Research Article

Measurement of Allocative Efficiency of Sugarcane Farms in District Charsadda-Khyber Pakhtunkhwa

Amjad Ali1, Abbas Ullah Jan1, Lal Almas2, Noor Piao Khan1 and Khurran Nawaz Saddozai1

1Department of Agricultural and Applied Economics, The University of Agriculture Peshawar, Khyber Pakhtunkhwa, Pakistan; 2Agricultural Business and Economics, West Texas AandM University, Canyon, TX USA.

Received | December 06, 2017; Accepted | November 06, 2018; Published | November 29, 2018

*Correspondence | Amjad Ali, Department of Agricultural and Applied Economics, The University of Agriculture Peshawar, Khyber Pakhtunkhwa, Pakistan; Email: [email protected]

Citation | Ali, A., A.U. Jan, L. Almas, N.P. Khan and K.N. Saddozai. 2018. Measurement of Allocative Efficiency of Sugarcane Farms in District Charsadda-Khyber Pakhtunkhwa. Sarhad Journal of Agriculture, 34(4): 955-962.

DOI | http://dx.doi.org/10.17582/journal.sja/2018/34.4.955.962

Keywords | Allocative efficiency, Stochastic frontier analysis, District Charsadda, Khyber Pakhtunkhwa

Introduction

Agriculture sector in Pakistan provides means of livelihood to more than 60 percent of the total population and serve as a source of foreign exchange earnings. However, because of rapid increase in input prices, the profitability of the agriculture sector is declining in Pakistan (Niala and Shahid, 2009).

Bashir and Khan (2005) suggest three possible ways to decrease cost of production namely by improving management practices, decreasing cost of inputs and by developing of high yielding varieties. However, only the first alternative seems to be achievable. Improvement in management practices refers to allocative efficiency, in which marginal production of each inputs equal to the ratio of input and output prices.

Sugarcane is among major crops of Pakistan and is under cultivation since long. It was grown in almost 1.13 million hectares during the year 2015-16. It is a source of income and employment for farming community throughout the year. In Pakistan it is mainly grown in Sindh, Punjab and Khyber Pakhtunkhwa. Pakistan has the resources to obtain per unit high yield of sugarcane crop, yet it has neither export sugar at a competitive price nor could get rid of import to meet local requirement. Per capita sugar consumption in South Asia including Pakistan is 25.83 kg year-1) (Azam and Mukarram, 2010). During the year 2014-15 total white sugar production was 4.65 million tonnes while consumption was 4.1 million tonnes (PSMA, 2014-15).

A vast body of literature pertaining to technical efficiency and agronomic aspects of sugarcane crops exists but no work has been done to estimate allocative efficiency of sugarcane crop in Khyber Pakhtunkhwa specifically in district Charsadda.

Due to lack of integrated planning in agriculture, inputs are recommended irrespective of soil type, inputs availability and market information. Trails are carried out in research stations and recommendations are made for wide area based on evidence derived from these trails. Rapid increase and revision in prices of energy, fertilizers and other agriculture inputs illustrate the need to explore whether sugarcane growers are allocating their resources efficiently or not?

Current study was directed in district Charsadda which is the largest sugarcane producing district of the province. Major part of its land is under agriculture activities. Its resources comprise fertile soil well suited for farming, climate and water. Main sources of irrigations are Rivers Jindi, Kabul and Swat flowing in district, which cover 86% of district Charsadda. Major crops grown are sugarcane, sugar beet, maize, tobacco and wheat along with some orchards and vegetables. Based on production district Charsadda contribute 32.21 to sugarcane production at provincial level (GoKP, 2015).

The objectives of the study were to estimate allocative efficiency of sugarcane grower in study area and its influencing factors. The findings will be used as base for recommendations.

Theoretical background

Farrell (1957) is considered first researcher who use and measure efficiency, he did this by comparing a firm’s actual and potential output from a given set of inputs. He argued two components of efficiency, namely technical and allocative efficiency. Technical efficiency was defined as the ability to produce maximum output from a given amount of inputs, while allocative efficiency refers to the ability to produce a given level of output at minimum cost.

The interpretation of allocative efficiency depends upon assumption researchers made about firm’s behavior. Farrell (1957) assumed cost minimization for a given level of output and defined allocative inefficiency as inability of growers to equate the ratio of marginal products of inputs with the ratio of their prices.

In current study, constraint allocative efficiency, which means that growers have fixed cash outlay which he allocates among various inputs during the entire sugarcane growing season. Also, it is assumed that grower’s objective is to maximize his output at a given cash outlay.

Materials and Methods

This study was conducted in district Charsadda. Randomly two villages Serdehri and Sheikabad were selected from union council Muhammad Nehri. Primary data was collected through a pre- structured questioner that was designed according to objectives of the study. Randomly 118 willing respondents were interviewed in study area. Out of total 118 sample size 64 were selected from village Sirdehri and 54 from Sheikhabad by using Cochran (1977) proportional sampling allocation technique given in Equation 1 as given in Table 1.

……(1)

Specification of functional form for an econometric model pose problem, because economic theory does not provide a precise guide in this regard. According to Kopp and Smith (1980) functional form has limited effect on empirical measurement of efficiency. Ogundari et al. (2010) reported that it is necessary to choose functional form well in advance because it influence the estimated results. Similarly, Hall (1978) and Godfrey and Wickens (1981) also reported that functional form has important implications for estimated results, forecast and policy analysis. According to Bashir and khan (2005) specification of model should be guided by visualization of the true research process which means that through a range of visible ideas and techniques exploring the research nature and findings. But for specification of model visualization need to be used with care.

The stochastic frontier production model of Aigner et al. (1977) and Meeusen and Van den Broeck (1977) was employed. According to Battese (1992) it has the property of self-dual which allow to estimate technical as well as allocative efficiency. According to Burki and Shah (1998) the concept of frontier is applied to production, profit and cost functions. Therefore, stochastic cost frontier analysis is used to estimate allocative efficiency of sugarcane growers in study area. The cost function shows the relationships between the prices of inputs and the amount of output that can be produced at these prices. Using vector of input prices, the cost frontier in its general form can be written as:

Where; Ci : represents the observed cost for each grower, pᵢ: price of nth input while Yi: represent the output.

In stochastic frontier analysis mostly Cobb-Douglas functional form is used. Thus, the cost frontier with the Cobb-Douglas form was specified as follow in Equation 2. Studies conducted by Bravo-Ureta and Pinheiro (1997) and Sajjad (2012) have also employed same technique.

...(2)

….(3)

…….(4)

Table 1: Respondents selection in two villages of District Charsada.

The results given in Table 2 revealed that the average sugarcane yield was 24566.78 kg acre-1 (60 tonnes/hec) in district Charsadda but varies from 18200 to 30480 kg/acre. Which show that average yield in study area was high compare to national level (50 tonnes/hec). Average seed, urea, DAP and farmyard manure applied were 2471.86, 115.70, 74.96 and 1543.43 kg respectively. Mean farm size was 2.36 acres having standard deviation of 0.83. On average tractor hours applied were 20.74 with standard deviation of 3.64 up to maximum 30.00 hours per acre. During entire season maximum numbers of irrigation applied were 17.09. Average human labor (both family and hired) was 44.02-man days, with minimum and maximum rate of 32 and 55 man days respectively. It indicates that sugarcane production is water as well as labor intensive crop. Average pesticides application per acre in study area was 2689.83 milliliter up to maximum 9000.00 milliliter. Variability by standard deviation implies that growers operated at different level of inputs which affect their yield level.

Table 2: Descriptive statistics of key explanatory variables used in the analysis.

MLE estimates were arrived by using the computer software (STATA). Maximum Likelihood and Ordinary Least Squares (OLS) estimates along with those of inefficiency model are presented in Table 3. Positive as well as significant (at 5% level) coefficients were noted for all explanatory variables except farmyard manure and pesticides. This implies that these variables have direct relationship with total cost of sugarcane production. The estimated cost elasticities show that one percent increase in total production and prices of explanatory variables given in Table 3 increase total cost by 0.097, 0.0023, 0.294, 0.217, 0.338, 0.083, 0.084, 0.224, 0.029 and 0.017 percent respectively.

Results on causes of allocative efficiency

Major sources of allocative inefficiency discussed in recent literature are the management qualities of respondents, such as education, age, experience etc. in current study estimates for inefficiency parameters has incorporated in Table 3. Negative sign associated with a parameter means that the associated variable has direct relationship with allocative efficiency.

In allocative inefficiency model family size and extension contacts were found negatively and significantly correlated with allocative inefficiency at 5% level. A possible reason of family size positive impact on allocative efficiency might be that larger household size ensure availability of labor for various activities to be carried out timely. At peak seasons large family size could deploy more labor to undertake farming activities such as weeding, plowing and harvesting timely compare to their counterparts and hence they are efficient. The result is in line with Sisay et al. (2015). Dipeolu and Akinbode (2006) found effect of household family size on allocative efficiency non-significant in his study. Similarly, Coelli et al. (2002) reported that larger families are causes of low efficiencies in less labor-intensive seasons.

Several researchers such as Khan (2012), Dipeolu and Akinbode (2006), Bravo-Ureta and Pinheiro (1993) and Bravo-Ureta and Evenson (1994) has reported positive association between extension contacts and allocative efficiency. They found that farmers with more extension contacts are relatively more efficiency compare to their partner growers.

Table 3: Ordinary Least Square (OLS) and Maximum Likelihood (ML) Estimates for Stochastic Frontier Cost Function in District Charsadda.

The estimated allocative efficiencies of individual growers of sugarcane have been presented in Table 4. Allocative efficiency ranges from zero to one. First column of Table 4 shows allocative efficiency level, second column presents frequency distribution of growers according to allocative efficiency level given in column 1. In district Charsadda majority (52.54 %) of growers were found in the range beyond 60%, while 47.45 % growers lie in the medium category. The results revealed that there is allocative inefficiency problem among majority of sugarcane farms.

The average predicated allocative efficiency in study area was found 0.63 with range from minimum 0.36 to maximum 0.90. None, of the sample respondent had a 100% allocative efficiency index. The mean level (0.63%) of allocative efficiency for sugarcane growers given in Table 4 is in line with study of Londiwe et al. (2014). He found 61.5% mean allocative efficiency level for sugarcane crop in South Africa. Similarly, Rangalal and B. Sen. (2013) found 0.23% allocative efficiency level for sugarcane crop in India. Hussain (1995) reported 42.5 % allocative efficiency level for wheat crop in Pakistan. Bashir and Khan (2005) reported 72 percent mean allocative efficiency score for wheat crop in Peshawar valley.

The presence of allocative inefficiency indicates that average growers can reduce [(1 – 0.63/0.90)] 30% of their average cost in order to achieve the allocative efficiency level of most efficient fellow grower without decrease in output level. In district Charsadda per acre average cost was noted Rs. 77083/-. 30% reduction mean that average grower can decrease his cost to Rs.53,958/- without any reduction in production.

Table 4: Frequency distribution of allocative efficiency of sample respondents in district Charsada.

Aigner, D.J., C.A.K. Lovell and P. Schmidt. 1977. Formulation and estimation of stochastic production function models. J. Econ. 6: 21-37. https://doi.org/10.1016/0304-4076(77)90052-5

Burki, A.A and H.N. Shah. 1998. Stochastic frontier and technical efficiency of farms in irrigated areas of Pakistan’s Punjab. Pak. Dev. Rev. 37: 3. 275-291.

Azam, M. and M. Khan. 2010. Significance of the sugarcane crops with special and reference to NWFP. Sarhad J. Agric. 26(2): 289-295.

Bashir, M. and D. Khan., 2005. An analysis of allocative efficiency of wheat growers in Northern Pakistan. Pak. Dev. Rev. 44: 4 Part II (winter 2005) pp. 643-657.

Battese, G.E. 1992. Frontier production function and technical efficiency: A survey of empirical application in Agricultural Economics. Agric. Econ. 7: 185-208. https://doi.org/10.1016/0169-5150(92)90049-5

Bravo-Ureta, B.E. and R.E. Evenson. 1994. Efficiency in agricultural production: The case of peasant farmers in Eastern Paraguay”. Agric. Econ. 10: 27-37. https://doi.org/10.1016/0169-5150(94)90037-X

Bravo-Ureta, B.E and A.E. Pinheiro. 1993. Efficiency analysis of developing country agriculture: A review of the frontier function literature. Agric. Resour. Econ. Rev. 22 (1): 88-101. https://doi.org/10.1017/S1068280500000320

Bravo-Ureta, Boris. E and A. E. Pinheiro. 1997. Technical, economic and allocative efficiency in peasant farming: Evidence from the Dominican Repulic. Dev. Econ. I: 48-67.

Byerlee, D. 1987. Maintaining the momentum in post-green revaluation agriculture: A micro level perspective from Asia. Univ. Michigan (MSU Int. Dev. Paper No. 10).

Cochran, W.G. 1977. Sampling Techniques, 3rd Edition. John Wiley and Sons, New York. pp. 37-45

Coelli, T.J., S. Rehman and C. Tirtle. 2002. Technical, allocative, cost and scale efficiencies in Bangladesh rice Cultivation: A non-parametric approach. J. Agric. Econ. 533607. https://doi.org/10.1111/j.1477-9552.2002.tb00040.x

Dipeolu, A.O. and S.O. Akinbode. 2006. Technical, economic and allocative efficiencies of Pepper production in South West Nigeria: A Stochastic Frontier Appraoch. J. Econ. Rural Dev. 17 (1).

Farrell, M.J. 1957. The measurement of productive efficiency. J. R. Stat. Soc. Ser. A, 120: 253-90. https://doi.org/10.2307/2343100

Godfrey, L.G. and M.R. Wickens. 1981. Testing linear and log-linear regressions for functional forms. Rev. Econ. Stud. 18: 487-96. https://doi.org/10.2307/2297160

GoKP. 2015. Crop Statestics

Green, W.H. 1990. Econometric Analysis. Macmillan press, New York.

Hall, R.E. 1978. Stochastic implication of the life cycle-permanent income hypothesis: Theory and Evidence. J. Pol. Econ. 78: 1971-87. https://doi.org/10.1086/260724

Hopper, W.D. 1965. Allocative efficiency in a traditional Indian agriculture. J. Farm Econ. 47: 611-24. https://doi.org/10.2307/1236276

Hussain, S.S. 1995. Analysis of Allocative Efficiency in Northern Pakistan: Estimation, Causes, and Policy implication. Pak. Dev. Rev. 34(4): 1167-1180. https://doi.org/10.30541/v34i4IIIpp.1167-1180

Khan, H. 2012. Measurement of technical, allocative and economic efficiency of tomato farms in Northern Pakistan. J. Agric. Sci. Technol. 1080-1090.

Kodde, D.A. and F.C. Palm. 1986. Wald criteria for jointly testing equality and inequality restrictions. Economertican. 54: 1234-1248. https://doi.org/10.2307/1912331

Kopp, R.J and V.K. Smith. 1980. Frontier production function estimation for steam electric generation: A comparative analysis. South. Econ. J. 47: 1049-59. https://doi.org/10.2307/1057240

Lau, L.J. and P.A. Yotopoulos. 1971. A test for relative efficiency and an application to Indian Agriculture. Am. Econ. Rev. 61(1): 97-109.

Londiwe, T., E. Mungatana and M. Labuschagne. 2014. Estimation of technical, economic and allocative efficiencies in sugarcane production in south Africa: A case study of Mpumalanga growers. J. Econ. Sustainable Dev. 5 (16).

Mceusen, W. and J.V. den Broeck. 1977. Efficiency estimation from Cobb-Douglas production function with composed error. Int. Econ. Rev. 18: 435-44. https://doi.org/10.2307/2525757

Naila, A. and Shahid. A. 2009. Agricultural input use efficiency in Pakistan: Key issues and reform areas. Managing natural resources for sustaining future agriculture. Res. Brief. 1(3).

Ogundari, K., T.T. Amos and S.O. Ojo. 2010. Estimating confidence intervals for technical efficiency of rainfed rice farming system in Nigeria. China Agric. Econ. Rev. 2(1): 107-118. https://doi.org/10.1108/17561371011017531

Pakistan Sugar Mills Association 2014-15. Annul Report. Government of Pakistan.

Rangalal, M. and B. Sen. 2013. Technical, allocative and economic efficiency in sugarcane production: A non-parametric approach. Int. J. Adv. Res. 1(6): 366-374

Subhash, R.C. 1985. Measurement and test of efficiency of farms in linear programming models: A study of West Bengal Farms. Oxf. Bull. Econ. Stat. 47 (4):371-86.

Sajjad, M. 2012. Economic efficiency of milk production in Khyber Pakhtunkhwa. Ph.D thesis submitted to the Department of Agricultural and Applied Economics. Univ. Agric. Peshawar, KPK Pak.

Schultz, T.W. 1964. Transforming traditional agriculture. Univ. Chic. Press, Chic.

Sisay, D., J. Haji, D. Goshu and A.K. Edriss. 2015. Technical, allocative, and economic efficiency among smallholder maize farmers in Southwestern Ethiopia: Parametric approach. J. Dev. Agric. Econ. 7(8): 282-291. https://doi.org/10.5897/JDAE2015.0652

Stefanou, E.S. and S. Sexena. 1988. Education, experience and allocative efficiency: A Dual Approach. Am. J. Agric. Econ. 70 (2): 338-45. https://doi.org/10.2307/1242074