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Journal of I=hil_plne Development

N.mber 35, Vol.rn= X,X, No2ondSe.,es=, 1..2






SOME IMPLICATIONS FOR PRICE STABILIZATION PROGRAMS Meyra Sebello Mendoza, 13.Wade Brorsen and Mark W. Rosegrant*

INTRODUCTION Considerableattentionhas been focusedon theimportance of risk in farmers' production decisions. Previous empirical studies of farmers' response under uncertainty in developed and developing economies have shown that agricultural production is negatively affected by risk. In studies of American agricultural farmers, Just (1974), Lin (1977), Traill (1978), Hurt and Garcia (1982), Brorsen, Chavas and Grant (1987), and Aradhyula and Holt (1989) found that risk significantly reduces output. Similarly, subsistence farmers in developing agriculturein Thailand (Behrman 1968), Kenya (Wolgin 1975); Niger (Adesinaand Brorsen1986), and Mexico (Moscardiand de Janvry 1977) decrease productionwithincreasedrisk. Filipinorice farmers have likewise been shown to moderately reduce levels of fertilizer usage with increasing risk (Rosegrant and Herdt 1981; Rosegrantand Roumasset1985). Knowledgeaboutthe impactof risk on Filipino farmers' supply decisions has important policy implications.If risk has a large negative impact on area and levels of output, stabilization policies aimed at reducing risk may generate large social benefits. Additionally, models estimating supply decisionsunder the risk-neutralassumptionmay be misspecified,if risk is an important variable. Because of statistical problems in omitting relevant variables, there could be efficiency gains in incorporating measures of risk in modeling farmers' production decisions.

*Mendozaisa ResearchAnalystat the International FoodPolicyResearchInstitute (IFPRI), Brorsenis an AssociateProfessorat the Departmentof AgriculturalEconomics, OklahomaState University,and Rosegrantis a ResearchFellowat IFPRI. Theauthorsacknowledgethehelpfulcommentsonearlierversionsofthispaperfrom DeborahBrown,LeeSchrader,AkinwumiAdesinaandtwoanonymousJouma/ reviewers forsuggestions that improvedtheexposition.Theviewsexpressedheredonotnecessarily representthose of IFPRI.



This studyanalyzestheimportanceof riskincornarea decisions of Filipinofarmers. About30 percentof total agriculturalarea is plantedto corn (NationalEconomicand DevelopmentAuthority 1991). Corn alsocontributed17 percentto grossvalue added in agriculture in 1988(NEDA1991). It isalsoconsumedasastaplefood carbohydratein placeof rice by some Filipinos,especiallythose locatedin the VisayasandMindanao areas. Over the past two decades,corn hasbeen increasinglyused in the manufactureof feedsfor poultryandlivestockraisingandthistrendis expectedto continuein thecomingyears. StudiesonricearearesponseinthePhilippines haveassumedthat producersare riskneutral(Mangahaset al. 1965;SisonandHayami 1977).Severalfactorssuggestthatriskmaybe importantin thecorn productiondecisionsof Filipinofarmers.First,domesticcornprices are characterizedby wide intertemporalvariations,indicatingthe potentialimportanceof riskin cornproduction decisions.Second,as a smallworldproducerandnetimporterofcorn,Filipinofarmersare highlyexposedto priceshockscomingfrom a very volatileworld grainsmarket.Third,theheavydependence of farmersontradersfor price informationsuggeststhat farmers may be susceptibleto "exploitation" by traders.Furthermore,becausemarketinformation servicessupposedlyavailableto thepublicare allegedlyinadequate toprovidefarmerswithaccurateandreliablemarketnews(Dy 1988; Olgadoet aL !977; DeomampoandSardido1982),Filipinofarmers may be unableto accuratelyreadand interpretmarketsignalsand translatethemintoprofits.Riskabsorption by farmersmaythenbe higherastheyare unableto transfertherisktothe market. The susceptibilityof rural incometo variabilityin corn prices indicatesthe importanceof riskinproducers'decisions,andthishas beenusedtojustifygovernment intervention inthecornmarketinthe Philippines. Attemptstosecureruralincomeby reducingriskthrough price stabilizationprogramsand regulatiOnof the importmarket dominatedgovernmentprogramsfor many years. Importationof grainshas been regulatedby the NationalFood Authority(NFA) throughquotarestrictions, includingimportbans,importtariffsand importlicensing. To containerraticpricefluctuations andsustainfarm income,theNFA hasattemptedtoinfluencedomesticcornpricesby enactingsupportpriceand ceilingpriceprograms.It has likewise directlyprocuredcorn at the farm level,thoughwithvery limited success.The actualvolumeof cornpurchaseddirectlyby the NFA accountedfor only2 percentof the totalproductionper year, on



average. This low level of purchases reflects the inability of the Philippine government to defend its domestic price policies (Pabuayon 1985). Additionally, because government-set prices are low, farmers were found to have sold a considerable portion of their corn harvest in the open market (Lantican and Unnevehr 1984). To stimulate corn production to self-sufficiency levels, price control programs have been complemented by production-enhancing programs. These production-augmenting programs include the National White Corn and Feedgrains Program in 1969-1973, Masaganang Maisan in 1974-1977, Maisan 77 Program in 19771982, Maisagana Program in 1982-1984, the Expanded Yellow Corn Assistance Program in 1984, and the Corn Productivity Enhancement Program in 1990. Typically, these programs contain a comprehensive package of technological and institutional support for small corn farmers, of which technical assistance on the proper use of fertilizers and chemicals, and of hybrid seed varieties, and institutional linkages to obtain access to credit, product outlets, marketing facilities, and cheaper inputs are salient features. Like other intervention programs, however, production programs have fallen short of targets. Knowledge of the importance of risk in farmers' production decisions may be valuable in understanding its likely impact in attaining development program objectives and evaluating program benefits, if any. The remainder of this paper is organized as followS: the theoretical and empirical specifications of the risk-responsive area allocation model are presented in section II and section III, respectively. The data are described in section IV, and the results for the riskresponsive model and comparison with a risk-neutral model are discussed in section V. Some policy implications of the study are summarized in section VI. THEORETICAL MODEL The risk-responsivearea decision model specified,here assumes that both productionand prices are stochastic.Output is represented by a stochasticproductionfunction: y-f(x,z,e)


where x is a vector of variable inputs, z is a vector of quasi-fixed inputssuch as land, and e is the stochastic error term representing weather and other random effects on production.



Let p be the probabilitydistributionof the output price, w be the vectorof variable inputprices,and qbe the vectorof quasi-fixedinput prices or opportunitycosts. The farmer isassumed to maximizethe expected utilityof wealth: Max E U (w + py. rx- qz)


where wis initial wealth, pyis gross revenues, and (rx+ zq)is cost of production. Since both price and yield are stochastic, the optimal riskresponsive input demand and output will be a function of the joint probability distribution of gross revenue (py). Define _-= E (py) as expected revenues and o as the second moment and, if necessary, higher moments of the joint probability distribution of crop revenues, py. The variable p7= E(py) measures crop revenue risk. The oplJmal level of use of quasi-fixed inputs, such as land, can then be expressed as;

z*--- f(w, r, q, py, _)


Thus, from equation (3), area planted is a function of variable input prices, quasi-fixed input prices, expected revenue, and revenue risk. EMPIRICAL SPECIFICATION Based on the theoretical model in equation (3) in Section II, the empirical model used in estimatingthecorn area response of Filipino farmers under risk is specifiedas: CORNAREA t = f (CORNREVt. 1,RICEREVt. 1, RICERISK t PUREAt, PAMOSOL r WAGE,, TIME_)



t2.1,/2 - CORNREVt.j.,)) J (S)

i,_j =_10j RICEREVt4



= ,j_1'(30j(CORNREVt/.



)1" (6)



where CORNAREA isthe area planted to corn, CORNREV is gross corn revenue (price times yield) per hectare, and RICEREV is gross rice revenue per hectare. By definitioh, corn area is specified as a function of revenues rather than prices as is often utilized in arearesponse analysis. As noted b_/Rosegrant and Kasryno (1992) and Sanderson, Quilkey and Freebairn (1980), the use of revenue is theoretically preferable to price as an exogenous variable because price and yield are nonstationary. The relative profitability of different crops is a function of the productivity of the crop, as well as of the price. Rice revenue, RICEREV, represents the opportunity costs of growing corn, as rice is the crop that competes directly with corn in terms of land usage. Revenue expectations assume a naive form and are equal to rates of return observed in the previous year. More area will be planted to corn if there is an anticipated increase in returns to corn. Thus, a positive relationship is expected between corn area and expected returns to corn. If returns to growing rice increase relative to those of corn, more land will be devoted to rice and less to corn, so that corn area is postulated to be negatively related tO increases in returns to rice. The variable PUREA is the price of urea fertilizer and PAMOSOL is the price of ammonium sulfate. Urea and ammonium sulfate are the two most popularly-used inorganic fertilizers in the Philippines. The cost of agricultural labor is included as WAGE. To capture the effects of other exogenous factors on corn area, the variable TIME is included. Following the theoretical specification in equation (3), risk is measured as a declining weighted three-year moving average of squared deviations of corn revenues, CORNRISK, and rice revenues, RICERISK. The weighing factors of revenues in corn and its competing crop, rice, are specified as 8j over the previous three years. As in Brorsen et al. (1987) and Adesina and Brorsen (1986), variability in the most immediate past is expected to have a larger influence on farmers' perception of risk than those in the distant past, so the weights used here are 0.60, 0.25, 0.15 for j = 1,2, 3. Sandmo (1971) and Ishii (1977) and Batra and Ullah (1974) showed in a model of perfect competition under uncertainty for a single product farm that if producers exhibit nonincreasing absolute risk aversion (ARA), increased risk will result in a reduction in input use. In a multiproduct farm (as in this study), the relationship between



risk and area is ambiguous and must be tested empirically. As demonstrated in numerous other studies, the area allocated to the planting of a crop decreases when the farmers' perception of risk in growing that crop increases. In terms of revenue risk for the competing crop, the relationship is positive as planting the other crop is perceived to be a more risky venture. For comparison, a constrained or risk-neutral area response model is also estimated. In this model, the revenue-risk variables are dropped from equation (4), While all other variables are retained. The efficiency gains, if any, of using the risk-reSponsive area allocation model can thus be identified. THE DATA The period of analysis covers 15 years from 1965 to 1989. Time series data on corn area and yield were obtainedfromthe 1989 Food and AgricultureProductionYearbook.The pricesreceivedby farmers for corn and rice and the prices paid by farmers for urea and ammonium sulfate are from the 1990 FAO Producer Prices. Real wage rate indices collected from the National Economic and Development Authority Statistical Yearbookwere used to reconstruct the 1965-1972 values missing from the date available on real wages for agricultural worker. All variables were transformed to their logarithms prior to estimation. As with most developing countries, the data obtained here may likely have been measured with errors, particularly corn area, the dependent variable (David et al. 1990). Random measurement errors in the dependent variable corn area do not, however; create serious econometric problems (Kmenta 1986). RESULTS AND DISCUSSION Table 1 summarizes the results of estimates of the unconstrained (risk-responsive)andconstrained(risk-neutral)cornarea models. As indicated by the adjusted coefficients of determination, the overall explanatorypower of the models in explainingthe variation in corn area decisionsof Filipinofarmers is relatively close. The overall Fstatistics are highlysignificantat the 1 percent level, indicating the highexplanatorypower of bothmodels. Compared to the risk-neutral model, most coefficients of the risk-responsive model have the correct signs consistent with a priori expectations and are highly







Constrainted Model Unconstrained Model Exogenous Variable

(risk neutral)

Intercept Corn Revenue, previous year (CORN REVt._ Rice Revenue, previous year (RICEREV,.,) Corn Revenue Variability (RICERISK)' Price of Urea (PUREA) Price of Ammonium


(PAMOSOL) Cost of Labor (WAGE) Trend (TIME) F statistic Adjusted Coefficient of Determination Durbin-Watson Test for Serial Correlation


Defined as (_. _j rREVENUE_i ' 3 ,j--1


(risk responsive)

-2.17 (-5.52)'** -0.002 (-0.25) n= 0.03 (1.08) "' n,a, n,a, 0.007 (0.86) _' -8,47X10 .6

-2,48 (-4.59)*** 0.03 (1.79)** -0,02 (-0.44) n= -0.04 (-0,44)"' -0.004 (-0.32) _' -5.04X10 "e

(-1.24)"' -0.03 (-0.28) I'lS 0.38 (4,73)*** 32.22*** 0.89 10,4

(-0.32) 째' -0,10 (-0.73) ns 0.58 (3.36)*** 15,29 .... 0.86 1,82

REVENUE tk:,._


where REVENUE is the rate of return at the farm level of crop k, k

** *** ns

equals 1 and 2 where 1 is corn and 2 is rice and O/= 6, .25, .15 for j= 1, 2,3. Values in parentheses are t-statistics. Significant at 10% probability level, one-tailed test. Significant at 1% probability level, one-tailed test. Statistically not significant.



significant at conventional significance levels, suggesting that they provide.a slightly superior fit than the constrained model. Moreover, results of the Durbin-Watson test indicate that the residuals obtained from the unconstrained model are white noise cofnpared to the serially-correlated error terms from the constrained model. This result indicates that there are efficiency gains in incorporating measures of risk in modeling corn production because estimates of tt_e standard errors are efficient, providing greater confidence in the power of statistical tests employed and in the validity of inferences drawn from the area-risk response model. Subsequent discussion is thus focused on results of the corn area-risk :model. Results show that expected revenues of corn, CORNREVt_1,exert a statistically significant and positive effect on corn area, indicating that more land is planted to corn as Ceturns to corn production increase. As expected, anticipated increasesDin rice revenues, RICEREVt_1,reduced the area planted to corn as farrners shifted more land to the planting of the more profitablerice and less la_d to corn, although the coefficient is statistically insignificant. Although the coefficients on input prices have negative signs,. prices of urea, PUREA, and ammonium sulfate, PAMOSOI, were not statistically significant at conventional levels. This may be because the level of fertilizer usage on corn farms is generally very low so that changes in fertilizer prices do not exert a significant influence on corn area allocation. The real wage rate, WAGE, had the expected negative sign, but was also statistically insignificant, possibly because of the high proportion of family labor used in corn production which may not be as responsive to changes in wages as hired labor. Of most interest is the result obtained for the corn revenue risk, CORNRISK. The coefficient on corn revenue risk which was statistically significant at the 10 percent probability level, had a negative sign. This negative relation between risk and farmer's production decision is consistent with the empirical findings obtained by Behrman (1968), Lin (1977), Traill (1978), Adesina and Brorsen (1986), and Brorsen, Chavas and Grant (1987), and with the theory on risk of Sandmo (1971), Batra and Ullah (1974), and Ishii (1977). This finding for Philippine corn suggests that increases in corn revenue risk will reduce the quantity of corn locally produced as less land will be planted by Filipino farmers to corn. Rice revenue risk, however, does not have a significant effect on corn area decisions. Since all variables are expressed in logarithms, the coefficients are direct estimates of elasticities. As presented in Table 1, the negative



sign on the coefficient of CORNRISK indicates that corn area would decrease with increases in expected variability in corn revenues, but the magnitude of response is very small, -0.04. The inelastic response of corn area to risk indicates that increased revenue risk resulting from highly volatile local and world market conditions and from the variability in corn yield does not translate into a large decrease in land planted to corn. This result is consistent with the findings obtained by Rosegrant and Herdt (1981) and Rosegrant and Roumasset (!985) for Philippine rice farmers that there is a statistically significant but small effect or risk on the level of input usage. This inelastic area response to risk perception obtained for Philippine corn is also comparable to that found in studies by Lin (1977) and Brorsen, Chavas and Grant (1987) for U.S. agriculture. SUMMARY AND POLICY IMPLICATIONS This paper investigatedthe importanceof corn revenue risk in the corn acreage decisions of Filipino farmers in the Philippines.As a small world producer, Filipino farmers are exposed to the wide variability in prices in thedomesticand worldmarkets, pointingupthe significance of risk in corn production.The existence of imperfect market knowledge and heavy dependence of farmers on traders for price informationfurther suggestthat Filipinocorn farmers' exposure to risk may be high, indicatingthe importanceof incorporatingrisk in modelingfarmers' corn area decisions. Results showed that perception of risk measured as variability in corn revenue significantly reduces the area planted to corn. This result provides evidence that revenue risk is a statisticallysignificant factor in Filipino farmers' corn area decision making. Estimates of cornarea response modelsthat do notaccountfor risk are likelyto be biased, and inferences derived from their results may lead to inappropriate policy recommendations. However, the magnitude of area response to risk is very inelastic,as indicatedby the area-risk elasticityof -0.04. Thus, increase in riskwould result in decreases in land planted to corn, but the magnitudeof the reductionin corn area would be small. The findings on the degree of risk-responsiveness in Filipino farmer's corn area decisions have important policy implications for programs affecting price stability. The small negative effect of increased revenue variability on corn area indicates that there are likely to be only small benefits to corn price stabilizationprograms.



These results are consistent with Newberry and Stiglitz (1981), and Spriggs and Van Kooten (1988) who showed that while there are benefits to commodity price stabilization programs, the gains are generally small. Net economic benefit of corn price stabilization programs in the Philippines may be smaller considering that the total outlays involved in implementing stabilization programs, particularly programs on open market operations and grain stock holding, are prohibitively high (Knudsen and Nash 1990). To the extent that the desire is for price stabilization programs for corn to achieve other social goals, such as stabilizing rural income, alternative policy instruments .for achieving stability, such as variable import tariffs, should be evaluated.




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Aggregate Corn Area Response Under Risk: Some Implications for Price Stabilization Programs