Section 4. The Determinants of Human Well-being

In this section, we consider how research on HWB and its determinants can illuminate the problem of selecting HWB indicators for ecosystem-based management. The focus is on methods that can and have been used to identify economic, social, and sometimes environmental factors that are correlated with and therefore likely to determine (in part) human well-being. These methods provide a way of assessing the connections between ecological and human systems, using human well-being as the metric by which to judge the strength of those links. The methods described below do not span the full set of potential ways of making such an assessment. In later versions of this document, the intent is to add, where warranted, other approaches.

The approach taken here is admittedly a reductionist view of human well-being and its determinants. First, we collapse the multiple domains or dimensions of human well-being into a single measure. While this measure is not observable directly, we use a framework that is based on either subjective, self-reported evaluations or inferred from observable behavior. Second, we assume that HWB can be expressed as a function of measurable, objective circumstances. There may be many other determinants, of course, that are not easily measured or even observable, but the challenge of selecting indicators for HWB can only be met if this second assumption holds.

With these assumptions, we can then formally represent HWB in the following way (Welsch and Kühling, 2009):

HWB = F(M,X,D,Q,U)

where HWB is an individual's stated well-being (the measurement of which is discussed below); M is the individual's income; X is a set of community or higher level "macro" factors that help determine HWB; D is a set of individual-level factors that help determine HWB; Q is a set of environmental conditions that determine the individual's HWB.; and U is a set of unobserved (or unmeasured) HWB determinants.

This equation provides a basis for formally and quantitatively assessing the links between a particular environmental quality attribute, Qi, and HWB:

Formula 1

which provides a theoretical construct for evaluating what environmental quality attributes are connected to HWB (i.e., is ∂F/∂ Qi > 0?) and to assess the strength of the connections (i.e., what is the magnitude of ∂F/∂Qi ?) (Welsch and Kühling, 2009).

Below, we consider three general strategies for bringing this equation to life. The first, generally known as life satisfaction or “happiness” studies, starts with direct measurement of HWB and then analyzes objective factors that correlate with that measurement. The other two are different approaches used in economics based on the willingness of individuals to sacrifice one good (usually taken as income) for others, or a “willingness-to-pay” (WTP) approach. The first of these is based on the actual behavior of individuals, either observed directly or inferred through market prices. The second of the WTP approaches is based on the stated preferences of individuals regarding their willingness-to-pay for one situation relative to another. Each of these three approaches uses the equations above in one way or another to derive quantitative estimates of connections between HWB and its determinants.

1.Direct, Subjective Measurement of Human Well-being

The question of an individual’s well-being can be addressed by taking a straightforward approach: Ask a person directly. The literature that has built up around this approach is generally known as life satisfaction or “happiness” studies. The types of measures used to assess HWB in this way fall into two categories: (1) measures that reflect an individual’s self-reported well-being in a global or holistic sense; and (2) measures that reflect an individual’s self reported well-being in the moment (Frey and Stutzer, 2002; Vitarelli, 2010).

Formula 2

This approach and methods to analyze life satisfaction and happiness originated in psychology but have been of found increasing interest to economists. The existence of several long-running, multi-national surveys provide a rich set of data for analysis (Frey and Stutzer, 2002):

  • The General Social Surveys, which asks: "Taken all together, how would you say things are these days—would you say that you are very happy, pretty happy, or not too happy?" (Davis, Smith, and Marsden, 2001).
  • The World Values Survey, which uses a ten-point scale and asks respondents: "All things considered, how satisfied are you with your life as a whole these days?" (Inglehart et al. 2000).
  • The Eurobarometer Surveys, which covers all members of the European Union and asks respondents: "On the whole, are you very satisfied, fairly satisfied, not very satisfied, or not at all satisfied with the life you lead?" (Noll, 2008)

Other approaches use the answers to multiple questions to address life satisfaction, such as the Satisfaction With Life Scale (Diener et al., 1985), which is composed of five questions and rates life satisfaction on a scale from one to seven.

As is the case for all data gathered through surveys, this approach is prone to a host of possible errors. A person’s self-reported global well-being can be influenced by moment-to-moment factors such as mood and immediate circumstances; it can also be affected by survey artifacts such as the order and wording of questions, the response scales used, and the selection of information given as context (Frey and Stutzer, 2002). Whether these factors produce systematic biases depends on how the data are used, as the potential problems are muted if their main use is not to compare levels in an absolute sense but rather to seek to identify the determinants of happiness.

With data on self-reported individual well-being, the framework above can be used to discern the determinants of HWB. The true level of HWB is modeled as a latent variable that is related to objective individual, economic, social, and environmental conditions, and the function above (usually in a linear form) can be estimated using ordered probit or logit regression (Welsch and Kühling, 2009). Among the most studied determinants is income (Hsieh 2003, Solberg et al 2002, Vera-Toscano et al 2006, Warr 1999, and many others). Across individuals within a given location, the general (and very robust) result is the people with higher incomes report higher levels of well-being (life satisfaction or happiness) - "income does buy happiness" (Frey and Stutzer, 2002).

Easterlin (1974, 1995, 2001), however, has found that while this result holds cross-sectionally, as incomes rise over time within a given area (such as a nation), everyone’s self-reported well-being does not necessarily increase. This result has been supported by laboratory experiments that look at the effects of individuals’ relative income on happiness (Smith et al. 1989, Tversky and Griffin 1991). Another interesting result comes from Alesina, et al. (2001), which found a strong negative relation between income inequality and happiness in Europe, but not in the United States. Another area related to income is unemployment, which many studies have shown to have strong, negative effects on well-being (Clark et al. 2001, Di Tella et al. 2001, Graetz 1993, Korpi 1997, Winkelmann and Winkelmann 1998).

Other individual circumstances play a strong role in determining self-reported well-being. A few areas are criminal victimization (Michalos and Zumbo 2000), housing and home-ownership (Diaz-Serrano 2006), and education (Hayo and Seifert 2003). Di Tella et al. (2001) show how inflation and unemployment both affect an individual’s well-being; Frey et al. (2009) show how terrorism in France and the British Isles exerts a strong negative effect on subjective well-being; and Frey and Stutzer (2000), in a study of Swiss cantons, show how the institutional right of individual political participation via popular referenda exerts a strong effect on happiness.

This approach has also been used to examine the relations between environmental conditions and subjective well-being, as shown in Table 1 (Welsch and Kühling, 2009; Ferreira and Moro, 2010). While research on measuring subjective HWB directly and exploring its determinants is growing, the literature has not yet expanded to cover the broad set of ecological goals associated with the Partnership’s efforts. Nevertheless, these studies and this method provide an interesting perspective on how links between ecological conditions and HWB can be assessed. If changes in these conditions have progressed to the point of having serious impacts on human systems, viewing the impacts through the lens of direct, subjective measurement of HWB would seem a fruitful avenue. Short of such changes, other methods (such as the ones discussed below) would seem more likely to provide a finer grained assessment of the links.

Table 1.


Becchetti et al. (2007)
Frijters and van Praag (1998)
Rehdanz and Maddison (2005)


Carroll et al. (2009)

Air pollution

Welsch (2002)
Welsch (2006)
Di Tella and MacCulloch (2007)
Luechinger (2009)

Airport noise nuisance

van Praag and Baarsma (2005)

Flood hazards

Luechinger and Raschky (2009)

Water pollution

Israel and Levinson (2003)

2.Revealed Preferences Methods

Standard economic theory is based on the assumption that observable choices made by individuals reveal their expected preferences. Individual utility is inferred from behavior, and is in turn used to explain the choices made (see Slesnick 1998 for an extended discussion). Behavior is therefore a way of inferring well-being, in that individuals are assumed to choose actions that are, from an ex ante perspective, the “best,” or the actions that maximize their well-being. Criticisms of this approach, and particularly the equating of utility and well-being, are legion. One of the leading lines is Kahneman (1999; see also Kahneman and Krueger, 2006; Kahneman and Sugden, 2005; Kahneman, Wakker, and Sarin, 1997), who distinguishes between decision utility (which is what economists analyze) and experience utility, which is akin to the moment-to-moment well-being discussed above. He argues that if the two utilities differ in their implications for public policy, experience utility should be favored over decision utility. A common example given to support this stance is one that features smokers: they may decide to have a cigarette (decision utility), yet be better off if they don’t (experience utility) (Read, 2004).

Nevertheless, although the revealed preference approach is not without its problems, it still offers a rich literature from which to draw, at least for the purpose of investigating links between environmental quality and human well-being. Below, we consider three methods that use actual behavior to assess the determinants of HWB: market-based approaches, hedonic analyses, and non-market behavior-based approaches.

Market-based Approaches

The most obvious way of discerning a link between environmental quality and human well-being is to look for environmental “goods and services” in the marketplace. Environmental resources are often inputs to market-based production processes. If so, their value can be measured directly, if the environmental resources are sold in a market; or inferred, if they are not themselves traded but the products they support are. Techniques for estimating the values in these cases are presented in standard benefit-cost textbooks (e.g., Zerbe and Bellas, 2006; Zerbe and Dively, 1994).

For example, Peters et al. (1989) examines the potential market value of non-timber forest products, such as fruits, latex, and tropical medicines, in a hectare of forestland. This value can be measured by calculating the net revenues per hectare from collecting these goods. Other studies use the costs of undesirable environmental change as a way of estimating the potential value of avoiding such change. Yohe et al. (1998) use the market value of land plus the cost of constructing protective sea walls to estimate the potential damage from sea level rise. The economic costs of climate change, and therefore the economic benefits of avoiding climate change, can also be estimated using this market perspective. Climate change will impact energy markets by shifting demand for energy resources, and the value of this shift can be used to infer these costs (Mansur et al., 2008). Similarly, a change in available water for an area through changes in climate can be valued using a demand model of water consumption in a watershed (Hurd et al., 1999).

The existence of markets for ecological goods and services provides an immediate pathway that connects ecological conditions to HWB. For Puget Sound, a potential source of relevant market-based data covers the commercial harvests of finfish and shellfish (Table 2) (Pacific States Marine Fisheries Commission , 2009). The volume of landings and the amount of revenues demonstrates the obvious value of these environmental goods. Exactly how these measures have or would respond to changes in the quality of their supporting habitat and other environmental conditions has not been the subject of systematic study, however.

Table 2.

Species common name


Commercial (non-tribal)

Commercial (tribal)

Landed weight (lbs)

Landed revenue ($)

Landed weight (lbs)

Landed revenue ($)

Landed weight (lbs)

Landed revenue ($)








Chum Salmon







Manila Clam







Dungeness Crab







Blue or Bay Mussel







Pacific Oyster







Coho Salmon







Chinook Salmon







Hedonic Analyses

Market goods often have multiple characteristics but are sold as a bundle. Analyzing such goods to discern the implicit price of each individual characteristic is an approach known as hedonic analysis. An existing house, for example, contains many characteristics that come as a bundle: numbers of bedrooms and bathrooms, square footage, size of lot, type of energy used, and so forth. If the good is fixed to a certain location, the characteristics of the location also become part of the bundle. Again, for an existing house, such location-specific characteristics include the quality of public schools, proximity to jobs, transportation networks, and even environmental amenities, such as air and water quality or proximity to open space. Each of these characteristics is not explicitly priced, yet the price of the house varies systematically with variation in their levels. Two types of bundled goods are analyzed with this approach: housing (or more generally, property) and jobs (wages).

Hedonic property models collect data on the prices of home sales and housing characteristics, which can include environmental quality and amenities. The expectation is that “good” features of a location (e.g., air and water quality) will be reflected by positive implicit prices for those features, while “bad” features (e.g., toxic waste sites) will have negative implicit prices. Hedonic wage models are based on the assumption that a job is a bundle of characteristics, which cover workplace characteristics as well as location-specific characteristics, including environmental quality and amenities. Here, the expected direction of implicit prices is the opposite of that for hedonic property prices. “Good” features will have a negative implicit effect because workers are willing to accept lower wages in locations with such features; “bad” features are associated with higher wages for the opposite reason. Although hedonic wage models are primarily used in environmental economics to value mortality risk, there are some studies that incorporate a broader set of environmental quality measures.

Exactly how one bounds a “location” for hedonic analysis is important. Most studies are limited to urban areas that have well-defined boundaries, or to other geographic units (counties, census blocks, and so forth) that have similarly well-defined boundaries. The characteristics of the bundled good are then taken from the features found within these boundaries. In contrast, Schmidt and Courant (2006) consider proximity to "nice" places (national parks, lakeshores, seashores, and national recreation areas) in an hedonic wage model. They found that amenities outside the metropolitan area generate compensating wage differentials, as workers are willing to accept lower wages to live in proximity to accessible “nice” places.

The hedonic approach has been used to estimate the values, as reflected in property prices or wage levels, for several types of environmental quality attributes, as shown in Table 3. Examples of studies that examine attributes that are more connected to ecological systems are briefly reviewed below:

  • Cho et al. (2009) examined amenity values of forest landscapes in the Southern Appalachian Highlands using a hedonic housing-price framework. Their results show that housing prices respond to the size and the density of forest-patches.
  • Bin and Polasky (2005) used a hedonic property price method to estimate how wetlands affect residential property values in a rural area. They found that i) a higher wetland percentage within a quarter mile of a property, ii) closer proximity to the nearest wetland, and iii) larger size of the nearest wetland are associated with lower residential property values.
  • Poor et al. (2007) investigated the value of ambient water quality throughout a local watershed in Maryland using a hedonic property value model, focusing on total suspended solids and dissolved inorganic nitrogen. Their results indicate that there is a substantial penalty imposed on property prices by higher levels of total suspended solids and dissolved inorganic nitrogen.
  • Bark et al. (2009) examined homebuyers' preferences for nearby riparian habitat in the metropolitan Tucson study area and the data incorporated into a hedonic analysis of single family residential house prices. The results indicate that high quality riparian habitat adds value to nearby homes and that instead of indiscriminately valuing "green" open space, nearby homebuyers distinguish between biologically significant riparian vegetation characteristics.
  • Bin et al. (2009) used data from the Neuse River Basin in North Carolina to provide empirical evidence on the effect of a mandatory buffer rule on the value of riparian properties. They found that a riparian property generally commanded a premium, but there was no evidence that the mandatory buffer rule had a significant impact on riparian property values when compared with the control group.
  • Netusil (2005) uses the hedonic method to examine how environmental zoning and amenities are related to the price of single-family residential properties sold between 1999 and 2001 in Portland, Oregon. The type of environmental zoning and the property's location affected the price effect of environmental zoning, while the type of amenity and its proximity affected a property's sale price.
  • Horsch and Lewis (2009) use hedonic analysis to estimate the effects of a common aquatic invasive species--Eurasian water milfoil (milfoil)—on property values across an extensive system of over 170 lakes in the northern forest region of Wisconsin. Their results indicated that property on lakes invaded with milfoil experienced an average 13% decrease in value after invasion.
  • Halstead et al. (2003) applies the hedonic method to estimate the effects of variable milfoil on shoreline property values at selected New Hampshire lakes. Results indicate that property values on lakes experiencing milfoil infestation may be considerably lower than similar properties on uninfested lakes, but that the results are highly sensitive to the specification of the hedonic equation.
  • Michael et al. (2000) used the hedonic approach to estimate the value for nine measures of water clarity for lakefront properties in Maine. They found that the value of water clarity varied across these measures, with the differences in implicit prices large enough to potentially affect policy decisions.

Table 3.

Air pollution

Anderson and Crocker (1971)
Chattopadhyay (1999)
Freeman III (1974)
Graves et al. (1988)
Harrison Jr. and Rubinfeld (1978)
Murdoch and Thayer (1988)
Nelson (1978)
Nourse (1967), Zabel and Kiel (2000)

Water quality

Boyle et al., (1999)
Leggett and Bockstael (2000)
Poor et al. (2006)
Epp and Al‐Ani (1979)
Gibbs et al. (2002)
Halstead et al. (2003)


Hall et al. (1978)
Nelson, J. P. (1982)
O'Byrne et al. (1985)
Taylor et al. (1982)

Solid waste sites

Havlicek et al. (1971)
Reichert et al. (1992)
Thayer and Rahmatian (1992)

Shore erosion protection

Kriesel et al. (1993)

Toxic waste sites

Kiel, K.A. (1995)
Kohlhase (1991)
Reichert (1997)
Smith and Desvousges (1986)
Smolen et al. (1992)

Non-market Behavior-based Approaches

For many recreational and other environmental experiences, there is no formal market that can be used to assess their value, either directly or indirectly as is done with the hedonic approach. If the experience requires some form of travel or other behavior that entails a cost (usually in terms of time), however, it is possible to infer how an individual values that experience in terms of their willingness-to-pay. The most common form of this approach is the travel cost method, which uses travel costs and visitation rates to a recreation site to estimate a demand function for that type of recreation (Clawson, 1959; Knetsch, 1963). Similar to the assumptions for hedonic models, the recreation “good” can be a bundle of characteristics, some of which are the environmental features important to the recreational experience. If data are available for visits to multiple sites with varying levels of those features, one can then estimate the contribution of a particular feature to the demand for that recreation, and from this estimate its value (Morey, 1981).

The travel cost method has been widely used to estimate the value of recreation. Loomis (2005) summarizes many of these studies for the purpose of assessing recreation values that could be applied to the U.S. National Forest system. Table 4 presents estimates from Loomis (2005) of seven different types of recreation, drawn from studies conducted in Oregon or Washington. As will be illustrated in the next section, the travel cost and other non-market behavior-based methods have been largely overtaken by the state preference approach. Nevertheless, there are some studies worth noting:

  • Murray et al. (2001) estimated the value of reducing beach advisories in Great Lakes beaches located along Lake Erie's shoreline in Ohio. They found that the across all visitors, the average seasonal WTP to encounter one less advisory was approximately $28 per visitor.
  • Egan et al. (2009) used a set of water quality measures developed by biologists in a study of recreation visits to 129 lakes in Iowa, and derived estimates of the willingness-to-pay for improvements in the water quality measures. The results demonstrated a significant WTP for water clarity as measured by the Secchi transparency, and that recreational trips decreased as concentrations of nutrients increased.
  • Massey et al. (2006) and estimated the benefits of reducing water pollution for recreational fishing when fishing takes place at multiple locations. They found only small impacts from improving water quality conditions in Maryland's coastal bays alone, but that improvements throughout the range of the species could increase abundance and associated beneficial increased catch rates.
  • Montgomery and Needelman (1997) also estimated the benefits of reducing water pollution for recreational fishing when fishing takes place at multiple locations. They estimated an annual benefit of $63 per capital per seasons from eliminating toxic contamination from New York lakes and ponds.
  • Johnstone and Markandya (2006) derived economic values for river quality indicators, including chemical, biological and habitat-level attributes, by developing a model of angler behavior that linked these attributes to visitation rates. The models could then be used to estimate the welfare associated with marginal changes in river quality.

Table 4. Average recreation values based on studies from Oregon and Washington that used the revealed preference approach (Loomis, 2005)


Value per day

Number of studies
















Wildlife viewing



3.Stated Preference Methods

Stated preference methods rely on survey questions that ask individuals to make a choice, describe a behavior, or state directly what they would be willing to pay for specified changes in non-market goods or services. This approach is controversial because in most cases it is not possible to verify independently the answers given to the survey questions, although experimental work has been conducted to investigate this issue (Murphy et al., 2005). Stated preference methods are increasingly used in economic studies of environmental quality because they offer the opportunity to estimate the valuation for anything that can be presented as a credible and consequential choice. Because they do tie willingness-to-pay to a hypothetical act of payment, they do not require observations of actual behavior and so they are the only economic methods that can measure non-use values.

The stated preference method can take the form of a contingent valuation survey, which asks respondents directly about the monetary value of a particular commodity or environmental change (Mitchell and Carson, 1989). A second approach, and one that is increasingly common, is the choice experiment or conjoint analysis approach (Holmes and Adamowicz 2003). This survey method gives respondents a set of hypothetical scenarios, each depicting a bundle of environmental attributes supplied at a given level, where the levels vary across scenarios. Also included (in nearly all cases) is a monetary cost, often characterized as a payment to a fund, a tax, or some other payment mechanism. Respondents are asked to express their preferences by choosing the most preferred alternative, ranking them in order, or rating them on some scale. By examining the tradeoff between the environmental attributes levels and the payment amounts, the willingness-to-pay for the different attributes can be estimated.

Although this approach has focused mainly on environmental economic issues, it has also been used to address other, non-environmental issues, including violent crime (Atkinson et al., 2005); urban amenities (Howie et al., 2010); broadband service (Tseng and Chiu, 2005); and public transit stop information (Caulfield and O'Mahony, 2009). Cook and Ludwig (2002) examined people's views of policies designed to reduce gun violence using a stated preference model. They asked respondents how they would vote on a policy that was described as having the potential to reduce gun violence by 30 percent. Stated preference questions were used to measure respondents' likelihood of using the high occupancy traffic lanes as a function of the toll level and time savings (Georgia State Road and Tollway Authority, 2005).

Stated preference studies are by far the richest literature for connecting environmental conditions to HWB, at least as measured in terms of individuals’ willingness-to-pay. Examples are cited in Table 5, which lists stated preferences studies that have estimated the willingness-to-pay for protecting a species (Richardson and Loomis, 2009). Below, a few of the many other studies are summarized:

  • Carson and Mitchell (1993) perform a single comprehensive CV analysis, asking a national random sample of U.S. households to value the change in water quality that results from moving from no pollution control to "swimmable" water quality nationwide. Their best estimate of annual benefits is $(1990) 29.2 billion.
  • Lyon and Farrow (1995) assessed the incremental net benefits of additional water pollution control investments beyond 1990. They concluded that these programs could have net benefits less than zero, but significant uncertainties remained.
  • Milon, J.W., and D. Scrogin (2005) estimated the benefits of restoring the Greater Everglades ecosystem in Florida. They cast the restoration in terms of ecological functions (water levels) and structural changes (species populations) and found higher WTP for the latter than the former.
  • Bell et al. (2003) used a stated preference survey to determine the WTP for a local coho salmon enhancement program in four Washington and Oregon coastal estuaries. They estimate this WTP to range between $37 and $120, depending on a household’s income and the type of program.
  • Hall et al. (2002) measured the benefit of an improvement in the quality of rocky intertidal zones in southern California resulting from additional regulation enforcement and access limitations. They presented respondents with a hypothetical reduction in illegal collecting and onsite habitat disturbance, which would increase the abundance of intertidal organisms, and found an average WTP of $6 per family-visit.
  • Viscusi et al. (2008) used the stated preference approach to estimate values for water quality ratings based on the US Environmental Protection Agency National Water Quality Inventory ratings. They found an average value of $32 for each percent increase in lakes and rivers in the region for which water quality was rated as “Good.”
  • Banzhaf et al. (2006) quantified the total economic value of ecological improvements to New York’s Adirondack Park from a reduction in acid rain. They estimated the WTP for these improvements to range from $48 to $107 annually.

Table 5. Examples of studies that use the stated preference approach to estimate the economic non-use value of a species (Richardson and Loomis, 2008)

Arctic grayling

Duffield and Patterson (1992)

Atlantic salmon

Stevens et al. (1991)

Bald eagle

Boyle and Bishop (1987)
Stevens et al. (1991)
Swanson (1996)

Bighorn sheep

King et al. (1988)

Blue whale

Hageman (1985)

Bottlenose dolphin

Hageman (1985)

Gray whale

Hageman (1985)
Loomis and Larson (1994)

Gray wolf

Duffield (1991, 1992)
Duffield et al. (1993)
Chambers and Whitehead (2003)

Humpback whale

Samples and Hollyer (1989)

Mexican spotted owl

Loomis and Ekstrand (1997)
Giraud et al. (1999)

Monk seal

Samples and Hollyer (1986)

Northern spotted owl

Rubin et al. (1991)
Hagen et al. (1992)

Northern elephant seal

Hageman (1985)

Peregrine falcon

Kotchen and Reiling (2000)

Red-cockaded woodpecker

Reaves et al. (1994)

Riverside fairy shrimp

Stanley (2005)


Olsen et al. (1991)
Loomis (1996)
Layton et al. (2001)
Bell et al. (2003)

Sea otter

Hageman (1985)

Silvery minnow

Berrens et al. (1996)


Cummings et al. (1994)

Steller sea lion

Giraud et al. (2002)

Striped shiner

Boyle and Bishop (1987)

Whooping crane

Bowker and Stoll (1988)

Wild Turkey

Stevens et al. (1991)


Given the flexibility of the stated preference approach, it is tempting to ignore the first two methods – direct, subjective HWB measurement and revealed preference approaches – and focus on the stated preference approach as the most fruitful, at least in terms of ongoing and future research. That approach can be difficult to apply for ecological systems, however, because presenting information on such systems in the context of a survey can be problematic (Boyd and Krupnick, 2009). For the first two methods, an individual does not need to understand or even be aware of entire system that connects ecological conditions and well-being. These methods are based on the actual experience of these conditions, however, because they use objective measurements of the “real” conditions as the basis for analysis. For stated preference surveys, the connections are explored by giving individuals information about various scenarios, which inevitably decompose the environment into a limited set of abstract conditions. This means that respondents do not experience the full set of “real” conditions, and so are likely to “fill in the gaps” in ways that present problems for gathering useful data (Boyd and Krupnick, 2009).

In any case, there is much more work to be done to relate changes in environmental conditions to changes in human well-being. (Stiglitz et al. 2009). One must be careful in drawing conclusion from the current literature, as the absence of evidence documenting the strength of a connection should never be taken as evidence of the absence of such a connection. Nevertheless, documenting such absences can identify potentially important areas for future research.

Key Points: Although human well-being cannot be observed directly, there are methods to assess the determinants of human well-being. Research has utilized these methods to investigate the strength of connections between economic, social, and environmental factors and HWB. There is still much work to be done, however, in documenting these connections, particularly those covering environmental factors in general and for Puget Sound in particular.




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