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Doktorarbeit / Dissertation, 2018
STATEMENT OF THE AUTHOR
ABBREVIATIONS AND ACRONYMS
LIST OF TABLES
LIST OF FIGURES
LIST OF APPENDIX TABLES
LIST OF FIGURES IN THE APPENDIX
1.2. Problem Statement
1.3. Research Questions
1.4. Research Objectives
1.5. Significance of the Study
1.6. Scope and Limitations of the Study
1.7. Organization of the Dissertation
2. LITERATURE REVIEW
2.1. Production System andBenefits of Haricot Beans
2.2. Conceptual Issues
2.2.1. Conceptualizing technology adoption
2.2.2. Conceptualizing efficiency
2.2.3. Conceptualizing welfare effect of technology adoption
2.3. Theoretical Framework
2.3.1. Theoretical perspectives of technology adoption
2.3.2. Theoretical perspectives of efficiency
2.3.3. Theoretical perspectives of welfare effect of technology adoption
2.4. Methodological Framework
2.5. Analytical Framework
2.6. Empirical Review on Farm Technology Adoption, Efficiency and Welfare Effect
2.7 Conceptual Framework
3. RESEARCH METHODOLOGY
3.1. Description of the Study Area
3.2. Sampling Design
3.2.1. Sampling technique
3.2.2. Sample size determination
3.2.3. The data
3.3. Methods of Data Collection
3.4. Methods of Data Analysis
3.4.1. Adoption study
3.4.2. Efficiency analysis
3.4.3. Welfare effect analysis
3.5. Definition of Variables, Measurements and Working Hypotheses
3.5.1. Adoption variables
3.5.2. Efficiency related variables
3.5.3. Variables included in the welfare effect analysis
4. Results and Discussion
4.1. Household and FarmCharacteristics
4.1.1. Household demographic and socio-economic characteristics
4.1.2. Institutional characteristics
4.1.3. Farm and household attributes
4.1.4. Agronomic practices of the households
4.1.5. Input useof the households
4.2. Determinants of Status and Intensity of Adoption of Improved White Haricot Beans
4.2.1. Determinants of the decision to adopt white haricot beans
4.2.1. Determinants of the intensity of adoption of white haricot beans
4.3. Efficiency Analysis
4.3.1. Technical efficiency analysis
4.3.2. Allocative efficiency analysis
4.3.3. Economic efficiency analysis
4.4. Welfare Effect Analysis
5. Summary, Conclusion AND RECOMMENDATIONS
5.2. Conclusion and Recommendations
This Dissertation is dedicated to my wife Tigist Teka; and my children Abel Daniel and Hanna Daniel.
By my signature below, I declare and affirm that this dissertation is my own work. I have followed all ethical and technical principles of scholarship in the preparation, data collection, data analysis and compilation of this dissertation. Any scholarly matter that is included in the dissertation has been given recognition through citation.
This dissertation is submitted in partial fulfillment of the requirements for a Doctor of Philosophy in Agricultural Economics degree at the Haramaya University. The dissertation is deposited in the Haramaya University Library and is made available to borrowers under the rules of the Library. I solemnly declare that this dissertation has not been submitted to any other institution anywhere for the award of any academic degree, diploma or certificate.
Brief quotations from this dissertation may be made without special permission, provided that accurate and complete acknowledgment of the source is made. Requests for permission for extended quotations from/or reproduction of this dissertation in whole or in part may be granted by the head of the school or department when in his or her judgment the proposed use of the material is in the interest of scholarship. In all other instances, however, permission must be obtained from the author of the dissertation.
Name: Daniel Masresha Amare Signature:
Abbildung in dieser Leseprobe nicht enthalten
Mr. Daniel Masresha was born in 1970 from his father Masresha Amare and mother Wubitu Kenea in Lalo-Kile district ofWest Wollega zone, Oromia region, Ethiopia. He attended his primary and junior school in his birthplace(Lalo Kile town) and secondary school in Dembi-Dollo, Bethel Evangelical Secondary School. After completing his high school, he joined Haramaya University of Agriculture in 1989 and graduated with a BSc degree in Agricultural Economics in 1992.
He was first employed in Oromia Industry and Trade Bureau, Ilubabor zonal office, where he served as a marketing team leader for three years (1993-1995). He attended his second degree in Acharya N.G. Ranga Agricultural University, Hyderabad, India (1996-1998)and obtained his MSc. degree in Agricultural Economics.
After his return to his home country, Mr. Daniel Masresha served as a project expert (1999-2000) for two years in Oromia Agricultural Development Bureau; served as manager of emergency rehabilitation program in Oromia region forfour years (2001-2004); served as Plan and Project team leader for one year (2005-2006) in Oromia Agriculture and Rural Development Bureau ; Plan and Marketing Division head in Oromia Water Works Design and Construction Enterprise for one year (2006-2007) and Food Security Building Coordinator in Hadiya zone of SNNPR in Lay volunteers International Association for two years (2008-2009). Currently, he is a lecturer in economics department, College of Business and Economics, Wollega University from end of 2009 to date. He also served Wollega University as a Plan and Project Directorate director on seconded basis (2010-2011) up until he joined Haramaya University for his PhD study in 2012.While pursuing his PhD study, he advised nine MScstudents that already graduated and is currently advising seven more students in their thesis work in Wollega University.
Above all, I would like to thank the Godalmighty for helping me in all aspects that are beyond my reach.My thanks and deep appreciation goes to my major advisor Dr. Belaineh Legesse for his valuable comments, constructive ideas and critiques beginning from the inception of the dissertationto the final stage. I would like to thankmy guidance committee members Dr. Jema Haji and Dr. Lemma Zemedu for their valuable ideas and constructive comments during the whole process of the dissertation. Their friendly approach and professional dedication throughout the process has been a source of inspiration. The dissertation would not have been completed without their generous support.
I would like to thank Dr. Girma Tesfahun for his critical comments during the preparation of the researchproposal that contributed to half-way work of this dissertation. I would also like to thank IFPRI- Ethiopia for offering me a timely and relevant training on impact assessment.
My past lovely and envisioned high school teachers (Ato Asefa Ayana, Ato Teferi Tadesse, Ato Mitiku, Miss Ulphu, Lissa, Helena, Adise and others) in Bethel Evangelical Secondary School (Dembi-Dollo), deserve special thanks for originally cultivating, guiding and showing me the direction of science in general.
My thanksalso goes to Wollega University for giving me the scholarship opportunity and the Ministry of Education of the EFDRE for sponsoring the research budget of my dissertation.
I would like to thank Professor Kumar B. Das and Dr. Sisay Debebe for their genuine support in editing parts of my dissertation. I would also like to extend my appreciation to Mr. Kidus Markos in Wollega University, otherfriends else-where and all members of the staff in the department of agricultural Economics of Haramaya University for the kind support they provided whenever needed.
Finally, I would like to give my heartfelt thanks to my lovely wife Tigist Teka for upholding my intention of study and taking care of our children on my behalf. My sweet thanks also goes to my children Abel Daniel and Hanna Daniel for tolerating the timely love they deserve from their father during my stay in the study. Thank you all !
ADB: African Development Bank
ADLI: Agricultural Development Led Industrialization
AE: Allocative Efficiency
BoFED: Bureau of Finance and Economic Development
CSA: Central Statistics Agency
CIAFS: Capacity to Improve Agriculture and Food Security
DEA: Data Envelopment Analysis
DF: Distance Function
EE: Economic Efficiency
EEA: Ethiopian Economic Association
ECX: Ethiopian Commodity Exchange
EPA: Export Promotion Agency
ESR: Endogenous Switching Regression
FAO: Food and Agriculture Organization of the United Nations
GDP: Gross Domestic Product
GTP: Growth and Transformation Plan
HYV: High Yielding Variety
IFPRI: International Food and Policy Research Institute
IPMS: Improving Productivity and Market Success
IV: Instrumental Variables Regression
IPW: Inverse Probability Weighting
ML: Maximum Likelihood
MoFED: Ministry of Finance and Economic Development
NNM: Nearest Neighbor Matching
PSM: Propensity Score Matching
SNNPR: Southern Nations, Nationalities and Peoples’ Region
SFM: Stochastic Frontier Model
TE: Technical Efficiency
TLU: Tropical Livestock Unit
UN: United Nations
WFP: World Food Programme
1. Sample size of households drawn from sample districts and villages
2. Definition of variables, measurements and working hypothesis in adoption study
3. Definition of variables, measurements and working hypothesis in production and cost functions
4. Definition of variables, measurements and working hypothesis for the determinants of technical, allocative and economic efficiencies
5. Definition of variables, measurements and working hypothesis in welfare effect analysis
6. Descriptive statistics of demographic and socio-economic characteristics of the households by adoption status
7. Descriptive statistics of institutional characteristics of the households by adoption status
8. Descriptive statistics of household and farm attributes by adoption status
9. Descriptive statistics of agronomic practices of the households on haricot beans farms by adoption status
10. Descriptive statistics of input use of the households on haricot beans plots by adoption status
11. Craggs’ Tobit alternative model output on determinants of adoption of white haricot beans
12. ML estimate of frontier (normal/half-hetroscedastic) production function
13. Two limit Tobit model output and marginal effects on determinants of TE
14. ML estimate of frontier cost function (Normal/half-normal: hetroskedastic)
15. Tobit model output and marginal effects on the determinants of AE
16. Tobit model output and marginal effects on the determinants of EE
17. Summary of the efficiency levels of haricot beans producer households by adoption status
18. ESR model Full Information ML Estimate on effect of adoption on crop income per adult equivalent
19. Conditional expected values for the observed and counter-factual of haricot beans income per adult equivalent
20. ESR model FIML Estimate on effect of adoption on food consumption expenditure per adult equivalent
21. Conditional expected values for the observed and counter-factual of food consumption expenditure per adult equivalent
1. Rogers’ innovation decision process model
2. Input-oriented technical, allocative and economic efficiency measures
3. Output-oriented firms’ technical, allocative and economic efficiencies
4. Conceptual framework
5. Administrative map of the study area
6. Frequency distribution of TE level of haricot bean producer households
7. Frequency distribution of AE levels of haricot bean producer households
8. Frequency distribution of Economic efficiency levels of haricot bean producer households
1. Conversion factor for adult equivalent
2. Frequency distribution of sample households’ by adoption status
3. Yield of white haricot beans by variety (ton/ha)
4. Multivariate Tobit ML estimate on determinants of adoption of white haricot beans
5. Mean marginal effects of the probability, conditional and unconditionalexpectation of adoption after Craggs’ model for significant variables
6. Comparison of production frontier models for TE analysis
7. Multicollinearity test for parametric stochastic Cobb-Douglass production function
8. Multicollinearity test for determinants of TE in two limit Tobit model
9. Marginal effects on probability of TE after Tobit
10. Marginal effects on expectation of TE after Tobit
11. Marginal Effects on Unc. Expectation of TE After Tobit
12. Multicollinearity test in parametric stochastic Cobb-Douglass
13. Comparison of models for allocative efficiency analysis
14. Multicollinearity test for determinants of AE in two limit Tobit model
15. Marginal effects on probability of determinants of AE after Tobit
16. Marginal effects on expectation of determinants of allocative
17. Marginal effects of unc.expectation of determinants of AE after
18. Multicollinearity test for determinants of EE in two limit Tobit model
19. Marginal effects on probability of EE after Tobit
20. Marginal effects on expectation of EE after Tobit
21. Marginal effects of overall (unconditional) expectation of EE after Tobit
1. Kernel density distribution of double hurdle model on determinants of adoption of improved white haricot beans
2. Kernel density graph of residuals for OLS production function
3. Kernel density graph of residuals in OLS cost function
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1. Daniel Masresha, Belaineh Legesse, Jama Haji, Lemma Zemedu. 2017. The determinants of adoption of white haricot bean high yielding varieties in East Shewa zone, south-eastern Ethiopia. Journal of Development and Agricultural Economic, 9(12):355-372.
White haricot bean is the major source of cash in domestic and internationalmarkets and serves as the cheapest source of protein diet forthe rural households. Despite thecontribution, its adoption is so low;and the efficiency status and welfare contribution of the cropis not well documented. In light of this, this research was meant to study adoption, efficiency and welfare effect of white haricot beans in the study area. A multi-stage sampling procedure was used to select three districts(nine villages)from east Shewa zone. A total of 394 sample households were randomly selected proportional to size at each sample unit. Both descriptive statistics and econometric models were used to analyze the data. Double hurdle model andparametric stochastic frontier model of Cobb Douglass type production and cost functionswere used to analyze the determinants of adoption and estimate production and cost efficiency scores,respectively. The determinants ofefficiency differentials among the householdswere analyzed using a two limit Tobit model. Endogenous Switching Regression model wasemployedto analyze theeffect of improvedwhite haricot beans adoption on the welfare of sample households.Adoption study revealed that the two decision tiers are independent and same or different factors affected the two tiers. The decision to adopt is positively and significantly influenced by frequency of extension contacts, land holding size, agricultural income, perception of the household heads (about price, contribution to soil fertility and nutritional importance), training, and crop diversification; and negatively by distance to market and form of possession of haricot bean plot (tenure). Intensity of adoption is positively affected bynon-farm income, contact with NGOs, and negatively with the number of dependents andform of possession of haricot beans plot (tenure).The mean level of technical, allocative and economic efficiency of haricot bean is 0.94, 0.92 and 0.87 percent, respectively.Technical efficiency is significantly and positively influenced by sex(male=1),membership in farmers cooperatives, education of the family, experience in haricot beans farming, use of certified seeds, income from the farm sector and crop diversification; and negatively affected by age of the households. Allocative efficiency differential is significantly and positively influenced by farming experience and household size; and negatively influenced by sex, distance to market and fragmentation of land. Economic efficiency is significantly and positivelyaffected bythe education of the family and household size, and negatively by distance to market and fragmentation of land. Adoption of white haricot bean was positively and significantly influenced by crop income with mixed results on food consumption expenditure of the households.Provision of improved extension services, enhancing the perceptions on the important attributes of the crop, training, and better access to market are proposed for first hand adoption ofwhite haricot beans while works on the creation of alternative sources of income (non-farmactivities) contributes more to the intensity of adoption.Supply of certified seeds, education of the households and family members, and access to resources (credit and other inputs, in particular for female headed households) are proposed to improve the technical efficiency. Allocative and Economic efficiencies could be improved through better access to markets, better educationof the households and discouraging fragmentation of land. Adoption of improved white haricot bean variety positively contributed to the income (welfare) of the households,even though the effect on households’ food consumption was unclear. A continuous research on the use of new crop varietiesthat are adaptable to a changing environmentis necessary to improve the welfare of the households.
Key words: White haricot beans, technology adoption, technical efficiency, allocative efficiency; economic efficiency; welfare effect; double hurdle model, Cobb-Douglass function, Tobit model and Endogenous Switching Regression model.
Ethiopia is the second most populous nation in Sub-Saharan Africa (SSA) with a population of about 94,351,000 million, as of 2017 (projected based on CSA, 2014). Agriculture is the major source of livelihoods for about 80-81% of the population of the country; accounts for 42.3% of the Gross Domestic Product (GDP) and generates 70% of the export earnings (UNDP, 2013; World Bank, 2014). Service and industrial sectors contribute 43.2 and 15.4 percent of the total GDP, respectively (MoFED, 2014). A substantial part of agricultural production, both for domestic consumption and export is provided by smallholder households.
Due to this importance of agriculture in the national economy, various policies and strategies have been implemented by the government to increase the production and productivity of the sector in Ethiopia. The first national intervention to enhance agricultural production and productivity was in the 1960s with Comprehensive Integrated Package Projects (CIPP) aimed at provision of modern inputs and access to extension which covered 16% of the farming population from 1968-1974. Then followed Minimum Package Program I (MPP-I) in 1971-1979; Minimum Package Program-II (MPP-II) in 1980-1985, Peasant Agricultural Development Program (PADEP) and Participatory Demonstration and Training Extension System (PADETES) in 1986-1995 and National Agricultural Extension Intervention Program (NAEIP) in 1995 (Stepanek, 1999; Demeke, 1995; Berhanu et al., 2006). In the Agricultural Development Led Industrialization (ADLI) strategy of Ethiopia, an intensification of smallholder agriculture was the focus to achieve productivity through improvement of access to technologies, provision of demand driven and efficient extension services (MOFED, 2010). Later on in the revised Agriculture Sector Policy and Investment Framework (PIF) during GTP-I 2010/11-2019/20 and Growth and Transformation Plan (GTP-II), sustainable increase in productivity and production was one of the strategic objectives set foreword (MoARD, 2010).
Existing alternatives for increasing agricultural production are increasing land under cultivation (horizontal expansion of resources), increasing productivity (technical change) per unit of resources and improvement of efficiency of farmers, or combinations of them. Given the limited nature of resources, extensive farming will be at the expense of existing natural resources unlikely to continue forever. Improvement of productivity of farmers could be achieved through improvement in quality of existing practices and institutional inefficiencies (in relation to the best efficiency levels) and through new technology adoption (transformation that results in an upward shift of production function). Parallel to the traditional extensive farming, access to technology has been one of the key strategies to improve the productivity and production of smallholder farmers that are engaged in production of cereals, oilseeds and pulse crops.
According to IFPRI (2010), pulse crops served as the second most important source of food in the national diet next to cereals and constituted 10% of agricultural value addition in Ethiopia. At the national level, pulses occupied 14% of the cultivated land yielding 2.86 million metric tons (11.4% of the total grain crop production) in 2013/14 meher season (CSA, 2014). Haricot bean is second to faba bean in terms of production with a production share of 19 percent (Ephrem, 2016). During the period 2005 to 2010, haricot beans exported to international markets ranged from 60,000 tons to 75000 tons annually (EPPA, 2010). Haricot beans constitutes 41 percent of export earnings from pulses (Ephrem, 2016). Over the years 2006-2012, dry bean export value for Ethiopia increased from 20 million to 100 million US dollars (FAOSTAT, 2015). Apart from the climbing type of haricot beans that grow in western Ethiopian highlands and Metekel zone, haricot bean crop, particularly grows (concentrated) in Southwestern (Wolayita and Sidama), the rift valley (northeastern) region, western lowland areas and East Hararghe zone of Ethiopia in sole and intercropped (widely) with maize and sorghum. Oromia region, especially east Shewa zone in the rift valley area is the major producer of white haricot beans, followed by Southern Nations, Nationalities and Peoples’ Region (SNNPR) and Afar Region; the first two regions constituting nearly 85 percent of the total production (Setegn et al., 2010; Ferris and Kaganzi, 2008).
In spite of the economic and food security importance, actual smallholder farm yields did not match estimated potential yields for pulses including haricot beans. The national average yield for haricot beans is 1.49 ton/ha (for white color type) and 1.34 ton/ha (for red color type). In Oromia (regional average) and east Shewa zone (study area) the yield stands to 1.57 ton/ha (for white color) and 1.57 ton/ha (red color), and 1.57 ton/ha (white color) and 1.68 tons/ha (red color), respectively (CSA, 2014). These figures are far below the average yield obtained at research sites (2.5-3 tons/ha) through use of improved varieties (EPPA, 2010).
Given the general farming circumstances, studies have shown that differences in technology use among farmers have resulted in differences in their production efficiency and consequently in disparity of income. Agricultural technology adopter households have benefited higher earnings and as a result, lower poverty (Kassie et al., 2011; Minten et al., 2007), increased consumption, income and asset holdings (Tsegaye and Bekele, 2012; Sosina et al., 2014), better nutrition (Kumar and Quiumbing, 2010); lower staple food prices (DeJanvry and Sadoult, 2002; Karanja et al., 2003), increased employment opportunities and earnings for the landless laborers (Binswanger and Von Braun, 1991). Hence, one can conclude that farming households and communities in poor countries could benefit from a farming business if appropriate and improved technologies are made available timely and adequately. Accordingly, an in-depth study was conducted on themes pertaining to technology adoption, efficiency and welfare effect of improved white haricot bean production to increase the relevance and its potential adoption.
Haricot beans, scientifically termed as Phaseolus vulgaris L., contribute to rural households’ income, as a high value crop compared to cereals. They are cost-effective sources of protein accounting for about 15% of the protein intake (second source of protein) and third source of food calorie (PABRA, 2005). Moreover, haricot bean has a short growth cycle of 70 days (Katungi et al., 2010); is a rich source of vitamin B1 and minerals such as phosphorous, copper, magnesium and iron which are vital for health (Serre, 2002); often grown by women; replenishes the soil with nutrient through atmospheric nitrogen fixation; which in turn improves productivity of cereals through crop rotations resulting in savings of smallholder farmers’ cost and reduction of the use of synthetic nitrogen (Katungi, 2010; FAOSTAT, 2008; IFPRI, 2010).
In the past two decades, wide spread poverty and malnutrition have been observed among the resource poor and the urban poor population of the sub-Saharan African countries(AfDB, 2012; Kimani et al., 2005). According to Ethiopia CSA (2017), country level poverty status for Ethiopia is estimated to 29.6 percent (Rural:30.4 and urban 25.7 percent) as of 2011.In East African countries including Ethiopia, despite improvements over the past decades, nearly 33.9% of the population is undernourished, underscoring the importance of increasing domestic food productivity (FAO et al., 2017). The key constraints to agricultural productivity in Ethiopia include drought, a decline in soil fertility due to land degradation as a result of high population pressure, poor linkage of input and output markets, low technology adoption rate (improved seeds, fertilizer, irrigation and modern agronomic practices), poor infrastructure (storage, processing, packaging and transportation) and market access, prevalence of pests and diseases, and low capacity and in-efficient governmental and private sector institutional services (Katungi, 2010; Dercon and Hill, 2009; Diao and Pratt, 2007; Odendo, 2004). In relation to haricot beans, lack of access to improved variety essentially due to higher seed price, poor quality, older and degenerated varieties, drought, poor soil fertility, poor linkage of input-output markets, and loss due to pests and diseases are the key causes of low productivity (Katungi et al., 2010; Fekadu, 2007).
Quite a number of interventions have been identified and implemented to address some of the challenges that hamper haricot bean production in Ethiopia. In this regard, a national meetings of bean sector stakeholders (EIAR, RARIs, universities, MoARD,ESE, local seed producers, local grain traders, local extension service workers from GOs and NGOs, Farmers’ cooperative unions, bean exporters, CIAT-PABRA, ECX and ACOS) was convened by EIAR since 2004 (Ephrem, 2016). The interventions included investment in the dissemination and promotion of existing technologies, capacity building works (such as trainings and awareness creation workshops for value chain actors, trainings on agronomic, harvesting and storage practices and development and use of resource manuals), improvement of infrastructures, strengthening market information, and informal seed systems, development and promotion of drought resistant varieties, and integrated soil and fertility management practices. For instance, in the rift valley region of Ethiopia, between 2004-2010, access to seeds on market demanded varieties has been increased from less than 20% to 60% across major beans growing areas by different actors; such as the International Center for Tropical Agriculture (CIAT), Pan African Research Alliance (PARA), and the Ethiopian Institute of Agricultural Research (EIAR) through integrated impact driven seed systems approach (Katungi et al., 2010).
Although a substantial amount of resources has been devoted to the development and provision of the required inputs over the past three decades, the overall adoption rateof agricultural technologies has been lower, as compared to other parts of the world (World Bank, 2008). In Ethiopia, evidences indicate that the adoption rate of modern farm technologies, including improved seeds is low. For example, at national level, the proportion of farm land area under different technologies such as fertilizer use, improved seeds, pesticides and irrigation in the belg season (2014) was 42%, 5%, 10% and 8%, respectively (CSA, 2014).
Improved technology adoption of cash crops depends on the degree to which smallholders are willing to give up production for own consumption; which is determined by their level of risk aversion, availability of local markets to purchase food, and the probability that marketable production will yield adequate profit to meet the household’s consumption need (Schneider and Gugerty, 2010). In this regard, studies related to haricot beans are scanty in the country. However, a number of related studies on other crops exist on the determinants of technology adoption. Examples are Asfaw et al. (2011), Ashraf et al. (2008), Endrias et al. (2012) and Khonje et al. (2015). Pertaining to haricot beans, Rahmato (2007) and Alemitu (2011) studied adoption of haricot beans in the SNNPRS of Ethiopia. However, they are limited in scope and area coverage.
Within the farm households, one could also observe differences in production efficiency due to a number of social, economic and environmental factors that characterize the households. Studies were conducted on production efficiency of other crops (example: Endrias et al., 2012; Musa et al., 2014; Sibiko, 2012), but, investigations on the production efficiency status (i.e. all forms of efficiency, including technical, allocative and economic efficiency) of haricot beans producer households does not exist for the study area and similar agro ecologies to the best of the knowledge of the authors. Moreover, a huge gap that exists between on farm and research site yield of the crop calls for investigation of the production efficiency of the crop and factors determining it.
Similarly, studies exist on welfare impact of adoption of agricultural technologies for other crops (example: Khonje et al., 2015; Kassie et al., 2014; Sosina et al., 2014; Smale and Mason, 2014; Simtowe et al., 2012; Breisinger et al., 2008; Minten and Barrett, 2006; Deininger and Okidi, 2003), but similar studies on haricot bean are lacking in Ethiopia.
This study filled-in the following three key research gaps. First, an adoption study part relatively covered a wider area; i.e., three districts in East Shewa zone of southeast Ethiopia. Second, in addition to filling the gap on the absence of any efficiency study on the crop in the study area, an investigation of the haricot bean production efficiency of smallholder farmers included allocative and economic efficiency analysis, as well, given that haricot bean is produced for export market. Thirdly, a study on the welfare effect of adoption of improved white haricot bean variety was conducted on smallholder households (not done before).
The basic research questions addressed in the study are the following:
1. What are the factors that influence the adoption of improved white haricot beans technology? Do the decision and intensity of adoption of improved white haricot bean producer households influenced by the same set of variables?
2. Are there efficiency differentials among haricot beans producer households in the study area? If yes, whatare the magnitudes (levels) of the differentials of technical, allocative and economic efficiencies of haricot bean producing smallholder households?
3. Are there efficiency differentials (technical, allocative and economic) among adopters and non-adopters of improved white haricot beans producer households?
4. What factors are linked to (influence) the efficiency differentials among households?
5. What is the welfare effect of adopting improved white haricot bean varieties by smallholder farmers?
The overall objective of the research was to study adoption of technology, efficiency and welfare effect of improved white haricot bean variety by smallholder farmers in East Shewa zone of southeastern Ethiopia.
The specific objectives of the study are to:
1. Identify factors affecting the adoption and intensity of use of improved white haricot beans by smallholder households in the study area;
2. Estimate the levels of technical, economic and allocative efficiencies of haricot beans producer households;
3. Identify the determinants of technical, allocative and economic efficiency differentials among haricot beans producing farmers; and
4. Measure the welfare effect of adoption of improved white haricot beans variety of smallholder households.
Investigation of technology adoption is important to map out the critical determinants of adoption and intensity of use of improved varieties. In addition, it could help in predicting adoption patterns, assisting adopters to sustain the process in cases of a relatively higher non-adoption rate; and to know the most favorable mechanism of marketing the new technology (Oster and Thornton, 2009). The study could also aid as a feedback in designing of new projects, programs and institutional reforms to enhance technology adoption. The data and results of adoption study could also provide baseline information for evaluations of technology on productivity, equity and other goals.Similarly, an evaluation of efficiency provides feedback for researchers and extension agents to improve the technical, allocative and economic efficiencies of haricot beans smallholder households.
Assessment of the impact of such technology helps to evaluate the effectiveness of programs performed in the past, provide feedback to concerned stakeholders on the returns to past investment, and gain political support for continued effort. It also helps to map the likely future institutional actions, and prioritize resource use and activities to benefit the rural community at large. The study could serve as an important source of information for planners, extension agents, governmental and non-governmental organizations; adds to existing knowledge in the area (for academic and research communities) and could provide the basis for further research.
The study focused on adoption of export type improved white haricot bean varieties in East Shewa zone of Southeastern Ethiopia. The export type white haricot bean varieties are: Awash 1, Mexican 142 and Awash Melka. The analysis covered rain-fed (meher season) agriculture only as the use of irrigation water for this particular crop was negligible in the study area.The study did not include climbing bean types in the analysis and is limited to east Shewa zone of south Eastern Ethiopia despite huge geographical variation of the crop in terms of adaptation and heterogeneity of the households.
In this study, white haricot beans improved variety technology adopters refers to those households that cultivated (produced) at least one of the export type improved white haricot bean varieties during the production year, and at least two years before. According to Reilly and Schimmelpfennig (1999), the adoption of a new variety of crops could take between 3 and 14 years. This time period might allow for sufficient adoption process and impact to occur among the households. Adoption in this study refers to the participation of the households in cultivation or production of improved white haricot beanvarieties; and not other packages of technologies that go together with this particular crop.Studies reveal that farmers rarely adopt a complete package due to economic, social and risk considerations (Million and Belay, 2004). Non-adopters are those households that do not cultivate or produce any of the export type improved varieties of white haricot beans, but other traditional haricot beans (such as red, black, stripe, etc.).
Technology adoption is dynamic in nature. There are a number of economic, social and environmental factors that affect technology adoption and production efficiency of the households over time. This may be better dealt with through use of panel data. Unfortunately, such types of data are rarely found and the use of cross sectional data may lead to failure in capturing inter-temporal variations. Similarly, the welfare impact study requires a complex data on income and consumption expenditure which are not recorded and kept at household level in developing countries like Ethiopia. Hence, the study was based on a back recall power of the household head on issues related to income, food consumption and expenditures on inputs used in the production process.
The results of the study on adoption, efficiency and impact may be area and crop specific. Application and use of the results of this study will be encouraged based on the similarity in agro-ecological, socioeconomic and institutional set up with the study area.
In general, although the study encountered the above mentioned limitations, efforts were made to ensure the data and results as nearly accurate as possible by taking precautionary measures on data collection, analysis, and interpretations of the results.
The dissertation is organized into seven chapters. The remaining part of the dissertation is organized as follows. The second chapter is a review of literature, followed by research methodology in the third chapter, results and discussions in the fourth chapter, summary, conclusion and recommendations in the fifth chapter, references in the six chapter, and appendices in the seventh chapter.
In this chapter, reviews on the production system and economic contribution of haricot beans, conceptual issues, theoretical issues, methodological framework, analytical framework and related aspects of technology adoption, efficiency and welfare effect of improved white haricot beans are presented.
In Ethiopia, in order to improve the productivity of smallholder households, an increased use of new crop varieties has been made including pulses. Pulses constitute the major cost-effective sources of food protein, local source of cash and export earnings for the country (Schneider and Anderson, 2010; IFPRI, 2010; Tadele, 2010).
Haricot bean, scientifically termed as Phaseolus Vulgaris L. is one of the twelve pulse species (faba beans, field pea, chickpea, lentil, grass pea, fenu-greek, lupine, soya beans, cow pea, pigeon pea and mung beans) concentrated in warmer regions along the rift valley of Ethiopia. Worldwide, it is cultivated extensively in North, South and Central America, Africa, Asia and throughout Europe. In Africa, it is widely grown in Kenya, Tanzania, Malawi, Uganda and Ethiopia (Derese, 2012). Haricot bean was introduced to northern Ethiopia around 16th century (Shimelis and Rakshit, 2005).
The crop is distinguished based on color into white, mixed (speckled), red, and other color types. About 36 haricot beans varieties were released in Ethiopia (MoARD, 2009). The most common types are the pure red and pure white (Ferris and Kaganzi, 2008). White haricot bean varieties include: Mexican 142, Awash 1 and Awash Melka. Other common types of haricot bean varieties are Red-Wolayita, Roba 1, Atendaba-brown, Speckled Ayenewu, Gofta, Zebra, Gobe Rash, Beshbesh, Melke, Tabor, Batagonia and Anger. White beans are grown in the central Ethiopia (Shewa) as a cash crop, colored beans in the southern part of Ethiopia for local consumption and climbing beans are grown in the North West (Metekel) and Western Ethiopia (Wollega). Climbers are grown along fences and on the edge of maize fields (Zelalem, 2002).
Haricot bean grows at an altitude ranging from 600 to 2200 m a.s.l., under suitable rainfall of 450-700 mm with duration of 85-95 days (1000-1700 m a.s.l) and 110 days (1500-2200 m a.s.l.). It is sown from end of June to mid-July, usually not intercropped, and harvested after three months in October. Farmers produce during both Meher and belg seasons with yield reduction observed in belg season (0.8-1.2 tons per hectare) and 2.4- 3.2 tons per hectare in meher season (Ferris and Kaganzi, 2008).
In different parts of Ethiopia, the crop is consumed, usually mixed with other cereals. It is also consumed boiled, fried, milled, or grounded, and prepared in the form of soups. Intercropping has been practiced with maize and sorghum to supplement farmers with additional income, through intensification, in eastern and western Ethiopia. At country level, the total production of haricot bean, 69.87% was utilized for household consumption, 18.75% for sale; while the remaining was used for seed; wage in kind, animal feed and other uses (CSA, 2012). Haricot bean is supplied mainly by smallholder households, private commercial farmers, state farms, imports and food aid. The market participants in haricot bean trade include producers (smallholders and commercial farms), wholesalers, retailers, part-time farmer- traders, brokers, agents, assemblers, processors, cooperatives, and consumers.
Ethiopia has recently become the 10th top exporter of pulses in the world market (Schneider and Anderson, 2010). The export destinations include: Sudan, Yemen, South Africa, UAE, USA, UK, Italy, Germany, Belgium and the Netherlands. Worldwide, top producer countries of haricot beans include Brazil, Mynamar, India, China, USA, Mexico, Tanzania, Uganda and Argentina as of 2008 (FAOSTAT, 2008).
According to Rogers (2003),“a technology is a design for instrumental action that reduces the uncertainty in cause-effect relationships involved in achieving a desired outcome”. He goes on defining an innovation “as a thought, practice, or project that is perceived as new by an individual or other unit of adoption”; while invention is the process by which a new idea is discovered or created. Innovations are defined as new or latest method, customs, or devices used to accomplish new job (Sunding and Zilberman, 2000).
Innovations can be classified according to forms such as mechanical (tractor and combine), biological (new seed varieties), chemical (fertilizers and pesticides), agronomic (new management practices), biotechnological innovations, and informational innovations that rely on computer technologies. Innovation can also be categorized intothe process (example, gene cloning) and product innovations (example, a new seed variety). Further, innovation can be categorized into yield increasing, cost reducing, quality enhancing, risk reducing, environmental protection increasing, and shelf-life enhancing (Sunding and Zilberman, 2000).
An innovation reaches the clients or end users through diffusion. According to Rogers (2003), diffusion is defined “as the process in which an innovation is communicated through certain channels overtime in the social system”. As pointed out in the definition, there are four key components of diffusion namely, innovation, communication channels, time and social system.
According to Feder et al. (1985), final adoption at the farmer’s level is defined as the long-run degree of use of new technology given that the farmer has full information pertaining to the technology and its’ potential uses. Technology adoption is a mental process through which an individual passes from first knowledge of an innovation to the decision to adopt or reject and to confirmation of this decision (Ban and Hawkins, 1996).Whereas, the adoption process is the change that takes place within individual pertaining to an innovation from the moment they first become aware of the innovation to the final decision to use it or not (Ray, 2001).
Adoption refers to the decision to use a new technology, method, practice, etc. by a firm, a farmer or a consumer. Farm level (household) adoption reflects a farmer’s decision to incorporate a new technology into the production process. On the other hand, aggregate adoption is the process of spreading or diffusion of a new technology within a region or population. Therefore, a distinction exists between adoption at the individual farm level and aggregate adoption, within a targeted region or within a given geographical area (Feder et al., 1985)
The rate of adoption is defined as the proportion of farmers who have adopted a new technology. The extent of adoption is the percentage of farmers using a technology at a specific point in time (e.g. the percentage of farmers using improved varieties). The intensity (degree) of adoption is defined as the aggregate level of use of a given technology within a household (Feder et al., 1985).
The definition given by Ban and Hawkins (1996), Feder et al. (1985) and Ray (2001) on adoption process; and definition by Feder et al. (1985) on adoption and intensity of adoption was used as an operational definition. The concept of adopters is meant as those households, that produce any of or at least one of the export type improved white haricot bean varieties during the survey year, and at least two years before. The time limit is based on a study by Reilly and Schimmelpfennig (1999) that the adoption of a new variety of crops could take between 3 and 14 years. Intensity (degree) of adoption refers to the area of land covered by export type improved white haricot beans at household level.
The term efficiency was derived from Latin word efficiens originating from Latin verb ex facio, meaning ‘to obtain something from’ (Blánquez, 1998). The application to economics of this view dates back to the ancient Greece with the term ‘economics’; referring to the efficient management of family home or estate. Three hundred and eighty years before Christ, Xenophone, next to Socrates, while defining economics explained the mechanisms of increasing one’s estate in terms of the two facets of efficiency. His notion that ‘efficient management of estate’ is the result of keeping home in good order, supervising, monitoring and caring for them, coincides especially with the term ‘static efficiency’, to mean the sound management of existing (given) resources. At that time, Xenophone also gave his wise answer to the then king of Barbara to the question raised as ‘what is the quickest way of fattening a horse?’ His reply was in terms of entrepreneurial ability, ‘the masters’ eye’, which is the ‘dynamic aspect of efficiency’.
This distinguished definition of efficiency towards preventing wastage (static), acceptance of risk and dangers arising from entrepreneurial speculation (dynamic) efficiency continued until the middle ages (Rothbard, 1995). With the arrival of modern age owing to the influence of mechanical physics (invention of the steam engine, law of thermodynamics and the principles of conservation of energy), the dynamic efficiency concept vanished to static efficiency aimed at preventing wastage of a given resource; the approach termed as the reductionist approach. Webster’s dictionary defines ‘efficient’ as ability to choose and use the most effective and least wasteful means of doing a task or accomplishing a purpose.
Technical efficiency, as borrowed from the mechanical physics continued to dominate static efficiency criteria. Later, through the effort of distinguished economists (Robbins, 1972; Lipsey, 1966; Alchian and Allen, 1964) an attempt to differentiate technical or technological efficiency from economic efficiency has been made once and for all. While technical efficiency is meant to minimize inputs in physical terms to produce a certain outcome, economic efficiency consists of the same, which is in cost terms (units of inputs multiplied by market price).
From Paretian perspective, a state of efficiency in an economic system occurs if no one can be made better-off without making someone else worse-off. This view finally was proved impossible to be achieved as there are no social welfare functions that meet the criteria of Pareto optimality and standards of liberalism. This is due to the fact that there are differences in individual preference and individual ordinal utility ranks cannot be added (Gamir, 1996).
With regard to the recent definition of efficiency, according to Kalirajan and Shand (1999), technical efficiency is defined as the capacity and willingness of an economic unit to produce the maximum possible output from a given bundle of inputs and technology; whereas, allocative efficiency is the ability and willingness of an economic unit to equate its specific marginal value product with its marginal cost. The combination of technical and allocative efficiency is said to be economic efficiency or over-all efficiency. Economic efficiency is defined as the capacity of a firm to produce a predetermined quantity of output at minimum cost for a given level of technology (Farrel, 1957; Kopp and Diewart, 1982). An economically efficient input-output combination would be on both the frontier function and the expansion function. The existence of technical inefficiency will in turn exert pressure on allocative efficiency resulting in a cumulative negative effect on economic efficiency (Bauer, 1990; Kalirajan and Shand, 1992).
Some authors do not make differences between definitions of productivity and efficiency. For example, Cooper et al. (2000) define both productivity and efficiency as the ratio between output and input. Productivity, according to Vincent (1968) and Lovell (1993), is the ratio between an output and the factors that made it possible. This ratio is easy to compute if the unit uses a single input to produce a single output. For several outputs and inputs, both have to be aggregated so that productivity remains the ratio of the two scalars. One can compute a partial productivity if concerned for a sole production factor, and a total factor (or global) productivity, in reference to all factors (Daraio and Simar, 2007).
As distinguished from productivity, efficiency is described as a distance between the quantity of inputs and output, and the quantity of input and output that defines a frontier, the best possible frontier for a firm in its cluster (industry). Even though efficiency and productivity are cooperating concepts, the measures of efficiency involve comparison with the most efficient frontier, can complete that of productivity based on the ratio of outputs on inputs and hence, is more accurate than that of productivity (Daraio and Simar, 2007). Lovell (1993) defines efficiency of a production unit in terms of a comparison between observed and optimal values of its output and input. The comparison can be in the form of the ratio of observed to maximum potential output obtainable from a given input, or the ratio of minimum potential to the observed input required to produce the given output. In both comparisons, optimum is defined in terms of production possibilities, and efficiency is technical. According to Koopmans (1951), an input-output vector is technically efficient if, and only if, increasing any output or decreasing any input is possible only by decreasing some other output or increasing some other input. The notion of efficiency is related to the concept of Pareto optimality. An input-output bundle is not Pareto-optimal if there remains the opportunity of any net increase in outputs or decrease in inputs.
There are other types of efficiencies so far distinguished in addition to technical efficiency. These include: scale efficiency, allocative efficiency (defined above), and structural efficiency. The scale efficiency has been developed in three different ways (Daraio and Simar, 2007). Farrell (1957) used the most restrictive technology having constant returns to scale (CRS) and exhibiting strong disposability of inputs. The model was developed in a linear programming framework by Charnes et al. (1978). Banker et al. (1984) expressed the CRS measure of efficiency as the product of a technical efficiency measure and a scale efficiency measure. Another method of scale uses nonlinear specification of the production function such as Cobb-Douglas or a translog function, from which the scale measure can be directly computed (Sengupta, 1995). As stated above, allocative efficiencyin economic theory measures a firm’s success in choosing an optimal set of inputs with a given set of input prices; this is distinguished from the technical efficiency concept associated with production frontier, which measures the firm’s success in producing maximum output from a given set of inputs. The concept of structural efficiencyis an industry level concept due to Farrell (1957), which broadly measures to what extent an industry, keeps up with the performance of its own best practice firms; thus it is a measure at the industry level of the extent to which its firms are of optimum size. A broad interpretation of Farrell’s notion of structural efficiency can be stated as follows: industry or cluster A is more efficient structurally than industry B, if the distribution of its best firms is more concentrated near its efficient frontier for industry A than for B.
The present study aims to compare each household’s technical, economic and allocative efficiency scores against the mean efficiency scores of all the sample households and the maximum production frontier. Accordingly, the efficiency definition put forth by Kalirajan and Shand (1999) and Lovel (1993) was taken as an operational definition.
The concept of welfare has been defined and understood in various ways, making it difficult to disentangle and catch in a similar fashion by the concerned individuals including scholars (George, 1995). According to the Oxford Dictionary’s (2001) definition, “welfare is defined as well-being; happiness; health and prosperity (of person, community, etc.,) and financial support from the state.” Historically, it has been related to happiness and prosperity, whereas its modern definition emerged in the 20th Century (Williams, 1976). Welfare could be defined in relation to an individual and to the collective, and may involve material as well as non-material needs (Greve, 2008). Welfare can also include acts of altruism, channels for the pursuit of self-interest, the exercise of authority, transition to work, and moral generation (Deacon, 1992). In a more restricted way, welfare has been defined biologically, for instance, as the amount of calorie needed to survive (Spicker, 1995). This definition is related to the traditional analysis of poverty.
Pigou (1950) stressed that the only obvious measure of welfare is money. Similarly, according to Van Praag and Frijerts (1999), welfare is the evaluation assigned by the individual to income or, more generally, to the contribution to our well-being from those goods and services that we can buy with money. According to Tinbergen (1991), welfare is measurable when mainly connected to individuals’ perception and utility of the use of income. In economic theory, welfare is commonly used to represent the utility derived from use of income (Greve, 2008). Individual welfare refers to the micro level and how utility can be maximized by choices made by the individual. Social welfare refers to the sum of all individual welfare in a society (Walker, 2005).
The welfare of an individual can be explained in relation to the poverty status evaluated on some standard criteria, namely the poverty line. The concept of poverty, poverty line and head count ratio originated first by Booth (1892) and Rowntree (1901). A broader definition of poverty, according to the World Bank (2000) is “a pronounced deprivation in well-being”. The definition usually links well-being to command over resources; those who do not have enough income or consumption above some adequate minimum threshold are designated as poor. The poor are then defined as those with a material standard of living as measured by income or expenditure below a certain level called poverty line (Atkinson, 1987; Ravallion, 1992). Often, poverty is measured in terms of absolute and relative poverty. In view of absolute poverty, “poverty is a lack of income in order to satisfy the essential requirements for physiological survival”. According to the relative approach, “poverty is a lack of income in order to reach the average standard of living in the society in which one live” (CEMAFI, 2003).
Aggregation of relevant characteristics of people is required for measuring poverty. This is termed as aggregation problem. Aggregation problem refers to how to pass from identification of poverty to the measurement of poverty (Sen, 1997). Among the existing poverty measurement indices, the best way of selection is by investigating whether it satisfies some of the desirable properties for instance, whether the indices are sensible to the number of poor or to the level of their income in relation to the poverty line). The most widely used include: the Head Count Ratio (HC) and the Poverty Gap (PG) (Bellu and Liberti, 2005). The Head Count (HC) ratio is the percentage of population, which is not above the poverty line. A poverty gap for an individual may be defined as the distance between the poverty line Z and his/her own income y. By aggregating individual poverty gaps for all poor individuals, we can get the aggregate poverty gap.
Poverty may also be measured tied to specific type of consumption: house poor, food poor, health poor, malnutrition and literacy (World Bank, 2000). According to Sen (1987), the broadest approach to well-being focuses on “capability” of individuals to function in the society. Similarly, Baulch (1996) described the broader dimension of poverty in terms of ‘a pyramid of poverty concepts’ including: Private Consumption (PC), Community Property Resources (CPR), State Provided Commodities (SPC), Assets, Dignity and Autonomy.
The definition and measurement of poverty has been often controversial and debatable depending on time and space based on the choice of welfare indicator (income or expenditure, household size and composition, and choice of appropriate poverty line (absolute or relative) to be used (Falkingham and Namzie, 2002).
One of the objectives of the research is to evaluate the welfare effect of adoption of export type improved white haricot beans on the smallholder households. According to the World Bank (n.d), “impact evaluations compare the outcomes of a program against a counterfactual that shows what would have happened to beneficiaries without the program”. Similarly, the International Initiative for Impact Evaluation (3ie) defines impact evaluation “as the analysis that measure the net change in outcomes for a particular group of people that can be attributed to a specific program using the best methodology available, feasible and appropriate to the evaluation question that is being investigated and to the specific context” (3ie, 2008). The impact evaluation is expected to answer the question how would outcomes of technology adopters’ well-being have changed if the intervention had not been undertaken.
The welfare impact of the household was evaluated in terms of the observed effects on household crop income and consumption expenditures /food/ of the household as adopted from Mendola (2007). Data on income and consumption was used as a proxy on the assumption that a person’s material standard of living determines their well-being. Hence, the operational definition of well-being in the present study follows the works of Van Praag and Frijerts (1999), Tinbergen (1991), Ravallion (1992), Jalan and Ravallion (2003) and Atikinson (1987); and Money-metric measures of poverty (Falkingham and Namzie, 2002).
Study on technology diffusion begun by Ryan and Gross (1943), in the United States in 1920’s to evaluate the improved farming practices (diffusion of hybrid seed corn in two Iowa communities) by U.S Department of Agriculture. The study grew rapidly in 1950’s and 1960’s and influenced the onset of similar studies in other countries (Dasgupta, 1989). In 1961, the classical five stage adoption process model was formulated by the North Central Rural Sociologists Committee (1961) as the major model until it was modified by Rogers and Shoemaker (1971). The model is composed of five stages, namely, awareness, interest, trial, evaluation and adoption. It considers adoption as a process that develops over time. Later the model has faced critics by Campbell (1966) and later by Rogers and Shoemaker (1971) who then designed the innovation decision process model (Duvel, 1991). The model was later revised by Rogers (1983) as follows.
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Figure1: Rogers’ innovation decision process model. Source: Rogers (1983)
Rogers’ diffusion of innovation theory explains the process of adoption of a given innovation by a society. The theoretical framework developed by Rogers has been used by several disciplines such as political science, public health, communications, history, economics, technology, and education (Dooley, 1999; Stuart, 2000).
The theoretical aspects for the explanation of the determinants of adoption of an innovation or a technology could be divided into three categories, namely innovation–diffusion model, perceptions of adoption, and the economic constraint model. The fact that technology is technically and culturally appropriate, but problem of adoption is one of asymmetric information and high search cost is the underlying assumption of the innovation-diffusion model (Feder and Slade, 1984; Shampine, 1998; Smale et al., 1994). The adoption perception model refers to the perceived attributes of technology condition by farmers that subjectively vary from individual to individual (Ashby and Sperling, 1992). The economic constraint model explains that inputs (such as credit, land, labor and others) are fixed in the short-run, limit production flexibility and affect technology adoption (Aikens et al., 1975; Smale et al., 1994; Shampine, 1998).
Researches on adoption behavior mainly focus on factors that affect if and when a particular individual begins using an innovation. It may be explained by more than one variable (a discrete choice, whether or not to utilize an innovation, or by a continuous variable). The measures of adoption may also point to both the timing and extent of new divisible innovation used by the individuals. For instance, a discrete variable denoting if the variety is being used by a farmer could be one measure of adoption of high-yielding seed at a certain time and the percentage share of farmer’s land planted with this variety may be another measure (Sunding and Zilberman, 2000). In cases of divisible technology, when such a measure of adoption is aggregated, it may be interpreted into diffusion. The percentage of farming population that adopted an innovation and the land share in total land which is utilized under new technology could be used as measures of diffusion (Sunding and Zilberman, 2000).
For households to adopt the new production technology successfully, they must first learn about them and how to use them properly in their farming system (Swanson and Claar, 1984). Farmers are assumed to maximize expected utility according to Von-Neuman and Morgenston (1944) utility function defined over wealth (W).
In modeling the satisfaction or utility derived from using the new varieties, the economic values or benefits associated with the improved variety over the traditional varieties needs to be considered. When confronted with a choice between two alternative practices, the ith farmer compares the expected utility of the modern technology, E mi (W) to the expected utility of the traditional technology, Eti (W). Since the direct measurement of farmers' perceptions and risk attitudes on this particular technology are not available, inferences can be made for variables that influence the distribution and the expected utility of the technology under long-run equilibrium (when the households have full information). These variables are used as a vector ‘X’s (attributes) of the choices made by farmer 'i' and εi is a random disturbance that arises from unobserved variation in preferences, attributes of the alternatives, and errors in optimization. Given the usual discrete choice analysis and limiting the amount of non-linearity in the likelihood function, Emi (W) and Eti (W) may be written as:
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Feder (1980) showed that optimal allocation of land under new crops declines with high variability of random variables and with high degrees of risk aversion. Just and Zilberman (1983) later showed that intensity of adoption depends on whether the new technology is risk increasing or decreasing and whether risk aversion is decreasing or increasing with wealth. In the presence of risks, the effect of output price on adoption of agricultural innovations is not determined; it can be negative, positive or zero (Feder, 1980). In theory, the optimal level of new technology use increases with higher output price if the elasticity of risk response to modern input is lower compared to the elasticity of the average yield response to modern input use.
Another important determinant of adoption of new technology (for profit maximizing entities) is the net return to the agent from new technology. Estimates of the marginal return to input or a marginal expansion in technology inform whether there are market or other problems that constrain adoption. For agents aimed at utility maximization, for instance in health and environment, the issue of return has no direct relation. The measurement of return even in the case of profit oriented agents is difficult due to lack of detailed cost related data on inputs, especially family labor, variations (heterogeneity) that exist across farms, and related management skills make the task of concluding that increment in return is solely caused by the new technology incomplete (Foster and Rosenzweig, 2010).
Learning is one of the determinants of technology adoption given that the direction of influence depends on situations. Learning takes place when new information affects behavior and results in outcomes for individuals. Learning does not imply that learning increases the use of an input or a new technology. What is learned may entail that new technology is not useful or not effective (Miguel and Kremer, 2007). Technological externalities depending on whether they are positive or negative externalities affect the adoption of new innovation through learning by individual agents (Foster and Rosenzweig, 2010).
Another determinant of technology adoption could be income. Income may affect the demand for technology (that augment wealth or well-being) in cases where imperfection in credit and insurance market exist, and when there are fixed costs needed for technology adoption. Wealthier individuals are likely to adopt a given new technology.
In general, given the complex nature of the adoption process, an approach to use the three models (innovation-diffusion model, perceptions of adoption, and the economic constraints model) to explain the determinants of adoption of individuals on a new innovation or technology improves the power of explanation (Morris et al., 1999; Gemeda et al., 2001).
This study looked into the determinants of adoption and intensity (degree) of use of improved variety of haricot beans crop with the aid of these three theoretical models. The factors lie within the domain of the demographic, economic, social features of the decision making unit, farm/farm specific attributes and existing policy scenarios.
According to Farrell (1957), in an input-oriented scheme, in the presence of firms which use two inputs (X1 and X2) to produce a single output (Y), under the assumption of constant returns to scale and knowledge of the unit isoquant of fully efficient firms (SS’), the measurement of efficiency could be possible.
If a given firm uses quantities of inputs defined by point P ( Figure 2) to produce a unit of output, the technical in-efficiency of that firm could be represented by the distance QP; which is the amount by which all inputs could be proportionally reduced without a reduction in output expressed in percentage terms as the ratio of (QP)/ (OP).
Technical Efficiency TE = OQ/ OP = 1- QP/ OP. Technical efficiency takes the values between 0 and 1. If the input price ratio represented by the slope of iscost line AA’ is known, allocative efficiency may also be known. The Allocative Efficiency (AE) of the firm operating at point P is defined as: AE = OR/ OQ. The distance RQ represents the reduction in production costs that would occur at the allocatively and technically efficient point Q’ instead of the technically efficient but allocatively inefficient point Q. The total Economic Efficiency EE = TE X AE = OR/ OP.
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Figure.2: Input-oriented technical, allocative and economic efficiency measures. Source: Coelli et al. (2005).
In an output oriented scheme below, practically firms operate at less than potential technical efficiency due to incomplete knowledge of best technical practices or other organizational factors that hinder the firms from operating on its technical frontier. The firm, thus, operates on actual or perceived production function located below the potential frontier (AP’ in Figure 3). In the same figure point C is allocatively inefficient even if it lies on the actual frontier. Whereas, point D is both technically and allocatively efficient as it is tangent to the price line. Maximum profit II4 is attained at point D. However, the firm would not achieve its potential economic efficiency (II1) located on the potential frontier. Both input-oriented and output oriented schemes yield the same result. In deciding on the orientation of a model one should also consider over which variables, decision making units (DMUs) have most control. If DMUs have more control over output variables than input variables, the model should be output-oriented; otherwise, the model should be input-oriented. Agricultural farms usually have more control over their inputs than their outputs (Coelli, 1995; Jaforullah and Premachandra, 2003). This study, therefore, used an input oriented model as the smallholders do have little or no control over output variable as they operate in an l uncertain physical environment.
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Figure3: Output-oriented firms’ technical, allocative and economic efficiencies.
Source: Coelli et al. (2005).
Underlying the economic concept of poverty analysis by Booth (1892) and Rowntree (1901), material welfare school (e.g., Javons, 1881; Marshal, 1920) based on the equivalence of income and welfare came into being; followed by a number of theoretical and methodological improvements until 1970’s first criticism by sociologists. In 1976, the axiomatic approach of Amartya Sen with a number of mathematically sophisticated indicators based on income and expenditure became practical.
Theoretically, the best indicator of welfare is the actual consumption of the individual, including food, other goods and services such as education and health (Falkingham and Namzie, 2002). Because of difficulty in quantification of the qualitative aspects of the welfare of individuals, traditionally, data on income and expenditure were used as a proxy on the assumption that a person’s material standard of living determines their well-being. These are termed as money-metric measures of welfare (Falkingham and Namzie, 2002). The choice between income and expenditure is dictated by data availability.
Money-metric measure of poverty is practically difficult to quantify accurately and criticized to be strictly a static concept and offers a limited picture of the household well-being (Falkingham and Namzie, 2002). As a result, alternative non- monetary measures such as household asset indexes (an aggregate measure of ownership of a list of attributes) has been used (Filmer and Pritchett, 1998; Montgomery et al., 2000; Sahn and Stifel, 2000). However, during transitory shocks on income, households may reduce their consumption in order to preserve their asset holdings. Agrawal (1991), in his study of the welfare impact of famine in Bangladesh, concluded that an exclusive focus on either expenditure or asset ownership gives a misleading result. Besides, money-metric measures are unable to capture the other aspects of well-being including community resources, social relations, culture, personal security and natural environment.
Multidimensional Poverty Index (MPI) is another methodology to measure poverty. Poverty is viewed as having multivariate direction in the social exclusion approach of Rene Lenoire (1974), functioning and capability approach by Sen (1980,1984,1985,1987,1992,1999), the United Nations Development Program (UNDP) Human Poverty Index (1990,1997,1998) and the fuzzy sets approach to poverty applied in Italy (Cerioli and Zani, 1990). Multidimensional Poverty Index (MPI) presents updated estimations for a number of data sets of various countries from time to time. Many countries of the world define poverty uni-dimensionally, in relation to income or consumption levels. But, the poor express poverty beyond income often including access to housing, health, education, employment, humiliation, personal security and more. No single indicator can uniquely capture a number of disadvantages that contribute to poverty (Alkire et al., 2016; OPHI, 2015).
Theoretical frameworks (models) for social welfare measures at household level include the conventional approach which basis on the utility theory, the reference dependent utility framework, the prospect theory, and the hybrid form. The approaches assume utility to be cardinal and interpersonally comparable to derive the social welfare function.
In the conventional welfare analysis, an individual has utility u (y) of consumption y, and one normally assumes that u ¢ > 0 and u ¢¢ < 0. This function predicts his behavior and measures his well-being.
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The key points in the conventional approach are that welfare measurement is not based on levels of income, but on changes from a reference point. The reference point could be e.g. one’s past consumption level or perhaps also the poverty line in the economy. Loss aversion, negative changes have a greater impact on welfare than gains of equal magnitude. In addition, the value function could be convex in the loss area (diminishing sensitivity). Moreover, the approach uses subjective probability distribution instead of objective distributions.
The reference dependent utility framework by Koszegi and Rabin (2006) basis both on current consumption utility level and the difference between utility level of current consumption and the reference point.
Prospect theory was developed by Kahneman and Tversky (1979); as an alternative to expected utility approach for decision making under uncertainty. Prospect theory deals with changes in well-being and it is silent on the level of well-being, whereas all conventional poverty and inequality measurement starts from income or utility levels.
A hybrid form of preferences was based on the reference dependent utility suggested by Koszgi and Rabin (2006), where well-being depends on the utility of current income and the deviation of current income from the base income (or reference income).
With respect to the approaches on measures of poverty, distinctively, there are ad hoc measures, axiomatic measures and measures based on the dominances of Lorenz curve or Generalized Lorenz curve. The ad hoc measure has been under use up until the axiomatic measure was developed by Sen (1976). The axiomatic measure is based on a set of properties that a poverty index should respect (axioms). The simplest (ad hoc measure) which are most widely used by Deaton (1997) in Ivory-cost and South Africa as desirable in poverty measurement includes: The Head Count Ratio (HC) and The Poverty Gap (PG).
Crop production affects the welfare of the farmers directly or indirectly. According to De Janvry (2002), crop production affects poverty directly by raising the welfare of poor farmers who adopt the technology through improved production for home consumption, more nutrition and higher gross revenue from sales, lower production costs, low yield risks, lower exposure to unhealthy chemicals and improved natural resource management. The indirect effects may be through market prices for food for net buyers, employment and wage effects in agriculture and other sectors.
Based on the reference dependent utility framework by Koszegi and Rabin (2006), the impact of the technology adoption has been compared between adopters and non-adopters in terms of its effect on welfare i.e. the mean change in household crop income and food consumption expenditures per adult equivalent.
This section refers to the philosophical position of the researchers, how the study was conducted systematically in view of existing scientific (standard) procedures, and what sort of critical decisions were made in the research process (including the logic behind them) so that they can be replicated or evaluated by others.
The study is an applied research based on quantitative methods with a combined research goalof description (i.e. offering a detailed picture of the subject under study), exploration (i.e. getting to know a phenomenon), explanation (i.e. causality study) and evaluation (i.e. seeking to judge the effect of a program of intervention). Knowledge generation (epistemology) followed an inductive reasoning, where we base on particular instances to generalize regarding the universe of the study.
The research used both metric (quantitative data that reflect the relative quantity / magnitude/amounts, or distance) and non-metric measures (qualitative data that reflect an individual attributes, characteristics or categories which might not be easily quantified). Three levels (scales) of measurements are operationalized to measure variables in this study under the broad category of metric and non-metric measurements. These include nominal, ordinal and ratio scales. While the nominal and ordinal scales are non-metric measurement scales; ratio scales belong to metric measurements. Examples of nominal measures are data on sex, marital status of the households, etc. Some examples of ordinal and ratio scale measures include data on education and income of the households, respectively.
The choice and measurement of variables were flexibly plannedahead to include all alternative valid measurements available in different forms due to threat of availability of information and intention of a more rigorous analysis. Alternative measures and standard units were incorporated to measure the same variable. Moreover, relatively more number of variables are incorporated assuming that adoption of a technology and efficiency of production are affected by complex sets of variables. Formats for gathering information on such alternative and complex set of variables measurement were included in the survey instrument and oriented to enumerators on the training session. Some measurements (dependent and independent) of variables required further processing of the primary data collected. Examples are efficiency scores, proportions used to measure intensity of adoption and measurements computed from conversion factors such as Tropical Livestock Unit or TLU, adult equivalent, per capita, etc.
In summary, the findings of the study could be applied to other areas with similar geographical settings. The generalization (external validity) of the result of the research to other geographical areas could be justified on the following grounds. First, the research (data collection) was fully based on random sampling technique enhancing the precision of the result (Cochran, 1977). Second, standard scientific procedures were adopted fordata collection, analysis, interpretation and in drawing conclusions in line with existing research ethical concerns.
While the above framework applies to all objectives of the study, some of the specific issues pertaining to each objective (separately) are presented as follows.
Adoption studies could be conducted based on either time series (panel) and/or cross-sectional data, depending on the objective and availability of data sets over time. However, time series data pertaining to the adoption of haricot bean varieties is lacking in Ethiopia in general and in the study area in particular. Therefore, a snap shot of technology adoption at a given point in time was taken with the assumption that few influential farm and farmer specific variables do not change over time. However, Information on some variables is also accessed using the recall power of the sampled households.
Technology adoption refers to an improved variety of white haricot beans that serve as a major source of income and nutrition (protein) for the rural households. Based on past sources on export markets and periodic reports, improved white haricot beans that were disseminated/ released in the area were Awash-I, Awash- Melkasa-II and Mexican- 142 variety; while the rest non-white colors (red, black and stripes) were considered (assumed) as local (traditional) haricot beans. Practically (after the survey), there were no local white haricot bean variety other than the aforementioned improved varieties in the study area. However, among the improved white haricot beans listed at the initial stage of this study, Mexican 142 variety was not recorded (totally not available) among the sampled households as the release was long beforethe rest of the improved white haricot bean varieties.
Given that time series data is lacking in the country in general and in the study area in particular, this study used a cross-sectional data for the efficiency analysis. Output and input data of haricot beans producer households) are expressed in kilograms (kg) per season of production in the frontier analysis. Data on prices of inputs and services (rents) paymentwere taken as reported directly by the households in the allocative efficiency analysis. In addition to the efficiency of haricot beans as a whole, efficiency analysis was conducted by adoption status (i.e. cohort analysis) to see whether efficiency differential existed among adopters and non-adopters of white haricot beans. The comparison, however, did not take into account variations due to selection bias in the program.
Efficiency analysis with the aid of frontier production functions begun with Aeigner and Chu’s (1968) reformulation of a Cobb Douglas model. In parallel, literatures have been devoted to Data Envelopment Analysis (DEA), as well (Greene, 2007). The major approaches applied to efficiency analysis include: parametric deterministic models by Aigner and Chu (1968) and parametric stochastic models by Aigner et al. (1977) and Meeusen and Van den Broeck (1977) for cross sectional and panel data, For panel data, stochastic model has been developed by Schmidt and Sickles (1984) and Cornwell et al. (1990) with semi parametric generalizations in which a part of the model is parametric and partly nonparametric. Moreover, nonparametric deterministic models for cross-sectional and panel data has been developed by Fare et al. (1985, 1994), and Cooper et al. (2000); and nonparametric stochastic model for cross-sectional data by Kumbhakar et al. (2004) and for panel data by Kneip and Simar (1996).
Among all, the two principal models commonly applied are: the non-parametric /deterministic/ Data Envelopment Analysis (DEA) and the parametric stochastic frontiers that involve mathematical programming and econometric methods, respectively (Lovell, 1993). They are different techniques of data envelopment. Both differ in accommodation of random noise and flexibility in the structure of production technology. The econometric approach is stochastic and attempts to distinguish between the effects of noise and the effects of inefficiency while the linear programming is deterministic. On the other hand, the econometric approach is parametric and in consequence suffers from functional form mis-specification while the linear programming is non-parametric and is immune from any form of functional mis-specification.
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