Yoji Kunimitsu. National Institute for Rural Engineering , Kannondai, Tsukuba City, Ibaragi Pref., , Japan

Transcription

1 Have the Agricultural Public Investments Improve Rice Prouctivity through Farmlan Usage Reallocation?: An Empirical Stuy on Japanese Pay-fiel Rental Transactions Yoji Kunimitsu National Institute for Rural Engineering 2-1-6, Kannonai, Tsukuba City, Ibaragi Pref., , Japan Tel: Selecte Paper prepare for presentation at the Southern Agricultural Economics Association Annual Tulsa, Oklahoma, February 18, 2004 Copyright 2004 by Y. Kunimitsu. All rights reserve. Reaers may make verbatim copies of this ocument for non-commercial purposes by any means, provie that this copyright notice appears on all such copies. 1

2 Abstract This paper aims to analyze the effects of pay-fiel consoliation projects by using the stochastic moel. Empirical results showe that the egree of effects, realize as a rise in rental rate an rental-area, vary in each region an that several factors influencing the project effects can be evaluate quantitatively. Key wors: Pay-fiel Consoliation Projects, Rental Rate, Rental Area, Stochastic Choice Moel, Supply an Deman Function for Renting JEL Classification coes: C25, D44, Q12, Q15, Q38, R58 2

3 Introuction Improving rice prouctivity is a critical issue in Japanese rice prouction because the average management scale of each farmer is about 1 hectare (ha) an still little progress has been mae in this improvement for many years. To overcome this situation, focus on agricultural policy is now stimulating Pay-fiel Rental Transactions (PRTs) along with public investment as seen by Pay-fiel Consoliation (PC) Projects (Fig. 1). In Japan, more than 15% of the agricultural buget has been spent on PC projects. Consequently, 60% of the total pay-fiels have been consoliate from small pay-fiels of irregular-shape to efficient fiels enowe with stanarize large parcels of farmlan (over 0.3 ha), irrigation an rainage canals an brunch roas. Ieally, the effects of PC projects are realize in two aspects. The first effect is improvement of agricultural prouctivity by moernization of agriculture with large agricultural machinery, high flexibility of water management an betterment of less fertile soil. In other wors, this effect is reveale by a high rental rate as a shaow price of farmlan through the process of increasing farmlan quality. The secon effect is realize as the scale merit in rice prouction by accelerating intensive farmlan use by efficient large scale farmers. Since the PC projects break ol farmlan ownership an establish new ownership or usage rights, implementation of the projects stimulate PRTs even though many pay-fiels belong to small-scale farmers. To substantiate these two effects, researchers nee to emonstrate not only an ascent in the rental rate relate to the improvement of prouctivity, but also an increase in the scale of management by efficient farmers through PRTs. Previous stuies suggeste ifficulty in farmlan reallocation among farmers by the aggregate prouction function approach proviing low elasticity of farmlan with regar to the rental rate (Goo, 1993 an Ito, 1993). Heonic price analyses trie to fin the causative factors on farmlan values by consiering of soil 3

4 characteristics (Ela, et al., 1994), urbanization (Plantinga an Miller, 2001) an site characteristics (Xu an Barkley, 1993 an Boisvert, et al., 1997). Those stuies coul not analyze the PRTs which consist of mutual ealings of farmers, so the response moel of iniviual farmers estimate from micro-ata shoul be use. However, estimating the prouction function or heonic price function from microata might have not succeee ue to the affects of the ifferences of iniviual farmers that were harly measure as tangible variables. It is also impossible to specify why farmlan is so inelastic in agricultural prouction by those methos, though those methos provie interesting fact finings about the farmlan situations. Aitionally, analyzing market situations with mixture ata of SD, which is common in those methos, causes the ientification problem especially regaring to the restricte market like Japanese farmlan situations that the local governments have controlle the rental rates in orer to protect the farmlan possession right of farmers. A lack of ata is also recognize in the statistics on farmlan situation affecte by PC projects an this becomes a bottleneck for empirical stuies. Even the Survey of Rice Prouction Cost (Ministry of Agriculture, Forestry an Fishery; MAFF), which was use in many of the previous analyses, inclues no information on consoliation fiel areas. The purpose of this stuy is to evaluate the project effects by moeling both supply an eman (S-D) sies of farmlan renting from micro-ata. To overcome low variability of the actual rental rate ata, the contingent valuation (CV) metho is employe to estimate the S-D functions in PRTs an ieal equilibriums of S-D are iscusse by simulating the effects of PC projects (Kunimitsu, 2003) regaring to policy issues in Japan. < Fig. 1. A pay-fiel consoliation project. > 4

5 Survey Design an Data Sources To obtain variable farmer reactions to the ifferent rental rate, the CV questionnaire survey was esigne for iniviual farmers whose pay-fiels were consoliate by PC projects two years before the survey. The question aske to the supply sie farmers was as follows. If you ha a chance to rent one parcel of pay fiel (A) to another farmer, woul you accept the rental rate B s yen/ha/year? Deman sie farmers were aske the following question. If you ha a chance to hire one parcel of pay fiel (A) from another farmer, woul you pay the rental rate B 1 yen/ha/year? Assume that you coul use other agricultural machinery in aition to your own an employ help to cultivate the fiel, if neee. Also, assume that the obligation rate of the set asie program is equal to the average rate for your town. Regaring Japanese rice prouction, eman sie farmers are far less than supply sie farmers although their rental areas from other farmers are larger an their nees of renting are greater than supply sie. Hence, the estimation error of the eman function will become larger ue to a smaller amount of ata. To improve statistical efficiency, a ouble boune question was employe (Hanemann et. al., 1991). A secon question that epene on the response to the first question was as follows. If you accept the above situation, are you going to pay higher rental rate ( yen/ha/year) for the rental fiel? or, If you reject the above situation, can you pay less rental rate ( B 2 yen/ha/year) for the rental fiel? L In these questions, one parcel of pay-fiel for rent (A) was assume to be 0.3 ha for Consoliate Fiels (CF) an 0.1 ha for Non-consoliate Fiels (NF). These areas are common in Japanese pay-fiels. A simple yes-no responses to above questions were aske to S-D farmers to B 2 H 5

6 realize the actual PRTs. Five rental rates for B were prepare, i.e. 50, 100, 200, 400, an 700 thousan yen/ha/year, an each value was propose to each group of farmers that were classifie ranomly as the same number. Questions for both the S-D sies were aske about both NF, wherein a pay-fiel was uner ba conitions before the PC project, an CF, wherein a pay fiel was uner goo conitions after the project. Thus, four kins of ata were collecte, i.e. (supply or eman sie) (NF or CF). The cross-sectional ata were compose from the survey of farmers conucte by Japan Institute of Irrigation an Drainage (JIID) with the assistance of MAFF in December 1999 (JIID, 2000). A total of 118 research sites were selecte from throughout Japan except for Hokkaio an Okinawa where the management style an rice varieties are ifferent from those of the other regions. The average project area was over 100 ha of pay-fiels, an most of the project sites were locate in the flat plain areas. The questionnaires were istribute to farmers who owne pay-fiels in the project sites. Average management scale of the farmers correspone to the average figure for mainlan Japan accoring to the Agricultural Census (MAFF). The results of the survey are shown in Table 2. <Table 1 Questionnaire Results> Empirical moel The empirical moel employe here consists of i) the S-D functions that show ecision making of farmers for renting base on iniviual ifferences of prouction conitions an ii) the simulation of the S-D equilibrium as ieal rental agreements. Stochastic functions were use in previous agricultural prouction analyses in orer to introuce iniviual ifferences of farmers into an equation (Kumbhakar, 1994 an Chambers an Quiggin, 2002). These analyses neee a convergence process, which sometimes faile, in the estimation of function to ientify those ifferences 6

7 that are not obtaine in actual ata. In this stuy, the S-D function for renting was irectly estimate to secure the estimation of farmer responses to PRTs after eriving those functions from the stochastic prouction function an by moifying them to the stochastic choice type moel. Each farmer is assume to prouce rice (Q) accoring to the Cobb-Douglas b c prouction function, such that Q = aa V E exp(u), ( b + c < 1), with pre-fixe farmlan (A), other input factors (V) an social an geographical influences (E). Here, bol character shows the vector, an a, b, c an are parameters which relate to the rice prouction structure. Variable u is the stochastic element which represents technological gaps among farmers, relating to ifferences in skills, knowlege an experiences of iniviual farmers, inherite farmlan quality an amount of information from consumers. The existence of the technological gaps makes farmers ecie ifferently even if they have the same management conitions. Given that farmers try to maximize the profit R = PQ P V V uner the technical constraints of the prouction function, the first orer conition with regar to V is = P ( Q / V) b c 1 = PacA V E exp( u), where P V is the price of V an P is the price of rice. Since A is restricte for farmers, the optimum rental rate PA(WTP) is ifferent among farmers accoring to their management conitions. The optimum rental rate PA(WTP), e.g. willingly pai by iniviual farmers, is assume to be ecie by the marginal prouctivity of farmlan as P A(WTP) = P( Q / A), that is, ln( P A ) = a' + b'ln( A) + c'ln( ) + 'ln( E) + eln( P) + = f ( X) + (1). P V P V 1 c 1 b c Here, a' = ln( a) + ln( b) + ln( c), b' =, 1 c 1 c 1 c c c' =, ' =, 1 c 1 c 1 e =, 1 c u ε = 1 c. Consequently, the rental rate can be ecompose into two parts: a systematic element which is a linear inex of the variable matrix X, an a stochastic element which represents intangible influences on the rental rate 7

8 but is ifferent from the observation error. When a farmer cultivates farmlan A 0 initially an rents one parcel of aitional farmlan A, the farmlan area after renting is A = A 0 ± A (A is negative for the supply sie an positive for the eman sie). Since in Eq. (1) is not error term, this function is harly estimate with market ata because of the lack of ata variability an the S-D separability. To specify this equation from the CV questionnaire ata, Eq. (1) shoul be moifie to stochastic choice type function. It is reasonable to presume that the supply sie s farmer woul agree with the rental rate ( B ) propose in the questionnaire, if the propose value was higher than s P A WTP) ( in Eq. (1). Given that the istribution of technological gaps shown by is i.i.. with zero means, the probabilities of acceptance are efine by using the cumulative ensity function G as follows: Probability of supply sie acceptance, s ; s s ln( B Pr( B PA ( WTP ) ) = Pr ) f ( X s σ > s s s s ) ε s s s s > = G[ γ ln( B ) X β ] (2). σ Here an subsequently, the superscripts s an show the S-D sie, respectively. A symbol shows the stanar eviation of, an an β are parameters. The eman sie farmers woul accept the propose rental rate ( B for first bit, B 2H an B 2L for secon bit), if the rate is lower than P A WTP) ( in Eq. (1). The eman function can be efine as a stochastic type in the same way as the supply sie as follows. Probability of yes in both answers, yy ; ln( B2H ) f ( X ) Pr( B2H PA ( WTP) ) = Pr = 1 G[ ln( B γ 2 σ σ Probability of no followe by yes, yn ; A WTP H H Probability of yes followe by no, ny ; 8 H ) X β Pr( B1 P ( ) B2 ) = G[ γ ln( B2 ) X β ] G[ γ ln( B1 ) X β ] ; )];

9 Pr( B2 P ( ) B1 ) = G[ γ ln( B1 ) X β ] G[ γ ln( B2 L A WTP Probability of no in both answers, nn ; ) X β Pr( P ( ) B2 ) = G[ γ ln( B2 ) X β ] (3). A WTP L L Parameters can be estimate by the maximum likelihoo estimation metho with the log sum of the likelihoo compose by response moels as above. Expecte signs of coefficients are as follows. The rational farmers lea the acceptance s s probability to π / B > 0 an π / B < 0. s s π / P = { π / G( )} { G( ) / P} is less than 0, because the first ifferential on right han of the equation is positive. The secon ifferential is negative ue to the negative sign in the function G( ) an e>0 in Eq. (1). Also, π / P > 0 ue to π / G( ) < 0 in Eq. (3). Similarly, the s negative sign of c brings about π / P > 0 an π / P < 0. The signs of π / A V in both the S-D sies cannot be etermine in avance, because these signs are relate not only to the parameter b of iminishing returns but also the total factor prouctivity a. If the total factor prouctivity changes in proportion to A, the affect of iminishing returns may be overwhelme an the sign of π / A may become the same as P A / A, but the signs of π / A may be opposite of π / PV. Equation (2) an (3) are the survival functions regaring to the propose rental rates for one farm parcel of A (ha), so the acceptance probability correspons to the number rate of farmers who accept the propose rental rate in PRTs. Since all parcels of farmlan are assume to be the same area an a stanar shape, acceptance probability woul correspon to the number rate of farmlan parcels, consequently area rate of rente farmlan. The ieal equilibrium of the S-D sies is efine at the intersection of the S-D functions ). At this point, the equilibrium rental rate ( * B ) an rental area ( N A L V ] ; * ) are ecie as, N * * s A = Pr( B > P ( ) ) N A = Pr( B * ( ) ) n N A Here, A WTP P A WTP * N shows the number of rente parcels of farmlan within a project site. N s an N are the total numbers of farmlan parcel participate in biing for the S-D sies, 9

10 respectively. In etail, transactions at one project site are assume to be ivie into n parts of biing, an large-scale farmers can participate in every biing, but small scale farmers can participate only once in one of the biing opportunities ue to the small farmlan area for biing. In practice, n is assume to correspon to the ratio (N s / N ) ), then, N / N s * s s * = G[ γ ln( B ) - X β ] = 1 G[ γ ln( B ) - X β ] (4). * s Pr( B * > P A ( WTP) ) an Pr( B * P A ( WTP) ) are substitute by stochastic function using the average value of explanatory variables X, supposing that all relate farmers in each sies woul be istribute consistently. Estimation Results of S-D Functions Table 2 shows the caniates for the explanatory variables of Eqs. (2) an (3). The prices of agricultural machinery, fertilizers, an pesticies were not inclue as caniates, because these prices correspon to nationwie market prices an have little variability among farmers (unifie by the constant of the equation). Tables 3 an 4 show estimation results of the S-D functions in both the NF an CF, respectively. Insignificant variables were exclue as compare to t-statistic at 15% level. Coefficients of the propose rental rate in both tables are significant as compare to t-statistic an show the correct signs base on the theoretical framework. Comparing the coefficients in NF an CF shows that the CF have greater value in both the S-D sies. Clearly, both S-D farmers reacte to the rental rates more sharply after the PC projects. These changes are shown more concretely by the rental rate elasticity to acceptance probability at the inifferent points where acceptance probabilities correspon to 0.5. The values of elasticity in the NF were 0.34 (supply) an (eman); those values in the CF were 0.44 (supply) an (eman). Thus, both elasticity values in the CF were higher than in the NF, but all values were less than 1.0 inicating inelasticity. An inelastic structure in 10

11 farmlan erive eman was also shown by previous stuies that estimate the trans-log cost function of aggregate agricultural prouction ). As for the estimate coefficients of rice price an wages, the positive coefficient shifts both S-D functions towar the right in the price-quantity graph, showing lower an higher affects on acceptance probabilities on the S-D sies, respectively. Thus, the higher price in rice an lower wages ten to encourage both S-D farmers to easily accept a high rental rate in spite of ifferent signs in coefficients of the estimate equations. The farmlan area A has a negative affect on the supply an a positive affect on eman. As iscusse in the former section, the coefficient of this variable can take positive an negative signs, but shoul take opposite signs between S-D. Therefore, the estimation results o not contraict the theoretical framework. Estimate coefficients of geographical classification show that farmers on the supply sie in urban areas tene to rent their pay-fiels at a high rental rate. This is because the rental rate was raise by strong intention for the lan use conversion of farmers (Shogenji; 1998). However, the situation was the reverse in LFAs, showing the low rental rate an easy renting from other farmers. <Table 2 Caniates of Variables for Estimation in Eqs. (3) an (4)> <Table 3. Estimation of the Supply Functions.> <Table 4. Estimation of the Deman Functions.> Simulation Figure 2 shows the S-D curves erive from Eq. (2) an (3) in the NF (S0, D0) an in the CF (S1, D1). Points A an B show the ieal equilibrium of PRTs for the NF an CF, respectively. Comparing point A to B inicates that the rental rate increase by four times an rental area increase by 40% because of the PC projects. Point C shows the supply-sie effect in which the eman function was stable while 11

12 the supply function shifte after the projects. Comparing point C to A emonstrates that the rental rate increase by 50% an rental area ecrease by 27% because of the projects. This is because farmers restarte cultivating their own pay-fiels with increase incentive when conitions of their pay-fiels were bettere by the projects (JIID, 2000). Furthermore, the buren for the PC project costs mae it impossible to rent their pay-fiels to others with low rental rate (Tanaa, 1993). On the contrary, comparing point A to D showing the eman effect only suggests that both the rental rate an rental area increase by 190% an 70%, respectively. This is because large-scale farmers coul attain efficient prouction with a high rental rate an larger rental area after the projects. Consequently, the rental rate highly increase with synergism of S-D effects an the rental area was improve moerately because of the PC projects with greater increase in the eman effect than ecrease in the supply effect, in spite of S-D effects offsetting each other. Table 6, calculate form the mean value of the explanatory variables in each region, reveals that the simulate rental rates correspon to the actual values an the orers of simulate values an actual values are almost the same. Most of the simulate rental areas are almost the same as the actual values, inicating consierable applicability of this moel, except for the Tokai region ). It is clear that rental rates increase rastically in all regions after the projects, especially in Tohoku, Kanto an Hokuriku, but, rental areas increase moerately in all regions. The increases in the rental areas of Tokai, Kinki, Chyu-Sikoku an Hokuriku were remarkable as compare to other regions. Therefore, the eastern part of Japan incluing Tohoku, Kanto an Hokuriku tens to attain high project effects that appear in the rental rate rather than the rental area, but the western part of Japan tens to attain high project effects in the rental area. Table 6 also shows that the simulate rental rates of all regions were lower than the actual values, especially in NF. Five out of seven regions attaine a higher 12

13 rental area by the simulation that shows ieal transactions. A transaction cost in the rental market may be cause by miss-ajustment between farmers an regulation by the government. However, from the gaps between the simulate values an actual values, the transaction cost seems to not be serious in the CF but serious in the NF. Therefore, it can be conclue that the PC projects reuce the transaction cost in the rental market. Table 7 shows the influences of low rice price, high wages an geographical situation on the project effects. The low price of rice an high wages brought about low rental rates an low rental areas in both NF an CF, but the ecreases in these inexes were remarkable in the CF. As a result, the effects of the PC projects shown by the ifferences in rental rate become lower than the whole country (status quo) in comparison of average situations, especially in the case of ecrease in rice price. As for geographical situations, the project effects on the rental rate in SUAs were higher, but the project effects on the rental area in SUAs were lower. Conversely, the effects in LFAs appeare through the rise in the rental area rather than the rental rate. <Figure 2. Changes in supply an eman by the PC projects in PRTs. > <Table 5. Regional effects of the PC projects (at equilibrium).> <Table 6. Influence of outsie factors on effects of PC projects.> Conclusions an Future Subjects Applying the stochastic choice moel to the regulate market incluing PRTs is useful for analyzing the capitalization mechanisms of PC projects an for evaluating causative factors. One of the remarkable results is that the PC project effects appears as a marke increase in the rental rate an a moerate increase in the rental area, in spite of the negative effect at supply sie overwhelme by the positive effect of the eman sie 13

14 farmers. In aition, the PC projects can ease the transaction costs, which came from miss-ajustment between farmers an affecte the rental transactions severely, by making pay-fiels usage more flexible. Secon, regional ifferences in agricultural an social situations bring about regional ifferences in the project effects. Namely, the project effects in the eastern part of Japan ten to appear as high rental rates because both S-D farmers have a strong will to continue their cultivation an outer situations such as the rice price an monoculture of rice suit their will. On the other han, the project effects in the western part of Japan appear as large rental areas because the small average farmlan area an many varieties of crops make it easy for farmers to rent their pay-fiels to other farmers. Thir, a ecrease in rice price negatively influences in the project effects. This may be a ilemma, that is, the PC projects are neee to improve rice prouctivity, but the ecrease of rice price erive from high rice prouctivity brings about negative effects on the PC projects. Aitionally, a change in the project site to LFAs makes the project effect appear higher in the rental area than the rental rate. This inicates the PC projects can be useful for protecting against farmlan abanonment cause by a lack of rental emaners in LFAs. Finally, there is a nee for further investigation to apply other kins of istribution functions to the moel, to improve questionnaire items an to test uniformity of the market structure. Furthermore, this moel may be applicable to analysis of the price of water, which has not been evaluate empirically, but constitutes an important factor in agricultural prouction. Acknowlegment The author is grateful to Drs. Shinichi Shogenji an Yasuhiro Nakashima, of University of Tokyo, for many valuable iscussions uring the course of this work sponsore by the Japan Institute of Irrigation an Drainage. 14

15 References Boisvert, R.N., T.M.Schmit, an A.Regmi, 1997, Spatial, Prouctivity, an Environmental Determinants of Farmlan Values, Amer. J. Agri., Econ.79(5), Chambers, R.G. an J.Quiggin, 2002, The State-contingent Properties of Stochastic Prouction Functions, Amer. J. of Agri. Econ. 84(2): Ela, R.L., I.D.Clifton, an J.E.Epperson, 1994, Heonic Estimation Applie to the Farmlan Market in Georgia, J. of Agri. an Applie Econ. 26(2): Goo, Y., 1993, The Policy Mix of Rice Price Policy an Manatory Set Asie Policy, (in Japanese) Economic Research, 44(1): Hanemann, M., J. Loomis, an B. Kanninen, 1991, Statistical Efficiency of Double-Boune Dichotomous Choice Contingent Valuation, Amer. J. Agri. Econ., 73(4): Ito, J., 1993, The rice prouction income an rental eman of farmlan uner manatory set asie policy, (in Japanese) Journal of Rural Economics, 65(3), Japan Institute of Irrigation an Drainage (JIID), 2000, e. Report on Economic Evaluations an Follow-Ups of Lan Consoliation Projects (in Japanese) Kumbhakar, S.C., 1994, Efficiency estimation in a profit maximizing moel using flexible prouction function, Agri. Econ., 10: Kunimitsu, Y., 2003, An Empirical Stuy on the Effects of Lan Consoliation Projects to Pay-fiel Rental Transactions, (in Japanese) Journal of Rural Economics, 75(3): Plantinga, A.J., an D.J.Miller, 2001, Agricultural Lan Values an the Value of Rights to Future Lan Development, Lan Econ. 77(1): Rustichini, A., M.A. Satterthwaite an S.R. Williams, 1994, Convergence to Efficiency in a Simple Market with Incomplete Information, Econometrica, 62(5): Shogenji, S., 1998, The Structure of Farmlan Market an Intensive Use of Farmlan, Genai Nousei no Keizai Bunnseki (in Japanese), Univ. of Tokyo Press: Tanaa, M., 1993, A Stuy on the Leasing of Farmlan an the Buren of the Farmlan Consoliation Cost, (in Japanese) Japanese Journal of Farm Management, Vol. 75, Xu, F., R.C.Mittelhammer an P.W.Barkley, 1993, Measuring the Contribution of Site Characteristics to the Value of Agricultural Lan, Lan Econ. 69(4):

16 Figure 1. A Pay-fiel Consoliation Project. Figure 2. Changes in supply an eman by the PC projects in PRTs. 16

17 Table 1. Questionnaire results. Table 2. Caniates of variables for estimation in equation (4) an (5). \ \ Data: SRPC ; Survey of Rice Prouction Cost by MAFF, AC ; Agricultural Census by MAFF, JIID; Research of JIID Note:1. Agress becomes 1 in the case of a farmer who enlarges his or her farmlan to more than 20% after consoliation, or 0 in otherwise. 2. Other Regions consist of Tohoku (6 pref.), Kanto (7 Pref.) an Kyushu (6 pref.). 17

18 Table 3. Estimation of the supply functions. Note: Significant at 5% level( ** ), at 1% level( * ). Table 4. Estimation of the eman functions. Note: Significant at 5% level( ** ), at 10% level( * ). 18

19 Table 6. Regional effects of the PC projects (at equilibrium). \ Note: 1. Actual values came from previous research ata (JIID 2000). 2. The estimation values are calculate by using the average values of explanatory variables in each region. Table 7. Influence of outsie factors on effects of PC projects. 19

20 <Foot Note> The heonic moel has the reuce form of supply an eman (S-D) functions an each factor of S-D cannot be specifie by this metho as explaine by Brown an Rosen (1982). The actual rental market may be too small to ensure market equilibrium, but Rustichini, et al. (1994) showe that the ineterminacy or inefficiency cause by traer bargaining behavior in a small market vanishes rapily uner a uniform price ouble auction with more than six or three traers per sie. Accoring to the ata on site, the rate n is approximately eight on the average, that is, one eman sie farmer rente from eight small scale farmers. This rate iffers in sites of the project, but appears stable for many years. Therefore, n rarely affects the equilibrium values even in the actual transactions. The elasticity in this paper is ifferent from conventional prouction function, but the following features were foun if this point was ignore for the comparison. That is, the elasticity for factor eman of pay-fiel estimate by Ito (1993) uring the perio was for the small-scale farmers an for the large-scale farmers. These values are similar to the estimation values calculate above for Goo (1993) also showe that the elasticity value of large-scale farmers was larger than that of small-scale farmers. Many cooperative groups for agricultural prouction were establishe in the Tokai region, an their management area excees the project site. Also, their large cultivation area makes the number of ata lower than other regions. Therefore, the actual rate of the rental agreement level in Table 6 shoul be consiere with some limitations. 20