The concept of and evidence for benefit capture are explored in a benefit cost analysis of selected water quality and infrastructure improvements in the Muskingum River corridor of South Eastern Ohio. Property tax revenues and CVM bid functions are estimated and implications for benefit capture and further research are developed.Resource /Energy Economics and Policy,

Ayalasomayajula, Radha

Hitzhusen, Frederick J.

Lowder, Sarah K.

English

Research Papers in Economics

 
 
 
 
Benefit Capture: Evidence from a River Corridor Benefit 
Cost Evaluation * 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Authors: 
Fred J. Hitzhusen 
Radha Ayalasomayajula 
Sarah Lowder 
Department of Agricultural, Environmental 
And Development Economics 
The Ohio State University 
2120 Fyffe Road 
Columbus, OH  43210 
 
 
 
 
 
*  Selected paper, American Agricultural Economics Assoc. 
Annual Meeting, Chicago, IL, August 5-8, 2001 
 
Copyright 2001 by Fred J. Hitzhusen, Radha Ayalasomayajula, Sarah Lowder.  All rights 
reserved.  Readers may make verbatim copies of this document for non-commercial purposes by 
any means provided this copyright notice appears on all such copies. Introduction 
  Pearce and others have suggested that environmental economics is concerned with not 
just the measurement but also the capture and internalization of benefits and costs.  Considerable 
research activity over the past 20 years or more has been concerned with non-market valuation 
techniques for measuring the economic benefits including non-use values of various 
environmental goods and services.  Less attention has been given to how some of these benefits 
could be captured in a real world policy context.  This is particularly true of benefits estimated 
with contingent valuation methods or surveys.  After all, this technique was originally known as 
hypothetical valuation and it was and still is argued by some authors that if you ask a 
hypothetical question you will get a hypothetical answer.  There are also tax-revenue 
implications not evident from changes in asset value (e.g., residential property) estimated in 
hedonic price analysis.   
This paper takes a fairly pragmatic approach to benefit capture extending both contingent 
valuation and hedonic pricing methods from a recently completed benefit cost analysis of several 
water quality and infrastructure improvements (e.g., zoning, upgrades to historic locks and dams, 
improved household septic systems, and a bike trail extension) in the Muskingum River corridor 
of SE Ohio.  Bid functions and property tax revenue functions are estimated to provide evidence 
on the potential for benefit capture.  Given that a hedonic pricing function measures implicit 
prices of property attributes, one might conclude that benefit capture can be directly observed in 
the changes in asset (property) value.  This is true for the owner of a property but school and 
other special districts (e.g., fire protection, waste management) and local governments may also 
gain or lose property tax revenue from changes in property values.  The tax revenue functions 
allow these changes to be estimated and reported to local special districts and governments.   Bid functions for the willingness to pay from the contingent valuation survey may also be 
able to identify significant explanatory factors that allow greater capture of CVM benefits 
estimated. The actual results of the foregoing bid functions and property tax revenue functions 
are presented for the two Counties of the Muskingum River corridor in SE Ohio.  The property 
tax function results are also presented for both local school districts and local governments with 
variations and implications discussed.  The CVM bid functions discussion focuses primarily on 
results regarding explanatory variables such as previous use of the corridor by boaters, whether 
or not respondents have ever visited the Muskingum River Museum, income, etc that might 
facilitate greater benefit capture.  Finally, a number of implications for both current policy and 
future research are developed. 
Hedonic Tax Revenue Functions 
The goal of the hedonic pricing research by Ayalasomayajula was to identify the 
structural, neighborhood and environmental factors that affect the variation in assessed-market 
property values in the corridor.  Characteristics that increase property values imply a higher tax 
base and revenue for the region. 
A first stage hedonic technique was applied to determine the factors affecting the 
variation in assessed market property value. Hedonic technique is the basic method used in the 
analysis of environmental amenities or disaminities related to property values, and the theory 
states that the implicit price of each characteristic is embedded in the price of the composite 
good. The use of the HPM technique is based on the assumption that the properties in the river 
corridor are differentiated goods and any marginal changes in the 'focus' or key variables are 
perceived by the residents. Any fluctuation in these variables will be reflected in the values of 
residential properties in the corridor. The impact of improvements in the corridor on the community as a whole will be reflected in fluctuation in the value of the properties as well as 
increase in tax base and annual revenue generated in the community. A simple algebraic 
calculation,  
tax millage*coefficient estimate* number of houses in the area/1000  
was used to estimate the annual increase in annual tax revenue due to the specific corridor 
improvement represented by the coefficient estimate.  Each coefficient represents a marginal 
increase in property value given a specific attribute such as zoning or effective household septic 
system. 
Estimation of Benefits to the Community  
Zoning regulations increase property values and imply higher tax revenues accruing to 
the community. Ideally, the expected increases in tax revenues to the zoned communities should 
be calculated using coefficients for each of these communities. One of the objectives of this 
research was to determine if there was any significant difference regarding the magnitude and/or 
sign of coefficients between results using data from only Morgan County versus using all three 
Counties in the corridor.  Morgan County is very different from the other two counties with 
respect to economic development indices, and is positioned between the two counties 
Muskingum and Washington. It was not possible to ignore Morgan County while using the river 
corridor as a unit for research purposes. On the other hand, we could not ignore the fact that 
Morgan County has different characteristics than the other two counties and hence potentially 
different results. Another objective was to isolate the effects of the focus variables in Morgan 
County. Consequently, the analysis was extended using a sample from only Morgan County, and 
another sample including Muskingum and Washington Counties. Table 1 presents the 
implications of zoning in increased tax revenues for Muskingum and Washington Counties. Table 1. Estimated Property Tax Revenues Increases from Corridor Improvements 
City  Tax Millage ($)  Coefficient 
Estimate 
Number of 
Houses in the 
Area 
Tax Revenue 
Increase ($) 
Zoning 
Zanesville  44.22  269 485 5782.21 
Marietta  43.18  269 464 5389.55 
Septic System 
Zanesville 44.22  67  1002  2975.38 
Marietta 43.18  67  726  2100.36 
 
The total increases in property tax revenue as a result of zoning in the cities of Marietta 
and Zanesville are estimated to be $11,172 annually. Similarly, the increases in tax revenues in 
the two counties as a result of increases in asset value due to rural septic systems are also shown 
in Table 1. The coefficient   of sewer has a negative sign, indicating that it does not contribute to 
increases in tax revenue. The interpretation of a negative coefficient is that it contributes to a 
decline in the asset value, and hence will result in a decline in tax revenue. However, the 
coefficient was not statistically significant even at 20 percent level of confidence, hence we did 
not include it in our analyses. 
Besides the increase in local government revenues generated due to increase in asset 
value, the asset increase also contributes to the tax revenues received by the school districts. 
Table 2 presents the increases in revenues from tax to the school districts. There are 6 school 
districts each in Muskingum and Washington Counties, and each school district levies a different 
rate of tax on the residents of the district. The millage rate for each county was weighted by the 
total value of the taxes and an average millage rate for each county was calculated. A total increase of $18,240 annually was estimated to accrue to the school districts as a result of the 
particular corridor improvements. 
Table 2. Estimated School District Tax Revenues Generated by Zoning and Septic System  
County  Tax Millage ($)  Coefficient 
Estimate  
Number of 
Houses in the 
Area 
Increase in Tax 
Revenue ($) 
Zoning 
Muskingum 24.61  269  1487  9844.07 
Washington 26.23  269  1190  8396.49 
Septic 
Muskingum 24.61  67  1002  1652.6 
Washington 26.23  67  726  1275.70 
 
Bid Functions and Benefit Capture 
Our Contingent Valuation survey of the general adult population of Ohio was designed 
based on the standard reference: Using Surveys to Value Public Goods: the Contingent Valuation Method 
by Carson and Mitchell.  In order to identify the characteristics of people who are willing to pay 
for the locks and dams, bike trail and/or septic program, we utilize bid functions.  A bid function 
explains willingness to pay (WTP) as a function of various characteristics of the respondent.  
Community leaders and policy makers could use this information to identify what constituency 
they should be targeting in legislative, referendum or fund raising efforts.   By soliciting only 
those people who are likely to vote favorably or contribute, transaction costs could also be 
reduced. 
Locks and Dams 
Three different equations will be presented to explain the probability that WTP for the 
maintenance, repair and operation of the Locks and Dams is positive (prob(WTPLDYES ≥ $10)).  Equation 1 modeled prob(WTPLDYES ≥ $10) as a function of (INC/HS)
-1 (the inverse of 
household income divided by household size), NOTIMPORTANT (a dummy variable for which 
a 1 indicates the respondent indicated that the locks and dams are not important), BOATED (a 
dummy variable for which a 1 indicates the respondent has previously boated along the 
Muskingum), LDOTHER (a dummy variable for which a 1 indicates the respondent has 
previously used a lock in a river other than the Muskingum) and MUSEUM (a dummy variable 
for which a 1 indicates that the respondent had previously visited the Ohio River Museum).  The 
results are shown in Table 3
1. 
Table 3: Results of Probit Estimation of WTPLDYES: Equation 1 
Variable Coefficient P  Value 
(INC/HS)
-1 -4785.830  0.0133 
NOTIMPORTANT -1.234  0.1421 
LDOTHER 0.594  0.3113 
MUSEUM 0.151  0.7101 
BOATED 0.858  0.2337 
 
The insignificance of the coefficients on LDOTHER, BOATED, and MUSEUM result from the 
intercorrelation of the three variables combined with the small size of the data set (87 
observations).  We therefore estimated three more simplistic equations reported in Table 4 
(Equation 2, 3 and 4) each of which incorporates either LDOTHER, BOATED, or MUSEUM as 
an independent variable
2.
3 
                                                 
1 These coefficients cannot be interpreted as marginal effects.  They are however of the same sign as the marginal 
effects.  If a coefficient is insignificant then its corresponding marginal effect is insignificant as well; a significant 
coefficient may have a corresponding marginal that is significant or that is insignificant. 
 
2 Equation 2 estimates prob(WTPLDYES ≥ $10) as a function of (INC/HS)
-1, LDNOTIMPORTANT, and 
LDOTHER.  Equation 3 estimates prob (WTPLDYES ≥ $10) as a function of (INC/HS)
-1, NOTIMPORTANT, and 
MUSEUM.  Equation 4 estimates prob(WTPLDYES ≥ $10) as a function of (INC/HS)
-1, LDNOTIMPORTANT, 
and BOATED.  The results are as shown in Table 7. 
 Table 4: Results of Probit Estimation for WTPLDYES: Equations 2, 3 and 4 
  Equation 2  Equation 3  Equation 4 
Variable Coefficient
2  P-value Coefficient P-value Coefficient P-value 
(INC/HS)
-1  -4370  0.015  -5100 0.0085 -4400  0.014 
NOTIMPORTANT  -1.31 0.038 -1.17 0.035 -1.06 0.054 
LDOTHER 0.9  0.075  NA NA NA NA 
MUSEUM  NA NA  0.57 0.071  NA NA 
BOATED  NA NA NA NA  1.118 0.046 
 
All of the coefficients are of expected sign and are significant to at least the 90% level.  The 
coefficient on (INC/HS)
-1 is negative for all three equations indicating that as income increases 
the probability of a positive WTP increases.  A person who believes the locks and dams are not 
important is less likely to report a positive WTP.  Equation 2 shows that respondents who have 
used locks and dams in a river other than the Muskingum are more likely to report positive WTP.  
Equation 3 indicates that respondents who have previously visited the Ohio River Museum are 
more likely to have a positive WTP.  Respondents who have boated in the Muskingum are more 
likely to have a positive willingness to pay. 
Septic 
The probability of observing a positive WTP for the proposed septic program 
prob(WTPSEPTICYES  ≥ $10) was modeled as a function of (INC/HS)
-1, FISH (a dummy 
variable that equals 1 if the respondent had previously fished in the Muskingum), MUSEUM, 
BTOTHER (a dummy variable that equals 1 if the respondent noted that poor water quality in a 
river other than the Muskingum prevented his use of it for recreational purposes).  The results are 
shown in Table 5. 
 
 Table 5: Results of Probit Estimation of WTPSEPTICYES: Equation 5 
Variable Coefficient P  Value 
(INC/HS)
-1  -10720 0.000 
FISH 2.384  0.001 
MUSEUM -1.090  0.044 
BTOTHER 0.640  0.061 
 
The income variable is of the expected sign.  Respondents who had previously fished in the 
Muskingum are more likely to report positive WTP.  Previous visitors to the museum are less 
likely to report positive WTP for septic.  Users of the bike trails that run along a river other than 
the Muskingum are more likely to have positive WTP for the septic program.  All of the 
coefficients are significant to the 95% level with the exception of BTOTHER which is only 
significant to the 90% level. 
Bike Trail 
The probability of WTP for the bike trail extension being positive (prob(WTPBTYES ≥ 
$10)) was explained as a function of (INC/HS)
-1, MRBIKETRAIL (a dummy variable for which 
a 1 indicates the respondent has previously used the bike trail that runs along part of the 
Muskingum), MUSEUM, MALE (a dummy variable for which a 1 indicates the respondent is 
male), ORBIKETRAIL (a dummy variable for which a 1 indicates the respondent has previously 
used a bike trail that runs along an Ohio river other than the Muskingum).  The results are shown 
in Table 6. 
 
 
 Table 6: Results of Probit Estimation of WTPBTYES: Equation 6 
Variable Coefficient P  Value 
(INC/HS)
-1 -5790  0.026 
MRBIKETRAIL 1.420  0.077 
MUSEUM -0.608  0.213 
MALE -0.799  0.016 
BTOTHER 0.793  0.034 
 
The probability of observing positive WTP for the bike trail increases as income increases; this 
effect is significant at the 95% level.  Respondents who have previously used the bike trail in 
question have a higher likelihood of reporting positive WTP; this coefficient is significant to the 
90% level.  Respondents who previously visited the museum are less likely to report positive 
WTP for the bike trail extension; this result is only significant at the 78% level.  Male 
respondents are less likely to report positive WTP; this is significant at the 98% level.   
Respondents who have used a bike trail that runs alongside another river are more likely to report 
positive WTP; this coefficient is significant at the 95% level. 
Summary and Conclusions 
The hedonic based tax revenue functions showed increases in tax revenue from zoning accruing 
to the cities of Zanesville and Marietta to be about two times larger than the increases from 
improved household rural septic systems in Muskingum and Washington counties.  The hedonic 
based tax revenue functions for school districts in Muskingum and Washington counties showed 
six fold differences between zoning and improved septic system impacts.  However, the annual 
revenue increases from zoning and household septic system improvements are generally larger to 
the school districts than to the local governments in Muskingum and Washington counties. 
 The CV instrument was not originally designed with bid function estimation in mind, and 
a limited number of observations were available.  Despite these limitations there are some 
general conclusions that can be drawn regarding what groups of people are more likely to have 
positive WTP for the three amenities. 
  Previous boaters of the Muskingum River and Museum visitors are likely to have higher 
WTP for the locks.  These two groups can be easily solicited using address lists of museum 
visitors and lock users.  Museum visitors are, however, less likely than non-museum visitors to 
have positive WTP for the bike trail or the septic program.  This would suggest that the museum 
visitors should only be solicited for the locks and not for bike trail or the septic program.  Users 
of bike trails that are adjacent to rivers other than the Muskingum are more likely to have 
positive WTP for both the bike trail and the septic program; this group of people could be 
targeted for both programs. 
 
  
References 
 
 
Ayalasomayajula, Radha “ An Economic Assessment of Property Value Variations Along the 
Muskingum River”. Unpublished Master’s Thesis, The Ohio State University, 2000. 
Carson and Mitchell, Using Surveys to Value Public Goods: the Contingent Valuation Method.  
Washington, D.C., Resources for the Future, 1989. 
 

BENEFIT CAPTURE: EVIDENCE FROM A RIVER CORRIDOR BENEFIT COST EVALUATION

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