The concept of technical and allocative efficiency

The concept of good and allocative efficacy good readiness- Basic ConceptThe term Technical Efficiency was first utilize by M. J. Farrell in 1957 in his creative paper and differentiated and disaggregated economic efficiency into two components i.e. managed efficiency and allocative efficiency. Coelli et al. (1999) define technical efficiency as the maximum possible output from a granted set of inputs and existing technology (Coelli, Rao, Battese, 1999). It has been also defined as the ratio of actual output and probable output of a farm unit i.e.In this sense TE refers to the manner in which the inputs or wareion resources atomic number 18 utilise. By this definition it is more closely associated with the techniques of build or down the stairsstanding of technology and deals with the behavior of how to asseverate an optimal train of production attendless of input-output price ratio. Hence, technical efficiency is also equivalent to agronomic Efficiency.The concep t of technical and allocative efficiency can be explained by the help of Figure 2.1 illustrated by Kalirajan and Shand (1999).Theoretically, we assume that all units of production ( cockeyed or gain) operate at potential boundary production function i.e. the points along the curve FF. Any aim of inefficiency with respect to this production function will be purely allocative. The rationalness may be that the offerr has no income to acquire inputs or is not willing to spend more for the marginal gist of inputs. Now suppose if the firm operates at point B by using I1 inputs and getting Y1 output. At this point the firm is both technically and allocatively efficient with a maximum moolah of 1. When the firm operates at Point-A with I2 level of inputs producing Y2 output points, earning 2 amount of profit. At this point the firm is technically efficient as it is operating at FF simply it is inefficient allocatively. It can improve its profit by 2/1 amount. But on real grounds, t he units of production operate at less than the level of its potential confines. The evidences are different technical, socio-economic, bio-physical, organizational and other un cognize factors (Ahmed et al., 2002 Ajibefun, 2008 Ozkan et al. (2009)). Thus the firm operates at its actual production function AA below the potential frontier FF. Let us suppose it operates at point C with I2 amount of inputs and producing Y3 put up and earning 3 profits. At this point the firm is neither technically nor allocatively efficient. It could maximise its profit to 4 levels by operating at point D utilizing I3 inputs and producing Y4 outputs.But on real grounds, the units of production operate at less than the level of its potential frontier. The reasons are different technical, socio-economic, bio-physical, organizational and other unknown factors ( Ahmed et al., 2002 Ajibefun, 2008 Ozkan et al. 2009). Thus the firm operates at its actual production function AA below the potential frontier FF. Let us suppose it operates at point C with I2 amount of inputs and producing Y3 ease up and earning 3 profits. At this point the firm is neither technically nor allocatively efficient. It could maximize its profit to 4 levels by operating at point D utilizing I3 inputs and producing Y4 outputs.Thus it is quite clear from Figure 2.1 that economic inefficiency is composed of two components of technical and allocative inefficiency. The total loss of the firm in profit toll operating at point C is 1-3. Within this loss, 3-2 and 1-2 are the technical and allocative inefficiency losses respectively.The efficiency scenarios in these models explain three reasons of farmers attri scarcelyes as discussed by Ellis (1988)Farmers hope to maximize profit with less input levels given by yield gap (Y0 Y3). Such behavior is referred as profit maximization behavior.Second reason may be the lack of correct allocation of inputs given by (Y3 Y2), andFarmers failure of operating in the most eff icient production function (Y3 Y3). This gap represents technical inefficiency level, andFarmers behavior to reduce his risk instead of maximizing profit....Technical Efficiency- History / EvolutionFarrell (1957) is known as the pioneer of efficiency literature when the frontier production model real by him, in one of his seminal papers, decomposed economic efficiency into two components i.e. technical and allocative efficiency. He defined TE as the ability of a firm to produce maximum output given a set of inputs under existing technology. Stated differently, technical inefficiency is the failure of attaining the maximum possible level of production given existing resources and technology (Bravo-Ureta Pinheiro, 1993). The adoption of new technologies after green revolution for enhancing farm output has acknowledged special attention as a means to accelerate husbandry ontogeny after Schultzs hypothesis that conventional agriculture was fully efficient (Schultz, 1964). The gro wth performance is not only determined by such technological innovations moreover also by the efficient management and utilization of such technologies. The importance of efficiency measures as a means of nurturing productivity a considerable amount of literature is effectuate focusing on agriculture (Bravo-Ureta Pinheiro, 1993).The efficiency analysis of units of agriculture inputs (land, labour, fertilizer etc.) has always been the focus of a number of studies since early 1960s. near of the studies baffle supported Schultzs efficient but poor hypothesis. Theodore Shultz stated this hypothesis in 1964 thatThe traditional agriculture is fully efficient in the allocation of inputs under an existing technology. The combination of crops being grown, the depth and number of cultivation, time of planting, fertilizing, watering and harvesting, the combination of tools, draft animals and equipment are all made with a fine regard for marginal costs.(Schultz, 1964)Sahota (1968) establi sh on his and many others incur supported Schultzs hypothesis in their empirical works. Based on his exact in Indian agriculture, Sahota (1968) concludes that the bug out of the evidences appear to support the hypothesis that the resources available to conventional Indian farmers have been, by and large, efficiently allocated (Sahota, 1968).A large number of frontier models were developed based on Farrells work which was than classified into parametric and non-parametric types. Aigner Chu (1968) were the initiators of deterministic parametric approach. They estimated a deterministic production frontier of a Cobb-Douglas type by dint of linear and quadratic programming techniques. Timmer (1971) further developed this procedure by introducing a probabilistic production frontier model. He estimated a series of production frontiers by displace extreme observations at each stage until the rate of change of parameter estimates stabilizes. These estimators had undefined statistical properties.Another class of frontier models was proposed by Afriat in 1972 known as statistical production frontiers. According to Afriats (1972) model, technical efficiency is a one-sided disturbance term with some explicit assumptions and frontier is estimated by system of maximum likelihood estimation (MLE). On the other hand if the disturbances are based on no a-priori assumptions, then corrected least squares (COLS) system is used to estimate the production frontier by just shifting the frontier upwards covering all negative disturbance terms.In 1977, Aigner et al. (Aigner, Lovell, Schmidt, 1977), and Meeusen and Broeck (1977) independently developed random frontier production model in which the computer error term was decomposed into two components. A one-sided positive component reflecting inefficiency and a two-sided error component covering measurement errors and the random do, which are not in control of the producer. Under this model the frontier could be could be estimated either by COLS or MLE. But in 1980, Greene found that the MLE are more efficient than COLS as the former method makes use of special statistical distributions for the disturbance terms e.g. exponential, half-normal or gamma distribution (Greene, 1980).Another mathematical programming method was developed by Charnes, Cooper and Rhodes (CCR) in 1978 which was a generalized form of Farrells (1957) method in terms of multi-input and multi-output vectors. Their method is well known as Data Envelopment Analysis or DEA. But their approach of measuring efficiency confounds the unbowed technical efficiency score with uncontrollable noise (Charnes, Cooper, Rhodes, 1978). Further developments in DEA were incorporated by Varian in 1985. He brought improvements in DEA by treating the deviations as having stochastic characteristics and split them into two components of technical efficiency and random noise (Varian, 1985).The Free Disposal Hull (FDH) model, introduced by Deprins et al. (1984), was originally designed as an alternate to DEA models. In FDH approach only strong (free) disposability of inputs and outputs is assumed by relaxing the convexity assumptions of DEA models. FDH models were initially treated as DEA models under variable returns to scale (VRS). The FDH models are traditionally represented as abstruse integer linear programming (MILP) problems. Further extensions in production frontier estimation are multi-equation models based on production, utility, cost or profit function specifications. Such extensions include the work of Kumbhakar (1987) Battese, Coelli and Colby (1989).In the decade of 1990s, the literature on TE expanded with the growing use of Z-variables in the application of Stochastic Frontier Approach (SFA). Previously, look forers used auxiliary or two-step regression on a set of socioeconomic, institutional and policy variables, alleged(prenominal) Z-variates to observe the effect of such variables on TE scores. A new method pr oposed by Wang and Schmidt allows a one-step procedure for calculation of TE and impose effects of such Z-variables (Wang Schmidt, 2002).Kalirajan and Obwona (1994) suggested another approach for modeling production behavior and technical efficiency of any production unit, known as Stochastic alter Coefficient Frontier Approach or SVFA. Under this method, like DEA, the potential output is estimated by allowing TE to vary by each individual input. Thus it makes proportion between firms performances easier in a sample of firms. It also facilitates to identify a benchmark of an excellent performing firm in terms of best practice in a sample (Kalirajan Shand, 1999).A recent approach, different from other sampling theory models, is Bayesian Approach (BA). The approach treats the uncertainty concerning which sampling method to use by mixing over a number of competing a-priori inefficiency distributions with a-posteriori model probabilities as weights. This approach overcomes the crit icism of imposing a-priori distributions on disturbance term as in SFA. But in Bayesian Approach, like SFA, the potential output to estimate TE varies over all inputs taken together. It also differentiates random effects and fixed effects issue for panel data (Kalirajan Shand, 1999).Developmetns are being made on the methods to make them more, efficient, flexible, easily computable and more policy oriented. curiously Bayesian and FDH approaches need more modifications and specifications.Efficiency Studies in Developing Countries Agriculture and Associated FactorsHere we cite some literature on efficiency estimates in agriculture sector of some growth countries with our main focus being on Pakistan. The findings regarding average efficiency scores and their relationship between different factors are summarized in the following paragraphs.Shapiro (1983) examined TE of Tanzanian cotton farmers using a Cobb-Douglas production frontier. His findings yielded an average TE of 66 percen t leading rejection of Schultzs (1964) hypothesis.Balbase and Grabowski (1985) invested TE in Nepali agriculture. His findings yielded 84 percent and 67 percent TE scores for rice and maize farms respectively. His analysis showed that nutrient levels, farmers obtaining up and income were significant factors influencing TE.Kalirajan and Shand (1985) examined TE of paddy farms in Indian state of Tamil Nadu. Their study proved non-formal education as significant positive factor in enhancing efficiency levels of farmers.Ali and Flinn (1989) have used a modified trans-log stochastic profit frontier to investigate profit efficiency of Basmati rice farms in Pakistan. They identify education, faith, late application of fertilizer and water paucity as key factors in profit losses.Ali and Chaudhary (1990) estimated efficiency for 220 farmers in Pakistani Punjab. According to his findings the average technical, allocative and economic efficiency were 84%, 61% and 51% respectively.Hussain (1991) canvass efficiency in Punjab province of Pakistan. His results showed a TE score ranging from 80 percent for rice region and 87 percent for sugarcane region.Bravo-Ureta and Evenson (1994) analyzed efficiency for 101 cassava and 87 cotton farmers from Eastern Paraguay. His findings showed 58%, 70% and 41% technical, allocative and economic efficiency scores respectively for cotton farmers. Whereas the corresponding persona for cassava growers were 59%, 89% and 52% respectively. His results evidenced farmers age, education, farm size, extension contacts and credit availability as significant factors influencing efficiency level of farmers.Another study conducted by Ali, Parikh and Shah (1994) in NWF province of Pakistan by using both behavioral and stochastic cost frontier functions. Among socioeconomic variables, farmers age, farm size, land fragmentation and subsistency were video display significant act on inefficiency levels.Ahmed et al. (2002) have analyzed TE of wheat growers in three provinces of Pakistan using a stochastic frontier production approach. The results yielded on average 32 percent losses due to technical inefficiency. The variables of age, education, extension function, farm to merchandise distance, farm size, and credit availability had significant influence on efficiency levels of farmers in the provinces. He also found that wheat farmers in Punjab were technically more efficient (70%) than their counterparts in Sindh (66%) and NWFP (63%). Tenants were technically more efficient than the owners and owner-cum-tenants.Dhungana et al. (2004) have used Data Envelopment Analysis approach to examine efficiency of Nepalese rice Farmers. The results revealed that 76, 87 and 66 percent technical, allocative and economic efficiency levels were achieved by farmers. The factors contributing in inefficiency were excessive use of input resources, farmers level of risk attitude, managers age and gender, education and family labour endowment. Hassan and Ahmed (2005) examined TE of wheat growers in a mixed farming system of Punjab province in Pakistan using a C-D production function. The mean TE was recorded about 94 percent. The key influencing factors of efficiency were education, punctual cultivation of crops, credit availability, sowing patterns and water availability.Bashir and Khan (2005) have conducted an efficiency analysis of 200 wheat farms in Northern region of Pakistan. They found high transmutation in yields of sample farms showing an average allocative efficiency of 72 percent in the study area. Farmers awareness, education level, farm size and level of fertilizer used were significant factors depriving farmers to achieve their optimum level of profits.Lambarraa et al. (2006) examined TE and productivity growth in the Spanish Olive sector. They found that farmers age, farm location tenure regimes of land and organic nature of farming techniques affect significantly the level of efficiency.Mari and Lohano ( 2007) have analyzed TE of onion, tomato and chili farms in Sindh province of Pakistan. The mean TE was found to be 83 percent, 74 percent and 59 percent for chili, tomato and onion farms respectively.A detailed study on TE of Russian agriculture has been conducted by Brock et al. (2007). They found interesting results under three organizational farming regime i.e. peasant farming, large corporate farming and household plots. The TE rankings were highest for household plots (81%) followed by corporate farms (74%) and peasant farms (70%). The peasant farms were least efficient.Analyzing efficiency of Nigerian food crops, Ajibefun (2008) has applied both SFA and DEA approach. He found only fine variation in average TE computed by both methods, i.e. 68 percent by SFA and 65 percent by DEA. Significant influencing factors were farmers age and education level.Kilic et al. (2009) have investigated TE of hazelnut production in Samson province of Turkey. Their study showed an average effici ency of 73.5 percent. Farmers education level and farm fragmentation were found as significant factor determining TE.A very recent work by Monchuk et al. (2010) on TE in Chinas agriculture reveals that heavy industrial enterprise and large percentage of rural labour force in agriculture sector tend to reduce TE. He suggests that air and water pollution have negative effects on agriculture production and growth of non-primary agriculture may lead to efficient use of labour resources.Factors Affecting Technical EfficiencyThere are various socioeconomic, infrastructural, institutional and policy factors that tend to influence technical efficiency of farmers, thereby depriving them from achieving a potential output from their available resources. assignment and probable solutions of such factors had been the focus of researchers and policy makers through decades. A summary table of the work of different researchers showing TE of different crops and significant factors touch on level of TE is given in Table 2.1Yield GapYield gap is the difference between the yields of the experimental station by researchers and yield from farmers plot. According to Gomez (1977), yield gap appears in two ways (see Figure 2.2)The yield gap between maximum yield of research station and potential farm yield. This gap emerges into the system due to the environmental factors (climate, rainfall, humidity, sunlight etc) and non-transferable technology to farmers fields from the research station.Second type of yield gap is the difference between potential farm yields to that of the actual farm yield gained by the farmer. This difference arises from the different biological and socio-economic factors.This study of these gaps is particularly important in the context of research and arises some questions that whether the research methodologies, technologies, environment, equipments and capital costs utilized at research station are appropriate to farmers field conditions? Whether the recomm ended technology is complete? Whether this can compensate or takes into consideration the less favorable socioeconomic, bio-physical and environmental conditions of rain-fed and resource-poor marginalized farmers (Dahal, 1996)? The process of estimating technical efficiency gaps should be taken in a doctrinal and realistic way. It is necessary to consider the farmers specific farm trials rather than the trials conducted at the research stations.Role of Marketing in Rural EconomyFarmers consider themselves as price takers and phone that they have no control over prices and are bound to accept whatever the price is offered. They do not know how to capture new market places nor how market demand and buyers preferences are ever-changing and which products are to grow to gain more profit from their produce. Farmers generally have knowledge and skills in agriculture production techniques but merchandise needs new skills, techniques and sources of information. Farmers armed with newe st business and merchandise skills will have better profit margins (Dixie, 2005).Rural businesses include input suppliers, product buyers, transporters, stock companies, processing companies and wholesalers. These intermediaries are often believed to exploit farmers and making unfair profits. Although they try to maximize their profit yet it is to accept that without these intermediaries farmers would not be able to connection with input and output markets and neither they would be able to sell their produce.Role of Marketing in Consumer welfareAs farmers desire is to receive higher prices, consumers desire to pay up lower prices. Farmers want to be paid as much share of consumer price as possible. These two conflicting goals balance when there is an efficient and low-cost marketing chain. Consumers preferences are constantly developing particularly in the case of horticulture crops. They need a marketing system that can respond to their changing demands and tastes. The market ing system should supply the volumes, variety and quality products that consumers demand.Fruits Marketing System in Pakistan (Aujla et al)Marketing includes a series of inter-connected activities involved in the flow of products and services from the point of production to the point of consumption at a profit. An efficient marketing system guarantees sustained agricultural growth as it affects both producers income and consumers welfare (Aujla, Abbas, Mahmood, Saadullah, 2007). The marketing of reapings in the Pakistan is supply based. Once a producer brings his produce to the market, the prices are decided by large traders at the spot such that he is bound to accept the prevailing prices. Most of the times the producers have to dispose off their commodities at throwaway prices (Hanif, Khan, Nauman, 2004).Several factors influence the efficiency of return marketing that include high perishability, seasonality, low quality, uneven prices and location of the products, the physical handling of produce and the institutional arrangements for facilitating these activities. The existing marketing system in Pakistan consists of aggregation, wholesale and death markets, which are briefly discussed belowAssembly MarketsAssembly markets are situated close to horticulture farm gate, generally situated in gauzy towns or sub-districts, where farmers bring their major portion of marketable scanty for sale to the shopkeepers, traders and retailers present in these markets. Most of the transactions in assembly markets involve small quantities of produce. Traders in assembly markets are not approved by any government agency, although in some cases town committees charge an entry fee from traders. Usually, these traders maintain no systematic record of transactions. The price formation is simple and based on direct negotiation between the traders and the farmers. Because the quantities involve small bulks the farmers may not mind small price differentials.Wholesale Mark etsWholesale markets are essential components of any marketing system, especially for horticulture crops because these markets provide farmers effective and profitable marketing outlets for their products. Adequately located, sized and managed wholesale markets serve as a basic instrument for promoting competition and help to improve consumers health and food quality control (FAO, 2001).Wholesale markets in Pakistan are unremarkably located in a district town or a major sub-division town. These markets are the main assembly centers for the fruit and vegetable unornamented of surrounding areas. These markets have better transportation, storage, communication and working conditions than those in the assembly markets. The example of wholesale market in Balochistan is that of Quetta, where the waste fruit produce of close districts are supplied. Wholesale markets have permanent auction floors and offices built by traders (commission agent) who hold an official permit for their activ ities. Each trader has sufficient space in the market to store produce for a few days or for longer periods at a nominal charge. Traders keep records of their daily transactions and depict them to the Market Committee. Market participants in wholesale markets including commission agents, wholesalers, retailers, shopkeepers and weighing men are also registered and licensed by Market Committees. Introduction of these measures have resulted in some improvements in these markets. Commission agents in wholesale market, charged 8 to 10 percent commission on the sale revenue (Hussain Abid, 2005). The major players in the wholesale market are commission agents, wholesalers, retailers and shopkeepers.Terminal MarketsTerminal markets are generally situated in large urban centers. Most of the marketable surplus of agricultural commodities is ultimately routed to these markets. The Karachi market is one of the best examples of this kind of market in Pakistan. Foreign trade is another reason f or the flow of the marketable surplus to this market. Traders in terminal markets are usually wholesalers who supply agricultural products to firms, industries and exporters. The majority of traders are buying agents, who buy from other wholesale markets through their agents or at one time when the produce is brought there from other regions. This market is well equipped with traders who are well established and mostly depending on supplies from growers and other wholesale markets. They have plan of attack to all modern facilities for approaching their agents in lower level markets. Many traders have their own trucking companies. Telephone and telegraph services are easily available for them.Fruit Marketing Channels in PakistanKhushk and Smith 1996Khushk and Smith (1996) have done a nice and detailed study of fruit marketing channels in Pakistan by concentrating particularly on mango production in Sindh province. According to them, agricultural marketing channels refer to the outl ets or routes through which commodities pass to reach final consumers. As produce moves along the marketing chain, its price increases because of opportunity cost incurred by each intermediary (Dixie, 2005). The existing fruit marketing channels in Pakistan by . are presented in Figure.....Figure 2.3 Marketing Channels for Fruits in PakistanThe marketing channels functionaries common in the country areProducerThe fruit growing farmers are dispersed geographically in the country. Majority of producer sell the harvesting rights of their orchards to contractors at the unfolding or in hanging fruits stage because they do not want to be involved in marketing complications. Also the farmers do not do not want to take the risk of price and income variation due to perishability, quality damage, and price seasonality. In addition, Khushk and Smith (1996) found another important reason reported by farmers is the lack of knowledge of marketing. commission agents are biased towards farmers tha n contractors and do not want to transfer market price information to farmers or provide them other facilities, like informal credits, transportation or information access at the market-place. By this way commission agents control the supply, demand and prices of market (Khushk Smith, 1996 Ali, 2004 Aujla, Abbas, Mahmood, Saadullah, 2007).ContractorThe contractor plays a main role in the marketing of fruits. He has close contacts with commission agents in the wholesale and terminal markets. While promise an orchard, the contractor estimates its yield and considers the expected costs to be incurred for supervision, labour, transportation, and marketing. Khushk and Smith (1996) report that more than 95 percent of mango contractors in Sindh province of Pakistan obtained loans from commission agents to pay the initial installments to the mango farmers and to pay an advance for labour and packing material. Once a contractor receives loan from commission agent, he is obliged to supply the produce to that commission agent.Commission AgentCommission agents act as a link between contractors in the field and wholesalers or retailers at wholesale market. They usually have their own transport companies and have offices and staff at wholesale markets of big cities, equipped with all communication facilities. They maintain contacts with market committees, market associations, wholesalers and retailers and influence the prices in fruit markets of Pakistan (Ali, 2004).WholesalerWholesalers perform their business in wholesale or terminal markets of the country. They do business with large quantities of farm products and deal in several commodities like vegetables, fruits and other agricultural produce within interregional markets and also supply produce to processing industries, exporters, and retailers according to their demand. They maintain contacts with commission agents in wholesale markets and retailers in the local area. Wholesaler usually purchase fruit from the com mission agents at open auction and sell in smaller quantities to the retailers and consumers. They mostly buy from the commission agents on a credit basis, and about one week after selling that quantity, they pay the commission agents. Some wholesalers also act as commission agents (Khushk Smith, 1996 Ali, 2004 Zulfiqar, Khan, Bashir, 2005).RetailerMarket activities come to end with the retailers. They buy and sell small quantities according to the demand of consumers in the area. A small number of fruit retailers occupy small shops in the main fruit markets or in the town. Moreover, a number of retailers are found standing at focal places of a town, particularly railway stations, bus stands, vicinity of courts, schools, and hospitals. Among fruit retailers there is a high degree of competition. Retailers buy fruit from the wholesalers on a 24-48 hour credit basis (Khushk Smith, 1996 Ali, 2004 Zulfiqar, Khan, Bashir, 2005).Importance of Market functionaries / IntermediariesAltho ugh a heavy literature is found on exploitative behavior of market intermediaries towards agriculture producers, especially in developing countries such as Pandit et al. (2005) Aujla et al. (2007), Khushk and Smith (1996) and many others, yet their role cannot be ignored (Dixie, 2005). It is often mis-understood how important traders are in taking agriculture produce from farm to the market. Their importance becomes more critical in case of fruits which are highly perishable in nature and need quick supply. The more dynamical the fruit trading sector leads greater competition among traders and greater volumes of produce taken out of rural farm lands resulting, ultimately, high income returns to the farming community. Farmers Selling promptly to consumers does mean higher profits but also greater risks. Market traders accept that risk such as non-payments, price decrease and marketing and handling losses (Khushk Smith, 1996). Therefore the intermediaries should be encouraged, not criticized (Dixie, 2005 Pokhrel, 2005).Marketing Margin AnalysisMarketing margins or farm-to-retail price spread are some functions of differences between farm-gate prices and retail prices, intended to measure the opportunity cost of providing marketing services including buying, grading, packing, transporting, storage, and processing (Khushk Smith, 1996 Wohlgenant, 2001). The prices paid to the rural sellers at farm-gate are much lower. But as the product moves along the production-marketing chain, its price increases such that the retailers achieve the highest price (see Figure 2.3).The farm-to-retail price spread of fruits in Pakistan is con