Impact of technology Adoption legume producing on Farmers’ Income: 

A Case study of Guraghe Zone   

Wolkite Univesity 

College of Business and Economics   

Department of Economics    

A Thesis submitted as partial fulfillment of the requirements for the 

award of MSc degree in Economics (Major in Development Economics) 

By: Sahlu Badishe   

Major advisor: Mekonnen Bersisa(PhD) 

Co-advisor Tesfaye Etensa (Ass Prof.)  

June, 2019 

Wolkite, Ethiopia 

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ACKNOWLEDGEMENT 

Above all, thanks to my God. I would like to express my truthful gratitude thanks to my major 

advisor Dr. Mekonnen Bersisa (PhD) for his valuable comments, suggestion, and support through 

deep follow up. He deserves special appreciation for the input he added on my work from the 

initial title adjustment as well as proposal writing to final thesis. I also thanks to my Co advisor 

Tesfaye Etensa (Ass Prof.) for his valuable comments, suggestion, and support. My deepest and 

sincere gratitude to my family always had been with me throughout my studies and my works. 

My beloved wife, Seble Zewdu who always push me and continuous helping with her smile that 

my works during my studied. My love I remember you anywhere because of no apprehensive 

action at any time and any work and I thank you so much for all your effort and support me! Also 

My Son Yeabsira Sahlu and My Daugther Danat Sahlu whole families appreciate and continuous 

helping idea giving time.  In Gurage zone agricultural development department experts, Abeshge 

,Gumer and Sodo woreda Agriculture and Natural Resource Office experts and 9 kebele  

agriculture development agents support in data collection times  any relevant data feed .I thanks 

also to my collogues of My Organization: Gurage      Zone Investment office the whole experts  

participate in my works facilitation. My Brother Birhanu Badishe  And Damench Badishe  by 

sharing the transport  cost other relevant fee. Finally but most significantly, I would like to 

express my deepest thanks to Ato lemma Shallo   (MA )  Economics  department of Wolkite 

University lecturere for his interest to  support me during data analysis using PSM and  thank you 

to all who supported me  throughout my studies. 

 

 

 

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Table of Contents 

ACKNOWLEDGEMENT .................................................................................................................................... iv 

Table of List .................................................................................................................................................. viii 

List of Figure ................................................................................................................................................... ix 

ACRONYMS AND ABBREVIATIONS .................................................................................................................. x 

Abstract .......................................................................................................................................................... xi 

CHAPTER ONE ................................................................................................................................................. 1 

INTRODUCTION ............................................................................................................................................... 1 

1.1. Background of the study ...................................................................................................................... 1 

1.2. Statement of the Problem ................................................................................................................... 3 

1.3. Research Questions ............................................................................................................................. 5 

1.4. Objectives of the Study ........................................................................................................................ 5 

1.5. Significance of the study ...................................................................................................................... 6 

1.6. Scope of the study ............................................................................................................................... 7 

1.7 Limitations of the study ........................................................................................................................ 7 

1.8 Organization of the study ..................................................................................................................... 7 

CHAPTER TWO ................................................................................................................................................ 9 

LITERATURE REVIEW ....................................................................................................................................... 9 

2.1. Definition of Basic Concepts ................................................................................................................ 9 

2.1.1. Definition of leguminous crops ......................................................................................................... 9 

2.1.2. Impact of Legume ......................................................................................................................... 9 

2.1.3. Importance of legumes ...............................................................................................................10 

2.1.4. Definition of Adoption ................................................................................................................10 

2.1.5. Agricultural Technology Adoption ..............................................................................................11 

2. 2. Theoretical Reviews ..........................................................................................................................13 

2.3. Experimental method and Non-Experimental methods ........................................................................15 

2.4. Propensity Score Matching ....................................................................................................................17 

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2.6. Empirical Review literature ....................................................................................................................18 

2.7. Conceptual Framework ..........................................................................................................................23 

CHAPTER THREE ............................................................................................................................................26 

RESEARCH METHODOLOGY ..........................................................................................................................26 

3.1. Description of the Study Area ................................................................................................................26 

3.2 Sampling Techniques and Procedures ....................................................................................................27 

3.3 Types and method of data collection .................................................................................................29 

3.4    Pre-Testing (Validity and reliability) .................................................................................................29 

3.5 Method of Data Analysis .....................................................................................................................29 

3.6.1.1. Nearest Neighbor Matching .........................................................................................................35 

3.6.1.3.Kernel Matching ............................................................................................................................36 

3.7. Description of Variables and their expected Signs ............................................................................40 

3.7.1. Dependent variables ...................................................................................................................40 

3.7.2. Explanatory Variables .................................................................................................................41 

3.7 Reliability and Validity .........................................................................................................................45 

CHAPTER FOUR .............................................................................................................................................46 

4.1. Descriptive Statistic Results ...............................................................................................................46 

4.1.1. Demographic characteristic of farm households ........................................................................46 

4.1.2 Education status of sample farm households ..............................................................................48 

4.1.3 Farming system and characteristics .............................................................................................48 

4.1.3.2 Cropping system ...............................................................................................................................50 

4.1.4 Adoption of technologies in legume farming ..................................................................................53 

4.1.4.1 Input supplying parties .....................................................................................................................53 

4.1.4.2 Trait preference of farmers ..............................................................................................................54 

4.1.5 Adoption status of sample households for main farm inputs .........................................................55 

Sources: Own computation from survey data (2019) ...............................................................................56 

4.1.6 Farmers’ typological arrangement ...................................................................................................56 

4.2. Econometric Analysis .........................................................................................................................59 

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4.2.1. Determinants adoption of legume technologies ........................................................................59 

4.3. Impact of Adoption of Legume legumes technology .........................................................................63 

4.3.1. Estimation of propensity score ...................................................................................................64 

4.3.2.   Matching adopter and non-adopters ........................................................................................66 

4.3.3. Common support condition ........................................................................................................66 

4.3.4. Choice of matching algorithm and matching ..............................................................................69 

4.3.5. Testing the balance of propensity score and covariates ............................................................71 

4.3.6 ATT estimation of impact of technology on HH income ..............................................................73 

4.4. Average treatment effects (ATE) with test ........................................................................................74 

4.5 .Matching estimators of the ATT based on the propensity score outcome ...........................................75 

4.5.1. Nearest neighbor matching (attnd.ado) .....................................................................................75 

4.5.2. Radius matching (attr.ado) .........................................................................................................76 

4.5.3. Kernel matching (attk.ado) .........................................................................................................77 

4.5.4. Stratification matching (atts.ado) ...............................................................................................77 

4.6. Sensitivity Analysis .............................................................................................................................78 

4.7. Consistency Testing ............................................................................................................................79 

4.7.1. Cronbach’s Alpha ........................................................................................................................79 

5.  Conclusions and Recommendations ........................................................................................................80 

5.1. Conclusion ..........................................................................................................................................80 

5.2. Recommendations .............................................................................................................................81 

5.3. Areas of Further Study .......................................................................................................................83 

REFERENCE ....................................................................................................................................................84 

Appendix.I Table of results ...........................................................................................................................90 

Appendix. II Questionnaire .........................................................................................................................102 

 

 

 

 

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Table of List--------------------------------------------------------------------------------viii 

Table.3.1 Sample distribution .......................................................................................................................28 

Table.3.2. Definition of dependent variables ................................................................................................40 

Table.3.3. Independent variables ..................................................................................................................44 

Table.4.1. Demographic characteristics of sampled farmer ..........................................................................47 

Table 4.2. Education levels of sampled household .......................................................................................48 

Table.4.3. Land use pattern of household .....................................................................................................49 

Table .4.4 agronomic activities of legume production ..................................................................................51 

Table.4.5. Plot size production of major crop ...............................................................................................52 

Table.4.6. Households’ preference of institution for input delivery .............................................................54 

Table.4.7. Variety's trait preference ..............................................................................................................55 

Table.4.8. Agricultural input adoption status ................................................................................................56 

Table 4.9. Adoption status of sample households for main farm inputs .......................................................58 

Table.4.10. Determinants adoption level of legumes technology .................................................................60 

Table. 4.11. Distribution of estimated propensity score ...............................................................................66 

Table.4.12 Performance of different matching estimator .............................................................................70 

Table .4.13.Pscore and covariates balance ....................................................................................................72 

Table .4.14. Chi-square test for the joint significance of variables ...............................................................72 

Table 4.15.Treatment effect on HH income .................................................................................................73 

Table .4.16. Average treatments effects ........................................................................................................74 

Table.4.17. ATT estimation with nearest neighbor matching analytical standard errors .............................75 

Table.4.18. ATT estimation with nearest neighbor matching bootstrapped standard error ..........................75 

Table.4.19. ATT estimation with in radius matching analytical standard error ............................................76 

Table.4.20 ATT estimation with in radius matching bootstrapped standard error .......................................76 

Table.4.21. ATT estimation with in kernel bootstrapped standard errors.....................................................77 

Table.4.22. ATT estimation with the stratification analytical standard errors ..............................................78 

Table.4.23. ATT estimation with the stratification bootstrapped standard errors .........................................78 

 

 

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List of Figure  

Figure 2.1 Diagram on conceptual framework .............................................................................................25 

Figure 4.1. psgraph propensity score matching distribution adoption of legumes technology .....................65 

Figure .4.2 propensity score before matching legumes technologies adoption ............................................66 

Figure-4.3. Propensity score graph propensity score matching distribution adoption of legumes technology 

after match. ...................................................................................................................................................67 

Figure .4.4. Propensity score after matching legume technologies adoption ................................................68 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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ACRONYMS AND ABBREVIATIONS 

CSA                       Central Statistics Agency  

EATA                   Ethiopian Agricultural Transformation Agency  

EEPA                   Ethiopia Export Promotion Agency  

GDP                     Gross Domestic Product  

GPS                       Generalized Propensity Score  

GZANRDD          Guraghe Zone Agricultural & Natural Resource   Development Department 

GZFEDD                Guraghe Zone Finance and Economics Development Department 

Ha                          Hectare  

ICARDA               International Center for Agricultural Research in the Dry Areas  

ICRISAT               International Crops Research Institute for the Semi-Arid Tropics 

IFPRI                    International Food Preparation Research Institution   

MOARD               Ministry of Agriculture and Rural Development 

NBE                      National Bank of Ethiopia  

NERICA              New Rice for Africa  

OPHI                   Oxford Poverty and Human Development Initiative   

PSM                    Propensity Score Matching  

SNNPRS            Southern Nation Nationality Peoples Regional State  

USAID              United States Agency for International Development  

USD                   United States Dollar  

WB                    World Bank  

WDR                 World Development Report 

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Abstract 

The importance of agricultural technology in enhancing production and productivity can be 

realized when yield increasing technologies are widely been used and diffused. Standing from this 

logical ground, this paper aimed to evaluate the impact of legumes technologies adoption on farm 

income on Guraghe Zone in Ethiopia. This study used cross sectional data that acquired from 

total of 204 households which were randomly and proportionately sample from 9 major legumes 

producer kebeles in three district of Guraghe zone stratifies sampling techniques.  Logit model 

was used to estimate to identify underlying factor that determine adoption of legume technologies 

(improve legume seed, fertilizers, chemicals, inoculants and farming techniques). PSM model was 

used to estimate to evaluate the impact of legume technologies adoption of farmers’ income. 

Descriptive statistics and econometric models were used to analyze the data.  The results from 

logit model indicate that educational level of household, the household headed, member of 

cooperative association, to advices to agricultural extension services, size of cultivated land for 

crop, credit access, off-farm participation and tropical livestock unit positively significant adopt 

of legume technologies adoption. If female of household headed and plot size for legumes crop 

cultivated purpose negatively significant influence of legume technologies adoption. Impact 

assessment of the marginal effect showed that farmers who had adopted legume technologies 

could enhance their annual total income level by 46.6% and the crop income particularly from 

grain legume has been increased by 88%. What about the impact based on the findings, the study 

suggests that strengthening the promotion of full package technology adoption will have crucial 

role towards improving the livelihood of households in the study area. In doing so, managing the 

possible influencing factors that affect adoption of legume technology should be a prerequisite. 

 Key words: Legumes Technology, technology adoption, Logit ,  PSM Model, Guraghe, Ethiopia 

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CHAPTER ONE 

  INTRODUCTION

1.1. Background of the study  

Globally, agricultural development is expected to have the potential of helping in trimming down 

poverty for 75% of the world's poor, who lives in rural areas and work mainly in farming. It can 

also contribute in raising incomes, improving food security and benefitting the environment. 

Agriculture accounts for one-third of GDP and three-quarters of employment in Sub-Saharan 

Africa (WB, 2013). 

Ethiopian economy is fundamentally agrarian where the performance of the agriculture sector 

dictates the entire economic performance of the country. Despite the reportedly growing 

importance of the manufacturing and the industry sectors, agriculture continues to account for 

nearly 36.7% of the gross-domestic product (GDP), more than 72% of labor employment and 

80.84% of foreign export earnings (NBE , 2015/16). 

Principal crops of Ethiopian agriculture include coffee, legumes, oilseeds, cereals, potatoes, 

sugarcane, and vegetables. The major staple foods in Ethiopia are grains (e.g. teff, wheat, barley, 

corn, sorghum, and millet), legumes, oils, ensete, fruits and vegetables. Grains are the most 

important field crops and the chief element in the diet of most Ethiopians. Exports are almost 

entirely from agricultural commodities, and coffee is the largest foreign exchange earner, the 

next sesame and legumes are estimated to be the third most important export crop in Ethiopia 

just next to sesame (MOARD, 2008). 

Pulses/Legumes are the second most important element in the national diet and a principal 

protein source. They are boiled, roasted, or included in a stew-like dish known as wot, which is 

sometimes a main dish and sometimes a supplementary food. Pulses, grown widely at all 

altitudes from sea level to about 3,000 meters, are more prevalent in the Northern and Central 

highlands. Pulses were a particularly important export item before the revolution. Major pulse 

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crops grown in the country are chickpea, haricot beans, lentils, faba bean and peas. Legumes in 

Ethiopia cover 12.42% of the total cultivated land and provide 11.89% of the total crop 

production of the country, which is 2.67 million tons (CSA, 2015). 

According to Legese (2004), feeding the rapidly growing population of Ethiopia by means of 

extensive farming is becoming unachievable due to limited opportunities for area expansion. 

Rather, the option that looks more likely is increasing yield through intensification, which 

involves adoption of different improved agricultural practices (Million and Belay, 2004). Despite 

the significant contribution of adoption of agricultural innovations for increasing production and 

income, adoption rate of modern agricultural technologies in the country is very low (Di Zeng et 

al., 2014 and Berihun et al., 2014). In order to raise the agricultural production and productivity, 

raise income, reduce poverty and to enhance the food security and children nutrition, on a 

sustainable basis in the developing countries like Ethiopia, large-scale adoption and diffusion of 

new technologies is very essential (Tsegaye and Bekele, 2012; Degye et al.,2013 and Di Zeng et 

al.,2014). 

  Despite the crucial role of legumes for poverty reduction and improving food security in 

Ethiopia, lack of technological change and market imperfections have often locked small 

producers into subsistence production and contributed to stagnation of the sector (Shiferaw and 

Teklewold, 2007). Even if several research and development efforts have attempted to facilitate 

productivity growth for small farmers, some of these efforts did not stimulate large-scale 

technology uptake and diffusion. This is mainly because of the limited understanding of farm 

level constraints, farmer preferences and the challenges related to better coordination of input 

supply and delivery of new technologies and market linkages for small producers. Therefore, this 

study aimed at filling the gap on identification of determinants behind lower adoption of   

legume technologies and evaluating the impacts of   adoption on the farmer′s incomes of 

household. 

The general motivations of  this paper is that eight main legumes widely grown in Ethiopia are: 

faba bean, chickpea, field pea, grass pea, and lentil for the cooler highlands and on haricot bean, 

groundnut   and soybean, for the warmer mid and low altitudes of the country. Overall, faba bean 

is the largest leguminous crop in Ethiopia followed by haricot bean and chickpea. Field pea and 

grass pea serve as an important food security crop in many areas and still account for more than 

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300,000 tons each. In total, Ethiopian legume production accounts for almost 3 million tons. At 

present, virtually all legume production in Ethiopia is undertaken by smallholder farmers with 

limited external inputs on plot sizes of up to 1.5 ha (CSA, 2013). 

Legumes grown in Ethiopia 2016/17 (2009 E.C.) covered 12.92% (1,624,773.23 hectares) of the 

grain crop area and 10.13% (about 29,442,665.89 quintals) of the grain production was drawn 

from the same crops. Faba beans, haricot beans (white), haricot beans (red), chick peas, lentils, 

grass pea, soya bean and  groundnut were planted to 3.40% (about 427,696.80 hectares), 0.63% 

(about 78,910.13 hectares), 1.68% (about 211,292.30 hectares), 1.79% (about 225,607.53 

hectares), 0.9%(about 113,684.63 hectares), 1.2%(about 151,268.58 hectares), 0.29%(about 

36,635.79 hectares) and 0.59%(about 74,861.23 hectares) of the grain crop area  

According to (IFPRI, 2010) report pulses are grown throughout the country. However, the lion 

share production is concentrated in the Amhara and Oromiya regions, which together account for 

92 percent of chickpea production, 85 percent of faba bean production, 79 percent of haricot 

bean production, and 79 percent of field pea production. The SNNPRG stands third in overall 

production of pulses by producing 10% of the faba bean, 18% of the filed pea, 3% of chickpea 

and 15% of haricot bean. Guraghe Zone in 2017/2018 productive season annual achievement 

report the main crop on average area of land  "belg" & "meher" 175,505.5 hectares cultivated, of 

which legumes 25,428.75 hectares sow under legumes crop about 6%  of the total sow different 

variety of certified legumes seed used. 

1.2. Statement of the Problem 

Based on the Multidimensional Poverty Index, Ethiopia ranks the second poorest country in the 

world just ahead of Niger (OPHI, 2015). Ethiopian economy is highly agriculture-dependent and 

it is characterized as subsistence-oriented. Use of improved seed holds the key to sustainable 

food crop production across the globe because seed is the basic agricultural inputs that brought 

improvement of agricultural productivity (Pelmer, 2005). Likewise, chemical fertilizer is 

regarded as a crucial component of farm inputs by small-scale farmers. In ideal farming 

condition, farmers should use fertilizer and improved seed together in order to achieve the 

optimal return of crop production (Nigussie et al., 2012).   

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 The development in cropping system helps for the improvement of standard of living of 

smallholder farmers who took the major part of the nations of Ethiopia. Despite the fact that 

farming technologies such as improved seed and chemical fertilizer is considered as contributing 

determinants for development of the worldwide agriculture, Ethiopia has chronicle poverty and 

food insecurity problem for a sustained period of time. One of the reasons for the prevalence of 

food insecurity is low rate of adoption of improved farm inputs. In fact different agricultural 

technologies have been released to improve productivity of smallholder farmers in the country 

(Hailu, 2008). But the national adoption rate could not exceeded 11% in major farming inputs 

such as improved seed and chemical fertilizers. As a result, low crop production and household 

income remained to be endemic problems in the country (Paul and Shahidur, 2012). 

The study area was conducted in 73.2% of residents are severe poor in the region (OPHI, 2017) 

of Ethiopia, SNNPRG regional state specifically in Gurage Zone. Even if so much has been done 

in developing improved technologies of legumes and in disseminating them in different parts of 

Ethiopia, understanding the drivers of adoption and the structure of the diffusion process is an 

essential component of any research aimed at tackling the challenges faced by resource poor 

households. There are in fact many studies on the adoption and impact of agricultural 

technologies (Asfaw et al., 2011; Tsegaye and Bekele, 2012; Degye et al., 2013 and Di Zeng et 

al., 2014). However, most of them focused only on identifying determinants of adoption and in 

analyzing the impact on wellbeing by considering adoption as a binary treatment (Asfaw et al., 

2011; Tsegaye and Bekele, 2012). But there are prospects to predict the factors affecting 

adoption level via constructing semi-experimental scenarios.  This study is designed to impact of 

technologies adoption of legumes producing on farmers’ income.  

Leguminous crops are the second crops both in production and consumption in Ethiopian 

farming system next to cereals. It has also the big market share in the export market and 

generating foreign currency for the national economy. Leguminous are the ultimate source of 

protein in diet complements of these substance-farming communities but are rarely the major 

focus of attention. Predominantly legume farming is carried out traditionally without the relief of 

agricultural technology. In recent years, the adoption of agricultural technologies such as 

improved seed, fertilizer, and farming equipments being utilized by the farming community but 

still the rate of adoption is in its lower level. More importantly, Gurage zone where this study 

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conducted, dominantly known in cereal production and most of the adoption studies associated 

with cereal crops while legumes are disregarded. Therefore, this study initiated to choose the 

study area to fill the mentioned knowledge gap. Furthermore, as technology has a dynamic 

nature, its effect varies along with time and hence continuous updating adoption effect is 

required then technologies adoption means as a full package form. In this regard, it is 

fundamental to researchers to measure the outcome of agricultural technology along with time 

and as a full package form.   

Variations in level of adoption of technology can be a result of generalization of farmers by 

decision makers. Farmers' initiative towards responding a technology varied due to not only on 

agro ecological determinants but also socioeconomic characteristics and technologies adoption is 

including appropriate improve seed varieties, appropriate chemical fertilizers, row planting, 

inoculants and integrated pest management use as a package form but not separately use. 

Drawing key characterization elements among farmers will have an indispensable importance 

towards customizing technology adoption. Thus, it is better to develop a typology of legume 

farmers based on their current status in technology adoption. 

1.3. Research Questions   

The study addresses the following major research questions: 

1. What are the factors that determine adoption of legume technologies? 

2. What is the impact of adoption of haricot bean, chickpea, faba bean and field pea 

varieties on households’ income? 

1.4. Objectives of the Study 

The main objective of this study was to evaluate the impact of legumes technology adoption on 

farmers’ income in Guraghe zone of Ethiopia. 

The specific objectives of the study are to: 

 Identify the determinants of adoption of haricot bean, chickpea, faba bean and field 

pea technologies. 

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 Evaluate the impact of adoption of haricot bean, chickpea, faba bean and field pea 

varieties on households’ income. 

 To identify the typology of farmers based on haricot bean, chickpea, faba bean and 

field pea producing their current technologies adoption status. 

1.5. Significance of the study  

Development of agricultural technology by itself is not enough to bring growth of farmers’ and 

improvement in livelihood. There should be an enabling policy environment which creates the 

condition where farmers have access to improved technologies and also to increase their 

production and productivity (Sitotaw, 2006). Dealing on adoption of agricultural technology 

from farmers’ livelihood perspective has a significance to draw the clear picture for policy 

makers involved in development and dissemination of new technologies.  

The result of this study could help stakeholders (agriculture offices, development partners, 

research institutions) to identify the pivotal issue to address the technologies in attaining the 

ultimate objectives. In addition, identifying determinants which determine success or failure of 

technology adoption has importance to guide future research. This study expected to point out 

the main determinants that influence the adoption level.    

Technologies to be recommended for adoption should insure the livelihood of farmers. And 

hence, impact studies enables researcher to identify their end towards the most pressing issues. 

With this respect, the study shows to what extent adoption of technology influence their 

livelihood. Once knowing the impact of technology, designing appropriate policy and extension 

service that is directed towards fostering the adoption level by identifying the potential factors is 

important. Besides, it is expected that this study would serve as introductory to undertake 

detailed and comprehensive studies in related scenario.  

Therefore, the study of adoption impact and determinants impeding the adoption of legume 

technology would provide useful insight to policy makers, strategic planners, and administrators 

in the formulation of appropriate agricultural policy. This study also serves as a springboard for 

further detail research in legume grain farming. 

 

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1.6. Scope of the study 

This study was in three district of Guraghe zone, which found in the SNNPR State. Farmers’ 

preference for of legume technology adoption packages is influenced by many factors.  During 

this analysis, factors influencing adoption of legumes   technologies with relevancy chemical fertilizer 

and pesticide by legumes producer of in Gurage Zone were the subject of the study. The study tried to 

assess that factors adoption of the technology, the intensity of use of the technology within the study area 

and to look at whether or not technology adoption led to higher financial gain to legume crops growers in 

Gurage Zone. And here specific issues connected with land use, socio-economic condition of home farms, 

and therefore the practice of legumes production with reference to the adoption of chemicals like fertilizer 

and pesticide; and opportunities of using those technologies in enhancing production have assessed. 

However, since this study is limited to technology adoption, it cannot provide detailed information about 

other related problems related to rural agriculture of the study area. Lack of adequate historical data is 

also another problem in this study. The available information also varies in many ways from year to year.  

In addition to this local problem, lack of related literature about legumes technologies adoption within 

Gurage Zone agriculture in general and within legumes  producers in particular is one of the significant 

limitations for this study. Therefore, the study has undertaken to fulfill its objectives inside the mentioned 

constraints.  

1.7 Limitations of the study  

The study was limited to three districts in Gurage Zone. It was designed in such a way that the 

sample was representative of the food legumes production potential of the area and yet it can 

hardly have sufficient external validity given the size of Gurage Zone both agro ecologies 

heterogeneity of the farming communities within. The study was prepared based on cross-

sectional data and hence does not look into the temporal dynamics of adoption of the 

technologies and the impact thereof. In addition, the impact assessments were limited to 

improved varieties despite the fact that the remaining technologies are usually recommended as a 

package. 

1.8 Organization of the study  

The rest of this thesis is organized in five sections. Section two, dealt with review of literature 

that includes definitions of concepts of legumes crops, adoption, agricultural technologies 

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adoption, stage of adoption, and theoretical and empirical reviews. Section three has presented 

methodology with a brief description of the study area, sampling method, and methods of data 

analysis. The results and discussion more detail in section four. Section five has presented 

conclusions and recommendation. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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CHAPTER TWO 

LITERATURE REVIEW 

2.1. Definition of Basic Concepts 

        2.1.1. Definition of leguminous crops 

 Leguminous crops are sources of protein for humans and animals, vegetable oil for human 

consumption, for human health, and resources for industries and bio-fuels.  They are important 

forage crops, groundcovers, and timber resources. Legume plants are notable for their ability to 

fix atmospheric nitrogen, due to a mutualistic symbiotic relationship with bacteria (rhizobia) 

found in root nodules of these plants. Legumes' nitrogen fixation ability resupplies depleted soil 

with nitrogen. Usage example: "The use of a leguminous crop, such as alfalfa, can provide a 

significant amount of nitrogen to subsequent crops in rotation and can replace the application of 

synthetic nitrogen fertilizer. This means reduced fertilizer cost and reduced fossil fuel 

consumption to produce the fertilizer". Grain legumes, also called pulses, are plants belonging to 

the family leguminosae (alternatively Fabaceae) which are grown primarily for their edible 

seeds. These seeds are harvested mature and marketed dry to be used as food or feed or 

processed into various products. Being legumes, these plants have the advantage of fixing 

atmospheric nitrogen for their own needs and for soil enrichment, thereby reducing the cost of 

fertilizer inputs in crop farming. Crops that are harvested green for forage and for vegetables are 

excluded, as well as those grown for grazing or green manure. Also excluded are the leguminous 

crops with seeds which are used exclusively for sowing, such as alfalfa and clover (FAO, 2010).  

2.1.2. Impact of Legume  

There are several factors that can help explain why the uptake and impact of legume technology 

is less well documented than is the case for some other major staples. Some are related to the 

relative importance of legumes and hence the absolute contribution of changes in legume 

technology and the importance that farmers may accord to opportunities for innovation. A 

second set of factors is related to the mechanisms for promoting legume technology and 

particularly the limitations of national seed systems for diffusing new varieties. A third set of 

factors relates to the way that statistics are collected about legume technology use (Robert Tripp, 

2011). 

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 2.1.3. Importance of legumes 

Legumes are known to perform multiple functions. Grain legumes provide food and feed, and 

facilitate soil nutrient management and mitigating climate change. Herbaceous and tree legumes 

can restore soil fertility and prevent land degradation while improving crop and livestock 

productivity on a more sustainable basis. Thus cultivation of such dual-purpose legumes, which 

enhance agricultural productivity while conserving the natural resource base, may be 

instrumental for achieving income and food security, and for reversing land degradation. 

Ethiopian farmers’ produce different legume crops mainly for food and feed, to fetch cash, and 

more importantly to restore the fertility of the crop land. Farmers' participation on pulses 

cultivation in the country has been increased nearly by double from 4.5 to 8.5 million farmers for 

the last nearly 20 years. Legumes contribute to smallholder income, as a higher-value crop than 

cereals, and to diet, as a cost- effective source of protein that accounts for approximately 15 

percent of protein intake. Moreover, pulses offer natural soil maintenance benefits through 

nitrogen-fixing, which improves yields of cereals through crop rotation, and can also result in 

savings for smallholder farmers from less fertilizer use. It also contributes significantly to 

Ethiopia’s balance of payments. 

2.1.4. Definition of Adoption  

The adoption of an innovation within a social system takes place through its adoption by 

individuals or groups. According to Federet al. (1985), adoption may be defined as the 

integration of an innovation into farmers‟ normal farming activities over an extended period of 

time. Dasgupta (1989) noted that adoption, however, is not a permanent behavior. This implies 

that an individual may decide to discontinue the use of an innovation for a variety of personal, 

institutional, and social reasons one of which might be the availability of another practice that is 

better in satisfying farmers' needs. 

Feder et al. (1985) classified adoption as an individual (farm level) adoption and aggregate 

adoption. Adoption at the individual farmers' level is defined as the degree of use of new 

technology in long run equilibrium when the farmer has full information about the new 

technology and its potential. In the context of aggregate adoption behavior they defined diffusion 

process as the spread of new technology within a region. This implies that aggregate adoption is 

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measured by the aggregate level of specific new technology with a given geographical area or 

within the given population. 

Rogers (1983) defines the adoption process as the mental process through which individual 

passes from first hearing about an innovation or technology to final adoption. This indicates that 

adoption is not a sudden event but a process. Farmers do not accept innovations immediately; 

they need time to think over things before reaching a decision. The rate of adoption is defined as 

the percentage of farmers who have adopted a given technology. The intensity of adoption is 

defined as the level of adoption of a given technology. The number of hectares planted with 

improved seed (also tested as the percentage of each farm planted to improved seed) or the 

amount of input applied per hectare will be referred to as the intensity of adoption of the 

respective technologies (Nkonya et al. 1997). 

2.1.5. Agricultural Technology Adoption  

The concept of technology adoption could be better conceptualized through understanding the 

difference between technology adoption and diffusion, which are highly interrelated but distinct 

concepts. Adoption is related to private utility mechanisms (Federet al., 1985; Feder and Umali, 

1993) and can be defined as “the choice to acquire and use a new invention or innovation” (Hall 

and Kahn, 2002), whereas “diffusion is the process by which an innovation is communicated 

through certain channels over time among the members of a social system” (Rogers, 

1983).Technology adoption is measured at one point in time while technology diffusion is the 

spread of a new technology across population over time (Thirtle and Ruttan, 1987).  

Rogers (1962) summarized the above definition of technology diffusion using the following four 

core elements: (1) the technology that represents the new idea, practice, or object being defused, 

(2) communication channels which represent the way information about the new technology 

flows from change agents suppliers (extension, technology suppliers) to final users or farmer, (3) 

the time period over which a social system adopts a technology and (4) the social system. 

Overall, the technology diffusion process essentially encompasses the adoption process of 

several individuals or farmers over time. Further, another study by Rogers (1995), defined the 

rate of adoption (speed of adoption) of a given technology. It is the relative speed with which 

farmers adopt technology; in this definition consideration is given to the element of a given 

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technology to the farmers. According to Feder et al. (1985), adoption can be categorized into 

individual or aggregate adoption. They defined individual adoption as the degree of use of a new 

technology in a long-run equilibrium when the farmer has full information about the new 

technology and its potential, whereas aggregate adoption is defined as the process of spread of a 

technology within a region. Further, their studies distinguished technologies that are divisible 

and non-divisible. Divisible technology in terms of resource allocation requires the decision 

process to involve area allocations as well as levels of use of the rate of application (for instance, 

improved seed, chemical fertilizer, and herbicide and pesticide). Whereas, technologies that are 

not divisible in term of resource allocation require how much resource to be allocated to the new 

and old technologies (for instance: mechanization, irrigation and better farm management 

practices such as uses of recommended agronomic practices). The application of the concept of 

adoption in empirical studies, therefore, requires making distinction between technologies which 

are divisible and non-divisible. This is because often times the nature of the technology dictates 

the terms on which adoption is conceptualized and analyzed. Therefore, adoption of improved 

agricultural technologies such as improved variety and/or chemical fertilizer can therefore be 

categorized as divisible technology, defined as farmers who planted at least one improved maize 

variety and/or use chemical fertilizer for maize, and non-adopters are those who did not grow 

any of the improved maize variety and/or used chemical fertilizer in maize farming. Adoption of 

recommended agronomic practices such as the use of timely planting, cropping system and seed 

spacing are categorized as a non-divisible technology, measured in terms of the status of use by 

smallholder farmers for planting. 

Rogers (1962) developed a technology adoption model, generalized the use of it in his book 

entitled as “Diffusion of Innovations”. He used the model to describe how technology spread in 

the social system. The technology adoption model describes the adoption or acceptance of a new 

product or technology. The process of adoption over time is typically illustrated as a classical 

normal distribution or bell-curve and use the mean and standard deviation to divide the normal 

adopter distribution categories. The model indicates that the first group of people to use a new 

product or technology is called innovators, followed by early adopters. Next come the early and 

late majority, and the last group to eventually adopt a product are called laggards. While 

explaining each of the categories the study by Rogers (1962) defined as: 

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 Innovators: These are the first individuals to adopt a given technology and hence they are 

willing to take risks, youngest in age, have the highest social class, have great financial liquidity, 

are very social and have closest contact with scientific sources and interacting with other 

innovators. 

Early adopters: These are those groups of individuals who are typically younger in age, have a 

higher social status, have more financial liquidity, advanced education, and are more socially 

forward than late adopters, which means more discrete in adoption choices than innovators.  

Early majority: Individuals in this category adopt technology after a varying degree of time. 

This time of adoption is significantly longer than the innovators and early adopters. Early 

majority tend to be slower in the adoption process, have above average social status, contact with 

early adopters, and seldom hold positions of opinion leadership in a system.  

Late majority: Individuals in this category will adopt technology after the average member of 

the society. These individuals approach technology with a high degree of skepticism, and after 

the majority of society has adopted the technology. Late majority is typically skeptical about 

technology, have below average social status, very little financial lucidity, in contact with others 

in late majority and the early majority, very little opinion leadership.  

Laggards: Individuals in this category are the last to adopt a technology. Unlike some of the 

previous categories, individuals in this category show little to no opinion leadership. These 

individuals typically have an aversion to change-agents and tend to be advanced in age. Laggards 

typically tend to be focused on “traditions”, likely to have lower social status, lowest financial 

fluidity, older of all other adopters, in contact with only family and close friends. 

2. 2. Theoretical Reviews 

Impact assessment is a process of systematic and objective identification of the short and long 

term effects of intervention on economic, social, institutional and environments. Such effects 

may be anticipated or unanticipated and positive or negative, at the level of individuals, 

households, or the organization caused by ongoing or completed development activities such as a 

project or program (Rover and Dixon, 2007, Omoto, 2003). Impact assessment evaluation is the 

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extent to which a project has caused desired or undesired changes in the intended users. It is 

concerned with the net impact of intervention on individuals, households, or institutions 

attributable only and exclusively to that intervention (Baker, 2000). Impact on income is a 

reward that the owners of fixed factors of production receive as a result of allowing their land, 

capital, and labor to take part in production.  

 The very focus of impact analysis was the contrast of adopters to the counter factual non 

adopters. Therefore, measuring the marginal effect of the adoptions of the new technology over 

the traditional practice is essential. According to FAO (2000), impact assessment is done for 

several practical reasons: (1) accountability – to evaluate how well we have done in the past, to 

report to stakeholders on the return to their investment, and to strengthen political support for 

continued investment; (2) improving project design and implementation - to learn lessons from 

past that can be applied in improving efficiency of research projects; and (3) planning and 

prioritizing - to assess likely future impacts of institutional actions and investment of resources, 

with results being used in resource allocation and prioritizing future projects and activities, and 

designing policies.    

 Technological change is very important in cases where there is limited scope for increasing 

agricultural production through increased use of input of factors like land (Solow, 1957). There 

are serious complexities associated with understanding the impact pathway through which 

agricultural technology adoption might affect household welfare. This is due to the fact that crop 

production can affect household welfare directly or indirectly. Crop production affects poverty 

directly by raising the welfare of poor farmers who adopt technological innovation though 

increased production, lowering cost of production, and improving natural resource management 

(Janvry et al., 2001).  

  Impact studies essentially have the same process as technology development itself. It typically 

does this by comparing outcomes between beneficiaries and control groups (AIEI, 2010).   

Since the data for this study was obtained from survey, non experimental impact evaluation 

design is preferred using propensity score matching method of analysis. According to 

Rosenbaum and Rubin (1983) and, Heckman et al., (1998), Propensity Score Matching is a non 

experimental method for estimating the average effect on social programs. The method compares 

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average outcomes of participants and non participants conditioning on the propensity score 

values. In order to make causal inferences, random selection of subjects and random allocation of 

treatments to subjects was required. In observational studies, random limitations of an 

observational study are that there may be random selection of subjects but not random allocation 

of treatments to subjects. When there is lack of randomization, causal inferences can’t be made 

because it is not possible to determine whether the difference in outcome between treated and 

control (untreated) subjects is due to treatment difference between subjects on other 

characteristics.  

Programs might appear potentially promising before implementation yet fail to generate 

expected impacts or benefits. The obvious need for impact evaluation is to help policy makers 

decide whether programs are generating intended effects; to promote accountability in the 

allocation of resources across public programs; and to fill gaps in understanding what works, 

what does not, and how measured changes in well-being are attributable to a particular project or 

policy intervention (Shahidur et al., 2010).  

Estimating the impact of the participation – in this case adoption of legume technologies - 

requires separating its effect from participating factors, which may be correlated with the 

outcomes. This task of “netting out” the effect of the program from other factors is facilitating if 

control groups are introduced. “Control group” consists of a comparable group of individuals or 

households who did not involve in the program, but have similar characteristics as those 

participating in the program, called the “treatment group”. In theory, evaluators could follow 

three main methods in establishing control and treatment groups: randomization/pure 

experimental design; non-experimental design and quasi-experimental design. In practice, in the 

social sciences, the choice of a particular approach depends, among other things, on data 

availability, cost and ethics to experiment. In what follows, brief descriptions of the main impact 

evaluation methods mentioned above are given.  

2.3. Experimental method and Non-Experimental methods  

Experimental method is randomized method, where the treatment and control samples are 

randomly drawn from the same population. In other words, in a randomized experiment, 

individuals are randomly placed into two groups, namely, those that involve in the program or 

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those that do not involve in the program. This allows the researcher to determine the 

participation impact by comparing means of outcome variable for the two groups. In the 

contrary, non experimental approach is used in cases where program placement is intentionally 

located. Non experimental methods are frequently used in practice either because program 

administrators are not too keen to randomly exclude certain parts of the population from an 

intervention or because a randomized approach is out of context for a rapid-action project with 

no times to conduct an experiment.  

Generally, randomized evaluations seek to identify a program’s effect by identifying a group of 

subjects sharing similar observed characteristics (say, across incomes and earning opportunities) 

and assigning the treatment randomly to a subset of this group. The non-treated subjects then act 

as a comparison group to mimic counterfactual outcomes. This method avoids the problem of 

selection bias from unobserved characteristics. However, the quality of impact analysis depends 

ultimately on how it is designed and implemented. Often the problems of compliance, spillovers, 

and unobserved sample bias hamper clean identification of program effects from randomization. 

In such cases, researchers then turn to non-experimental methods. The basic problem with a non 

experimental design is that for the most part individuals are not randomly assigned to programs, 

and as a result, selection bias occurs in assessing the program impact (Shahidur et al., 2010).  

The essential idea of the before and after estimator of an impact evaluation approach is to 

compare the outcome of interest variable for a group of individuals after participating in a 

program with outcome of the same variable for the same group or a broadly equivalent group 

before participating in the program and to view the difference between the two outcomes as the 

estimate of average treatment effect on the treated. Cross-section estimators use non-participants 

to derive the counterfactual for participants in which case it becomes quasi-experimental method.  

A quasi-experimental method is the only alternative when neither a baseline survey nor 

randomizations are feasible options (Jalan and Ravallion, 2003). The main benefit of quasi 

experimental designs are that they can draw on existing data sources and are thus often quicker 

and cheaper to implement, and they can be performed after a project has been implemented, 

given sufficient existing data. The principal disadvantages of quasi-experimental techniques are 

that the reliability of the results is often reduced as the methodology is less robust statistically; 

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the methods can be statistically complex and data demanding; and there is a problem of selection 

bias.   

2.4. Propensity Score Matching   

Propensity score matching (PSM) is one of the quasi-experimental methods, which constructs a 

statistical comparison group that is based on a model of the probability of participating in the 

treatment, using observed characteristics. Participants are then matched on the basis of this 

probability, or propensity score, to nonparticipants. The average treatment effect of the program 

is then calculated as the mean difference in outcomes across these two groups. The validity of 

PSM depends on two conditions: (a) conditional independence (namely, that unobserved factors 

do not affect participation) and (b) sizable common support or overlap in propensity scores 

across the participant and nonparticipant samples. Different approaches are used to match 

participants and nonparticipants on the basis of the propensity score. They include nearest 

neighbor (NN) matching, caliper and radius matching, stratification and interval matching, kernel 

matching and local linear matching (LLM). Regression-based methods on the sample of 

participants and nonparticipants, using the propensity score as weights, can lead to more efficient 

estimates.  

PSM is not without its potentially problematic assumptions and implementation challenges. First, 

PSM requires large amounts of data both on the universe of variables that could potentially 

confound the relationship between outcome and intervention, and on large numbers of 

observations to maximize efficiency (Bernard et al., 2010). Second, related to the previous point 

one can never be entirely sure that it has actually included all relevant covariates in the first stage 

of the matching model and effectively satisfied the conditional independence assumption (CIA). 

Furthermore, PSM is non-parametric: that does not make any functional form assumptions 

regarding the average differences in the outcome. Although the first stage involves specification 

choices - e.g., functional form like logit and probit, empirical analyses tend to find impact 

estimates that are reasonably robust to different functional forms. Moreover, if unobservable 

characteristics also affect the outcomes, PSM approach is unable to address this bias (Ravallion, 

2005).  

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Irrespective of its shortcomings, PSM model was employed to evaluate the impact of adoption 

(as a binary treatment variable) on the income of household because it is very appealing to 

evaluators with time constraints and working without the baseline data that it can be used with a 

single cross-section of data.  

2.5. Generalized propensity score matching:   

Currently, propensity score matching methods are extended to be applied in settings with 

continuous treatments, where the focus is on assessing the heterogeneity of treatment effects 

arising from different treatment levels, that is, different amount of intensity of adoption of 

improved legume varieties. Generalized Propensity Score (GPS) or Dose Response Function is a 

continuous treatment estimator developed by Hirano and Imbens (2004).The GPS method relies 

on the assumption that selection into different levels of adoption of legume technologies is 

random, conditional on observable characteristics (unconfoundedness) which could be important 

determinants of intensity of adoption. In this study, generalized propensity score matching was 

employed to assess the impact of intensity of adoption legume varieties (adoption as continues 

treatment variable) on the adopter households by discarding non-adopter from the model.   

After this study to shows that the difference between adoption of legumes technologies adopters 

and none adopters based on annual income & income from legumes crops. According to this the 

number of legumes technologies adopter increase then after agricultural production & 

productivity increases.   

2.6. Empirical Review literature  

As is the case in many developing countries with an agrarian economy, agricultural technology 

adoption has got a number of processes. It has both spatial and temporal dimension. It is argued 

that technology adoption is not a one of static decision rather it involves a dynamic process in 

which information gathering, learning and experience play pivotal roles particularly in the early 

stage of adoption and diffusion (Assefa and Gezaghegn, 2010).   

Technology can be adopted when it is found to be beneficial while dropped over time if loss is 

entertained due to increasing cost of inputs, falling of yields or shift to other more profitable 

technology (Dinar and Yaron, 1992). There are various reasons that brought agricultural 

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technologies to be adopted or brought for failed to do adoption. Quite much of the studies have 

been generated on determinants of technology adoption both domestically and internationally. 

Farmers move from learning to adoption to continuous or discontinuous use over time. The 

characteristics of both the user and the technology are important in explaining adoption behavior 

and the pathway for adoption. The lag between learning and adoption, and the possibility of 

discontinuation imply that a longer period will be required for the majority of farmers to use the 

technology than if adoption was a one off decision leading to continuous use. This picture has 

been clearly demonstrated by the adoption process of the technology in the four regions of 

Ethiopia considered in this study.   

The study conducted in Ethiopia and western Kenya using probit analytical model shows that 

gender, agro-climate zone, manure use, hired labor and extension service has a significant effect 

towards adoption of improved seed and fertilizer (Salasya et al., 1998, Cropenstedt et al., 2003). 

On the other hand a study conducted in the coastal low lands of Kenya shows that non 

availability and high cost of seed, unfavorable climate conditions, perception, and insufficient 

soil fertility has a negative and significant effect on adoption of technology. 

The study conducted in Morena district of India, on wheat production, found that knowledge of 

farmers which may be acquired through education, training, and availability of information and 

the credit facility has a significance positive contribution to the adoption of improved technology 

(Kansana et al., 1996). Nkonya et al.,(1997), analysis factors affecting adoption of improved 

maize seed and fertilizer in Northern Tanzania indicated that farm size, education and frequency 

of visits by extension agents significantly and positively influenced maize seed adoption where 

as the factors such as farmers’ age, family labor and yield variability have not significantly 

influenced improved maize seed adoption. Batz et al., (1999) a study conducted in Meru district 

of Kenya to find out factors affecting rate and speed of adoption of technology, less risky 

technology is preferable and easily adopted.   

Misfin (2005), on his study carried out to determine factors influencing adoption of triticale in 

Farta wereda of Amhara region using Logistic regression model, maximum likelihood estimation 

procedure, traced that distance to market center, distance to all weather road, access to leased-in 

land, perception about superiority of yield of triticale, livestock holding, off/non farm income 

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and input price were found to influence farmers adoption decision of triticale (wheat crop).  

According to Yanggen et al., (1998), in Africa fertilizer application is determined by human 

capital (basic education, extension and health); financial capital (income, credit and assets); yield 

response (bio-physical technology and extension), basic services (infrastructure and quality 

control) and input output price (structure conduct and performance of subsector, competition and 

equity). 

Foyed et al., (1999), in a study of adoption and associated impact of technology; conducted in 

the western hill of Nepal draw that a balanced investment in research and extension is needed to 

ensure adoption at the household level. The study found that the typical reason for failing of 

adoption is either lack of know how or supply of the technological inputs. So, in conditions 

where official sources are not available, farmer to farmer interaction is important. Farmer to 

farmer information flow can be built on by extending by the involvement of farmers in 

technology development and by developing methods that enables to enhance their current roles 

in technology dissemination.   

 Yu et al., (2011), in a study conducted on cereal technology adoption in Ethiopia, to examine the 

extent of adoption of fertilizer seed technology package and factors affecting the adoption of 

same using nationally representative secondary data, found that variables affecting the adoption 

of the new technology, like access to extension service, the level of adoption at the district level, 

and the experience of farmers using fertilizer in other crops, have a significant effect on the 

probability of accessing fertilizer and improved seed by farmers. Specialization, together with 

wealth and risk aversion, also plays a major role in explaining crop area under fertilizer, which 

should be related to better access to technology-related knowledge.  

 According to Feder et al., (1982) the conventional explanations for the sequential adoption 

process are: lack of credit, limited access to information, aversion to risk, inadequate farm size, 

and inadequate incentive associated with farm tenure arrangements, insufficient human capital, 

absence of equipment to relieve labor shortage, and inappropriate transportation infrastructure. 

Hailu (2008) used the probit and Tobit models to examine factors influencing adoption and 

intensity of teff technologies in Ethiopia. The study revealed that farmers education level, 

frequency of DA officer visits, credit availability and knowledge of farmers have positive 

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influence towards technology adoption and adoption intensity whereas variables like age of 

farmers, number of family labor, frequency of risk were inhibiting adoption of technology.  

 Saha et al., (1994) divide the adoption process into three stages: information collection, decision 

on whether or not to adopt, and decision on how much to adopt. Filho (1997) applied both probit 

and logit models and duration analysis to explain the maize growers’ behavior in the adoption of 

new technologies. He found that both economic (such as yield level, income, and cost of 

adoption) and non-economic (such as behavior of adopters' factors influences a farmer’s decision 

to adopt the new technologies of maize. It shows that decision to adopt the sustainable 

technologies for maize is positively related to his/her contact with government/non-government 

organizations, the farmer’s understanding of the negative effect of chemicals, the available labor 

force in the family and the soil fertility. Filho (1997) further concludes that the adoption is 

negatively related to farm size. According to Foster and Rosenzweig (1996), agricultural 

technology adoption decision was seriously been determined by imperfect information, risk, 

uncertainty of institutional constraints, human capital, input availability and infrastructural 

problems. 

The study by Degye (2013) in Eastern and Central highlands of Ethiopia identified the 

determinants of adoption of chemical fertilizer, high yielding crop varieties and improved 

livestock breeds and their interdependence by using multivariate probit model. The results verify 

that adoptions of these three agricultural technologies were significantly interdependent of each 

other. Uses of chemical fertilizer were positively affected by use of irrigation water, gross 

agricultural income, distance to research institution and farming system. Whereas the adoption of 

high yielding variety were positively determined by land allocated to cash crops, gross 

agricultural income, distance to research institution and farming system; where adoption of 

improved livestock breeds were positively affected by amount of cultivated land and distance to 

research institution while it negatively affected by farming experience of household and distance 

to nearest road.  

Similar studies were also done on factors affecting the adoption and intensity of use of improved 

forages in South Wollo, north east highlands of Ethiopia by Hassen (2014), using the double 

hurdle model. The finding of this study suggests that the likelihood of adoption were enhanced 

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by age of household head, ownership of livestock, and access to credit and extension service. 

Where farm size, off/non-farm income, distance to all weather roads and markets, distance to 

input and credit offices were found to adversely affecting the likelihood of adoption of improved 

forages. The intensity of adoption of improved forages was enhanced by sex of household head 

[being male], labor availability, and farm size where it is adversely affected by household size, 

off/non-farm income, distance to all weather roads and markets and distance from development 

agent office. Similarly, the study by Abreham and Tewodros (2014) identified level of education, 

social participation, access to credit, labor availability, farm size, achievement motivation and 

market distance as the major socio economic factors that affect the intensity of adoption of 

coffee in Yerga Cheffe District in Gedeo Zone of SNNP Regional State of Ethiopia by using 

Tobit model.  

Using Logit model, Debelo (2015) assessed factors influencing adoption of Quncho tef in Wayu 

Tuqa district of Ethiopia. Results revealed that family labor availability, participation of farmers 

in agricultural trainings, education level of the household head, livestock holding (TLU), 

farmer’s ability of meeting family food consumption and frequency of extension contact were 

enhancing the decision to adopt Quncho teff. In this study, age of household head, owning oxen 

and distance from household residence to market center was found to influence adoption of 

Quncho tef negatively.  

Similarly, Berihun et al. (2014) examined the determinants of adoption of chemical fertilizer and 

high yielding varieties in Southern Tigray Ethiopia by using Probit model. Sex of household 

head, land ownership, use irrigation, access to credit, contact with extension worker and 

participation in off farm activities were found to be positively affecting the adoption of chemical 

fertilizer, whereas plot distance, distance to the nearest market and livestock holding affected the 

adoption negatively. The adoption of high yielding varieties was positively affected by land 

ownership, access to credit, use of irrigation and livestock holding where as it is negatively 

affected by age of household head and distance to the nearest market.   

As discussed above, the empirical evidence on the adoption and its determinant in Ethiopia 

generally indicate that the adoption rate of agricultural technologies was relatively low with 

considerable personal and spatial heterogeneities. They suggest that the rate and intensity of 

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adoption of agricultural technologies is notably influenced by socioeconomic factors such as 

livestock holding, farm size, active family member and so on and other organizational factors 

such as access to credit, input and output market, agricultural extension services etc. Even 

though there are many adoption studies throughout Ethiopia, there is a clear bias towards major 

cereal crops or key cash crops within the geographic scope of the crops’ ideal agro-ecologies. 

Unlike previous studies, this study focuses on estimating the determinants of adoption of legume 

technologies on farmer’s income in Guraghe Zone of Ethiopia where legumes are not the 

dominant crops. In addition, most of the studies above were in locations with entirely different 

socioeconomic and biophysical features compared to Guraghe Zone. 

In general, the above reviewed study shows that most of the researchers focused on improved 

seed and chemical fertilizers, improved seed varieties based on agro climates it depends on these 

variables positively and negatively significantly influence. These researchers the study of 

technology adoption more focus improve seed varieties & chemical fertilizers or improve seed 

varieties and other technologies were separately used. Technologies mean dynamics to change 

over times.  The best of my special attention technology adoption as a full package means, 

improve appropriate seed varieties, appropriate chemical fertilizer application, row planting 

based on spacing , integrated pest management (IPM), proper agronomy management to be done 

from site selection up to storing on time used on each household level, both agricultural practices 

incorporate as packaging form  but not separately use . Those practices always to be done 

appropriate time because one of miss the technologies component fails the new technologies.      

2.7. Conceptual Framework 

The conceptual framework of adoption and impact of legumes technologies on the welfare of 

farm households starts with identification of the driving factors for adopting improved 

agricultural technologies. These factors include external dynamics (environmental factor, for 

example, like unfavorable weather condition, land degradation, erratic rainfall, and low fertility 

status of land), demographic characteristics of the household (e.g. age, education level etc.) and 

other social and institutional factors (availability of new information, availability of new 

technology, availability of credit etc.).  

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Then after, adoption of legume technologies is expected to have considerable economic 

advantage in terms of increase in yield, increase in marketable surplus of farm households and 

ultimately in reducing food security and poverty. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Diagram on conceptual framework 

 

 

 

 

Figure 2.1 Diagram on conceptual framework 

Source own computation survey data 2019 

 

legumes 
technology 
adoption

Household  
characteristics 

(eg) 

Sex of hh head

Education of hh 
head

Economic  
factors(eg)

Farm size

Livestock holding

Off-farm  activity

Out put (eg)

Annual income

Income from 
legumes crops

Institution factor (eg)

Member of farmers 
coop asso

Access to credit

Access to extension 
serviecs & market

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CHAPTER THREE 

RESEARCH METHODOLOGY 

3.1. Description of the Study Area 

Gurage Zone  is one of  the 15 administrative Zone including Hawassa city and 4 special woreda 

in Southern Nations, Nationalities, and Peoples' Region (SNNPR) state in south- Western 

Ethiopia situated at  8ْ10N 38ْ15E. It has 13 districts and two towns. Guraghe is bordered on the 

Southeast by Hadiya and Yem special woreda, on the West, North and East by the Oromia 

Region, and on the South east by Silt'e CSA (2009). 

 The altitude in Guraghe zone ranges from 1000 to 3600 m.a.s.l. The Zone has four agro-

ecological zones essentially based on altitude, namely extreme highlands (above 3000 m.a.s.l), 

highland (2300 to 3000m.a.s.l), midland (1800 to 2300 m.a.s.l), and lowland (below 1800 

m.a.s.l) covering 4.1%, 27.5%, 65.3%, and 3.1% of the zone, respectively. The natures of 

topography in the zone exhibit, broadly speaking, three categories: The mountainous highland 

represented by the Guraghe mountain chain, dividing the zone east to west, having an elevation 

of 3600Mts. The plateau flat lands, the area covered by "Amora and Ambusa meda". The low 

stretching area, the western fringe of the rift valley and the Wabe Gibe valley having an 

elevation of 1000Mts.The area receives an average annual rainfall of 700-1600mm and minimum 

and maximum temperature of 7.5oC and 32oC, respectively GZFEDD, annually socio economic 

report (2017).  

Total area of Guraghe zone is about 5893 square kilometers accounting for 5.6 % of SNNPRG. 

Only about 73.64 % of the zone is arable land under crop production, whereas 11.85% is grazing 

land, 11.74% covered with forest, and the remaining 3.77% of the zone is under other land use 

forms (GZANRDD, 2017). The districts in the Guraghe zone are known for their bimodal 

rainfall pattern and hence highly suitable for agriculture. They have two distinct seasons; i.e, 

Belg (from March to June) and Meher (from July to January). 

According to GZFEDD report (2017) the zone has estimated total populations of about 

1,724,324   out of which about 836,896 are male and 887,428 are female. Out of total population 

of the zone, more than 93.76% is dependent on agriculture and 87.76% lives in rural areas. Major 

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crops grown in the zone are wheat, maize, teff, barley, faba bean (Vicia faba), field pea (Pisum 

sativum), chickpea, haricot bean, sorghum and Enset (Ensetumventricosum), Peper, coffee and   

are also grown in the zone the location of map shows (Appendix I.13) .  

3.2 Sampling Techniques and Procedures   

In this study, three stage sampling technique was employed. First, major legume producing 

districts in Gurage zone based on legumes production potential and suitable agro ecologies for 

legumes growers were identified with the help of as key informants to use in Gurage Zone 

Agricultural and Natural Resource Development Departments through many times experienced.  

At this stage, three major legume producing districts were selected purposively. The districts 

were Abeshge, Gumer and Sodo based on relative importance of legumes in the crop production 

system. Then, three kebeles were randomly selected from each district. Accordingly, Mida 

Tedele, Bido Tedele, and Abiko Kebles were selected from Abeshge district. Abesuja, Esen, 

Dirbo and  Sunen  Kebeles were selected from Gumer district. Similarly, Sewaty geda, Wudget 

and Wacho kebeles were randomly selected from Sodo district. Finally, out of total 9 kebles a 

sample of 102 household headed legumes technologies adopter and 102 non adopter random 

selected based on each kebele  agriculture development agent through past time recorded data  

204 farm households in total 78, 88 and 38 sample households from Abeshge, Gumer and Sodo, 

respectively- were selected proportionately across kebeles based on their total households. 

Ordinarily multi-stage sampling is applied in big inquires extending to a considerable large 

geographical area, say, the entire Gurage Zone. There are two advantages of this sampling design 

viz., (a) It is easier to administer than most single stage designs mainly because of the fact that 

sampling frame under multi-stage sampling is developed in partial units. (b) A large number of 

units can be sampled for a given cost under multistage sampling because of sequential clustering, 

whereas this is not possible in most of the simple designs. This sample size represent in the target 

populations, Research methodology methods and techniques C.R.KOTHARI (1990). Table3.1 

shows the distribution of the sample households across the three Kebeles in each of the districts. 

 

 

 

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Table.3.1 Sample distribution 

District  Kebele Number 

of  

household  

Adopter Non 

adopter 

Number of sample 

household 

Total (%) 

Sodo    19 19 38 18.63 

 Sewaty geda  311 5 6 11 5.392 

 Wudget  366 6 6 12 5.882 

 Wacho  451 8 7 15 7.353 

Abeshge    39 39 78 38.24 

 Mida tedele  1160 20 20 40 19.61 

 Bido tedele 810 14 14 28 13.73 

 Abiko  300 5 5 10 4.9 

Gumer   44 44 88 43.13 

 Abesuja  862 14 15 29 14.21 

 Esen  1200 21 20 41 20.1 

 Dirbo & sunen  534 9 9 18 8.82 

   102 102 204 100 

 

 

 

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3.3 Types and method of data collection 

Both qualitative and quantitative types of data were collected from primary and secondary 

sources. The study was started with a series of short visits to the study sites for rapport 

development with the key actors in the legumes development continuum. Then a reconnaissance 

survey was conducted with a brief checklist to identify and document the key socioeconomic and 

biophysical features of the study area and major challenges and opportunities of improved 

legume production and marketing. The visits and the preliminary surveys were used, among 

others, to develop the instrument for the formal survey of the study. 

The primary data were obtained by the use of semi-structured questionnaires by interviews with 

the farm households. The adoption and impact survey was carried out from first week of 

December to January 30 of 2018. The data were collected by enumerators (Agricultural staff 

experts and development agents   under supervision of the researcher. In order to facilitate data 

collection, the enumerators were trained regarding the objectives of the study, content of the 

questionnaire, and data collection procedure. 

Data were collected on several issues including households’ demographic characteristics, asset 

endowments, importance of   legumes, access and adoption of  legume technologies, household 

income and its source, access to market, access to credit and membership in different rural 

institutions.  

3.4    Pre-Testing (Validity and reliability)  

To ensure validity and reliability of the research instrument, the researcher will ensure that the 

Questions that are asked are in conformity with the research objectives of the study and a pilot  

test of the research instrument will be conducted and a calculation using office Microsoft excel  

will be computed for question reliability and validity assessment  

3.5 Method of Data Analysis  

The data collected was analyzed using both descriptive statistics and econometric model. 

Descriptive statistics includes mean, standard deviation (SD), frequency, percentage, graph and 

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tabular representation, which has been mostly used to examine the socio economic and farming 

characteristics of households.   

3.6 Econometric model 

The legumes technology adoption being used, a farmer’s was taken as an adopter if he or she 

sows certified improved seed use, chemical fertilizer application, IPM and row planting; either 

independently or both with their indigenous seeds and manure. The dependent variable, 

technology adoption, has the value of 1 for adopters and 0 for non-adopters. In this regard an 

econometric model employed while examining probability of farm households’ agricultural 

technology adoption decision was the logit model. Often, logit model is imperative when an 

individual is to choose one from two alternative choices, in this case, either to adopt or not to 

adopt. Hence, an individual i makes a decision to adopt legumes technologies if the utility 

associated with that adoption choice (V1i) is higher than the utility associated with decision not to 

adopt (V0i). Hence, in this model there is a latent or unobservable variable that takes all the 

values in (-∞, +∞). According to Alexander Spermann (2009) these two different alternatives 

and respective utilities can be quantified as: Yi* = V1i - V0i and the econometric specification of 

the model is given in its latent as:     

We start by deriving the odds ratio in a way that makes explicit the relationship between the 

estimated logit parameters �and the error term  �i: - Suppose that a continuous latent variable yi
* 

can be modeled as a linear function of K explanatory variables (covariates), xki , for k= 1,…,k for 

individuals i= 1…N.  The equation for yi
* can be written as 

yi
* =�0+�1x1i + �2x2 +……+ �kixi + �i                                                                          1         

If we allow the explanatory variables, including the constant term, to be represented by the 

vector xi
′, then equation 1 can be represented in matrix notation as 

�i
*
 = xi� + �i                                                                                            2 

However, the researcher observes only the explanatory variables and a binary (0, 1) variable yi, 

which indicates whether yi
* exceeds the threshold of zero  

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 �� = �
1 ��, ��

∗ > 0
0  ��ℎ������

                                                                                                                    3                           

To make statements about the probability that y�  = 1  (or equivalently, ��  
∗  >0 we need to 

express the probability in terms of on error term with a known distribution. Substituting ��   
′ � +

��  for ��
∗   allow us to write the probability that ��    > 0  in terms of the probability that the error 

term takes on a range of values 

Pr (��
∗ >

�

��
) = ��(��� + �� >

�

��
) = ��(�� > −

��
′ �

��
)                         4                                                         

If the error term has term has mean zero and is symmetric (which is true for the standard logstic 

and standard normal distributions) then  

��(�� =
�

��
) = ��(��

∗ >
�

��
) = ��(�� <

��
′ �

��
)                                       5 

Equation 5 holds for any arbitrary scaling of � and � (eg; 
�

�
 and

�

�
). Thus, because the distribution 

of � is unknown, the��(�� =
�

��
) cannot be evaluated without an additional step (Greene and 

Henser, 2010).  To address that problem, the typical solution is to divide both ε and β by the 

standard deviation of ε:  ε/σ and β/σ.  Those transformations makes ��(�� =   
�

��
) a cumulative 

distribution function (CDF) of a standard logistic (logit) variable, which is easy to calculate for 

logistic. 

For the logit model, the standard deviation of 
�

�
=

�

√�
 the cumulative distribution function for 

logit model is 

��(�� =
�

�������,��
) = ��(

��

�
< ��

�

�
) =

�

����� (���  
′ �

�
)
                                   6 

  This derivation explicitly shows the important role of � in making any statements about 

probabilities.  

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Many researchers prefer to estimate logit rather than probit models because of the odds ratio 

interpretation of the logit coefficients.  The odds for individual i are expressed as the ratio of the 

probability ��to 1− �� where �� =  ��( �� =  1|��������, ��). 

���� =
��

����
=   exp (��

′   
�

�
)                                                                       7 

The odds ratio is the ratio of the odds in equation 7 for two different values of an explanatory 

variable. This is easiest to derive for a binary variable. Let ������� ���ℎ�������� ��������1�  

the indicator for adoption status and �������� be the corresponding coefficient the odds if 

individual �was � �������  (������� ���ℎ�������� ��������1� = 1) and odds if individual � 

was � ��� ������� (������� ���ℎ�������� �������� 1� = 0) are: 

���� ��� ������� = exp (
����������� ��������⋯�����

�
)                             8 

���� ��� ��� ������� = exp (
���������⋯������

�
)                                    9 

Therefore, the odds ratio is the ratio of the odds, which simplifies to the exponentiated   

Coefficient. 

���� ����� =
���� ��� �������

���� ��� ��� �������
= exp (

��������

�
)                                10   

 X′ is vectors of exogenous variables that explain adoption of legumes technologies adopter (e.g. 

age of household head, sex of the household head, education, membership to an agricultural 

association, access to credit, etc). Therefore, on the basis of dependent variables indicated: 

legumes technologies adoption, logit model was applied independently for each binary dependent 

variable;  

Given below    

� = � + �1�1 + �2�2 + �3�3 + �4�4 + �5�5 + �6�6 + �7�7 + �8�8+�9�9 + �10�10 + �11�11 + 

�i ……………. (1)                  

Where Y, is the dependent variable that is measures the level of adoption legumes 

technologies.   

X1- Access to credit  

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 X2- Extension Agents’ Contact 

X3- Technology access (access to improved seed/DAP fertilizer)    

X4-Market access of product   

X5-Tropical Livestock Unit  

 X6- Education level    

X7- Membership to an Association  

 X8 -Gender of household head 

X9 -   Land Size for cultivation purpose of household headed  

X10-   Plot size for legumes crop for cultivation purpose of household headed   

X11    Off-Farm Participation  

 �-The constant term (intercept)  

 βi- regression parameter for the it explanatory variables which indicates the slope of the  

predictor variable ui= the error term of the model. 

3.6.1. Propensity Score Matching Method (PSM) for impact analysis  

Impacts are discreet (usually binary) variables. Treatments are heterogeneous in the population 

(Heckamn et al., 1997. Robin 1997), developed a framework that each household has two 

potential outcomes; an outcome when adopting technology (y1) and not adopting technology 

(y0). If we let the adoption status d, d=1 for adoption of technology and d=0, for not adoption, 

then it is possible to write the observed outcome y of the household performance as a function of 

the two potential outcomes as   

� = ��1 + (1 − �)y0     ………………………………..(1)                                                                                           

The causal effect of the adoption on its observed outcome y is the difference between the two 

outcomes (y1-y0). But because of the realization, the potential outcomes are mutually exclusive 

that is only one of the two outcomes has been observed at a time (Nguezet et.al, 2011). It is also 

impossible to measure the individual effects of adoption in any household. However, it can be 

possible to estimate the mean effect of adoption on a population household. Such mean 

parameter is called average treatment effect (ATE) (Imben and Wooldridge, 2009).   

ATE= 
�

�
∑

���(�(��)��

�(��)(���(��)

�
���  ………………………………… (2)                                                                                              

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  34 
 

Where n is the sample size, n1=∑��, is the number of treated variable i.e. the number of seed 

fertilizer technology adopting farmers and p(xi) is a constant estimate of propensity score 

evaluated at x. It is possible to employee logit specification to estimate the propensity score. 

Propensity score matching pursues a targeted evaluation of whether adopting a modern seed - 

fertilizer technology causes farmers to improve their performance.  There will be problem of 

avert and hidden biases and deal with the problem of noncompliance or indigenous treatment 

variable. In order to remove such biases Robin (1974) introduces ignorablity (conditional) 

assumption which postulates, the existence of a set of covariate x, which controlled for renders 

the treatment outcomes (y1 and y0). The estimation using the conditional independent 

assumption) or they are based on a two stage estimation procedure, conditional probability of 

treatment called propensity score.   From this we can develop two interrelated stages:   

 Estimating the propensity score- The first step in PSM method is to estimate the propensity 

scores by using logit models. Caliendo and Kopeinig (2008) noted that the logit model which has 

more density mass in the bounds could be used to estimate propensity scores, P(x) using a 

composite characteristics of the sample households and matching will then be performed using 

propensity scores, p-score, of each observation. Matching algorism will be selected based on the 

data to be collected after undertaking matching quality test. Overlapping condition or common 

support condition will be identified, estimating the average treatment effects of both outcomes 

(ATE1 and ATE0) after estimation of the propensity scores, seeking an appropriate matching 

estimator is the major task.   

There are various matching estimators, which include the nearest neighbor matching, caliper and 

radius matching, stratification and interval matching, kernel and local linear matching (Caliendo 

and Kopeinig, 2008).The treatment effects will be estimated based on matching estimators 

selected on the common support region (owusu and Awudu, 2009).The average treatment effects 

can be estimated using the inverse propensity weighing estimates as stated in IPSW (Nguezet,, 

2011) using matching techniques of Kernel  Matching  (KM),  Nearest  Neighbor  Matching  

(NNM)  and Radius Caliper Matching (RCM).  

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3.6.1.1. Nearest Neighbor Matching 

 Caliendo and Kopeinig (2008) said that NN matching is the most straightforward and frequently 

used matching estimator in PSM. The individual from the control group is chosen as a matching 

partner for a treated individual with the least distance in terms of propensity score (Becker and 

Ichino, 2002). Several variants of nearest neighbor matching are proposed, e.g. NN matching 

‘with replacement’ and ‘without replacement’. In the former case, an untreated individual can be 

used more than once as a match, whereas in the latter case it is considered only once. Matching 

with replacement involves a trade-off between bias and variance. If we allow replacement, the 

average quality of matching will increase and the bias will decrease while increasing the 

variance. This is of particular interest with data where the propensity score distribution is very 

different in the treatment and the control group. A problem which is related to nearest neighbor 

matching without replacement is that estimates depend on the order in which observations get 

matched. Hence, when using this approach, it should be ensured that ordering is randomly done. 

It is also suggested to use more than one nearest neighbor matching. Reduced variance will result 

from using more information to construct the counterfactual for each participant, with increased 

bias that results from on average poorer matches (Caliendo and Kopeinig, 2008).  

3.6.1.2. Caliper Matching:  

To avoid the problems of bad matches resulted from the Nearest Neighbor matching; economists 

impose a tolerance level on the maximum propensity score distance (caliper). Imposing a caliper 

works in the same direction as allowing for replacement. Bad matches are avoided and hence the 

matching quality rises. However, if fewer matches can be performed, the variance of the 

estimates increases. Applying caliper matching means that an individual from the comparison 

group is chosen as a matching partner for a treated individual that lies within the caliper 

(‘propensity range’) and is closest in terms of propensity score (Caliendo and Kopeinig, 2008).   

 Dehejia and Wahba (2002) suggest a variant of caliper matching which is called radius 

matching. The basic idea of this variant is to use not only the nearest neighbor and limit itself 

within each caliper but all of the comparison members or observations within the caliper. The 

benefit of this approach is that it uses only as many comparison units as available within the 

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caliper and therefore allows for usage of extra (fewer) units