An Investigation into Factors Affecting Productivity of Produces by Karoun Agriculture and Industry...

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I nternational Journal of Scientifi c Research i n Knowledge (I JSRK), 1(8), pp. 263-275, 2013Available online at http://www.ijsrpub.com/ijsrk

ISSN: 2322-4541; 2013 IJSRPUB

http://dx.doi.org/10.12983/ijsrk-2013-p263-275

263

Full Length Research Paper

An Investigation into Factors Affecting Productivity of Produces by Karoun

Agriculture and Industry Company

Mehdi Basirat1, Mehrafarin Latif

2*, Ali Asghar Esfandiari

3

1Assistant Professor and Head of Economics Department, Science and Research Branch, Islamic Azad University, Khouzestan,

Iran2MA Student, Department of Economics, Science and Research Branch, Islamic Azad University, Khouzestan, Iran3Assistant professor, Department of Economics, Science and Research, Islamic Azad University, Khouzestan, Iran

*Corresponding Author: [email protected]

Received 10 June 2013; Accepted 15 July 2013

Abstract. Due to the limitation of production factors, our world today is in a vital need for enhanced productivity, in bothdeveloped and developing countries. Hence, productivity and provision of proper approaches will lead to the improvement of

economic situation. This study investigates productivity of production factors in Karoun agriculture and industry in the periodbetween 1981 and 2010. The results obtained from the fitting of the model indicate that variables have the condition for the

convergence test and there is a long-term balance correlation between the variables. In order to investigate the short-term

dynamics and its correlation with long-term relationships, error correction model (ECM) was used. Results obtained from this

model indicate that, as expected, ECM sign is negative and its value is 0.61. In order to calculate productivity, detailed

productivity method (mean and final productivity) was used. The results obtained from this study demonstrate that the mainvariables, such as labour and capital, affect productivity. Tensions obtained from fitting of the model demonstrate the share of

each factor. The tension of capital and labour is respectively 0.013 and 0.373 in a way that productivity of capital was

increased in some years and decreased in some others, showing a fluctuant decreasing trend on the whole. On the other hand,

productivity of labour increased in some years and decreased in some years, but it showed a fluctuant increasing trend on the

whole.

Key words: productivity, production function, Karoun Agriculture and Industry Co., Total factor productivity (TFP),

Regressive distributed Lags (ARDL)

1. INTRODUCTION

Productivity is regarded as one of the importantconcepts of economics as it shows the relationshipbetween the use of production factors and the

produce. On the whole, productivity could beconsidered a combination of efficiency andeffectiveness. Efficiency refers to conducting a task

accurately and is related to beneficial use of theresources. Effectiveness means accurate task, meaningthat it is possible to produce more outputs by a lesseruse of inputs although the output might not have thequality desired by the consumer. In terms ofproductivity, the first important issue is that the taskneeds to be accurate and beneficial and then this tasksneeds to be performed in the best possible way. Ifthese two conditions are met, it could be ensured thatproductivity has been realized. The increase in

productivity of production process will be translatedinto usage of a certain level of inputs to have more

production (Abtahi and Kazemi, 2006).The significant production inputs are the labour

and capital factors. Therefore, the two concepts of

productivity of labour and productivity of capital are

in fact the efficient use of the labour and capitalfactors. In addition, total factor productivity (TFP)shows the efficient use of the combination of thementioned factors. Growth of TFP not only effects ofquantitative growth of production inputs, but is also

one of the important factors that lead to economicgrowth. It is in fact a type of management in the useof production resources. Nowadays, sugar is regarded

as an important and strategic good and the ability isproducing this produce is both economically andpolitically significant. However, the recent efforts ofthe government in producing sugarcane and itssecondary produces are of great importance. KarounSugarcane Agriculture and Industry is among thecentres for producing sugarcane and refining it intosugar and the related produce in Iran (Khoozestanprovince) (Saeedi, 2001). This article aims to answerthe following questions:

Is there is a strong and direct correlation betweencapital and productivity in Karoun agriculture and

industry?Has productivity of capital and labour increased in

the period under study?


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1.1. Theoretical Foundation

Productivity is one of the new criteria for measuringprogress in production. This concept adopts an

economic perspective to investigate the application of

production factors as well as the access to pre-determined goals. There is a difference here betweenthe concepts of production and productivity.Production does not necessarily mean higherproductivity, but it is the produced output. On theother hand, productivity is the ratio of producedoutputs to consumed inputs. Therefore, it is possiblethat in a given year, production is increased butproductivity is decreased. For instance, the totalannual sugarcane harvest or sugar production of anagency might be satisfactory without any satisfactionwith regard to improvement of productivity of theinputs. In other words, the increase in production isthe result of the higher use of materials and inputs.Meanwhile, if the ratio of the increase in production tothe increase in used materials shows a decrease in theratio of output to input, it indicates productivity of theagency has decreased. In other words, productivitydecreases when the input illogically increases incomparison to the output (Rahmati Andami, 2007).When analysing the performance of an agency,productivity, efficiency, and effectiveness are usuallyconsidered to be the same and are used alternatively.

However, it should be noted that these words aredifferent from one another. Efficiency is the ratio ofactual output to the standard or required output, or it isthe ratio of the work that is done to the work thatneeded to be done. Effectiveness refers to the degreeof achieving the goals and productivity refers to the

ratio of outputs to inputs.A significant issue is that according to physical

laws in machines, efficiency, which is the result offace capacity to present capacity, is never 1 and it isalways lower than 1. However, when it comes to

human beings, proper motivation and leadership could

help to make this quotient higher than 1.Effectiveness is the degree of achieving the set goals.In other words, effectiveness shows to what extent

efforts have been translated into the intended results.However, the usage and productivity of resources forachieving the goals is related to efficiency. In fact,effectiveness is related to performance and humansatisfaction of performed efforts. Efficiency is related

to proper exploitation of resources; in other words,productivity is the combination of efficiency andeffectiveness. It could be seen that efficiency has aquantitative aspect but effectiveness has a qualitative

aspect. In addition, efficiency and effectiveness arenot necessarily in the same direction and their changesare not the same. This is due to the fact that efficiencyincludes the results and outputs that are not

necessarily all desirable and ideal. Therefore, properattention should be paid when using such terms asefficiency, effectiveness and productivity. Based onthe above explanations, it could be said thatproductivity has three criteria:

1. Efficiency (doing a task successfully); 2.Effectiveness (performing the right task); 3.Consistent application of production factors; in otherwords, it means consistently performing a taskwithout wasting time or resources or without wasting

labour and machines. Therefore, productivity isconsistent performance of the right task and it

indicates efficiency, effectiveness and the consistencyof performance. The increase in productivity as theresult of better application of the inputs used in theproduction process of an agency is usually translated

into an increase in production and a decrease in thecosts of production, thus benefiting the agency.However, the opposite of this trend is not necessarilyright. In other words, the increase in profits could notalways be regarded as the increase of productivity.This is because profits of an agency could be theresult of the increase in demands or the increase in theproducts prices, thus not being related to the better

use of resources by an agency. Even whenproductivity is low, an agencys profits mightincrease. The increase in profits could be the result ofbetter application of production resources (Varzeshi

and Esfandiari, 2009).

2. PREVIOUS STUDIES

Tavakoli et al. (2000) measured and analysedproductivity of production factors in large industrygroups in Iran in the period between 1972 and 1993.

They investigated the indices of productivity in thetwo groups of detailed and total productivity ofproduction factors. In terms of detailed indices,productivity of labour and capital was considered asthe ratio of production to the related input and in

terms of total productivity indices, productivitygrowth was measured and analysed with regard to thea series of production factors using basic solo anddivisia indices. Moreover, they used an exponentialfunction to estimate capital inventory. The resultsobtained from calculation of productivity indicesindicated that detailed labour productivity had a 48.9percentage growth and capital productivity had a 13precent negative growth in the period under study.Study of detailed productivity as per industrialactivities indicated that labour productivity had anincreasing trend except for paper and carton

industries. However, capital productivity showed agrowing trend only in the resources belonging to non-metal mineral products, fundamental metals, and othervarious industries and demonstrated a decreasingtrend in other industries.


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Amini (2002) measured and analysed the factorsaffecting productivity in non-oil sectors of the Iranianeconomy and economic prospect in the thirddevelopment plan. He first explained the factorsaffecting labour and capital productivity and then

investigated the total performance of non-oil sectorand the performance of each sub-sector in the periodbetween 1956 and 1999. He then provided a prospectfor them in the third development plan and calculatedthe share of productivity of total factors of growth in

the period between 1956 and 1999. In this study,agriculture sector demonstrated the best performance

and other services sector (including services otherthan transport, warehousing, and communications)had the worst performance with regard to efficiency ofusing production factors. On the whole, production

sectors enjoyed a high degree of productivity in theuse of resources. In contrast, service sectors wereeither not successful or only slightly successful interms of efficient use of production factors. Share ofproductivity in production growth in the whole non-oil sector was 13.8 precent and it was predicted toincrease to 20.6 precent in the Third DevelopmentPlan.

Ghalambaz et al. (2008) estimated the productionfactor and productivity models in Karoun Gas and OilExploitation Co. productivity and its productionfactors was estimated for the period between 1986 and

2008. In order to conduct this study, first thereliability of variables was tested using the augmentedDickeyFuller test (DFT). Then, the error correctionmodel was estimated using Engle and Granger co-integration approach. The results demonstrated that inthe period under study, the function is CobbDouglasand the mean of productivity growth in Karoun Gasand Oil Exploitation Co. is 8.3 precent. The tension is0.64 for the labour, 0.15 for the capital and 0.55 forenergy. The result obtained by Wald test indicates thatin comparison to the scale, the return in increasing is1.34. Ernesto (2002) conducted a study on the

productivity of industries in Mexico and found outthat the increasing competition in import and accesses

to US markets have a positive and significant effecton the total productivity of production. In addition,

foreign investments too have a positive effect on theproductivity of factors in industries in this country,although the overflow resulting from this capital to thecountrys industries is slight. Krueger and Tuncer(2006) studied productivity growth in manufacturing

industries in Turkey based on public and privatesectors and claimed slower productivity growthcoincided with periods of a more stringent traderegime their study also showed that despite the factthat the rate of growth ofTFPwas about the same in

the public and private sectors, levels of inputs andproduction factors in the public sector enterpriseswere much higher than in their private sectorcounterparts. Dirk Pilat (2009) compared productivityof industries in South Korea with the productivity of

similar functions in the US and Europe. Although inthis study, productivity of some industries like leather,metals and machineries was to a similar level asEuropean industries, the TFP of Korean industries was26 precent of the productivity in the American

industries. According to Pilat, such factors as capitalintensity, savings as the result of production scales in

industries, and levels of education of the labour forcewere among the most influential factors in thedifference between productivity of industries in SouthKorea and the US. and Reddy (2010), studied the

productivity trend in Andhra Pradesh products inIndia. In order to calculate the productivity of the fourindustries of cotton, tobacco and coke, food productsand paper, Trans log production function and divisiaindex were used. Their explanatory variables infunctions included capital inventory, labour force, andconsumed fuel. The variable of time was added to theproduction functions as an input to study the technical

progress and the GDP was also used as a dependentvariable. After calculating TFP using divisia index,they concluded that TFP in all industries except forcotton products industry had a decreasing trend. TFP

index of cotton products industry was increasedduring this period despite having some slightfluctuations. Desini et al. (2010) used econometricmethods to study and test the role of internalrestructuring (such as using modern technology andstructural changes) and external restructuring (such asentering and exiting the market and changes in marketshare) on productivity growth in industries inEngland. They concluded that external restructuringaffects 50% of changes in labour productivity and90% of changes in the TFP. Thirtle and Bottomley(2012) measured productivity of total production

factors in agriculture. This paper analyses therelationship between production functions and TFP

index. The results showed that intensive growth inagricultural productivity was proportional to other

economic sectors. Rosegrant and Evenson (2012)investigated changes of efficiency in a paper whosetitle was Agricultural productivity and Growthresources in south Asia. The results showed that ahigh rate of efficiency was estimated to invest in

public researches, which is indicative of profitabilityand need for continuity of this investment. Efficiencyis the origin of wealth and growth possibility isprepared in long-term based on new theories ofeconomic growth of efficiency.


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Table 1: Summary of series Unit-Root test

Table 2: Summary of series Unit-Root test

Table 3: Autoregressive Distributed Lag Estimation ARD(1,2,0,1,1,2,0,0)

3. METHODOLOGY

The methodology used in this study is descriptive-analytic. Te data used in this study was collectedusing the library method from Karoun Agriculture andIndustry Co. in the period between 1981 and 2010.

3.1. Calculation of Productivity

In order to calculate productivity, two methods havebeen recommended by economists: econometric andnon-parametric methods. In the econometric method,

calculation of productivity is performed throughestimating a production function or a cost function. Inthe non-parametric method, productivity is determinedby using mathematical planning or by calculating theindex value (Abtahi and Kazemi, 2006). This studyused the first method. Therefore, if the productionfunctions is assumed to be as follows:y= (x1 , x2, , xn) (1)

It is possible to calculate average productivityAPxi and final productivity MPxi of each productionfactor, assuming that the following conditions arefixed:


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(2)

(3)

By estimating one production function, MPxi

could easily be calculated. With regard to CobbDouglas function, MPxi is as follows:

(4)

Where Y refers to the product, Xi the input, and eithe elasticity of each input.

3.2. Total Factor Productivity (TFP)

After the Second World War, many economistsfocused their studies on TFP. TFP (labour and capital

simultaneously) in fact indicates the averageproduction for each unit of the whole productionresources. This index indicates the balance of thechanges in productivity of labour and capital. GDPgrowth or added value in each economic sector isperformed in two resources (Valizadeh Zonooz,

2005).1. Input Increase (Labour and Capital); 2.

Structural Improvement (machineries, equipment forimproving the quality of labour and management)

Detailed productivity, including labour and capitalproductivity, might not be able to explain the increasein the efficiency of production factors and thus the

current conditions of the industry. Meanwhile, TFPcould explain how production factors could be usedproperly and productively as it takes into account thechanges in those factors that leave the biggest role inthe production process, and thus it could be used tohave proper economic policies by industrial-economicpolicy-makers.

TFP growth rate could be calculated using thefollowing equation:

KPALPAPFTkL

(5)

where TFP is the total factor production growth,APL is labour productivity growth, APK capitalproductivity growth, nL is the productive tension oflabour input, nK is the productive tension of capitalinput.

3.3. Regressive distributed Lags (ARDL)

Unreliability of the variables might lead to artificialregression, and thus damage the confidence aboutestimated coefficients. Therefore, based on the co-

integration theory in modern econometrics, whenusing time series in estimation of the models, it isnecessary to use the approaches that take into accountboth reliability and co-integration.

Using the models that have short-term dynamicswill help to have more exact estimated coefficients.On the whole, a dynamic model is one in whichvariables lags could be considered as in equation 6(Tashkini, 2005).

(6)

In order to decrease biased estimators in smallersamples, it is recommended to enter a large number of

lags for variables in the model.

(7)

The above model is called ARDL, in which:

(8)

(9)L is the operator of the lag, Lnxt=xt-n is a vector of

fixed variables like intercept and the virtual variables

of time trend. Microfit software estimated theequation times (m+1)

k+times. M is the maximum

number of lags that is set by the researcher and k isthe number of explanatory variables. Then, oneSchwartz-Bison, Akaike, Hannan Quinn criterion is

used to choose one of the questions:

3.4. Error Correction Model (ECM)

Co-integration between a series of economic variablesis the basis for using ECM. ECM is in to fact relatedshort-term fluctuations (short-term imbalance) of

variables to their long-term values. According toEngle and Granger, each long-term correlation has a

short-term ECM that guarantees the balance and viceversa.

These models are in fact a type of detailed balance

models in which forces affecting short-termconditions and the speed of getting close to long-term

balance value are measured by entering the stable

error terms of a long-term correlation (Tashkini,2005).

3.5. Research Model

The model used in this model is linear and CobbDouglas (Debertin, 1997; Darisavi, 2001).Ln SHT = Ln A+1Ln Szk +2Ln MAM+ 3 LnN+4Ln I+5Ln IM+6Ln KF+ 7Ln MSIB

where SHT is produced sugar, SZK is the areaunder cultivation; MAM is consumed water; N is the

labour, I is the Capital (equipment), IM is theagricultural machinery asset, KF is the phosphorousfertilizer, and MSIB is total frost time.


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4. RESULTS

4.1. Reliability

Use of traditional econometric approaches for

experimental studies is based on the assumption thatvariables are reliable. Therefore, augmented unit rootDFT is used. Based on the conducted tests, it could beconcluded that in Table 1, SHT, IM, N, MSIB, KF,SZK variables are reliable and I and MAM variables

become reliable after one difference(Table 2).Using the dynamic models coefficient, the long-

term correlation between the variables is tested. To dothis, the following equation is used to calculate therequired statistic.

106/0

138/0-5/84

The statistic is -5.84. Therefore, comparing thecalculated value of -5.84 and the critical valuepresented by Banerjee, Dolado and Master at the levelof 99%, (-5.04), the null hypothesis of the model isrejected and the existence of a balance long-termcorrelation between the models variables isconfirmed. The results obtained from estimating thedynamic model indicate that capital, machinery assets,

labour, area under cultivation, and amount ofconsumed water leave positive and significant effectson production. In addition, phosphorous fertilizer andthe total frost hours have negative and significanteffect on production. Determining coefficient of the

model is 97% and the value of statistics of Durbin-Watson test (2.76) and f=37.7620, show the high

explanatory feature of the model and lack of co-integration and significant between generalcoefficients in the estimated model (Table 3).

The following results could be gained by observingthe pathological tests:

a). F statistic to determine the presence or absence

of co-integration is 3.08 and the minimum level ofsignificance of the value in parenthesis is 0.10. Takinginto account the error level of 0.05 and comparing itwith the minimum level of significance in the nullhypothesis, which indicates there is no co-integration,the hypothesis with regard to existence of co-

integration is rejected(Table 4).b). F statistic to determine the right or wrong

function shape is 0.0017. Taking into account theerror level of 0.05 and comparing it with the minimumlevel of significance of 0.96, the null hypothesis,

which indicates there is an accurate function shape, isconfirmed(Table 4).

c). F statistic for determining the similarityvariance is 0.33. Taking into account the error level of0.05 and comparing it with the minimum level of

significance of 0.56, the null hypothesis, whichindicates there is no different variance, isconfirmed(Table 4).

The long-term correlation between the modelsvariables is estimated using the following equation:

LnSHT=0/013LI+0/059LIM+0/373LN+0/167LSZK-0/073LKF+2/02LMAM-0/112LMSIB-16/39C

(2/08) (2/10)(2/92)(3/03)(-2/21)(2/46)(-3/17)(-1/78)

Values in the parenthesis are the t-statistic relayedto the coefficients. The results obtained from

estimating the long-term dynamic model indicate thatproduction is positively and directly correlated withlabour, capital, machinery assets, area undercultivation, and amount of consumed water. In otherwords, each 1% increase in labour, capital,machineries assets, area under cultivation, and theamount of consumed water increase production asmuch as 0.37, 0.013, 0.059, 0.16, and 2.02 precent

respectively. Production has a negative correlationwith the total number of frost hours and phosphorousfertilizer, meaning that for each 1% increase in thetotal number of frost hours and phosphorous fertilizer,

production decreases as much as 0.11 and 0.073precent respectively(Table 5).

The Coefficient for the error correction term issignificant and its expected sign is negative. The valueof this coefficient is 0.61, meaning that about 61precent of the deviations in the variable of productionfrom the long-term balance value is balanced after oneperiod (Table 6).

4.2. Stability Test

The stability of the coefficients is studied using the

CUSUM test, showing that the coefficients of theestimated model in the period under study are stable

(Figure 1).

4.3. Reaction Function

Reaction functions show the reaction of endogenousvariables to shocks imposed on the models variables(Figure 2).


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Table 4: Diagnostic Tests

Table 5: Long-Run Estimation ARDL

Table 6: Error Correction Representation for the Selected ARDL Model

Table 7: Variance Decomposition


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Fig. 1: CUSUM and CUSUMQ tests for coefficients stability of ARDL model

Fig. 2: Reaction Function

4.4. Variance Analysis Test

If a shock is imposed on the dependent variable, itwill show what the percentage of the fluctuations is

the result of the variable itself and which percentage isthe result of the fluctuations in the other variables.

The results obtained from this test indicate that onaverage 55% of the fluctuations in the first five years

was the result of production logarithm, 4% was theresult of machineries assets logarithm, 2% was theresult of the logarithm of the area under cultivation,18% was the result of the logarithm of the amount of

consumed water, 15% was the result of the logarithmof the phosphorous fertilizer, and 2% was the result ofthe logarithm of the total frost hours (Table 7).

Fig. 3: Variance Decomposition

4.5. Calculation of Productivity

The results obtained from calculating the detailed

productivity (final productivity and averageproductivity) and TFP are provided in Table 8 and 9based on the following equations:

(10)

(11)

where Y is the product, Xi is the input, and ei is the

tension of each of the inputs.


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5. CONCLUSION

Enhancement of productivity is production factorstranslated into a decrease in prices. The decrease inthe level of prices, including production factors, leads

to the decrease of average costs of production ofgoods and services in the market and the increase ofprofitability of products of manufacturing enterprises.From the perspective of micro-economics, capital andlabour are two important factors in the function of

production and they play a significant role inincreasing production. Based on the type of

production function, which is linear and CobbDouglas, it directly and increasingly affectsproduction. Agricultural section is one of importanteconomic sections in the country. Much attention is

payed to this section for its important role to supplythe peoples food and to provide the primary materialsof many industries. Low productivity is one of theserious problems in Iran. This problem is observablein all pre- and post-productivity steps. Efficiencygrowth is a necessary factor for continued economicgrowth in each country. In order to increase ofefficiency in Irans economy, we must pay special

attention to agricultural section which is one of theimportant sections and the main economic activities inthe country. Because increasing of efficiency growthaccording to special economic structure of the country

in this section can help us achieve economic goals.Realizing direction of efficiency growth in Iranagricultural section, either for having poor economicinfrastructures or for competition on the internationalscene to reach better economic success, help us to leadour productivity equipments and resources, accordingto which we can reach our proper place ininternational relations. So, doing productive projectsand their appropriation in different agricultural sub-sections are necessary. The main variables like labourand capital impact productivity. Productivity of thelabour was increased in some years and decreased in

some others, but it shows a fluctuating decreasingtrend on the whole. On the other hand, the results

obtained in this study demonstrate that capitalincreased over the period of the study. On the other

hand, productivity of capital was increased in someyears and decreased in some years, but it showed thehighest amount of decrease in 2000. On the whole,capital productivity has a fluctuant decreasing trend.Therefore, labour and capital productivity was not

increased over the course of the study. Therefore,since two types of capital is used, the productivitytrend of both types of capital is studied. Since the TFPwas increased in some years and decreased in someothers but had a fluctuant-increasing trend on the

whole and since capital and machinery asset wasincreased over the course of the study, it could be said

that there is not a strong and direct correlationbetween capital and productivity (Table 9). Based onthe estimation of the long-term coefficients of themodel, it could be claimed that all variables of themodel, except for phosphorous fertilizer and total frost

hours, are positively and significantly correlated withproduction. In better words, each 1% increase inlabour, capital, machineries assets, area undercultivation, and amount of consumed water increaseproduction as much as 0.37, 0.013, 0.059, 0.16, and

2.02 precent respectively. On the other hand, each 1%increases in the total number of frost hours and

phosphorous fertilizer decreases production as muchas 0.11 and 0.073 precent respectively (Table 5).

Policy Recommendations:

1. Increasing labour productivity through increasingthe share of educated labour force in the total labourforce due to the high capability of the skilledworkforce in using modern technologies in production2. More attention to the problems in the sugarproduction machinery lines, and particularly withregard to the sources of producing sugar wastage at

factories;3. More beneficial use of dry days during the periodof harvest and doing harvest in the cooler months inorder to prevent the reduction of harvested sugarcane

at the end of the period, which usually coincides withthe hot days of the year;4. More attention to destroying the weed andparticularly resistant weeds like cockspur grass, whichhave become the main target of chemicals in recentyears.

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Tashkini (2005). Applied econometrics using Microfit. Edition 1, Tehran cultural and ArtisticDibagaran Institute.

Darisavi H (2001). Estimation of the productionfunction of sugar and sugarcane production in

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Debertin D (1997). Agricultural manufacturingeconomy.Translated by Dr. MohammadGholiMoosanezhad and Dr. Reza Najarzadeh.

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Saeedi N (2001). investigation of employment statusin Khoozestan province and the effect ofsugarcane development projects and alternativeindustries on employment in the provinces, MA

thesis, Economics, Ahvaz Science and ResearchUniversity, pp. 14-21.

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Varzeshi M, Esfandiari A (2009). Measurement and

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Table 8: Total Factor Productivity and partial productivity

Average

productivity

of Capital

Average

labor

productivity

Average

productivity

of CapitalMachinery

Average

productivity of

the Acreage

Elasticity

of

Capital

Elasticity

of

CapitalMachine

Elasticity

of Labor

061 36-E1/3903/1 1-E

4309/0

444004/9 10694/1 1411/1 66/1

0603-E

64/9

10309/3 1-E069/6

66969/ 10694/1 1411/1 66/1

0633-E

0040/0

196119/0 1-E

13/0

9436009/ 10694/1 1411/1 66/1

0663-E

916/9

096636/1 1-E

9/0

460/ 10694/1 1411/1 66/1

0693-E

6693/9

63916/1 1-E

3016/

1149/ 10694/1 1411/1 66/1

064

34-E

113/0

0096/3 14-E

91660/3

9199344/9 10694/1 1411/1 66/1

0639-E

66/9

316610/1 14-E9964/

31109009/3 10694/1 1411/1 66/1

06 60-E1/040694/1 14-E

044/3

16340069/6 10694/1 1411/1 66/1


11/13


273

0664-E

3/9

100043/0 1-E

333/6

104303/9 10694/1 1411/1 66/1

06 69-E4/333016/1 1-E

6690/9

63304/9 10694/1 1411/1 66/1

061

63-E

3166/03319/1 1-E

444/

60046/9 10694/1 1411/1 66/1

060 64-E99/64094/0 14-E

364/3

39044/6 10694/1 1411/1 66/1

0636-E

9963/0

6063/3 1-E

410/3

649444/4 10694/1 1411/1 66/1

0666-E

94/6

3600/1 1-E

306/0

649494/ 10694/1 1411/1 66/1

0696-E

114/3

63999/0 1-E

116/3

940693/ 10694/1 1411/1 66/1

064 93-E46/0 0114/6 1-E6003/0 443410/4 10694/1 1411/1 66/1

06 96-E1/0913430/1 1-E

304/6

04346/4 10694/1 1411/1 66/1

06 96-E6666/ 3091990/1 1-E9446/0 94943/ 10694/1 1411/1 66/1

0693-E

014/0

666/1 1-E

3669/0

616194/30 10694/1 1411/1 66/1

066-E

4/6

34/0 1-E043/ 3004/ 10694/1 1411/1 66/1

061 9-E193/00619/1 1-E

09/3

36443/ 10694/1 1411/1 66/1

060 9-E36/1111/0 1-E

13/0

410696/ 10694/1 1411/1 66/1

063 9-E06/0399/3 1-E

36/0

141196/ 10694/1 1411/1 66/1

0669-E

93/9

61010/1 1-E

3064/6

63/ 10694/1 1411/1 66/1

069 9-E39/

91161/0 1-E99/0

16630/ 10694/1 1411/1 66/1

0649-E

44/

404194/1 1-E

660/

93094/6 10694/1 1411/1 66/1

06 9-E/60493/1 1-E

036/3

13411/4 10694/1 1411/1 66/1

069-E

4/6

04619/3 1-E

6/3

9946/4 10694/1 1411/1 66/1

0694-E

0/3

06990/3 1-E10/ 46190/ 10694/1 1411/1 66/1

0641-E

63/

0093/1 1-E13/3 3036469/ 10694/1 1411/1 66/1

Table 9: Total Factor Productivity and partial productivityElasticity

of Acreage

Marginal

Productivity

of Capital

Marginal

Productivity

Capital

Machinery

Marginal

Productivity

of labor

Marginal

productivity

Acreage

Total Factor

productivity

061 03/1 34/3 E 34- 664/ E 1- 0460160/1 330/1 46/1

060 03/1 13/ E 3- 064/3 E 1- 4116/1 0393034/0 03/0

063 03/1 463/0 E 3- 946/ E 1- 610444/1 39699/0 4/0

066 03/1 14614/ E 3- 1603/0 E 1- 333499/1 39060/0 406963/0

069 03/1 46/4 E 3- 919/9 E 1- 3466/1 0369936/0 941/0

064 03/1 919/0 E 3- 9004/0 E 1- 39/1 63603/1 43496/0

06 03/1 00/ E 3- 136/6 E 1- 10044/1 90600/1 49/1

06 03/1 334/3 E 66- 333/0 E 1- 649439/1 4139/1 3066/1

06 03/1 999/ E 6- 39/0 E 1- 334044/1 093/1 390/0


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Basirat et al.


274

06 03/1 614/6 E 6- 449/3 E 1- 134/1 36400/1 416/1

061 03/1 46/0 E 69- 043/4 E 01- 3033014/1 09330/1 1614/0

060 03/1 910/9 E 6- 4/0 E 1- 4691031/1 39334/1 60394/0

063 03/1 69/0 E 6- 433/0 E 1- 003013/0 4061/1 11969/3

066 03/1 343/4 E 91- 900/ E 1- 01040/1 16019/0 010/0

069 03/1 6306/3 E 90- 4394/0 E 1- 9913/1 136369/0 4636/0

064 03/1 0039/3 E 99- 0/ E 1- 01333/0 3364/1 00143/3

06 03/1 939/3 E 94- /0 E 1- 04109144/1 9116939/1 1104/0

06 03/1 140/ E 94- 49/ E 1- 019699/1 9040613/0 4310/0

06 03/1 6401/3 E 99- 346/ E 01- 3433646/1 46416/6 049/6

06 03/1 16136/4 E 4- 634/6 E 1- 1610/1 699/0 1/3

061 03/1 96609/0 E 9- 604/0 E-1 34904960/1 3463/0 4436/0

060 03/1 143/ E 9- 99/ E 1- 666346/1 963343/0 6140/0

063 03/1 33/0 E 9- 013/0 E 1- 433314/1 016390/0 16344/3 066 03/1 06934/ E 9- 6/0 E 1- 396130/1 0313436/0 6444/0

069 03/1 30/ E 9- 69/ E 1- 434499/1 0904334/0 130/0

064 03/1 04690/ E 9- 39/4 E 1- 030019/1 31106/1 0030/1

06 03/1 6630/0 E 9- 343/0 E 1- 00396/1 963363/1 004/1

06 03/1 99/4 E 9- 99/0 E 1- 0436414/1 434033/1 04/0

06 03/1 6/6 E 9- 044/9 E 1- 3/1 013/0 0/0

06 03/1 3163/ E 43- 36/0 E 1- 3404/1 490196140/0 6144/0


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275

Mahdi Basirat holds a Ph.D. in economics and Assistant Professor and Head of Economics Department,

Science and Research Branch, Islamic Azad University, Khouzestan, Iran. His areas of interest include

econometrics and macroeconomics.

Mehrafarin Latif is a graduated student of M.A economics, Department of Economics, Science and

Research Branch, Islamic Azad University, Khouzestan, Iran in 2013.

Ali Asghar Esfandiari holds a Ph.D. in economics. His areas of interest include econometrics and

microeconomics.

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