Indicators and Methods for Constructing a US Human ...

United States Environmental Protection Agency Office of Research and Development, Gulf Ecology Division Gulf Breeze, FL 32561

Report # EPA/600/R-12/023 March 2012 http://www.epa.gov/ord/

Indicators and Methods for Constructing a U.S. Human Well-being Index (HWBI) for Ecosystem Services Research

Photo credits for the cover Clouds in the sky – Microsoft.com Family holding hands on bea c h—Microsoft.com B o a r d w a l k — U . S . F i s h a n d W i l d l i f e S e r vi c e ( U . S . F W S ) Rainbow—Microsoft.com K a y a ke r – U . S . E P A Gre a t Bl ue H e r o n – U.S. FWS Cypress swamp– Heather Smith Inside Cover U.S. EPA Additional phot o c r edit inf or m ation Background pictures for well-being domain pages(*and on page 13): Social Cohesion (holding ha nds)—Microsoft.com Ed u ca t io n ( ap p l e) —M i c r osof t.c om C o n n e c t i o n t o N a t u r e ( b o y a nd b u t t e r f l y ) — U . S . F W S Health (leapfrog couple)—Microsoft.com L i v i n g S t a n d a r d s ( d o l l a r b i l l s ) — V e e r Im a g e s ( Mi c r o s o f t p a r t n e r ) L e i su r e T im e ( s u n di a l ) —M i c r osof t .c om S a f e t y a nd S e c u r i ty ( v a u l t ) — P h o t o s . c o m ( M i c r o sof t p a r t n e r ) C u l tu r a l F u l f il l m ent ( boa ts) —M i c r osoft .com

Acknowledgements

This Indicators and Methods for Constructing a U.S. Human Well-being Index (HWBI) for Ecosystem Services Research Report was prepared by the U.S. Environmental Protection Agency (EPA), Office of Research and Development (ORD), National Health and Environmental Effects Research Laboratory (NHEERL), Gulf Ecology Division (GED). The following task members provided written materials and technical information throughout the preparation of the document. Lisa M. Smith, Office of Research and Development

Heather M. Smith, Student Services Contractor

Jason L. Case, Student Services Contractor

Linda C. Harwell, Office of Research and Development

J. Kevin Summers, Office of Research and Development

Christina Wade, Student Services Contractor

Disclaimer:

This document has been reviewed in accordance with U.S. Environmental Protection Agency policy and ap proved for publication.

i

Contents

Courtesy of Chuck Felix; freedigitalphotos.net

Acknowledgements………………………………………………………………………………………………………………………………. i Abstract………………………………………………………………………………………………………………………………………….………… iv Introduction……………………………………………………………………………………………………………………………………………. 1 Data Sources and Quality Assurance…………………………………………………………………………………….………….. 5 Well-being Domains and Indicators…………………………………………………………………………………….……………. 6 Connection to Nature…………………………………………………………………………………………………………………

7

Biophilia……………………………………………………………………………………………………………………………….

9

Cultural Fulfillment……………………………………………………………………………………………………………………

10

Activity Participation…………….…………………………………………………………………………………………….

12

Education……………………………………………………………………………………………………………………………….…….. 13 Social, Emotional and Developmental Aspects……………………………………………………………….…….

15

Basic Knowledge and Skills of the Youth………………………………………………………………………….…… 17 Participation and Attainment……………………………………………………………………………………………….

18

Health………………………………………………………………………………………………………………………………………….… 21 Personal Well-being……………………………………………………………………………………………………………..

23

Life Expectancy and Mortality………………………………………………………………………………………………

24

Physical and Mental Health Conditions………………………………………………………………………………..

28

Lifestyle and Behavior………………………………………………………………………………………………………….. 32 Healthcare……………………………………………………………………………………………………………………………

34

Leisure Time………………………………………………………………………………………………………………………………..

36

Time Spent…………………………….…………………………………………………………………………………………..

38

Activity Participation……………………………………..……………………………………………………………………

38

Working Age Adults…………………………………………………………………………………………………………….

39

ii

Living Standards………………………………………………………………………………………………………………………… 41 Wealth………………..…………………………………………………………………………………..…………………………

43

Income..……….…………………………………………………………………………………………………………………….

44

Work…………………………………………………………..……………………………………………………………………… 45 Basic Necessities.……………………………………………………………………………………………………………….. 46

Safety and Security….……………………………………………………………………………………………………………….. 48 Actual Safety.……………………………………………………………………………………………………………………..

50

Risk…………………..………………………………………………………………………………………………………………..

52

Perceived Safety.………………………………………………………………………………………………………………..

52

Social Cohesion………………………………………………………………………………………………………………………… 53 Social Engagement.…………………………………………………………………………………………….………………

55

Attitude Towards Others and the Community…………………………………………………………….………

56

Family Bonding………………………………………………………………………………………………………………….

59

Democratic Engagement……………………………………………………………………………………………………

60

Social Support……………………………………………………………………………………………..……………….……

63

Summary Table of Data and Available Spatial Scales……………….…………………………………………………. 64 Constructing the Composite Index of Well-being………………………………………………………………………… 66 Current Status and Next Steps.…………………………………………………………………………………………………………. 69 References…………………………………………………………………………………………………………………………………………….. 70 Appendices……………………………………………………………………………………………………………………………………………. 75 A Descriptive statistics and histograms used to establish metric distributions…….…………………………………… 76 B Contribution weights for domains and elements of well-being…………………………………………………………….. 100 C Graphical summary of indicator development and index construction methodologies…………………………. 102

iii

Abstract Humans are dependent upon the services provided by nature, and unless we effectively account for the range of values from ecosystems in our efforts to protect the environment, we cannot sustain human well-being. In light of this dependence, a national measure of well-being is needed which is responsive to changes in the provisioning of ecosystem services as well as service flows from economic and social sectors. To conceptualize the eco-human linkages we must identify the measurable components of wellbeing that can be related to ecosystem service provisioning. The indicators and metrics used in existing well-being indices provide a basis for developing a core set of domains to develop such a composite measure of well-being; however these indices lack the ability to link well-being endpoints specifically to service flows from different types of capital. This report suggests a core set of well-being domains that can be linked to ecosystem services via their relationship to economic, environmental and societal wellbeing. The development of indicators and metrics used as domain measures are described and the methodologies for constructing a composite human well-being index (HWBI) are detailed. The HWBI is intended to be used as a sustainability indicator for evaluating the provisioning of ecosystem, economic and social services in a predictive modeling framework, allowing decision makers to use alternate scenarios to assess potential impact on communities.

“Ecosystem services incorporate the language of economics and business, through their valuation, and the language of development, through their support for human well-being. Efforts to support the long-term sustainable supply of those services are as important to human well-being and survival as they are for nature itself.” Mainka Susan A. , Jeffrey A. McNeely and William J. Jackson. 2008 Depending on Nature: Ecosystem Services for Human Livelihoods. Environment: Science and Policy for Sustainable Development . 50(2):42-55.

Courtesy of Roxanne Lavelle

iv

Introduction One of the primary goals of the United States Environmental Protection Agency’s (USEPA) Sustainable and Healthy Communities Research Program (SHCRP) is to assess, valuate, and provide comparisons of changes in ecosystem services resulting from local, regional, and national decision making. Valuation is generally thought of as a conversion of ecological services to their extrinsic value to humans in economic terms. However, many of the services provided by healthy, resilient ecosystems have intrinsic value (e.g., traditions and customs, belief systems, values and attitudes, security, disability recovery, happiness) that are difficult to valuate in traditional ways (Boyd 2008). The conceptual relationship between the quality of the environment and its services to human well-being is well established and generally accepted and may have profound implications for policy-making and sustainability (Daily et al. 2009). While accepted, the determination of the quantitative “value” of the intrinsic and extrinsic services of ecosystems is more elusive and requires broader thinking than more straightforward economic approaches. To better understand the contribution of ecosystem services to overall human wellbeing we must first describe human well-being and delineate a core set of indicators that represent the state of society across time, culture and scale. Additionally, the relationship of ecosystem services to these aspects of well-being must be evaluated in context of service flows from human, built and social capitals (categorized as economic and social services) (Figure 1). Most frequently, well-being indices are designed to address specific policy objectives and are driven by economic and social measures. While some composite indices of well-being include measures of environmental quality, ecosystem condition or health outcomes related to environmental exposures, the concept of ecosystem services and the potential impact of loss of services has not been addressed for environmental accountability and decision making. What is evident from our extensive literature review of human well-being research, however, is that holistic measures of well-being should be Figure 1. Capital Flows in a Sustainable Society; adapted from J. inclusive of the elements of societal Fiksel, A Framework for Sustainable Materials Management, Journal well-being as described in terms of of Materials, August 2006 subjective well-being and meeting basic human needs, economic wellbeing, and environmental well-being (Summers et al. in press). Therefore the objective of human well-being research within SHCRP is to develop a national human well-being index (HWBI) for the United States that describes well-being by integrating endpoint measures of these elements and to ultimately show how changes in service flows from different capitals (economic, social and ecosystem services provisioning) are reflected in this composite index.

1

Describing Well-being Our approach to linking the provisioning of ecosystem services to human well-being is anchored in the development of an index of well-being for the United States based on indicators and metrics derived from existing measures of well-being. Groups of indicators described by suites of metrics are commonly aggregated to evaluate components of well-being (domains). The domains we have identified for developing a United States index of well-being are influenced by service flows from different capitals. More specifically, the domains of well-being described in this document were adapted from the framework for the Canadian Index of Well-being (CIW) released in April 2011 and closely resemble domains described in the Organisation for Economic Co-operation and Development (OECD) Better Life Index (http://www.oecdbetterlifeindex.org/). Note that not all indicators represented in the domains of the CIW and OECD were used, and in some cases we chose additional indicators, as appropriate for a well-being index for the United States. The proposed index of well-being for the United States represents the following eight domains of human well-being: • • • • • • • •

Social cohesion Education Connection to nature Health Living standards Leisure time Safety and security Cultural fulfillment

Ecosystem Services

Well-being Domains

Human Well-being

Photos Courtesy of U.S. EPA (Ecosystem Services), photostock (Well-being Domains), and Microsoft.com (Human Well-being)

The metrics chosen here for index development reflect measures of the human condition as opposed to the quality and quantity of goods and services supporting society. The metrics describing service flows will be used to model well-being as an endpoint measure in a predictive modeling framework. Therefore, the HWBI described in this document is an “ends” measure separated from the “means”. By doing so, we can ultimately develop alternate scenarios for decision support tools for managers and policy makers. Information quantifying the delivery of social and economic services, and ongoing research within SHCRP seeking to measure ecosystem functions and quantify goods and services provisioning will provide information for model input (Figure 2). Modeling efforts will involve Relative Importance Values (RIVs) like those described in the section titled “Constructing the Composite Index of Well-being” on page 67 of this report. RIVs will also be used to link each service (ecosystem, social and economic) to each well-being domain by establishing their subjective importance. A conceptualized modeling framework highlighting ecosystem goods and services is presented in Figure 3 which delineates the components of the composite index of well-being. This report provides a short description for each well-being domain chosen for the construction of the HWBI, how each relates to economic and social drivers, and emphasizes the relationships to ecosystem goods and services. The domain descriptions are followed by the domain indicators and their corresponding metrics. A summary of metric data is provided, and metric selection criteria and quality assurance are briefly described. Finally, the methods used to construct the composite HWBI are described on pages 66-68 and are illustrated in Appendix C.

2

Ecosystem Functions and Processes

Ecosystem Goods and Services

Water Quality Regulation

Usable Water

Nutrient fixed into biomass Denitrification rates Advective nutrient removal rates Dilution rates Nutrient burial rates Contaminant burial and removal rates Freshwater inflow rates Heat flux rates Sediment loading rates Light attenuation rates Pathogen removal Surface/groundwater ratios Net production of dissolved oxygen

Drinkable, swimmable, and fishable water Temperature moderation Salinity moderation Water clarity maintenance DO moderation

Availability of Habitat/Refugia

Habitat/Refugia

Area of habitat Habitat complexity Temperature Dissolved oxygen concentrations Salinity Depth Light availability Soil/sediment characteristics Contaminant concentrations Habitat connectivity Animal and plant abundances Sediment loading rates Erosion rates

Productive terrestrial and aquatic environments Maintenance of habitat structure Habitat characteristics maintained in viable range

Water Quantity Regulation

Available Water

Precipitation rates Aquifer recharge rates Surface water reservoir capacity Salt water infiltration rates

Fresh water supply

Atmospheric Regulation

Usable Air

CO2 fixed into biomass Carbon content of biomass Carbon burial rates Soil and sediment carbon content Ozone, CO, NO2 and SO2 removal rates Particulate removal rates Temperature CH4 emissions

Clean Air Air pollutants removed Temperature moderation

Food and Fiber Provisioning

Food, Fiber, and Energy

Stable Climate Greenhouse gas reduction

Row crop production Timber production Livestock production Fishery production Fuel production

Natural Hazard Protection

Flood Protection

Water retention capacity of soils Wetland floodwater receiving capacity Storm surge attenuation rates

Flood and storm surge buffer Retention/removal of precipitation Attenuation of peak flows Attenuation of storm surge

Biodiversity Regulation

Biodiversity

Number of species per functional role Plant and animal diversity indices Number of charismatic species Fragmentation statistics

Functional Stability Functional redundancy maintained Indirect existent uses maintained Habitat heterogeneity supported

Green Space

Recreation and Aesthetics

Area of recreational space Travel distance to "natural" areas Diversity of recreational opportunities Aesthetic quality

Recreational opportunities Direct use activities Access to natural areas

Figure 2. Final ecosystem goods and services flowing from ecosystem functions and processes (identified by EPA ecologists). 3

Figure 3. A conceptualized modeling framework showing the components of the composite index of well-being highlighting ecosystem goods and services inputs, ( Smith et al. 2013). 4

Data Sources and Quality Assurance An extensive review of existing well-being indices was performed to determine the current indicators and metrics in use. As stated previously, the categories of domains, indicators, and metrics were mainly adapted from the Canadian Index of Well-being and the OECD Better Life Index because they contained the most complete set of measurements identified in the review of all potential indices. Data collected by the following institutions and organizations was used in our index (* most used data sources): • • • • • • • • • • • • • •

Centers for Disease Control and Prevention (CDC) *1 U.S. Census Bureau *2 General Social Survey (GSS) 3 Gallup, Inc. (Gallup Brain, Gallup Healthways) 4 Bureau of Labor Statistics (BLS) Bureau of Economic Analysis (BEA) U.S. Department of Health and Human Services (HHS) Federal Bureau of Investigation (FBI) American National Election Study (ANES) National Center for Education Statistics (NCES) Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) National Oceanic and Atmospheric Administration (NOAA) Association of Religion Data Archives (ARDA) University of South Carolina Hazards and Vulnerability Research Institute (HVRI)

These data sources were chosen based on the following criteria: 1. Availability and access: The data were publicly available and easy to understand, access and extract. 2. Reliability and data credibility: The sources collected data in a manner that was vetted by the professional community and had metadata available for review. 3. Spatial preference: County-level data were the lowest geospatial level preferred, and could be rolled into larger scales as needed. In the absence of county-level data, or when it was not feasible to pull county-level data (i.e., data only available from local governmental sites; lack of compiled data from a single source), state, regional, and national-level data were used. 4. Coverage: The data were available for a large portion of the United States. 5. Chronological history and the likelihood that the data will continue to be collected: Data had a good history of collection or consistent collection. The goal was to initially create a time series beginning with the year 2000 and continuing through 2010; however, if the data were not available from a single data source for all years, other sources containing similar measurements were used to complete the time series. 6. Subjective and objective data: Both subjective and objective data were included. 1 CDC Behavioral Risk Factor Surveillance System data is derived from telephone surveys and therefore does not include persons without a home telephone number. 2 U.S. Census Bureau American Community Survey 1-year estimates are only available for geographic areas with populations greater than 65,000 people.. 3 Questions asked may or may not be repeated in subsequent survey years and is only available on a biennial basis. 4 Gallup Healthways is proprietary data and only pre-calculated index values are publicly available at the national level.

5

Well-Being Domains and Indicators

Photo credits can be found on the inside front cover.

Domains are collections of indicators and metrics used to describe different components of human well-being (see Fig. 3). Although these components are often interrelated, domains are commonly identified for use in well-being indices in an attempt to separate the main aspects of the human condition to be measured by serving as the foundation for selecting and developing indicators. Ultimately, these domains correspond with one or more of the three main elements of well-being: economic, environmental, and societal well-being (societal includes basic human needs and subjective well-being) that constitute a multidimensional approach to modeling human well-being.

The following pages contain descriptions of the chosen well-being domains, including how each may relate to economic and social services, and emphasize the relationships to ecosystem goods and services. The linkages of ecosystem services to a domain might not be as obvious or as widely known. We have highlighted prior research that has shown, both directly and indirectly, how ecosystem services can influence each domain of well-being. Unfortunately there is a lack of metrics depicting direct ecosystem service-domain relationships that are widespread or have good coverage across the United States. This will likely change with time due to increasing interest and research in this area of well-being studies. Details of the domain indicators and corresponding metrics are also included. Each metric description includes basic information such as the data source(s) and years available, as well as calculations performed to create the final datasets. We examined the distribution for all metrics using descriptive statistics for pooled data (2000-2010) to determine the appropriate method for evaluating the data. The graphical results and statistical summaries for each metric are included in Appendix A.

6

Domain: Connection to Nature

Biophilia (2)

7

Connection to Nature Humans’ connection with nature is a subjective trait most easily explained by the biophilia hypothesis. Biophilia is a term coined by prominent evolutionary biologist and entomologist, Edward O. Wilson, who defined it as the “innately emotional affiliation of human beings to other living organisms” and hypothesized that this psychological, and possibly genetic, phenomena arose due to humans’ long time interaction with the natural environment (Wilson 1984, 1993). Biophilia is most evident by the popularity of zoos and outdoor activities and in people who have non-economic motivations for the protection of natural areas and biodiversity, such as positive experiences of an area, solastalgia, and having affection or sympathy for nonhuman species (Wilson 1993, Serpell 2004, Chawla 2006, Higginbotham et al. 2007, Martin-Lopez et al. 2007, Nisbet et al. 2011).

Courtesy of Evgeni Dinev; freedigitalphotos.net

Biophilia is also evident in other domains of well-being such as spiritual and cultural fulfillment, education, health, and leisure time. Humans, however, are experiencing an increasing disconnection with nature through urban development and technology— especially noted in children as the “nature-deficit disorder” and coincides with rising trends in obesity, attention deficit disorder, and depression (Wilson 1993, Kellert 2005, Louv 2005). An attempt to correct this growing disconnection and to incorporate the health of the environment in land use planning is through “biophilic design”, which aims to enhance human physical health, psychological benefits, and productivity by fostering a human-nature connection (Baldwin et al. 2011). Although our detachment with nature will never completely rid us of the desire to associate with nature, it can weaken our appreciation for nature and decrease our well-being (Kellert 1997).

Economic and social services have significant direct and indirect effects on the connection to nature domain. For example, economic programs and funding can increase or decrease natural areas, either by putting aside more areas or decreasing those areas through capital investment (e.g., new infrastructure, mining/extraction activities). Additionally, social services such as activism, community and faith-based initiatives, justice (e.g., environmental justice), and public works can affect policies that support ecosystems or can possibly be used as indirect measures of our connection to nature.

Relationship to Ecosystem Services Biophilia is largely affected by biodiversity and amplified by access to nature and exposure to diverse, healthy ecosystems (Wilson 1993). Nature Relatedness (NR), similar to biophilia, has been used to quantify our connection to nature (Nisbet et al. 2011). Natural areas and green spaces are needed for humans to experience nature and increase NR, which is most often accomplished through the ecosystem goods of recreation and aesthetics. The total area of these spaces directly affects the availability and diversity of recreational and aesthetic opportunities and the health of the ecosystem and its ability to provide other services such as water and air quality regulation (EPA 1997, MEA 2005, Pongsiri and Roman 2007). Additionally, due to the interconnectedness of plants and animals occupying these areas, biodiversity is especially important for the functioning of the ecosystem and of humans psychologically (Kellert 1997, MEA 2005, Chavas 2009, Nisbet et al. 2011). 8

Courtesy of Microsoft.com

Indicator: Biophilia

Spiritual Fulfillment Metric Variable: BEAUSPRT Source: General Social Survey (GSS) Source Question or Measurement: GSS variable BEAUSPRT, I am spiritually touched by the beauty of creation Alternate Source: N/A Years Available: 1998*, 2004 Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of respondents who answered “Many times a day”, “Every day”, and “Most days”. *1998 values were used for imputation purposes only

Connection to Life Metric Variable: ALLOFLFE Source: General Social Survey (GSS) Source Question or Measurement: GSS variable ALLOFLFE, You may experience the following in your daily life, if so how often? Experience a connection to all of life Alternate Source: N/A Years Available: 2004 Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of respondents who answered “Many times a day”, “Every day”, and “Most days”

9

Domain: Cultural Fulfillment

Activity Participation (2)

10

Cultural Fulfillment This domain captures metrics that measure opportunities that afford people and communities access to fulfilling their cultural needs. Indicators are multi-faceted and may represent cultural interests, cultural identity, and/or connection to nature (i.e., visits to national parks). Cultural indicators encompass valuesdriven metrics that examine the concepts of the “self” that centers around vital interconnections with others and the environment (Centre for Rural and Remote Mental Health 2009).

Courtesy of iStockphoto

While there are many variations of the specific definition, few would argue the important role of spirituality and culture within populations. Cultural values are in many ways integral to vital communities ,yet are rarely considered in most well-being indices. Investment in museums, cultural centers, and other similar gathering places offer educational opportunities to help mitigate inequities typical of cultural exclusion. Faith- and community-based activities, such as festivals, concerts, arts and crafts shows, etc.,. further strengthen social cohesion by preserving cultural and spiritual heritage. Moreover, it is the environmental culture that is often the harmonizing factor that supports community vitality when obvious economic disparity would otherwise cause discord (“A Tale of Two Aspens” 2011).

Relationship to Ecosystem Services Cultural ecosystem services represent the “non material benefits people obtain from ecosystems through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences” (MEA 2005, p. 40). For many populations, culture and spirituality are strongly connected with the environment. Swan and Raphael (1995) noted that Aboriginal Australians holistically view “health” as harmonized, inter-relating factors that include spiritual, environmental, ideological as well as mental and physical aspects that, collectively, are identified as “cultural well-being”. The social, sacred, and cultural aspects of ecosystems significantly contribute to Native American well-being but are often overlooked in qualitative assessments. Native Americans seek cultural and spiritual fulfillment by communing with nature, praying and meditating, fishing and hunting, collecting herbs, and conducting vision quests or other ceremonies (Burger 2011).


The interwoven relationship between humans and the landscape is manifested in cultural diversity and heritage, educational values and ecological knowledge, social relations and sense of place (MEA 2005, Rössler 2006, Schaich et al. 2010). The tangible and intangible heritage associated with the human nature interface is tightly coupled with people’s involvement in environmental conservation (Philips 1998). Thus it follows that cultural and spiritual fulfillment is influenced by our connection to natural systems and an opportunity to identify with our heritage through visits to natural historical sites, national parks, and celebrations revolving around cultural landscapes and nature’s bounty. 11


Indicator: Activity Participation

Performing Arts Attendance Metric Variable: PERARTS Source: U.S. Census Bureau & Bureau of Labor Statistics - Current Population Survey Source Question or Measurement: Census variables PESA1A through PESA9A (Attended jazz, classical music, opera, musical stage play, non-musical stage play, ballet, or modern, folk, tap performance, or visited an art museum/gallery or art/craft fair/festival) Alternate Source: N/A Years Available: 2002 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the percentage of people who responded “yes” to any of the variables

Rate of Congregational Adherence Metric Variable: TOTRATE Source: Association of Religion Data Archives, U.S. Church Membership Data, Religious Congregations and Membership Studies Source Question or Measurement: ARDA variable TOTRATE, All denominations/groups--Rates of adherence per 1,000 population Alternate Source: N/A Years Available: 2000, 2010 Smallest Geospatial Level Available: County Calculation Methods: N/A

12

Domain: Education

Social, Emotional and Developmental Aspects Basic Knowledge and Skills of the Youth Paticipation and Attainment

13

Education The domain of education is defined as the outcomes derived from the formal and informal transfer of knowledge and skills and is measured using standardized test scores, literacy rates, educational attainment and participation, and various social, emotional, and developmental aspects in childhood. Education has been referred to as a basic capability leading to the expansion of other capabilities and is fundamental to well-being (Terzi 2004). Educational progress and benefits influence other well-being domains and may be measured at the individual level by economic returns by subjective feelings of achievement and accomplishment, or at a societal level by creating a skilled workforce with enhanced worker productivity, lower crime rates, and greater civic participation (Guhn et al. 2010, Hill et al. 2005). Economic and social services provide funding and other programs that influence the access to and opportunities for education. Educational services provide programs are aimed at reaching more students, especially those with disabilities or other special circumstances, and hiring qualified teachers. Community and faith-based initiatives may also act in this manner to reach additional children and families. Communication through public broadcasting and public service announcements helps educate the public about various issues (e.g., public health issues). Financial assistance in the form of grants, scholarships, and student loans is also essential to allow opportunities for post-secondary education

Relationship to Ecosystem Services Ecosystems provide a plethora of learning opportunities at many levels of education. Some areas may be designated as public learning centers and accessible to all ages, while post-secondary educational institutions may use natural areas for teaching and scientific research (EPA 1997). Environment-based education programs and school grounds greening in elementary and secondary schools have shown several positive effects on the mental health and brain development in early and middle childhood. These benefits include improved standardized test scores and problem-solving skills, decreased symptoms of attention deficit disorder, and enhanced cooperation and interpersonal skills, all of which lead to a better educational experience and improved well-being (Lieberman and Hoody 1998, Louv 2005, Guhn et al. 2010).

Courtesy of U.S. FWS

Courtesy of U.S. FWS

Ecosystem research is also integral to innovation and the progression of society. By studying the function and uses of organisms, we are able to discover untapped sources of pharmaceuticals, crops, and other goods and also transfer that knowledge into art, other scientific fields, and practical affairs (Wilson 1993). Local environmental knowledge is also important in providing historical accounts of an area. These accounts contribute to scientific research and environmental management, but also contribute to various cultural aspects of the area. (Huntington 2000). Continual research on ecosystems is crucial for understanding how ecosystems provide services that effect human well-being, as well as understanding how our actions affect the provisioning of these services.

14

Courtesy of photostock

Indicator: Social, Emotional and Developmental Aspects

Preprimary Education and Care Metric Variable: CONFACT Source: Bureau of Labor Statistics- American Time Use Surveys Source Question or Measurement: Time spent reading to/with household children identified by activity code 030102 (and where the youngest household child was between the ages of 3 and 5 years old). Alternate Source: N/A Years Available: 2002-2008 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the percentage of parents who have children that reported time spent (*incidence, not actual time spent) reading to/with their children

Bullying Metric Variable: BULLY Source: Centers for Disease Control and Prevention- Youth Risk Behavior Surveillance System Source Question or Measurement: During the past 30 days, on how many days did you not go to school because you felt you would be unsafe at school or on your way to or from school? Alternate Source: N/A Years Available: 1999*-2009; biennial Smallest Geospatial Level Available: State Calculation Methods: Calculated as the percentage of students in grades 9-12 who responded with 1 or more days. *1999 values were used for imputation purposes only

15


Indicator: Social, Emotional and Developmental Aspects (continued)

Child Physical Health Metric Variable: CHLDHLTH Source: U.S. Department of Health and Human Services- National Survey of Children’s Health Source Question or Measurement: NSCH Indicator 1.1: In general, how would you describe [child name]'s health? Would you say [his/her] health is excellent, very good, good, fair, or poor? Percentage of children (age 0-17 years) in excellent or very good health Alternate Source: N/A Years Available: 2003, 2005, 2007 Smallest Geospatial Level Available: State Calculation Methods: N/A

Social Relationships and Emotional Well-being Metric Variable: CHLDSOCIAL Source: U.S. Department of Health and Human Services- National Survey of Children’s Health Source Question or Measurement: NSCH Indicator 2.5: How many children often exhibit caring, respectful behaviors when interacting with other children and adults? Percentage of children (age 6-17 years) who often exhibit positive social skills . "Often exhibit" is defined as answering "usually" or "always" to at least 2 of the 4 questions [S7Q53; S7Q52; S7Q54; S7Q59]. Alternate Source: N/A Years Available: 2003, 2007 Smallest Geospatial Level Available: State Calculation Methods: N/A

16


Indicator: Basic Educational Knowledge and Skills of the Youth

Mathematics Skills Metric Variable: MATHTEST Source: National Center for Education Statistics- National Assessment of Educational Progress Source Question or Measurement: Percentages at or above each achievement level for mathematics, grade [4, 8] by year, jurisdiction, and All students [TOTAL]. Alternate Source: N/A Years Available: 2000, 2003, 2005, 2007, 2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the average of the percentages in grades 4 and 8 at or above achievement level.

Reading Skills Metric Variable: READTEST Source: National Center for Education Statistics- National Assessment of Educational Progress Source Question or Measurement: Percentages at or above each achievement level for reading, grade [4, 8] by year, jurisdiction, and All students [TOTAL]. Alternate Source: N/A Years Available: 2002, 2003, 2005, 2007, 2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the average of the percentages in grades 4 and 8 at or above achievement level.

17


Indicator: Basic Educational Knowledge and Skills of the Youth (continued)

Science Skills Metric Variable: SCITEST Source: National Center for Education Statistics- National Assessment of Educational Progress Source Question or Measurement: Percentages at or above each achievement level for science, grade [4, 8] by year, jurisdiction, and All students [TOTAL]. Alternate Source: N/A Years Available: 2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the average of the percentages in grades 4 and 8 at or above achievement level.


Indicator: Participation and Attainment

Adult Literacy Metric Variable: ADULTLIT Source: National Center for Education Statistics- National Assessment of Adult Literacy (NAAL) Source Question or Measurement: Indirect estimate of percent lacking Basic prose literacy skills and corresponding credible intervals. Percent [age 16 and older) lacking basic prose literacy skills. Those lacking Basic prose literacy skills include those who scored Below Basic in prose and those who could not be tested due to language barriers. Alternate Source: N/A Years Available: 1992*, 2003 Smallest Geospatial Level Available: State Calculation Methods: N/A. *1992 values were used for imputation purposes only. 18


Indicator: Participation and Attainment (continued)

Participation Metric Variable: PARTNEDU Source: U.S. Census Bureau & Bureau of Labor Statistics - Current Population Survey Source Question or Measurement: CPS variables PETYPE- School enrollment 2 or 4 year college, PRTAGE- single year of age Alternate Source: N/A Years Available: 2000-2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the percentage of people aged 18-24 enrolled in post-secondary education

High School Completion Metric Variable: HSGRAD Source: U.S. Census Bureau- American Community Survey Source Question or Measurement: ACS variables C0, C12, C18, C24, C30, C84, C90, C96, C102, C108, C114, C120, C126. Population totals and percentages who obtained a high school (or equivalent) diploma or higher for age groups 18-24 and 25 and older Alternate Source: N/A Years Available: 2005-2009 Smallest Geospatial Level Available: County Calculation Methods: Percentages of attainment were summed within each age group and then averaged together using population totals as weights

19


Indicator: Participation and Attainment (continued)

Post-Secondary Attainment Metric Variable: UNIVGRAD Source: U.S. Census Bureau- American Community Survey Source Question or Measurement: ACS variables C0, C24, C30, C108, C114, C120, C126. Population totals and percentages who obtained a bachelor’s degree or higher for age groups 18-24 and 25 and older. Alternate Source: N/A Years Available: 2005-2009 Smallest Geospatial Level Available: County Calculation Methods: Percentages of attainment were summed within each age group and then averaged together using population totals as weights.

20

Domain: Health

Personal Well-being Life Expectancy/Mortality Physical and Mental Health Lifestyle and Behavior Healthcare

21

Health The domain of healthy populations includes health outcome measures of personal well-being, life expectancy and mortality, and physical and mental health conditions. This domain also incorporates lifestyle behavior and healthcare, all of which influences a population’s health status. Food utilization (a part of food security) also falls into this domain because of its connection to healthy behaviors. Other outside influences such as environmental quality (e.g., clean air days, clean water, etc.) are captured through indicators of ecosystem services. The connections between economic services and human health are so numerous and complex that an entire sub-discipline of economics, known as health economics, has emerged. Economic assessments of healthrelated interventions are critical to decision makers because expenditure on health care in the United States has outpaced the general rate of inflation (Meltzer 2001). Social services are also strongly tied to human health. Many large organizations within the U.S. government were formed to protect and enhance the health of the U.S. population, and several well-known private organizations, such as the American Red Cross, United Way of America, and Ronald McDonald House Charities, provide health-related services to populations in need.

Relationship to Ecosystem Services


The impact of environmental quality and condition on human health is well known. Yet the connection between ecosystem services and human health and development is a relatively new field of study. McMichael et al. (2003) points out that climate change is known to have an adverse affect on human health and that an estimated 83% of medicinal goods have yet to be discovered and used for human benefit from tropical vegetation, much of which could be lost forever if biodiversity continues to decline. Ecosystem condition also has direct impacts on human health resulting from bacterial contamination, air pollution, and toxic algal blooms (Cox et al. 2003). Access to nature, even if only through a window view, provides restorative experiences that can improve psychological and physiological health (Van Den Berg et al. 2007).

Greenspace and connection to nature have been linked to healthy physical, cognitive, and behavioral development, especially in children and youth. For instance, sensatory stimulation promoted positive healthyrelated behaviors by affecting interpersonal processes among a group working in a community garden (Hale et al. 2011). Children and youth living in greener neighborhoods had lower BMI after 2 years, presumably due to increased physical activity or time spent outdoors (Bell et al. 2008). Children also see improvements in motor fitness, balance, and coordination when provided with a natural landscape for play (Fjortoft 2004). Lifestyle is responsible for the bulk of the current avoidable disease burden, making the impact of ecosystem services on healthy behaviors that much more important (de Hollander and Staatsen 2003). Courtesy of Microsoft.com

22

Courtesy of freestockphoto.com

Indicator: Personal Well-being

Perceived Health Metric Variable: PRCVDHLTH Source: Centers for Disease Control and Prevention (CDC)- Behavioral Risk Factor Surveillance System (BRFSS) Source Question or Measurement: CDC variable GENHLTH, Would you say that in general your health is excellent, very good, good, fair, or poor? Alternate Source: Gallup Healthways variable H36, Would you say your own health in general is excellent, very good, good, fair, or poor? Years Available: 2000-2010 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of people who responded that their health was “Excellent”, “Very Good” or “Good”

Life Satisfaction Metric Variable: LIFESATIS Source: Centers for Disease Control and Prevention (CDC)- Behavioral Risk Factor Surveillance System (BRFSS) Source Question or Measurement: CDC variable LSATISFY, In general how satisfied are you with your life? Alternate Source: Gallup Healthways variable WP15, In general, are you satisfied or dissatisfied with the way things are going in your own personal life? Years Available: 2005-2010 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the proportion of people who are satisfied with their life (Gallup), and “Very satisfied” or “Satisfied” with their life (BRFSS)

23


Indicator: Personal Well-being (continued)

Happiness Metric Variable: HAPPY Source: General Social Survey (GSS) Source Question or Measurement: GSS variable HAPPY, 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? Alternate Source: Gallup Healthways variable WP6878, Did you experience happiness a lot of the day yesterday? Years Available: 2000, 2002, 2004, 2006, 2008, 2009 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of respondents who answered “Very happy” or “Pretty happy” (GSS); and the percentage of respondents who answered “Yes” (Gallup)


Indicator: Life Expectancy and Mortality

Life Expectancy at Birth Metric Variable: LIFEXPCT Source: CDC- Compressed Mortality Files Source Question or Measurement: Compressed Mortality Files- all Alternate Source: N/A Years Available: 2000-2007 Smallest Geospatial Level Available: County Calculation Methods: Calculated using CDC’s Compressed Mortality Files and Fergany’s (1971) methods. Life expectancy was determined by county-level age group rates; missing or zero age group rates were imputed from the next higher spatial level (state or national) 24

Courtesy of the CDC

Indicator: Life Expectancy and Mortality (continued)

Cancer Mortality Metric Variable: CANCMORT Source: CDC- Compressed Mortality Files Source Question or Measurement: Number of deaths due to malignant neoplasms and various cancer diseases, age-adjusted (ICD 113 Group Codes GR113-020 through GR113-044, excluding GR113-037) Alternate Source: N/A Years Available: 2000-2007 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of deaths that were cancer-related

Infant Mortality Metric Variable: INFMORT Source: CDC– Compressed Mortality Files Source Question or Measurement: Compressed Mortality, 1999-2007, Age group =30) Alternate Source: N/A Years Available: 2004-2008 Smallest Geospatial Level Available: County Calculation Methods: N/A


Indicator: Lifestyle and Behavior

Teen Smoking Rate Metric Variable: TEENSMK Source: CDC- Youth Risk Behavior Surveillance System (YRBSS) Source Question or Measurement: Percentage of children in grades 9-12 who smoked cigarettes on 20 or more days in the past 30 days Alternate Source: N/A Years Available: 1999*-2009; biennial Smallest Geospatial Level Available: State Calculation Methods: N/A. *1999 values were used for imputation purposes only

32


Indicator: Lifestyle and Behavior (continued)

Healthy Behaviors Index Metric Variable: HBI Source: Gallup Healthways Source Question or Measurement: The Healthy Behaviors Index (HBI) is a mean of four items recoded to reflect the positive responses only. The four items are Gallup variables H11 (Do you smoke?), M16 (Did you eat healthy all day yesterday?), H12A (if respondent reported exercising 3-7 times per week), and H12B (if respondent reported eating 5 fruits and vegetables per day, 4 or more times per week). Alternate Source: CDC- BRFSS variables RFPAMOD (Risk factor for moderate physical activity defined as 30 or more minutes per day for 5 or more days per week, or vigorous activity for 20 or more minutes per day on 3 or more days), FRTINDEX (summary index based on the calculated number of daily servings of fruits and vegetables), and SMOKER2 and SMOKER3 (Four level smoker status: Every day smoker, Someday smoker, Former smoker, Non-smoker). Years Available: 2001-2010 Smallest Geospatial Level Available: County Calculation Methods: The average index value was calculated for each county (Gallup). The average of the variables was computed at the respondent level following the same recoding procedure as Gallup (CDC)

Teen Pregnancy Metric Variable: TEENPREG Source: CDC- VitalStats Birth Data Files Source Question or Measurement: CDC variables for year, county of residence, and age of mother Alternate Source: CDC- WONDER, CDC variables for year, county of residence, and age of mother Years Available: 2000-2008 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of births to mothers in the age groups “under 15” and “15-19” as a percentage of all births

33


Indicator: Lifestyle and Behavior (continued)

Alcohol Consumption Metric Variable: ALCOHOL Source: CDC- BRFSS Source Question or Measurement: 1) CDC variables DRINKANY, DRNKANY2, DRNKANY3, and DRNKANY4, During the past month have you had at least one drink of any alcoholic beverage such as beer, wine, wine coolers, or liquor? 2) CDC variables ALCDAYS, ALCDAY3, ALCDAY4, and ALCOHOL, During the past 30 days, how many days per week or per month did you have at least one drink of any alcoholic beverage? 3) CDC variables NALCOCC, AVEDRNK, and AVEDRNK2, On the days when you drank, about how many drinks did you drink on the average? Alternate Source: N/A Years Available: 2000-2010 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of people who drank on average more than one drink per day using the variables listed above


Indicator: Healthcare

Population with a Regular Family Doctor Metric Variable: FAMDOC Source: CDC- BRFSS Source Question or Measurement: BRFSS variables PERSDOC and PERSDOC2, Do you have one person you think of as your personal doctor or health care provider? Alternate Source: Gallup Healthways variable H13, Do you have a personal doctor? Years Available: 2000-2010 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of respondents who answered “Yes”

34


Indicator: Healthcare (continued)

Satisfaction with Healthcare Metric Variable: SATISHLTHC Source: Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Source Question or Measurement: NCAHPS variable H_HSP_RATING_9_10, How do patients rate the hospital overall? Patients who gave a rating of 9 or 10 (high)

Alternate Source: N/A Years Available: 2008, 2009 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the average percent of patients who gave a rating of 9 or 10

35

Domain: Leisure Time

Activity Participation Time Spent Working Age Adults

36

Leisure Time Leisure time is time that individuals have to voluntarily engage in pleasurable activities when they are free from the demands of work or other responsibilities. It is commonly deemed as necessary for basic survival and has increasingly been referred to as a domain of the “good life” (Smale et al. 2010). Suggested metrics of this domain are the amount of time spent on specific leisure activities, types of activities, frequency of participation, and expenditures on leisure activities. Measures of work hours and continuous sleep time can be used as surrogate measures indicating the amount of time available for leisure activities. Enjoyable activities may also act as “restorers” that facilitate the individual’s recovery from stress as the result of positive social interactions or relaxation that lead to increased positive emotions (Pressman et al. 2009). Participation in leisure time activities has been positively linked to both physical and mental health measures (Williams and Patterson 2008, Krueger et al. 2009). Leisure time also provides for psychological detachment from work which in turn promotes well-being and productivity (Sonnentag et al. 2010). Leisure time activities also provide opportunities for social interactions through group participation (e.g., clubs, sports, religious organizations) and expand the size of social networks, enhancing social cohesion. Higher income has been positively associated with increased leisure time as it relates to more disposable income; however, in the U.S. the cost of the loss of leisure time due to increased work hours has continued to rise since the 1950s (Talberth et al. 2007).

Relationship to Ecosystem Services Specific activities individuals engage in can be linked to access and exposure to nature and greenspace. According to Korpela and Kinnunen (2010), time spent in interaction with nature is significantly correlated to both life satisfaction and relaxation, contributors to our subjective well-being and health. Among a variety of leisure time activities evaluated, exercise, spending free time outdoors, and interacting with nature were the most effective activities for recovery from work stress (Korpela and Kinnunen 2010). These activities are closely tied to recreational opportunities and aesthetics, biodiversity, usable water (swimmable, fishable), and clean air. The U.S. downward trend in the amount of free time afforded to individuals places increased value on the amount of time available outside work. The potential impact of outdoor activities and interactions with nature on our wellbeing exemplifies the contribution of ecosystem goods and services that support these leisure Courtesy of U.S. EPA activities.

37


Indicator: Time Spent

Leisure Activities Metric Variable: LEISURE Source: Bureau of Labor Statistics- American Time Use Survey Source Question or Measurement: Time spent on socializing, relaxing, leisure and sports identified by activity codes 12xxxx-13xxxx (where “xx” indicates any numbers to complete the 6-digit activity code from the coding lexicon). Alternate Source: N/A Years Available: 2002-2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the average percentage of time involved in these activities


Indicator: Activity Participation

Physical Activity Metric Variable: PHYSACTIV Source: Centers for Disease Control and Prevention- Behavioral Risk Factor Surveillance System Source Question or Measurement: CDC variable EXERANY2, During the past month/30 days, other than your regular job, did you participate in any physical activities or exercise such as running, calisthenics, golf, gardening, or walking for exercise? Alternate Source: N/A Years Available: 2000-2010 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of people who answered “yes”

38


Indicator: Activity Participation (continued)

Average Nights on Vacation Metric Variable: VACATION Source: Bureau of Labor Statistics (BLS)- American Time Use Survey, Trips Survey Supplement Source Question or Measurement: BLS variable TUTRV2- Main purpose for the trip, and BLS variable TUTRV5Total nights away from home Alternate Source: N/A Years Available: 2004-2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the average number of nights away from home when the main purpose was vacation or visiting friends/relatives


Indicator: Working Age Adults

Adults Working Standard Hours Metric Variable: NORMWRKHRS Source: Bureau of Labor Statistics- American Time Use Survey Source Question or Measurement: Work and work-related activities identified by activity codes 0501xx (where “xx” indicates any numbers to complete the 6-digit activity code from the coding lexicon) Alternate Course: N/A Years Available: 2003-2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the percentage of work activity duration during daytime hours (9 am to 5 pm) from total work activity duration

39


Indicator: Working Age Adults (continued)

Adults Working Long Hours Metric Variable: LONGWRKHRS Source: U.S. Census Bureau- Current Population Survey Source Question or Measurement: CPS variable PEHRUSLT, # hours usually worked at all jobs Alternate Course: N/A Years Available: 2002-2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the percentage of employed respondents reporting that they work 50 hours or more per week

Adults who Provide Care to Seniors Metric Variable: SENIORCARE Source: Bureau of Labor Statistics- American Time Use Survey Source Question or Measurement: Adult care activities identified by activity codes 0304xx, 0305xx, 0404xx, 0405xx (where “xx” indicates any numbers to complete the 6-digit activity code from the coding lexicon). Alternate Course: N/A Years Available: 2002-2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the percentage of adult care activities duration from total activities duration

40

Domain: Living Standards

Wealth Income Work Basic Necessities

41

Living Standards In the simplest of terms, living standards may be best described as “the physical circumstances in which people live, the goods and services they are able to consume and the economic resources to which they have access” (New Zealand Economic Social Report 2010). Living standard indicators tend to be largely economic in nature, characterized by demography and geography. Income level is the most dominate class of metrics used for evaluating standard of living followed by living conditions which includes housing status, household crowding (rooms per person), and state of housing repair. Home ownership, household assets, and other measures of material affluence were used to evaluate wealth.


Economic and social services aim to improve the living standards of a population. Economic services provide a means to accumulate and distribute wealth ,while many social services help to improve living conditions among the most impoverished within the community. Poverty metrics (e.g., income- and housing-related) figure prominently in living standard assessments because there is a close relationship between standards of living and attainment of basic human needs. However, wealth disparity alone cannot fully account for standards of living. Current research suggests that conceptualizing basic human needs in light of multi-dimensional wellbeing may provide a more comprehensive picture relative to living standards (Sen 1993, Sumner 2004, Waglé 2008). For example, indices that exclude time use measures may be missing non-market activities that may enhance standards of living without significantly contributing to household income (Folbre 2009). Further, the perception of living standards is often an overlooked influence on a population’s overall well-being.

Relationship to Ecosystem Services Ecosystem services may greatly influence living standards both monetarily and non-monetarily. Coastal and Great Lake ecosystems, for example, create approximately 100 million jobs nationwide (National Ocean Economics Program 2009). Ecosystems such as wetlands or grasslands provide regulating services that may reduce infrastructure cost by using existing natural capacity for increasing the availability of clean and safe drinking and recreational water. Urban greenspace helps mitigate environmentally-borne health-related illness such as asthma thus reducing healthcare-related costs and stress. Easy access to natural space provides opportunities for culturallyfulfilling, quality recreational activities for those populations who are most likely to have the least amount of leisure-time available.

Courtesy of USDA NRCS

42

Courtesy of Fotolia

Indicator: Wealth

Median Home Value Metric Variable: HOMEVAL Source: U.S. Census Bureau- American Community Survey Source Question or Measurement: ACS variable B25077, Median value of owner-occupied housing units Alternate Source: N/A Years Available: 2004-2009 Smallest Geospatial Level Available: County Calculation Methods: N/A

Mortgage Debt Metric Variable: MTGDEBT Source: U.S. Census Bureau- American Community Survey Source Question or Measurement: ACS variable B25081, Mortgage status of owner-occupied housing units Alternate Source: N/A Years Available: 2004-2009 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of owner-occupied housing units with no second mortgage or home equity loan

43


Indicator: Income

Median Household Income Metric Variable: MEDINCOME Source: U.S. Census Bureau– SAIPE Source Question or Measurement: Median household income, in dollars; number Alternate Source: N/A Years Available: 2000-2009 Smallest Geospatial Level Available: County Calculation Methods: N/A

Incidence of Low Income Metric Variable: POVERTY Source: U.S. Census Bureau– SAIPE Source Question or Measurement: All ages in poverty; Percent Alternate Source: N/A Years Available: 2000-2009 Smallest Geospatial Level Available: County Calculation Methods: N/A

44


Indicator: Income (continued)

Persistence of Low Income Metric Variable: POVPERSIST Source: General Social Survey (GSS) and U.S. Census Bureau Source Question or Measurement: 1) U.S. Census Bureau weighted average poverty threshold for the year 1986. 2) GSS variable REALINC: Family income on 1972-2006 surveys in constant dollars (base = 1986). 3) GSS variable FINALTER: During the last few years, has your financial situation been getting better, worse, or has it stayed the same? 4) GSS variable HOMPOP Household Size and Composition (see Appendix D: Recodes in the General Social Surveys, 1972-2008 Cumulative Codebook for more information about the GSS variables) Alternate Source: N/A Years Available: 2000-2008; biennial Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of respondents who answered “Stayed the same” for GSS variable FINALTER, while using the responses to GSS variables REALINC and HOMPOP to determine what respondents were below the U.S. Census poverty thresholds


Indicator: Work

Job Quality Metric Variable: JOBLOSE Source: General Social Survey (GSS) Source Question or Measurement: GSS variable JOBLOSE, Thinking about the next 12 months, how likely do you think it is that you will lose your job or be laid off- very likely, fairly likely, not too likely, or not at all likely? Alternate Source: N/A Years Available: 2000-2008; biennial Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of respondents who answered “Not too likely” or “Not at all likely” 45


Indicator: Work (continued)

Job Satisfaction Metric Variable: JOBSATIS Source: General Social Survey (GSS) Source Question or Measurement: GSS variable SATJOB1, All in all how satisfied would you say you are with your job? Alternate Source: Gallup Healthways variable WP9045, Are you satisfied or dissatisfied with your job or the work you do? Years Available: 2002, 2006, 2009 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of respondents who answered “Very Satisfied” and “Somewhat Satisfied” (GSS), and the percentage of respondents who answered “Satisfied” (Gallup)

Courtesy of Fotolia

Indicator: Basic Necessities

Housing Affordability Metric Variable: HOMEAFFORD Source: U.S. Census Bureau- American Community Survey Source Question or Measurement: ACS variable B25092, Median selected monthly owner costs as a percentage of household income, Total Alternate Source: N/A Years Available: 2004-2009 Smallest Geospatial Level Available: County Calculation Methods: N/A 46

Courtesy of Fotolia

Indicator: Basic Necessities (continued)

Food Security Metric Variable: FOODSECURE Source: U.S. Census Bureau- Current Population Survey Source Question or Measurement: Census variable HRFS12M1, Food Security Summary Status, 12-month Alternate Source: N/A Years Available: 2005-2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the percentage of households that responded “Food Secure – High or Marginal Food Security”

47

Domain: Safety and Security

Actual Safety Perceived Safety Risk

48

Safety and Security Based on Maslow’s hierarchy of basic human needs, once physical needs are relatively satisfied, safety needs take precedence. Personal security can be linked to unemployment, poverty, education level and social cohesion, and is most often evaluated using crime rates, number of accident-related injuries and deaths, and perceived safety. In basic terms, safety and security can be described as freedom from harm (physical security—personal and national) but is also described by measures related to financial security. Economic security is evaluated in the event of unemployment, sickness, widowhood, old age and disability. These measures can be linked to economic services of employment and income and heavily associated with social nets provided through the provisioning of social services — particularly financial assistance and healthcare. Our sense of safety and security can be altered in the wake of technological and natural disasters due to degradation of ecosystems, economic loss, and increased reliance on social safety nets and recovery services. From the ecosystem services perspective, clean water and air, sufficient food production, and natural hazard protection significantly contribute to our sense of safety and security through direct relationships to our health via exposure to pathogens and contaminants, food supply, and prevention of loss of life and property (MEA 2005). Additionally, there is a comfort derived from knowing that we are not on the brink of environmental problems and that a natural system will be conserved for future generations (Higginbotham et al. 2007).

Relationship to Ecosystem Services The domain of safety and security is frequently evaluated using violent crime and property crime rates combined with measures of perceived neighborhood safety. Green spaces in urban areas have been linked to a reduction in neighborhood crime, especially in inner city neighborhoods (Kuo and Sullivan 2001). Urban green spaces provide opportunities for simultaneous users and increased throughput which in combination deter criminal behavior; however, densely vegetated areas often evoke feelings of insecurity (Kuo and Sullivan 2001, Kuo 2010). In some cases, natural areas appreciated for aesthetic and therapeutic value and recreational opportunities may also be perceived by some as “scary” places, concealing criminal activities or harboring dangerous animals, poisonous plants, and vector borne disease (Louv 2005, Milligan and Bingley 2007). In reference to accident-related injuries, more specifically traffic accidents, there are opposing views on the role of roadway vegetation. Roadside aesthetic appeal has been reported to Courtesy of Microsoft.com positively affect driver behavior by promoting a calming effect and reducing speeding and driver fatigue (Cackowski and Nasar 2003). Conversely, traffic engineers and city planners purport that roadside vegetation introduces collision hazards, reduces traffic visibility, and distracts drivers (Wilde 2010). Similarly, public perceptions may present conflicting valuations of ecosystems such as wetlands, which are valued for species diversity, habitat and recreational areas, but also depreciated because of associated vector borne diseases such as West Nile virus (Barbier et al. 1997). Because of the multitude of conflicting perceptions, the fear of nature and lack of public knowledge regarding ecosystem goods and services benefits, the evaluation of the contribution of ecosystems to safety and security is not as clear cut as the influence of economic and social drivers. However, clarifying these relationships through education and inclusion of public perception and preferences could help mitigate these differences towards a better understanding of the linkages between ecosystems and the domains of wellbeing. A common understanding of nature’s benefits is vital to sustainable well-being. 49


Indicator: Actual Safety

Property Crime Metric Variable: PROPCRIME Source: National Archives of Criminal Justice Data Source Question or Measurement: NACJD variables BURGLRY, LARCENY, MVTHEFT, ARSON, Number of burglary, larceny, motor vehicle theft, and arson offenses Alternate Source: N/A Years Available: 2000-2005, 2008 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the total (sum) number of property crimes per 100,000 people. Population estimates were provided by the NACJD (variable CPOPCRIM) and reflect the total population served by reporting agencies.

Violent Crime Metric Variable: VIOLCRIME Source: National Archives of Criminal Justice Data Source Question or Measurement: NACJD variables MURDER, RAPE, ROBBERY, AGASSLT, Number of murder, rape, robbery, and aggravated assault offenses Alternate Source: N/A Years Available: 2000-2005, 2008 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the total (sum) number of violent crimes per 100,000 people. Population estimates were provided by the NACJD (variable CPOPCRIM) and reflect the total population served by reporting agencies.

50

Courtesy of NOAA

Indicator: Actual Safety (continued)

Loss of Human Life Metric Variable: NATHAZHLOSS Source: University of South Carolina, Hazards and Vulnerability Research Institute Source Question or Measurement: SHELDUS dataset, Fatalities and injuries from hazardous weather Alternate Source: N/A Years Available: 2000-2010 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the number of fatalities and injuries from hazardous weather per 100,000 population

Accidental Morbidity and Mortality Metric Variable: ACCMM Source: CDC – Compressed Mortality Files Source Question or Measurement: Number of deaths due to external causes (ICD-10 Group Codes V01 through Y89), excluding deaths caused by natural hazards and intentional deaths (ICD-10 group codes X30X39, X60-X84, Y85-Y89) Alternate Source: N/A Years Available: 2000-2010 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the number of deaths per 100,000 population that were accident-related

51

Courtesy of Imaresz; Pixabay.com

Indicator: Risk

Social Vulnerability to Environmental Factors Metric Variable: SOVI Source: University of South Carolina, Hazards and Vulnerability Research Institute Source Question or Measurement: Social Vulnerability Index (SoVI®) for the United States, SoVI Score. This index estimates a population’s ability to prepare for, respond to, and recover from environmental hazards. Higher scores indicate more vulnerability. Alternate Source: N/A Years Available: 2000, 2007, 2008 (*2007 and 2008 data points reflect aggregate 2005-09 and 2006-10 indices, respectively) Smallest Geospatial Level Available: County Calculation Methods: N/A

Courtesy of Suat Eman; freedigitalphotos.net

Indicator: Perceived Safety

Community Safety Metric Variable: PRCVDSAFE Source: Gallup Healthways Source Question or Measurement: Gallup variable WP113, Do you feel safe walking alone at night in the city or area where you live? Alternate Source: N/A Years Available: 2009 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of people who responded “Yes”

52

Domain: Social Cohesion

Social Support Social Engagement Attitude towards Others and the Community Family Bonding Democratic Engagement

53

Social Cohesion The ties that bind humans together in society have a large bearing on our personal well-being and the wellbeing of our community (Putnam 2000, Smith 2003). A social network propagates opportunities to enhance the quality of life to all of its members, and creates a safety net for difficult times. A cohesive community allows open discussion and resolution of difficult problems, and gives its members a sense of identity (Jeannotte et al. 2002). Social participation of all concerned citizens is essential to obtaining environmental well-being (Mann 1992). Indicators of social cohesion vary greatly, with the most common indicator being volunteering rates. Measures of the health of one’s social network typically revolved around qualitative assessments of existing relationships and quantitative assessments of the size of the network. Feelings and behaviors associated with trust and reciprocity are often used as a proxy for community cohesion. Divorce rates, migration patterns, family demographics, and charitable contributions were some of the more objective measures used to measure cohesiveness. Courtesy of iStockphoto.com

Social services can establish social norms that promote cohesion, repair and strengthen family cohesion, and provide safe, equitable working environments which foster healthy coworker relationship development. Economic services impact social cohesion by creating equitable wages and redistributing wealth, thereby relieving tensions between different social-economic classes (Rupasingha et al. 2006), and they allow businesses to generate excess revenue to be given back to the community.

Relationship to Ecosystem Services: Greenspace and access to nature promote pro-social behavior and help mitigate some of the negative antisocial behaviors associated with crowding and urbanization (Kuo and Sullivan 2001, Kuo 2010). Natural spaces within communities afford people opportunities to interact with others beyond their own family dynamics through proximate open areas reserved for recreational and cultural activities, such as festivals and picnics. A healthy natural environment also helps provide a sense of community by enhancing feelings of pride and a stronger sense of kinship among its citizens who share the common goal of making their community a better place to live (EPA 1997).


54

Courtesy of the U.S. FWS

Indicator: Social Engagement

Participation in Group Activities Metric Variable: GRPACTV Source: General Social Survey Source Question or Measurement: GSS variable MEMNUM, Could you tell me whether or not you are a member of any type of organization? Alternate Source: N/A Years Available: 2004 Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of people who are members of one or more groups

Volunteering Metric Variable: VOLNTR Source: Bureau of Labor Statistics and the U.S. Census- Volunteering in America Source Question or Measurement: Volunteer rate (equals the percentage of Current Population Survey respondents who reported that they had performed any unpaid volunteer work) Alternate Source: N/A Years Available: 2002-2009 Smallest Geospatial Level Available: State Calculation Methods: N/A

55


Indicator: Social Engagement (continued)

Children Participating in Organized, Extracurricular Activities Metric Variable: CHLDACTV Source: U.S. Department of Health and Human Services- National Survey of Children’s Health Source Question or Measurement: Percentage of children aged 6-17 years old who participate in one or more organized activities outside of school Alternate Course: N/A Years Available: 2003, 2007 Smallest Geospatial Level Available: State Calculation Methods: N/A

Courtesy of Savit Keawtavee; freedigitalphotos.net

Indicator: Attitude toward Others and the Community

Trust Metric Variable: CANTRUST Source: General Social Survey (GSS) Source Question or Measurement: GSS variable CANTRUST, Generally speaking, would you say that people can be trusted or that you can't be too careful in dealing with people? Alternate Source: N/A Years Available: 2004, 2008 Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of respondents who answered “people can almost always be trusted” and “people can usually be trusted.”

56


Indicator: Attitude toward Others and the Community (continued)

City Satisfaction Metric Variable: CITYSATIS Source: Gallup Healthways Source Question or Measurement: Gallup variable WP83, Are you satisfied or dissatisfied with the city or area where you live? Alternate Source: N/A Years Available: 2009 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of respondents who answered “Satisfied”

Belonging to Community Metric Variable: CLSETOWN Source: General Social Survey (GSS) Source Question or Measurement: GSS variable CLSETOWN, How close do you feel to your town or city? Alternate Source: N/A Years Available: 2004 Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of respondents who answered "Very Close" and "Close"

57


Indicator: Attitude toward Others and the Community (continued)

Discrimination Metric Variable: DISCRM2 Source: Centers for Disease Control and Prevention (CDC)- Behavioral Risk Factor Surveillance System Source Question or Measurement: CDC Variable RREMTSM1, Within the past 12 months on average, how often have you felt emotionally upset, for example angry, sad, or frustrated, as a result of how you were treated based on your race? Alternate Source: N/A Years Available: 2005-2006 Smallest Geospatial Level Available: County Calculation Methods: Calculated as the percentage of respondents who answered anything except “Never” (CDC)

Helping Others

Metric Variable: HELPFUL Source: General Social Survey (GSS) Source Question or Measurement: GSS variable HELPFUL, Would you say that most of the time people try to be helpful, or that they are mostly just looking out for themselves? Alternate Source: N/A Years Available: 2000-2008; biennial Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of people who responded “Try to be helpful”

\

58


Indicator: Family Bonding

Parent-child Reading Activities Metric Variable: CHLDREAD Source: Bureau of Labor Statistics- American Time Use Survey Source Question or Measurement: Adults reading to children identified by activity codes 030102 and 040102. Alternate Source: N/A Years Available: 2002-2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the percentage of parent-child reading activity duration from total activities duration

Exceeded Screen Time Guidelines Metric Variable: WATCHTV Source: Centers for Disease Control and Prevention- Youth Risk Behavior Surveillance System Source Question or Measurement: Percentage of children in grades 9-12 who watch television 3 or more hours per day Alternate Source: N/A Years Available: 2001-2009; biennial Smallest Geospatial Level Available: State Calculation Methods: N/A

59


Indicator: Family Bonding (continued)

Frequency of Meals at Home Metric Variable: MEALS Source: Bureau of Labor Statistics- American Time Use Survey Source Question or Measurement: Time spent by children, aged 15-17 years old, eating at home with their parents, identified by activity codes 11xxxx (where “xx” indicates any numbers to complete the 6-digit activity code from the coding lexicon). Alternate Source: N/A Years Available: 2003-2009 Smallest Geospatial Level Available: State Calculation Methods: Calculated as the percentage of time spent eating at home with parents by children (aged 15-17) from the child’s total eating time


Indicator: Democratic Engagement

Trust in Government Metric Variable: TRUSTGOV Source: American National Election Study (ANES) Source Question or Measurement: ANES Variable VCF0604, People have different ideas about the government in Washington. These ideas don’t refer to Democrats or Republicans in particular, but just government in general. We want to see how you feel about these ideas. How much of the time do you think you can trust the government in Washington to do what is right – just about always, most of the time, only some of the time? Alternate Source: General Social Survey (GSS) variable POLEFF17, Most government administrators can be trusted to do what is best for the country. Years Available: 2000-2008; biennial Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of respondents who answered “Most of the time” or “Just about always” for the variable VCF0604 (ANES), and the percentage of respondents who answered “Strongly agree” or “Agree” for the variable POLEFF17 (GSS). 60


Indicator: Democratic Engagement (continued)

Voter Turnout Metric Variable: VOTRTOUT Source: U.S. Census Bureau- Current Population Survey Source Question or Measurement: Percentage of U.S. citizens aged 18 and older that voted Alternate Source: N/A Years Available: 2000-2008; biennial Smallest Geospatial Level Available: State Calculation Methods: N/A

Interest in Politics Metric Variable: POLINTRST Source: American National Election Study (ANES) Source Question or Measurement: ANES variable VCF0310, Some people don’t pay much attention to political campaigns. How about you, would you say that you have been/were very much interested, somewhat interested, or not much interested in the political campaigns (so far) this year? Alternate Source: General Social Survey (GSS) variable POLINT and POLINT1, How interested would you say you personally are in politics? Years Available: 2000-2008; biennial Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of people who answered “Somewhat interested” or “Very much interested” for variable VCF0310 (ANES). Calculated as the percentage of people who answered “Very interested”, “Fairly interested”, or “Somewhat interested” for variable POLINT, and the percentage of people who answered “Very interested” or “Fairly interested” for variable POLINT1 (GSS).

61



Registered Voters Metric Variable: REGVOTRS Source: U.S. Census Bureau- Current Population Survey Source Question or Measurement: Percentage of U.S. citizens aged 18 and older (eligible voters) that are registered to vote Alternate Source: N/A Years Available: 2000-2008; biennial Smallest Geospatial Level Available: State Calculation Methods: N/A

Voice in Government Decisions Metric Variable: VOICENGOV Source: American National Election Study (ANES) Source Question or Measurement: ANES Variable VCF0609, Please tell me how much you agree or disagree with this statement: Public officials don’t care much what people like me think. Alternate Source: General Social Survey (GSS) variable POLEFF11, People like me don't have any say about what the government does. Years Available: 2000-2008; biennial Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of respondents who answered “Disagree” for variable VCF0609 (ANES), and the percentage of respondents who answered “Disagree” or “Strongly disagree” for the variable POLEFF11 (GSS).

62



Satisfaction with Democracy Metric Variable: SATDEM Source: General Social Survey (GSS) Source Question or Measurement: GSS Variable DEMTODAY, How well does democracy work in America today? On the whole, on a scale of 0 to 10 where 0 is very poorly and 10 is very well. GSS Variable SATDEMOC, On the whole, are you very satisfied, fairly satisfied, not very satisfied, or not at all satisfied with the way democracy works in the United States? Alternate Source: N/A Years Available: 2000, 2004 Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of respondents who answered 6 through 10 for the variable DEMTODAY, and “very satisfied” and “fairly satisfied” for the variable SATDEMOC


Indicator: Social Support

Close Family and Friends Metric Variable: CLSFRNDFAM Source: General Social Survey (GSS) Source Question or Measurement: GSS variable NUMPROBS, Of these (NUMCNTCT) friends and relatives, about how many would you say you feel really close to, that is close enough to discuss personal or important problems with? (variable NUMCNTCT: Not counting people at work or family at home, about how many other friends or relatives do you keep in contact with at least once a year?). Alternate Source: N/A Years Available: 2002 Smallest Geospatial Level Available: GSS Region Calculation Methods: Calculated as the percentage of respondents who answered 6 or more friends or relatives 63

Summary Table of Data and Available Spatial Scales

Cultural Fulfillment Education

Health

Connection to Life Spiritual Fulfillment Activity Participation Performing Arts Attendance Rate of Congregational Adherence Basic Educational Knowledge Mathematics Skills and Skills of Youth Reading Skills Science Skills Participation and Attainment Adult Literacy High School Completion Participation Post-Secondary Attainment Social, Emotional and Bullying Developmental Aspects Child Physical Health Social Relationships and Emotional Well-being Preprimary Education and Care Healthcare Population with a Regular Family Doctor Satisfaction with Healthcare Life Expectancy and Mortality Asthma Mortality Cancer Mortality Diabetes Mortality Heart Disease Mortality Infant Mortality Life Expectancy Suicide Mortality Alcohol Consumption Lifestyle and Behavior Healthy Behaviors Index Teen Pregnancy Teen Smoking Rate Personal Well-being Happiness Life Satisfaction Perceived Health Physical and Mental Health Adult Asthma Prevalence Conditions Cancer Prevalence Childhood Asthma Prevalence Depression Prevalence Diabetes Prevalence Heart Attack Prevalence Coronary Heart Disease Prevalence Obesity Prevalence Stroke Prevalence

METRIC VARIABLE ALLOFLFE BEAUSPRT PERARTS TOTRATE MATHTEST READTEST SCITEST ADULTLIT HSGRAD PARTNEDU UNIVGRAD BULLY CHLDHLTH CHLDSOCIAL CONFACT FAMDOC SATISHLTHC ASTHMORT CANCMORT DIABMORT HRTDISMORT INFMORT LIFEXPCT SUICDMORT ALCOHOL HBI TEENPREG TEENSMK HAPPY LIFESATIS PRCVDHLTH ADLTASTHMA CANCER CHLDASTHMA DEPRESSION DIABETES HRTATTACK HRTDISEASE OBESITY STROKE

REGION

METRIC

STATE

DOMAIN INDICATOR Connection to Nature Biophilia

COUNTY

LOWEST AVAILABLE SCALE

X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X

64

Summary Table of Data and Available Spatial Scales (continued)

Time Spent Working Age Adults

Living Standards

Basic Necessities Income

Wealth Work Safety and Security

Social Cohesion

Actual Safety

Perceived Safety Risk Attitude toward Others and the Community

Democratic Engagement

Family Bonding

Social Engagement

Social Support

65

METRIC Physical Activity Average Nights on Vacation Leisure Activities Adults Working Long Hours Adults Working Standard Hours Adults who Provide Care to Seniors Food Security Housing Affordability Median Household Income Incidence of Low Income Persistence of Low Income Median Home Value Mortgage Debt Job Quality Job Satisfaction Accidental Morbidity and Mortality Loss of Human Life Property Crime Violent Crime Community Safety Social Vulnerability to Environmental Factors Trust City Satisfaction Belonging to Community Discrimination Helping Others Interest in Politics Registered Voters Satisfaction with Democracy Trust in Government Voice in Government Decisions Voter Turnout Parent-child Reading Activities Frequency of Meals at Home Exceeded Screen Time Guidelines Participation in Organized, Extracurricular Activities Participation in Group Activities Volunteering Close Friends and Family

METRIC VARIABLE PHYSACTIV VACATION LEISURE LONGWRKHRS NORMWRKHRS SENIORCARE FOODSECURE HOMEAFFORD MEDINCOME POVERTY POVPERSIST HOMEVAL MTGDEBT JOBLOSE JOBSATIS ACCMM NATHAZHLOSS PROPCRIME VIOLCRIME PRCVDSAFE SOVI CANTRUST CITYSATIS CLSETOWN DISCRM2 HELPFUL POLINTRST REGVOTRS SATDEM TRUSTGOV VOICENGOV VOTRTOUT CHLDREAD MEALS WATCHTV CHLDACTV GRPACTV VOLNTR CLSFRNDFAM

REGION

INDICATOR Activity Participation

STATE

DOMAIN Leisure Time

COUNTY

LOWEST AVAILABLE

X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X

Constructing the Composite Index of Well-being Composite score

Well-being Index Elements Domains Indicators Metrics

Specific measurements

Normalizing and Imputing Metric Values Based on the distribution of data for each metric and the variety of metric units, we elected to use the normalization procedure used by the Organisation for Economic Co-operation and Development (OECD) Better Life Index (OECD 2011). Normalization is done using OECD’s formula which converts the original values of the metrics into proportions that range between 0 (for the worst possible outcome) and 1 (for the best possible outcome). The formula is: (value to convert - minimum value) / (maximum value - minimum value) When an metric measures a negative component of well-being the formula used is: 1 - (value to convert - minimum value) / (maximum value - minimum value) Prior to normalization, we identified outlying values falling beyond the far fences of a box-and-whisker plot (i.e. less/greater than 3 interquartile ranges from the 1st and 3rd quartiles, respectively). We set the minimum and maximum values to the lowest and highest values within the far fences, and set the identified outliers to these extremes. Finally, normalized values were linearly rescaled between 0.1 and 0.9 (rescaled value=.8*normalized value + .1) to allow for potential improvements and declines beyond what was observed in the data. A mean value imputation method was used as a substitute for missing county-level metric data points. County groupings were created based on a combination of the Rural-Urban Continuum Code (RUCC) classifications (USDA, 2013) and the Gini Index (GINI) for household income inequality quintile bandings (US Census, 2012). This RUCC-GINI combination helped to account for the relative spatial relationship of a county to the nearest large urban center and its measured income dispersion. A mean value was calculated across all years where metric data were available and within each RUCC-GINI band in an effort to calculate imputed metric values using data from counties exhibiting similar characteristics. In the few cases where data were not available within a RUCC or GINI delineation, a county’s related state or GSS region data were used to substitute for missing values, as appropriate. 66

Constructing the Composite Index of Well-being (continued) Calculating the Domain Score The mean of the normalized metric values are used to calculate the individual indicator scores. Domain scores are then calculated as the sum of the indicator scores divided by the total possible score for all the indicators in that domain. The domain score is weighted by the contribution of the domain to the elements based on relative importance values (RIVs) and prioritization weights.

Domain and Element Weights The approach used to develop and apply domain and element weights for calculating the composite index of well-being is outlined in Figure 4. Relative importance values (RIVs) for each of the domains were derived using qualitative data based on professional opinion and public perception. During roundtable discussions, professionals in relevant fields (e.g., ecology, sociology, etc.) assigned RIVs to relationships between each domain and element of well-being based on an ordinal rating scale ranging from 0 (no relationship) to 5 (very strong relationship). The elements of well-being used to develop these relationships are described in Summers et al. (2012) and are briefly defined below: •

Economic Well-being—Sense of well-being derived from financial stability

•

Environmental Well-being—Sense of well-being derived from having opportunities to experience healthy, natural environments

•

Societal Well-being—A combination of well-being derived from having the opportunity to meet the requirements for healthy human growth and development (Basic Human Needs) and the perception of life as a whole based on opportunities and achievements (Subjective Well-being) Domain Scores Professional Opinion Assigned Domain-Element RIVs and Element- Well-being RIVs

Element Sub-Index Scores

Adjusted RIVs Domain Contribution Weight

Contribution to Well-being (Target Prioritization Weights)

Prioritization Weights

Prof essional Opinion Rank Data f or Domains

Prioritization Weights

Public Perception Rank Data f or Domains and Elements

Element Contribution Weight

Weighted Element Index Scores

Human Well-being Index (HWBI)

Figure 4. Steps for deriving relative importance values (RIVs) as domain and element weighting factors for construction of the human well-being index (HWBI)

67

Constructing the Composite Index of Well-being (continued) Individual researchers in the workgroup were also asked to rank randomly selected groups of domains based on their perceived contribution to overall well-being. Additional rank data were collected for these same components utilizing a public perception convenience survey (70 respondents). Prioritization weights were generated for rank data from both populations using techniques borrowed from the analytic hierarchy process (Saaty 1980). The weights were combined into a target weight for estimating the contribution of domains to elements and elements to overall well-being. The differences between the RIV-based calculated contribution of each component and the target weights was used to derive an adjustment factor which was applied to the original RIV values resulting in adjusted RIV values for all linkages. From the adjusted RIVs, the estimated contribution of each domain to each element and of each element to overall well-being was calculated. Appendix B contains the contribution weights for both the domains and the elements. For detailed methodology regarding the development of RIVs used for domain and element weights in the construction of the composite index refer to Smith et al. (2013).

Calculating the Element Index scores and the Composite Index of Well-being Each domain score is multiplied by the corresponding domain contribution weight resulting in an element sub-index score. Eight sub-index scores are calculated for each element (one for each domain). The product of the sub-index scores are calculated for each element to produce the element index score. Each of the element indices are then multiplied by the corresponding element contribution weight yielding the weighted index score for each element. The composite index of well-being is the sum of the weighed element scores. The methods are described for the national scale index, but may be applied at smaller scales where data are available. The detailed methodologies for constructing the composite index of well-being are illustrated in Appendix C.

68

Current Status and Next Steps Many obstacles exist in developing comparable measures of human well-being--lack of consistently available data, transparency of performance of indicators and domains and cultural differences in the perception of well-being. We are aware that even within disciplines, the aggregation of indicators to create an index for evaluating well-being is highly contentious and that many researchers argue that summary indices have no value as tools in policy forums (Booysen 2002, Saltelli 2007). Additionally, lack of scientific robustness has rendered many sustainability indices inadequate in the policy arena (Böhringer and Jochem 2007). Perhaps for these reasons, many well-being measures have been relegated to specific areas of economic and social policy. However, composite indices represent an aggregate of the most widely accepted measurements within a particular discipline (i.e., Courtesy of iStockphoto sociology, economics, ecology, health) and the individual indicators used to develop the composite measure are based on quantitative values, generally recognized qualitative assessments, and sound methodologies. The ultimate goal of this research is to create a balanced index of well-being for the U.S that will illustrate the importance of ecosystem services in context of social and economic drivers which also adequately emphasizes the degree to which environmental factors influence well-being endpoints. The majority of the selected metrics and indicators developed for this report represent environmental, economic and social elements at state and regional scales. We can aggregate information to provide a national scale picture of well-being for the U.S., but data gaps may present challenges for applying the index at finer resolutions. The index will be tested within the Sustainable and Healthy Communities Research Program’s place-based projects to identify modifications that may be needed for application at smaller scales. Validation of the index in place-based projects will be a transition of the human well-being research into community-based sustainability projects. Although the metrics and indicators may vary depending on scale, preference and data availability, the domains described for the index should transcend scale, culture and time. Relative importance values can be developed at the community level and applied as weighting factors in index construction; however, the methodologies described for constructing the index should not be affected by scale. Courtesy of sakhorn38; freedigitalphotos.net Future research will focus on testing and modifying the index for application in various community typologies, as well as for specific populations (e.g., tribes) and across generations (inter-generational equity). The suite of human well-being indices are intended to be used in conjunction with other sustainability measures to provide information to assist communities with selecting appropriate measures for establishing and evaluating community sustainability goals.

69

References A Tale of Two Aspens. “Marketplace”. American Public Media. 30 June 2011. [online] URL: http:// marketplace.publicradio.org/display/web/2011/06/30/pm-a-tale-of-two-aspens/. Baldwin, R. F., R. B. Powell, and S. R. Kellert. 2011. Habitat as architecture: Integrating conservation planning and human health. Ambio 40:322-327. Barbier, E. B., M. Acreman, and D. Knowler. 1997. Economic valuation of wetlands: A guide for policy makers and planners. Ramsar Convention Bureau, Gland, Switzerland. Bell, J. F., J. S. Wilson, and G. C. Liu. 2008. Neighborhood Greenness and 2-Year Changes in Body Mass Index of Children and Youth. American Journal of Preventive Medicine 35(6):547-553. Böhringer, C., and P. E. P. Jochem. 2007. Measuring the immeasurable - a survey of sustainability indices. Ecological Indicators 63(1):1-8. Booysen, F. 2002. An overview and evaluation of composite indices development. Social Indicators Research 59(2):115-151. Boyd, J. 2008. Counting Nonmarket, Ecological Public Goods—The Elements of a Welfare-Significant Ecological Quantity Index. Resources for the Future Discussion Paper, RFF DP 07-42, Washington, D.C., USA. Burger, J. 2011. Valuation of environmental quality and eco-cultural attributes in Northwestern Idaho: Native Americans are more concerned than Caucasians. Environmental Research 111(1):136-142. Cackowski, J.M., and J.L. Nasar. 2003. The restorative effects of roadside vegetation: Implications for automobile driver anger and frustration. Environment and Behavior 35(6): 736-751. [online] URL: http://facweb.knowlton.ohiostate.edu/jnasar/crpinfo/research/RoadsideEB2003.pdf. Centre for Rural and Remote Mental Health. 2009. Key directions for a social, emotional, cultural and spiritual wellbeing population health framework for Aborininal and Torres Strait Islander Australians in Queensland. Queensland, Australia. Chavas, J. P. 2009. On the productive value of biodiversity. Environmental and Resource Economics 42:109131. Chawla, L. 2006. Learning to love the natural world enough to protect it. Barn 2:57-78. Cox, M., R. Johnstone, and J. Robinson. 2003. A conceptual model of impacts of environmental change on human well-being. The Airs Waters Places Transdisciplinary Conference on Ecosystem Health in Australia, 2003, Newcastle. University of Queensland, Newcastle, Australia. Daily, G. C., S. Polasky, J. Goldstein, P. M. Kareiva, and 5 others. 2009. Ecosystem services in decision making: time to deliver. Frontiers in Ecology and the Environment 7(1):21-28. De Hollander, A. E. M., and B. A. M. Staatsen. 2003. Health, environment and quality of life: an epidemiological perspective on urban development. Landscape and Urban Planning 65(1-2): 53-62. 70

References (continued) Fergany, N. 1971. On the Human Survivorship Function and Life Table Construction. Demography 8(3): 331334. Fjortoft, I. 2004. Landscape as playscape: The effects of natural environments on children's play and motor development. Children, Youth and Environments 14(2):21-44. Folbre, N. 2009. Time Use and Living Standards. Social Indicators Research 93(1): 77-83. Guhn, M., A. M. Gadermann, and B. D. Zumbo. 2010. Education: A report of the Canadian Index of Wellbeing (CIW). University of British Columbia, Kelowna, British Columbia, Canada. Hale, J., C. Knapp, L. Bardwell, M. Buchenau, J. Marshall, F. Sancar, and J. Litt. 2011. Connecting food environments and health through the relational nature of aesthetics: Gaining insight through the community gardening experience. Social Science & Medicine 72(11):1853-1863. Higginbotham, N., L. Connor, G. Albrecht, S. Freeman, and K. Agho. 2007. Validation of an Environmental Distress Scale. EcoHealth 3:245-254. Hill, K., D. Hoffman, and T. R. Rex. 2005. The value of higher education: Individual and societal benefits. Arizona State University, Tempe, Arizona, USA. Huntington, H. P. 2000. Using traditional ecological knowledge in science: Methods and applications. Ecological Applications 10(5):1270-1274. Jeannotte, M. S., D. Stanley, R. Pendakur, B. Jamieson, M. Williams, and A. Aizlewood. 2002. Buying in or dropping out: The Public Policy Implication of Social Cohesion Research. Strategic Research and Analysis (SRA), Strategic Planning and Policy Coordination, Department of Canadian Heritage, Ottawa, Canada. Kellert, S. R. 1997. Kinship to mastery: Biophilia in human evolution and development. Island Press, Washington, D.C., USA. Kellert, S. R. 2005. Nature and childhood development. Pages 63-89 in Building for life: designing and understanding the human-nature connection. Island Press, Washington, D.C., USA. Korpela, K., and U. Kinnunen. 2010. How is leisure time interacting with nature related to the need for recovery from work demands? Testing multiple mediators. Leisure Sciences 33(1):1-14. Krueger, A. B., D. Kahneman, C. Fischler, D. Schkade, N. Schwarz, and A. A. Stone. 2009. Time use and subjective well-being in France and the U.S.. Social Indicators Research 93:7-18. Kuo, F. E., and W. C. Sullivan. 2001. Environment and crime in the inner city, does vegetation reduce crime? Environment and Behaviour 33:343-367.

71

References (continued) Kuo, F.E. 2010. Parks and Other Green Environments: Essential Components of a Healthy Human Habitat. National Recreation and Park Association, Research Series 2010. [online] URL: http://www.nrpa.org/ uploadedFiles/Explore_Parks_and_Recreation/Research/Ming%20(Kuo)%20Reserach%20Paper-Final150dpi.pdf. Lieberman, G. A., and L. L. Hoody. 1998. Closing the achievement gap: Using the environment as an integrating context for learning. State Education and Environment Roundtable. Poway, California, USA. Louv, R. 2005. Last child in the woods: Saving out children from nature-deficit disorder. Algonquin Books of Chapel Hill, Chapel Hill, North Carolina, USA. Mann, H. 1992. The Rio Declaration. Proceedings of the Annual Meeting. American Society of International Law, Washington, D.C., USA. Martin-Lopez, B., C. Montes, and J. Benayas. 2007. The non-economic motives behind the willingness to pay for biodiversity conservation. Biological Conservation 139:67-82. McMichael, A. J., D. H. Campbell-Lendrum, C. F. Corvalan, K. L. Ebi, A. K. Githeko, J. D. Scheraga, and A. Woodward. 2003. Climate Change and Human Health Risks and Responses. World Health Organization, Geneva, Switzerland. Meltzer, M. I. 2001. Introduction to health economics for physicians. The Lancet 358(9286): 993-998. Milligan, C., and A. Bingley. 2007. Restorative places or scary spaces? The impact of woodland on the mental well-being of young adults. Health and Place 13(4)799-811. [online] URL: http:// www.sciencedirect.com/science/article/pii/S1353829207000111. Millennium Ecosystem Assessment (MEA). 2005. Ecosystems and human well-being: Synthesis. Island Press, Washington, D.C., USA. Kildow, J. T., C. S. Colgan, and J. Scorse. 2009. State of the U.S. Ocean and Coastal Economies 2009. National Ocean Economics Program, Moss Landing, CA, USA. [online] URL: http://www.oceaneconomics.org/ NationalReport/. New Zealand Economic Social Report. 2010. Ministry of Social Development, Wellington, New Zealand. [online] URL: http://socialreport.msd.govt.nz/documents/economic-standard-of-living-social-report -2010.pdf. Nisbet, E. K., J. M. Zelenski, and S. A. Murphy. 2011. Happiness is in our nature: Exploring nature relatedness as a contributor to subjective well-being. Journal of Happiness Studies 12:303-322. Organisation for Economic Co-operation and Development (OECD). 2011. OECD Better Life Initiative. OECD Publishing, Paris, France. doi:10.1787/9789264116627-en Philips, A. 1998. The nature of cultural landscapes: A nature conservation perspective. Landscape Research 23: 21–38. 72

References (continued) Pongsiri, M. J., and J. Roman. 2007. Examining the links between biodiversity and human health: an interdisciplinary research initiative at the U.S. Environmental Protection Agency. EcoHealth 4:82-85. Pressman, S. D., K. A. Matthews, S. Cohen, L. M. Martirem, M. Scheier, A. Baum, and R. Schulz. 2009. Association of Enjoyable Leisure Activities with Psychological and Physical Well-Being. Psychosomatic Medicine 71(7): 725–732. Putnam, R. D. 2000. Bowling alone: The collapse and revival of American community. Simon & Schuster, New York, New York, USA. Rössler, M. 2006.World Heritage cultural landscapes: A UNESCO flagship programme 1992–2006. Landscape Research 31:333–353. Rupasingha, A., S. J. Goetz, and D. Freshwater. 2006. The production of social capital in US counties. Journal of Socio-Economics 35(1):83-101. Saaty, T. L. 1980. The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill International Book Company, New York, New York, USA. Saltelli, A. 2007. Composite Indicators between analysis and advocacy. Social Indicators Research 81(1):65-77. Schaich, H., C. Bieling, and T. Plieninger. 2010. Linking Ecosystem Services with Cultural Landscape Research. GAiA-Ecological Perspectives for Science and Society 19(4):269-277(9). Sen, A. 1993. Capability and well-being. In M. Nussbaum, and A. Sen, editors. The quality of life. Clarendon Press, Oxford, United Kingdom. Serpell, J. A. 2004. Factors influencing human attitudes to animals and their welfare. Animal Welfare 13:145– 151. Smale B, H. Donohoe, C. Pelot, A. Croxford, and D. Auger. 2010. Leisure and Culture: A report of the Canadian Index of Well-being (CIW). [online] URL: http://www.ciw.ca/Libraries/Documents/Leisure_and_Culture -Full_Report.sflb.ashx. Smith, L. M., J. L. Case, H. M. Smith, L. C. Harwell, and J. K. Summers. 2013. Relating ecosystem services to domains of human well-being: Foundation for a U.S. Index. Ecological Indicators 28:79-90 Smith, L. M., J. L. Case, L. C. Harwell, H. M. Smith, and J. K. Summers. 2013. (in press). Developing relative importance functions to model well-being: An example using ecosystem services. Human Ecology 41(4) Smith, T. W. 2003. Altruism in contemporary America: A report from the National Altruism Study. National Opinion Research Center, University of Chicago, Chicago, Illinois, USA. Sonnentag, S., C. Binnewies, and E. J. Mojza. 2010. Staying well and engaged when demands are high: The role of psychological detachment. Journal of Applied Psychology 95(5):965-976. 73

References (continued) Summers, J.K., L.M. Smith, J.L. Case, R.A. Linthurst. (2012). A review of the elements of human well-being with an emphasis on the contribution to ecosystem services. Ambio 41(4):327-340. Sumner, A. 2004. Economic Well-being and Non-economic Well-being: A Review of the Meaning and Measurement of Poverty. World Institute for Development Economics Research (WIDER) Research Paper 30. United Nations University. Swan, P., and B. Raphael. 1995. Ways Forward: National Consultancy Report On Aboriginal And Torres Strait Islander Mental Health. Canberra: Australian Government Publishing Service. Talberth, J., C. Cobb, and N. Slattery. 2007. The Genuine Progress Indicator 2006: A tool for sustainable development. Redefining Progress, Oakland, California, USA. Terzi, L. 2004. On education as a basic capability. Fourth International Conference on the Capability Approach: Enhancing Human Security, 5-7 September 2004, Pavia, Italy. University of Pavia, Pavia, Italy. United States Environmental Protection Agency (EPA). 1997. Community-Based Environmental Protection: A Resource Book for Protecting Ecosystems and Communities. Washington, D.C., USA. Van Den Berg, A. E., T. Hartig, and H. Staats. 2007. Preference for Nature in Urbanized Societies: Stress, Restoration, and the Pursuit of Sustainability. Journal of Social Issues 63(1):79-96. Waglé, U. R. 2008. Multidimensional poverty: An alternative measurement approach for the United States? Social Science Research 37:559-580. Williams, D. R., and M. E. Patterson. 2008. Place, Leisure, and Well-being. Pages 105-119 in Eyles, J., and A. Williams, editors. Sense of Place, Health and Quality of Life. Ashgate, Burlington, Vermont, USA. Wilson, E. O. 1993. Biophilia and the Conservation Ethic. Pages 31-42 in Kellert, S. R., and E.O. Wilson, editors. The Biophilia Hypothesis. Island Press, Washington, D.C., USA. Wilson, E. O. 1984. Biophilia. Harvard University Press, Cambridge, Massachusetts, USA.

74

Appendices

75

Appendix A Descriptive statistics and histograms used to establish metric distributions

76

Connection to Nature

77

Cultural Fulfillment

78

Education

79

Education

80

Education

81

Health

82

Health

83

Health

84

Health

85

Health

86

Health

87

Health

88

Leisure Time

89

Leisure Time

90

Living Standards

91

Living Standards

92

Safety and Security

93

Safety and Security

94

Social Cohesion

95

Social Cohesion

96

Social Cohesion

97

Social Cohesion

98

Social Cohesion

99

Appendix B Contribution weights for domains and elements of well-being

100

The contribution weights for the domains and elements of well-being were derived using rank data from professional opinion and public perception and calculated using many steps (see Figure 4, page 67). These weights were necessary to estimate the final contribution of each domain to each element and of each element to overall well-being in constructing the composite index, and to ultimately model human well-being in the United States.

Weighting factors applied to domain scores in the calcuation of element sub-index scores Domain Connection to Nature Cultural Fulfillment Education Health Leisure Time Living Standards Safety and Security Social Cohesion

Economic Well-Being Environmental Well-Being Societal Well-Being 0.087 0.148 0.097 0.118 0.030 0.148 0.106 0.179 0.088 0.190 0.128 0.212 0.071 0.143 0.118 0.153 0.093 0.103 0.166 0.169 0.111 0.109 0.110 0.121

Weighting factors applied to Element scores in the calculation of the Human Well-being Index (HWBI) Element Overall Well-being Economic Well-Being 0.328 Environmental Well-Being 0.313 Societal Well-Being 0.359

101

Appendix C Graphical summary of indicator development and index construction methodologies

102

Connection to Nature Domain Score and Element Sub-Index Scores

Biophilia (2) Societal Well-being Score (SoWB3)

Environmental Well-being Score (EnWB3)

CON_NATWtCON_NAT-SoWB

CON_NATWtCON_NAT-EnWB

Economic Well-being Score (EcWB3) WtCON_NAT-EcWB

CON_NAT

Connection to Nature CON_Nat

Biophilia Spiritual Fulfillment: Percentage of people who are spiritually touched by the beauty of creation (GSS)

103

Connection to Life: Percentage of people who experience a connection to all of life (GSS)

Cultural Fulfillment Domain Score and Element Sub-Index Scores

Activity Participation (2) Societal Well-being Score (SoWB8 )

Environmental Well-being Score (EnWB8 )

CULTWtCULT-SoWB

CULTWtCULT-EnWB

Economic Well-being Score (EcWB8 )

CULTWtCULT-EcWB

Cultural Fulfillment Score CULT

Ti m eS Activity Participation pe nt Performing Arts Attendance: Percentage of people who attended any performing arts or art museum/fair/festival (Surveys of Public Participation in the Arts/Census)

Adherence: Capture congregational membership. (The Association of Religion Data Achieves)

104

Education Domain Score and Element Sub-Index Scores

Social, Emotional and Developmental Aspects Basic Knowledge and Skills of the Youth Paticipation and Attainment

105

Health Domain Score and Element Sub-Index Scores



HLTH

Societal Well-being Score (SoWB4 )

HLTH

WtHLT_POP-SoWB

WtHLT_POP-EnWB


HLTH

WtHLT_POP-EcWB

Health Score HLTH

Personal Wellbeing

Perceived Health: Percentage of people who reported good health (GSS/Gallup)

Life Satisfaction: Percentage of people who are satisfied with their life (Gallup) Happiness: Percentage of people who are happy (World Database of Happiness/GSS)

Physical and Mental Health

Disease Prevalence: Percentage of people with cancer, diabetes, coronary heart disease, stroke, heart attack, adult/childhood asthma (CDC)

Depression Prevalence: Percentage of people with depression (SAMHSA)

Overweight and Obesity Prevalence: Age-adjusted prevalence of overweight and obese adults (CDC)

Life Expectancy and Mortality

Life Expectancy: Average numbers of life years at birth (CDC)

Infant Mortality: Infant deaths per 1,000 live births (CDC) Disease Mortality: Percentage of deaths from suicide, cancer, diabetes, heart disease, and asthma (CDC)

106

Health Domain Score and Element Sub-Index Scores (continued)



HLTH

Societal Well-being Score (SoWB4 )

HLTH

WtHLT_POP-SoWB

WtHLT_POP-EnWB

Health Score HLTH


HLTH

WtHLT_POP-EcWB

(Continued)

Lifestyle and Behavior

Teen smoking: Percentage of teens who smoke daily (NIH) Teen pregnancy: Percentage of births to mothers under 18 years old (CDC) Healthy Behavior: Index of healthy behaviors (Gallup) Alcohol Consumption: Percentage of adults who consume more than one alcoholic beverages per day (CDC)

107

Healthcare

Satisfaction: Percentage of people satisfied with hospital stay (HCAHPS)

Family Doctor: Percentage of people with a regular family doctor (Gallup)

Leisure Time Domain Score and Element Sub-Index Scores

Activity Participation Time Spent Working Age Adults

108

Living Standards Domain Score and Element Sub-Index Scores

Wealth Income Work Basic Necessities

109

Safety and Security Domain Score and Element Sub-Index Scores

Actual Safety Perceived Safety Risk

110

Social Cohesion Domain Score and Element Sub-Index Scores


111

Social Cohesion Domain Score and Element Sub-Index Scores (continued)


112

Weighted Element Index Scores and the Composite Index of Well-being

Environmental Well-being Element Sub-Index Scores

Economic Well-being Element Sub-Index Scores

EnWB1=SOC_COHWtSOC_COH-EnWB

WtSOC_COH-EcWB

EcWB1=SOC_COH

EnWB2=EDUWtEDU-EnWB

WtEDU-EcWB

EcWB2=EDU

EnWB3=CON_NATWtCON_NAT-EnWB

WtCON_NAT-EcWB

EcWB3=CON_NAT EcWB4=HLTH

EnWB4=HLTHWtHLTH-EnWB

WtHLTH-EcWB

WtLIV_STD-EnWB

EcWB5=LIV_STDWtLIV_STD-EcWB

EnWB5=LIV_STD

EcWB6=LEI_TIMWtLEI_TIM-EcWB

EnWB6=LEI_TIMWtLEI_TIM-EnWB

EcWB7=SAF_SECWtSAF_SEC-EcWB

EnWB7=SAF_SECWtSAF_SEC-EnWB

EcWB8=CULTWtCULT-EcWB

EnWB8=CULTWtCULT-EnWB

EcWBindex=EcWB1*EcWB2*EcWB3*EcWB4*EcWB5*EcWB6*EcWB7*EcWB8

EnWBindex=EnWB1*EnWB2*EnWB3*EnWB4*EnWB5*EnWB6*EnWB7*EnWB8

HWBI=(EcWBindex*WtEcWB)+(SoWBindex*WtSoWB)+(EnWBindex*WtEnWB)

SoWBindex=SoWB1*SoWB2*SoWB3*SoWB4*SoWB5*SoWB6*SoWB7*SoWB8 Societal Well-being Element Sub-Index Scores

SoWB1=SOC_COHWtSOC_COH-SoWB SoWB2=EDU

WtEDU-SoWB

SoWB3=CON_NATWtCON_NAT-SoWB SoWB4=HLTHWtHLTH-SoWB SoWB5=LIV_STDWtLIV_STD-SoWB SoWB6=LEI_TIMWtLEI_TIM-SoWB SoWB7=SAF_SECWtSAF_SEC-SoWB SoWB8=CULTWtCULT-SoWB

113

This page was intentionally left blank.

114

Courtesy of Roxanne Lavelle

Indicators and Methods for Constructing a US Human ...

Indicators and Methods for Constructing a US Human ...

Suggest Documents

Indicators and Methods for Constructing a US Human

Constructing performance appraisal indicators for

Constructing indicators for measurement and ... - Population Council

A Survey of Methods for Constructing Rooted

Constructing Indicators for Measuring Provincial Sustainable ...

Constructing Coincident Economic Indicators for Emerging ... - EAF

Geometric methods for constructing cosmological

A study on constructing capability indicators for C++

A Survey of Methods for Constructing Covering Arrays - Institute for

Various Methods for Constructing Auto-BÃ¤cklund Transformations for a

A Review of Entomological Sampling Methods and Indicators for

A Review of Entomological Sampling Methods and Indicators for ...

OSMnx: New methods for acquiring, constructing ...

Three Methods for Constructing Parallel Gatekeeping ... - MultXpert

methods for constructing composite indices - Istat

Practical methods for constructing suffix trees - Wisc

Constructing Software-intensive Methods: A Design ...

Constructing Meaning in a Science Methods Course

Handbook on Constructing Composite Indicators - OECD.org

Constructing Institutional Measures: Indicators of ...

Constructing Aggregate Environmental-Economic Indicators - EconStor

A simple technique for constructing two-step Runge-Kutta methods ...

Methods of Constructing Quantum Channels for Teleporting a General ...

US 20090269765 A1 Compositions And Methods For