Impacts of Norwegian Milk Quotas on Output Growth: A Modified Distance Function Approach

Quota regulations that prevent output expansion of farms and reallocation of output between farms can cause lower growth in output and productivity. The aim of this study was to explain the output growth rate of Norwegian dairy farms since 1976, and to decompose it into output, input, socioeconomic and technical change components. Instead of using the standard distance function approach for multi‐output technologies, we use a growth rate formulation, which automatically removes the farm‐specific effects. This formulation also helps to impose non‐negativity constraints on marginal products of inputs (input elasticities), which are often violated for many observations, especially when flexible functional forms are used. The farm‐level panel data cover three periods: before the quota scheme was introduced (1976–1982); the period with the most output‐restricting quota scheme (1983–1996); and the period with a more flexible quota scheme (from 1997 onwards). Results show that the milk quota regulations had a significant constraining effect on output growth, in particular on milk output in the period 1983–1996. Furthermore, the output mix has shifted towards meat production for the average farm. What emerges from this study is that output growth and technical change are negatively influenced by policy aims where productive performance has not been the primary objective, and that there is scope for increased farm growth if the quota regime is liberalised.     

Productivity Growth in German Dairy Farming using a Flexible Modelling Approach

A random‐coefficients specification of an output distance function is used to measure and decompose productivity growth in German dairy farming. This specification can accommodate heterogeneity with respect to the technology employed by dairy farms, allowing for specialised and non‐specialised farms to be included in the analysis. The proposed modelling approach is favoured by the data when compared with the conventional translog specification of distance functions. The average total factor productivity growth rate for German dairy farms is estimated at 1.1%, with the technical progress component contributing most of this growth.     

Introduction to the Special Feature: Evaluating the Impact of Agricultural Projects in Developing Countries

This article provides an introduction to the special feature on impact evaluation of agricultural projects in developing countries. The special feature is motivated by an increased interest in impact evaluation both within the economics profession and the development community. The article highlights methodological issues in conducting such evaluations and discusses the current literature that empirically assesses the effectiveness of agricultural projects. A synthesis of the articles presented in the special feature and their contribution to the literature are noted as well as conclusions on the next steps in evaluating agricultural projects in developing countries.     

Productivity and Environmental Performance in Marketing Cooperatives: An Analysis of the Spanish Horticultural Sector

The paper analyses the productivity of marketing cooperatives incorporating environmental inputs/outputs. In the European agriculture, expectations for attaining sustainable and competitive agriculture rely to a great extent on the cooperative sector's ability to adapt to new market conditions. These challenges have led marketing cooperatives in the fruit and vegetables sector to consider improvements in productivity and sound environmental performance. The study analyses the total factor productivity related to environmental variables in this sector using a parametric‐stochastic approach and panel data on Spanish cooperatives over the period 1994–2002. Additionally, the determinants of environmental productivity are examined econometrically. The estimates obtained show an increase in efficiency for the period under study and a relationship between productivity changes and management factors, such as labour quality, capital intensity and environmental spillover.     

A trait-based model of the potential demand for a genetically engineered food crop in a developing economy

We predict the potential demand of smallholder farmers for genetically transformed varieties of a food crop, the cooking banana of the East African highlands. Farmer demand for planting material is derived in an agricultural household model that accounts for variety traits and missing markets. The demand for candidate host varieties is predicted using a Zero‐Inflated Poisson (ZIP) regression system. The fitted model is used to illustrate the sensitivity of farmer demand for improved planting material to (a) investments in research and development, represented by the effectiveness of gene insertion and expression, and (b) other public investments in education, extension, and market infrastructure that support diffusion. By comparing the characteristics of agricultural households we demonstrate that the choice of host variety can have social consequences, favoring one rural population compared with another. Clients for transgenic banana planting material are likely to be poorer, subsistence‐oriented farmers in areas greatly affected by biotic constraints. A model of this type might be useful in assessing the investments needed to support the systematic dissemination of improved planting material. The approach can be generalized to other crop biotechnologies for smallholder farming systems, particularly in developing economies.     

Examining the relationship between farm size and productive efficiency: a Bayesian directional distance function approach

Achieving sustainable food security and increased farm income will depend on how efficient production systems are in converting available inputs to produce outputs. Using data from Malawi, we estimate a Bayesian directional technology distance function to examine the relationship between farm size and technical efficiency. Our results support the existence of an inverse relationship between farm size and productive efficiency, where small farms are more efficient than large farms. On average, farms exhibit inefficiency levels of 60%, suggesting that productivity could be improved substantially. Improving productive efficiency and food security will require farms to operate in ways where the size of cultivated area is matched by nonland production inputs such as labor, fertilizer, and improved seeds. The results highlight the need for policies that could incentivize farmers to adopt productivity‐enhancing technologies and, where possible, to allocate excess land to lease markets.     

Diet transformation in Africa: the case of Ethiopia

Africa's food systems are changing fast amid rapid economic growth, emerging urbanization, and structural transformation. In this study, we use four rounds of nationally representative data from Ethiopia to document changes in household food consumption patterns over a period of unprecedented economic growth. We find that while the share of food in the total consumption basket is declining, food quantities and calorie intakes have considerably increased between 1996 and 2011. A decomposition analysis suggests that this was mostly driven by improvements in household incomes—a finding that is consistent across the calorie distribution. Also, the content of the food basket is changing with a gradual shift toward high‐value foods such as animal products, fruits and vegetables and processed foods. Overall, this diet transformation has important implications for the food security debate and for agricultural and food policy in the country.     

Patterns in Technical Efficiency and Technical Change at the Farm‐level in England and Wales, 1982–2002

English and Welsh farm‐level survey data are employed to estimate stochastic frontier production functions for eight different farm types (cereal, dairy, sheep, beef, poultry, pigs, general cropping and mixed) for the period 1982 to 2002. Differences in the relative efficiency of farms are explored by the simultaneous estimation of a model of technical inefficiency effects. The analysis shows that, generally, farms of all types are relatively efficient with a large proportion of farms operating close to the production frontier. However, whilst the frontier farms of all types are becoming more efficient through time because of technical change, it is also the case that the efficiency of the average farm for most farm types is increasing at a slower rate. In addition, annual mean levels of efficiency for most farm types have declined between 1982 and 2002. The factors that consistently appear to have a statistically significant effect on differences in efficiency between farms are: farm or herd size, farm debt ratios, farmer age, levels of specialisation and ownership status.     

The impact of decoupled subsidies on productivity in agriculture: a cross‐country analysis using microdata

The decoupling of direct payments from production introduced in the reform of the Common Agricultural Policy is expected to make production decisions more market‐oriented and farmers more productive. However, ex‐post analyses of the productivity of farms have yet to uncover any evidence of a positive impact of the decoupling policy on farm productivity. Using Irish, Danish, and Dutch farm‐level data, we identify whether the decoupling policy has contributed to productivity growth in agriculture and farm product adjustment behavior. We find some evidence that the decoupling policy had significant positive effects on farm productivity and behavioral changes related to farm specialization.     

Impacts of Vessel Capacity Reduction Programmes on Efficiency in Fisheries: the Case of Australia’s Multispecies Northern Prawn Fishery

Capacity reduction programmes, in the form of buybacks or decommissioning, have had relatively widespread application in fisheries in the US, Europe and Australia. A common criticism of such programmes is that they remove the least efficient vessels first, resulting in an increase in average efficiency of the remaining fleet, which tends to increase the effective fishing power of the remaining fleet. In this paper, the effects of a buyback programme on average technical efficiency in Australia’s Northern Prawn Fishery are examined using a multi‐output production function approach with an explicit inefficiency model. As expected, the results indicate that average efficiency of the remaining vessels was generally greater than that of the removed vessels. Further, there was some evidence of an increase in average scale efficiency in the fleet as the remaining vessels were closer, on average, to the optimal scale. Key factors affecting technical efficiency included company structure and the number of vessels fishing. In regard to fleet size, our model suggests positive externalities associated with more boats fishing at any point in time (due to information sharing and reduced search costs), but also negative externalities due to crowding, with the latter effect dominating the former. Hence, the buyback resulted in a net increase in the individual efficiency of the remaining vessels due to reduced crowding, as well as raising average efficiency through removal of less efficient vessels.     

Yield Effects of Tissue Culture Bananas in Kenya: Accounting for Selection Bias and the Role of Complementary Inputs

We analyse yield effects of tissue culture (TC) banana technology in the Kenyan small farm sector, using recent survey data and an endogenous switching regression approach. TC banana plantlets, which are free from pests and diseases, have been introduced in East Africa since the late 1990s. Although field experiments show significant yield advantages over traditional banana suckers, a rigorous assessment of impacts in farmers’ fields is still outstanding. A comparison of mean yield levels between TC adopters and non‐adopters in our sample shows no significant difference. However, we find evidence of negative selection bias, indicating that farmers with lower than average yields are more likely to adopt TC. Controlling for this bias results in a positive and significant TC net yield gain of 7%. We also find that TC technology is more knowledge intensive and more responsive to irrigation than traditional bananas. Simulations show that improving access to irrigation could lift TC productivity gains to above 20%. The analytical approach developed and applied here and the finding of negative selection bias may also be relevant for the evaluation of other agricultural technologies.     

Challenging Small‐Scale Farming: A Non‐Parametric Analysis of the (Inverse) Relationship Between Farm Productivity and Farm Size in Burundi

We use a non‐parametric approach to investigate the (inverse) relationship between farm productivity and farm size. A kernel regression is used on data of mixed cropping systems to study the determinants of production including different factors that have been identified in literature as missing variables in the testing of the inverse relationship such as soil quality, location and household heterogeneity. Household data on farm activities and crop production were gathered from 640 households in 2007 in two Northern provinces of Burundi. Our results do not reject the findings of an inverse relationship between farm size and productivity. However, we find that size returns vary substantially with farm size, that is, between 0.2 for the smallest farms and 0.8 for the largest farms. Other factors that significantly affect production include soil quality. Finally, we find a significant positive association between food security and farm size.     

Heterogeneous information exposure and technology adoption: the case of tissue culture bananas in Kenya

Classical innovation adoption models implicitly assume homogenous information flow across farmers, which is often not realistic. As a result, selection bias in adoption parameters may occur. We focus on tissue culture (TC) banana technology that was introduced in Kenya more than 10 years ago. Up till now, adoption rates have remained relatively low. We employ the average treatment effects approach to account for selection bias and extend it by explicitly differentiating between awareness exposure (having heard of a technology) and knowledge exposure (understanding the attributes of a technology). Using a sample of Kenyan banana farmers, we find that estimated adoption parameters differ little when comparing the classical adoption model with one that corrects for heterogeneous awareness exposure. However, parameters differ considerably when accounting for heterogeneous knowledge exposure. This is plausible: while many farmers have heard about TC technology, its successful use requires notable changes in cultivation practices, and proper understanding is not yet very widespread. These results are also important for other technologies that are knowledge‐intensive and require considerable adjustments in traditional practices.