Habtamu Alem

Research Scientist

(+47) 907 81 274
habtamu.alem@nibio.no

Place
Oslo

Visiting address
Storgata 2-4-6, 0155 Oslo

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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

Purpose – The purpose of this paper is to explore the economic performance of Norwegian crop farms using a stochastic frontier analysis. Design/methodology/approach – The analysis was based on a translog cost function and unbalanced farm-level panel data for 1991–2013 from 455 Norwegian farms specialized in crop production in eastern and central regions of Norway. Findings – The results of the analysis show that the mean efficiency was about 78–81 percent. Farm management practices and socioeconomic factors were shown to significantly affect the economic performance of Norwegian crop farms. Research limitations/implications – Farmers are getting different types of support from the government and the study does not account for the different effects of different kinds of subsidy on cost efficiency. Different subsidies might have different effects on farm performance. To get more informative and useful results, it would be necessary to repeat the analysis with less aggregated data on subsidy payments. Practical implications – One implication for farmers (and their advisers) is that many of them are less efficient than the estimated benchmark (best performing farms). Thus, those lagging behind the best performing farms need to look at the way they are operating and to seek out ways to save costs or increase crop production. Perhaps there are things for lagging farmers to learn from their more productive farming neighbors. For instance, those farmers not practicing crop rotation might be well advised to try that practice. Social implications – For both taxpayers and consumers, one implication is that the contributions they pay that go to subsidize farmers appear to bring some benefits in terms of more efficient production that, in turn, increase the supply of some foods so possibly making food prices more affordable. Originality/value – Unlike previous performance studies in the literature, the authors estimated farm-level economic performance accounting for the contribution of both an important farm management practice and selected socioeconomic factors. Good farm management practices, captured through crop rotation, land tenure, government support and off-farm activities were found to have made a positive and statistically significant contribution to reducing the cost of production on crop-producing farms in the Central and Eastern regions of Norway.

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

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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

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Abstract

This paper compares technical efficiencies (TEs) and technological gap ratios (TGRs) for dairy farms in regions of Norway, accounting for differences in working environments. We used the state-of-the-art stochastic meta-frontier approach to estimate TEs and TGRs to account for regional heterogeneity, and the ‘true’ random-effect model to account for farm effects. The dataset used was farm-level balanced panel data for 24 years (1992–2014), with 5442 observations from 731 dairy farms. The results of the analysis provide empirical evidence of small regional differences in TEs, TGRs, and input use. Furthermore, the results may provide support for the more regionally specific agricultural policy, in terms of support schemes and structural regulations.

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 doctoral thesis incorporates an integrated framework for the measurement and analysis of the performance of Norwegian farms, focusing on crop-producing and dairy farms. Farm-level datasets were used in the analysis. The thesis comprises an introductory chapter and five independent research articles. The aim of the first article is to explore the effects of model specifications and estimate short-run and long-run inefficiency. We used the transcendental logarithmic (translog) cost function and the analysis is based on unbalanced farm-level panel data for the period 1991–2013 from 455 Norwegian farms that specialise in crop production in the Eastern and Central regions of Norway. It was found that cost efficiency scores are sensitive to how the inefficiency is modelled and interpreted. Empirical analysis demonstrates that the magnitude of long-run inefficiency (5%) is lower than the level of short-run inefficiency (6%). It would be possible to reduce crop production costs by, on average, up to 5% if shortfalls in managerial capabilities were reduced. Such shortfalls in farmers’ management abilities derive from such factors as lack of farming experience and lack of farm ownership. On the other hand, it would be possible to reduce crop production costs by up to 6% if transient inefficiencies could be eliminated. On average, actual costs could be reduced by 11% without reducing output if both forms of inefficiency were eliminated from Norwegian crop production. Policy interventions to this end might include providing training in farm-management practices, and policy changes to ease rigidity in farm ownership. The objective of the second article is to measure the economic performance of two crop-producing Norwegian farms while accounting for both unobserved heterogeneity and environmental variables. The analysis employs a translog cost function and is based on unbalanced farm-level panel data comprising 3,855 observations (1,004 observations from the central region and 2,884 from the eastern region). We found that the mean minimum costs for the period 1991–2013 were approximately 93% and 92% of the actual production costs for crop farms in the central and eastern regions, respectively. The marginal effects of crop rotation, land tenure, off-farm activity, direct government support, and experience positively correlated with the economic performance of crop farms. In both regions, the marginal contribution of these variables to economic performance increased for the period 2000–2013 compared to 1991–1999. The aim of Article 3 is to measure the contribution of productivity and price change to changes in the profitability of crop-producing family farms in Norway. The results indicate that the average annual productivity growth rate for grain and forage production decreased by 0.11% per annum over the period 1991–2013. Profits decreased by 0.14% per annum primarily due to the effect of the trend of increasing input prices and a decline in total factor productivity. Interventions to improve the productivity of farms would also improve farm profitability.

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.