# Agricultural Sedimentation Impacts on Lakeside Property Values

Somskaow Bejranonda, Fred J.Hitzhusen and Diane Hite

A hedonic pricing model is developed to estimate the effects ofpolicies to control agricultural sedimentation on lakeside property values at 15Ohio state park lakes, Using an LA/AIDS demand system, we estimate changes in social welfare that result from upstream soil conservation practices and/or lake dredging activity, while holding other property characteristics constant. Policy simulation results suggest that lakeside residents generally have

ahigher willingness to pay on an annualized basis for sediment reduction from upstream soil conservation than for lake dredging. This has important implications for soil conservation policy, particuhwly intargeting improvements inthe economic efficiency ofthe Conservation Reserve Program.

Introduction

Soil erosion from agriculture and the downstream impacts ofsedimentation continue tobe an important environmental problem in the U.S. and elsewhere. The Conservation Reserve Program (CRP) in effect since the 1985 Farm Bill in the U.S. has reduced gross erosion from agriculture by paying farmers toremove themost erosive lands from row crop production. However, under the 1996 Farm Bill, this program is being downsized in terms of acreage covered, and the average payment per acre has been reduced to control program costs. Some fear that these policy changes will increase soil erosion and downstream sedimentation. Earlier studies provide national aggregate offsite cost estimates for soil erosion. Clark, Havercamp and Chapman estimate the total annual offfarm costs for all agricultural erosion sources to range from $3–$ 13billion with apoint estimate of $6.1 billion, ofwhich $2.2 billion was attributable to cropland erosion (in 1980 dollars). Loss ofrecreational value accounted for the largest share of costs, near]y 33910with boating being the largest subgroup. Other high impact recreationists orusers

SomskaowBejranondaisanassistmrtprofessorwiththeFacultyofEconomics,KasetsartUniversity,Bangkok,Thailand.FredJ.Hitzhusenisa professorwiththeDepartmentofAgricultural,EnvironmentalandDevelopmentEconomics attheThe Ohio State University, and Diane Hite is an assistant prufessor atthe Mississippi State University Department ofAgricultural Economics. We would like toacknowledge the valuable assistance ofpmfessurs Donald Haurin and Alan Randall atOhio State University in this research effort We would also like to thank two anonymous referees whose insights helped to improve this manuscript, and their percent oftotal costs included municipal and industrial (14.8Yo), water storage facilities (11.3%) dredging (8.5%), and preservation values (8.2%). Ofallerosion sources, cropland comprises the largest share at38%. Ribaudo’s (1986) reanalysis of Clark et al.’s estimates resulted in a point estimate at $7.1 billion. Thus, the costs are not trivial. Sedimentation caused by agricultural soil erosion is a major source of damage and economic loss throughout the crop producing regions ofthe U.S. Our analysis focuses onlakeside property values in reservoirs and lakes in Ohio which, as a transition state in terms of physical and demographic characteristics, represents conditions found in the Corn Belt, Appalachia and the Northeast regions oftheU.S. Farmland covers approximately 15.4million acres ofland area inOhio, but itisthe 7790offarmland under cultivation that contributes most ofthe sediments that eventually damage waterways, Two studies inparticular have estimated annual agriculturally related off-site soil erosion costs inOhio. In 1983, the Soil andWater Division of the Ohio Department of Natural Resources (ODNR) estimated direct off-site cost ofremoving soil erosion sediment inOhio at$160 million/year. Inaddition, the Ohio Alliance forthe Environment (1988) estimated the annual cost ofremoving sediment from Ohio’s lakes, waterways, harbors, and water treatment plants at $162 million. Both of these studies focused on the accounting costs of a clean up which do not necessarily account for the full social costs oferosion. Macgregor (1988) found that sedimentation at 46 Ohio State park lakes resulted in per-boaterBejranonda, Hitzhusen, and Hile Agricultural Sedimentation Impacts 209

losses to non-resident recreationists ranging from less than $0.01 to $11.95 per ton ofsediment, with

anaverage of$0.49 per ton ofsediment. However, this research did not capture the impact of sediment onlakeside residents’ property values. Based

on anecdotal evidence from state park rangers and others, we hypothesize that residents adjacent to the lakes will suffer welfare losses if sediments render the lakes to be less desirable for boating, fishing, swimming, and any other activities due to shallow depths and/or change in water quality. Scenic views may also be lost due to increases in weeds and algae. These impacts lead toadecline in both property and recreational values for lakeside property owners. The impacts ofsedimentation on recreation and property values have been recognized and addressed by the state ofOhio. For example, the Division of Watercraft of the Ohio Department of Natural Resources allocates almost $2 million annually for dredging lakes; this constitutes almost one-seventh ofthe total budget ofthe Waterways Safety Fund. The Waterways Safety Fund consists of boater registration fees paid every three years (depending on factors such as size and power of the boat) and a one-half of one percent tax on marine gasoline. Thus, costs ofdredging are borne by individuals who use the lake (primarily boaters), but not necessarily by those who live on the lake, Furthermore, upstream farms are the major

source of sediment run-off, but farmers pay for dredging only ifthey boat, and are not required to directly compensate downstream users. The question ofwho should be responsible for compensating downstream users for soil erosion damages continues to be debated. A U.S. Soil Conservation Service study (1990) suggests that there are two ways to reduce accumulation ofsediment inthe lakes. The first method entails dredging the lake to directly remove accumulated sediment, The second procedure istocontrol upstream soil erosion by employing soil conservation practices such asconservation tillage and no-till. The major objective ofthis study istomeasure the social costs of sediment accumulation to lakeside property owners, and toestimate the benefits that are realized from dredging and upstream soil conservation practices. The benefits from the two policies differ inthat dredging directly reduces sediment in specific areas of a lake and thereby increases depth, while changing upstream soil conservation practices reduces the sediment entering the lakes without increasing the depth. We use atwo-stage hedonic price model (HPM) to analyze the welfare impacts ofthese policies on lakeside property values atstate park lakes, includingboth vacation and year round residences, From the empirical model, we estimate the implicit price of a change in environmental quality associated with different sedimentation reduction programs, thus enabling us toestimate the social benefits accruing from agiven policy. The use ofthe HPM inabenefit-cost framework is well established, Since the formalization ofhedonic theory byRosen (1974), anumber ofauthors have used the HPM toestimate implicit prices and demand forhousing characteristics, such asLinneman (198 1), Parsons (1986), and Quigley (1984). An important branch of this literature deals with theproblems ofestimating nonmarginal changes in implicit prices, such as in Bartik (1987, 1988), Brown and Rosen (1982), and Epple (1987). A growing literature has used the HPM to measure welfare changes in implicit markets for environmental quality, for example, Mendelssohn (1984), Nelson (1978), Palmquist (1988), and Palmquist, Roka and Vukina (1997). We use acomplete demand system technique in a way similar to Driscoll, Alwang and Dietz (1994). The advantage ofthis methodology isthat it allows us to recover parameters oftheempirical indirect utility function from which we directly calculate compensating variation measures ofwelfare changes. Our paper is organized as follows. First we estimate the first-stage ofthe hedonic price function from which we obtain implicit prices for characteristics of lakeside properties such as structural, community, and ambient environmental quality. Then, weuse Deaton andMuellbauer’s (1980) Linear Approximate/Almost Ideal Demand System (LA/AIDS) to estimate the second stage demand equations for structural and environmental characteristics of lakeside properties. Finally, the compensating variation (CV), which we derive directly from theLA/AIDS indirect utility function, isused to estimate lakeside residents’ willingness to pay for improved environmental quality at the lakes. The policies we examine include increasing the depth ofthe lakes through adredging program and reducing the rate of sediment inflow entering the lakes by employing upstream soil conservation practices, as well as various combinations of dredging and soil conservation.

Methodology
Sampling and Data

Several criteria were used to select the Ohio State park lakes for this study. First, lakes with aminimum of 100 water surface acres were chosen, and210 October 1999 Agricultural and Resource Economics Review private residences within 4,000 feet of the lake perimeter were included inthe sample frame. 1Second, the lakes are located throughout the state in

areas that have different levels and sources ofsoil erosion that are broadly representative ofnot only Ohio, but other parts ofthe Eastern and Midwestern U.S. Lastly, the lakes have different horsepower (HP) regulations. A total of 15 state park lakes were chosen based on the foregoing criteria, and were categorized into two groups based onHP regulation. Eight ofthe lakes have HP greater than 10, including Buckeye Lake, Caesar Creek, Grand Lake St. Marys, Indian lake, Rocky Fork, Lake White, Mosquito Lake, and Lake Loramie. The second group oflakes that have HP equal toorless than 10 are Harrison Lake, Madison Lake, Lake Logan, Kiser Lake, Guilford Lake, Wolf Run, and Jackson Lake, The 15state parks lakes are divided into two groups (or markets) because we hypothesize that lakeside property rents between these two markets differ depending on property characteristics as well as activities available atthe lakes. For example, people who like to water ski will reside at the lakes that have HP greater than 10, while those who enjoy sailing or fishing will be

more likely to live at the lakes that do not allow higher HP. Once the study sites were determined, acrosssectional data setofassessed values and structural characteristics of2,677 randomly selected lakeside properties were gathered from county auditors’ offices. A total of 2,297 observations were drawn from lakes with HP limits greater than 10, and 380 observations were drawn from lakes with HPlimits less than 10.2The property locations were charted

onarea topographic maps andwe were thus able to addlocational variables, such asdistance ofaproperty to alake and miles tothe nearest metropolitan center. Inaddition, county level demographic characteristics were obtained through U.S. Census data. Data on structural, locational, community and environmental characteristics were used as independent variables inthe first stage estimate ofthe HPM. Structural characteristics include: lot size in sq. ft. (LOT); the size of the house in sq. ft. (DWELL); number of rooms (RM); number of

14,000 feet was chosen as a cutoff since the majority of lakeside residences areclustered within this distance. The lakes areinrural areas, and land beynnd 4,000 feet generally reverts back to agricultural use. 2Because sales data are typically sparse for such vacation homes, assessed values andthechamcteristics wereusedratherthan actual transactions prices. Following aprocedure used inthe urban economics literature, a subset of actual sales data was collected, and a predictive model estimated inorder to adjust forthe differences between assessed prices and actual sales prices (see Bejranonda, 1996, for details). This model was the one actually used in calculation of implicit marginal prices forproperty characteristics. full-baths (FB) and half-baths (HB); age of the building inyears (OLD); dummy variables for airconditioning (AC), heat (H), basement (BS), garage (GAR), fireplace (FP); and dummy variables for structural improvements such aspatio and deck (IMP). Also included is a measure of the nearest distance between a property and lake in feet (DSTL). In addition, community characteristics of the properties and lakes were included such as county population (POP), the distance from property tothe nearest central business district inmiles (CBD), and the unemployment rate in the county where the lake is located (UMEMP). The variables ofmost interest, the environmental characteristics ofthe lakes, were added to the data setand include the average depth3 ofthe lakes infeet (ADEP). ADEP canbe viewed as avariable that partially characterizes the type of lake on which agiven property islocated. We view annual sediment accumulation (STPS), and average annual sediment dredged incubic yards per acre foot per year (DRED) as policy variables; that is, itis through dredging and sediment inflow controls that changes to the status quo levels of the lakes (ADEP) can be impacted. Itshould be noted, however, that the way inwhich these variables impact lake depth differs significantly. Dredging activity impacts specifically chosen areas of a lake, especially lake-access points and channels. On the other hand, physical forces, such as weather and hydrology govern sediment inflow and deposition. The result isthat the ADEP, DRED and STPS are fairly independent since ADEP is aphysical variable, measured on levels for awhole lake, DRED isastrategic variable determined bypublic choice, and STPS isrelated toboth physical characteristics of a watershed and farming practices. Dredging activities will significantly impact small areas ofa lake, and cannot realistically have amajor impact

onthe average depth ofalarge body ofwater, such

as the lakes inthis sample. In addition, STPS and ADEP are probably not very interrelated because the lakes are located in different areas ofthe state where the size, volume, and terrain are very different, resulting in varying degrees of sedimentation inflow andimpact ondepth. Even inthe smallestlakes, the annual sediment inflow is small compared tooverall lake volume and therefore will not significantly affect average depth. The dependent variable consists of the annual

3Larger lakes had multiple variables foraverage depths asmeasured at several locations since there is considerable variation in lake depth depending on factors such as proximity to streams, agricultural fields, etc. 4Measured as apercent ofthe lake volume or sediment inflow.Bejranorrda, Hitzhusen, and Hite Agricultural Sedimentation Impacts 211

rental equivalent ofthe property. To convert total housing value into anannual rental rate, werely on asset value theory which suggests that the price of

ahouse isequal tothe sum ofthe netpresent value ofhousing services over an infinite time horizon, i.e. P = Z;=~W(1 + i)r,Inthe preceding, tis the year, P is the property price (or market value, in this case), Risthe annual rent forhousing services, and i is the discount rate. From the above expression it is evident that the annual rental rate R is given by i *P, orrate ofinterest times total house price orvalue; we adopt an 8% discount rate. The

reason for using arental rate rather than the actual property value is quite simple: all of the policy variables are couched in annual terms, so that the rental equivalent is necessary to calculate appropriate welfare measures. Seetable 1fordescriptive statistics ofthe variables included inthe first stage estimates. First Stage Estimate

The Hedonic Pricing Model (HPM) has gained great popularity for use in measuring the welfare impacts ofchanges inenvironmental quality. Inthe HPM, the implicit prices of property characteristics, including environmental quality areembedded inthe transaction price and/or property rent value. We theorize that property rent should beafunction of the structural, community, and environmental characteristics of the properties. The regression model used is given by
(1) log(RENT) =a. +al log(LOT)

+a2DWELL +as DWELLSQ +a40LD+a5RM+a6FB +a7HB+a8AC+a9H +alOGAR+all BS +a12 IMP
+a13FP+a14log(DSTL)

Table 1. Summary Descriptive Statistics ofVariables Used in Hedonic Model and Estimated Coefficients ofthe First Stage Limited and Unlimited HP Markets

Limited HP Market (HP s 10) Unlimited HP Market (HP > 10)

Coeff. Coeff. Variable Mean Estimate t-value Mean Estimate t-value Dependent Variable

RENT 4,480,74 6,630.94 Regressors Intercept 0.5412 0.718 7.7607*** 53.264 LOT 28,939 0.2087*** 5.587 15,327.35 0.0756*** 6,310 DWELL 930.43 0.0022*** 8.099 1,188.85 0.0008*** 14.567 DWELLSQ –3.lE-7*** –5,684 –9E.8*** -4.65 OLD 32.28 –0.0028” -1.868 35.85 –0.0042*** –12.407 RM 4.58 0.0604*** 3.071 5.09 0.0526*** 7,700 FB 0.92 0.0368 0.536 1.15 0.0982*** 5.109 HB 0.39 –0.0522 -0.667 0.31 0.0003 0.017 AC 0.13 0.0328 0.401 0.21 0.1212*** 5.936 H 0.83 0.0579 0.856 0.83 0.1717*** 8.318 GAR 0.36 0.0026 0.050 0.46 0.1736*** 10.691 BS 0.49 0.3417*** 6.267 0.44 0.0427*** 2.543 IMP 0.76 0.2140*** 3.659 0.65 0.0880*** 4.863 FP 0.37 0.0665 1.262 0.35 0.1377*** 8.182 DSTL 563.12 -0.0909*** –4.781 552.22 -0.1838*** –26.006 POP 5,017.46 0.0003*** 9.004 5,914.86 5.7E-6** 1.984 CBD 8.48 0.1121 0.778 9.20 -0.0190 -0.814 UNEMP 7.84 -0.0211 –0.580 6.72 –0.0373*** -5.397 ADEP 7.93 0.0875*** 6.432 6.35 0.0238*** 4,693 ADEPSQ -0.0007” –1.775 –0.0001 *** -5.194 STPS 0.28 –0.1545*** –3.067 0.13 -O.11O4*** -9,812 DRED 44.30 0.1812*** 5.568 7.52 O.1O85*** 10.076

R* = 0,7831 Adj R’ = 0.7704 R’ = 0,6938 Adj R’ = 0.6910 F-Statistic = 61.542 N = 380 F-Statistic = 245.440 N = 2297
*Significant at0.10 level. **Significant at0.05 level. ***significant at0,01 level.212 October 1999
+a15POP+a16CBD +a17UNEMP +a18ADEP
+alg ADEPSQ
+a20log(STPS)
+azl log(DRED) +&
where the ai’s are estimated coefficients and s is the error term, and log represents the natural logarithm. The model specification is a mixed loglinear model that arises from the observation that the variables that represent price, lot size, distance tolake, net annual sediment inflow, and the annual dredging rate appear to be log-normally distributed. Based onvarious criteria, such asF-tests and explained variation, this specification out performed others, Equation (1) is estimated separately for the limited HP (HP s 10) and unlimited HP (HP > 10) markets utilizing ordinary least squares. The ADEP and ADEPSQ coefficients arehypothesized to be positively and negatively related to price, respectively, i.e., property rents should increase with the lake depth at adecreasing rate; this is so because increased depths not only provide increased possibilities for recreational activities, but may exhibit decreased turbidity as well.5 In addition, ifthe rent variable is exponentiated, then the marginal implicit price (MIP) ofADEP isgiven by dRent/tiADEP = (alg + 2. U19oADEP)

.Rent; this condition suggests that increasing depths will have a positive effect as long as ADEP > –uIs/ 2“a,g. STPS is expected to have anegative sign, meaning that higher rates of sediment inflow entering the lakes result inlower property rents. The coefficient ofDRED isexpected tohave apositive impact onproperty rent, i.e., the greater the annual sediment dredged from the lakes, the higher the property rent. The coefficients (cq’s) derived from equation (1)

are used to calculate the marginal implicit prices foreach horsepower market;6 themarginal implicit prices areultimately used asdependent variables in the second stage estimate ofmarginal willingness to pay.

Second Stage Estimate

The Almost Ideal Demand System (AIDS) we use to estimate demand for property characteristics

5We attempted to include ameasure ofturbidity in tbe regressions, but itwas found tobeinsignificant, perhaps because themeasures were not consistently gathered across the lakes inthe sample. 6Obtained intbe usual manner by taking the first derivative ofEq 1 with respect to each characteristic. Agricultural andResource Economics Review

was introduced by Deaton and Muellbauer (1980), and subsequently used in the hedonic framework by Parsons (1986) and others. The budget share form ofthe model is given by: (2) 

## w,=cl,+Xjyulog Pj + (3,log (Y/P)
where Pis aprice index defined by (3) 10gP = Ci. +Z~ Clklogp~ + 1/2 ZjXk ‘Y~j

logp~logpj.
Yiin equation (2) is total expenditure on property characteristics from the first stage model; in this
case, Yjrepresents the total price ofthe property. The qtare quantities ofagiven housing characteristic ateach property andpirepresents the i~hcharacteristic’s implicit price. The non-linearity of the full AIDS model presents estimation difficulties. Therefore, we use the linear approximation ofthe demand system, which incorporates the Stone Price Index defined as: (4)log P*=2j Wjlogpj j=l,2,.

... n. Then the LA/AIDS model that we use for our estimates is derived from substituting equation (4) into (2), and can be written as: (5) W, = ~j + 2j y~, 10gpj + ~~10g(Y/P*) + vi
where W, isthe budget share spent on the ithgood and equals piqfli, the total expenditure of the household; CYi,

~ti, (3,are the parameters to be estimated; and vi 1sthe disturbance term. In order to use the LA/AIDS to estimate the demands for housing and property characteristics,

we assume the following:
1.

2.

3.

4. Housing markets are segmented by HP regulation and are in equilibrium. The supply ofhousing and property characteristics is exogenous and varies across markets. Household preferences are weakly separable in

a.

b.

c. housing and property characteristics and all other goods; HP regulation and all other housing and property characteristics; and housing and property characteristics included-in the ‘an~ysi_s and characteristics excluded from the analysis. The hedonic price functions are non-linear.
Based on assumptions (3) and (4) and equations (2)-(5), the empirical model for the LA/AIDS is:

()Xfi (6) W,= CL,

+~jyijlog Pj+(3,log
~Bejranonda, Hitzhusen, andHite Agricultural Sedimentation Impacts 213

where Wi,Vu,and piareparameters tobeestimated. In equation (6), the Pj’s represent the empirical MIPs for housing characteristics derived from the non-linear hedonic price functions of the limited andunlimited HPmarkets. The MIPs consist ofthe derivatives ofthe hedonic price function with respect to each property characteristic. The variable X*H is the ‘mythical’ adjusted expenditure based

onthe nine housing and property characteristics in the first stage estimate. X*His calculated for each household inthe sample by summing the property characteristics times marginal implicit price from the first stage regression, That is, X*H = Z?=,

~i~~i~ ~i is the budget share for the i~”housing and property characteristic, where i = 1,2,

....9 represents characteristics oflot size, dwelling size, the number of rooms, the number of full-baths, average depth ofthe lake, the amount ofsediment dredged from the lake, the accessibility tothe lake (orthe inverse distance from property to the lake), and environmental quality (or the inverse rate of sediment inflows), and an index variable ofstructural amenities respectively.7 The definitions ofthe last two variables arediscussed inthe next section. We assume that there aretwo ways that changes inenvironmental quality take place. First, dredging

can be used to increase average depth ofthe lake

over time, and second, the rate at which sedimentation accumulates in a lake can be lowered through upstream or off-site soil management practices that decrease sediment inflows. In addition to investigating impacts ofsingle policies, we address the combined effects of offsite soil management with dredging. We use adirect measure of welfare change ofenvironmental quality by calculating empirical compensating variation (CV). CV iscalculated toevaluate the lakeside property owners’ willingness to pay for increased levels ofenvironmental quality at the lakes that result from increasing average depth of the lakes and/or decreasing the sediment inflow entering the lakes. Equation (7) gives the algebraic representation of CV for the LA/AIDS model as developed by LaFrance (1991). 

# (~ (=:)p~’p’ (7) CV=X$H

- exp ~ 

# )

(logflH

- log P“”) +log P*’

.

Here, PADEPO isthe MIP for the original average depth of a lake, and PADEP1 is the MIP for the

7Amenities included inarecentral AC, central heat, garage, basement, fireplace, and structural improvements. average depth thatresults from dredging projects at thelakes. Asimilar equation holds forthe marginal implicit price for reduced sediment inflows from conservation practices as given by the variable STPS. X$!$!

represents the adjusted total expenditure that aproperty owner spends on lakeside property given the initial lake depth, and log P* is the estimated Stone Price Index under the initial lake depth (Osuperscript) andincreased depth (1 superscript), In this paper, we first calculate predicted implicit prices under the baseline scenario and then recalculate them by varying thepolicy (ADEP and STPS) variables. Thus we are able tocalculate CV inorder to simulate changes in welfare tolakeside residents of adredging project that removes sediment, asoil management program that reduces soil inflows or combinations ofboth. We assume that dredging increases average lake depth by 0.5, 1.5, and 2.0 feet, respectively; the depth values were chosen because ODNR officials suggested that dredging the lakes more than 3feet would cut into theoriginal base ofthe lakes atmany locations. We estimate benefits from soil conservation practices todownstream lakeside residents by assuming that under a corn-soybean-wheat rotation, changing from a conventional to reduced-till soil management system will reduce the rate of sediment inflow by 50% andchanging toano-till practice will reduce sediment inflow by 75%. These estimated changes in sediments inflow are based on discussions with soil scientists at Ohio State University and USDA. These scientists were asked to suggest realistic sediment inflow reductions for the soils and topography inthe areas ofthe 15lakes. Equation (7) is also used to produce estimates using these criteria.

Results

The system of share equations represented by equation 6was estimated using 3SLS. Horsepower regulations are considered tobe anexogenous factor that can be used to identify the two markets in this research; to test this hypothesis, an F-test was performed todetermine whether the characteristics of lakeside properties at low HP lakes were significantly different from those at unlimited HP lakes. The results confirm that the 15 state park lakes can be categorized into two markets, which represent the limited (HP s 10)and unlimited (HP
> 10) HP markets. From table 1,the limited and unlimited HP markets have adjusted-R2’s of0.77 and 0,69, respectively. Most of the variables in both markets are statistically significant and have214 October 1999 Agricultural andResource Economics Review
the hypothesized signs, including the environmental characteristics: lake depth, sedimentation rate, and annual dredging. Note that for the lake depth variable, ADEP, increasing depth has a positive impact on price until depth reaches 62,5 feet for low HPlakes, and 119feet forunlimited HP lakes. The results indicate that fordeeper lakes, sediment impacts are lower, and furthermore, that when dredging of deeper lakes is undertaken, property rents increase with increases in depth. By comparing the marginal implicit price atthe global mean (i.e., calculated atthe mean ofthe full data set), the results in table 2 show that environmental characteristics will have more impact on the limited HP than the unlimited HP market. This isprobably because the lakes within the limited HP market are smaller lakes located in areas with higher rates of sediment inflow than lakes within the unlimited HP market. It is interesting to note that the limited HP lakes are deeper on average than the unlimited HP lakes, but tons dredged in limited HP lakes are nearly six times greater than in unlimited HP lakes. This is aresult ofthe fact that the amount of sediment dredged as apercent of lake volume within the limited HP market is higher than inthe unlimited HP market because of the smaller water area in the limited HP lakes. In both markets, coefficients of the average depth variable confirm that property rents increase at a decreasing rate with increases in average depth of the lakes; however, as expected, the effect is greater in the limited HP lakes. Judging from the coefficients for STPS, sediment inflows have a larger negative impact for limited HP lakes than for unlimited HP lakes, which can once again be explained by the fact that the limited HP lakes are smaller than the unlimited HP lakes. We limit our reporting ofthe results ofthe LA! AIDS estimates ofshare demands intable 3to the equations for environmental characteristics, ADEP, ISTPS (inverse ofsedimentation rate),8 and Table 3. Estimated Share Demand Equations for Environmental Characteristics from LA/AIDS Model—Homogeneity and Symmetry Restrictions Imposed

Characteristic Share Dependent Variables ADEP ISTPS’ DRED Intercept 0.122*** 0.198 0.169*** PLOT 0.012 -lE-5*** 0.0001 PDWELL 0.031*** O.011*** 0.004*** PRM 0.060*** –0,005*** –0.001*** Pm –0.004”” –0.009*** –0.003*** PAMEN 0.001 –0.007*** 0.002*** PDSTL 0.002*** –0,001*** –0.001 *** PADEP -0.122*** 0,011*** 0.008*** PSTPS O.011*** -0.0002 –0.0001*** PDRED 0.008*** -0,0001 –0.008*** Expenditure 0.021*** -0.011 –0.013***

Wrverse ofsedimentation rate. **Significant at0.05 level, ***significant at0.01 level.

DRED. PLOT, PDWELL, PRM, PFB, PAMEN, PDSTL, PADEP, PSTPS, and PDRED represent the estimated marginal implicit prices for lot size, dwelling size, amenities, distance from lake, average lake depth, sedimentation inflow and dredging, respectively. Compensating variation (CV) which isthe lakeside residents’ willingness topay (WTP) for improved environmental characteristics at the lakes iscalculated byusing theformula from equation (7). Table 4 demonstrates that lakeside residents at the limited HP lakes (HP s 10) have higher willingness topay per acre foot ofsediment removed and forreduced tillage induced decreases in the rate of sediment inflow into the lakes than

Table 4. Average Benefit Estimates from Changing Average Lake Depth and Rate of Sediment Inflow per Acre-Feet of Sediment Removed

8[nthe second stage estimate we use inverse sedimentation rate as a positive indicator ofenvironmental quality. Welfare Measure ($)/Acre-feet of Sediment Removed Increasing ADEP ?ZOChange Limited HP (feet) in STPS Market Unlimited HP Market Table 2. Estimated Implicit Marginal Prices for Environmental Characteristics

Implicit Marginal Prices Calculated at Global Mean

Variable Limited HP Unlimited HP AverageDepth $445.11 $178.15 Sediment Inflows -5,855.05 -5,831.160 Dredging 80.85 67.47 0.5 1.5 2.0

0.5 1.0 2.0 0.5 1.5 2.0 50%

75% 50% 50% 50% 75% 75% 75% 5.1865

5,1887 5,1839 85.1197 115,9008 6.7645 5.6025 5.4454 8.7071 6.3440 6.0322 0,1529

0.1532 0.1532 23.2166 31.6747 0.3296 0.2106 0.1956 0.5413 0.2854 0.2531Bejranonda, Hitzhusen, and Hite Agricultural Sedimentation Impacts 215

Table 5. Benefits Per Acre-Foot ofSediment Removed by Changing Average Depth ofLake and Reducing the Rate ofSediment Inflow

Benefit ($)/Acre-Foot ofSediment Removed Increasing ADEP (feet) % Change in STPS

Lake 0.5 1,5 2.0 50% 75%
Guilford L. 3.7008 3.6929 3.6848 125.5111 171.1935 Harrison L. 9.8501 9.9348 9.9644 96.2951 132,1008 Jackson L. 0.5435 0.5346 0.5297 6.2530 8.5645 Kiser L. 5.0200 5.0442 5.0501 206.5446 281.6765 Logan L. 5.5172 5.5303 5.5309 97.1821 132.2841 Madkon L. 14.2017 14.3311 14,3785 96.7835 Wolf Run 131.8663 2.7362 2.5217 2.4132 319.9989 434.9351
Limited HP 5.1856 5.1887 5.1839 85.1197 115.9008 Buckeye L. 0.2461 0.2394 0.2394 5.9322 8.6867 Caesar C. 0.1008 0.0987 0.0977 8,5148 116159 Grand L, St. 0.0510 0.0511 0.0512 11.7051 Indian L. 0,1732 16.2407 0.1737 0.1740 22.2746 L. Loramie 0,5365 31.0991 0.5377 0.5382 220,2547 L. White 1,6340 308.4172 1.6364 1.6373 110,7026 168.6167 Mosquito L. 0.0821 0.0817 0.0815 26.1506 38.0314 Rocky Fork 0.2019 0.2008 0.2002 39.7651 49.8141
Uotimited HP 0.1529 0.1532 0.1532 23,2166 31.6747

those who live attheunlimited HPlakes (HP> 10). This isbecause most ofthe limited HP lakes such

as Lake Logan, Kiser Lake, Guilford Lake, and Wolf Run have not previously been dredged, and thus lakeside residents are willing topay more per
acre foot of sediment removed to obtain au increased depth at these lakes, However, lakeside residents have higher willingness to pay per acrefoot of sediment reduction by reducing the rate of sediment inflow through upstream soil conservation practices than by increasing the average depth ofthe lakes through dredging; this maybe partially due to a reduction in turbidity that accompanies sedimentation reduction. It should be noted here that sediment inflows translate into potentially significantly varying depth impacts on lakes of different sizes. While dredging affects the depth ofall lakes uniformly, sedimentation impacts depend on local soil characteristics aswell aslake volume and sedimentation disposition patterns; this leads to quite different CV estimates for soil conservation

vs. dredging policies. The results show that apolicy combining dredging and upstream soil conservation practices results inhigher benefits tolakeside residents than if only adredging project is employed, and are also higher at the limited HP lakes than the unlimited HP lakes, depending on lake volume. WTP, as measured byCV, increases with increased levels of soil conservation, and although positive, WTP for dredging actually decreases with increased dredging activity, This may be a result of the fact that dredging equipment may intrude on recreational activities. Furthermore, once farmers make afixed investment in equipment that reduces sediment, benefits will continue toaccrue into the future, and the mode ofproviding thebenefit does not involve potentially intrusive on-site activity. Tables 5and 6show thebreakdown ofestimated benefits reported intable 3asexperienced by lakeside residents ateach lake location under dredging and/or upstream soil conservation practices. Among the limited HP lakes, lakeside residents at Madison Lake gain the highest benefits interms of property rent ifdredging or the combination procedure are implemented. Alternatively, lakeside residents at Wolf Run will gain more benefit in terms ofincreases inproperty rent ifonly upstream conservation practices are implemented. Among the unlimited HP lakes, lakeside residents atLake White achieve higher benefit gains under dredging

or the combination project, whereas lakeside residents atLake Loramie enjoy higher benefits when only upstream soil conservation practices are employed. ODNR has engaged in annual dredging that is ongoing currently at Buckeye Lake, Indian Lake, Lake Loramie, Grand Lake St. Marys, and Rocky Fork, even though thebenefits received bylakeside residents at these locations are less than those in the limited HP lakes. This result can be partially explained by a study conducted by Lehman et al,216 October 1999 Agricultural and Resource Economics Review
Table 6. Benefits Per Acre-Foot ofSediment Removed by Combining Changes in Average Depth ofLake and Reducing the Rate ofSediment Inflow

Benefit ($)/Acre-Foot ofSediment Removed

Rate ofSediment Inflow Reduced Rate of Sediment Inflow Reduced by 50% and Increasing ADEP (Ft) by 75% and Increasing ADEP (Ft)

Lake 0.5 1.5 2,0 0.5 1,5 2.0
Guilford L. 4.5173 3.8813 3.7913 5.5703 4.2842 4,1124 Harrison L. 10,8724 9.9434 9.8321 12.6649 10.6753 Jackson L. 0.6716 10.4302 0.5721 0.5555 0.8304 0.6344 0,6053 Kker L. 5.9934 5.2698 5.1779 7.2549 5.7601 5.5717 Logan L, 7.1856 5.9613 5.7997 9.2500 6.7512 6,4261 Madison L. 16.7263 14,7369 14.4934 20.4522 16.2523 15,7192 Wolf Run 8.4652 4,4054 3.8095 14,4935 6.5131 5,4114

Limited HP 6.7645 5.6025 5.4454 8.7071 6.3440 6.0322 Buckeye L. Caesar C. Grand L. St. Indian L. L. Loramie L. White Mosquito L. Rocky Fork
Unlimited HP 0.2487 0.2006 0.0597 0.1963 2.7376 8.4366 0.1196 0.5969
0.3296 0.2368

0.1301 0.0534 0.1754 1.2899 4.0247 0.0936 0.3318 0.2354

0.1204 0.0526 0.1728 1.1079 3.4457 0.0902 0.2978 0.2730

0.3094 0.0707 0.2332 4.4698 16.0764 0.1606 1.0271 0.2465

0.1679 0.0575 0.1891 1.9057 6.9101 0.1081 0.4831
0.2106 0.1956 0.5413 0.2854 0.2432 0.1492 0.0558 0.1837 1.5823 5.6806 0.1014 0.4138
0.2531

(1995) which shows that lobbying ofOhio House representatives by lakeside homeowner’s associations is an important factor in obtaining dredging funds from ODNR. Such organizations donotexist atalllakes, andlarger lakes, like Indian Lake, have politically active lobby groups which have been successful in obtaining dredging funds. One might argue that lakeside residents who gain benefits from dredging should bear some of the dredging costs by paying higher property taxes or levies. The results ofthis study also show that implementing upstream soil conservation practices can generate relatively more benefits to downstream lakeside residents interms ofthe property rent equivalent than dredging.

Summary and Conclusions

The main objective ofthis study is toestimate the economic impact of sedimentation on lakeside residential property values atOhio state park lakes, and provide some economic evidence for an optimal combination of changing upstream soil conservation practices and downstream dredging projects to reduce the sedimentation problem. A Hedonic Price andLA/AIDS model was developed to estimate the impacts of sedimentation on lakeside property values. The important environmental factors that affect property values (interms ofannual rent) are the average depth ofthe lakes, the rate of sediment entering the lakes as a percent of lake volume, and the amount of sediment annually dredged, There are four main conclusions that can be drawn from this empirical study. First, on-site factors, such as the average depth of alake and the amount of sediment dredged annually have positive impacts on lakeside property rent. Lakeside property rents increase atadecreasing rate aslakes become deeper, and increase directly with the amount of sediment dredged, Alternatively, the off-site (upstream) environmental factor, measured by the rate of sediment inflow entering the lakes, has anegative influence on lakeside property rent, meaning higher rates of sediment inflow directly lower the property values. Second, the environmental factors are substitutes for one another. Third, lakeside residents at the limited HP lakes (Harrison Lake, Guilford Lake, Jackson Lake, Kiser Lake, Logan Lake, Madison Lake, and Wolf Run) have more to gain from increases in average lake depth and reductions in sedimentation rates if

a dredging project and/or upstream soil erosion control are proposed than do residents at the unlimited HP lakes (Buckeye Lake, Caesar Creek, Grand Lake StMarys, Indian Lake, Lake Loramie, Lake White, Mosquito Lake, and Rocky Fork). Finally, lakeside residents generally have a higher willingness topay per acre-foot ofsediment reduc-Bejranonda, Hitzhusen, and Hite ,., ,”
,. . . --- Agr[cuaurtu aealmenrauon Impacts LII
tion for upstream soil conservation practices than for lake dredging. This may be inpart due tothree factors: first, the presence of dredging equipment

can be an environmental disamenity in and of itself, second, reduced sediment inflow may be related to reductions inturbidity, and finally, dredging must be repeated continually while upstream conservation practices may have long term implications for reduced sediment inflow. The foregoing suggests that implementing the upstream soil conservation practices will generally provide more economic benefits to downstream lakeside residents in terms of increasing property rent than increasing average depth of the lakes through dredging. Therefore, targeting soil erosion control based on off-site damages to downstream lakeside property values islikely toresult inhigher societal benefits. This has important implications for the downsized CRP within the current Farm and Food Bill. Targeting CRP lands based on avoided downstream sediment impacts will be critical. For example, CRP contracts on 21.2 million acres in the U.S. expired on September 30, 1997 and the U.S.D.A. accepted only 11.7 million ofthose acres into the new program. Another five million new acres were added due to anew set of sign-up criteria where other factors than erosion potential are considered in the decision to retire a parcel of land. It is arguable that such incentive programs may notnecessarily bethebest incentive mechanisms, andtaxes orpenalties rather than subsidies could also be considered tooptimize net social economic benefits. Forexample, society might decide to impose penalties based on downstream damage of soil erosion from upstream soil loss above the T-level and subsidize reductions in upstream soil loss below the T-level. This, ofcourse, shifts property rights from upstream to downstream users. This study focuses on a single category of offsite economic impacts from sedimentation. However, other off-site damages on downstream lakeside residents (such as boater value loss), municipal and industrial users (such as flood control and water treatment costs) should be explicitly incorporated tooptimize thefull social netbenefits from soil conservation. Italso would beuseful tosurvey lakeside renters and owners todetermine the actual extent towhich they aretaking depth and sediment inflow into their decision to purchase or rent a given property. Finally, itmay be possible to utilize benefit transfer methods to generalize these results from Ohio toother states, particularly inthe North Central, North East, and Appalachian regions oftheU.S. Acurrent research project atOhio State University is investigating this potential (Hitzhusen and French, 1998),

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