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Integrating Social Science Theories Relevant to Development Transitions

Several social science theories relevant to the notion of types of development transitions are also relevant to both the Wilcox-Gubler-Colwell hypothesis and Holling's resilience theory. Our starting point is the theory of the Environmental Kuznet's Curve (EKC) from the discipline of development economics. Nobel Prize winner Simon Kuznets proposed that with respect to inequality, economic development is not linear but rather an inverted 'u' shape; economic development is a transition from an initial state of relative equality to an end state also of relative equality, but in the midst of economic development nation-states display high levels of economic inequality (line C in Fig. 5.1 is a Kuznets curve). While various scholars have shown that Kuznets curves are not universally applicable (see Park et al. 2007), Kuznets curves have become a simple but powerful method for empirically testing hypotheses about transitional states. The EKC hypothesizes that certain indicators of environmental degradation tend to get worse as modern economic growth occurs until average income reaches a certain point over the course of development (Grossman and Krueger 1995). Urban air pollution and deforestation have been cited as examples of environmental quality variables that follow the EKC. Recent

Fig. 5.1 Environmental risk transition framework

empirical work has applied this understanding to the issue of HPAI (Spencer 2013), indicating that disease occurrence may be most likely where settlements are undergoing the most intense transitions. Such evidence suggests that a deeper understanding of “transition” is warranted.

While some environmental quality indicators such as landfills and biodiversity do not seem to follow the EKC, supporters of the theory have argued that this may be more to do with issues of scale. Traditionally, most of the empirical work on EKC has been based on inter-country analysis of cross-sectional data. Often global regions (groups of countries) have been the unit of analysis. Most commonly, the metrics chosen for the predictor variable have been Gross Domestic Product (GDP) or GDP per capita, often adjusted by purchasing power parity or the Human Development Index (HDI). To address the scale issue we are examining whether the EKC is valid at the lowest level of government administration–the commune or municipal ward—for the entire country of Vietnam using readily available census data. The outcome variable is HPAI in domestic poultry. We are faced with the challenge that at the commune/ward level metrics such as GDP or HDI are not valid or are difficult to measure (e.g., because data are not available). Furthermore, using a binomial variable—whether a place is rural or urban (as classified by the government) has two problems: (1) one cannot test the non-linearity of the curve and (2) it contradicts our fundamental premise that a significant number of places are not easily classifiable as being either rural or urban. Thus, we are forced to find a new metric reflecting development, which is ordinal with at least three levels. The “urbanicity” method from the field of urban geography is useful for classifying place (Allender et al. 2008; Dahly and Adair 2007; McDade and Adair 2001; Vlahov and Galea 2002). Though most of the urbanicity metrics in the literature are continuous scale metrics, we adapt the principles to create an ordinal scale metric.

Another social science theory relevant to understanding the relationship between development transitions and EID comes from Smith (1990), who proposed an environmental risk transition where the environmental factors leading to ill health were categorized as traditional or modern. This categorization is based on the premise that the major environmental causes of traditional diseases are problems at the household level (e.g., water, sanitation, food availability and quality, ventilation and indoor air pollution). As these are addressed during development there is an increase in the relative importance of the major environmental causes of more modern diseases which operate at the community level (i.e., urban air quality, occupational hazards, toxic chemicals, and motorization). As these are addressed in richer societies a further transition occurs to increase the importance of environmental hazards at the global level (e.g., global warming, land-use change) (Holdren and Smith 2000; McGranahan et al. 2000; Smith and Akbar 2003).

Figure 5.1 shows the environmental risk transition framework in which traditional risks fall with social and economic development, transitional risks rise and then fall, and modern risks rise throughout the development process. Smith and Ezzati (2005) write that limited or no research has attempted to apply this framework to emerging and reemerging infectious diseases caused by evolving human activities such as those associated with trade, tourism, terrorism, and human interactions with natural environments.

Others have hypothesized forest (a natural habitat) (Grainger 1995; Mather 1992, 2007; Rudel 1998), agrarian (Hall 2004; Rigg 2005), and urban (Douglass 2000; Friedmann 2005) transitions. A forest transition occurs when an initial surge in economic activity spurs deforestation, but as economic activity continues to intensify and cities grow larger, a 'turnaround' occurs, and deforestation gives way to reforestation. The agrarian transition has been defined as a number of inter-related phenomena. These include agricultural extensification and intensification (the amount of agricultural land is hypothesized to follow a Kuznets curve as extensification precedes intensification, but intensification then leads to the abandonment of marginal land); increased integration of production into market-based systems of exchange; heightened mobility of populations both within and across national borders as people are attracted to opportunities both within and outside of the agricultural sector; and processes of environmental change that reflect new human impacts and new valuations of resources (Akram-Lodhi 2004; Rigg and Vandergeest 2012; De Koninck 2004). The urbanization transition includes two parallel processes: population concentration (population is hypothesized to increase linearly) and the development of socio-physical infrastructure to manage the inevitable conflicts and problems associated with higher density living (infrastructure is hypothesized to increase linearly). The urban transition in developing countries describes societies that have rapidly changed from rural to urban forms of social and physical organization in relatively short time periods (Douglass 2000; Montgomery et al. 2004) such as those found in Southeast Asia.

The broader implications of these simultaneous and related transitions remain unexplored in general, and more specifically, as Wilcox and Colwell (2005) argue, in relation to how they produce feedbacks that affect natural communities and ultimately their pathogens, animal host, and human populations. A better understanding of the relationship between development transitions and EID is critical for improving our ability to predict and respond to EID. This is particularly true in Vietnam where government policies have facilitated what can broadly be called a “transition to the market” (Arkadie and Mallon 2003; De Vylder 1990; Fforde and Vylder 1996). Economic policies have driven changes in the built environment that have created new ecological health risks (Oliveira et al. 2004; Smith 1997), migration to cities has simultaneously uprooted residents from local social networks and placed them into new neighborhood associations, water user-groups, and other forms of social organization (Crane 1994; Spencer 2007). These new socio-physical ecologies present new challenges that, in turn, require new forms of social organization and governance, many of which do not yet exist, to provide basic services such as water and sanitation, education, housing, and public health.

A final social science perspective that has received little attention to date in the developing world relates to theories of behavioral decision making and perceptions of risk. This field draws primarily from economics, psychology, philosophy, anthropology, and cognitive science. Researchers have developed tools such as the Social Amplification of Risk Framework (Kasperson et al. 2003; Pidgeon et al. 2003) to describe and explain the societal processing of risk signals, but tests of such frameworks are rare because of the difficulty in predicting when risk amplification conditions are likely to occur (Frewer et al. 2002). A central insight of decision theory is that risk responses are based on socially constructed perceptions of risk. That is, risk means different things to different people; it cannot be measured independent of our minds and cultures. People prioritize risks in different ways, depending on their beliefs about the need to try to reduce a risk (Douglas and Wildavsky 1982; Hofstede 1984; Park et al. 2007). Research conducted in the developed world suggests that perceived risk is related strongly to feelings of control and trust (Slovic 2000). Some authors suggest that people perceive low risk during modernization because they feel they are in control of technology, nature, or society and that regulatory authorities can be trusted (Bauman 1992; Beck 1999; Giddens 1992). Over time, however, perceived risk increases as feelings of control and trust are eroded. Some people will respond swiftly and comprehensively to a risk event and others will respond more slowly, depending on a range of psychological and socio-cultural variables and environmental conditions. However, the relative importance of various elements in CNH systems (i.e., socio-ecological and socio-psychological factors) in determining perceptions of and responses to the risk of EID in rapidly developing societies has not been examined.

To understand the relationships between characteristics of decision makers, their environmental context, and their risk responses, the field of decision science uses a wide range of methodological approaches and analytical tools. Qualitative interviews and focus groups permit an in-depth exploration of risk perceptions and responses, allowing participants to describe beliefs and experiences in their own words, rather than as a choice between predetermined survey responses (Pope and Mays 1995). These methods are useful in defining the range and variability of conceptualizations of risks such as disease outbreaks and how they might relate to environmental change (O'Brien 1993). Quantitative methods (e.g., decision analysis, process tracing, surveys) are more readily applied with larger samples and allow precise measurement of the information integration strategies decision makers use to determine their risk response. Applied to the problem of understanding the relationship between modernization and EID, we can test whether variation in individuals' risk responses is related to environmental change as represented in our degree of modernization metric.

Combining decision and risk research methods applied at the household level with environmental economics methods applied at the communeand national-level analyses provides an opportunity to look for converging evidence for hypothesized relationships between constructs in our social-ecological model of EID. Identifying variation in risk responses to EID is only of theoretical interest if it furthers our understanding of the causal variables and structure in the coupled natural-human system that lead to the behavior. An understanding of the system is also crucial to making such results practically useful. For instance, observing variance in HPAI risk responses in Vietnam provides an empirical basis for making predictions about other diseases and other developing countries that have not been studied directly.

In sum, the conceptual framework introduced in this chapter relies heavily on the integration of multiple social science theories and methods from diverse disciplines (e.g., environmental economics, geography, decision and risk science, urban and regional development, and spatial information science). Identifying key components of these theories relevant to the notion of types of development transitions provides a coherent approach to analyzing the complex interactions among natural and human systems at diverse spatial, temporal, and organizational scales. In the next section we provide reasons for the choice of important elements to characterize CNH systems.

 
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