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Abstract

We investigated whether diversification and/or structural change would improve Norwegian agriculture. Using a flexible technology approach to account for different technologies, we assessed economies of scope and scale of dairy and cropping farms, including regional differences. We fitted translog cost functions to farm-level panel data for the period 1991–2014. We found both economies of scope and scale on the farms. Dairy farms have an economic incentive to integrate dairying with cropping in all regions of Norway, and vice versa. Thus, policy makers should eschew interventions that inhibit diversification or structural change and that increase the costs of food production.

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Abstract

This paper addresses the endogeneity of inputs and output (which is mostly ignored in the stochastic frontier (SF) literature) in the SF panel data model under the behavioural assumption that firms maximize returns to the outlay. We consider a four component SF panel data model in which the four components are: firms' latent heterogeneity, persistent inefficiency, transient inefficiency and random shocks. Second, we include determinants in transient inefficiency. Finally, to avoid the impact of distributional assumptions in estimating the technology parameters, we apply a multi-step estimation strategy to an unbalanced panel dataset from Norwegian crop-producing farms observed from 1993 to 2014. Distributional assumptions are made in second and third steps to predict both persistent and transient inefficiency, and their marginal effects. Keywords Efficiency; Endogeneity; Returns to the outlay; Panel data

Abstract

Previous studies estimating TFP and its components have been criticized for not considering farm heterogeneity in their model. Moreover, the studies focused on the technical evaluation of a sector. However, the technical evaluation alone reveals how well farmers use the physical production process. There is a need to closely examine the cost efficiency of the farmers. In this study, we used a cost function (dual) approach to facilitating the decomposition and estimation of TFP components. Using a translog stochastic cost function, we estimated the level and source of productivity and profitability change for crop producing family firms in Norway. We used the true random effect to account for farm heterogeneity. The analysis is based on 23 years unbalanced panel data (1991-2013) from 455 crop- producing firms with a total of 3885 observations. The result indicates that average annual productivity growth rate in grain and forage production was - 0.11 % per annum during the period 1991-2013. The profit change was 0.14 % per annum.

Abstract

This paper examines the recent advances in stochastic frontier models and its implications for the performance of the Norwegian crop producing farms. Unlike the previous studies, we used a cost function in multiple input-output frameworks to estimate both long-run (persistent) and short-run (transient) inefficiency. The empirical analysis is based on unbalanced farm-level panel data for 1991-2013 from 455 Norwegian farms specialized in crop production with 3885 observations. We estimated seven SF panel data models grouped into four categories regarding the assumptions used to the nature of inefficiency. The estimated cost efficiency scores varied from 53 % to 95%, showing that the results are sensitive to how the inefficiency is modeled and interpreted. Keywords: cost function, short and long-run inefficiency, agriculture, panel data

Abstract

In this article, we estimate the progress of Total Factor Productivity (TFP) in the Norwegian grain production sector. Previous studies conducted in TFP estimation can be criticized for estimated production function relied on the assumption that the underlying technology is the same for all regions and firms face similar environmental conditions. In reality, agricultural firms in different regions resource endowment, adoption of new technology, and innovation might be different because of farmers face different production opportunities. For this study, we classified the country into two main grain producing regions with district level of development, and hence production technologies. We used farm level balanced panel data for 19 years (1996-2014) with 1463 observations from farms specialized in grain production. We applied the ‘true' fixed effect stochastic frontier model to estimate region level efficiency and source of productivity changes. The result of the analysis shows that there has been a productivity improvement in the sector, and technical change has had the main source of productivity change.

Abstract

The Horn of Africa includes Ethiopia, Djibouti, Eritrea, Kenya, Somalia, Sudan and Uganda and is the poorest region on the continent. More than 40 per cent of the population of over 160 million is living in areas prone to extreme food shortages (FAO, 2011). In mid-2011 the world became witness to a widespread food crisis in the Horn of Africa, which has escalated into acute shortages of food notably in the regions of southern Somalia, northern Kenya, southeastern Ethiopia and Djibouti. The U.N. Humanitarian Coordinator for Somalia on July 20 declared that severely reduced food access, acute malnutrition, and high crude mortality rates indicate ongoing famine conditions in the Bakool and lower Shabelle regions in southern Somalia (UN, 2011). USAID estimates that 2.8 million people in southern Somalia and 12.4 million people in Djibouti, Ethiopia, and Kenya require immediate, lifesaving humanitarian assistance (USAID, 2011). Furthermore, more people in Eritrea, Uganda, Sudan and South Sudan are also facing a worrying food situation. The causes of food crisis are both environmental, structural and avoidable factors have taken in a broader spectrum of problems affecting the region. This paper addresses in detail some important causes and aggravating factors of famine in Horn of Africa and recommend possible interventions to tackle food shortage and famine in a sustainable way.