META-ANALYTICAL ESTIMATES OF VALUES OF ENVIRONMENTAL SERVICES ENHANCED BY GOVERNMENT AGRICULTURAL CONSERVATION PROGRAMS DISSERTATION

Transcription

1 META-ANALYTICAL ESTIMATES OF VALUES OF ENVIRONMENTAL SERVICES ENHANCED BY GOVERNMENT AGRICULTURAL CONSERVATION PROGRAMS DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Ayuna Borisova-Kidder, M.A. ***** The Ohio State University 2006 Dissertation Committee: Professor Alan Randall, Adviser Professor Timothy Haab Professor Brent Sohngen Approved By: Adviser Agricultural, Environmental, and Development Economics Graduate Program

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3 ABSTRACT There are growing needs and interest in obtaining generalized non-market value estimates in today s research world. Meta-analysis techniques have been explored by economists as a potential basis of policy analysis conducted by various government agencies in the area of natural resources. This research originated as an attempt to contribute to a broader estimation of benefits from USDA conservation programs and used meta-analysis to generalize empirical value estimates of three major groups of environmental services: wetlands, improvements in surface water quality and terrestrial habitat. While this work does not present national benefits of the programs, it provides important meta-analytical results, comments on meta-analysis as a tool for programs evaluation, and draws some necessary valuation studies guidelines. The valuation context for welfare estimates of the effects from government agricultural conservation programs is complex. Programs create various effects that change the flow of services which can be valued in terms of welfare estimates and virtual prices. For example, wetland restoration programs can result in improved floodwater control which provides reduced flood damage. It creates environmental benefit of avoided flood damage which is valuable to people. This study reports a meta-analysis of more than 30 US valuation studies estimating wetland value per acre, a meta-analysis of more than 40 US valuation studies ii

4 estimating willingness to pay per household per year for improvements in surface water quality, and a meta-analysis of 11 US valuation studies estimating benefit per acre for terrestrial habitat services. Results indicate that mean value per acre of wetland services is $ (in 2003 US dollars). American household s annual mean willingness to pay associated with surface water quality change is $ Benefit per acre of terrestrial habitat is estimated to average $ One of our main criticisms of meta-analysis is that it can obscure important information when it includes only averaged numerical representations from primary valuation studies, estimated means are treated as deterministic, and no distinction is made between study means based on smaller and larger number of observations. This research employs a technique to simulate data used for calculation of averaged representations in wetland contingent valuation studies. As a proof of concept exercise, meta-regression results based on the full simulated data and means of this data demonstrate consistency across estimators. Simulation can become an attractive technique of meta-analysis as it expands degrees of freedom and due to differences in sample size and precision of estimates can improve meta-analytical estimates. iii

5 TABLE OF CONTENTS Abstract ii List of tables.....viii List of figures...x Chapters 1. Introduction Research problem Research objectives Outline of dissertation Valuation theory and methods Principles of welfare change measurement Non-market valuation theory Functions and values of resources Valuation context for conservation programs Meta-analysis Theory and methods Applications in non-market valuation theory...33 iv

6 3.3. Special challenges in meta-analysis of non-market valuation Meta-analysis and its application for benefit transfer Meta-analyses of non-market valuation Meta-analysis of US wetland valuation studies Introduction Wetlands and their types Wetland valuation literature and data review Meta-analytical variables, modeling and regression results Wetland values across ERS Farm Resource Regions Comparisons of wetland meta-analytical studies Conclusions Meta-analysis of improvements in US surface water quality Introduction Surface water quality Surface water quality literature and data review Meta-analytical variables, modeling and regression results WTP for improvements in surface water quality across ERS Farm Resource Regions Comparisons of water quality meta-analytical studies Conclusions Meta-analysis of US terrestrial habitat studies Introduction Terrestrial habitat v

7 Terrestrial habitat literature and data review Meta-analytical variables, modeling and regression results Conclusions A meta-analysis of simulated data sets Meta-analytical estimates of the wetland CVM dataset Simulation Meta-analytical estimates of the simulated wetland CVM dataset and their implications Conclusions Summary and conclusions Introduction Summary of meta-analytical results Limitations and future considerations of the study.134 List of references 136 List of meta-analyses references Appendices Appendix A: Wetland meta-analytical dataset (in year 2003 US dollars).152 Appendix B: Surface water quality meta-analytical dataset (in year 2003 US dollars).165 Appendix C: Terrestrial habitat meta-analytical dataset (in year 2003 US dollars)..181 vi

8 Appendix D: Wetland contingent valuation studies meta-analytical dataset: means of original and simulated dataset (in year 2003 US dollars) vii

9 LIST OF TABLES Table Page 1. Valuation methods and associated welfare measures Some meta-analyses carried out in the field of environmental valuation Description of wetland meta-analytical variables Wetland meta-analytical results Mean income and wetland values per acre across ERS Farm Resource Regions based on the primary data, in 2003 US dollars Comparison table of some results from wetland meta-analyses (standard errors in parentheses) A spectrum of water quality benefits Water quality ladder Description of surface water quality meta-analytical variables Regression results for meta-analysis of improvements in surface water quality Mean income and WTP for surface water quality improvements across ERS Farm Resource Regions based on the primary data, in 2003 US dollars Comparisons of meta-analytical results for improvements in surface water quality...98 viii

10 13. Terrestrial habitat regression variables Terrestrial habitat regression results Description of wetland CVM meta-analytical variables Wetland CVM meta-analytical results and their comparisons with the results from full wetland dataset (all methods) Comparisons across meta-analytical results based on the wetland original and simulated CVM datasets Meta-analytical results from the simulated wetland dataset..126 ix

11 LIST OF FIGURES Figure Page 1. The valuation context for conservation programs 3, Compensating variation Equivalent variation Consumer surplus Replacement cost and marginal willingness to pay functions ERS Farm Resource Regions Mean wetland values per acre based on the meta-regression results, in 2003 US dollars (swamp in Fruitful Rim Region estimated by replacement cost method is a default category set at 0) Fresh surface- and ground-water withdrawals Mean WTP for surface water quality improvements based on the meta-regression results, in 2003 US dollars ( types of waterbody other than FRESH in Southern Seaboard region estimated by phone method is a default category set at 0)...95 x

12 CHAPTER 1 INTRODUCTION 1.1. Research problem Conservation programs in agriculture have a long history of existence. Laws creating them were first enacted in the late 1930s, in response to drought and the dust bowl. Conservation programs evolved slowly and were amended infrequently until the 1985 Food Security Act when four major new conservation programs were enacted (the Conservation Reserve Program, the Sodbuster, the Conservation Compliance, and the Swampbuster). Over the years, the context of conservation programs has changed significantly. Programs now concentrate not only on on-farm productivity and soil erosion problems, but also on water quality and wildlife habitat protection. Public attention is also increasing to open space and agricultural heritage issues as well (Batie, 2001). Overall federal spending for conservation has grown substantially with current annual expenditures of nearly 20 billion dollars (Zinn, 1999). With many programs conducted there is little systematic evaluation of their benefits. On July 22 nd 2004, the Secretary of Agriculture Ann M. Veneman publicly announced the kickoff of the Natural Resources Conservation Service/Agricultural Resource Service Conservation Effects Assessment Project (CEAP), promising a fiveyear effort to study the collective environmental benefits of government conservation programs on agricultural land. The accompanying press release signals recognition, at 1

13 the highest levels of the United States Department of Agriculture, of the need for estimates of benefits attributable to conservation programs. Public benefits from conservation programs include enhanced natural resources that help sustain agricultural productivity and environmental quality while supporting continued economic development, recreation, and scenic beauty. Proper evaluation of the total social benefits of recreation and environmental resources and the incorporation of these values in government decision-making would improve the efficient allocation of resources and thus increase the welfare of society Research objectives The valuation context for welfare estimates of the effects from government agricultural conservation programs is complex (Figure 1). Programs create various effects, for example, the Conservation Reserve Program can provide habitat for species. This process is influenced by program incentives, environmental conditions and producer decisions. Effects are followed by changes in services, for example, by wildlife improvement. Economists then measure values of services in terms of welfare estimates and virtual prices. These estimates vary, to name the least, due to people s preferences, demographics, and price (money, time, distance). Agricultural conservation practices result primarily in environmental goods that are public and that are not traded in markets. Estimating their dollar value, therefore, requires use of non-market valuation studies. Using meta-analytical techniques on the breadth of existing environmental valuation studies, our analysis will focus on obtaining 2

14 values for services provided by wetlands, terrestrial habitat and improvements in surface water quality in US context. BOX 1 BOX 2 BOX 3 BOX 4 Programs (e.g. Conservation Reserve) Effects (e.g. habitat) Services (e.g. wildlife Improvement) Values ( welfare measures, virtual prices) Program incentives Environmental conditions Producer decisions Environmental conditions Producer, consumer decisions Preferences Demographics Price (money, time, distance) Substitutes, complements Figure 1. The valuation context for conservation programs The main objectives of this study are to: 1) Obtain meta-analytical estimates of US valuation studies for groups of services provided by wetlands, surface water quality improvements and terrestrial habitat: a) perform a meta-analysis of US studies that estimate values of wetland services. Generalizing the results, estimate statistical relationship between values and a set of explanatory variables to analyze variations among the dollar values per acre of wetlands; b) perform a meta-analysis of US studies that estimate the value of improvements in US surface water quality. Generalizing the results, estimate statistical relationship between values and a set of explanatory 3

15 variables to analyze variations among the willingness to pay for improvements per household per year; c) perform a meta-analysis of US terrestrial habitat benefit estimates. The study will research whether any systematic trends can be observed from the breadth of environmental valuation studies conducted to date in the areas of wetlands, surface water quality and terrestrial habitat valuation. 2) Analyze methodological issues of meta-analysis as a suitable tool for policy assessment of agri-environmental initiatives in the USA; 3) Identify weak informational or methodological links in valuation studies; 4) Discuss contribution of obtained meta-analytical values to the broader objective of estimating program benefits; 5) Perform a simulation exercise to randomly generate individual observations used for calculation of averaged representations in wetland contingent valuation studies. Evaluate meta-regression results produced by a simulated dataset, and discuss potential use of simulation in meta-analysis Outline of dissertation The first chapter of the study introduces the research problem and discusses main objectives. The second chapter reviews basic welfare economics and focuses on important implications of non-market valuation theory on meta-analysis. It discusses general valuation framework for environmental benefits from government agricultural conservation programs. It then identifies a diverse range of valuation methods that have been applied to value wetlands, water and habitat functions such as contingent valuation 4

16 method, hedonic pricing, travel cost method, production function approach, net factor income approach, total revenue estimation, opportunity cost, energy analysis and replacement cost. The third chapter lays out a theoretical framework for conducting a meta-analysis. It discusses special challenges of application of meta-analysis in non-market valuation. The fourth chapter obtains and analyzes meta-analytical estimates of US valuation studies for groups of services provided by wetlands, surface water quality and terrestrial habitat. It describes specification and functional form of meta-regression functions, provides regression results and their interpretations. The fifth chapter introduces a meta-analysis of simulated datasets. It addresses one of the main criticisms of a meta-analysis associated with using averaged representations from primary studies. It adopts simulation technique to randomly generate individual observations used for calculation of averaged representations in wetland contingent valuation studies, and analyzes obtained meta-regression results. Chapter six provides an overall discussion of the results from previous chapters, summarizes the conclusions and provides future research needs. Finally, four appendices labeled by letters A through D are included with metaanalytical datasets for wetlands, water quality, terrestrial habitat, means of original and simulated wetland contingent valuation studies.. 5

17 CHAPTER 2 VALUATION THEORY AND METHODS 2.1. Principles of welfare change measurement Prior to drawing up a framework for a meta-analysis, it is helpful to overview some core welfare economics and consider issues concerning the selection of descriptive, analytical and evaluation methods. Researchers develop estimate of value for wetlands, terrestrial habitat and improvements in water quality from a human perspective using a variety of market and non-market valuation methods. Some methods can be generally controversial, likewise their results or interpretations of those results, as the methods involve clear deviations from ideal welfare measures. There are several alternative ways to represent preferences in welfare economics: 1. Utility Function. Consider a consumption set L { l 0 1 } L X = R+ = x R : x for l =,... L A function v: X R 1 is a utility function representing the preference relation 1 2 if, for all x, x X, x f x v( x ) v( x ) (1) We typically denote the utility function by v(x). 6

18 2. Indirect Utility Function. We obtain the indirect utility function by substituting the utility maximizing levels of x for a given set of prices, p, and income, m, into the utility function. or [ ] ψ ( m, p) = v x1( m, p), x2 ( m, p),..., xn ( m, p) (2) [ ] ψ ( m, p) = max v( x): px = m + (3) The indirect utility function gives the maximum utility level obtainable with a given set of prices and income ( p, m ). The indirect utility function is an ordinal ranking of price and income combinations. 3. Cost Function. The cost function gives the minimum cost way of obtaining a particular utility level for a given set of prices. n c( u, p) = min pi xi s. t. v( x) = u x 0 i= 1 [ ] = min px: v( x) = u (4) x 4. Money Metric Utility Function. The money metric utility function gives the minimum cost of obtaining the utility of the vector x when prices are p. Specifically, m( p, x) = C( u( x), p) (5) The money metric defines the minimum cost of buying bundles as least as good as x. The money metric utility function is sometimes called the minimum income function or the direct compensation function. 7

19 5. Money Metric Indirect Utility Function. For the price vectors p and p 0 and income m, the money metric indirect utility function is defined by 0 0 µ ( p, p, m) = c( ψ ( p, m), p) (6) The indirect money metric utility function defines the minimum cost of buying bundles at prices p that yield utility at least as large as the one obtained when prices are p 0 and income is m. The money metric indirect utility function is sometimes called the indirect compensation function. 6. Ordinary Demand. An ordinary demand function specifies the optimal levels x for a given set of prices and income. [ ] x( m, p) = arg max v( x): px = m (7) It is obtainable from the indirect utility function via Roy s identity. x i ( m, p) = ψ ψ [ c( u, p), p] p [ c( u, p), p] m i = ψ ( u, p) pi ψ ( ui, p) m (8) 7. Hicksian Demand. A Hicksian or compensated demand function specifies the cost minimizing levels x for a given set of prices and specified utility level. [ ] It is obtainable from the cost function via Shephard s lemma. h( u, p) = arg max px: v( x) = u (9) c( u, p) h( u, p) = p i (10) Changes in environmental quality can affect individuals welfares through changes in prices they pay for purchased goods; changes in prices they receive for their 8

20 factors of production; changes in the quantities or qualities of nonmarket goods; and changes in the risks individuals face (Freeman, 1983). 1. Ideal welfare change measures. Consider two alternative states of the world. In the first state (or status quo), the consumer faces prices p 0 and has income m 0. In the second state, the consumer faces prices p j and has income m j. The utility maximizing consumer will obtain utility level ψ ( m 0, p 0 ) in the initial state and utility level ψ ( m j, p j ) in state j. Here ψ ( p, m) is the indirect utility function yielding the maximal level of utility with prices p and income m. If all we care about is which state is optimal, this measure is sufficient. Utility is an ordinal measure, so the consumer is better or worse off as ψ ( p, m ) is greater than or 0 0 less thanψ ( p, m ). The difficulty with using this ordinal measure is that it allows no comparisons across individuals and gives no indication of the strength of preference for 0 0 { p m } j j, versus{ p, m }. j j 2. Definitions of Compensating and Equivalent Variation. Sir John R. Hicks (1941) proposed two willingness to pay measures to allow for monetary measurement of welfare change. 9

21 Definition 1 (Equivalent variation). Equivalent variation (EV) is defined as the amount of money paid to an individual with base prices and income that leads to the same satisfaction as that generated by a price and income change. j j EV = c( ψ ( m, p ), p ) c( ψ ( m, p ), p ) j = c( ψ ( m, p ), p ) m j 0 0 (11) If there is no change in income between the initial price and income pair and the subsequent one, we can also write EV as follows given that j j 0 0 c( ψ ( m, p ), p ) = c( ψ ( m, p ), p ) j j EV = c( ψ ( m, p ), p ) c( ψ ( m, p ), p ) j 0 j j = c( ψ ( m, p ), p ) c( ψ ( m, p ), p ) (12) In equation 11, c( u, p) is the cost or expenditure function, ψ ( m, p) is the indirect utility function, p 0 is the initial price vector, p j is any other price vector, m 0 is initial income and m j is subsequent income. EV is the amount of money one has to give to a consumer so that he/she could attain the utility level possible with the new prices and income while facing base prices and only having base income. EV can be thought of as the amount of money the consumer would accept in lieu of the price change. EV measures the difference in attaining the initial utility level at the initial and subsequent prices. EV is negative if the price and income change would make the consumer worse off. Definition 2 (Compensating variation). Compensating variation (CV) measures the net revenue of a planner who must compensate the consumer for a price 10

22 0 0 change after it occurs, bringing him/her back to the utility levelψ ( m, p ). CV would be negative if the planner would have to pay the consumer a positive level of compensation because the price change makes him/her worse off. CV is defined implicitly by j j j j CV = c( ψ ( m, p ), p ) c( ψ ( m 0, p 0 ), p ) j j = m c( ψ ( m 0, p 0 ), p ) (13) If there is no change in income between the initial price and income pair and the subsequent one, we can also write CV as follows given that j j 0 0 c( ψ ( m, p ), p ) = c( ψ ( m, p ), p ) j j 0 j CV = c( ψ ( m, p ), p ) c( ψ ( m, p ), p ) = c( ψ ( m, p ), p ) c( ψ ( m, p ), p j ) (14) CV is negative of the amount of money the consumer would be just willing to accept from the planner to allow the price change to take place. CV measures the difference in attaining the initial utility level at the initial and subsequent prices. Compensation takes place after the price and income change, so that compensating variation uses the after change prices. Consider Figure 2 below. 11

23 X 2 CV x (m, p 0 ) x (m-cv, p 1 ) x (m, p 1 ) U 0 U 1 X 1 Figure 2. Compensating variation At the prices and income (m, p 0 ) the optimal demand is at x(m, p 0 ) along the indifference curve U 0. As the price of good one falls, the budget line rotates outward and the consumer moves to point x (m, p 1 ) along the indifference curve U 1. CV measures the amount of income that must be taken away from the consumer at the new lower prices to leave him/her at the old utility level. At the new prices and this lower income level he/she consumes at point x (m-cv, p 1 ). CV is the distance between the two budget lines along the vertical axis. Consider Figure 3 below. At the prices and income (m, p 0 ) the optimal demand is at x (m, p 0 ) along the indifference curve U 0. As the price of good one falls, the budget line rotates outward and the consumer moves to point x (m, p 1 ) along the indifference curve U 1. EV measures the amount of income that must be given to the consumer at the old prices to make him/her as well off as with the lower prices. At the new prices and this 12

24 higher income level he/she consumes at point x (m+ev, p 0 ). EV is the distance between the two budget lines along the vertical axis. X 2 EV x (m+ev, p 0 ) x (m, p 1 ) U 1 x (m, p 0 ) U 0 X 1 Figure 3. Equivalent variation 1. Definition of consumer surplus. Consider the demand for a product xi ( p, m ) and a change in price from p i 0 and pi 1. The area to the left of the ordinary demand curve for good i is called the change in consumer surplus associated with the change in price from p i 0 and pi 1. Mathematically it is given by p 0 1 pi i CS= x i ( p, p, m) dp i i i (15) Consider figure 4 below. The area to the left of the demand curve between the two prices of good i is equal to consumer surplus. 13

25 p i p i 0 p i 1 xi ( m, pi, p i ) x i Figure 4. Consumer surplus If preferences can be represented by a quasilinear indirect utility function ψ ( m, p) = g( p) + m (16) then compensating variation will be equal to equivalent variation and both are equal to the integral in equation (15). With other preferences, there will be a divergence between compensating variation, equivalent variation and consumer surplus. With quasilinear preferences, when there are no wealth effects for good i, the measure in equation (15) is referred to as Marshallian consumer surplus. When good i is a normal good, Marshallian consumer surplus overstates compensating variation and understates equivalent variation for both increases and decreases in p i. If good i is an inferior good, Marshallian consumer surplus understates compensating variation and overstates 14

26 equivalent variation for both increases and decreases in p i. If the wealth effects for the goods in question are small, consumer surplus will be very close to both CV and EV. If the good being considered is one among many, because changes in real income will be spread around among many goods, the wealth effects will be small and the error from using consumer surplus instead of EV or CV will be small for the good in question. There exists big literature on conditions, ways for deriving Hicksian measures from Marshallian measures and their differences (e.g., Gorman 1953, Willig 1976, Randall and Stoll 1980, Vartia 1983, Hanemann 1991). Understanding principles of welfare measures is necessary to identify what the adequate valuation tools are in each given case, to correctly interpret data analysis of economic outcomes for the purpose of decision making, and to recognize the limitations and fields of applicability of each valuation approach. Relationship of the principles of ideal welfare measures to the use of valuation methods in practice has important implications for meta-analyses conducted by this research Non-market valuation theory Over the years, economists have developed non-market valuation techniques to reveal representative monetary values for non-market public resources. The monetary measure of the change in the person s welfare is the change in his/her monetary income that he/she would consider equivalent to the change in his/her welfare. Monetary value is thus defined in terms of a tradeoff that the person would be willing to make, whether framed in terms of willingness to pay (WTP) or willingness to accept (WTA). 15

27 Revealed preferences approach, from observations of demand behavior with respect to marketed goods, infers preferences that generated this behavior and applies for non-market goods that are complements to purchase of market goods. It includes methods such as travel cost and productivity evaluations, which infer individuals values for environmental quality by observing their behaviors in related markets. Stated preferences approach presents subjects with tradeoffs through a survey or experiment, infers preferences and measures WTP or WTA from survey responses and applies for any non-market good. It includes such methods as contingent valuation and contingent choice experiments, which use surveys to directly elicit individuals values. Contingent valuation allows a sample of people who benefit from a particular resource to tell researchers directly, through surveys, what they are willing to pay for some improvement in environmental quality. One of the strengths of this method is that it can capture both use value (e.g., drinking water use) and nonuse value (protection of threatened aquatic species) (Mitchell and Carson, 1993). Because of this versatility, it is the most widely used method, although it is controversial because, critics say, people are reporting hypothetically on their willingness to pay rather than observing their actual spending of money, possibly biasing the resulting valuation estimates (Hanemann, 1991). These concerns can be addressed with careful survey design and implementation (Carson et al. 2001). Another widely used approach to valuing water ecosystem services is the travel cost method, a revealed preference approach that is based on how people make recreational choices (Smith and Desvousges, 1986). The underlying principle is that 16

28 people spend time and money to travel and use a site for recreation. There are two main versions of this method (Freeman, 1993). The first version estimates a statistical relationship between the number of visits at a site and the level of travel expenditures by visitors and uses that relationship to estimate the total value of recreation services provided by the site to all users. The second version uses statistical analysis to examine how specific site characteristics influence decisions to recreate at different sites and then to infer the economic value of those characteristics. Although many environmental goods are not traded in markets, their presence may have an affect on property values. The hedonic property value method takes advantage of this connection (Smith and Huang 1995). Land prices are usually higher for land parcels close to lakes or estuaries because of the views and boating or fishing opportunities. By statistical analysis, the part of land values due to these environmental services can be separated out. The method controls for other variables influencing land prices, so that any remaining price differential is a measure of the willingness to pay for the unpriced environmental good. Finally, the change in productivity method recognizes that when changes in environmental quality affect the production of marketed goods, these effects can be captured by observing what happens in a related market. So if water pollution reduces fish catches or acid rain reduces timber productivity, we can value those impacts with the price of the resource, e.g., fish or timber. Consider the example of wetlands that provide breeding areas and increased food supply for various nearby fisheries. If these fisheries are commercially exploited, then the value of a wetland can be measured in part by the 17

29 dollar value of the increase in fish catches resulting from the wetland. This method requires an interdisciplinary approach involving biologists and economists. Information obtained by revealed and stated preferences methods can be used for the benefit transfer. For example, values for recreational fishing in a particular state may be estimated by applying measures of recreational fishing values from a study conducted in another state. The basic goal of benefit transfer is to estimate benefits for one context by adapting an estimate of benefits from some other context. Benefit transfer is often used when it is too expensive and/or there is too little time available to conduct an original valuation study, yet some measure of benefits is needed. It is important to note that benefit transfers can only be as accurate as the initial study. To better conceptualize the economic value of natural resources the concept of total economic value was developed (Randall and Stoll, 1983). The economic values can be categorized into distinct components of the total economic value according to the type of use. Direct use values are derived from the uses made of wetlands or water resources and services, for example wood for energy, water for irrigation and the natural environment for recreation. Option value is related to the preference, or willingness to pay, to maintain the possibility of future use. The concept of option value includes preferences for preserving an environmental asset for possible future use by current (philanthropic value) or future generations (bequest value). Indirect use values are associated with the indirect services provided by their natural functions, such as storm protection or nutrient retention. 18

30 The nonuse or passive-use values are those that do not have a direct use and usually encompass existence and bequest values. Literature offers a lot of examples on passive use values including work on damages from the Exxon Valdez Oil spill (Carson et al. 1995) Functions and values of resources Wetlands, water resources and terrestrial habitat provide different economic functions that are derived from their ecological and physical functions. The range of services provided is related to direct geophysical processes, such as sediment retention, provision of flood and storm buffering capacity, but it extends to wider climatologic, biological, and socio-cultural functions, including impacts on local and global climate change and stabilization, preservation of biodiversity, and provision of natural environmental amenities. In addition, program effects provide ecological processes enabling the extraction of goods and services in the form of natural resources such as water, fish and other edible animals, wood, and energy, and they provide the natural surroundings for recreational activities (see Larson et al. 1989; Barbier 1991, 1997; Brouwer et al. 1999; Woodward and Wui 2001; Brander et al. 2004). A diverse range of valuation methods have been applied to value wetlands, water and habitat functions such as contingent valuation method, hedonic pricing, travel cost method, production function approach, net factor income approach, total revenue estimation, opportunity cost, and replacement cost. Table 1 lists the methods used in the 19

31 literature with a short description of each method and the welfare measure that it estimates. The contingent valuation method is the method capable of estimating non-use values. By directly asking respondents to state their WTP or WTA for (hypothetical) changes in environmental quality or quantity it provides estimates of the technically precise welfare measures of compensating and equivalent surplus. The hedonic and travel cost methods estimate the Marshallian consumer surplus, which approximates, and is bounded by, the compensating variation and equivalent variation welfare measures (Randall, Stoll 1980 and Hanemann 1991). The production function approach estimates changes in consumer and producer surplus resulting from quantity or quality changes in environmental good that is used as an input in production process. The market prices method estimates the economic value of commercially traded products and services on the basis of their market prices. Usually it does not deduct market value of other resources used to bring ecosystem products to market. The net factor income approach also estimates changes in producer surplus by subtracting the costs of other inputs in production from total revenue, and ascribes the remaining surplus as the value of the environmental input. The remaining methods involve clear deviations from the ideal welfare methods. The total revenue approach simply estimates values as the total revenue received from the sale of goods and services derived from the environmental resource in question. 20

32 The opportunity cost takes the values of the next best alternative application of the resources used to provide the ecosystem function being valued. This reflects the cost of supplying the good or service and not the surplus associated with its use. The replacement cost approach places values on ecosystem services by estimating the cost of replacing them. This approach is based on the assumption that if individuals incur cost to replace ecosystem functions, then the lost services must be worth at least what people are willing to pay for them. Replacement cost as an engineering method is not based on social preferences for ecosystem services, or individual s behavior in the absence of those services, and is unlikely to approximate consumer and producer surpluses. Energy analysis measures the total amount of energy captured by natural ecosystems (amount of carbon fixed, or gross primary production) as an estimate of their potential to do useful work in the economy. It generates an upper bound on the economic value of the products of an ecosystem and involves a protocol for valuing energy. The diversity in welfare measures being estimated makes it necessary to clearly distinguish between the different valuations techniques in the meta-analysis. Some methods involve clear deviations from ideal welfare measures. Although we may have a priori expectations as to the direction of any bias associated with each valuation method, it is not possible to make sensible adjustments to the observed valuation estimates to correct for these biases (Brander et al. 2003). 21

33 Valuation method Short description Welfare measure Contingent valuation Hypothetical questions to obtain WTP Compensating or equivalent surplus Travel cost Estimate demand (WTP) using travel costs to visit site Consumer surplus Hedonic pricing Estimate WTP using price Consumer surplus in differentials and characteristics of related products relation to housing market Production function Estimate value as an input in Producer surplus production Net factor income Assign value as revenue of an associated products net of costs of other inputs Producer surplus Replacement cost Opportunity cost Market prices Energy analysis Cost of replacing the function with an alternative technology Value of next best alternative use of resources (e.g., agricultural use of water and land) Assigns value equal to the total market revenue of goods/services Economic value of total amount of energy captured by natural ecosystems Source: modified table from Brander et al. (2003). Table 1. Valuation methods and associated welfare measures. Value larger than the current cost of supply (baseline) Consumer surplus, producer surplus, or total revenue for next best alternative Total revenue Value of the products of the ecosystem 2.4. Valuation context for conservation programs The valuation context for welfare estimates of the effects from government agricultural conservation programs is complex (Figure 1). Programs create various effects, for example, the Conservation Reserve Program (CRP) can provide habitat for species. This process is influenced by program incentives, environmental conditions and producer decisions. Effects are followed by changes in services, for example, by wildlife 22

34 improvement. Economists then measure values of services in terms of welfare estimates and virtual prices. These estimates vary, to name the least, due to people s preferences, demographics, and price (money, time, distance). The range of benefits provided by the environment is very broad and difficult to measure. While Figure 1 lays out a whole research agenda, this research will focus on performing three meta-analyses with respect to wetlands, surface water quality and terrestrial habitat, and make some inferences of the results to program effects. BOX 1 BOX 2 BOX 3 BOX 4 Programs (e.g. Conservation Reserve) Effects (e.g. habitat) Services (e.g. wildlife Improvement) Values ( welfare measures, virtual prices) Program incentives Environmental conditions Producer decisions Environmental conditions Producer, consumer decisions Preferences Demographics Price (money, time, distance) Substitutes, complements Figure 1. The valuation context As the Nation s largest user of land and water resources, agriculture significantly affects the natural environment. United States Department of Agriculture (USDA) has designed many programs to mitigate the negative environmental effects of agriculture. In the agricultural sector, environmental and natural resource program benefits are associated with policy directions such as to: 23

35 Reduce runoff of agricultural chemicals and of manure from livestock facilities, which can pollute surface water; Decrease a loss of wetlands, where a loss of wetlands means a loss of the benefits they provide: improving water quality, reducing soil erosion, conserving surface water, improving subsurface moisture, contributing to flood control, enhancing an area's natural beauty, and providing habitat for migratory waterfowl and other wildlife; Reduce soil erosion, which diminishes recreation activities, increases water treatment costs and dredging of navigation channels, silts up drainage and irrigation channels, and causes the sedimentation of reservoirs; Avoid improper management of land, which ultimately harms the environment through sedimentation, pollution of surface waters, and loss of highly productive and unique soil. Conservation programs improve environmental quality. Improving environmental quality leads to enhanced ecosystem health and augments the public s enjoyment of recreational activities. Along with the increasing expenditures on conservation, a number of important issues have been raised, including: How should conservation funds be allocated among geographic areas? Should funds be concentrated in fewer watersheds or distributed over a wider geographic area? 24

36 Should funding priorities be given to areas with the worst environmental problems, or to areas that have made some environmental improvements? What criteria should be used to target resources for conservation? Should conservation programs target least expensive resources or resources that are most vulnerable to environmental damage? If farmers are paid for conservation, what should payments be based on? (i.e. should payments be based on the adopted conservation practices or the amount of environmental benefits provided?) These issues are not only intellectually challenging but also policy relevant. While challenges are daunting to incorporate these complexities into the design of conservation policies, payoffs are potentially high. Conservation policy tends to ignore the relationships between alternative environmental benefits. Such relationships can take two forms: interactions or correlations (Wu and Boggess 1999). The interaction refers to the cause-effect relationships among alternative environmental benefits. For example, improving water quality enhances fish habitat. The correlation refers to the situation where two environmental benefits are jointly produced by the same conservation effort, although these two benefits have no cause-effect relationship. Citing another illustration, the CRP reduces soil erosion by retiring lands from crop production; it also reduces groundwater pollution, although groundwater pollution and soil erosion have no direct cause-effect relationship. In their examination of the effect of ecosystem linkages on the targeting of conservation efforts, Wu and Boggess (1999) found that ignoring the interaction between 25

37 different environmental benefits would not lead to misallocation of conservation funds only under very restrictive conditions. For example, if the relationships between different benefits are the same across watersheds, then misallocation would not occur only when (a) all benefits are proportional to one another, (b) the targeted benefit is the same across the watersheds, or (c) all funds are allocated to one watershed. Otherwise, misallocation would occur. Specifically, if the direct benefit increases the indirect benefit at an increasing (decreasing) rate, targeting only a direct benefit would over- (under-) fund watersheds with a larger amount of total direct benefit. The degree of misallocation increases as the curvature of the relationship between the indirect benefit and direct benefit increases. The above conditions suggest that information about the tradeoffs between alternative environmental benefits and their social values is required to ensure optimal targeting of conservation efforts. This type of information is difficult to obtain, and relatively little information is currently available. However, physical and biological scientists are increasingly investigating these relationships, and payoff for this information is potentially high. Ecosystems are connected spatially simply because conservation upstream affects water quality downstream. When conservation is targeted based on an on-site physical criterion, spatial linkages of ecosystems are ignored. In a recent study, Watanabe, Adams, and Wu (2003) explored the importance of spatial linkages in the targeting of conservation efforts in the upper Grande Ronde River Basin in Oregon. Based on their findings, the heterogeneous nature of riparian conditions and stream morphology must be 26

38 considered to allocate restoration activities efficiently. Localized effects of restoration efforts on water quality are important to achieve small water quality improvement. The above suggests that in the presence of spatial connections and ecosystem linkages, a system approach must be adopted when targeting resources for conservation. Policy designs that ignore ecosystem complexities or formulas based on political consideration, or keyed to a specific on-site physical criterion, are likely to result in substantial losses in economic efficiency. There is a causal relationship between a possible distribution of conservation programs as mediated through a targeting mechanism and environmental benefits. More specifically, the size, the placement, and management of programs affect physical variables. These decisions then change biological parameters, which impact habitat quality and fish and wildlife population. These altered environmental conditions ultimately are reflected in the values public places on the environment. All these issues, although they are not directly addressed by this dissertation, deserve high amount of interest and consideration when making decisions about conservation programs. 27

39 CHAPTER 3 META-ANALYSIS 3.1. Theory and methods Meta-analysis has become a widely accepted tool in today s research world that demands generalized results, encompassing a family of rigorous procedures used in a variety of disciplines. Meta-analysis is defined as the statistical analysis of the findings of a collection of empirical studies (Glass, 1976). Meta-analysis can also be described as the regression analysis of regression analyses that attempts to integrate and explain the literature about some specific relationships between variables (Stanley, 1989). There are standards in conducting meta-analysis that can be defined a priori. The execution of meta-analysis generally involves the following steps: 1) Defining the population of relevant studies; 2) Searching for relevant studies; This process typically involves the use of both electronic and manual searches. The appropriateness of specified electronic databases will vary as a function of the research topic or domain. The key to a good electronic search is to identify the pertinent electronic databases and keywords or phrases to be used in the search. Electronic searches should be supplemented with manual searches which take the form of reviewing the reference lists of articles (identified in the electronic search or other seminal works), 28

40 conference proceedings and programs, and contacting researchers in the topic domain to obtain additional published and unpublished studies. Searching for and locating studies is considered to be a judgmental process, so as with the other steps, the search process should be clearly documented in the meta-analysis. Meta-analysis is susceptible to a number of problems. Some critics argue that meta-analysis necessarily mixes elements that are too dissimilar to warrant integration. Meta-analysts have been said to use "overly broad categories" which in fact confuse rather than clarify important distinctions in the literature. This apples-and-oranges problem can affect both dependent and independent variables. Another common criticism of meta-analysis concerns the quality of the primary research included in reviews ( garbage in, garbage out ). It has been claimed that metaanalysis is too inclusive and too willing to accept data from poorly designed studies in an effort to be comprehensive. In principle, exclusivity has some merit, but reviewers invariably disagree about what constitutes good quality research. The "garbage in, garbage out" complaint reflects concern with the study retrieval phase of meta-analysis. Like the complaint about apples and oranges, it is directed more at implementation than at meta-analysis itself. Construct validity problem arises when there is a reason to question how firm the understanding of the reviewers of what is being tapped by the variables under study. In this regard, the contribution depends on the capabilities of the reviewer. 3) Data abstraction and coding; This step involves extracting data on the variables of interest, sample sizes, reliability of measurement, and other specified characteristics of the study of interest to 29

41 the researcher (e.g., research design, participant type, number of authors, and year of publication, published vs. unpublished sources, and so on). Again, because this process is judgmental, it is a good idea to have this done, if feasible, independently by multiple raters. Coders should be trained to ensure that they are very familiar with the coding scheme and have a shared understanding and common frame-of-reference for the coding process and variables. 4) Statistical analysis (choice of metric, choice of model, etc.). The most basic form of a meta-analytical model is: y = α+ βx + ε (17) i i i where i indexes each observation (i.e. each individual study estimate), y is the dependent variable (for example, a WTP estimate per household adjusted to a certain year), α and β are parameters to be estimated in the model and x is a matrix of explanatory variables, including methodology, site, and household characteristics, and ε is the error term with mean zero and constant variance. Many studies collected for meta-analysis produce multiple estimates per study depending on whether authors used different methods, looked at different locations within the same region or sought estimates for more than one proposed improvement. Datasets which contain multiple estimates from each study can be treated as panel datasets. This raises a special set of econometric questions. Unobservable and systematic effects which are not accounted for in the variable specification of a classical regression model can result (Shrestha, 2000). 30

42 Classical OLS model is of the form stated above. A generic panel model, however, is of the form: y ji = α j + β' x ji + εi (18) where j indexes the individual study. Panel datasets can be modeled as either having a group-specific constant term or a group-specific disturbance effect. The fixed effects approach takes α j to be a group-specific constant term in the regression model. The random effects approach specifies that α j is a group-specific disturbance (Greene, 1997). The Fixed Effects model is of the form: Yij = α j D j + Xijβ + ε ij (19) where α j is the study specific constant term, D j is the dummy for each study. The fixed effects model is also referred to as the Least Squares Dummy Variable (LSDV), which refers to the Least Squares technique used in estimation. The random effect model is of the form: yij = α + β' x ji + ε ji + µ j (20) whereµ j is the group specific disturbance effect and has a zero mean and variance of σ µ. The random effect model is a generalized regression model with generalized least square being the efficient estimator. These random researcher effects, if they exist, need to be controlled for individual specific effects that may impact reported empirical estimates. Certain test statistics are used to determine which of the models to choose. One of these tests is the Breusch and Pagan Lagrange multiplier statistic The Lagrange 31

43 multiplier statistic tests whether a group effect specification is significant (H0: µj = 0). Using the Breusch-Pagan LM test, it can be found if random effects are present in the model. The generally accepted way of choosing between fixed and random effects is running a Hausman test. The Hausman test checks a more efficient model against a less efficient but consistent model to make sure the more efficient model also gives consistent results. The Hausman test tests the null hypothesis that the coefficients estimated by the efficient random effects estimator are the same as the ones estimated by the consistent fixed effects estimator. If they are (insignificant P-value, Prob>chi2 larger than 0.05) then it is safe to use random effects. If P-value is significant, then fixed effects should be used. F-test and Lagrange Multiplier test can be used to choose between fixed effects and OLS, and random effects and OLS specifications respectively. Meta-analytical dataset can be also treated as hierarchically structured data and analyzed by multilevel modeling techniques (Goldstein, 1995). Multilevel modeling (MLM) is a family of statistical procedures that try to come to terms with influences that are located on different levels. Bateman and Jones (2003) fitted two- level MLM in their meta-analysis of British woodland recreational values, Johnston et al. (2005) followed their example and used MLM in their meta-analysis of improvements in water quality. Roberts (2004) discusses the question of whether or not multilevel techniques are really that much better than OLS techniques. Multilevel modeling requires large samples (less than 10 units at any level are too few for reliable conclusions involving that level) and is most needed when a dataset s intraclass correlation is large. Parameter estimates obtained from multilevel modeling are not much different from OLS estimates. Although 32

44 multilevel modeling is a relatively new field of statistical research, the impacts from its development are already being felt in the community of researchers. As this field continues to grow, the utility and familiarity of multilevel modeling will also expand. However this growth must be garnered with the caution that was echoed by Goldstein (1995): There is a danger... that multilevel modeling will become so fashionable that its use will be a requirement of journal editors, or even worse, that the mere fact of having fitted a multilevel model will become a certificate of statistical probity. That would be a great pity. These models are as good as the data they fit; they are powerful tools, not universal panaceas. (p. 202) All meta-analytical datasets of this study were analyzed and tested in order to choose a preferred regression specification. The argument is made in favor of using OLS due to thinness of each dataset and a relatively large number of included dummies that can be characterized as study-specific variables. Wetland dataset consists of 33 studies that produce 72 observations of wetland value per acre, and uses 26 dummy variables. Water quality dataset utilizes 98 observations of WTP from 40 studies and uses 20 dummy variables. Terrestrial habitat dataset includes 11 studies that provide 23 observations of benefit per acre, and employs 6 dummy variables. Wetland contingent valuation studies dataset includes 16 studies that produce 25 wetland values, and uses 15 dummy variables Applications of meta-analysis in non-market valuation In non-market valuation, meta-analysis has a big potential for development. According to J.C.J.M. van den Bergh et al. (1997), examples of some of the most 33

45 promising issues where meta-analytical techniques can be applied are the following: evaluation of environmental costs, forecasting of non-direct effects of economic activities on the environment, assessment of the effectiveness of alternative environmental policy instruments, exploration of the appropriate political level of intervention to contain environmental damage, and political acceptability of alternative environmental instruments by decision-makers. Meta-analysis has seen some applications in environmental and natural resources economics (table 2 provides the list of some meta-analyses carried out in the field of environmental valuation). Representative applications include Smith and Huang (1995) on air pollution; Dalhuisen (2003) on residential water demand; Rosenberger and Loomis (2000) on outdoor recreation. Standard references for technical aspects of meta-analysis are Hedges and Olkin (1985), Hedges (1992), and Lipsey and Wilson (2001). Economists have focused on empirical studies evaluating air quality using hedonic property prices. Smith and Huang (1995) have identified 37 studies between 1967 and 1988 offering hedonic price function estimations using air pollution measures. 34

46 # Study Topic 1 Smith and Kaoru (1990) outdoor recreation 2 Walsh et al. (1992) outdoor recreation 3 Smith and Huang (1993, 1995) air pollution 4 Sturtevant et al. (1995) fresh water fishing 5 Bateman and Jones (1995) woodland recreation 6 Rush (1995) wildlife benefit estimates 7 Smith and Osborne (1996) visibility at national parks recreation, environmental amenities, health risks 8 Carson et al. (1996) 9 Loomis and White (1996) endangered species 10 Brouwer et al (1999) wetlands 11 Rosenberger and Loomis (2000) recreation 12 Bergstrom, Boyle, Poe (2000) groundwater quality environmental impacts of agri-environmental policies 13 Oltmer, Nijkamp, Florax, Brouwer (2000) 14 Mosquera, Côté, Jennings and Reynolds (2000) marine reserves 15 Platt and Ekstrand (2001) water recreation 16 Woodward and Wui (2001) wetland services 17 Sayman and Onguler (2001) WTA-WTP disparity 18 Loomis and Shrestha (2003) outdoor recreation households water supply systems 19 Ukoli-Onodipe (2003) 20 Banzhaf and Smith (2003) air quality 21 Nelson (2003) airport noise 22 Schipper, Nijkamp, Rietveld (2003) aircraft noise, air pollution 23 Brander, Florax, Vermaat (2003) wetland services environmental valuation 24 Gen (2004) studies surface water quality 25 Johnston et al. (2005) improvements 26 Johnston et al. (forthcoming) recreational fishing values 27 Manley, Van Kooten, Moeltner and Johnson (2005) carbon costs and benefits Table 2. Some meta-analyses carried out in the field of environmental valuation 35

47 3.3. Special challenges in meta-analysis of non-market valuation The statistical techniques used in meta-analyses vary due to heterogeneity of studies and topics addressed. Some issues in conducting meta-analysis in environmental economics arise to quality of original valuation studies. The quality of the meta-analysis is directly related to the quality of the original study ( garbage-in, garbage-out ). Completeness and availability of data is crucial for a high quality meta-analysis. The availability of original studies that match the context of specific policy site is often limited because of variation in site characteristics or available substitutes, and even if a sufficiently similar study site was found, most primary research was not designed for meta-analysis or benefit transfer purposes. Methodological approaches used in studies are non-standard which makes generalization of results difficult. Another problem is related to the heterogeneity among studies. In medicine and sciences, the context of research is highly standardized and replicated. Frequently metaanalysis in medicine is used for clinical studies which often have quite underdeveloped protocols, and observations are very different in terms of patient s history, time of the illness, etc. In economics researchers are rewarded for originality and innovation. Most valuation studies still fall into category of method development. As a result, accounting for heterogeneity is not straightforward. However, two things make this problem worse. First, how to account for quality difference between studies? There is a tendency in economics to publish experimental results that have a positive result (found something), while not publishing findings where the results are negative (found that something did not happen) or inconclusive, resulting in publication 36

48 bias. It is especially relevant for environmental economics that as a young discipline has relatively few journals for dissemination of research results. The other problem is associated with poor reporting of statistical results and not providing sufficient information about the statistical characteristics of the sample observations. Providing insufficient or incomplete information may not be detrimental to the study, but it may compromise the comparison of results among different studies, and is thus important for a proper and justifiable construction of a good database. Different methodological approaches applied in valuation studies cause inconsistencies in results and their interpretations Meta-analysis and its application for benefit transfer The literature often suggests that the use of a meta-regression model to estimate benefit values for a new policy site would allow researchers to control for various site, method, or study specific effects on benefit estimates. Given that a meta-regression function fully represents the empirical valuation functions or the random sample of those functions across the sites and resources, it is plausible in theory that the value of the new site or the resource could be uncovered using the meta-regression model. Krupnick (1993) discussed some of the earlier applications of benefit transfer in the U.S. Federal regulatory decisions. The U.S. Environmental Protection Agency (EPA) suggests that off-the shelf benefit transfer methodology should be used where possible (Freeman 1984, Desvousges et al. 1992). The high cost, extended time requirement, and funding uncertainty for empirical studies to estimate non-market values are primary factors that often lead analysts to transfer benefits. 37

49 Freeman (1984) highlighted the application of benefit transfer in a broader framework of benefit-cost analysis as required by President Reagan s 1981 Executive Order Despite the mixed performance of benefit transfer in past assessments (Smith et al, 2002), welfare measures estimated using such methods are increasingly incorporated as central components of benefit cost analyses (Bergstrom and De Civita 1999). Suggestions are made that meta-analysis would serve as an improved technique to benefit transfer (Smith and Kaoru 1990, Sturtevant et al. 1998). While benefit transfer studies had been conducted over the years, the conceptual foundations received renewed interest in the early nineties in the U.S. For example, a special issue of Water Resources Research (1992), the Association of Environmental and Resource Economists proceedings volume from a benefit transfers workshop and more recently works of Desvousges et. al. (1998) are devoted to benefit transfer issues. The use of transferred data in environmental studies has grown during the 1990s. A sampling of these efforts would include Kirchhoff et al. (1997), Smith and Kaoru (1990), Feather and Hellerstein (1997) and Kirchhoff (1997, 1998). Performing benefit transfer using meta-analysis has several advantages (Shrestha and Loomis 2003). First, information is utilized from a number of studies providing rigorous measures of central tendency. Second, methodological differences in the original studies can be controlled for when calculating a value from the meta-regression equation. Third, by setting the explanatory variables specific to a policy site, the analyst can potentially account for differences between the study site and the policy site characteristics. Finally, the benefit estimate using meta-analysis is likely to be a better 38

50 approximation of the value of the resource at a new policy site. Based on this potential, US EPA (2000) guidelines characterize meta-analysis as the most rigorous benefit transfer exercise. (p.87). Nonetheless, some authors advise caution in direct policy applications of meta-analysis (Poe et al. 2001). Conducting higher quality meta-analyses can improve benefit transfers and lead to better application of generalizable results. 39

51 CHAPTER 4 META-ANALYSES OF NON-MARKET VALUATION 4.1. Meta-analysis of US wetland valuation studies Introduction For many years people treated wetlands as worthless land areas and insisted that they be drained or reclaimed (some people still do). The government even encouraged the destruction of wetland areas through its funded water resource development projects. Estimates show that during the 1600 s, over 200 million acres of wetlands existed in the lower 48 states. Today, barely half of the original wetlands remain, most of which are in the southeastern United States (U.S. EPA). Time passed when values of wetland ecological services and functions were not recognized. Wetlands are highly productive ecosystems, and are able to capture energy and support a wide variety of flora and fauna. They have a role in providing water quality protection in the catchment by filtering pollutants such as sediments, nutrients, organic and inorganic matter and pathogens. Wetlands provide protection against floods and opportunities for recreation activities such as boating, fishing, and birdwatching. Many wetlands have cultural significance and provide opportunities for scientific research. Increased awareness of the value of wetlands has resulted in many regulations and programs designed to protect wetlands and the benefits they provide. 40

52 A number of wetland valuation studies have been undertaken in the literature to determine value of wetlands and their services. This research utilizes a technique of metaanalysis as a tool to summarize results of existing studies. There are three wetland valuation meta-analyses in the literature (Brouwer et al. 1999; Woodward and Wui 2001; Brander et al. 2003). They each estimate a subset of the literature and collect worldwide observations. The first two studies review temperate wetlands and do not include their socio-economic and geographical characteristics. The third study considers socioeconomic characteristics and expands the scope of estimation to tropical wetlands. This meta-analysis examines a subset of the valuation literature available for wetlands located in the USA and attempts to contribute towards a general framework of estimating benefits from wetland services enhanced by U.S. government conservation programs. Meta-analytical results can be used to understand the influence of methodological and study specific factors on estimated wetland value per acre. They can also be used to forecast value for services in unstudied sites. After obtaining a meta-regression equation, one can estimate or forecast value in unstudied sites by calibrating the equation to fit the new site. Benefits transfer efforts could be substantially improved if value of wetlands services were better known. Meta-analytical results can also be used to give direction to future wetland research as to what needs to be examined more carefully. They can give greater insight into the important variables that need to be studied when carrying out research in the wetland valuation field. 41

53 Wetland policy decisions usually require comparing the values of wetland benefits to the values of alternative use benefits. Knowledge of wetland value would help allocate agency budgets and determine the level of monetary incentive necessary to induce owners to preserve wetland. Valuation of wetland benefits can help better achieve wetland conservation objectives and may become applicable in the environmental assessment processes. The purpose of this analysis is to perform a meta-analysis of US wetland valuation studies, estimate a standardized shadow price, such as the dollar value per year of one acre of wetland area and try to develop a consistent estimator of wetland value in terms of a set of predetermined explanatory variables. The analysis examines the wetland valuation literature and existing wetlands meta-analyses while creating a research dataset. It first considers common methods used to measure economic benefits of wetlands and then discusses obtained meta-analytical results, compares them to the results from other studies and identifies problems and prospects of applying a meta-analysis to wetland valuation Wetlands and their types Wetlands are highly complex ecological ecosystems. There is no exact universal definition of wetlands. In the U.S. for regulatory purposes under the Clean Water Act, the term wetlands means "those areas that are inundated or saturated by surface or ground water at a frequency and duration sufficient to support, and that under normal circumstances do support, a prevalence of vegetation typically adapted for life in saturated soil conditions. 42

54 Wetlands generally include swamps, marshes, bogs and similar areas (EPA Regulations 40 CFR 230.3(t)). A UNESCO-based intergovernmental treaty on wetlands adopted in the Iranian city of Ramsar in 1971 defines wetlands the following broad way: areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six meters, and points out that wetlands: may incorporate riparian and coastal zones adjacent to the wetlands, and islands or bodies of marine water deeper than six meters at low tide lying within the wetlands. Wetlands vary widely because of regional and local differences in soils, topography, climate, hydrology, water chemistry, vegetation, and other factors, including human disturbance. Indeed, wetlands are found from the tundra to the tropics and on every continent except Antarctica. In 1979, a comprehensive classification system of wetlands and deepwater habitats was developed for the U.S. Fish and Wildlife Service (Cowardin et al. 1979). Under this system, wetlands are of two basic types: coastal (also known as tidal or estuarine wetlands) and inland (also known as non-tidal, freshwater, or palustrine wetlands). Major classes include marine, estuarine, riverine, lacustrine, and palustrine wetlands. Previous wetland meta-analyses adopted their own wetland classification schemes. Woodward and Wui (2001) specified wetlands as coastal and non-coastal. Brander et al. (2003) used the following groupings: mangrove, unvegetated sediment, salt/brackish marsh, fresh marsh and woodland. From the ecological point of view, characterizing wetland as a coastal marsh is not enough to identify it either as a swamp or 43

55 marsh. More unity in classification of wetlands by valuation studies is desirable to better understand and interpret values of wetlands of different types. Cowardin s classification system appears to be the most detailed and scientifically accepted system. However, most primary valuation studies do not specify a type of wetland in terms of Cowardin s typology and do not provide enough information to place it according to the classification. This analysis does not use Cowardin s typology because of the lack of detailed ecological information about wetlands provided by studies and thinness of the data. It classifies wetlands in four major groups: freshwater marsh, saltwater marsh, prairie pothole and swamp Wetland valuation literature and data review There is substantial literature on wetland valuation, including three meta-analyses (Brouwer et al. 1999; Woodward and Wui 2001; Brander et al. 2003). The first two studies do not include socio-economic and geo-referenced characteristics for the wetland sites in the analysis. Brander et al. (2003) extend their sample to near 213 observations including tropical wetlands and estimates from more countries, as well as socio-economic and geo-referenced variables in the form of GDP per capita, population density, and latitude. Richard Bishop compiled an extensive wetland bibliography list that contains 439 studies (Bishop, 2005). As Brander et al. (2003) point out generally, the literature can be classified into three groups by the primary focus of the study. First, some studies calculate value(s) for a specific wetland site (Acharya 2000; Cooper and Loomis 1991; Lant and Roberts 1990). Second, some studies review or compare already existing wetland valuations (Barbier et 44

56 al. 1997; Anderson and Rockel 1991). Third, some studies develop a specific methodological innovation for non-market valuation of wetlands (Bateman and Langford 1997; Barbier 1991; Whitehead and Blomquist 1991; Haab and McConnel 1997; Pate and Loomis 1997). Many studies also combine elements of these three categories. Within the group of studies that primarily attempt to estimate values for specific wetland sites, some estimate values for alternative wetland management strategies (Bann 1997; Barbier and Thompson 1998). Other studies use value transfer rather than primary valuation techniques to value a specific wetland (Farber 1992; Gren 1995). Within the group of studies that focus on methodological issues in wetland valuation, some empirically test different survey or estimation techniques using real or hypothetical data whereas others are purely conceptual (Turner 1991; Bergstrom 1993; Bystrom et al. 2000). Candidate studies for this meta-analysis are wetland valuation studies within the USA. Electronic databases such as American Economic Association s EconLit ( the University of Michigan-Dissertation and Thesis Abstracts ( the Water Resources Abstract Index; gray literature including western regional research publications; American Agricultural Economists Association s publications ( were searched for necessary studies. A big part of literature findings was retrieved from the environmental valuation databases such as EVRI ( EnValue ( and existing wetland valuation bibliographies. The following keywords were used: wetland valuation, wetland value, wetland services, meta-analysis, nonmarket valuation, environmental 45

57 benefits. Each article was then reviewed and considered for inclusion in the metaanalysis. Most of the results from the original valuation studies are reported in various formats and need to be transformed and manipulated to establish uniformity across studies. The criteria for study selection were the following: 1) the study was conducted in the USA; 2) the study allows calculation of wetland value per acre; 3) the study provides sufficient information about resource, context and study attributes. Sufficient information includes description of wetland geographical location, type, size, or wetland name that allows to identify these characteristics from other sources; detailed discussion of wetland services valued in the study; described methods and concepts of valuation; stated monetary results on total value, willingness to pay, value per acre or other information that allows to calculate wetland value per acre. After review of 81 studies the data from 33 of these studies were identified with sufficient information to allow inter-study comparisons. These 33 studies produced 72 separate observations of wetland value 1. The number of wetlands represented in the data set is less than the number of observations because multiple observations are taken from some studies. Multiple estimates from a single study are available due to in-study variations in such factors as the methods applied, wetland types and services affected. The studies include twenty-two journal articles, seven research reports or academic papers, two chapters in a book, one PhD dissertation and one Master s thesis. 1 The author has expanded a U.S. subset of a wetland meta-analytical dataset of Richard T. Woodward and Yong-Suhk Wui (2001) by adding 14 more observations from 7 wetland valuation studies. 46

58 Data extracted from the studies was first coded into Microsoft Excel spreadsheet and then regressions were run using statistical programs such as SAS and SPSS. This dataset, unlike other wetland meta-analyses, contains only US observations. It includes socio-economic and geographical characteristics of wetland sites. Because of our desire to synthesize wetland values from all different services, we use annual value per acre of wetland in 2003 US dollars. WTP per household is not applicable here because some methods (net factor income, energy analysis, replacement cost, production function and market prices) do not lead to a WTP per household measure. However, if WTP per household is available, then the value per acre can be calculated with knowledge of the relevant population and the wetland s size. When capitalized values were reported, they were annualized assuming constant value per year and using discount factors provided in the studies or a 6% rate in the studies that did not state a discount rate Meta-analytical variables, modeling and regression results Dependent variable is the logarithm of value per acre of wetlands adjusted to year 2003 dollars using consumer price index, LNVALUE. Building on the conceptual framework, the variables used in this analysis are grouped into socio-economic variables, wetland size, types and functions, methodological and regional variables. Table 4 below describes all variables used in the meta-analysis. 47

59 Socio-economic variables include annual household income in the study area adjusted to U.S dollars, INCOME, and year of wetland value, YEAR. It is a variable that is included to control for refinements in wetland valuation that have occurred over time. Over the years, growing awareness of improved environmental benefits may have gradually led to an increase in value per acre of wetlands. Therefore, studies done in earlier time periods may be expected to have smaller value estimates compared to studies done in later time periods. YEAR is a qualitative variable coded as 1969=1, 1970=2 for the n year period in which all studies were done. Wetland size variables characterize amount of wetland acres valued in the study (ACRES) and share of wetland acres in the area (SHARE). SHARE 2 is a continuous variable with the range between 0 to1 as reported by the National Resource Inventory 1997 data. SHARE represents approximate share of wetland acres based on the total land acres in the area. It is adopted in this research as a measure of scarcity of wetlands. Wetland types are represented in the analysis by four general groups: freshwater marshes, saltwater marshes, swamps and prairie potholes. Of the 72 wetland values per acre, 39 are for freshwater marshes, 20 are for saltwater marshes, 6 for swamps and 7 are for prairie potholes. Wetland functions are flood control, water quality, water supply, recreational fishing and commercial fishing, bird hunting and bird watching, amenity and habitat. 2 First, to obtain value of SHARE each observation in the dataset was assigned a five-digit Federal Information Processing Standards (FIPS) code, where first two digits represent a state, and the next three digits represent a county. Then SHARE was determined as a ratio of a number of wetland acres by FIPS code (all values of variable xfact, number of acres, where fsawet, wetland classification, is a category 1=prior converted, 2=converted wetland, 3=artificial wetland, 4=farmed wetland, and 5=wetlands in the NRI 1997 database) and total number of acres (variable xfact, number of acres). 48

60 Methodological variables include a set of variables characterizing method of estimation such as contingent valuation method, hedonic pricing, travel cost method, replacement cost, net factor income, production function, market prices and energy analysis method. Regional variables include variables characterizing geographical location of wetlands on the map of ERS Farm Resource Regions. Figure 4 below demonstrates this map. The following regions are represented by the dataset: Heartland (5 observations from 4 studies), Northern Crescent (20 observations from 7 studies), Northern Great Plains (8 observations from 3 studies), Fruitful Rim (13 observations from 8 studies), Mississippiportal (12 observations from 7 studies), Southern Seaboard (9 observations from 2 studies), Prairie Gateway (1 observation from 1 study), and Eastern Uplands (4 observations from 1 study). Values from variables representing Southern Seaboard, Northern Great Plains, Prairie Gateway and Eastern Uplands regions were combined into one variable ALL OTHER REGIONS in order to avoid influence of study-specific effects. 49

61 Variable Description Frequency Dependent variable LNVALUE Socio-economic variables INCOME YEAR ACRES SHARE Wetland size Wetland types FRESHWATER MARSH SALTWATER MARSH SWAMP PRAIRIE POTHOLE Logarithm of value per acre of wetland, U.S. year 2003 dollars Annual household income, U.S. year 2003 dollars (divided by 1000) Year in which study was conducted, coded as 1969=1, 1970=2, etc. Amount of wetland acres used in the study valuation Share of wetland acres in the area by FIPS codes as reported by the NRI 1997 data Coded as 1 if a wetland is a freshwater marsh, 0 if not Coded as 1 if a wetland is a saltwater marsh, 0 if not Coded as 1 if a wetland is a swamp, 0 if not Coded as 1 if a wetland is a prairie pothole, 0 if not Number of studies Mean Wetland functions FLOOD WATER QUALITY WATER SUPPLY RECFISH Reduced damage due to flooding and/or stabilization of the sediment for erosion reduction, coded as 1 if a wetland function is noted in the study, 0 if not Reduced costs of water purification, coded as 1 if a wetland function is noted in the study, 0 if not Increased water quantity, coded as 1 if a wetland function is noted in the study, 0 if not Improvements in recreational fisheries either on or off site, coded as 1 if a wetland function is noted in the study, 0 if not Table 3. Description of wetland meta-analytical variables 50

62 COMFISH BIRDHUNT BIRDWATCH AMENITY HABITAT Methodological variables CVM HP TCM RC NFI PFMP EA PUBLISH Regional variables HEARTLAND NORTHERN CRESCENT Improvement in commercial fisheries either on or off site, coded as 1 if a wetland function is noted in the study, 0 if not Hunting of wildlife, coded as 1 if a wetland function is noted in the study, 0 if not Recreational observation of wildlife, coded as 1 if a wetland function is noted in the study, 0 if not Amenity values provided by proximity to the environment, coded as 1 if a wetland function is noted in the study, 0 if not Nonuse appreciation of the species, coded as 1 if a wetland function is noted in the study, 0 if not 1 if study used Contingent Valuation Method, 0 if not 1 if study used Hedonic Pricing Method, 0 if not 1 if study used Travel Cost Method, 0 if not 1 if study used Replacement Cost Method, 0 if not 1 if study used Net Factor Income Method, 0 if not 1 if study used Production Function or Market Prices method, 0 if not 1 if study used Energy Analysis Method, 0 if not 1 is study is a journal article, 0 if not 1 if study conducted in the region 1 if study conducted in the region (continued) Table 3. Description of wetland meta-analytical variables 51

63 FRUITFUL RIM MISSISSIPPI PORTAL ALL OTHER REGIONS 1 if study conducted in the region 1 if study conducted in the region 1 if study conducted in Eastern Uplands, Northern Great Plains, Prairie Gateway, or Southern Seaboard, 0 if not (continued) Table 3. Description of wetland meta-analytical variables The econometric model is based on a maintained hypothesis that measured wetland value per acre, y, is a function of the wetland types and size, in matrix x a; socioeconomic characteristics, x o ; wetland services provided, x s ; the methodology used and regional variables, x m ; and a constant term, a. The fit was substantially improved by using logarithms of per acre value. The estimated linear model is ' ' ' a a o o s s m m ln y= a+ b' x + b x + b x + b x +u; (21) where a is the constant term, u is a vector of residuals and the b s are the estimated coefficients on the respective explanatory variables. In our model all right-hand-side (RHS) variables are linear resulting in the semilog functional form common for meta-analysis. While linear models are also common, the semi-log form was chosen based on its statistical performance, ability to capture curvature in the valuation function, and because it allows independent variables to influence the dependent variable in a multiplicative rather than additive manner. After testing, fixed effects, random effects and multilevel modeling were rejected, and OLS was chosen as a preferred regression specification. Statistical considerations such as multicollinearity, autocorrelation, and heteroskedasticity were also tested for and 52

64 these were judged to present no overriding problems. The Durbin-Watson test for autocorrelation was not significant, a scatter plot of residuals did not show big evidence of heteroskedasticity, and a simple correlation matrix indicated reasonably accepted correlation between independent variables. Further evidence against the existence of a multicollinearity problem was the observation that regression coefficients and standard errors remained quite stable across the full and reduced models. For our semi-log model the coefficients measure the constant proportional or relative change in the dependent variable for a given absolute change in the value of the explanatory variable. Therefore, coefficient of for the binary variable indicating that the wetland provides water quality function means that, ceteris paribus, the value of the wetland will be % higher than the average when the function is provided. For the explanatory variables expressed as logarithms, the coefficients should be interpreted as elasticities, the percentage change in the dependent variable given a percentage change in the explanatory variable. Some coefficients from the meta-analysis permit tests of some underlying hypotheses as follows: a) Income- value per acre is positively associated with income. b) Year- over time, value per acre increases as people become more aware and conscious of environmental issues. This research considers two models of wetland value: first, without regional variables, and second, with regional variables included in the regression. The metaanalytical results are represented in the table 4 below. 53

65 Variables Model without regional variables (standard errors in parentheses) Model with regional variables (standard errors in parentheses) (CONSTANT) (2.307) (2.861) INCOME.138*** (.043).095 (.059) YEAR ACRES.174*** (.052).197*** (.053) -4.17E-007 (.000) -3.85E-007 (.000) SHARE * (3.256) (3.905) FRESHWATER MARSH SALTWATER MARSH PRAIRIE POTHOLE WATER SUPPLY WATER QUALITY FLOOD RECFISH.207 (1.134) (1.186) * (1.208) * (1.136) * (1.474) (1.516) (.919).929 (1.016) 1.877** (.743) (.738).277 (.636) (.626).644 (.626) (.613) Table 4. Wetland meta-analytical results 54

66 COMFISH BIRDHUNT BIRDWATCH AMENITY HABITAT.926 (.951) (.899) (.688).015 (.651) 2.344*** (.827) 1.570* (.825) ** (1.041) (.972) (.761).023 (.711) PUBLISH 1.769* (.926) 2.489** (1.025) PFMP ** (1.065) * (1.057) CVM HP TCM NFI EA HEARTLAND NORTHERN CRESCENT MISSISSIPPI PORTAL ALL OTHER REGIONS k (number of independent variables) *** (.870) (.989) (1.607) (1.617) (1.175) (1.150).472 (1.203).628 (1.237) 5.196*** (1.790) 5.296*** (1.657) (1.059) 2.681** (1.008) (1.919) (.832) N R 2 (R 2 -adjusted) (0.522) (0.602) F 4.372*** 4.972*** Durbin-Watson (continued) Table 4. Wetland meta-analytical results 55

67 Meta-analytical results produce coefficients on variables that measure the change in the dependent variable for a given absolute change in the value of the explanatory variable. When the dependent variable is in a natural logarithmic form and an explanatory variable in question is not, the interpretation is as follows: ln( WTP) ln( WTP) = Birdwatch 1 (22) when a wetland provides bird watching opportunities, so if the coefficient of BIRDWATCH is 2.344, then ln( value) ln( value) = ln( value) = = (23) 1 value which means that, ceteris paribus, wetland value per acre rises by 234.4% if a wetland provides BIRDWATCH function. The results demonstrate some important relationships. In model without regional variables the coefficient of INCOME is positive (0.138) and significant, so is coefficient for the variable YEAR (0.174) supporting the hypotheses that observations with higher income produce higher value per acre, and over time value per acre increases as people become more aware and conscious of environmental issues. Coefficient on ACRES is negative but statistically insignificant. SHARE has a negative significant coefficient suggesting that the higher the share of wetlands in the area (the more substitutes are available), the lower wetland value per acre, Regarding the influence of wetland type on the wetland value, differences in value associated with different wetland types are indicated by the coefficients on these dummy variables. Coefficients of SALTWATER MARSH and PRAIRIE POTHOLE are positive and significant indicating that saltwater marshes and prairie potholes tend to 56

68 have significantly lower values than swamps. Coefficient of FRESHWATER MARSH is positive but insignificant. The coefficients on the wetland functions variables are estimates of the extent to which the presence of each service changes the value per acre. Presence of wetland functions identified by variables WATER SUPPLY, WATER QUALITY, FLOOD, RECFISH and COMFISH, and BIRDWATCH tend to result in higher than average values. However, only the coefficient of BIRDWATCH variable (2.344) is significant suggesting that wetlands that provide bird watching opportunities have values that are percent higher than average. Wetlands that provide functions identified by variables BIRDHUNT, AMENITY and HABITAT tend to have lower than average values. However, only coefficient of AMENITY (-2.093) is statistically significant. The results for the valuation methodological dummies suggest that CVM estimates tend to have percent significantly lower values than replacement cost values. Coefficients of TCM and PFMP are also negative but insignificant. Coefficient of EA is positive and significant (5.196) suggesting that estimates obtained by this method result in higher values than estimates from replacement cost method. It is expected that estimates from replacement cost method will differ from estimates from contingent valuation method as these methods are based on different theoretical concepts. Replacement cost method is not limited by demand and it is perfectly elastic to scale, while contingent valuation method is. The meta-regression results demonstrate that replacement cost method and energy analysis estimates produce wetland values higher than contingent valuation and travel cost methods. Engineering approach to obtaining wetland value per acre does not take 57

69 demand for wetlands and their functions into consideration which can lead to overvaluing wetlands and providing more of them than would be justified by welfare considerations.. Figure 5 below demonstrates an approach to thinking about differences in values provided by so-called demand and nondemand methods. It shows replacement cost (RC) and marginal willingness to pay (MWTP) functions in their relation to a number of wetland acres and dollar values. RC function is constant providing the same amount of dollars for an acre of wetlands. MWTP tends to decrease as number of acres increases. This can characterize behavior of most demand method functions, including contingent valuation. MWTP is bigger than RC when scale is smaller than x, but smaller than RC if scale is bigger than x. We can not be sure that each wetland in the dataset is optimally scaled; therefore, predictions about comparisons between estimates obtained by demand and nondemand methods can not be made. $ MWTP RC x Wetland acres (scale) Figure 5. Replacement cost and marginal willingness to pay functions 58

70 Coefficient of PUBLISH is positive and significant (1.769) suggesting that a publication bias is present and observations from journal articles tend to have higher values than estimates from non-published sources. In model with regional variables coefficients on INCOME, SHARE, PRAIRIE POTHOLE, WATER QUALITY, AMENITY and CVM keep the same signs but are no longer significant. The rest of the variables demonstrate similar and consistent relationships with the dependent variable. Wetlands located in Northern Crescent region tend to have significantly higher than average values (coefficient of NORTHERN CRESCENT is 2.681) Wetland values across ERS Farm Resource Regions Since the early 1900's, USDA analysts have sought to identify patterns in U.S. farming that might further the understanding of differences in financial performance of farms and the economic well-being of farm households. This meta-analysis looks at ERS Farm Resource Regions to help better understand allocations of environmental benefits and conservation programs expenditures by the region, and try to explore a national pattern of benefits and costs of the conservation programs. 59

71 Figure 6. ERS Farm Resource Regions The table 5 below demonstrates list of regions (column 1) with the number of observations for each region (column 2) and the number of studies represented by these observations (column 3), income values as means of the study means (column 4), and wetland values per acre across ERS Farm Resource Regions based on the study means (column 5) and based on the set default category (column 6) in 2003 US dollars. The default category of a wetland in the meta-regression is a swamp located in Fruitful Rim region with the value per acre estimated by the replacement cost method with all dummies set at 0. Its mean value per acre as estimated by the regression equation for model with regional variables in table 4 is $64.54 (column 6, table 5). Column 6 demonstrates mean values per acre based on the default wetland in Heartland, Northern Crescent, Mississippi Portal and All other regions. 60

72 Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Region n # of studies Income (means of the study means) Wetland value per acre (means of the study means) Estimated wetland value per acre (dummies set at 0) Heartland Northern Crescent Fruitful Rim Mississippi Portal All other regions (Eastern Uplands, Northern Great Plains, Southern Seaboard and Prairie Gateway) Table 5. Mean income and wetland values per acre across ERS Farm Resource Regions, in 2003 US dollars Figure 7 below demonstrates differences in mean values per acre across ERS Farm Resource regions. The meta-regression results provide important robust regional characteristics with reduced study specific effects. These are premiums/discounts for other regions given that default is normalized at 0. 61

73 Heartland Northern Crescent All other regions Mississippi Portal Figure 7. Mean wetland values per acre based on the meta-regression results, in 2003 US dollars (swamp in Fruitful Rim Region estimated by replacement cost method is a default category set at 0) Comparisons of wetland meta-analytical studies There exist three wetland meta-analyses in the literature conducted by Brouwer et al. (1999), Woodward and Wui (2001) and Brander et al. (2003). Brouwer et al. (1999) evaluates only contingent wetland valuation studies, their dataset includes 103 worldwide observations from 30 unique studies. The dependent variable is WTP per household per year for the preservation of specific wetland aspects in SDR Special Drawing Rights (1SDR~1.5US$ on 1995). Woodward and Wui (2001) examine 39 wetland valuation studies that produce 65 observations of wetlands located worldwide. Their dependent variable is a natural logarithm of the value per acre of wetland estimated in 1990 US dollars. 62

74 These studies do not include socio-economic and geographical characteristics of wetlands in meta-regression models unlike Brander et al. (2003) who collect 202 world observations from 80 unique studies; their dependent variable is a vector of wetland values per hectare per year in 1995 US dollars. This analysis considers 33 US studies that produce 72 observations of US wetland value per acre. The dependent variable is a natural logarithm of wetland value per acre per household per year in 2003 US dollars. Table 6 demonstrates results from this metaanalysis as they compare to results from Woodward and Wui (2001) and Brander et al. (2003). 63

75 Variable Woodward and Wui (2001) Brander et al. (2003) Borisova-Kidder (2006) Dependent variable Number of studies and observations Model A natural log of the value per acre of wetland in 1990 US$ (mean 4.92). Mean value per acre in 2003 US$ is studies ( ), 65 world observations A vector of wetland values per hectare per year in 1995 US$ (mean 2800). Mean value per acre in 2003 US$ is studies ( ?), 202 world observations A natural log of wetland value per acre per household per year in 2003 US$ (mean 5.57). Mean value per acre is $ studies ( ), 72 US observations OLS results OLS results OLS results R R 2 -adjusted F df (degrees of freedom) Socio-economic variables *** 5.50*** 4.372*** GDP PER CAPITA/INCOME ** (0.46).138*** (.043) POPULATION DENSITY ***(0.12) YEAR (0.04) -.174*** (.052) - Table 6. Comparison table of some results from wetland meta-analyses (standard errors in parentheses) 64

76 Wetland types and size ACRES **(0.11) -0.11**(0.05) -4.17E-007 (.000) SHARE * (3.256) COASTAL (0.68) - - FRESHWATER MARSH ** (0.59).207 (1.134) SALTWATER MARSH (0.42) * (1.208) PRAIRIE POTHOLE * (1.474) SWAMP/WOODLAND ** (0.42) Wetland functions FLOOD (0.77) 0.14 (0.55).277 (.636) WATER SUPPLY (1.54) (0.71) (.919) WATER QUALITY (1.54) (0.71) 1.877** (.743) HUNTING ** (0.43) - FISHING (0.36) - RECFISH (0.56) (.626) COMFISH (1.01) (.951) AMENITY *** (0.95) 0.06 (0.39) ** (1.041) BIODIVERSITY (0.81) - HABITAT AND NURSERY (0.59) (0.35) - BIRDHUNT ** (0.52) (.688) BIRDWATCH 1.804** (0.59) *** (.827) MATERIAL ** (0.42) - FUELWOOD *** (0.45) - Table 6. Comparison table of some results from wetland meta-analyses (continued) 65

77 Methodological variables PUBLISH (0.71) * (.926) CVM 1.49** (0.73) HP 5.043** (1.12) (1.54) TCM (1.05) 0.01 (0.65) RC 2.232** (0.89) 0.63 (0.81) NFI (0.90) 0.19 (0.61) PF (production prices) (0.75) *** (.870) (1.607) (1.175).472 (1.203) - MP (market prices) (0.53) - PFMP ** (1.065) OC (opportunity cost) (0.72) - EA (energy analysis) *** (1.790) Table 6. Comparison table of some results from wetland meta-analyses (continued) GDP per capita/income and value per acre in this study and work by Brander et al. demonstrate consistent positive significant relationship. Woodward and Wui (2001) does not include income in their meta-regression. Coefficient of YEAR is positive significant according to the results of this study, it is positive but insignificant with Woodward and Wui (2001). Brander et al. (2003) do not consider this variable in their meta-regression. 66

78 Woodward and Wui (2001), Brander (2003) find decreasing returns to scale in their analyses. The results of this study demonstrate insignificant decreasing returns to scale. All three studies employ different classifications of wetland types. Woodward and Wui (2001) distinguish only coastal wetlands in their research, however, this definition from ecological point of view is too broad to determine whether a coastal wetland was a freshwater marsh, saltwater marsh, or both. They do not find a significant relationship of this variable with the wetland value. Brander et al. (2003) include three times more world observations than Woodward and Wui (2001). It is not surprising that they have a more detailed classification of wetlands (mangrove, unvegetated sediment, salt/blackish marsh, fresh marsh and woodland) that primarily is driven by continents characteristics. They find freshwater marshes to have negative significant relationship and woodlands to have positive significant relationship with the value per acre. This study has four wetland types such as freshwater marshes, saltwater marshes, prairie potholes and swamps. These are the types of wetlands represented by 33 used valuation studies. Saltwater marshes and prairie potholes tend to have significantly lower values than swamps. Three meta-analyses consider different wetland functions, more or so depending on valuation studies used and their own interpretation of descriptions of functions provided. Woodward and Wui (2001) find AMENITY and BIRDHUNT to have significant negative relationship and BIRDWATCH to have significant positive 67

79 relationship with the value per acre. Brander et al. (2003) receive negative significant coefficients on functions HUNTING, MATERIAL and FUELWOOD (-1.1, and respectively). This study determines a positive significant relationship between bird watch function and value per acre (the coefficient of BIRDWATCH is 2.344). It also finds that coefficient of AMENITY is negative significant (-2.093) suggesting that wetlands that provide this function tend to have percent power values than average. Woodward and Wui (2001) observed that the hedonic pricing and the replacement cost produce higher values than CVM, while Brander et al. (2003) find that CVM estimates are higher than estimates from other valuation methods and that the hedonic pricing leads to rather conservative estimates. Results of this study find CVM estimates to be lower than estimates from replacement cost method which supports findings of Woodward and Wui (2001). So far, the findings from the three existing wetland meta-analyses demonstrate overall consistencies in signs, magnitudes and significance levels of important continuous variables such as INCOME, YEAR and ACRES. Inconsistencies arise in results for wetland types and functions which can be due to different classification schemes adopted by each meta-analysis. This meta-analysis has made improvements by making thoughtful considerations of policy relevant variables such as INCOME, ACRES and SHARE and controlling for study-specific effects. Existing wetland valuation datasets are still very thin on the amount of studies and observations; therefore researchers encounter a problem of degrees of freedom and have to ignore available data about precision of estimates in the original studies. Wetland 68

80 values vary enormously depending on their types, functions, socio-economic, geographical characteristics and study methods. So, caution must be advised when trying to use results from wetland meta-analyses for benefit transfer Conclusions The analysis overviews wetland valuation literature and identifies important socio-economic, geographical, physical and methodological variables that influence wetland value per acre. The findings suggest that observations with higher income have higher wetland value per acre and over time value per acre increases as people become more aware and conscious of environmental issues. Wetland size has insignificant decreasing returns to scale. Share of wetlands has a significantly negative impact on the value per acre. Saltwater marshes and prairie potholes tend to have significantly lower value than swamps. Of all wetland functions considered the coefficients of BIRDWATCH variable (positive) and AMENITY (negative) are significant. The results for the valuation methodological dummies suggest that estimates from replacement cost method tend to have the highest values. CVM estimates tend to have significantly lower values than average, while EA methods tend to have significantly higher values. Wetlands located in Northern Crescent region tend to have significantly higher values per acre than on average. Results from wetland meta-analyses conducted in the literature and their demonstrated inconsistencies urge caution in using them for benefit transfer exercises. 69

81 There is a clear need for more and higher quality valuation studies. The science of estimating economic values for wetlands is still relatively new and evolving and methods are continually being refined and enhanced. Future wetland valuation studies should focus on including and fully reporting detailed geographical, physical, socio-economic and methodological characteristics of wetlands, and providing the supporting documentation with the raw data, information on confidence intervals and variance of key variables. Standards in reporting wetland valuation data are highly necessary to achieve uniformity across studies and avoid misinterpretations of the data. There exist various methods to estimate value of wetlands and their services in the literature. Many of these methods are based on different theoretical concepts. This leads to worsening apples and oranges problem with this wetland meta-analysis. If more data is available over the time, estimates from conceptually different methods should be examined separately. Opportunities for further research related to this study come from the following general areas: additional valuation studies on wetlands, conducting similar studies in other locations, standardizing data reporting and meta-analysis approaches, and wetland research. 70

82 4.2. Meta-analysis of US valuation studies of improvements in surface water quality Introduction Clean water is essential to human survival as well as to aquatic life. The quality of surface water in rivers and streams, lakes, ponds and wetlands is determined by interactions with soil, transported solids (organics, sediments), rocks, groundwater and the atmosphere. It may also be significantly affected by agricultural, industrial, mineral and energy extraction, urban and other human actions, as well as by atmospheric inputs. About 40 percent of monitored streams in the U.S. do not meet water quality standards (U.S. EPA). This means they are not safe to drink, swim, and fish or, in some cases, boat. Water quality improvements produce a wide variety of benefits, including use and non-use ones. Environmental economists conducted many valuation studies to estimate people s willingness to pay for improvements in surface water quality. It is important for policy analysis to summarize results of such studies to determine what influences and causes variations in WTP for improvements in surface water quality. This analysis reports on the application of meta-analysis to identify systematic patterns in WTP for improvements in surface water quality. The work makes a contribution towards estimating national benefits from improvements in surface water quality due to government agricultural conservation programs. 71

83 Surface water quality Surface water is water that is on the Earth's surface, such as in a stream, river, lake, or reservoir. Source: U.S. Geological Survey ( Figure 8. Fresh surface- and ground-water withdrawals In its National Water Quality Inventory: 1998 Report to Congress, EPA concludes that 40 percent of the nation's assessed waterways remain too polluted for fishing and swimming. Over 290,000 miles of 840,000 miles of assessed rivers and streams do not meet water quality standards. EPA also assessed nearly half of all lakes, reservoirs and ponds, finding nearly half polluted. Of the Great Lakes, 90 percent of their shoreline miles were assessed; of those, 96 percent of the shoreline miles indicated pollution exceeding water quality standards to protect human health. Although threats remain, EPA found that ground water quality generally remains good and can support many different uses. 72