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 of policies to control agricultural

sedimentation on lakeside property values at 15 Ohio 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

a higher 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 in targeting improvements in the economic efficiency of the Conservation

Reserve Program.

Introduction

Soil erosion from agriculture and the downstream

impacts of sedimentation continue to be 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 to remove the most 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 $3–$ 13 billion with a point estimate of

$6.1 billion, of which $2.2 billion was attributable

to cropland erosion (in 1980 dollars). Loss of recreational value accounted for the largest share of

costs, near] y 33910with boating being the largest

subgroup. Other high impact recreationists or users

Somskaow Bejranonda is an assistmrtprofessor with the Faculty of Economics, Kasetsart University, Bangkok, Thailand. Fred J. Hitzhusen is a

professor with the Department of Agricultural, Environmental and Development Economics at the The Ohio State University, and Diane Hite

is an assistant prufessor at the Mississippi State University Department

of Agricultural Economics. We would like to acknowledge the valuable

assistance of pmfessurs Donald Haurin and Alan Randall at Ohio 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 of total costs included municipal

and industrial ( 14.8Yo), water storage facilities

(1 1.3%) dredging (8.5%), and preservation values

(8.2%). Of all erosion sources, cropland comprises

the largest share at 38%. 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 of the

U.S. Our analysis focuses on lakeside 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 of the U.S. Farmland covers approximately

15.4 million acres of land area in Ohio, but it is the

7790 of farmland under cultivation that contributes

most of the sediments that eventually damage waterways, Two studies in particular have estimated

annual agriculturally related off-site soil erosion

costs in Ohio. In 1983, the Soil and Water Division

of the Ohio Department of Natural Resources

(ODNR) estimated direct off-site cost of removing

soil erosion sediment in Ohio at $160 million/year.

In addition, the Ohio Alliance for the Environment

(1988) estimated the annual cost of removing 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 of erosion.

Macgregor (1988) found that sedimentation at

46 Ohio State park lakes resulted in per-boater
Bejranonda, Hitzhusen, and Hile Agricultural Sedimentation Impacts 209

losses to non-resident recreationists ranging from

less than $0.01 to $11.95 per ton of sediment, with

an average of $0.49 per ton of sediment. However,

this research did not capture the impact of sediment on lakeside 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 to a decline in

both property and recreational values for lakeside

property owners.

The impacts of sedimentation on recreation and

property values have been recognized and addressed by the state of Ohio. 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 of the total budget of the 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 of dredging 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 if they boat, and are not required to

directly compensate downstream users. The question of who 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 of sediment in the lakes. The first method

entails dredging the lake to directly remove accumulated sediment, The second procedure is to control upstream soil erosion by employing soil conservation practices such as conservation tillage and

no-till. The major objective of this study is to measure the social costs of sediment accumulation to

lakeside property owners, and to estimate the benefits that are realized from dredging and upstream

soil conservation practices. The benefits from the

two policies differ in that 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 a two-stage hedonic price model (HPM)

to analyze the welfare impacts of these policies on

lakeside property values at state park lakes, including both 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 to estimate the social benefits accruing from a given policy.

The use of the HPM in a benefit-cost framework

is well established, Since the formalization of hedonic theory by Rosen (1974), a number of authors

have used the HPM to estimate implicit prices and

demand for housing characteristics, such as Linneman (198 1), Parsons (1986), and Quigley (1984).

An important branch of this literature deals with

the problems of estimating 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 a complete demand system technique in a way similar to

Driscoll, Alwang and Dietz (1994). The advantage

of this methodology is that it allows us to recover

parameters of the empirical indirect utility function

from which we directly calculate compensating

variation measures of welfare changes.

Our paper is organized as follows. First we estimate the first-stage of the hedonic price function

from which we obtain implicit prices for characteristics of lakeside properties such as structural,

community, and ambient environmental quality.

Then, we use Deaton and Muellbauer’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 the LA/AIDS indirect utility function, is used

to estimate lakeside residents’ willingness to pay

for improved environmental quality at the lakes.

The policies we examine include increasing the

depth of the lakes through a dredging 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 a minimum of 100 water surface acres were chosen, and
210 October 1999 Agricultural and Resource Economics Review

private residences within 4,000 feet of the lake

perimeter were included in the sample frame. 1Second, the lakes are located throughout the state in

areas that have different levels and sources of soil

erosion that are broadly representative of not only

Ohio, but other parts of the 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 on HP

regulation. Eight of the 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 of lakes that have HP equal to or less

than 10 are Harrison Lake, Madison Lake, Lake

Logan, Kiser Lake, Guilford Lake, Wolf Run, and

Jackson Lake, The 15 state 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 at the 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, a crosssectional data set of assessed values and structural

characteristics of 2,677 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 HP limits

less than 10.2 The property locations were charted

on area topographic maps and we were thus able to

add locational variables, such as distance of a property to a lake and miles to the nearest metropolitan

center. In addition, county level demographic characteristics were obtained through U.S. Census data.

Data on structural, locational, community and

environmental characteristics were used as independent variables in the first stage estimate of the

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 are clustered within this distance. The lakes are in rural areas,

and land beynnd 4,000 feet generally reverts back to agricultural use.

2 Because sales data are typically sparse for such vacation homes,

assessed values and the chamcteristics were used rather than actual transactions prices. Following a procedure used in the urban economics literature, a subset of actual sales data was collected, and a predictive

model estimated in order to adjust for the 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 for property characteristics.

full-baths (FB) and half-baths (HB); age of the

building in years (OLD); dummy variables for airconditioning (AC), heat (H), basement (B S), garage (GAR), fireplace (FP); and dummy variables

for structural improvements such as patio 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 to the nearest central business district in miles

(CBD), and the unemployment rate in the county

where the lake is located (UMEMP).

The variables of most interest, the environmental characteristics of the lakes, were added to the

data set and include the average depth3 of the lakes

in feet (ADEP). ADEP can be viewed as a variable

that partially characterizes the type of lake on

which a given property is located. We view annual

sediment accumulation (STPS), and average annual sediment dredged in cubic yards per acre foot

per year (DRED) as policy variables; that is, it is

through dredging and sediment inflow controls that

changes to the status quo levels of the lakes

(ADEP) can be impacted. It should be noted, however, that the way in which 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 is that the ADEP, DRED and STPS are

fairly independent since ADEP is a physical variable, measured on levels for a whole lake, DRED

is a strategic variable determined by public choice,

and STPS is related to both physical characteristics

of a watershed and farming practices. Dredging

activities will significantly impact small areas of a

lake, and cannot realistically have a major impact

on the average depth of a large body of water, such

as the lakes in this sample. In addition, STPS and

ADEP are probably not very interrelated because

the lakes are located in different areas of the state

where the size, volume, and terrain are very different, resulting in varying degrees of sedimentation inflow and impact on depth. Even in the smallest lakes, the annual sediment inflow is small compared to overall lake volume and therefore will not

significantly affect average depth.

The dependent variable consists of the annual

3 Larger lakes had multiple variables for average depths as measured

at several locations since there is considerable variation in lake depth

depending on factors such as proximity to streams, agricultural fields,

etc.

4 Measured as a percent of the lake volume or sediment inflow.
Bejranorrda, Hitzhusen, and Hite Agricultural Sedimentation Impacts 211

rental equivalent of the property. To convert total

housing value into an annual rental rate, we rely on

asset value theory which suggests that the price of

a house is equal to the sum of the net present value

of housing services over an infinite time horizon,

i.e. P = Z;= ~ W(1 + i)r, In the preceding, t is the

year, P is the property price (or market value, in

this case), R is the annual rent for housing 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, or rate of interest times total house

price or value; we adopt an 8% discount rate. The

reason for using a rental 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. See table 1 for descriptive

statistics of the variables included in the first stage

estimates.

First Stage Estimate

The Hedonic Pricing Model (HPM) has gained

great popularity for use in measuring the welfare

impacts of changes in environmental quality. In the

HPM, the implicit prices of property characteristics, including environmental quality are embedded

in the transaction price and/or property rent value.

We theorize that property rent should be a function

of the structural, community, and environmental

characteristics of the properties. The regression

model used is given by

(1) log(RENT) = a. + al log(LOT)

+ a2 DWELL + as DWELLSQ

+a40LD+a5RM+a6FB

+a7HB+a8AC+a9H

+alOGAR+all BS +a12 IMP

+ a13 FP + a14 log(DSTL)

Table 1. Summary Descriptive Statistics of Variables Used in Hedonic Model and Estimated

Coefficients of the 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 at 0.10 level.

**Significant at 0.05 level.

***significant at 0,01 level.
212 October 1999

+a15POP+a16CBD

+a17 UNEMP + a18 ADEP

+ alg ADEPSQ

+ a20 log(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

to lake, net annual sediment inflow, and the annual

dredging rate appear to be log-normally distributed. Based on various criteria, such as F-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 are hypothesized

to be positively and negatively related to price,

respectively, i.e., property rents should increase

with the lake depth at a decreasing 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, if the rent variable is exponentiated, then the

marginal implicit price (MIP) of ADEP is given by

dRent/tiADEP = (alg + 2. U19 oADEP) . 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 a negative sign,

meaning that higher rates of sediment inflow entering the lakes result in lower property rents. The

coefficient of DRED is expected to have a positive

impact on property 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

for each horsepower market;6 the marginal implicit

prices are ultimately used as dependent variables in

the second stage estimate of marginal willingness

to pay.

Second Stage Estimate

The Almost Ideal Demand System (AIDS) we use

to estimate demand for property characteristics

5 We attempted to include a measure of turbidity in tbe regressions,

but it was found to be insignificant, perhaps because the measures were

not consistently gathered across the lakes in the sample.

6 Obtained in tbe usual manner by taking the first derivative of Eq 1

with respect to each characteristic.

Agricultural and Resource 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 of the model is given by:

(2) w, = cl,+ Xj yulog Pj + (3, log (Y/P)

where P is a price index defined by

(3) 10g P = Ci. + Z~ Clklog p~ + 1/2 ZjXk ‘Y~j

log p~ log pj.

Yi in equation (2) is total expenditure on property

characteristics from the first stage model; in this

case, Yj represents the total price of the property.

The qt are quantities of a given housing characteristic at each property and pi represents the i~hcharacteristic’s implicit price.

The non-linearity of the full AIDS model presents estimation difficulties. Therefore, we use the

linear approximation of the 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~, 10g pj + ~~ 10g (Y/P*) + vi

where W, is the budget share spent on the ith good

and equals piqfli, the total expenditure of the

household; CYi,~ti, (3, are the parameters to be estimated; and vi 1s the 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 of housing 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,+ ~j yij log Pj + (3,log ~
Bejranonda, Hitzhusen, and Hite Agricultural Sedimentation Impacts 213

where Wi,Vu, and pi are parameters to be estimated.

In equation (6), the Pj’s represent the empirical

MIPs for housing characteristics derived from the

non-linear hedonic price functions of the limited

and unlimited HP markets. The MIPs consist of the

derivatives of the hedonic price function with respect to each property characteristic. The variable

X*H is the ‘mythical’ adjusted expenditure based

on the nine housing and property characteristics in

the first stage estimate. X*H is calculated for each

household in the 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 of lot size, dwelling size,

the number of rooms, the number of full-baths,

average depth of the lake, the amount of sediment

dredged from the lake, the accessibility to the lake

(or the inverse distance from property to the lake),

and environmental quality (or the inverse rate of

sediment inflows), and an index variable of structural amenities respectively.7 The definitions of the

last two variables are discussed in the next section.

We assume that there are two ways that changes

in environmental quality take place. First, dredging

can be used to increase average depth of the 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 of single policies, we

address the combined effects of offsite soil management with dredging. We use a direct measure of

welfare change of environmental quality by calculating empirical compensating variation (CV). CV

is calculated to evaluate the lakeside property owners’ willingness to pay for increased levels of environmental 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 ~

)

(log flH - log P“”) + log P*’ .

Here, PADEPO is the MIP for the original average

depth of a lake, and PADEP1 is the MIP for the

7 Amenities included in are central AC, central heat, garage, basement,

fireplace, and structural improvements.

average depth that results from dredging projects at

the lakes. A similar equation holds for the marginal

implicit price for reduced sediment inflows from

conservation practices as given by the variable

STPS. X$!$!represents the adjusted total expenditure

that a property owner spends on lakeside property

given the initial lake depth, and log P* is the estimated Stone Price Index under the initial lake

depth (O superscript) and increased depth (1 superscript),

In this paper, we first calculate predicted implicit prices under the baseline scenario and then

recalculate them by varying the policy (ADEP and

STPS) variables. Thus we are able to calculate CV

in order to simulate changes in welfare to lakeside

residents of a dredging project that removes sediment, a soil management program that reduces soil

inflows or combinations of both. 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 3 feet would cut into

the original base of the lakes at many locations. We

estimate benefits from soil conservation practices

to downstream 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% and changing to a no-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 in the areas of the 15 lakes. Equation (7) is also used to produce estimates using

these criteria.

Results

The system of share equations represented by

equation 6 was estimated using 3SLS. Horsepower

regulations are considered to be an exogenous factor that can be used to identify the two markets in

this research; to test this hypothesis, an F-test was

performed to determine 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 of 0.77

and 0,69, respectively. Most of the variables in

both markets are statistically significant and have
214 October 1999 Agricultural and Resource 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 HP lakes, and 119 feet for unlimited HP lakes.

The results indicate that for deeper 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 at the

global mean (i.e., calculated at the mean of the 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

is probably 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 a result of the fact

that the amount of sediment dredged as a percent

of lake volume within the limited HP market is

higher than in the 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 of the results of the LA!

AIDS estimates of share demands in table 3 to the

equations for environmental characteristics,

ADEP, ISTPS (inverse of sedimentation 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 of sedimentation rate.

**Significant at 0.05 level,

***significant at 0.01 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

is the lakeside residents’ willingness to pay (WTP)

for improved environmental characteristics at the

lakes is calculated by using the formula from equation (7). Table 4 demonstrates that lakeside residents at the limited HP lakes (HP s 10) have

higher willingness to pay per acre foot of sediment

removed and for reduced 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 [n the second stage estimate we use inverse sedimentation rate as a

positive indicator of environmental quality.

Welfare Measure ($)/Acre-feet

of Sediment Removed

Increasing

ADEP ?ZO Change 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

0.2531
Bejranonda, Hitzhusen, and Hite Agricultural Sedimentation Impacts 215

Table 5. Benefits Per Acre-Foot of Sediment Removed by Changing Average Depth of Lake

and Reducing the Rate of Sediment Inflow

Benefit ($)/Acre-Foot of Sediment 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 at the unlimited HP lakes (HP> 10).

This is because most of the 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 to pay 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

of the 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 of all

lakes uniformly, sedimentation impacts depend on

local soil characteristics as well as lake volume and

sedimentation disposition patterns; this leads to

quite different CV estimates for soil conservation

vs. dredging policies.

The results show that a policy combining dredging and upstream soil conservation practices results in higher benefits to lakeside residents than if

only a dredging project is employed, and are also

higher at the limited HP lakes than the unlimited

HP lakes, depending on lake volume. WTP, as

measured by CV, 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 a fixed

investment in equipment that reduces sediment,

benefits will continue to accrue into the future, and

the mode of providing the benefit does not involve

potentially intrusive on-site activity.

Tables 5 and 6 show the breakdown of estimated

benefits reported in table 3 as experienced by lakeside residents at each lake location under dredging

and/or upstream soil conservation practices.

Among the limited HP lakes, lakeside residents at

Madison Lake gain the highest benefits in terms of

property rent if dredging or the combination procedure are implemented. Alternatively, lakeside

residents at Wolf Run will gain more benefit in

terms of increases in property rent if only upstream

conservation practices are implemented. Among

the unlimited HP lakes, lakeside residents at Lake

White achieve higher benefit gains under dredging

or the combination project, whereas lakeside residents at Lake 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 the benefits received by lakeside

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 of Sediment Removed by Combining Changes in Average

Depth of Lake and Reducing the Rate of Sediment Inflow

Benefit ($)/Acre-Foot of Sediment Removed

Rate of Sediment 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 of Ohio House

representatives by lakeside homeowner’s associations is an important factor in obtaining dredging

funds from ODNR. Such organizations do not exist

at all lakes, and larger 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 of this study also show that implementing upstream soil conservation practices can generate relatively more benefits to downstream lakeside residents in terms of the property rent equivalent than dredging.

Summary and Conclusions

The main objective of this study is to estimate the

economic impact of sedimentation on lakeside

residential property values at Ohio 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 and LA/AIDS model was developed to

estimate the impacts of sedimentation on lakeside

property values. The important environmental factors that affect property values (in terms of annual

rent) are the average depth of the 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 a lake and the

amount of sediment dredged annually have positive impacts on lakeside property rent. Lakeside

property rents increase at a decreasing rate as lakes

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 a negative 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 St Marys, Indian Lake, Lake Loramie,

Lake White, Mosquito Lake, and Rocky Fork). Finally, lakeside residents generally have a higher

willingness to pay per acre-foot of sediment reduc-
Bejranonda, Hitzhusen, and Hite ,., ,” ,. . . --- Agr[cuaurtu aealmenrauon Impacts LI I

tion for upstream soil conservation practices than

for lake dredging. This may be in part due to three

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 in turbidity, 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 is likely to result in higher

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

of those acres into the new program. Another five

million new acres were added due to a new 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 not necessarily be the best incentive

mechanisms, and taxes or penalties rather than subsidies could also be considered to optimize net social economic benefits. For example, 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, of course,

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 to optimize the full social net benefits from

soil conservation. It also would be useful to survey

lakeside renters and owners to determine the actual

extent to which they are taking depth and sediment

inflow into their decision to purchase or rent a

given property. Finally, it may be possible to utilize benefit transfer methods to generalize these

results from Ohio to other states, particularly in the

North Central, North East, and Appalachian regions of the U.S. A current research project at Ohio

State University is investigating this potential

(Hitzhusen and French, 1998),

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