Habtamu Alem
Research Scientist
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CVBiography
Alem holds Ph.D. and M.Sc. in Economics from the Norwegian University of Life Sciences (NMBU). He works as a research scientist at the Norwegian Institute of Bioeconomy Research (NIBIO) in the Department of Economics and Society. Alem was a researcher for the Ethiopian Institute of Agricultural Research and an executive officer for the Norwegian Institute of Agricultural Research. He is now the project coordinator for the SYSTEMIC project, which covers eight EU nations.
His research interest is food and nutrition security; Environmental and production economics; Climate change; Econometrics; impact assessment; circular economics; cost benefit analysis; consumer economics; Risk analysis; and topics related to development economics (developing and developed countries)
Authors
Habtamu AlemAbstract
Previous application of the stochastic frontier model and subsequent measurement of the performance of the crop sector can be criticized for the estimated production function relying on the assumption that the underlying technology is the same for different agricultural systems. This paper contributes to estimating regional efficiency and the technological gap in Norwegian grain farms using the stochastic metafrontier approach. For this study, we classified the country into regions with district level of development and, hence, production technologies. The dataset used is farm-level balanced panel data for 19 years (1996–2014) with 1463 observations from 196 family farms specialized in grain production. The study used the true random effect model and stochastic metafrontier analysis to estimate region-level technical efficiency (TE) and technology gap ratio (TGR) in the two main grain-producing regions of Norway. The result of the analysis shows that farmers differ in performance and technology use. Consequently, the paper gives some regionally and farming system-based policy insights to increase grain production in the country to achieve self-sufficiency and small-scale farming in all regions.
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Lecture – Resource Use Efficiency and Agricultural Sustainability: Evidence from Norway
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The book discusses scientific, technical, and sociological aspects of sustainable agricultural value chains, focusing on coffee and tea production. The book advocates a value-chain strategy and highlights the importance of tracking the effects of climate change. Increasing the amount of value-added products via irrigation is critical to combating climate change and achieving sustainable development. Furthermore, blockchain technology has the potential to transform agricultural business models and supply chain networks.
Authors
Habtamu AlemAbstract
Previous application of the stochastic frontier model and subsequent measurement of the performance of the crop sector can be criticized for the estimated production function relying on the assumption that the underlying technology is the same for different agricultural systems. This paper contributes to estimating regional efficiency and the technological gap in Norwegian grain farms using the stochastic metafrontier approach. For this study, we classified the country into regions with district level of development and, hence, production technologies. The dataset used is farm-level balanced panel data for 19 years (1996–2014) with 1463 observations from 196 family farms specialized in grain production. The study used the true random effect model and stochastic metafrontier analysis to estimate region-level technical efficiency (TE) and technology gap ratio (TGR) in the two main grain-producing regions of Norway. The result of the analysis shows that farmers differ in performance and technology use. Consequently, the paper gives some regionally and farming system-based policy insights to increase grain production in the country to achieve self-sufficiency and small-scale farming in all regions.
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Purpose The study measures the technology gap and performance of the Norwegian dairy farms accounting for farm heterogeneity. Design/methodology/approach The analysis was based on a meta-frontier and unbalanced farm-level panel data for 1991–2014 from 417 Norwegian farms specialized in dairy production in five regions of Norway. Findings The result of the analysis provides empirical evidence of regional differences in technical efficiencies, technological gap ratios (TGRs) and input use. Consequently, the paper provides some insights into policies to increase the efficiency of dairy production in the country across all regions. Research limitations/implications The author used a meta-frontier approach for modeling regional differences based on a single-output production function specification. This approach has commonly been used in the economics literature since Battese et al. (2004). To get more informative and useful results, it would be necessary to repeat the analysis within terms of multiple input-output frameworks using, for instance, the input distance function approach. Moreover, the author estimated the meta-frontier using the non-parametric approach, thus it is also a need for further analysis if the values are different by estimating using a parametric approach. Practical implications One implication for farmers (and their advisers) is that dairy farms in all regions used available technology in the area sub-optimally. Thus, those lagging the best-performing farms need to look at the way the best-performing farmers are operating. Policymakers might reduce the gap is through training, including sharing information about relevant technologies from one area to another, provided that the technologies being shared fit the working environment of the lagging area. Moreover, some of the dairy technologies they use may not fit other regions, suggesting that agricultural policies that aim to encourage efficient dairy production, such as innovation of improved technology (like breeding, bull selection and improved feed varieties) through research and development, need to account the environmental differences between regions. Social implications For both taxpayers and consumers, one implication is that the contributions they pay that go to subsidize dairy farmers appear to bring some benefits in terms of more efficient milk production that, in turn, increases the supply of some foods so possibly making food prices more affordable. Originality/value The paper contributes to the literature in several ways. In contrast to Battese et al. (2004), the author accounts for farm-level performance differences by applying the model devised by Greene (2005), thus may serve as a model for future studies at more local levels or of other industries. Moreover, the author is fortunate to able to use a large level farm-level panel data from 1991 to 2014.
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Habtamu AlemAbstract
Growing environmental concerns have prompted governments to make sustainable choices in agricultural resource use. Evaluating the sustainability of agricultural systems is a key issue for the implementation of policies and practices aimed at revealing sustainability. This study aimed to evaluate the performance of Norwegian dairy farms, accounting for marginal effects of environmental (exogenous) variables. We adopted the dynamic parametric approach within the input distance function framework to estimate the performance of Norwegian dairy farms, focusing on the technical efficiency and determinates. For comparison, we also estimated the static parametric model, which was used by previous studies. We used unbalanced farm-level panel data for the period 2000–2018. The result shows a mean technical efficiency score of 0.92 for the dynamic model and 0.87 for the static models. The empirical result shows that the previous studies that focused on the static model reported a biased result on the performance of dairy farms. The dynamic efficiency score suggests that Norwegian dairy farms can reduce the input requirement of producing the average output by 8% if the operation becomes technically efficient. The environmental variables have a different effect on the performance of the farmers; thus, policymakers need to place special focus on these variables for the sustainable development of the dairy sector.
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Habtamu AlemAbstract
The study aimed to extend the static concepts of multiproduct technical efficiency and determinants into a dynamic setting within the input distance function framework. The existing literature in performance analysis of the dairy farms in Norway based on static modelling and thus ignores the inter-temporal nature of production decisions. The empirical application focused on the farm-level analysis of the Norwegian dairy sector for 2000- 2018. The dynamic efficiency allows analysing the performance of dairy farms in regards of inter-temporal optimization of the investment behaviour. The analysis shows that the static model efficiency study in the previous studies underestimate the performance of the dairy farms. The marginal effects experience positively correlated with dairy farm technical efficiency whereas copped subsidy and asset debt ratio negatively correlated to the performance of the dairy farm.
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Habtamu AlemAbstract
The objective of this paper is to examine the economic performance of crop-producing farms accounting for unobserved heterogeneity,environmental variables, and regions. The empirical analysis was based on a translog cost function and unbalanced farm-level panel data for 1991–2013 from the 455 crop-producing farms with 3,885 observations (1,004observations from the central region and 2,881 observations from the eastern region). We found that the mean minimum costs were about 93% and 92% of the actual 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 were positively associated with crop farm economic performance. The marginal contribution of these variables on economic performance increased in the years 2000–2013 compared with the years 1991–1999 in both regions.
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Habtamu AlemAbstract
From a theoretical perspective, it is well stated that the farm's decision on the use of inputs depends on the farmer's ability to make an efficient decision over time. The existing literature in performance analysis of the dairy farms based on static modeling and thus ignores the inter-temporal nature of production decisions. This paper aims to construct a dynamic stochastic production frontier incorporating the sluggish adjustment of inputs, to measure the performance of dairy farms in Norway. The empirical application focused on the farm-level analysis of the Norwegian dairy sector for 2000- 2018. The dynamic frontier estimated using the system Generalized Method of Moments estimator. The analysis shows that the static model in the previous studies underestimates the performance of the dairy farms.
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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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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.
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Habtamu AlemAbstract
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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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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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.
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Habtamu AlemAbstract
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.
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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.
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Habtamu AlemAbstract
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.
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Habtamu AlemAbstract
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.
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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.
Division of Food Production and Society
SYSTEMIC
SYSTEMIC is an international research project that will map knowledge about the global food system. The project takes on a farm to fork approach, ranging from climate and sustainable food production, health and nutrition, to consumer behaviour.
Division of Food Production and Society
DairyMix. Multi-criteria assessment, decision support and management tools for sustainable circular mixed farming systems for dairy production
A total of 10 research institutions from Europe and Latin America have joint forces to develop a tool for dairy farmers. The overall aim is to increase sustainability and reinforce climate friendly mixed crop and dairy production systems.