of her choosing i over j can be described as

푝(푖) = 푝푟표푏푎푏푖푙푖푡푦 (푣푖 + 휀푖 >= 푣푗 + 휀푗)

In other words, the probability of a person choosing an alternative is the same as the probability that the utility thereof is greater than the utility of all other alternatives in the choice set.

Attribute-based analyses of choices generally model choices using a conditional logit model. The error component of a utility function is assumed to have a Gumbel distribution, so that the probability of choosing i is modeled with the logistic curve

푝(푖) = 푒푣푖⁄∑ 푒푣푗 푗

while 푣푖 is defined as

푘 푣푖 = ∑ 훽푘 푋푖 푗

k Where 훽푘 is the slope coefficient of attribute X . The model is estimated with the Maximum

Likelihood technique, and transforms the logistic curve into a linear regression function (Ben-

Akiva and Lerman 1985). The effect of each attribute is equal to its marginal utility, which is

k equal to 훽푘for attribute X . Knowing the marginal utility of an attribute, the attribute’s utility at any given level can be calculated by multiplying the value of that level by the marginal utility.

When an individual experiences a change in the level of an attribute, for example as a result of a new policy, the resulting change in the individual’s economic welfare can be measured using the Hicksian compensating variation (CV). CV can be estimated as

0 1 푣푗푛 − 푣푗푛 퐶푉 = − 훼

0 1 in which α is the marginal utility of money, while 푣푗푛 and 푣푗푛 represent the respective utilities of the initial and new situations. To derive the marginal utility of money it is necessary to include a monetary attribute among the attributes in the choice experiment (Adamowicz et al. 1998). For example, including a subsidy attribute measured as the rate of payment means that α can be calculated as the slope coefficient (훽푘) of the subsidy attribute derived from the choice analysis.

4.2.3 Choice attributes and levels

Prior to data collection, we conducted focus groups with county officials and local residents to identify appropriate policy attributes and levels. These focus groups were conducted in each of the three counties we surveyed prior to going into the field, with participants in each group numbering less than 10. We asked participants for their perception of the degree of success of stocking rate policies and what the major challenges were. We also asked them if they thought the government was using the most appropriate policy tools. If they were, we asked what changes to the existing policies would make them more effective, but if the current policies were considered ineffective, we asked what alternative policy tools would be better. Once we had an idea of the range of different types of policy tools that respondents thought would be appropriate, we asked respondents what they thought the realistic minimum and maximums for these policies could be (e.g. minimum maximum stocking rates or subsidy rates). Though we began by conducting the focus groups in a more formal way, handing out questionnaires for participants to fill in, we found over time that it was more effective to have informal group conversations with participants, allowing them to present their perspectives and opinions in a more in-depth way.

Through these conversations with local stakeholders, I selected five types of policies to test in the choice experiment: stocking rate limits, subsidy rates, overgrazing fines, land contract durations, and future pension plans. I included the stocking rate limit as an attribute so that I would eventually be able to measure the tradeoffs that herders make between livestock numbers and various policy incentives. The other attributes I chose included those that are currently being used (i.e. subsidies and fines) as well as hypothetical incentives that policymakers and herders thought might be both realistic and effective forms of utility to offer in exchange for smaller

67 herds (i.e. tenure security and future pension plans). Therefore, these five policies were chosen as the choice experiment attributes. Consultation with the focus group participants elicited realistic ranges of levels to set for each attribute. Xilinhot and East Ujumqin were found to have similar enough conditions that all attributes and levels were set identically. However in Xianghuang many of the land holdings were much smaller than in the other two counties. The Xianghuang government decided that, in order to ensure that all households received a subsidy that it considered adequate, those households with less than 500 mu of land should receive a subsidy on a per-person basis. The subsidy rate that these households receive is 3,000 yuan per person. For these households, I decided that the attribute levels should also be set on a per-person basis.

Table 2 summarizes the final array of attributes and levels that was decided upon.

Table 2: Summary of Attributes and Levels used in the choice experiment

ATTRIBUTE LEVELS NOTES STOCKING RATE (MU PER 23 15 mu = 1 ha. Higher values SHEEP UNIT) represent lower stocking 20 density. 15 8 PENSION (YUAN/MONTH) 350 This pension would be received 550 upon reaching retirement age. 750 950 LAND CONTRACT DURATION 30 years All land is owned by 70 years government and leased to Lifelong (not inherited) herders. Current leases are for Permanent (inherited) 30 years. SUBSIDY 2.5/mu or 3,000/person Grassland-animal balance 6/mu or 4,500/person subsidy, which is paid to herders 9.5/mu or 6,000/person throughout Inner Mongolia. FINES Withhold 40% of subsidy if stocking rate exceeded If stocking rate exceeded withhold 100 RMB of subsidy for every extra sheep No fine

4.2.4 Survey and statistical design

The attributes and levels decided upon in the focus groups were configured in many different combinations to generate hypothetical scenarios which were then presented to herders.

If every possible combination of attributes and levels were used in this experiment, I would have had to use over 300,000 choice sets. I therefore chose a small sample of those choice sets that would be able to capture the most information about participants’ preferences.

We used a Bayesian D-optimal main effects design generated with the software JMP

(SAS Institute Inc. 2013). D-optimal designs are efficient, particularly when using Bayesian algorithms with informative mean utility values for attributes taken from prior studies (Ferrini and Scarpa 2007; Kessels et al. 2008). Though I did not have prior data on herders’ preferences

69 for different levels within an attribute, most attributes had interval-based levels with a logical preference order so I decided it was safe to estimate preferences, which Kessels et al. (2008) regard as appropriate for such situations. Furthermore, I had extensive prior knowledge to help me with these estimations due to my experience conducting research in the study region. My design consisted of 32 different choice sets in the choice experiment, and I divided these into eight different survey versions, each containing four different choice sets. To avoid respondent fatigue, I gave each respondent four sets of choices to complete.

Each choice set was shown to participants as a table with each column representing a choice, and each row describing the different levels of the attribute in each choice. Figure 1 shows an example of a choice card as presented to herders. For the status quo option, herders were generally assumed to know what their current attribute levels were, but if they were not sure these would be written down for them on the choice set. The enumerators were employees of the local office of the regional Environmental Protection Bureau and were able to tell participants what average status quo levels were if the participants themselves were not sure.

In addition to the choice experiment questions, the survey instrument also included a semi-structured questionnaire which was administered following the selection of choices. The questionnaire varied somewhat between counties to adapt to local conditions, but the main themes were consistent. Questions sought to uncover reasons behind respondents’ choices as well as information on any loans, perceptions and strategies regarding future livelihoods, off- farm income, recent environmental shocks, subsidies received, opinions on different grazing systems, and data on grazing inputs and productivity.

Figure 1: Sample choice card

Attributes Choice A Choice B Status Quo

Stocking Rate 8 15 (mu/sheep)

Pension (yuan/month) 750 550

Subsidy Rate (yuan/mu) 2.5 6

Overgrazing fine If overgrazing, withhold 100 If overgrazing yuan per extra sheep withhold 40%

Land Contract Length Lifelong (don’t inherit) 70

4.2.5 Data Collection

Data were collected over the course of three weeks in May and June. Participant households were chosen from a list of households that had already participated in Li and Hou’s panel survey. Randomization of samples was not necessary because we were mostly choosing households from an existing panel dataset which only had 60 households in each county, and we tried to include all households in each of the three counties that had participated in the earlier panel survey. However, the participating households for the panel survey had been selected using random sampling. Where panel households were not available we added new households. These new households were not randomly chosen but were suggested by local officials. In total, we surveyed 56 households in Xilinhot (49 from panel and seven new), 55 in East Ujimqin (50 panel and five new), and 77 in Xianghuang Banner (50 panel and 27 new). Among surveyed households in Xianghuang Banner, 21 received subsidies on a per mu basis (16 panel and five new), while 56 received subsidies on a per person basis (34 panel and 22 new).

4.3 Results

Though I initially thought that it would be acceptable to combine surveys with different subsidy measurement systems into a single model, I eventually decided that this might compromise the models. Therefore, of the 752 surveys collected, 224 were excluded from modeling because they measured the subsidy rate on a per-person basis rather than a per-area basis, in accordance with how these households receive subsidies in practice. Of the 528 remaining surveys, 44 were from respondents who responded to the surveys serially, meaning they made the same choice on each of the four surveys they were shown. Examination of the choices that these serial responders were shown, combined with the fact that they mostly came from the same few communities, led us to conclude that their responses were probably protest responses or answered without careful deliberation. These responses were therefore not used in the model, leaving 484 remaining surveys to be modeled, corresponding to 121 respondents.

The data were modeled in the statistical software Stata, with three different models being generated: an ordinary-least-squares (OLS) regression model, a naïve logit model (i.e. one that models effects across individual observations without consideration of which herder is choosing), and a fixed effects logit model (which controls for the identity of the herder making the choice). These three models vary in their degree of complexity, and to obtain a model that had the best balance between simplicity and accuracy, I decided to start by testing all three. In each choice set, the difference in each attribute level between choice A and choice B was calculated. Because it is theoretically arbitrary as to which of the two choices in an observation is assigned to column A or B, bootstrapping was used to randomly assign the choices to A and B over 500 different simulations. The marginal effects of the attributes were then modeled in all

500 simulations, and the mean coefficients and p-values of the attributes was calculated over all

72 simulations. In this way, I ensured that the arbitrary assignment of choices to column A or column B would not influence the model output. Table 3 shows the marginal effects and significance values for the three models, averaged over 500 simulations. For continuous variables, the marginal effects show the change in utility given by a unit change in that attribute.

For categorical variables (i.e. the land tenure and fine variables), the marginal effects are broken up so that each row represents a level change, and the marginal effect represents the change in utility given by the specific level change indicated. The models included the attributes and level changes mentioned above, as well as two dummy variables for the three counties that participants came from.

The bootstrapping method described above led to variations in the number of observations over the 500 simulations for the fixed effects model. This is because the fixed effect model automatically drops respondents who only choose A or only choose B. Because the assignment of columns A and B changes with each simulation, the number of respondents who only choose A or only choose B varies by simulation, meaning that the number of observations being dropped also varies by simulation. Therefore, Table 4 does not give a specific number of observations or respondents for the fixed effects model.

Table 3: output of three choice models

OLS LOGIT Fixed Effects Marginal P-value Marginal P-value Marginal P-value effect effect effect Stocking Rate -0.005 0.105 -0.006 0.072 -0.007 0.134 Pension 0.000 0.223 0.000 0.209 0.000 0.361 Subsidy Rate 0.001 0.055 0.002 0.037 0.002 0.086 Land Contract -0.011 0.763 -0.013 0.726 -0.026 0.548 30 yrs -> Life Land Contract 0.205 0.000 0.182 0.000 0.186 0.000 Life -> 70 yrs Land Contract 0.149 0.007 0.165 0.004 0.171 0.029 70 yrs -> Permanent Fine: none -> -0.094 0.130 -0.095 0.117 -0.084 0.311 40% of subsidy Fine: none -> -0.062 0.204 -0.082 0.114 -0.090 0.177 ¥100/extra sheep Fine: 40% of 0.020 0.567 0.021 0.544 0.022 0.560 subsidy -> ¥100/extra sheep Xianghuang 0.011 0.496 0.011 0.492 0.000 . County Xilinhot -0.009 0.510 -0.009 0.507 0.000 . County N 484 484 Varies Number of 121 121 Varies Respondents

As can be seen in Table 3, most attributes and levels were not found to be statistically significant in any of the three models. The main exceptions were two of the land tenure level changes, and the subsidy rate. A change in land tenure from a lifelong contract to a 70-year contract was found to be highly significant in all three models, and a contract change from 70- years to permanent was also significant in all three models. The subsidy rate was significant in the logit model but just outside of the 0.05 significance threshold in the other two models. The

74 stocking rate effect did not fall within the 0.05 significance threshold in any of the models, but it came close in the logit model with a p-value of 0.072.

The magnitudes of the coefficients varied greatly, with the land tenure level changes having by far the largest marginal effects. The fine level changes also had sizeable marginal effects, but none were statistically significant. The signs of the effects were as expected, the most important being that changes toward longer land tenure were associated with increased utility, subsidy increases were associated with increased utility, and increased rangeland required per livestock unit associated with decreased utility.

Because the naïve logit model contained more significant effects than the other two models, I decided that it was the most useful of the three models and should be used for further analysis. The next step in the analysis was to control for characteristics of the survey participants by adding them to the model. The logit model was thus run again with eight additional variables: the respondent’s age, sex, household size, herd size, rangeland lease size, house surface area, level of education attained, and a dummy variable for whether or not they use the traditional

Mongolian migratory grazing practice called otor. Table 4 summarizes the output of this model.

Of the 121 included in the previous model, I did not have data for these new variables for 22 of them, so the subsequent model only contained 99 households, or 396 observations.

Table 4: Output of naïve logit model with herder characteristics included

Marginal p- value Effects Stocking Rate -0.010 0.013 Pension 0.000 0.158 Subsidy Rate 0.002 0.025 Land tenure -0.021 0.605 30 yrs -> Life Land tenure 0.181 0.000 life -> 70 yrs Land tenure 0.264 0.000 70 yrs -> permanent Fine: none -> -0.089 0.192 40% of subsidy Fine: none -> -0.128 0.033 ¥100/excess sheep Fine: 40% of 0.040 0.273 subsidy -> ¥100/excess sheep Xianghuang 0.027 0.505 Xilinhot -0.003 0.505 Age -0.001 0.493 Sex -0.021 0.506 Household 0.014 0.406 Size Herd Size 0.000 0.526 Rangeland 0.000 0.479 lease size House surface 0.000 0.484 Area Education -0.018 0.429 Practice of 0.012 0.501 otor N 396 Number of 99 Participants

While none of the participant characteristics were themselves significant in the model, they did increase the significance some of the attributes. The significance of the two significant land tenure level changes increased further, as did the subsidy rate. In addition, the stocking rate showed significance, as well as the change in fine policy from having no overgrazing fine to being fined ¥100 for every excess sheep unit.

To calculate the herders’ average willingness-to-pay (WTP) for longer land tenure, and willingness-to-accept compensation (WTA) for more grazing restrictions, I used the marginal effects in Table 4. First, using the subsidy marginal effect, I found that each unit of utility was equal to about 50 yuan/mu. To calculate the herders’ average Willingness-to-pay (WTP) for an increase in land tenure, I used the marginal effect of the level change from a lifelong lease to a

70-year lease. Because the tenure level change from 30 years to lifelong was not statistically significant, I treated the lifelong-70 year change as the equivalent of a 30-year-to-70-year change, or a 40-year extension of herders’ current leases. I found that herders were willing to pay

9.05 yuan/mu on average for this land tenure extension. This suggests that households with the median rangeland holding (4500 mu) would thus be willing to pay ¥40,725 for an increase in land tenure security over 40 years. Using a discount rate of 10%, herders would be willing to pay

0.92 yuan/mu every year over the additional 40-year period, or ¥4,140 per year. This is a considerable amount as it would be equivalent to giving up more than half of the current grassland-animal balance subsidy of 1.71 yuan/mu. Furthermore, herders would be willing to pay

22.25 yuan/mu (¥100,125 total for a household with 4,500 mu of rangeland) to change their lease from a 30-year lease to a permanent lease. With a 10% discount rate, herders would be willing to pay 2.2 yuan/mu (¥9,900) in perpetuity.

4.4 Discussion

4.4.1 WTA and WTP Based on the marginal effects of the change in stocking rate in Table 4, herders would be willing to accept no less than 0.5 yuan/mu for every extra mu that is required to graze each sheep. For a typical household with 4,500 mu, the WTA for adding an additional mu per sheep is thus ¥2,250. Based on a median herd size of 525 sheep units, the typical stocking rate is 8.6 mu/sheep. For a family with 525 sheep, changing their stocking rate from 8.6 to 9.6 mu/sheep would mean reducing their herd to 469 sheep, or giving up 56 sheep for ¥2,250. This would be equivalent to valuing each sheep at ¥40. Because the typical market value of an adult sheep in the area was ¥600 in 2015, this WTA for giving up one sheep is extremely low. Considering that there is a history of not enforcing existing stocking rate rules, and that the vast majority of herders graze far in excess of legal limits, it is likely that herders still imagine that stocking rate limits would not be enforced in the hypothetical situations that we presented them. Although we tried to emphasize that enforcement would be strict, the notion that the rules are not serious may have been difficult to dispel. This perception may have reduced the effect of changes in the stocking rate limit, leading to the surprising WTA figures we see here. My WTA figures may therefore be representative of what herders are willing to accept for changes in the stocking rate rules, but changes in these rules should not be equated with changes in actual stocking rates.

In accordance with one of the goals of this study, we could envision a policy where land tenure extensions, instead of cash subsidies, were given as compensation for herders who agree to reduce their grazing intensity. According to my results, Extending herders’ land contracts from 30 years to 70 years would make herders willing to change their stocking rate by 18.1 mu/sheep. For a household with 4,500 mu and 525 sheep, this would mean changing their

78 stocking rate from 8.6 mu/sheep to 26.7 mu/sheep, or reducing their herd size by 356 sheep to only 169 sheep. Because my analysis values this transaction as the equivalent of 40,725 yuan, each sheep would be valued at 112 yuan. It should be noted that this is considerably different from the 40 yuan/head value when the stocking rate changes by only one mu/sheep. This demonstrates that my WTA values for grazing intensity reductions do not have a linear relationship with the number of livestock that herders would be expected to get rid of. Therefore, the stocking rate WTA that I have obtained may not be reliable enough to apply in policy formulation. This may be due to a perceived lack of credibility of the stocking rate rules, as well as the way that herd size changes are being measured. In future, it may be more effective to ask herders not how much they are willing to change their stocking rate, but how many fewer head of livestock they would be willing keep.

It may be regarded as counter-intuitive that stocking rates were not significant in the model before the inclusion of herder characteristics, but became significant after the inclusion of herder characteristics. However, this outcome can be interpreted as showing that the importance of stocking rates in decision-making becomes clearer perhaps only after controlling for individual herder attributes such as age and education. Herders of different ages (i.e., experience level) and education may have different approaches to stocking management, and not controlling for these factors may obscure the fact that the stocking rate is exerting an influence on choices.

This influence may only be observable once herder characteristics are added to the model.

4.4.2 Land Tenure and Grazing Management

Our quantitative results regarding the importance of land tenure corroborate findings from the questionnaire portion of my survey, in which respondents frequently expressed anxiety about the possibility of losing their land when their leases run out, or being allotted a smaller holding than they currently hold. The widespread nature of these concerns suggests that the 30- year land leases that are currently the norm throughout Inner Mongolia are regarded by herders as too short. The fact that a large portion of these leases are up for renewal within the next decade is also likely to be putting at the forefront of herders’ minds the question of securing tenure for their land. Herders appear to experience great uncertainty regarding their children’s future livelihoods. In my focus groups and surveys respondents often expressed the view that economic necessity compels herders to maintain stocking rates above the legal limit. A connection between land tenure and overgrazing is supported by Banks (2001), who argues that the current tenure system creates disincentives for sustainable land management.

In practice the Chinese government has rarely applied enhancement of land tenure security as a tool to tackle overgrazing, though laws such as the 2001 Law of the People’s

Republic of China on Desert Prevention and Transformation have hinted at such a strategy. This law lists the granting of 70-year leases as one of several possible incentives that local governments may grant at their discretion to leaseholders for engaging in anti-desertification activity (Nelson 2006). According to Ho (2001), it is a long-term goal of the central government that leaseholders under the Household Contract Responsibility System (HCRS) will eventually be granted leases in perpetuity. However, up to now there has been little movement toward this goal. Prior to 1996, the Grassland Law mandated 30-year leases for herders, after which a reform allowed for the granting of leases of up to 50 years (Nelson 2006). While studies have mentioned the implementation of 50-year leases in parts of Xinjiang (Banks 2001) and the Tibetan Plateau

(Yeh and Gaerrang 2011), as of the year 2000 30-year leases were still the norm on China’s rangelands, and these same leases are generally still in force considering that most leases date from the late 1990s (Ho 2000, 2001) . At the time of the present study, all leases in the three surveyed counties were for 30 years. In the study area, the main activity related to land tenure is an initiative started in 2014 by the government of Inner Mongolia to clarify the precise boundaries of herders’ leases. However, this effort deals with land tenure security only with respect to reducing boundary disputes and illegal grazing on herders’ land by neighboring herders, and is not related to the duration of leases (Government of Inner Mongolia 2014).

4.5 Conclusion

Although strengthening land tenure security has often been suggested as a possible strategy to improve environmental sustainability in grazing systems in China, most research has been qualitative in nature (Banks 2001; Wang et al. 2010) . My study may represent the first time that the importance of land tenure in this system has been quantitatively demonstrated. While my results do not appear to give a precise estimate of how much reduction in grazing intensity could be achieved through a given extension of land contracts, they do show that herders consider land tenure reform to be more desirable than any amount of cash subsidy that the government would be able to provide. The Chinese government and the government of Inner Mongolia already spend billions of yuan each year on eco-compensation payments to herders, but even these expenditures fail to bring about the desired reductions in grazing intensity (Xie et al. 2015).

While increasing subsidy rates further could achieve better environmental outcomes, the amount of increase that would be necessary might be beyond the scope of government budgets. My results suggest that land tenure reform could represent a degree of utility to herders that could

81 substitute for further increases in subsidies. However, to ensure that such changes translate into greater achievement of the government’s environmental goals, reforms would have to be applied together with stricter enforcement of existing stocking rate regulations. Government officials in

Inner Mongolia have been discussing stricter enforcement of stocking limits, and while such a move would be likely to cause a degree of economic hardship and discontent among herders, these consequences could be tempered by simultaneously strengthening herders’ land tenure rights.

Chapter 5 - Thesis Conclusions

In this thesis I sought to investigate how stocking rates on Inner Mongolia’s rangelands are influenced by variability in environmental as well as socio-political conditions. Both analyses yielded significant and informative results. The shocks analysis revealed that different types of environmental shocks are associated with very different stocking rate outcomes. The choice experiment revealed that among the various types of government policy changes that might incentivize stocking rate reductions on the part of herders, lengthening of land contracts appears to have the greatest potential. In short, both analyses provide needed empirical knowledge to give a firmer grounding for the already extensive theoretical knowledge on grazing systems in

Inner Mongolia. Therefore, notwithstanding some limitations in the analyses (which were discussed in their respective chapters), the overall objectives of the thesis have been successfully met.

The findings of this thesis have relevant implications for the formulation of grassland policy in Inner Mongolia. The findings of Chapter 3 suggest that overgrazing may be a greater

82 problem in areas where droughts are the most frequent type of environmental shock, and a lesser problem in areas where snowstorms are more common. With climate change, the frequency of the two types of shocks across the landscape is expected to change, and stocking rates are likely to be influenced by these shifts. Policy-makers may benefit from adjusting their management policies spatially and temporally to match these dynamics (e.g., through the deployment of index-based livestock insurance schemes that use environmental shocks as an index), thereby improving their deployment of resources and avoiding putting greater-than-necessary restrictions on herders’ activities. Allowing herders to take full advantage of good grazing conditions will put them in a better position to maintain lower stocking rates during poor conditions. The findings of Chapter 4 suggest that lengthening herders’ land contracts could improve herders’ material well-being while simultaneously increasing incentives to reduce overgrazing. Such changes in land tenure could be especially effective if combined with stricter enforcement of realistic grazing regulations. Lengthening land contracts could be a win-win situation for both herders and the government, as it could improve herders’ livelihoods and protect rangeland ecosystems while making the government less dependent on financial rewards or punishments to achieve its environmental goals.

In addition to implications for policy, the findings of this thesis also carry implications for theory as well as for the direction of future research. One area of theory for which the findings of the thesis have relevant implications is that of Social-Ecological Systems. The findings demonstrate that herders’ management decisions put a large emphasis on managing system uncertainties. As mentioned in the literature review, the predictability of resource systems has a heavy influence on the degree to which resource users organize to conserve resources

(Ostrom 2009). The findings of this thesis lend support to this view. However, the findings also

83 suggest that we might do well to extend this concept to include not only predictability in the resource system, but also predictability in the governance system. Like ecosystems, political systems are dynamic and ever-changing, and difficulty in predicting the future development of policies like land tenure can limit the ability of herders to invest in long-term rangeland conservation.

The findings of this thesis support several avenues of future research. The shocks analysis shows evidence of a relationship between environmental shocks and stocking rates, while existing literature provides a plausible explanation (differences in livestock mortality). However, the analysis does not empirically demonstrate this mechanism. Future studies could analyze differences in livestock mortality induced by different kinds of shocks, as well as the pace and scale at which herds are rebuilt following shocks. Regarding the choice experiment, future research could address the limitations of this study. One limitation is that the study did not manage to infer with precision the reduction in stocking rates that livestock herders would be willing to accept in exchange for improvements in land tenure security. This was partly due to stocking rate restrictions not being taken seriously, and partly due to the fact that the study asked herders about stocking rates rather than actual numbers of livestock. While the first problem will be challenging to confront, the second problem could be addressed by asking herders in future surveys not how much they are willing to change their stocking rate, but how many head of livestock they would be willing to give up. Another possible avenue for future research would be to explore the potential for the Inner Mongolian grazing system to be less driven by government regulation and more driven by herder self-organization. One possibility would be to explore herders’ attitudes toward the different variables and bundles of rights that define tenure systems.

This could be another useful application of choice analysis.

References

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