Why Metafrontier Analysis?

Efficiency estimation¹¹ using stochastic frontier analysis (SFA) and or data envelopment analysis (DEA) often assumes homogeneous production technology for all farmers in the

10 Ideally, systematically well documented farming information would have been prefer as compare to recall information; since recall information could aggravate the problems of outlier in statistical estimation; This could be a draw back and so has to be kept in mind for interpretation.

11 Recent developments in frontier modeling and efficiency measurement have been well documented by many authors (T. J. Coelli, 1995)(Bravo-ureta & Pinheiro, 1977)(Andrew, 2010). Thus, a comprehensive review of the overall literature would require a whole research paper. Therefore, references cited may be consulted for further details on recent methodological developments.

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dustry. However, often in agricultural production, farmers in the same industry due to a vari-ety of reasons such as quality and availability of important agricultural infrastructures (e.g.

roads, ports etc), resource endowment (e.g. human and financial capital) and climatic con-straints may be forced to operate under different production technologies. Such variation or differences in available stocks of physical, human and financial resources in terms of quality and availability compel farmers to use different production technologies (e.g. different plant variety or different bundle of input quality, application rate/frequency and amount).

As stated by Battese et al, (2004) “Technical efficiencies of firms that operate under a given production technology, which is assumed to be defined by a stochastic frontier production function model, are not comparable with those of firms operating under different technolo-gies.” Thus, failure to account for these technological differences risk attributing production shortfall due to technological gaps to technical inefficiency of farmers in that industry,¹² hence, the need to employ an analytical method which allows technology gaps to be distin-guished from technical inefficiency.

In this study, we adopt the metafrontier model proposed by Battese et al, (2004). This method enables the estimation of comparable technical efficiencies and technological gaps ratios for farms under different technologies relative to the potential technology available to the industry as a whole¹³. In an agricultural production context, the use of a metafrontier is usually justified when statistical test together with prior knowledge confirm farmer’s in dif-ferent regions uses difdif-ferent production technologies. Physical conditions (such as climate, soil and infrastructure) and other constraints prevailing in the production environmental may prevent farmers from making full use of certain production techniques even though it is avail-able to them. Occasionally and in exceptional cases, application of such high output technol-ogy may be possible but only in the long run after scientist have introduced technologies which are able to deal with barriers posed by such physical condition (e.g. drought or frost resistant varieties). In other circumstances, farmers are not constrained by their physical envi-ronment in making use of a better production technology but still are not in a position to use such technologies because of lack of required infrastructure and or financial/investment con-straints. Such situation typically arises with perennial fruit crops such as mangoes where

12 Labelling unobserved heterogeneity in production technology as inefficiency is inappropriate since it leads to bias estimation of the technical inefficiency parameters.

13 Technological gaps due to climate conditions cannot be closed through policy or intervention programs, how-ever, such empirical information is worth noting in shaping future policies in such regions.

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duction cycles span many decades. Often it is not feasible for farmers to replant their trees in the short or medium term even though more productive varieties may have become available since the initial planting (Villano et al (2010). Farmers in the northern savannah zone of Ghana face different climatic and soil conditions as well as lagging behind in terms of infra-structural development compared to that of the middle and the southern zones and this may induce variability in production practice.

We therefore use the metafrontier model to study and compare the productivity and effi-ciency of mango farmers in the different production regions of Ghana. The model enables us to separate causes of performance inefficiencies due to poor production practice (i.e. technical inefficiency) from that of technological lags (i.e. technology gap between the region and the industry). Empirical information gathered from technical efficiency and technology gaps es-timates could be used in two different ways in designing performance enhancing programs:

1) Technical efficiency estimates (i.e. a component of the analysis which measures the distance from an input-output point to the regional/group frontier) could be used in de-signing performance enhancing programs involving changes to the management capa-bilities and effectiveness of how farmers use available technologies and resources in that region/zone to achieve higher yields.

2) Estimates of technology gaps (i.e. a component of the analysis which measures the distance between the group frontier and the industrial frontier) could be used in de-signing programs to enhance the production environment to enable farmers’ access the best production techniques available in the Ghanaian fruit production industry.

Stakeholders and policy makers can influence the production environment through labour laws, market regulations and infrastructural development such as building roads to facilitate transport of both inputs and outputs. However, policy makers have very limited capacity in influencing certain physical conditions such as soil texture, structure, water holding capacity and infiltration rate or temperature, humidity and rainfall patterns as well as some cultural characteristics (e.g. religions, local traditions, customs and norms) of the production environ-ment. If empirical analysis confirmed that such physical conditions prevent a region from making full use of available technologies and hence cause such regions to exhibit technology gaps or lags: Such information is worth noting but renders little or no help in designing pro-grams to directly alter such physical conditions in order to improve the production environ-ment. Such information however, does shed light on the suitability or comparative

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tage¹⁴ of such regions in producing such a crop, hence, could shape future agricultural devel-opment policies towards such regions.

With this in mind, lessons learned from this study hopefully should assist both farmers and policy makers to device appropriate strategies to aid improve production efficiency and hence output in the sector. This could be achieved as farmers receive help and advice on how to im-prove production efficiency to enable them operate as close possible to their regional frontier and or policy makers improving the production environment through legislation and infra-structural development. Such specific targeting measures are cost effective and economically sustainable and will help improve the performance of the fruit crop industry as a whole.