Firm behaviour under uncertainty: a simple parametric model

In this paper, we model production technology in a state‐contingent framework. We assume that all the firms use the same stochastic technology, but they may have different risk attitudes and information sets, and ex post they may operate in different production environments. Firms maximise ex ante their preference function subject to a stochastic technology constraint; in other words, they are assumed to act rationally, thereby leaving no room for either technical or allocative inefficiency. We provide a simple parametric functional form to represent the state‐contingent technology. Using simple numerical examples, we illustrate how optimal input–output choices are dramatically affected when firms have different preferences and information sets. Thus, we show that the observed disparateness of production choices among different firms can actually be attributed to the stochastic nature of the decision environment.     

Event‐specific data envelopment models and efficiency analysis*

Most, if not all, production technologies are stochastic. This article demonstrates how data envelopment analysis (DEA) methods can be adapted to accommodate stochastic elements in a state‐contingent setting. Specifically, we show how observations on a random input, not under the control of the producer and not known at the time that variable input decisions are made, can be used to partition the state space in a fashion that permits DEA models to approximate an event‐specific production technology. The approach proposed in this article uses observed data on random inputs and is easy to implement. After developing the event‐specific DEA representation, we apply it to a data set for Western Australian barley production data. Our results highlight the need for acknowledging stochastic elements in efficiency analysis.     

Representing climatic uncertainty in agricultural models – an application of state‐contingent theory

The state‐contingent approach to production uncertainty presents a more general model than the conventional stochastic production approach. Here we investigate whether the state‐contingent approach offers a tractable framework for representing climatic uncertainty at a farm level. We developed a discrete stochastic programming (DSP) model of a representative wheat–sheep (mixed) farm in the Central West of NSW. More explicit recognition of climatic states, and associated state‐contingent responses, led to optimal farm plans that were more profitable on average and less prone to the effects of variations in climate than comparable farm plans based on the expected value framework. The solutions from the DSP model also appeared to more closely resemble farm land use than the equivalent expected value model using the same data. We conclude that there are benefits of adopting a state‐contingent view of uncertainty, giving support to its more widespread application to other problems.     

Production economics in the presence of risk*

This paper provides an overview of the literature on production under the influence of risk. Various specifications of stochastic production function such as models with additive and multiplicative uncertainty, Just and Pope model, output‐cubical, state‐allocable and state‐general models are discussed. Further, criteria determining optimal producer behaviour are derived for deterministic production technology and for various kinds of state‐contingent technologies such as output‐cubical, state‐specific, state‐allocable and state‐general technologies. Finally, a brief discussion is presented about the drawbacks of each of these specifications of technology.     

State‐contingent analysis of farmers’ response to weather variability: irrigated dairy farming in the Murray Valley,Australia

The agricultural sector is commonly regarded as one of the most vulnerable to climate change. Current understanding of the impact of climate change on this sector relies on the underlying assumptions about farmers’ possible responses to weather variability, including changes in crop choice, input combinations and land management practices. Many previous analyses rely on the implicit (and restrictive) assumption that farmers operate under a fixed technology set across different states of nature. This assumption, represented through stochastic production or profit functions, is commonly made but seldom tested and may understate farmers’ responses to climate change if state‐contingent production technologies are, in reality, more flexible. The potential for farmers to adapt production technologies in response to unforeseen events is at the core of the state‐contingent approach. Advanced in Chambers and Quiggin (2000), the theory contends that producers can manage uncertainty through the allocation of productive inputs to different states of nature. In this article, we test the assumption that farmers’ observed behaviour is consistent with the state‐contingent production theory using farm‐level data from Australia. More precisely, we estimate the milk production technology for a sample of irrigated dairy farms from the southern Murray–Darling Basin over the period from 2006–2007 to 2009–2010.     

Heterogeneity of Long‐run Technical Efficiency of German Dairy Farms: A Bayesian Approach*

In parametric efficiency studies, two alternative approaches exist to provide an estimate of the long‐run efficiency of firms: the dynamic stochastic frontier model and the generalised true random‐effects model. We extend the former in order to allow for heterogeneity in the long‐run technical efficiency of firms. This model is based on potential differences in firm‐specific characteristics and in firms’ inefficiency persistence. The model is applied to an unbalanced micro‐panel of German dairy farms over the period 1999 to 2009. Estimation of long‐run technical efficiency and inefficiency persistence is based on an output distance function representation of the production technology and estimated in a Bayesian framework. The results suggest that heterogeneity in long‐run technical efficiency of farms is mostly attributed to discrepancies in farm‐specific factors rather than differences in farms’ inefficiency persistence. Farm size is positively related to long‐run technical efficiency while subsidies exert a negative effect on the long‐run technical efficiency of farms. Inefficiency persistence is found to be very high, but heterogeneity in this persistence is low.     

A copula‐based approach to the simultaneous estimation of group and meta‐frontiers by constrained maximum likelihood

Existing approaches to the meta‐frontier estimation consist of two stages where the estimates of the local frontier parameters obtained in the first step are used to estimate meta‐frontier parameters by means of a linear or quadratic minimisation procedure in the second. Since it was shown by Schmidt (Review of Economics and Statistics 58: 238) that the second step is equivalent to constrained maximisation of a likelihood function, we extend this idea and offer a copula‐based approach to the estimation of the parameters of both meta‐ and group frontiers in a one‐step setting. In this way, we ensure a single data‐generating mechanism for the estimated parameters, expand the set of potential meta‐frontiers and account for the fact that shocks to the individual production units may be correlated with shocks to the local technological environment as a whole. We apply our estimation methodology to a data set on the world agriculture and find that the deviations from the group frontiers are positively correlated with deviations from the meta‐frontier, which is a conclusion that is impossible to reach without accounting for stochastic dependence between the two deviation types represented by a copula.     

Modern Rice Technologies and Productivity in the Philippines: Disentangling Technology from Managerial Gaps

Using cross‐sectional farm‐level data from 3,164 rice‐farming households in the Philippines, we measure the impact of modern rice technologies on farm productivity while disentangling technology gaps (the distance between production frontiers) from managerial gaps (differences in technical efficiency). To do so, we combine a recently developed stochastic production frontier framework with impact evaluation techniques to control for biases stemming from observables and unobservables. First, we find an adequate control group using propensity score matching to mitigate the effect of biases from observable variables. Then, we test for biases that might arise from unobserved variables using a stochastic frontier framework corrected for self‐selection. Finally, we estimate meta‐frontiers to assess productivity differences between adopters and non‐adopters. The analysis shows that the adoption of certified seeds has a significant and positive impact on productivity, efficiency and net income in rice farming.     

Estimating Technical Efficiency and Production Risk under Contract Farming: A Bayesian Estimation and Stochastic Dominance Methodology

We investigate production risk, technical efficiency and risk attitudes amongst contract and independent farmers. We use a Bayesian parametric approach and stochastic dominance quantile regression methods to compare technical efficiency and risk attitude of smallholders in Nepal. Using farm‐level data, we find that contract farmers appear to show lower inefficiency and lower production risk. Additionally, contract and independent farmers can increase output by reducing the scale of operation. Regardless of the commodity produced and farming arrangement (contract or independent production), we find that labour, land and other inputs are risk‐augmenting, while the role of capital is mixed. We find a second order stochastic dominance (SSD) for lentils, and first order stochastic dominance (FSD) for tomatoes, ginger and HYV paddy seed commodities. Finally, contract farmers are more risk averse than independent farmers, regardless of the commodity produced.     

What determines the efficiency of Australian mining companies?

We examine the firm‐specific determinants of technical efficiency in Australian mining companies using data envelopment analysis (DEA). To do so, we employ panel data sourced from individual mining companies listed on the Australian Securities Exchange (ASX) over the period 2010–2014. To ensure valid statistical inference in the presence of serial correlation between DEA efficiency scores, we apply Simar and Wilson's two‐stage bootstrap method. We find that ownership concentration, firm size, firm age, product portfolio, product diversification and growth status significantly contribute to efficiency gains. However, other firm‐specific factors, such as capacity utilisation, financial risk and overseas operations appear to have limited impact on the technical efficiency of mining firms.     

Small U.S. dairy farms: can they compete?

The U.S. dairy industry is undergoing rapid structural change, evolving from a structure including many small farmers in the Upper Midwest and Northeast to one that includes very large farms in new production regions. Small farms are struggling to retain competitiveness via improved management and low‐input systems. Using data from USDA's Agricultural Resource Management Survey, we determine the extent of U.S. conventional and pasture‐based milk production during 2003–2007, and estimate net returns, scale efficiency, and technical efficiency associated with the systems across different operation sizes. We compare the financial performance of small conventional and pasture‐based producers with one another and with large‐scale producers. A stochastic production frontier is used to analyze performance over the period for conventional and pasture technologies identified using a binomial logit model. Large conventional farms generally outperformed smaller farms using most economic measures—technical efficiency, various profitability measures, and returns to scale.     

Valuing seasonal climate forecasts in a state‐contingent manner

We applied state‐contingent theory to climate uncertainty at a farm level to assess the value of seasonal climate forecasts in the Central West region of NSW. We find that modelling uncertainty in a state‐contingent manner results in a lower estimate of forecast value than the typical expected value approach. We attribute this finding to a more conservative long‐term farm plan in the discrete stochastic programming (DSP) model, which is better balanced for climate uncertainty. Hence, a climate forecast, even though it still revises probabilities held by farmers, does not call forth such large changes in farm plans and associated farm incomes. We then use the DSP model to assess how attributes of a hypothetical forecasting system, particularly its skill and timeliness, as well as attributes of the decision environment, influence its value. Lastly, we assess the value of current operational forecast systems and show that the value derived from seasonal climate forecasts is relatively limited in the case study region largely because of low skill embodied in forecasts at the time when major farm decisions are being made.     

Drought policy: a graphical analysis*

The standard approach to modelling production under uncertainty has relied on the concept of the stochastic production function. In the present paper, it is argued that a state‐contingent production model is more flexible and realistic. The model is applied to the problem of drought policy.     

Estimating product‐specific and multiproduct economies of scale with data envelopment analysis

Nonparametric cost frontier estimation and subsequent analysis of the relative efficiency of firms has historically been conducted without critically examining the shape of the cost frontier. The shape of the cost frontier has been examined using additional parametric estimation methods to recover potential cost savings from multiproduct and product‐specific economies of scale. This paper presents and tests an approach to estimate multiproduct and product‐specific economies of scale using data envelopment analysis. Data for the study are simulated assuming an underlying production technology. Nonparametric estimates of efficiency, multiproduct scale, product specific scale, and scope economies are compared to those of the assumed production technology. Results show that the nonparametric approach accurately estimates multiproduct economies of scale and product‐specific economies of scale under alternative inefficiency distributional assumptions.     

Annual and cropping season environmental production conditions effects on smallholder technical efficiency in sub‐Saharan Africa: Evidence from Ethiopia

This article investigates the impact of environmental production conditions on smallholder farmers’ technical efficiency in Ethiopia for wheat, maize, and sorghum farming. We use a household panel data set with annual and cropping season environmental production conditions to estimate the technical efficiency scores. The results show that including environmental production conditions in the stochastic frontier has a significant impact on farmers’ technical efficiency scores. Technical efficiency scores improve when environmental production conditions are incorporated in the stochastic frontier. Thus, accounting for environmental production conditions reduces the inefficiencies that otherwise may be attributed to the characteristics of smallholder farmers.     

The Contingent Tariff of the Special Safeguard Mechanism: What Happens When Markets are Imperfectly Competitive?

Contingent tariffs for agri‐food commodities have been proposed as a Special Safeguard Mechanism (SSM) in the Doha Round negotiations by the G33 group of countries as an instrument to control downward spikes in their border prices and/or surges of imports. The objective is to safeguard the livelihood security of farm households in poor countries. To date, most analysis of such tariffs uses stochastic partial equilibrium models with perfect competition. Yet in many markets for such commodities, imperfectly competitive market intermediaries play an important role in determining producers' prices, as do state trading enterprises (STEs). A stochastic partial equilibrium model of a typical importing country situation is specified in which there are either imperfectly competitive domestic intermediaries with a contingent tariff or an STE. The role of these intermediaries in influencing price behaviour and livelihood security in the presence of contingent tariffs alters the conclusion based on models of perfect competition. Using Monte‐Carlo simulation, it is shown that the efficacy of a contingent tariff is substantially reduced as the number of firms declines because increasingly they absorb the tariff, and the procurement price and producer surplus do not increase to the extent that they do under perfect competition.     

Off‐farm work,technical efficiency,and rice production risk in Taiwan

This article investigates the differences in yield production, production efficiency, and yield risk for farmers both with and without off‐farm work. Using a nationwide survey of rice farmers in Taiwan, we estimate two stochastic production frontier models that accommodate technical inefficiency and production risk simultaneously for farmers both with and without off‐farm work. The stochastic dominance criterion is then applied to compare the differences in the distributions of the estimated technical efficiency and yield risk between groups. The empirical results indicate that these two groups of farmers use resources in different ways, and off‐farm work is not necessarily associated with lower technical efficiency. For farmers in the lower percentiles of the efficiency distribution, those with off‐farm work are more efficient than their counterparts without off‐farm work. In addition, farmers with off‐farm work face higher production risk and this result is robust for the entire distribution.     

Determinants of Adoption of Protected Designation of Origin Label: Evidence from the French Brie Cheese Industry

This paper investigates empirically the determinants of agro‐food firms’ adoption of the Protected Designation of Origin (PDO) label. A unique dataset containing firm‐level cost and production information on the French Brie cheese is used, covering the period 1980–2000. The Brie cheese data are especially relevant as PDO Brie producers have coexisted with other non‐PDO producers since 1981. To evaluate the producers’ incentive to opt for PDO certification, we use a structural switching regression model which incorporates cost and production structure variables. Results show that PDO certification is less attractive the higher the costs of raw materials and the greater the size of the company. PDO Brie cheese production costs are estimated to be on average 40% higher than those for non‐PDO Brie. The PDO production process could be technically inefficient when compared with the unconstrained non‐PDO manufacturing; yet, PDO producers benefit from a price premium on their product which offsets their higher production cost.     

Measuring technical efficiency of dairy farms with imprecise data: a fuzzy data envelopment analysis approach

This article integrates fuzzy set theory in the data envelopment analysis (DEA) framework to compute technical efficiency scores when input and output data are imprecise. The underlying assumption in conventional DEA is that input and output data are measured with precision. However, production agriculture takes place in an uncertain environment, and, in some situations, input and output data may be imprecise. We present an approach of measuring efficiency when data are known to lie within specified intervals and empirically illustrate this approach using a group of 29 dairy producers in Pennsylvania. Compared to the conventional DEA scores that are point estimates, the computed fuzzy efficiency scores are interval bound allowing the decision maker to trace the performance of a decision‐making unit at different possibility levels.