In the paper, firstly, we statistically investigate the willingness to pay (WTP) for multifunctionality sustained by improvement project of agriculture and agricultural village. We apply the double bounded version of contingent valuation method (CVM) to estimate the mean of WTP. This version is extended one of dichotomous choice CVM and generally thought to be an effective method relative to original dichotomous choice CVM. However, because, in double bounded version of CVM, respondents are continuously asked the question that you could pay a listed value or not twice, 1st WTP derived from 1st responses and 2nd WTP derived from 2nd responses might not follow same distribution.
So, We estimate the mean of 1st WTP estimated by using 1st responses only and the mean of 2nd WTP estimated by using 2nd responses only. In all survey area, the mean of 1st WTP is larger than that of 2nd WTP.
Secondly, we test the change of responses between 1st bound and 2nd bound by using nonparametric method. We decide to use Gehan's test statistics W to do hypothesis test. This statistics are modified by Mantel and good for testing the sameness of distribution between two samples. This statistics also can be used even in the case that samples are arbitrary left or right censoring.
We now suggest new method applying bootstrap method to nonparametric test and do hypothesis test by this new method. As a result, in 6 survey area, except one survey area, we reject the null hypothesis that the difference of responses between 1st bound and 2nd bound is not significant statistically at 5% significance level. So, we conclude that when estimating WTP by using double bounded version of CVM, change of responses between 1st bound and 2nd bound generates in these area.
Finally, from our results of tests, we think that we have to take the change of responses into account to build new model when using double bounded version of CVM. This is our rested problem.

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The development of a variety of stated preference methods has been an important agenda in the field of environmental valuation during the last decade. It leads to the wide use of those methods for costbenefit analyses. In this article, choice modeling is examined as to whether or not it is reliable for the evaluation of agri-environmental policy measures such as the direct payment and rice terrace partnership program. A choice modeling approach has an advantage in evaluating the multi-attribute environmental goods compared with contingent valuation method. Rice terrace has two different kinds of environmental impacts, i. e., environmental benefits (multifunctionality) and damage. Both environmentally positive and negative impacts should be evaluated simultaneously. Otherwise, the result is considered to be biased. The choice modeling can accommodate a number of attributes. Therefore, it is quite a useful tool to assess the environmental value of multi-attribute goods. Environmental benefits are classified into two categories.
One is the conservation of rural landscapes and ecosystems, and the other is disaster prevention. As for environmental damage, water contamination caused by the excessive application of pesticide and fertilizer is presented.
A mail survey was conducted in July, 2001. Two types of questionnaires were mailed to 400 general households in Kamogawa-city. Samples were randomly chosen from the telephone directory. The difference of those questionnaires appears in an order of attributes and their explanation. Exchanging the attributes between an environmental benefit and damage enables an analysis of an order effect. Their overall response rates were 74.5% and 82.0%, respectively.
This experimental choice technique produced marginal willingness-to-pays for three types of environmental impacts with statistical significance of key variables. Marginal willingness-to-pays for water contamination were at least twice as high as those of two environmental benefits.
An order effect of the questionnaire design was tested using likelihood ratio test and t test. Consequently, an order effect was statistically revealed. This finding suggests the choice modeling is not an ideal tool for environmental valuation. It is also affected by some biases due to respondents inconsistent attitude toward environmental risk.

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The Farmland and Rural Improvement Project (FRIP) system had met socioeconomic needs during the time the Agriculture Basic Law (ABL) was enacted. 440 publications and revisions of project execution guidelines between FY 1960 to 1995 addressed the need to modify the LRIP systems. They were remarkable in number in the early 1970's when the Comprehensive Agricultural Policy began and in the early 1990's when the Uruguay Round Agreement on Agriculturel was accepted.
The FRIP can be evaluated as follows. On the one hand it had been associated as productivity improvement and selective enlargement in agriculture. On the other hand, it also had seen the to preservation and converstor of agricultural resources including farmland and irrigation water in the process of urbanization, as well as to improvements in living conditions in rural areas during the process of farmers and non-farmers living side by side. The FRIP can also senn from vienpoint consist of the Farmland Improvement Project (FIP) mainly to improve agricultural productivity and the Rural Improvement Project (RIP) to improve living conditions as well as to prevent rural areas.
This is very important to view the FRIP history and to analyze the structure and movement of the FRIP budget in ABL times. When we divide the FRIP budget between the LIP budget and the RIP budget, we can find out the several principles of annual FRIP budgets and understand the significance of structural changes as follows.
The FIP budget share in annual public works budgets had decreased in close correlation with percentages of Gross Agricultural Product in GDP. The RIP budget share, however had increased in close correlation with percentages of non-farmers living in rural villages. Consequently there had been little change of the FRIP budget share.
These changes in FRIP budget structure had brought a certain level of FIP investment per hector, with no increase since FY 1980. In addition the RIP investment had played a role in narrowing the gap of the living infrastructure investment per capita between urban and rural areas.

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