LIE-JURY Hickman State University This is to certify that the dissertation entitled 'IHE IMPACT OF BASE PRICE POLICY IN THE AGRICULTURAL SECIOR: 'IHE TUH /<1-v) L = A e (R /P ) 1 (w/P ) 2 2 a f f - /l-V) 8(1-V) (P /P ) 3 Z ........ 4.3.20 nf f 119 (b/l-vm - P /1-v) +3 NH» X = A e (R /P ) l (w/P ) 2 nf 3 a f f -13 /(1—v) 5(1-v) (P /P ) 3 Z ........ 4.3.21 of f 3 where v =: F; K = profit maximizing capital demand; :1 i=1 i x L = profit maximizing fertilizers demand; R = rate of a interest; w= wage rate; P = price of fertilizers; P = nf f price received by farmer ; Z = area sown. By“ substituting input demand functions into production function, the market price augmented supply function is obtained, and it is formulized as follows: “-F/(l-v) 43 /(l-v) Y = A(R /P ) 1 (w/P ) 2 a a f f 2? /(1-V) 25(1-V) (b/(l-V))t (P /P ) 3 2 e ........ 4.3.22 nf f where Y =agricultural output. a The above concepts of profit maximization and of supply function are unambiguous within the deterministic framework of the model postulated by economic theory. They need more delicate interpretation when uncertainties about both pro- duction and price are introduced.(16) In our model it is assumed that farmers maximize the mathematical expectation of profit, input prices are known with certainty and output price is statistically indepen- dent of the production function disturbance, with 120 t * expectation P and P = E(P ) = p(0). f f f Under these assumptions, the farmer’s expected profit is 3 Ear) = p (0) E(Y ) —Z P x ....... 4.3.23 a i=1 i i th th where P price of i input, and X j input. In our case i J igjz 1,2,3. 0n the supply side it is assumed that farmers respond to base prices efficiently and quickly by transforming information obtained from the government into their ex- pected market price. The expected market price enters into their decision-making process, and performs a crucial role in determining the level of output. Realized output creates the agricultural income of that sector, and induces income flows in the whole economy. Thus, it may well be argued that base prices affect the pattern of aggregate demand by inducing income flows in the economy and stimulating the level of output in agricultural sector. To trace the aggregate effects of such inducements (17) on the demand side, income flows must be analyzed. Y , is essentially determined by the market price, so d , and the level of output, X , given the exogenous input ef f 121 prices of capital, labor, and fertilizers. Y = d X ........ 4.6.1 ac ef f ° Equation 4.6.1 defines value-added income in the sector. The second group of income earners in the economy consists of laborers. Wage income, Y , at the aggregate level, can be set up as a linear funzzion of agricultural and industrial incomes, since the level of incomes in both sectors are the major determinants of the labor demand in the economy as a whole. Thus, assuming wages are exoge- nous, wage income can be expressed as a function of incomes in the agricultural and industrial sectors. This relationship can be formulated in a linear form as follows: Y = a + a X + a Y ........ 4.6.2 wp 30 31 nc 32 so The third class of income flows to profit earners. It is the mark- up income, Y , in the industrial sector, which 2 can be set up as a unique function of wage income; it is Equations 4.6.1, 4.6.2, 4.6.3 allow us to specify total expenditure or aggregate demand function in the economy in the following form, 122 In the two-sector economy, the cost of acquiring the base consumption levels, B, of agricultural and industrial products, i and i respectively, is defined as f nf B =‘6 d + G-d ........ 4 6 5 c f ef n en where d is the price of industrial production. en Aggregate demand above the base level, (G -B ), is up c split between the two sectors, according to the marginal budget shares m and m . Determination of the consumption f nf function of the two sectors above the base level is as (18) follows: C = e + (m /d )(G —B ) ........ 4.6.6 f f f ef np c C = e + (m /d )(G ~B ) ........ 4.6.7 nf n n en np c Substituting 4.6.5 into 4.6.6 and 4.6.7, we can simplify agricultural and industrial consumption functions. They are C = a + a G - a R ........ 4.6.8 f 70 71 npf 72 en 123 where a = (l-m)9 , a = m , a = m 9 , 80 f 81 n 82 n f G =G /d , R =d /d npn np ef en ef en Up to this point the foreign sector is ignored. In order to see the base price effect on trade the following identity is introduced. The Turkish economy is charecterized as an agricultural surplus economy. Even though productivity in agriculture is low, and inefficient and less developed agricultural technology is in effect, more food is produced than the country’s subsistence demand. The surplus agricultural production which is subjected to export, E , can be defined x as E = Y - C ...... . 4.6 10 x ac f By exporting agricultural surplus, a base for imports of industrial semi-manufactured goods is established. Assuming there is no hardship exporting agricultural surplus as a small country with high world food demand due to insufficient supply of food, Turkey may hasten the country’s economic development by increasing the size of agricultural surplus. 7. Statistical inference Goldberger defines econometrics as a social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of econo- 124 (19) mic phenomena. On the other hand, Theil’s view of econo- metrics is as the empirical determination of economic (20) laws. Definitional approaches to the art of econometrics along these lines are common in the economic literature. Besides improvements in data collecting, use of compu- ters, and estimation techniques, the task of the economet- rician is to seek good results to provide basic evidence in identifying, clarifying, or verifying problems encountered in economic theory. Good results depend basically on the art of the econometrician, which, in the words of Malinvaud, is to find the set of assumptions which is both sufficiently specific and sufficiently realistic to allow him to take the best possible advantage of the data avail- able to him.(21) One other task of the econometrician is to fit the econometric methods in current use to the needs of economic policy.(22)When our concern becomes economic policy, two important aspects of the real policy-making problem need to be recognized. They are the high degree of uncertainty resulting from random events and the nature of the decision-making process.(23)The uncertainty that decision makers must face comes from two sources. First, the system is not perfectly known, it is complex and its basic struc- ture is not well defined. Second, it is subject to unex- pected random technological, political, and natural events. The former is among the exogenous uncertainties, the 125 latter is among the endogenous uncertainties. Thus a policy maker in order to affect economic events, faces classical problem of decision making under uncertainty.(24) Our purpose in this study is to contribute to the decision process by making conditional forecasts of the outcomes of the alternative courses of action which are taken by the government by announcing base prices in the area of agriculture. For this purpose, we will discuss the estimation process and the econometric properties of our base price augmented agricultural model in the remaining part of this chapter concerned with endogenous uncertain- ties. 7-1 A_f9£!§l_ig!§§:izstigy In econometrics our main concern is statistical infer- ence. Descriptive statistics is relevant only as statis- tics which summarizes various characteristics of the data, such as averages, measures of dispersion, etc. In statis- tical inference such characteristics related to a popula- tion are also used. However, the two are different and the difference lies in the fact that in the field of descrip- tive statistics such measures represent ends in themselves, but in statistical inference they are only means in the (25) process of inquiry. Statistical inference is concerned with generalizations 126 or simplifications, as we put it, about the population on the basis of information provided by a sample. Such a procedure is frequent in everyday life. For instance, we make generalizations on the life expectancy of a person, given his sex, age, etc., on the basis of our past experi- ence. In statistical inference this is done in a more scientific way because the way in which the sample is selected is also taken into account, and generalizations are expressed in specific probability terms.(26) In general we are not interested in knowing everything in a population, but are concerned with only some of its characteristics, which we call parameters. The purpose of sampling and statistical inference is to make judgments about population parameters. These judgments are guesses endowed with a specific degree of reliability and they can be of two types. One is concerned with the estimation of parameter and the other with testing some hypothesis about it. Judgements in the form of hypothesis testing involve an a prior assumption about the value of a parameter. If the sample information provides evidence against the hypothesis, the hypothesis is rejected, otherwise, it is accepted. The evidence provided by the observations in the sample is for the purpose of hypothesis testing, summarized in the form of a test statistic. This is then used in arriving at a conclusion concerned with the hypothesis. We face a serious problem with regard to hypothesis 127 testing. It is whether to accept a false hypothesis instead of a true one. Blaug, arguing about the dark spot of statistical inference, states that if we are worried about the danger of accepting false hypotheses, we raise the level of significance at which we screen hypotheses, in consequence of which we will also end up rejecting some true hypotheses; on the other hand, if we are worried about the danger of rejecting true hypotheses, we lower the level of significance and that means we will accept some false hypotheses.(27)Therefore,one may well argue that the cutoff point where we begin accepting hypotheses is entirely arbitrary and depends upon our normative judgements. Thus, resting on solely statistical hypotheses, testing for the purpose of validation exercise does not produce conclu- sive results from the point of Blaug’s dilemma about making a type two error.(28) To reduce the destructive effect of such an error, one may suggest testing a model from the standpoint of predic- tive power. If a model predicts economic events accu- rately, a set of relations depicted within a model struc- ture is said to be unassailable only within the sample period in which parameters of the model structure are estimated.(29)A mode ,predicting economic events properly does not mean that the model subject to our concern is valid beyond the sample period, since a proper model needs to predict economic events that we are concerned with experiments. Yet, the science of economics has no proper 128 laboratory to make such experiences for validation exer- cisis. In this study our model is kept limited within the sample period of 1962-1980. Therefore, our simulation exercise will be bounded with the set of information which sample period carries. From the point of statistical inference, the problem of estimation carries considerable similarities to hypotheses testing, since both are concerned with questions of some unknown population parameter. However, in estimation, unlike in hypothesis testing, we make no prior claims about the credibility of the parameter. The problem of point estimation is that of producing an estimate that will represent our guess about the value of the parameter. An estimator, O, is commonly considered to be desirable, or we may say a best guess, if it satisfies the following properties: A) For small samples: 1. 9 is an unbiased estimator of O, 2. O is an efficient estimator of 9, 3. O is a best linear unbiased estimator of e. B) For large samples: 1. O is an asymptotically unbiased estimator of 03 2. Q is consistent estimator of 93 3. O is an asymptotically efficient estimator of (30) 129 Having defined the desirable properties of an estimator, we need to depict a technique to generate esti— mates which carries the desirable properties mentioned above. The appropriate technique which we may adopt depends upon maintained assumptions about residuals or disturbance term u .There are two possible ways of rationa— lizing the insertion of the u. term in a functional rela- tionship. First, in explaining human behavior the list of relevant factors may be extended infinitely. Many of the factors, however, will not be quantifiable: and even if they are, it is not usually possible in practice to obtain data on all of them. Even if we can do that, the number of factors is still almost certain to exceed the feasible number of observations, so that no statistical means exist for estimating their influence. Moreover, many variables may have very slight effects, so that even with substantial quantities of data, the statistical estimation of their influence will be difficult and uncertain.(31)In this case disturbance u represents the net effect of the excluded variables. It is well known that such an assertion about residuals creates difficulties in estimation, and down- grades the reliability of an estimator because the exis- tence of an omitted variable or excluded variable creates bias and worst of all, inconsistency in estimating parame- (32) ters. A second justification for the presence of a distur- bance term is to assume that in the total effect of all 130 relevant factors, there is a basic and unpredictable element of rondomness in human responses which can be adequately characterized only by the inclusion of a random (33) variable term. The initial specification of the relationship needs to include some assumptions about the probability distribution of the disturbance term. Usual assumptions about distur- bance term or residuals are as follows: 1. E(u /X ) = 0 for all i. 1 1 2. Cov(u , u ) = 0 for all i=j i j 2 3. Var(u /X ) = i i 4. Cov(u , X ) = 0 i i The first assumption states that the conditional expected value of u , conditional upon the given X , is i i zero. That is, the average or mean value of the residuals corresponding to any given X should be zero. The i assumption postulates that the disturbances u and u are i J' uncorrelated. Technically, this assumption is known as the assumption of no serial correlation, or no autocorrelation. The third assumption represents the assumption of homosce- dasticity, or equal variance. Assumption 4 states that the disturbance u and the explanatory variable X are uncorre- lated. Assumption four is automatically fulfilled if the X variable is non random or nonstochastic.(34)The assumption that the residuals u are normally distributed is needed to i make confidence interval statements and to apply the test 131 (35) of significance for finite samples. Rowever, assympto— tically valid tests do not require specific distributional assumption about the residuals. Once we ensure that the residuals of an equation satisfy the above assumptions, the technique which is known as OLS (ordinary least squares) provides desirable esti- mates of the parameters subject to investigation. OLS technique is applied only to a single equation in isolation from a larger economic model. For example, the demand equation for a particular commodity is typically one in a system of equations that determines the equilibrium price and quantity in the market for that commodity; the economic model for a market will generally include a demand equation, a supply equation, and an equation describing the equilibrium process in the market.(36)Under certain circumstances, regular OLS technique no longer gives consistent estimators.(37)When our concern is focused upon simultaneous equations models, we need to investigate dif- ferent methods of estimation to obtain reliable estimates of structural parameters. In econometric literature, simultaneous equation esti- mators are classified under two categories: One of them is the so-called single equation methods, the other one is system methods. In a single equation methods we estimate each equation separately, using only the information about the restrictions on the coefficients of that particular 132 equation. The restrictions on the coefficients of the other equations are not used. In the system methods we estimate all equations jointly, using the restrictions on the parameters of all equations as well as the variances and covariances of the residuals. The most commonly used single-equation methods are OLS (ordinary least squares), ILS (indirect least squares), ZSLS (two-stage least squares), and LIML (limited-informa— tion maximum likelihood). Besides those, 3SLS (three-stage least squares) and FIML (full-information maximum likelihood) methods are among the system estimation met- (38) hods. All simultaneous equation estimation methods discussed here except OLS(39)have some desirable asymptotic proper- ties. These properties become effective in large samples. Unfortunately, our knowledge in the small sample properties of these estimators is not complete. Most of the evidence on the small sample properties of the simultaneous equation estimators comes from sampling (i.e., Monte Carlo) experi- ments.(40)The essence of MOnte Carlo study is that various sets of parameter values are specified for postulated distributions underlying a model; repeated numerical drawings from the resultant distribution generate a large number of samples of finite size. Various estimating techniques are applied to these samples, and the sampling distributions of the estimates are studied in relation to 133 the true value of the parameter and to theoretical expecta- (41) tions about asymptotic distributions. The results are conditional on the numerical values used to generate the samples, but a range of such studies can build up valuable information. Among the empirical studies, general agreement can be found on the question of bias. The OLS estimates display the greatest finite sample bias, but the means of the sampling distributions are not usually significantly from the true values.(42)The evidence about OLS bias is not in complete agreement. Quandt’s study, for example, found that OLS bias is almost invariably less than the ZSLS bias when there is substantial multicolinearity among the exo- genous variables.(43)Cragg, on the other hand, considered six different degrees of colinearity in the exogenous variables and found that OLS is badly biased, even though multicolinearity is present.(44)Ris results suggest that multicolinearity can produce a substantial increase in the bias of the consistent estimators, as well. More important, however, the danger of using OLS estimators in a simultaneous-equation context arises in hypothesis testing. Because the sampling distribution of the OLS estimator is centered around a biased expectation, rather than the true value of a ngameter, there is a risk of making incorrect inferences. BOwever, it is known that inference proce- dures producing consistent estimators work reasonably well. 134 The state of current knowledge on the various struc- tural coefficient estimators is well summarized in Cragg’s study. Cragg studies OLS, ZSLS, UBX (k-class estimator), LIML, 3SLS and FIML, and makes the following arguments about the performance of mentioned procedures. Given the small differences among the estimators and the variability in the relative performances, OLS is usually the poorest method and 3SLS and FIML are better than ZSLS, UBX, and LIML. In most cases, differences in the central tendencies of the distributions of consistent estimators from the true values of the coefficients were not very serious, but large disturbances and multicolinearity could change this conclu- sion. On criterion, FIML and LIML seemed slightly superior to other methods. The differences of its medians from the true values is a serious problem for OLS. This feature, rather than wide dispersions, is the reason for the poor rankings of OLS. It weighted more heavily against OLS when larger samples were used. Cragg concludes that the use of the standart errors of the consistent methods would lead to reliable inferences, but this was not always the case. The standard errors of OLS were not useful for making infer* ences about the true values of the coefficients.(46) The experiments performed give no clear guidelines for the choice of an estimator for econometric models. The results suggest that, because the consistent estimators do not differ greatly and their relative performances are sensitive to the data and structure studied, ZSLS may well 135 be the best estimator to choose, since it is the cheapest and easiest method to compute.(47) The Nager study also shows that ZSLS has the smallest bias in all cases, and the asymptotic standart errors of two stage least squares give a rather satisfactory picture of the variability of the estimates about the true value.(48)This is not true for least squares in all cases considered. 7-2 §sgsh§§ti9-39991fisstigs-ssé-s§iisstigy When econometric models are correctly specified, statistical theory provides well defined procedures for obtaining point and interval estimates and evaluating the performance of various linear and usually unbiased estima— tors.(49)Bowever, uncertainties usually exist about the stochastic specifications underlying the econometric model. In this section, disturbance term will be introduced to the model and be specified with the underlying assumptions upon it. Specifying the production function in agriculture, we have selected a production function which represents a Cobb- Douglas form. Zellner, Kmenta, and Dréze have found that classical least squares provides consistent estimators of the parameters of the Cobb-Douglas production function. With the normality assumption, these parameters are also unbiased and maximum-likelihood estimators.(50)8ince base price augmented supply function is also in Cobb-Douglas form, due to duality between production and cost functions, 136 it can be estimated by least square technique, assuming that farmers maximize the mathematical expectations of their profit, input prices are known certainty and the output price is statistically independent of the supply 3 function disturbance with expectation P . The later assumption is reasonable because off disturbance- representing factors such as weather and any kind of unpre- dictable variations.(51)The base price augmented supply function relates a farmer’s profit-maximizing supply of output to the variables that can be considered exogenous to his decisions. Thus following Zellner, Ementa, and Dreze, the least square technique is found to be appropriate to estimate unknown parameters of the base price augmented supply function. Stochastic specification of the demand side equations are relatively easy. It is assumed that every equation contains an additive error term representing uncertainty concerning random causes on dependent variables. Distur- bances are assumed to be uncorrelated with the disturbance of base price augmented supply function. Furthermore, it is assumed that they are nicely behaved. On the demand side, unknown parameters are estimated by employing two— stage least square procedure to every equation of the model. Since all equations are overidentified by exclusion restrictions, ZSLS estimates are consistent and asymptoti- (52) cally equal to LIML. 137 8- Ih§-szszeasts-!ggel The complete system of equations, constructed from the guidelines established in this chapter is set out below. The logarithmic form of related variables is represented by the letter 2. Stochastic equations of the model: PF = a + a OF + a QF(-l) + u ........ 4.8.1 10 11 12 1 ZYA7 = a + a 2W a ZPXRA + a ZPNF + a ZASV 20 21 22 23 24 + a TT + u ........ 4.8.2 25 2 YWP = a + a XNC + a YAC + u ........ 4.8.3 30 31 32 3 YZP = a + a YWP + u ........ 4.8.4 40 41 4 GNP = a YWP + a YZP + a YAC + u ........ 4.8.5 51 52 53 5 CF6 = a + a GNPF + a REF + u ........ 4.8.6 60 61 62 6 CN6 = a + a GNPN + a REN + u ........ 4.8.7 70 71 72 7 Identities and definitional equations: W = W7/PEF ........ 4.8.8 PNF = PN7/PEF ........ 4.8.9 138 DEN, ASV, XNC, TT. 319999stissl_li§:_9£_sxshgl§ 3 ASV .......... Area sown (10 hectares) = RA/PEF ... .10 = DEFtYA7 .. .11 = GNP/DEF ..... .12 = GNP/DEN ........ .13 = CN/DEN ........ .14 = CF/DEF ........ .15 = DEF/DEN ........ .16 = l/REN . ........ .17 YAC - CF .18 Endogenous variables in the system of equations are YA7, YZP, GNP, CF6, CN6, YAC, and predetermined variables are OF(-l), OF, W7, PN7, DEF, CF .......... Domestic agricultural consumption (105TL.) by current prices. Agricultural consumption normalized by agricultural prices (by 1974 prices) Domestic industrial consumption by current prices (105TL.). Industrial consumption normalized by EX ...... GNP ..... GNPF ..... GNPN .00... .0... PF7 O ......... PN? 00.0 000000 PNF 0.0.000... PXRA ..... . ... PEF .... ...... 0F .0... .0. OF(-l) ...... REF ....... REN . ......... RA 0...... 0. TT .... ...... w 0...... O "7 0...... O XNC ..... ..... YA? ........ . YAC .......... 139 industrial prices (by 1974 prices). Exports by current prices. Gross national product by current prices. GNP normalized by agricultural prices. GNP normalized by industrial prices. Prices received by farmers (1974:100). Price of fertilizers (1974:100). Price of fertilizers normalized by farmer’s expected prices. Interest rate normalized by farmer’s expected prices. Farmer’s expected prices (1974:100). Base price index (1974:100). Base price at time t-l. Relative price of agricultural products. Relative price of industrial products. Real interest rate. Time trend Average wages. Wage index (1974:100). Industrial production by current prices. Agricultural production by 1974 prices. Agricultural income by current prices. 140 YWP .......... Wage income by current prices. YZP ..... ..... Mark-up income by current prices. DEF .......... Agricultural price index (1974=100). DEN .. ........ Industrial price index (1974=100). Theoretical analysis in economics inevitably contains a causal sequence which links economic events to certain activities. In economic theory we find different causal sequences which are suggested by various economists as an ideal type. Those causal links require different policies and social actions to produce desired results.(53)ln this study we have developed a simple macro economic model to depict the causal link between the government-announced base price and the major aggregate variables of the economy. The scheme of this causal sequence is given below. The starting point of our model is the base or support prices. On the supply side, base price is seen as a unique determinant of the farmers expected price in the market.(54)Assuming there is no change in input prices, any change in base prices produces a parallel change in farmer’s expected prices. Therefore, the optimum level of factors which are to be used in production will change. Thus it can be argued that base prices are the major deter- minant of the level of output in the agricultural sector within the framework of our model, holding all exogenous factors constant. 141 Once a decision has been made about the level of pro- duction, the amount of output which is harvested will determine income in the agricultural sector, given the exogenous market price of output. Agricultural income, together with industrial output, regulate the volume of wage income, and thus the volume of mark-up income. Since aggregate demand is set up among all these income earning groups, base price level impact on aggregate demand occurs as a consequence, and thus on the domestic level of agri- cultural and industrial demands. Up to the this point we have argued that the major impact of base prices is seen not only on the supply side alone, but on the demand side as well. We have shown that this effect stemmed from the scheme of income flows of the economy. Since base price affects both the level of output and the level of consumption in the agricultural sector and the level of consumption in the industrial sector, it determines both sector’s deficits or surpluses as well. A developing country’s objective is to cover its deficit by trading its surplus output in foreign markets. Defining such an objective function for a representative country, we can carry base price policy impact on country’s balance of trade as well, as depicted in our model. The causal sequence proposed here makes it possible to trace the impact of base price policy change on major macroeconomic variables as indicated above. To validate 142 our model, we have applied it to the Turkish economy, in which base price policy has been used as an effective policy instrument by the Turkish government for a long period of time. Our estimation period is between the years of 1962 and 1980. 143 8.2. Causal formulation of the model “ff--- :53: 25f"""32%if""‘ii}???"""fééiééiwfflé: ""ffiééff """""""" 35:: :39: :3: 3:5: 35:: 255:: :35: 5:5: "_II;;:I“' "u:;i: :::: 144 8-3. Estisstsg-£9:!-9f-ihs_§tgshsstis_sggst1999 PF = -.0048 + .831 or + .255 0F(-1) .. 4.8 —.178 19.03 2.77 2 R = .998 DW = 2.50 F = 3257.39 (2, 15) ZYA7 = -4.973 - .104 zw - .002 ZXRA — .719 ZNF + — .276 - .927 - .145 -1.072 .481 zsv +.027 TT ....... .257 2.702 2 R = .935 DR = 1.75 F = 37.61 (5. 13) YWP = - 615.309 + .0017 XNC + .023 YAC ....... — .137 .133 1.822 2 R = .968 UN = 1.39 F = 241.08 (2, 16) YZP = 8272.85 + 1.588 YWP ....... 1.785 21.537 2 R = .967 UN = 2.67 F = 496.37 (1, 17) GNP = 144.704 YWP - 104.741 YZP + 5.290 YAC 4.164 2.276 R2= .968 DW = CF6 = 685561 + 6.425 8 2 R = .840 DW = 2.80 2.331 .109 GNPF - 147766 REF .298 1.56 F - 1.868 (2. 16) 41.91 145 CN6 = - 44071.1 + .292 GNPN - 130552 REN .. ..... 4.8.3.7 - 1.227 43.249 - 2.436 2 R = .995 DW = 1.96 F (2,16)= 1692.97 8.4 Result of the structural estimation The base price augmented macro model consists of eighteen equations, eleven of which are identities or defi- nitions. Definitions explain the deflated value of input process, agricultural value added (measured by current prices) and exports. The results of the structural estimation are provided. Each functional relationship is assumed to be linear in parameters. The model as a whole achieves a sound level of statistical significance, as evidenced by the associated t values of the estimated parameters reported under them. The signs of all parameters appear to be reasonable in terms of a prior expectations. However, there is an indication of malticolinearity in equation 4.8.3.2. Since (55) it is not a problem for prediction, coping with it is found irrelevant from our considerations. By simulating the model during the period for which historical data for all variables are available, a valida- tion test is performed. The comparison of the original data series with the simulated series for each endogenous variable presents a good match for real world behavior. Therefore, the performance of the model in the historical run can be utilized as a supporting argument in the 146 validation. The summary statistics for each endogenous variable are introduced on the Table 18, and the related graphics for the historical run are in appendix. TABLE 18. RESULTS OF HISTORICAL SIMULATION (SUMMARY STATISTICS) Endogenous R RMS error Mean abso- Mean error variable lute error ET""m"T§§§§""T6§Z£"m""T8328""'""TIEQEIBé" YA7 .9627 .0431 .0351 .30468—02 YAC .9996 .7587E 05 .4645E 05 .1334E 05 YWP .9855 9372 5786 427 YZP .9595 .25298 05 .1406E 05 1062 GNP .9982 .6312E 06 .53OOE 06 .lOZlE 06 CPS .9115 .1047E 06 .79258 05 .5101E 05 CN6 .9927 .21898 06 .197OE 06 .10518 06 EX .9753 .6714E 05 .52308 05 -5683 CHAPTER V POLICY SIMULATION 1. Introduction In the previous chapters, particularly Chapters III and IV, base prices announced by the government were discussed as a major determinant of agricultural output. The doc- trine that farmers in developing countries respond perva- sively to changes in prices is the a prior hypothesis maintained for the supply side of the model.(l) The general debate over supply responsiveness has been reviewed. In our model the supply responsiveness was founded on base price rather than market price because of the presumption that farmers prefer certainty over uncer- tainty in real life.(2)In reality, there is no discernible difference between the farmer’s price and the base price in general.(3)This is because of the fact that the buyer’s market is imperfect and it is inefficiently organized, where the government, being the major purchaser, has the power to impose its price as the true market price. There- fore, base price change positively affects the level of output; and this can be justified on theoretical ground, (4) and it has been justified empirically in the literature. Once the level of output is determined, given the price 147 148 received by farmers, it determines the large proportion of agricultural family expenditures. Therefore, the effects of base price policy spread through out the economy by effecting income flows and thus demands for agricultural and industrial goods. The income effect induced by the government interven- tion in the agricultural sector is traced in the model developed. The basic purpose of this chapter is to trace the economic consequences that would have resulted from changes in the base price policy. The policy analysis consists of two sets of counterfactual simulations. The first one is to trace the economic consequences of the world market price of agricultural products by assuming that the government is able to predict it consistently and replace it by the base price to eliminate or to reduce domestic market uncertainty. The second one is to trace the policy outcome resting upon the assumption that the government increases base price as much as the increase in the industrial sector price; that is to say, that govern- ment policy is aimed toward keeping relative prices con- stant. 2-§s§s_:yg_priss-pglisy The sample period investigated has been divided into four subperiods. Subperiods reflect a change in government policy toward agriculture in Turkey. 149 Between 1963 and 1969, farmers were protected against world-wide low prices of agricultural goods. The average level of protection measured by the percentage deviation from the world market price is about 3.7 percent. The level .of protection in this period varied between .7 per- cent and 6.9 percent. Despite protection, the farmer’s relative well-being in the domestic market was weakening because of the increase in the relative prices in favor of the industrial sector. In fact, the industrial sector price increased 12.3 percent above the increase in the base price.(5)Therefore, it can be said that farmers did not utilize fully the benefit of the price protection against the world market because farmers were left unprotected in the domestic market. Such a policy was the result of the import substituting strategy of the development plan. The period between 1970 and 1974 showed a shift in the agricultural support policy by which it weakened the farm- ’ position further in the domestic and in the inter- ers national market. In this period the base price was set 31.3 percent below the level of the world market on the average. Such a policy was aimed toward increasing the market share of Turkish agricultural goods by increasing their competitiveness in the world market. Therefore, farmers did not utilize the benefits of the world market price becau?§)of the restricted price policy of the government. Industrial prices, on the other hand, were 20.1 percent above the level of the base price 150 In the years 1975-1977, the government pursued a sup- port policy comparable to the policy implemented during the period of 1963-1969. The basic impetus of the policy pursued was again to protect farmers against low interna- tional market prices, but not to protect them in the domes- tic market against the industrial sector. Therefore, the spirit of the policy was to transfer income from agricul- ture to industry in order to stimulate industrial sector growth. Base price was increased by 9.6 percent on the average between 1975 and 1977 and an increase yielded 7.4 percent protection against world markets. On the other hand, rela- tive prices changed in favor of farmers at the margin. The years after 1977 witnessed high inflation and more impor- tant than that, political turmoil which damaged the produc- tive capacity of the industrial sector. The base price, on the other hand, increased by 59.2 percent on the average until 1980. Such an increase may seen remarkably high, considering the previous period’s averages. However, it was still 25.7 percent below the industrial price. There— fore, the increase in base price was only in money price and basically was aimed to gain the political support of the rural population. Thus farmers were again protected unfavorably in domestic and international markets. 151 3. Simulation results The economic consequences of the two alternative base price policies were evaluated by comparing the out- comes of the alternative scenarios, scenario 1 and scenario 2, with the base run. Scenerio l is the case where the base price matched the world market price, and scenario 2 is the case where the base price matched the industrial price increase. Therefore, scenario 1 assumed no protection against the world market and this reflects the government behavior, which placed greater reliance on the market forces. It is a basic policy shift to use market incentives rather than protection. Scenerio 2 assumed corective action taken by the government in favor of the agricultural sector by deter— mining the base price to keep the relative base price constant. It is a policy aimed to establish a planned balance between sectors in order to divert the inflationary effects of an import-substituting policy.(7) The analysis presented is based upon projections of the model developed in this study. It is important to remember that all model results are conditional upon the specific base price scenarios and the value of exogenous variables in the base case that are being used. 152 TABLE 19. PERCENTAGE DEVIATION OF THE FARMER’S PRICE FROM THE BASE RUN EXPECTED (in percent) Years Scenario 1 Scenario 2 1963 - 2.5 7 8 1964 - 4.3 9 4 1965 - 6.8 8 5 1966 - 1.7 -3.8 it 12.6 11.94 1967 - 4.5 (1.7) 15.3 (3.38) 1968 - 2.6 16 2 1959 t - 2.6 13 8 1970 13.2 15.2 1971 30.4 19.2 1972 38.7 30.24 30.8 19.94 1973 62.2 (22.02) 31.3 (11.73) 1974 6.7 3.2 1215 - 8 1 1 5 1976 - 7 7 -7.6 3.7 6.7 1977 - 7 0 (-.56) 14.8 (7.09) 1928 19.6 64.6 1979 13.8 23.73 99.2 95.87 1980 37 8 (12.53) 123.8 (29.74) t Underlined years shows start of policy shift. it Shows period averages with standard deviations in parenthesis. 153 4. Ih§_f§:!§§1§_§§9§9199-92199 Table 19 presents percentage divergence of the farmer’s expected price from the base run price. Scenerio l projected a decline in the farmer’s expected price compared with the base run during the period of 1963- 1969. Such a decline was expected, since farmers’ prices were being protected in the base run by the government against the world market. Scenerio 2, on the other hand, projected a 11.94 percent increase in the expected price above the base level. For the period between 1970 and 1974, scenario 1 pres- ented an average increase in the base price of 31.3 percent and scenario 2 of 20.13 percent, respectively, above the base run. In the 1975-1977 period, the base price was lowered by 7.45 percent from its base run level in order to trace world market developments in scenario 1, and it was increased 7.63 percent above the base run to balance the industrial sector price upsurge in the scenario 2. As a result, the expected price of farmers ran 7.6 percent below and 6.7 percent above the base run respectively. The period from 1978 to 1980 resulted in a 27.73 per- cent increase in the farmer’s expected price above the base case upon a 25.69 percent average price increase in scenario 1, and a 95.87 percent increase against a 100.62 percent increase in the base price in scenario 2. 154 TABLE 20. PERCENTAGE DEVIATION OF AGRICULTURAL OUTPUT FROM THE BASE RUN (in percent) Years Scenerio l Scenerio 2 1963 - .43 1 34 1964 - .74 l 61 1965 — 1.17 1.45 1966 - .30 - 66 2.12 2.02 1967 — .79 ( .29) 2.57 ( .55) 1968 - 74 2.70 1959 - 46 2.32 1970 2.22 2.55 1971 4.83 3.16 1972 5.98 4.62 4.88 1.82 1973 8.96 ( 3.11) 4.95 ( 1.82) 1974 1.12 .55 1919 - 1.36 .29 1976 - 1.30 -1.28 .65 1.13 1977 - 1.19 (-.09) 2.45 (1.16) 1929 3.20 9.15 1979 2.30 3.76 12.86 12.40 1980 5.79 ( 1.81) 15.19 ( 3.05) 155 Table 20 compares total agricultural output with the base case under the proposed scenarios. Scenario 1 predicted a .66 percent average decline in agricultural sector production, while scenario 2 predicted a 2.02 percent increase between 1963-1969. In the second period (1970-1974), the world price effect is much stronger than the domestic price effect. The world price of agricultural commodities increased above the increase in domestic prices of the industrial sector by about 7.3 percent. There are two reasons for such an increase: first, the price of agricultural commodities grew 13.1 percent on the average; and secondly, the Turkish lira devalued by about 10.03 percent against the US dollar. As a result, agricultural output showed a 4.63 percent increase above the base run in scenario 1, while the increase in scenario 2 stood at 3.23 percent. For the third period, 1975-1977, scenario 1- predicted a mild decrease in the agricultural output of about 1.25 percent on the average. Scenario 2, on the other hand, showed a 1.09percent increase in the output. For the last period, 1978-1980, both scenarios predicted a good performance above the base case. Output increased by 3.53 percent in scenario 1, while it increased 3.77 percent in scenario 2. The outcome of this experiment confirms that the 156 agricultural sector responds to base price policy effi- ciently. Supply response to the base price was the a prior hypothesis of the model developed in the previous chapter. Even though base price policy can be used to stimulate agricultural output, it is a limited tool, considering the elasticity of agricultural output, it is a limited tool, considering the elasticity of agricultural output to base price. The base price elasticity of agricultural output is .083 for the base run, .059 for scenario 1, and .036 for scenario 2.(8)Considering that scenario 1 represented the true market price, it can be claimed that output response to the base price decreases as the base price increases above the level of the market price. This conclusion is important from the stand point of policy. 6. Income flows Table 21 presents percentage changes relative to the base case in the flow of incomes as a result of base price policy changes. Scenario 1 projected a mild decrease in the mark-up income by .37 percent and agricultural income by .67 percent on the average. A strong decline in wage earnings is observed during the first period. Wage earn- ings deviated from the base run by .71 percent. By con- trast, high prices in scenario 2 showed a favorable increase for the wage earners, compared with the others. 157 TABLE 21. PERCENTAGE DEVIATIONS OF AGRICULTURAL INCOME, WAGE INCOME, AND MARK-UP INCOME FROM THE BASE RUN Years Agricultural income Wage income Mark-up income 1963 — 43 - .48 - 23 1964 - 74 - 81 - 39 1965 - 1.16 -l.27 - .63 1966 .30 - .67 .32 - .71 .17 - .37 1967 .79 .83 .46 1968 .74 .78 .44 1999 .46 .49 .28 1970 2.22 2.29 1.43 1971 4.83 4.63 4.89 4.63 3.28 2.78 1972 5.98 5.98 4.28 1973 8.96 8.87 6.94 1974 1.16 1.13 .95 1979 - l 36 -1.13 -l.l4 1976 - 1.30 -1.28 -1.27 -1.25 -1.17 -l.12 1977 - 1.19 -1,15 -1.04 1919 3.20 3 04 2.83 1979 2 30 3 76 2 16 3 53 2.07 3 39 1980 5.79 5.40 5.27 158 TABLE 21. (CONT’D.). ----------- (in percent) Years Agricultural income Wage income Mark-up income 1963 1.34 1.47 .70 1964 1.61 1.76 .85 1965 1.45 1.57 .79 1966 2.12 2.02 2.26 2.14 1.20 1.15 1967 2.57 2.71 1.50 1968 2.74 2.83 1.60 1999 2.32 2.40 1.42 1970 2.55 2.62 1.64 1971 3.16 3.22 3.20 3.23 2.15 2.32 1972 4.88 4.89 3.50 1973 4.95 4.90 3.84 1974 .56 .54 .45 1929 .29 28 .24 1976 .65 1.13 .63 1.09 .55 .98 1977 2.45 2.37 2.15 2929 9.15 8.69 8.09 1979 12.86 12 40 1.21 3.77 11.58 11.17 1980 15.18 1 42 13.84 159 In the scenario 2, wage income increased 2.14 percent above the base case. The increase for agricultural income was 2.02 percent and for mark-up income was 1.15 percent. For the second period, 1970-1974, world price development created a much more beneficial position for agricultural earners, compared with the others. Agricultural and wage incomes increased by 4.63 percent and mark-up income increased by 2.78 percent. For this period, scenario 2 prices grew at a lower rate compared with scenario 1. The average increases for scenario 2 were 3.22 percent for agricultural income, 3.23 percent for wage income, and 2.32 percent for mark-up income. For the third period, a decline in the base price due to decline in the world market price resulted in almost an equal decline for the three income groups. However, agri- cultural and wage incomes declined more than the mark-up income. Higher prices compared to scenario 1 were simu- lated in scenario 2 and this gave a favorable increase for agricultural and wage incomes. For the last period, 1978-1980, prices increased above the base case in both scenarios, however, the scenario 2 price was higher than the scenario 1 price. Both scenarios projected an increase in income earnings relative to the base run. Price increase stimulated agricultural income in both scenarios, while it stimulated mark-up income much more strongly in scenario 2. Scenario 1 and scenario 2 160 projections for agricultural income show 3.76 percent and 12.40 percent increases for wage income 3.53 percent and 3.77 percent increases, and for mark-up income 3.39 percent and 11.17 percent increases, respectively. Considering income flows, high base price created unfa- vorable developments for the wage income, compared with agricultural and mark-up incomes in general. 7. Structure of demand The change in the structure of demand as a result of a change in simulated base price policies is presented in Table 22. These results reveal an important fact that one should consider in base price strategy. It is the base price effect on industrial consumption demand. For the first period, 1963-1969, industrial consumption declined 7.78 percent in scenario 1, while it increased 19.91 percent in scenario 2. Agricultural consumption, on the other hand, showed a slight decrease in scenario 1, compared to a 1.39 percent increase in scenario 2. In the second period, both scenarios projected increase in indus- trial and agricultural consumption. Scenario 1, however, stimulated consumption more than scenario 2, due to the high base price simulated in scenario 1. In the third period, scenario 1 projected a 1.65 percent decline for industrial consumption, while it projected a .72 percent decline for agricultural consumption. In the scenario 2 22. 161 Industrial consumption Scenario 1 -15.43 -14.69 -14.75 .74 .50 .49 .49 10.18 10.93 13.76 -7.78 (6.74) .79) Scenario 2 47.55 31.96 18.30 12.22 11.42 10.64 7.29 6.53 6.67 8.93 19. (14. (3. .10 3. PERCENTAGE DEVIATIONS OF INDUSTRIAL INCOMES FROM THE BASE RUN (in percent) AND AGRICUL- Agricultural consumption Scenario 1 .21 65) - .53 05 13) 3.60 .66 (.21) 2.76 (1.76) .96 1.17 1.47 1.74 1.83 1.53 N .94 .31 .16 Scenario 2 (1. .39 .34) .93 06) .63 .64) 1974 prices 162 the increase in industrial consumption is 1.42 percent, and in agricultural consumption is .63 percent. In the last period, 1978-1980, both scenarios again projected an increase in the industrial and the agricul- tural consumption. The percentage increase above the base case is much stronger in scenario 2, compared to scenario 1. Industrial consumption increased 14.67 percent in scenario 2, while there was a corresponding increase of 4.45 percent in scenario 1. Agricultural consumption, on' the other hand, grew 2.24 percent above the base case in scenario 1 and 7.4 percent in scenario 2. Comparing the growth rates, it can be argued that base price policy affects the pattern of consumption; a high base price stimulates industrial consumption more than it stimulates agricultural consumption. Turkey’s dependence on the agricultural sector in the country’s export is emphasized in Chapter 1. Therefore, the effect of base price policy on export potential is examined in the simulation exercises. The assumption main- tained in the simulations is the country’s ability to export its bumper product without any constraint. Scenario 1 projected a 1.26 percent decline in agricul- tural exports for the period of 1963-1969. For the same period, the scenario 2 projection was a 3.98 percent 163 TABLE 23. PERCENTAGE DEVIATIONS OF AGRICULTURAL EXPORTS FROM THE BASE RUN (in percent) Years Scenario 1 Scenario 2 1963 .75 2.32 1964 1.26 2.75 1965 1.97 3.98 1966 .58 -1.26 4.09 3.98 1967 1.63 ( .50) 5.32 (1.47) 1968 1.56 5.68 1959 1.06 5.29 1970 6.11 7.01 1971 13.41 8.78 1972 21.44 20.51 17.51 13.35 1973 54.26 (19.82) 29.97 (10.62) 1974 7.34 3.50 1929 8.66 1.83 1976 10.20 -9.46 5.12 8.88 1977 9.52 ( .77) 19.69 (9.50) 1929 18.12 51.87 1979 8.04 15.41 44.98 49.83 1980 20.06 ( 6.45) 52.65 ( 4.22) 164 increase. Both scenarios showed a good performance for agricultural exports. However, scenario 1, which repre- sented higher price, performed well compared with the scenario 2 export performance. The increase in agricultu- ral exports is 20.51 percent for scenario 1, and 13.35 percent for scenario 2. In the third period, 1975-1977, exports showed weak performance in scenario 1 relative to the base run perfor- mance because of a simulated decline in the base price. A simulated increase in scenario 2, however, created a push in agricultural export by 8.88 percent above the base case. During last period, 1978-1980, simulated base price in both scenarios gave a big stimulus to agricultural exports. While the projected increase was 15.41 percent above the base case in scenario 1, scenario 2 projected an export boom for agricultural commodities due to a simulated high base price. Concerning the country’s exports, it is concluded that a high price stimulates agricultural exports if there is no marketing constraint. 9- Mgssggsgngsis-ssghilitx One of the targets of agricultural support policy is to (9) reduce price and income instability. Once price and income stability is achieved, it can well be argued that macroeconomic stability will occur as a consequence. 165 Therefore, macroeconomic stability is examined through a coefficient of variations calculated for the base case, in scenario 1 and scenario 2. The coefficient of variations for every endogenous variable in the model is presented in (10) Table 24. TABLE 24. COEFFICIENT OF VARIATIONS (in percent) Variables Base run Scenario 1 Scenario 2 PEF 1.09 1.28 .80 YA7 .16 .17 .19 YAC 1.46 1.89 1.54 YWP 1.51 1.54 1.58 YZP 1.32 1.35 1.39 GNP 1.66 1.69 1.74 CN6 .75 .74 .73 CF6 .27 .25 .25 EX 1 61 1.78 l 84 Comparing the coefficients of variations listed in Table 24, it can be seen that macroeconomic instability has been reduced in the base case for the period of 1963-1980. Therefore, government base price strategy achieved its target concerning stability. However, such a conclusion should not be final, and the subject should be elaborated. First, a high base price, as should be noticed, reduced instability in the farmer’s expected price. This outcome of 166 the experiment confirms the hypothesis maintained about the farmer’s behavior under uncertainty. A high base price reduces uncertainty in the agricultural sector; therefore, instability was reduced in scenario 2. Second, the macro- economic instability which occurred in scenario 1 is margi- nal. For example, the coefficient of variation in agricul- tural output is .16 percent for the base run and .17 per- cent for scenario 1; however, the contribution of scenario 1, the world market price scenario, to agricultu- ral output is 4.12 percent (11)more than the base run. Even tough the base price strategy of the government achieved macroeconomic stability compared to the other two scenarios, the economic gain resulting from the marginal instability in scenario 1 is quite important. Our conclusion in this study concerning stability is that support price policy has not been effective in reducing macroeconomic instability, given the implied tradeoff with the agricultural output. This conclusion confirms previous studies on this subject in the (12) literature. 10. Final remarks The macroeconomic implication of the base price policy in Turkey has been investigated in the framework of the base price augmented supply model of an open and regulated agricultural sector. 167 The simulated increase in the base price led to an increase in the level of agricultural output supplied; however, it was noticed that the output response to base price decreased as divergence from the world market price increased. Other things being equal, an increase in the base price above the base case stimulated the flow of incomes as a result of an increase in the output supplied. A high base price led to a depression in the wage income, compared to the mark-up and agricultural incomes. It may also be noticed that a high base price changes income distribution in favor of the agricultural sector. Base price policy affected patterns of consumption as appears in two equations in the model. It is noticed that increasing the base price above the level of the world market would stimulate industrial consumption (demand for industrial goods) more than agricultural consumption (demand for agricultural goods). The opposite trend is observed when base price is determined below the level of the world market price. Since increased base price and agricultural income altered the demand and consumption for agricultural and industrial products, it is therefore logical to expect that income changes would also affect foreign trade in general. In the absence of restricting trade policies, demand for imports would increase. The increase in the agricultural supply, on the other hand would result in an increase in 168 the exportable surplus product. In fact, the simulation exercise confirms the increase in exports due to the increase in the base price . However, the exports gain should not be unique strategy of the government base price policy without considering world market prices. SUMMARY Development has been misunderstood and/or misjudged by identifying it with the level of industrialization. As a result of such identification, most of the developing coun- tries, including Turkey, have devoted their resources to rapid industrialization without giving much attention to the agricultural sector. However, historical records clearly show that no country has moved from chronic stagna- tion into the take-off stage of economic development without first achieving a substantial gain in agricultural production. Therefore, it can be claimed that economic growth in the developing countries depends heavily upon improving the performance of the agricultural sector, just as it did in the more advanced countries at earlier stages of their development. Turkey’s agricultural sector and government price policy have been investigated in this study in order to expose opportunities in the agricultural sector to improve Turkey’s development performance. The economic development of Turkey was reviewed chrono— logically in Chapter I to evaluate explicit policies which consequently became the working rules of the Turkish economy, in which the state assumed the task of capital accumulation to accelerate the pace of industrialization. 169 170 Government policies carried out during the planned period were not able to produce a rapid industrialization similar to Western Europe. Additionally, it has been seen that Turkey’s economic moved out of the development phase in the late 70’s with steady deficits in the balance of payments. To enlarge the exportable surplus of the agricul- tural sector which Turkey has, the competitive advantage was seen as a way of easing the pressure of the balance of payments deficit. In Chapter III, the role of the agricultural sector in the process of development was investigated, and it was concluded that an export-led strategy of development without employment can be achieved by stimulating agricul- tural output. In order to recognize structural rigidities in the agricultural sector, as well as its potential to improve output in that sector, land use and its character— istics and inputs in agricultural production were investi- gated. The role of the government in economic life was discus- sed in Chapter III. The government was found to have a critical and important role in economic life. The govern- ment was seen as an arena in which and for which various participants compete for power. Cost was defined as a function of power, which was a function of the relative use of government by interested parties. Base price was given as an example of power-related cost in the economy. From 171 this point of view, the base price experience in Turkey has been investigated and it was concluded that the system lent itself to political influence and thus decisions that might be more political than economic in setting agricultural prices. Even though the base price system lent itself to poli- tical influence and thus to decisions that might be more political than economic, the resulting economic consequen- ces of implied policy were important. Therefore, to mea- sure and evaluate the effects of government involvement through the price mechanism, a base price augmented supply model was developed. On the supply side of the model, it was assumed that farmers respond to the base price by transforming information obtained from the government into their expected market price. The expected market price enters into their decision-making process and performs a crucial role in determining the level of output. The level of output, given the price received by farmers, determines a large proportion of agricultural family expenditures and thus base price policy effects spread throughout the economy by inducing income flows, consequently, demand for agricultural and industrial goods. Given agricultural production and the level of domestic demand, exportable surplus was calculated as a residual. Parameters of the model were estimated. Having infor- mation about estimates, the model was simulated for the 172 period for which historical data were available. The model was validated by comparing the original data series with the simulated series for each endogenous variable in this chapter. In the last chapter, Chapter V, two base price policy scenarios were defined for policy simulations. They were scenario 1, based upon the world market price, and scenario 2, based upon the domestic industrial price. Simulated results of these scenarios were evaluated comparing them to the base case results derived from the historical base price policy of the government. CONCLUSION The role of agriculture in economic development has been studied from different viewpoints in the literature. Models constructed to analyze various agricultural policies differ from the stand point of assumptions about the price of output, and whether it is fixed by government policy in the regulated model, as opposed to being determined by supply and demand in the open model. The relevance of one or the other model depends on the institutional context of the specific economy. In any actual situation it is impor- tant to know which variables are subject to direct control and which are to be determined as a result of market forces.(l) In this study, we have presented a model to trace the macroeconomic effects of regulated agricultural price. The treatment of agriculture was based on a partial analysis in a macroeconomic equilibrium. In that framework, resource flows to agriculture incorporated sectorial income flows into a general equilibrium model. In this context, exoge- nous support price (base price) became a sole determinant of the expected price of output, which determines input demands and thus the level of output. The supply of output has been viewed as the link between agricultural and 173 174 industrial consumption. Consequently, the supply of and the demand for agricultural output have become the sole determinant of exportable surplus. The macro economic model in this study demonstrates that the impact of base price policy in the agricultural sector is quite important, considering the economic conse- quences of implied government policy, and the following conclusions are derived from the experience of Turkey: 1. Agricultural supply responds to base price policy positively; however, this response decreases when higher divergence from world market price occurs. 2. Base price increase above the world market price stimulates agricultural income and mark-up income while it depresses wage income. In such a case, the increase in the agricultural income is much stronger than the increase in mark-up income. Therefore, it is claimed that base price policy has affected income distribution in Turkey; however, the effect did not favor the agricultural sector, due to distorted agricultural price as a result of government involvement. 3. Base price policy affects patterns of consumption. High base price induces industrial consumption more than it induces agricultural consumption. Since high base price induces agricultural consumption more than it induces production, exportable surplus in agriculture accumulates. 4. Base price policy reduces macraeconomic instability in general. However, the economic gain resulting from 175 stability due to government involvement is less than the economic gain resulting from the marginal instability implicated by world market price. Although the data used have been crude and results are treated as merely tendencies resulting from supposed policy decisions, a dramatic shift came from two alternate scenarios compared with the base case. As expected, the findings indicated that an increase in the base price leads to increase in output supplied and the income of farmers and it stimulates demand for industrial commodities. As opposite effect occurs when base price is decreased. From the perspective of our analysis, base price policy as portrayed in the base case is inefficient compared it to the world price scenario. Overall gain resulting from world market price in agricultural output is greater than that of the base case. Base price policy in Turkey has been used for political advantage without giving much attention to its economic consequences. This study suggests that the government should proclaim base price as close as possible to the world market price if the govern- ment is not about to abolish it. APPENDIX A DATA RELATED WITH FIGURES 176 DATA RELATED WITH FIGURES Industrial Heal Growt rate, time Real GNP production wages Employment industrial production 1962 100.00 100.00 100.00 100.00 0.00 1963 109.69 111.42 104.92 102.44 11.40 1964 114.16 123.55 117.22 103.38 10.90 1965 117.74 135.21 123.09 95.94 9.40 1966 131.85 155.66 123.09 100.36 13.10 1967 137.40 167.98 128.96 101.96 7.90 1968 146.56 190.38 134.82 101.56 13.30 1969 154.51 210.38 140.58 104.58 10.50 1970 163.41 213.21 140.68 102.75 1.40 1971 180.04 231.84 134.82 107.20 8.70 1972 193.44 255.06 128.96 108.43 10.00 1973 203.84 283.98 134.82 111.35 11.30 1974 218.90 307.45 134.82 112.49 8.30 1975 236.32 334.98 152.40 114.33 9.00 1976 255.04 368.36 169.99 116.74 10.00 1977 264.95 405.80 169.99 118.05 10.20 1978 272.54 441.97 128.96 123.03 6.60 1979 271.44 408.47 128.96 123.97 -5.60 1980 268.47 385.98 99.65 122.25 -5.50 Population INV/GNP INV IND IND (in millions) PUB/PHV PROD/GNP PROD INDEX 28 90 0.00 0.00 13.90 100.00 29 65 19.44 96.80 14.10 111.40 30 40 14.47 111.90 15.00 123.50 31 10 14.41 112.70 15.90 135.20 31 90 15.05 114.50 16.40 155.70 32 70 16.09 114.70 16.90 168.00 33.60 16.61 122.60 18.00 190.40 34.40 18.01 117.70 18.90 210.40 35.30 18.42 112.00 18.10 213.20 36.20 18.50 101.30 17.80 231.80 37.10 16.72 99.20 18.30 255.10 39.09 16.85 88.50 19.30 284.00 39.07 17 24 92.40 19.50 307.40 40.06 17 08 101.60 19.60 335.00 41.08 19 91 106.30 20.00 368.40 42 13 21 64 117.10 21.20 405.80 43 20 22 88 100.80 22.00 432.60 44 31 20 82 127.50 20.80 408.50 45 40 19 27 127.30 19.90 386.00 IND=Industry PROD=Production INV=Investment PUB=Public PRV=Private 177 CPI WPI Public Private Budget Import labor labor deficit productivity (million TL) (thou- sand 3) n.a n.a n.a n.a -100 n.a 100.00 100.00 100.00 100.00 -5 n.a 102.70 99.00 105.70 102.90 -614 537 107.80 104.30 130.20 107.60 1100 572 112.20 111.70 135.10 123.60 -691 718 122.30 119.90 190.10 120.30 99 685 128.90 122.10 194.40 124.10 -692 764 136.50 132.20 n.a n.a -1826 801 153.60 144.80 n.a n.a 255 948 182.10 169.40 122.50 147.10 -5646 1171 222.40 195.60 231.20 156.30 31 1565 264.70 236.70 217.80 165.40 -2853 2099 322.50 300.40 171.20 147.40 -4201 3775 318.60 334.60 209.70 147.40 -1402 4739 371.60 392.60 172.20 179.90 —4312 5129 469.70 504.30 141.90 185.60 -44030 5796 779.20 774.80 132.40 195.80 -40958 4599 460.20 1357.00 109.60 174.90 -95958 5069 152.10 2581.90 n.a n.a -107044 7909 178 EXPORTS EXP AND EXP AND WORK Crude WORK REM AND CRED Birth Rate (thousand 3) (per thousand) 411 411 755 4.80 464 473 712 4.80 490 560 805 4.60 523 638 934 4.60 496 589 865 4.30 537 644 953 4.60 588 729 1092 4.30 677 950 1462 4.30 885 1356 1736 4.30 1317 2057 2400 4.40 1532 2715 3222 4.40 1401 2827 3253 3.90 1960 3272 3910 3.90 1753 2736 3291 3.90 2288 3270 4144 3.90 2261 3244 4521 3.10 2910 4604 7223 3.10 Period AGR Imports Imports Imports Exports Employment Investment Consmp Raw Mat Agricultural (percentage shares in total) Exports Exports Petrolium Imports Imports Imports Mining Inds Prod xof Raw Mat EEC USA ME&NA (percentage shares in total) Imports Imports Exports Exports Exports Exports Exports EBC Others EEC USA ME&NA EEC Others (percentage shares in total) 179 Planning University Teaching staff UCL/AP UCL/AP Periods Enrollments Universities Public Private 1963-1967 45304 30070 24.20 31.70 1968-1972 68427 5990 20.60 28.40 1973-1978 85863 8800 28.60 34.20 1979-1984 133547 12717 47.80 32.30 Strikes Strikes Public Private 100.00 100.00 90.90 98.80 73.50 125.50 135.60 324.70 AGR=Agricu1ture EEC=Common Market Countries ME&NA=Midd1e East and North Africa EBC=Eastern Black Countries EXP=Exports WORK=Workers in abroad CRED=Capita1 transactions USL=Unit labor cost AP=Average productivity APPENDIX B DATA AND FIGURES RELATED WITH ESTIMATION 180 we fit he he he 56 r mocc-~.c w¢c~¢~m.¢ u~mmocr.c u-m¢e~.c momewmu.c mcrco-.c .rhrmmo .momNoc .mcwmrm .rpwcpe .maccmm .m-mr~ .mmemrm .Ncoucw .mmewcd .cmeceu .¢¢mc- .mc~¢- .ccCcmu kc he No «C h: hc ¢ wc¢-0h.o me-oeoc oucmmmN.o mdo¢mo~oa w>¢~mmuoc ucc~¢~uoo .mwhomo .m~m~o¢ oPMMGOm .ucmam¢ .Nomown oom¢m0N oh¢>m0N omommmm ocwo¢- owmsswu .drrmeN o~¢c-N a QC Lat—N m onN¢mr ommcoeu oNMurmd ocwewrw concave oocmume Gourmem mo—wccw Ooh¢~rw Mo¢¢mcw mo¢crwu nomchu w.mruh~ ~omc¢h~ Oothéd doctrwu momccmu m.omcr~ m.N—wru J A. comegd hNoewou ONQONoA Jamaaou doomuou omocu.~ moaacou memco.c m¢-c.~ Nohuooa hmmnao on. oaamhooo ooo¢~ooo sucommofi OOmMOwos dNGOhhoo mcm¢~ooé OONmuwoa Nooo¢ho3 .hh m2—J fiDoQQOA o. go~o~ ¢D.w~o~ afioshau cocoho~ o:.mho~ aco¢~¢~ odom~o~ 6 ..Mhau 93.aho~ Gonc~o~ oaooova DoomOOH Oc.~oo~ 36.300A ooomOOA aflo¢00~ 00.noca GD.N00~ 29 no ue> ~<> on Nova Nvm~ mead Nova Nova Nowu Nvan Nova Nova Noam Noon Noqd vau wood wood Noon Nvmq Nova Nvmq 181 c 56 w~mm¢-.o o¢~¢¢0¢ ohemmom .hONOFA o¢¢hwma co0N¢Nm conmmoo ohm¢¢o~ ro¢um¢~ doow¢¢m moomoo¢ nocmcom noho~¢m nooemmn dc.uNNm #ohONum coONfimN camchuw PC.Nmmol xm m 0.0mmcu oomoccu oonrc~ courwed oohumtw cocmmmu oocwuru o.Necc~ oohecru oo¢Nomu oouomwu Oomewmu coccemw o.m~mmu oo¢m¢wu co¢omru oohonwu CoONNMu coheumu >w¢ N ha madohhooo PO mmoo¢~§oo NC mcficwmwoc so woobomdoc .Ommohw ocm¢m¢~ ocehooo oowmdoe .como¢m oON¢NQN .OQQMHN oaaodod oocmwfiu oc~0~v~ .Ommanu ocmhhcu O.GQ¢NG o.fim¢¢m coauo~h uzx hZ—ma a ogoomod ca.ohma Gnomhau noosh0H oaochou 03.m50« nzc¢~oq oo.nho— 53. NFOH oc.~ho~ Geocho~ naoaoaa caooowq oao~oaq an.ooo~ ooomowa aa.¢oo~ 33omoo~ OooNooa Du .wh uZ—A mood Nvaq Noon Nona Nov— Ncad N00— Nvad Nomd Nua— Nova Nomu Nvou No¢~ N0¢~ Noon Noam N09— N09— 182 m chPCuoc cccn¢~uc ccovcwof cccrc~.c cccrc~oc occmmdoc Cccrcdoc dciwococcc.c ccurcaoc cccrcwoc ocomc~.c ~ruu00000¢.c urumooococ.c denuooocCc.c urnwccooca.c .cuuoococc.c denuococcc.c urlwocCoccoc acuwccoooeoc d w ~o¢m~.o couum.¢ muoeo.m ¢co¢—.~ m~0me.~ cmmm~.H cacao.~ ovcsosoo emm~¢0.c mmccem.o «Resum.c ec~c~¢.c o~¢n_¢.c rmmmsm.c mometnoc same—n.a cwmmm~.o mm-c~.c mo-¢~.o 53 n mmwco.¢ mmehr.w ¢¢ons.n ~¢w0~.~ owowuow mshewod ococoou N¢Ncem.c eucreeoc ommcr¢.c ucc¢m¢or ~N0©u¢or mo~mam.¢ nwmnwm.c mammmror «cNOcrur o~crrr.c heueuror acocmror hum N mQNFN.m mommnmoo mommmmoc oomwMo.o anmnm.o momwmwcc GOGGOoa wuthmoa mnmst.c NNucmeoo NNgom¢oo QNNO¢¢oo omN¢hcoo newcomoc MMNmNmoe. m¢mwomoc mnmoamoo mnqocmoo nmaocmoo ~2m cum uZ~4 a aa.owo~ OOoONoH cc.mho~ oo. 2. on o9.c~o~ ocomhoa 2.1.2.3 ooomhou aa.-o~ co.aho« ocoahoa 9.160?— oaomoou o:.~oo~ ocoocqu oo.mvo~ aao¢oou noomoo~ oc.~oou CH Nova Noun wood Neon Neon Nwwn Noun ~09— Nowu Nova Nwm~ Nova Noqd Nova Neva Noqq NoOu Nova Nova 183 m amoebO¢ ~rnchOocwoc douccmu mocucmu .owmcmu crouccm ~nuucoocom.c cc.r¢mm ~.e~w- c.ac¢~o “sorc.~ a~m~.H~ mcocoe.c ~N~¢¢.~ mem-0.c -eo¢m.¢ ccwmm.~ acomm.~ bqu ¢ rmcm.- rwmem.m ermo.~ roc¢m.~ mcm¢~.~ ¢~m-.« ecccc.~ mmmceh.c ~90m~o.c ~c¢mcm.c mm~¢o¢.c po~c~¢.s ~¢o-¢.c m:mm~¢.3 mocmc¢.s ceommm.o com~om.c emcmmm.c rzwo m hmoom.c mmewm.m hawmm.m momrw.~ cc~c¢.~ domc~.H ccccc.~ mc¢0c~.c mmmmmm.r -m¢c¢.c mmmcc¢.e o¢~cmr.r ~c>emm.e mos¢~m.r smormn.c -¢¢m~.¢ m~m»m~.c vemc»~.e Fuwc N ommmm.¢ oo~c¢.~ mmmmm.~ ommdm.~ «somd.~ -ooc.~ 90395.4 «memom.o --~¢.c --~¢.a Neoas¢.: eoemhn.c ¢~o~om.o omeomn.s omecmm.c -m¢mn.o Nmeamn.c mnenmn.3 ~ma hzamm .mh .2 co. 8 9H 2... 2.0.— Oc.m~o~ o:.-o~ nooo~o~ oc.m~0~ ooo¢~od n3.m~m~ afiomhma oo.~po~ Q3.asm~ 6c.9oo~ ca.ao@~ Ojohooa oaoooo~ OD. mood cac¢oo~ ofi.m00~ on "C36? emca "wueqm CPNxxOZ cqqmqmum pgapga mu 4;“ CwfliflfiiflififififiiNsaw*#¥*§*##§§§*§**i#*§**§*****§§**w##«wfswfiazvfififififiiiwwwfiififkfi#wiifiwioiw ##fiiwwfiwfiwfiw *éfiwwflfiwfis Qo—m .moH~z~4 m M d .vmrcrw .thuun oaoawo~ mww~ .Cmepru .mnhawm O).o~o~ Muvq o.~m¢¢c .N~N~¢~ ncomsad nvw~ n.9emmr n.aummw ooa-®~ moc~ o.wmcwr $.Na30h oo.e~o~ now“ o.~murm c.~0mwm onom~®~ mvwa m.mmmc~ o.¢¢mmm bo.+~9~ nova v.Mu¢¢r @.D©Dn¢ n:.m~?~ New“ wompcdfi o.a~NmN n .N~o~ mvv~ 4 ocofiowc o.mmow~ oo.«~o~ nova .w or.cm¢> «.samma 03.3sfiq mwmq no.~mfic mu~m~ma oo.$oo~ nvma Oc.o»n¢ Nowq¢ma 05.nowa Muw~ o~.m~r¢ ~.d00m~ 03.~v¢~ mvwa nmodrmr 6N.~acc 1so¢oo~ Mesa oc.¢wpm co.n¢mm oo.mu®~ fivw~ o¢.drmw 33.00fm eq.?oo~ Myra nc.r~nw Cw.fiom¢ fig.*uau Neva a3> an» mu r: ......37 now 2......y..> :znnsquxm mmmawm mznh .1: 3:. 185 LEGENDS FOR APPENDIX B YA7=Index of real agricultural production YPC=Agricultural production by current prices (105TL.) CF=Agricultural consumption by current prices (105 TL.) CN=Industria1 consumption by current prices (105 TL.) XNC=Industrial production by current prices (105 TL.) ASV=Area sown (103 hectares) EX= Agricultural exports less agricultural imports (105TL.) PN7=Index of fertilizer prices PF7=Index of price received by farmers W7=Wage index R=Interest rate DEF7=Agricultura1 price index DEN7=Industria1 price index QF7=Base price index WEF7=World price index YWP=Wage income by current prices (106 TL.) 6 YZP=Mark-up income by current prices (10 TL.) APPENDIX C DATA RELATED WITH SIMULATION RESULTS o 1“0.51 W. x ‘ ;,c-nmwweaa.oun+ ,nw;»w~nmmam¢$ww.fizw ,; «armcsn-o .-awmwwwwwanmmma.onwawz. in . sonum~».cn _a,¢.»ww°o~.oy . ; «drumoa.or; ,wjmmwwunuwmum. mwpnu» - ,,~ouunnn.onwx: .‘ ;x..wmvw.«arwmm¢.01mw . , «creams.ohg:- . ,+,;~w«a+wmm««qm.q‘:, . so-ms¢~.o O‘CG ‘ a '136 mgr E I 1L: . .1 -.---Nndenmuo cm no ~04; qu4<> sanmo-.ou:‘ ., .., .+.om~»_u og< Am«>v ZOHBQDQOZL A mewmuomm mmuamw mi—w .«a;wzss o<>m -.oo.uz~4 Io<>ml ‘ .om mzun cs¢.. m~¢.~ ~m~.s; oms.~ o¢2.2-: o-.~ -o.~ coo.~ c~o.~ o¢o9.o ammo.o m-¢.o mmom.o, o-a.o eoem.o mnsm.o cmom.o .cee~.o masses MMDOHW I meo— AO¢N.~ mowod mm~.s : moo¢.o oa~m.o ,o¢~Q.o ommw.o mmow.o too-.o mcsw.o .. 1.11.]. r; .wmmmsm.o «igmqshue mm WZ~J nbmos mhw~ macs. asen apes, new" .chmu m~m~ N~o~ -¢~ u~¢~ 000— «own pom— ooo~ nova Voou mood O. . o so msos.o- .\ owWflxw.sMumhmomOoolu,xqs ;‘1 .loo m¢s3.o-; wavy» mwmo,uo-1omw . _,: on.m-o.o , ,.ws -uhwwoumomaaow,z;, mo-umos.o- .nooflmmms.o1«. sofasmo.ou- ‘mo woo~.o_ on moos.o- «xwommoos.on ,oo mono.ora «o-uos¢.or on mnm¢.o .. y‘mqmumdao ...; , so mo¢~.o snoommmoouokr 17 8 1 .awddaamwwx vac mo.sooa :33. .mo 3:; ‘ osso x2wo .No mass 4 so wsooo.o + i so wsosm.o +§ so mesom.o +* so m--.o I so woizo :74- + so momm~.o . so moos~.o .« co mssno.o .. oo mseom.o . oo m~¢s¢.o + co mmsom.o .« oo wsoem.o + co omoom.o + co w¢mo~.o + co osms~.o + no wmo¢~.o + co oosm~.o + co usm-.o oosssm mwoo<> .t.ouss_u o2< .scnqosoq so soon i v r .w. .33 mznwsm Ao¢wv nuonm Bzmzzso >2 oneoauoxs sooneszJHz a n m.m zs_mmm> xcmmwuoma mooswm wzos «sos.snoo #9 Nu, Nu hU Nu Nu (bu co 0. 09 cu Cu 09 cu cu Cu 09 --ou ,wm~ow.° whme¢com ‘uowwmoo muudwoo w-h~.o w¢Nm~.o mowedoo mahosoo, wNmsmoo womm¢oo ‘wmmNMoo w~¢NMoo wamom.o whoomoo wmmhmoo mcomwuo w¢¢mwoo wh¢mwoo J .+.ouhh~u 924 .*_4<3bu< mo hams uzwu co co no mo (mo me we mo mo +s mo + + + 7+» +§ +¢ +* +* .m0 m2—4 a~>w .¢@ w2~4 u~o¢~.o mosms.o oomms.o msesm.o momom.o u-~m.o mo¢o~.o w-o~oo wdsm~.o macaauo .seoo .moss A.m¢oo .osmo .snoo .m-m .soo¢w .omo¢ owhs~m AQZ>V WMUHmL BZMZZDO >3 mZOUZH 3342 .MJ mmbflam *5 9d 9 >435 new ZD—ntu> rfinnUJDZL abozun UEH. 0“ Cu mu m9 mu mu .mo mu mu wmoom.o meMNuo mmocmoo wmmmmoo (moommoo wo~o~.o o~oo~.o uso¢~.o oo¢s_.o .Nmmo :.ooos .Nmoo .ooo¢ .m~m¢ «soon. .smom usmm~- .nsos -443Hu< hp” UCNJ omw~ who" mswu hhqu -vnwa mhwu «ova mhm~ Nsvu uhou thu wowu coma howw coo— mood ¢Oo~ mood Du 183' _ do bofiu MgJogomquo .71 :{,o mo monomo: w. ;. , mowmoos.o{ mo monuoo «oguwsmoc Jozmamm.o .,rwmumm ,Jo mono.ou «nmumnN-Ol. $0 mmoc.cs ‘ . Jo usmsow: Jo‘usom.ou ,mJngmmom.oam- .5 mm Na .ol Jowmsoo.oumwr oo,usoa.o- ,woanoawommu Jdfifluwwm mw34<> .+.owhh~u 07d so asaqdahu< mo humhu< §+ «zoo co co woo mo .mo +§ #* poms + m. ¢* +* +* +* +* +* .3? no mo no mo no mo mo mo no we no mo no .so oz”; 520“ .oo wzsm wmmo¢oo w¢-Noo wmmNAoo wmqvmuo. wwwmhoo mmwomoo w¢mcm.o (upmam.o wmoomoo wmth-o mm¢cho wOMONoo woomo~.o- umso~.o womhuoo ommo~.o- womoaoo m¢0m~oo uwhhuu Alev mmUHma BZHEZDU WQ BZOUZH Lblxmdz .vm xza$am I.“.mll)\l ‘I J ’ ‘.“l‘.l‘. ...1‘,,J,)'m ,J.I-J, ca 09 09 mg my my mu mu mu mu mu mu mu mossn.o wmummoo wm~¢—o0 mummmoo. wwmcpoo‘ wmmmmou w¢mmmoo mmOQVoo w~Nmmoo wOOmuoo womm~oo m¢~m~oo wo¢m~oo woomdoo .ssoo. o¢¢mw -soosm Idam¢ A ...owsssu coo ...ocosoo lo sooa mmUHmL PZmKZDO wm L20 .mm mmDOHm W r-NNDIJJ, N.“ ’—~;‘~Jfl.lfl. ’.31\1J"—(sb still/.-n‘a J..Q . -\ J’-d.d COG N am up- n. 1-, msxonms.ou + ,_, t w. .m» mo woso.on,+ w.. . 1 ms moss.oy . 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NH- W,Wm.swwmm,1orm 1 mo,omoo.ou-w »‘ »;»¢qmo«wmmm.o _,»,_ mo,oJ~o.or,., .,wmmmmm~nnowm-,, noioo-.ount., wumnm,ooan~J.oumm ... mo oumn.ougl nah.oommenqaumm H,,;, mo mono.or ,_wwsoqoosm.omwwwy mo moss.ou, ,.,n;msmmooo4ommm, w ,..J%E~. . Lu scan,, ..mw34<> .+.ouhh~u cz< 7:134:3‘ Away memosxm AdeBJDquua me *+ #04; sumsu< so co co ac ,;-lcc +* oo o mo *+ mo + mo +§ mo +§ mo ,q+*, mo +§ mo ,mow no + * mo ,+*,,mo +§ mo +*, mo .sooozoo omsoo.o womoo.o oooomJo wmsoo.o, osmoo.o nwoooo.ox. moccaoo mcwdwoo wHMhmoo oJomo.o» oooos.o, osoms.o, oomms.o gmooooyow wcooooo womsoo.ofl w¢cmooo woomo.o» ouhh~u .OHQ flawam, so cu cu ,au 69 ,mu- mv 09 mo .mo. mu mo. mu nu umu my mu omoNo.o. oJJoJ.o .ommom.o wooso.o osoooqo wmomo.o ommoo.o wmooo.o oooos.o uo¢¢m.o omoo¢.o omoom.o osoom.o usmmm.o .mmmm monom.o, wmhmw.o wwhdwoc -ou w Pmc~¢.~ #mwN¢ou ¢mchou sword-A ewwm~od N~O-o~ c¢mroou domcrou Cuwwoua me¢0fioo Nommmwoz ~h¢mm¢oo £m~m0moo m¢amhmcs cm¢c¢moo thrumoo Naomfimog ONOCONoO a~<>w ¢so~.>o:v m N¢¢wr.u cemcmos ummeuou deucu.u NmNm~oH quusou mmusc.~ mmmdr.u ¢c-mc.c oa¢¢¢c.c mm¢m~oor momucooc shorom.c sedsrc.r deconvoc Ommrsmoc Hmmw0hor «omcps.c o~<>w N «oom~.o s~.¢~.o o~oo~.o aaooo.o ooooo.o omooo.o oasoo.o «womso.: momoo.o wosoo.o smmooo.c ooooso.o ooooso.o sosomo.: oomooo.o osooss.o msmooo.o oo~o~o.o 54> ZDHBUSQOZL JflbeJDOHmud dfiuz studs .m a 03.3mwa fisoohoa oaoasc~ daohnom flaoo~o~ OOomhqu an¢~oa 03-MhOH Gaomhmod aaoahoH 03.650d econoam aa.nom~ anosoo~ accoown aaomoou fifio¢oo~ ca.noo~ om hzumm om .NU mdmdfi mom 2C.wdu> «Cmmuuadm wwwxwm mzuh uz~4 uZ—a h mvo~ Mood mood moan moaa mvwd Mvo~ Mood Mood mood mead m~o~ Mvad mood mo@~ mum“ flood mvwd wz~4 198 st sc sr sr st sr sc m ummmsmcoc uNrmmo¢oc wcscccmoc mc—secwoc wmmmrc~oc umdsumfi.c us¢Nuunoc onc¢mem QKWPCOm .mcm~¢¢ .scmccm osoccmm o—mmmom .smsuam .rmwscm ocsrsmN .semcmm .—¢s~nN Nu<>u sa so sf so sm ss so ¢ wwsscoooo wfiosc—m.a wsmmscm.c wtm--.0 wu~c¢o~.c ueuomm~.c uosmc-oo .mocamm oomc¢mm oom~¢s¢ .~m¢~0m osc~o¢m o¢c~ocm .mm¢m0~ .NscmsN o~mNc¢~ .c¢q~m~ .mmemNN ou<>u muUHmL EZQZZDU >3 BZOUZH Jw so so sc so s0 s: so N wss-wmoo wsoow¢¢o7 mONwomNoc wuoaudNoa mNNNNsuo: wN~¢Nm~oc mecmcdoo .Oswoas sandmsm .Dao¢o¢ .~@¢msm comt¢Nm .¢domom .mmsawm .Nrwmsm omMSONN .mN¢¢mN .ams¢MN u<> oo~ uzmm a OfiofimOH 030$N6~ OCofiFO~ OOoshOd ODoOPOA OQomh@~ ODo¢hwd 090Mh0~ ODoNs¢~ OOodhOd GDoflhOH GO. 000." OOomoad ODoKO¢d Ofio©00a DfioMOOfl fi00¢00~ GDoMOOH a— Mvo~ m0m~ mva~ Mvwd fiku Nova newu Mead Mood avad fiuw~ nvau nvad man mood vad Mood nova ..x 199 T ocOOGNN odcrouu coc¢c~s ~.—rso¢ Nos¢mrm m.©0©~m o.~wcc~ moom¢CN cosmmma ¢osm-~ ¢wos~0m mmocm¢s cco~cso cmodwwe m¢.¢urm dcosocm Oco¢£c¢ r¢.srs¢ Nexsu ¢ .@N~0¢N .~¢OsN~ oocmmms Nous¢um Nomcmwm n.N~NNm ¢o¢o¢0N soNusoa uo¢~sm~ mosrfiuu csoc¢ow WNomcss Gmo¢¢¢o ucossmo mmocmco mNomuNm c®.coo¢ cGoONm¢ gmxsw ¢sc~s>ozfi m .0~N¢- .HNuegu n.¢rrcc comsmer ~.m¢mcr ~.N-~r o.._rmcm o.Ncsm~ ~.mscr~ n.0crru ~N.¢~s¢ Nr.swms em.rrsc N¢.¢c¢c m¢.rpcm ~0.mr~m QMo¢rO¢ ¢~.rcs¢ Gazsw muUHm; 923z330 *2 EEOUZH N oQMNoaN .co¢sn~ soumooo co0smmm ooomomm cosm~mN moNNmo~ o.m~o¢~ o.ms¢- coauomo encamos Oo.~m30 smooso¢ aaomNfl¢ 3m.~30m 10.0N3m c¢.ammN OoomsDN azs mU UOWmUUOxQ mu—muw w2~s A: mead mood mV@~ mead hwwa fivma Mama nvod Muoa Mvwa nuoa nvod Mood mvod Mama fivoq mwo~ Mead wZ~A .Na 9.: .— m e m N o .cmomsn .~como¢ .ossows .smooon o2.oooo nooo .osmmco .oonoo~ .smocoo .nosoow oo.osoo mooo .mmcsso .oosoNo .oocooo .Nswoeo oo.osoo nooo c.oooso c._oooo o.oorso o.o~omo oo.ssoo mooo ¢.o¢coo o.~mo~s o.ossss o.~ooos oo.oso~ mooo s.mcmom o.¢o¢om m.opsom o.oomom oo.msoo mooo ~.oom¢m s.smmom o.ososm o.¢¢mmm oa.¢soo Moos ~.¢wss¢ s.oomon o.scoss o.oooo¢ os.ssoo mooo o.mrscm o.o¢oom n.oscos o.oo~m~ oo.~so~ nooo m m.¢moo~ o.o¢sm~ m.so~ms o.mmooo oo.osoo woos 2 o.omo- ~.ss¢- m.csosm ¢.oommo oo..soo mooo m.omsc~ o.~cmo~ o.ooscs o.smomo oo.oooo nooo o.oocos ¢.oo~oo o.mooso ~.~o¢mo oo.oooo mood o.owmoo m.oosos o.o¢ooo o.ooom~ o..soo~ mood m.mocso «.smoss «.mooso o~.oooo oo.oooo mood ¢.oo«co (.somoo o.os¢oo oo.m¢mo oo.moo~ mooo ~.oacmo o.ocooo m.om.oo :o.ooom oo.¢ooo mooo s.¢osro o..¢omo m.osooo oo.oom¢ oo.oooo mooo Nosst oooso ooosw ass oo noooz; szozzou so ozouzo ;:uogonn n.~ zrorsl> m:wnmuamm wmsmom uzos do wzn x433 moN 7.0—wows dcmmuuoxo mu~duw mzus Nu wz—J 202 szumo .s~ ozud m ¢ m N a so uccmmssoc so w~m¢c~ooo so wm¢0s¢soc so woosdosoo ooogoOH . so rc¢mco¢oc so uraACN¢go so oummmcmoc so meNNO¢.) o).Oso~ . st noomNCN.c so usmm~sNoo so m¢oosm~.( so M~GMMMNoz oo.osoa . sc uorrmOAoc so wco¢s0aoo so wo¢o¢o~.c so w~o¢no~.o ooossod . so u~c0~m~.c so uuocrmuoo so wocOKmuoc so ms¢Nmm~oo ancesOA o st usomNm~.o so w¢msm-o3 so w¢mmr-or so wc0~¢Ndoz o3.msOH . sr anMchoc so wcso~o~.o so wnmodouoc .mNsomo ooo¢so~ o¢soscs .oe¢oms omsccws .MHmNoo oo.Msm~ oucoccm oo¢¢so¢ .merw¢ .smno;m oo.NsoH odoumcm o¢o~oom ooccrrr odomom¢ acousou .~0scum .Nu¢on .mNrmdr .Nomomm oooosod .¢CNNCN .NMCsON oc~c9ew com¢m0N oocooo~ .m0m~MN ochcMN omormsm .s¢socN cococoa o¢c¢CNN .onmNN .socwwN .moanN ooosoo~ .mNsuoN .s~¢¢o~ oo¢0#rs .3oo¢sN 03.00ou .m~mmc~ osmmasa oNOsoc~ oomssod ooomvo~ oeo¢mc~ o¢omoo~ .amr¢c# .aosooN oo.¢oo~ .Omoccu ovurmos amo¢uCu .~¢oN~N ooomoou «mum anew nucw mu om ZOHELZDmZOU AdePJDUHde adflds ‘UI' m.\.4 B Is. a.\u\l\19o))f- b \a..—4/—Jfla or. 4 - moo~ mood Mood m~o~ moo~ mood mood mv0~ fivo~ m~o~ mood Mood mvou mvmd mood Mood Mood Mood .17—‘4 203 so sr so so so so sc s: r urm-~o.c uNNsm-.c wmcmN—~.c Lm~m-~.c wmc¢mc~.c oNouwc~.c uc~m~c~.c mocmcco.c .roooca ocom¢¢m .NMcsms .osmcus .sCNmmc .oucmoo .Noec¢e .c¢mscm .cuommm .mosmsm so so so so so so so N so mo3¢o~doo w~a~¢-oo wusmmoduo wasNdaaoo moomooucz moo¢oo~oo .oNsomo .NoNNos oooomoo oacosNo .Assnoo cocoodo .Nuomos o¢om¢ss osmomso .NmNooo oomom¢s onsdos sznmo a ODoGQOA 66o0s0~ 060ms0~ oaoss0~ oaocsod OGomsou 030¢sOH OfiomsOA oo.NsO~ ODodsOA GDoGsOA GOO GOO“ ooowOOH O‘osOOH oOoocou oogmoou 630¢00~ n o. mood :3 N72... 00000000000000000000000000.0000... 0.00000000000000000000 00 00000000 000000000 Ncmou ¢ m uOchAMoo so wcmmcodoo womocwnoo so mNcumuuor wmmoouuoo so usONouacc uuommuuoo so wccowuu.c wsomoouoo so wmoaocAof we-oo~.o so mmmomouoc uoso~o~oo so mumo~cHoc oomoooo ouoccmc omooooo ooooNso oomommo oeacouc .mmcoos .uuucss o¢~mm¢s osadmrs .smmuos .mocrac oooomoo ooo-se .sosumo oo¢m~¢c .NcmoOm .sommom omNoNom accommr oomumom .Norssr domuw oomuw DmN HA xcwmooooo mooaow ozos so lzso moor: somso uuuqom crsxmoz z< oommoooao moomom ozos so ozo. 207 LEGENDS FOR APPENDIX C (1) Variable name indicates the original time series. (2) 0 at the end of each variable indicates base run. (3) l at the end of each variable indicates simulated series for scenario 1. (4) 2 at the end of each variable indicates simulated series for scenario 2. FOOTNOTES INTRODUCTION (1) A.P. Thirlwall. grggth-§gd_flsyslgps§nt (New York: John Willey & Son., 1977), p. 39. (2) Ibid., p. 39. (3) The world Bank. E9219-9929192!§9t-3929:t-12§9 (Wash— ington D.C: The World Bank, 1980), p. 112. (4) Ibid., p. 59. (5) Ibid., pp. 59-60. CHAPTER I (1) State Planning Organization, ngglgpmgnt_£1§g, Eiggt_§iy§_!g§§_!§§; (The Central Bank of the Republic of Turkey, 1963), p. 6. (2) Ibid., p. 6. (3) State Institute of Statistics, Iggkiygid§_19p1gmggl ggligmggig_§g;_1111 (The 50th year of the social develop- ment of Turkey) (Devlet Istatistik Enstitusu Matbaasi, 1973), p. 45. (4) Clarence Zuvekas, Jr., Egggggig_ygy§lgpgggg (New York: St. Martin’s Press, 1979), pp. 84-85. (5) 6.8. Ferguson. Miss9§9999919-1h99£! (Illinois: Richard D. Irwing, Inc. 1972), p. 35. (6) Ibid., p.54. (7) Ibid., p.481. (3) Adam Smith. Ibs-flsslth-9f-!stiggs (Ed. Edwin Cannan, Chicago: The University of Chicago Press, 1976), Book IV Ch. 2. (9) G.L-S. Shackle. Ihs-X§§:§_9£_§ish-Ih§9:! (London: Cambridge University Press, 1967), p. 10. (10) A.P. Thirlwall (1977), p. 213. 208 209 (11) P. Yotopouloa & J-B- Nugent. 59999919§-9£ Dsyelgpnsn: (New York: Harper & Row Publishers, 1976), p. 365. (12) A.P. Thirwall (1977). P. 214. (13) State Planning Organization. 2929192999£-21991_12§§ (Ankara: The Centarl Bank of the Republic of Turkey, 1963), p. iii. (14) Ibid., p. iii. (15)Ibid., p. i. (16)State Planning Organization, Qgg§1gpmggtJj1ggL §§§99§_§1g§_!g§§ (Ankara: The Central Bank of the Republic of Turkey, 1969), p. 1. (17) Lester 8- Pearson. Eartne£§-in-ye!elepsent (New York: Praeger Publishers, 1969), p. 321. (18) "Turkey, Special Sponsored Section,’ Ingestgz. August. 1983. p. 2. (19) The Republic of Turkey. In!9:na§igg_U§!9£§n§9! (New York: K.L.L. Brothers International Inc., 1983), p. 4. (20) A.P. Thirlwal (1977), p. 123. (21) State Institute of Statistics, §§§11§§19§1_1§§§bggk gf_19§kgyL 1919 (Ankara: SIS, 1973), p. 233. (22) John White. 2199899 29 2929192299! (London: The Overseas Development Institute Ltd., 1967), p. 93. Institutional- CHAPTER II (1) G.W. Forster & M.C. Leager. Elenen§§-9£-észigyl£szal 599993199 (New York: Prentice Hall, Inc., 1950), pp. 12-13. (2) Thirlwall (1977), p. 83 (3) Clarence Zuvekas (1979), p. 205. (4) J.W. Mellor and U. Lele "The Interaction of Growth Strategy, Agriculture, and Foreign Trade: The Case of India," 19 G.S. Tolley, P.A. Zadrozny editors Trade; Agri: 9912222_299-9229192!sn§ (Ca-bridge; Ballinser Publisher Comp., 1975), pp. 93-113. 210 (5) Little, Scizavsky and Scott reach the same conclu- sion in ”A major study of industrialization and trade policies in seven developing countries," cited by Zuvekas (1979), p. 265. (6) s. Chakravarty. 992i591-999-2929;onsent_El§nninz (Cambridge: MIT Pres, 1969), p. 55. (7) J.W. Mellor and U. Lele (1975), p. 95. (8) Ibid., pp. 13-14. (9) C. Zuvekas (1979), p.205. (10) Oddvar Aresvic. Ihe-éxrionltnrgl-ye!elonn§nt_9£ ngkey (New York: Praeger Publishers, 1975), p. 8. (11) geegegie__gepert (Ankara: Turkiye Ticaret odalari Birligi, 1973), p. 143. (12) Development Plan (1963). (13) Qonnt:x-5929:t-£or-19:kex (Rome: FAO Mediterra- nean Development Project, 1959). (14) Necmi Sonmez, "The Existing Land Ownership and Land se Conditions in Turkey.” Xen:hook-9!-£ssgltx-9£_ Agrisgl: Qge (1963). P. 54. (15) Development Plan (1969), p. 342. (16) Country Report for Turkey (1959), p, 5. (17) Statistical Yearbook of Turkey (1973). (18) Necmi Sonmez (1963), p. 64. (19) This estimation was done basically from data in the 1963 Statistical Yearbook. (20) All figures cited here are country averages. (21) Data derived from Statistical Yearbook (1983). U E (22)Statistical Yearbook of Turkey 1971, 1983; culti- vated area covers crop area sown and fallow, vineyards, vegetable and fruit gardens. (23) geege!;e_gepegg (Ankara: Turkish Agricultural Association, Publication(127), 1979), p. 182. (24) Morris Singer, The Economic Advance 9T Terkey 1938-1960 (Ankara: Ayyildiz Matbaasi A.S., 1977), p. 198. 211 (25) Ihe_ue9hanization_9f_Inrki§h_égrignltnre (Ankara: The Ministry of Finance Publication, 1975), pp. 118-125. (26) John H. Mellor. Ihe-!en-ggononi9§_9£_§ronth (London: Cornell University Press, 1976), p. 106. (27) Terkey (Paris: OECD Economic Surveys, 1982), p. 11. (28) The actual variation or dispersion as determined from the standard deviation is called absolute dispertion. N N If the absolute dispertion is the standart deviation s and the average is the mean "x", the relative dispersion is called the coefficient of variation, V= s/x, and is generally expressed as a percentage. It is independent of units used. For this reason it is useful in comparing distributions where units may be different. (29) The average is the simple arithmetic mean of the volume of export index. CHAPTER III (1) A. Downs. An_§sononio-Iheorx-o£-Qenosragy (New York: Harper & Brothers Publishers, 1964), p. 3. (2)". J. Samuels, "State Law, and Economic Organiza- tion." Researoh-in_Lag-an§-§ogiolosx (Volume 2): 65-69. (3) Define Pareto Optimality-such an optimum is normal- ly reached when it is no longer possible to improve the position of one actor in the exchange system without injur- ing another. (4) Randall Barlett, Economic Foundation of Political Beyer (London: Collier Macmillan Publishers, 1973), p. 6. Ibid. (6) Milton Friedman. Qanitaliss-an§-£reeéos (New York: Mc Grow Hill, 1962), Cited by Barlett (1973), p.6. (7) Paul A. Samuelson, "The Pure Theory of Public Expen- ditures." Beg:ea-9f-§sonosis_ané-§tati§tigs (November. 1954) cited by Barlett (1973). (8) Barlett (1973), p. 7. (9) Samuels (Volume 2): 67-71. 212 (10) Downs, p. 35. (ll) Cited by Downs, p.35. (12)Ibid., p. 59. (13) Barlett, pp. 7-12. (14) Samuels, "State Law and Economic Organization," 68. (15) Warren J. Samuels, "Organization and Control in The political Economy," (Unpublished Work.) (16) Ibid. (17) Warren J. Samuels, "Cost and Power," (Unpublished work.) (18) Ibid. (19) Ibid. (20) "F. Birtek and C. Keyder, "Agriculture and The State: An Inquiry into Agricultural Differentiation and Political Alliances, The Case of Turkey," The gegrrel_9f Peasant §tngie§ (1975): 446-467. (21) Ibid., 450. (22) Ibid., 453. (23) Ibid., 455. (24) Development Plan (1963), p. 129. (25) A Ulusan, ”Policies Towards Agriculture and The Inter-Sectoral Distribution of Income in Turkey: Some pre- liminary Results." Begeargh_£rozran-in-2e2elopnent-§tngie§ (Princton: Discussion Paper (75), 1977). (26)Cited by Somel (1979): 310. (27)Ibid., 311. (28) Ibid. (29) Jan K-enta. Elenents-9£-§sononetriog_ (New York: Macmillan, 1971) and George G. Judge & Others, Iheorx-ané-£rastise_o£-§gononetriosl (New York: John Willey and Sons, 1980). (30) Lewis(1954), and Ranis and Fai (1961) are well known proponents of this stand. 213 (31) Cited by Somel (1979). (32) Birtek and Heyder (1975): 452. (33) Kutlu Somel, "Agricultural Support Policies in Turkey: A Survey of Literature," MEIE-§SQ§1§§_19_ Qegelepgegr Vol 6 (1979): 327-339. (34) 0. Varlier. Tn:kixe-!arininda-!ani§§l_Qesishe Eek: nolodisi-29-192:ak-§oln§nnn (Ankara: State Planning Orga- nization, publication no: 1636) (35) Findings of Aksoy study. Cited in Somel (1979). (36) State Planning Organization, Q§!l§§-A§iQ§_X§Bil§9 Destakleme Alimlarini Tree;ere_ge_QegerTeggirme (Ankara: SPO, publication no: 1564) CHAPTER IV (1) Vernon L. Sorenson editor, Agricultural Market Ana- Tyeie (Wisconsin: J. Nielson and V. Sorenson, 1964), p. 73. (2) It was Knight’s contribution to show that presence of uncertainity about the future may allow entrepreneours to earn positive profits despite product exhaustion and competitive equilibrium. Production takes place in antici- pation of consumption, and since the demand for factors is derived from the expected consumers’ demand for output, the entrepreneur is forced to speculate on the price of his final product. The product price is not determined unless the price of output is known. The entrepreneur resolves this dilemma by guessing the price at which output will sell, thereby translating the marginal physical products of the factors hired into anticipated marginal value products. Although the factors hired must be awarded their antici- pated value of the marginal product, the entrepreneur as residual claimant may make a profit if realized total receipts prove to be greater than forecasted total receipts. Fore more detail see Blaug (1980). (3) Arnold 8: Packer. Hodel§_o£_§oononis_§z§ten§ (Massachusetts: MIT Press, 1972), pp. 13-14. (4) John N. FerriS. Azri99lt9£§l-!9:ket_énalysi§ (1964). p.230. (5) Ibid., p. 227. (6) Ibid., p. 228. 214 (7) Such an expectation model is suggested by Andersen (1979) and Meyer and Rasche (1980). In such a model, it is assumed that prices received by farmers respond base price immidiately provided that it is announced in advance and believed (or otherwise expected). (8) K.C. Hoguki, Macroeconomic Models (New York: Harper and Row, 1971), p. 82. (9) There is a high degree correlation between base price at time (t-l) and base price at time t. The estimated correlation coefficient is .96. (10) Paul Zarembka, Toward_a_1heorx-o£_fioononis-fleyelop: meg; (San Fransisco: Halden Day Inc., 1974). (11) This was first introduced by Mc. Fadden in "Cost Revenue and Profit Functions," Ag_§eeeererrie_Appreeeh_re Preggegieg Theery (Danial Mc. Fadden editor, Amsterdam: 1972). (12) Lawrence Lou and Pan A. Yotopoulos, "A Test of Relative Efficiency and Aplication for Indian Agriculture," American Economic Review 61 (1971): 94-109. (14) P. Zarembka, (1974). (15) The Duality approach states that the profit maxi- mizing factor demands are given by X! = dG/dWi/P i=1,...,m, and the supply function is given by m Y1: = 177? - :dG/dw /P (w /P) 1 1 1 Thus we can first specify profit function 13, and then input demand function 12 and supply function 14. See Zarembka(1974) for more detail. (16) A. Zelner, J. Kmenta, J. Dreze, "Specification and Estimation of Cobb-Douglas Production Models," Eeere: EQEEiQQ (October, 1966): 784-795. (1?) Lance Taylor. Maoro-!9del§-£92-De!el92198-9999tries New York: McGraw Holl Comp., 1979), pp. 73-83. (18) Ibid., p. 75. (19) Arthur S. Goldberger, Eeegegie_Theery (New York: John Willey and Sons. Inc., 1971), p. 1. 215 (20) H. Theil. Erinoiples-o£-fioonoaetries (New York: Jhon Willey and Sons. Inc., 1971), p. l. (21) E. Malinvaud, ”Econometrics Faced with the Needs of Macroeconomic Policy,” 6 Eeegererriee (Novamber, 1981): 1363-1375. (22) Ibid. (23) Arnold H. Packer (1972), p. xii. (24) Ibid., p. 5. (25) J. Kmenta, p. 3. (26) Mark Blane. Esononik-lheor¥-in-zetsoonest (Canb- ridge University Press, 1980), p. 704. (27) Ibid. (28) Ibid., p. 707. (29) Ibid. (30) J. Kmenta, p. 154-168. (31) J. Jhonston, Eeeregerrie_gerhege (New York: Mc. Graw Hill, 1978), pp. 36-41. (32) Peter Schmidt, EQQQQEQEEiQQ (New York: Marcel Dekker, Inc., 1976), pp. 39-40., and also see G.G Judge and others (1980), pp. 411-413. (33) J. Jhonston (1961), p. 11. (34) Damodar Gujarati, Basic Econometrics (New York: Mc. Graw Hill, 1978), pp. 36-41. (35) G. Madala (1977), p. 76. (35) Herry H. Kelejian and Wallace E. Oates, Terregge: tion_t9-§§999!§t£isg (New York: Harper and Row, 1974). p- 224 (37) OLS technique is still appropriate for a reqursive system of equations if covariance matrix is diagonal. (38) Maddala (1977), p. 231. (39) The OLS does not give consistent estimates of the parameters because of the correlation between the residual and the regressor, whereas the other methods give consis- tent estimates. 216 (40) Kmenta p. 583. (41) Johnston p. 408. (42)Ibid., p.410. (43) Ibid., pp. 410-417. (44) J.C. Cragg, "On the Relative Small Sample Proper- ties of Several Structural Equation Estimators," Eoononetrisa 35 (1967): 89-110. (45) Johnston, p. 413. (45) Cragg (1967): 109. (47) A.L. Nagar, "A Montecarlo Study of Alternative Simultanous Equation Estimators," ESQQQEQSEiSQ 28 (1960): 573-590. (48) Ibid. (49) Judge p. 4. (50)Ze11ner, Kmenta, and Dreze (1966): 784-795. (51) The expected profit maximizing supply curve is defined as a function of input prices normalized by the expected output price. The major source of deviations from optimality is the difference between anticipated and rea- lized prices which occur due to human errors. Since pro- duction function disturbance results largely from acts of nature, it is reasonable to assume that normalized input prices are independent of the disturbance of the supply function. See Maddala p. 251. (52) If the equation under consideration is overiden- tified, ZSLS gives asymptotically efficient estimates to LMIL or they are assymptotically equal. (53) Maurice Dobb, Theories of Value and Distribution Sigee_Ageg_§gith (Cambridge: Cambridge University Press, 1973), p. 30. (54) The adaptive expectation model was also assumed for the farmer’s transformation function. Under such an assumption, transformation function turned to the form of Pf= a0+ alOf+ a2Pf(t-l)+ u. Considering this information true, the model was esti- mated and resulted in R2 compared with the R2 obtained from 217 the original form. Since it is higher rhan the above one, the original function is employed in the model according to the principle of max R2. (55) Maddala p. 186. CHAPTER V (l) Farmer’s price responsiveness has been discussed in J-R- Behr-an. §unplx-e§299§e_in-§nderdexel9299-58519915229 (Amsterdam: North Holland, 1968), and for Turkish Experi- ence, S. Imrohoroglu and H. Kasnakoglu, "Supply Response in Turkish Agriculture.” MEIQ-§tudies-in_D929192299: 6 (1979): 327-339. (2) Implicit in the definition of the supply function are the expected price of the product and its inputs. Farmers are conscious of many of the important factors affecting price, such as production, consumption, consumer income, prices of competing products, and the government support program. If the government support program was instrumental in establishing price, than farmers would be attentive to the prospective government program. Outlook information influences expectations. See J.N. Ferris, pp. 227-228. (3) M. Donnez Celik. In:kixelde-$arinsal-Desteklens Ei: xa£-Eolitik§§inin-Etkinlis; (Ankara: Maliye Bakanlisi Tet- kik Kurulu, 1979). (4) Ekmekcioglu and Kasnakoglu (1979): pp. 113-143, and 1: Bulnus. Tarigsal-Eixat-919susuna-2eylet_Mudahalesi (Ankara: AITIA yayini, 1979). (5) Prices mentioned here are the indices of prices used in the model estimation. (6) The increase in the world market price is 13.07 percent: however, the increase was coupled during the translation to domestic price, due to devaluation of the Turkish lira in that period. (7) Changes in the price differentials between the agri- cultural and industrial sectors have actually moved against the agricultural sector. This means that price differen- tials have moved to retard, not stimulate, the agricultural sector’s development. (8) Elasticities are calculated by using the first and the last period averages. 218 (9) Kutlu Somel (1979): p. 281. (10) Coefficient of variations are calculated based on the sample period of 1963-1980. (11) The simple average of percentage deviations from base case to scenario 1. (12) Somel (1979): 275-323. 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