Farmers’ participation decision in high value market and its effect on food security of smallholder avocado producers in Sidama region, Ethiopia

Farmers’ participation decision in high value market and its effect on food security of smallholder avocado producers in Sidama region, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Farmers’ participation decision in high value market and its effect on food security of smallholder avocado producers in Sidama region, Ethiopia Tibebu Legesse, Aneteneh Ashebir, Kehabtimer Kebede This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3124184/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Food insecurity is an enduring, critical challenge in Ethiopia. Linking farmers to high-value markets continue to be a viable option for breaking the food insecurity. Many studies have emphasized on factors determining smallholders’ participation in high value markets. Despite their undeniable importance, these studies have methodological limitations since they neglected the importance of this for food security. Thus, this study aimed to fill a knowledge gap in the area by investigating the factors influencing smallholder avocado farmers’ participation in a high-value market channels and how it impacts households' food security in the study area. This study employed a cross-sectional research design and multistage sampling techniques to achieve the objectives of the study. Both primary and secondary data were collected. The primary data were collected from randomly selected 389 avocado producers using a semi structured questionnaire. Secondary data were collected from journals, annual reports, websites and different published and unpublished materials. Descriptive statistics, inferential statistics, and propensity score matching model were used to analyze the data. The result of the binary logit model revealed that the participation of avocado producers in a high-value market channels was influenced by age, educational status, the quantity of avocados sold, and price of avocado in quintal and market information. The ATT estimation of PSM model indicated that the participation in high-value market channels had a positive and significant influence on the food security in the study area. Given the substantial contributions participation in the high-value market channels to food security, concerned bodies in Ethiopia should encourage more households to participate in the high-value market channels. Avocado Participation in high-value market channels Impact Food security Propensity score matching model Figures Figure 1 Figure 2 Introduction Almost 1.3 billion people worldwide do not have access to adequate food to consume, 22.8% of these people living in Sub-Saharan Africa (FAO, 2019 ). In the sub-Saharan Africa, smallholder agriculture has long dominated the economy and will continue to play a crucial role for the foreseeable future (Gollin, 2014 ). Despite agriculture being the main source of income in Africa, it is inadequate to feed the continent's expanding population (African Union, 2013).The sector's limited market integration and subsistence status, however, continue to be significant obstacles. The agricultural sector also dominates the Ethiopian economy, contributing 34.1% of GDP, 79% of export revenues, 79% of the labor force, and 70% of the raw materials used in industries (Asrat et al., 2022 ; Endalew et al., 2022 ; Gebremariam et al., 2021; Wordofa et al., 2021 ; Zegeye et al., 2022 ). Agriculture in the country is mostly dependent on rainfall, carried out on a small scale, and has limited access to technology, extension assistance, market information, and financial access, all of which have lowered agricultural productivity (Kifle et al., 2022 ; Nakawuka et al., 2018 ). Indeed, the country's agricultural production growth has lagged behind the pace of population expansion (Regasa et al., 2021 ). Horticulture farming has been identified as one of the agricultural sub-sectors that is expanding quickly and a potential driver of poverty reduction for low-income smallholder households (Mkindi, 2011 ; Barrett et al., 2012 ). Horticultural crops refer to fruits, vegetables, ornamental and medicinal plants (Amao, 2019 ). Avocado is one of the horticultural crops with high economic value. Avocado (Persea Americana Mill.) is originated in Mexico (Muhammad, 2015. After pineapple, avocado is the fruit that is traded the most, it makes up around 25% of all tropical fruits. Avocado has nutritional benefits such as potassium, unsaturated fatty acids, proteins, and fat-soluble vitamins that are uncommon in other fruits (Duarte et al., 2017). The fruit is also utilized as a raw ingredient in the pharmaceutical and cosmetic industries (Duarte et al., 2017). Africa has a strong interest in avocado cultivation and marketing. South Africa, Ethiopia, Cameroon, Rwanda, and Kenya continue to be the continent's top producers of avocados (FAO, 2019 ). In Ethiopia, avocado is grown on a total of 20,908.00 hectares, with a production of 104,492 tonnes. The output of avocados in Ethiopia has historically ranged from 13,888 tonnes in 2001 to 104,492 tonnes in 2019 (FAOSTAT, 2020 ). Approximately 36% of the nation's annual avocado production comes from the Sidama region (CSA, 2021). Linking Smallholder farmers to high value market may offer the opportunity to produce and sell high-value products, translating their vertically-coordinated relationships into premium prices and letting them capture a bigger share of the price paid by final consumers (Hussein & Suttie, 2016 ; De Janvry & Sadoulet, 2020; Kilelu et al., 2017 ). Indeed, there is evidence in that participation of small-scale farmers in high-value markets presents opportunities to improve their productivity, income, food security, and reduce poverty (Belay, 2018 ; Sharp et al., 2007 ; Barrett et al., 2012 ; Mmbando et al., 2015 ). In current days, small-holder farmers have more options to participate in high-value markets due to the booming supermarkets and agro-processing industries, which may help them earn more money and have more reliable access to food (Hernandez et al., 2007 ). However, for the majority of smallholders in developing regions, sustained participation in high-value markets remains difficult. Recent empirical evidences shows that smallholders frequently quit high value market upon entry due to substantial post-entry transaction risks (Anderson et al., 2015, Michelson, 2017 , Lambrecht and Regasa, 2018, Romero and Wollni, 2018). According to Mossie et al., ( 2020 ) some issues such as poor infrastructure, grading systems, insufficient market information and communication between farmers, traders and consumers pose a significant hindrance to high value market accessibility. Ethiopia has been implementing agricultural development lead industrialization (ADLI) policy since the early 1990s. In order to attain such a development goal, there should be a market oriented production, which ensures marketable surplus as well as efficient and effective marketing system that will enable farmers to gain the actual profit from what they produce. Given reliable market, an increase in market participation in turn makes it easy for farmers to shift into commercial farming, increasing economic growth (Jari and Fraser, 2009 ). The marketing channels used by farmers to sell their avocados in the study area can be split into two groups: high-value market channels and traditional market channels. Traditional market channels represented by Village markets, whilst, the high-value market channel represented by processor (Yirgalem agro-processing industry, Hawassa juice house and supermarkets). There are few research on the impact of smallholder farmers' participation in high-value markets on food security in Ethiopia. Many studies have emphasized on factors determining smallholders participation in high value market (Tamirat et al., 2018; Getahun et al., 2018 ; Girmalem et al., 2019 ; Kassa et al., 2017 ; Mengesha et al., 2019 ; Tarekegn et al., 2020 ; Abate et al., 2019 ; Biggeri et al., 2018 ; Gebremedhin et al., 2019 ; Habtewold et al., 2017 ; Kyaw et al., 2018 ; Warsanga & Evans, 2018 , Despite their undeniable importance, these studies have methodological limitations since they only look at the factors that influence smallholders’ participation in the high value market, neglecting the importance of this for food security. Thus, this study aimed to fill a knowledge gap in the area by investigating the factors influencing smallholder avocado farmers' participation in a high-value market channel and how it impacts households' food security in Aleta Chuko district, Sidama Region of Ethiopia. Material and methods Description of the study area This study was conducted in Aleta Chuko district, Sidama Region, Ethiopia. It is situated in the Sidama region, 62 kilometers south of Hawassa, the Sidama region's capital, and 330 kilometers south of Addis Ababa. Its precise location ranges from 38004'E to 38024'E and 6046'N to 7001'N. The district is divided administratively into 26 rural and 5 urban Kebeles. The Aleta Chuko district has a total population of 209,886, of which 102,215 (48.7%) are men and 107,671 (51.3%) (CSA, 2021). A rough estimate of the district's land area is 32.2 square kilometers. The area has lowland agro ecological zones and varies in altitude from 1400 to 2000 meters above sea level (CSA, 2021). Research design Cross-sectional data were used in this study. It refers to the type of data in which the data required for analysis need to be collected at a single point in time from the sample to represent population. It is convenient for the qualitative and quantitative descriptive study. Data source and methods of collection Both the primary and secondary data were collected for this study. Primary data was collected from avocado producers who are participated in traditional and high value market channel. Primary data collected through checklists, semi-structured questionnaires, and field observations. Prior to the survey, the questionnaire was pre-tested on 14 farmers (7 traditional and 7 high value market channel participants) to evaluate the appropriateness of the design and clarity of the questions, relevance of the questions and to estimate the time required for an interview. Three enumerators who completed a first degree and have knowledge of the local culture, and language of the community were recruited to conduct the interview. They were given appropriate training including field practices, in order to make them understand the objectives of the study, the contents of the interview schedule, how to approach the respondents and conduct interview. The secondary data used for this study, obtained from a review of many sources, including academic journal articles, books, government reports and research works published by various researchers. Sampling techniques The purpose of this study was to analyze factors affecting smallholder farmers’ participation in high value market and its impact on food security. So, smallholder avocado producer farmers in Aleta chucko district are the target population of this study. Multi-stage sampling techniques were used to draw representative sample. In the first stage, Aleta chuko district was purposefully chosen from 36 districts in the Sidama region for its significant potential for avocado production. In the second stage, four Kebeles from 12 avocado producing kebeles were chosen randomly with the help of districts’ agriculture and natural resource office experts. In the third stage, 389 sample avocado producers were drawn from the 14,748 total avocado producers of the chosen kebeles using Yemane's (1967) formula at 5%level of error. We applied this formula to determine sample size because it is the most appropriate formula when the study population size is known [51]. Consequently, it has been widely used by many recent studies [52–54] in determining the sample size for their studies. Finally, the sample size was distributed using the probability proportional to the sample size. $$n= \frac{N}{1+N\left({e}^{2}\right)}----------------------------\left(1\right)$$ Where, n = sample size, N = total avocado producer households, and e = is level of precision (0.05). $$n= \frac{\text{14,748}}{1+\text{14,748}({0.05}^{)2}}=389 ------------------------\left(2\right)$$ Method of data analysis Measuring food security Due to the intricacy of the food security concept, choosing an acceptable food security indicator is the most difficult problem (Hendricks, 2005 ). This is due to the fact that none of the metrics adequately represent the idea of food security. As a result, one of the indicators from (Lele et al., 2016 ), which divided indicators into eight groups based on the underlying data source, was employed in the current investigation. These might all be put to use in different ways. Individual or household recollection, national observations, market observations, prevalence and depth of undernourishment, anthropometric measurements, breastfeeding and sanitation, clinical data, composite indices, and multidimensional measures are the sources of data used to create the indicators. Among the aforementioned indicators, individual or household recall indicators are regarded as the simplest technique to collect pertinent information from household using survey questionnaires. One of the most crucial aspects of household food security is the total quantity of calories consumed by each member of the home for each food item (Berry et al., 2015 ). In this study, it was calculated how many calories were consumed differently by the treatment and control groups. Interviewees were asked to provide information about the types and quantities of food that their households had consumed in the seven days prior to the survey. The processes that were involved in translating the family's physical food consumption into calories consumed were as follows. For each food item consumed, local measuring units were first transformed into a standard unit of measurement. Second, the national food composition table created by the Ethiopian Health and Nutrition Research Institute was used to convert each food item ingested to calories (EHNRI, 2022). Third, the total number of food calories consumed was multiplied by 24 hours to get daily quantities. Using a conversion factor for adult equivalent, the total food calories per household were changed to an adult equivalent (AE) unit (Stock et al., 1997). The calculated average kilocalorie (kcal) demand per adult household equivalent per day was compared to the minimum subsistence kcal requirement for Ethiopia, which was defined by FDRE (EHNRI, 2022) at 2200 kcal. As a result, the study employed 2200 kcal as a precise cutoff point to classify households as either food secure or food insecure. Finally, the household was classified as food secure if their physical food consumption in kcal was greater than or equal to 2200 kcal/day/AE, whereas a household was classified as food insecure if it consumed less than 2200 kcal/day/AE. Using a generally used model of effect evaluation, such as that in (Seng, 2016, and Ngema et al., 2018 ), it is possible to evaluate how market entry affects household food security. Y = βX + γI∗+ε and Y = 1, Yi∗ > 0; 0, Yl∗ ≤ 0 Where Y is the household’s HDDs per capita, β represents the coefficient, and X is a vector of household and farm characteristics and other factors expected to affect household food security. “I” is a dummy for high-value market participation, γ is the coefficient capturing the effect of high-value market participation on household food security, and ε stands for random errors. Statistical analysis Data were edited, coded, entered, and cleaned to make it ready for analysis. After doing this, data analysis techniques such as descriptive statistics, inferential statistics, and econometric models were used. To give summary statistics of quantitative data related to the socio-demographic, economic, and institutional features of sample households, descriptive statistics including percentage, frequency, and mean were employed. To determine whether there were any statistically significant differences in the observations between the high-value market channel and traditional market channel participants, inferential statistics including the t-test and Chi-square test were performed. The impact of farmers’ participation in the high-value market channel on food security was analyzed using the propensity score matching (PSM). Specification of propensity score matching (PSM) model The following three expected biases make it difficult to estimate the impact of treatment on outcomes: (1) the selection of observables as a result of sampling bias, (2) the choice of a comparison group in the presence of externalities, and (3) the choice of an unobservable as a result of differences between the treated and control groups in the distribution of their unobserved characteristics (Wooldridge, 2012). The coefficients on the control variables in basic regression or logistic models would be the same for participants and non-participants. Due to this constraint, the majority of studies in the literature used the PSM model to assess how treatments affected outcomes (Smale et al., 2012 ; Rosenbaum et al., 1983). The PSM method enhances regression's capacity to generate accurate causal estimates due to its non-parametric approach to the balance of variables between the treated and control groups (Connife et al., 2000 ). According to Caliendo and Kopeinig ( 2005 ), the implementation of PSM involves six steps. These are: PSM estimation; choosing a matching algorithm, checking for overlap (common support); matching quality test, impact estimation, and sensitivity analysis. Estimating the propensity score This involves the identification of the probability of participation in high value market. When estimating the propensity score, two choices have to be made. The first one concerns the model to be used for the estimating the probability of participation in high value market channel, and the second one is about the variables to be included in that model (Caliendo and Kopeinig, 2005 ). For binary dependent variables the models that are used to estimate the probability of participation against non-participation households are logit or probit models. However, the choice between the two is not as problematic as they provide the same result (Gujarati and Porter, 2009 ). In practice, a model (Logit or Probit for binary treatments) is estimated in which participation in treatment is explained by a number of pre-treatment variables. Predictions from this estimation are then used to construct the propensity score, which runs from 0 to 1 (Rosenbaum et al., 1983; Aku et al., 2018 ). This study used the Logit model to estimate the propensity score even though both models produce results that are essentially equivalent. Participation in the high-value market channel, which has a value of 1 if a farmer is high value market channels participant and 0 traditional market channels participant, served as the dependent variable for the logit model's estimation. Rosenbaum and Rubin claim (Rosenbaum et al., 1983), the logit model can be specified as: $$Pi=\frac{{e}^{zi}}{{ 1+e}^{zi}}---------------------------------$$ 3 Where \(Pi\) is the probability of participation in high-value market channel $$zi={\beta }\text{o} +\sum \beta ixi+ui----------------------------$$ 4 Where, \(i\) = 1, 2, 3, - --, n, \({\beta }\text{o}\) = intercept, \(\beta i\) =regression coefficients to be estimated \(ui\) = a disturbance term, and \(xi\) =variables The probability that a household belongs to the traditional market channel is: $$1-p=\frac{1}{{ 1+e}^{zi}}--------------------------------$$ 5 Then the odds ratio can be written as $$\frac{Pi}{1-p} = \frac{{ 1+e}^{zi}}{{ 1+e}^{-zi}}---------------------------------$$ 6 The left-hand side of Eq. (6) \(\frac{Pi}{1-p}\) is simply the odds ratio in favor of participation in the high-value market channels. It is the ratio of the probability that the household would participate in the high-value market channel to the probability that it would not participate in the high-value market channels. Lastly, by taking the natural log of Eq. (4) the log of odds ratio can be written as: $$\text{Li}=\text{ln}\left(\frac{pi}{1-pi}\right)=\text{ln}\left({e}^{{\beta }\text{o}+{\sum }_{j}^{n}={1}^{\beta jXij}}\right)=Zi------------------$$ 7 $$zi={\beta }\text{o}+{\sum }_{j}^{n}={1}^{\beta jxji+\epsilon i}-------------------------$$ 8 Where, \(\text{L}\text{i}\) is the log of the odds ratio in favor of participation in the high value market channels, which is not only linear in \(Xji\) , but also linear in the parameters. Predictor variables that affect the selection process, participation in high-value market channels, and the outcome of interest should be included in the matching theory in the propensity score created by the logit model (Bryson et al., 2002 ; Jalan and Ravallion, 2003 ; Bergstra et al., 2019 ). Eleven independent variables were proposed and utilized in the model as a result of this recommendation. These factors were found, and it is thought that they control whether households participate in high-value market channels or traditional market channels. The common support region determination The conditional independence assumption (CIA) and the common support condition (CSC) must both be satisfied for the PSM method's results to be considered legitimate (Caliendo et al., 2008). The Confoundedness Assumption (CIA), which asserts that a treatment must satisfy the requirement of being exogenous, contends that any systematic difference in results between a treatment group and a control group with the same values for characteristic X can be attributed to the treatment. The common support or overlap requirement indicates that there is enough similarity between the traits of the treated and untreated units to identify suitable matches (or common support). Households for which there is no match are removed since there is no foundation for comparison. It is done by eliminating observations whose propensity scores are lower than the minimum and greater than the maximum of the participant and control groups, respectively (Caliendo and Kopeinig, 2005 ). Choosing matching algorithm There most widely used matching algorithms are (NNM), kernel-based matching (KBM), radius-based matching (RBM), caliber-based matching (KBM), and Kernel-based matching (KBM) (Caliendo et al. , 2008; Caliendo and Kopeinig, 2008 ). Among them, a matching estimator that bears low pseudo R 2 , results in large matched samples and insignificant explanatory variables after matching should be chosen among them (Dehejia and Wahba, 2002).Therefore, NNM, KBM, RBM, CBM, and KBM estimators were utilized in this work. Testing the matching quality The matching quality of the best matching algorism should be tested for its quality using different parameters. The basic idea is to compare the situation before and after matching and check if there are any differences after conditioning on the propensity score. The matching quality of the best matching algorism identified in step three should be tested for its quality using different parameters. Chi-square test for the joint significance of variables, bias reduction after matching and number of covariates results in significant differences between two groups should be checked as a criterion of matching quality (Caliendo and Kopeinig, 2005 ). Impact estimation After matching quality tests, impact estimation needs to be conducted using the average treatment effect on the treated (ATT). It is the mean outcome difference between intervention participants and non-participants matched by PSM. Hence, the ATT for the individual can be defined as the difference between the expected outcome variable; (food security based on our study with and without participating high value market channels).The ATT can then be calculated as follows after determining the propensity scores: $$ATT=E p\left(X\right) \left\{\right(E (Y │D=1, P(X)-E (Y0│D=0, P\left(X\right)\left)\right\}$$ Where, ATT represents Average Treatment effect on the treated group. The symbol “│” stands for conditional on Ep(X) denotes the expectation with respect to the distribution of propensity score in the entire population and D denotes intervention participation indicator which is equal to one (1) if a farmer participated in the intervention and zero (0) if otherwise. The estimation of ATT clearly depends on the characteristics of the two groups: treated and control for (Y1│D = 1) and (Y0│D = 0)} respectively as explained above. Sensitivity analysis Sensitivity analysis is final step in PSM application. An assumption of the matching method is based on Conditional Independence (CIA), which states that the researchers should observe all variables that are at the same time influencing the participation decision and outcome variables (food security). However, this assumption is non-testable since the data are uninformative about the distribution of the untreated outcome for treated groups and vice versa (Becker and Caliendo, 2007 ). The estimation of treatment effects with matching estimators is based on the selection of observables assumption. As a result, if there are unobserved variables that affect assignment into treatment and the outcome variable simultaneously, a hidden bias might arise which invalidate the CIA and it results in biased estimates of ATTs (Rosenbaum, 2002 ). Since estimates are not robust against hidden biases, it is important to test the robustness of results to depart from the identifying assumption. However, it is impossible to estimate the magnitude of selection bias with non-experimental data. To address such problem, in this study, sensitivity analysis was applied accordingly (Rosenbaum, 2002 ). Hypothesis, variable description and expected sign Finding the factors that influence avocado producers to participate in high value market channels and how this affects their food security is important. It is also necessary to investigate the relationships between these factors and the dependent variables to determine which factors have a significant impact. In light of this, the following dependent, independent, and outcome variables were identified and hypothesized for this investigation (Table 1). Dependent Variable Participation in the high-value market, which has a value of 1 for high-value market channel participants and 0 for the traditional market channel participants, is the study's dependent variable. Outcome variable Food security, as evaluated by household food intake in calories, is the study's outcome variable. Independent Variables The independent variables which are expected to influence avocado households’ participation in high-value market channels are presented below. Sex of the household head This is a dummy variable which takes a value 1 if the household head is male and 0 otherwise). Male household heads have been reported to have a better tendency in searching market alternative for the sale of avocado than female household heads. Male households are participated in the vegetable market more than females and females are disadvantaged in marketing because of unequal distribution of resources as well as cultural barriers (Banchamlak & Akalu, 2022 ). Thus, sex of household head was hypothesized to influence farmers participation in high value market positively Age of the household head This is a continuous variable and defined as the number of year of household head age. In this study, Age of household’s head is expected to be negatively affect high value market participation since the older farmers are more likely to participate in village market. Previous study by Edosa, ( 2018 ) found that age has negative effect in market participation decision. Thus it was expected to be negatively correlated with high market channel participation. Education level of household head Educational level of the household head is continuous variable measured in a number of years spent in formal school. Household heads with more years of formal education expected to have a higher ability to accept new ideas and innovations, and therefore would be more willing to sale avocado through high value market channel. Better educated farmers tend to be more innovative and are therefore more likely to participate in modern market channels (Habtamu, 2014 ; Banchamlak and Akalu, 2022 ) Thus, education level of household head was hypothesized to influence farmers’ participation in high value market positively. Family size This variable is a continuous explanatory variable and measured in adult equivalent ratio of the household members. It is assumed that household with more adult household members produce large amount of avocado with expected quality and supply to the high value market. The involvement of smallholders in in the value chain market influenced by family size of the household (Kassa et al., 2017 ).Therefore, this variable was hypothesized to have positive influence on high value market participation. Quantity of Avocado produced It is a continuous variable measured in quintals. High avocado production was hypothesized to have a positive effect on high value market participation. Farmers producing small quantities are likely to sell their products to traditional market within a village rather than selling to modern market. According to Chalwe ( 2011 ) an increase in output is a motivation to produce and sell more and produce more that ultimately increases income Therefore, it was hypothesized that quantity of avocado produced would have positively relationship with high value market participation. Price of avocado This is a continuous variable measured in price of avocado in the current price in birr per quintal. When the price of the product is promising, farmers are motivated to sell their product to a particular market chains. This makes the supply to be directly related with a price offer. For example, Warsanga et al., (2018) found that supermarkets paid horticultural suppliers higher than what they got in traditional market. Therefore, this variable was hypothesized to have positive relationships with high value market participation. Market information It is dummy variable that takes a value of 1 if the farmer’s obtained market information and zero otherwise. According to Jari and Fraser ( 2009 ) as a result access to market information is a positively correlate with the market participation of farmer. A more access to market information could contribute to an increase in smallholder farmers’ participation in modern high value markets. Therefore, access to market information hypothesized to influence the high value market participation positively. Avocado marketing Experience This is a continuous variable measured in number of years. A household with better experience in avocado marketing is expected to participate more in high value market channel than the one with the less experienced. Oduniyi et al., ( 2021 ) found beef farmers market experience positively determine farmers participation in high value market channel. Therefore, higher avocado marketing experience anticipated to have a positive relationship with high value market participation. Frequency of extension contact This refers to the number of contacts that the respondent made with extension agents. Here, the frequency of contact between the extension agent and the farmers is hypothesized to be the potential force, which accelerates the effective dissemination of adequate agricultural and market information to the farmers. Previous study by Tarekegn et al., ( 2020 ) found that access to extension service has positive and significant role on the farmers participation in high value chain market. As per this study, it was hypothesized that frequency of extension contact influence farmers participation in high value market positively. Access to credit service It is measured in terms of whether respondent farmers accessed or not credit service. It is Dummy variable which takes value of 1 if the farmer has access to credit in terms of availability of credit sources and possibility of getting credit and 0 otherwise. Farmers who have access to credit may overcome their financial constraints and therefore buy inputs (Hagos et al., 2020 ). Farmers without cash and no access to credit will find it very difficult to attain and adopt new technologies. Thus, in this study, it was expected that access to credit increase the probability of participation in high value market positively. Distance to the high-value market This variable was measured by kilometers from the production area to the high value market. The closer they are to the high value market; the more likely it is that, the farmer will receive valuable information and choose the channel to supply avocado. On the other hand, if the distance to the high value market increases, the transportation cost will also increase and the farmers obligated to sell their avocado to traditional village market. The studies by Girmalem et al., ( 2019 ); Mengesha et al., ( 2019 ) found that the distance to the market has the negative influence on the farmer’s participation in market. Therefore, it was hypothesized that the modern market is far from the farm the farmer more selling to traditional market chain and then the income of farmer become low. Thus, distance to the high value market was expected to have negative relationships with high value market. Table 2 Variable description and their expected signs Variables Type variable Expected sign Sex of the household head (1 = Male, 0 = Female) Dummy +ve Age of the household head (Years) Continuous -ve Education level of household head (Year of schooling) Continuous +ve Family size ( ADE) Continuous -ve Quantity of Avocado sold (Quintal) Continuous +ve Price of avocado/quintal (Birr) Continuous +ve Market information (1 = Yes, 0 = No) Dummy +ve Avocado marketing Experience (Years) Continuous +ve Frequency of extension contact (Number of days ) Continuous +ve Access to credit service (1 = Yes, 0 = No) Dummy +ve Distance to the high-value market (KM) Continuous -ve Results and discussion Characteristics of sample households The mean ages of the sampled respondents who participated in the high-value market channels and the traditional market channels were 35.53 and 42.23 years, respectively (Table 1). The t-test (-4.393) result shows that there was a statistically significant mean age difference between farmers participating in the high-value market channel and those in the traditional market channels at a 1% level of significance. Study’s finding revealed that the typical family sizes of high-value market channels and traditional market channels participants were 5.0 and 6.42, respectively. The t-test (-3.605) demonstrates that, at the 1% level of significance, there was a statistically significant difference in the mean family size between high-value market participants and traditional market participants. Regarding the level of education in the households, farmers who participated in the high-value market channels had a greater mean number of school years (7.95) than farmers who participated in the traditional market channels, who had a mean of 2.55 school years. The t-test score (23.283) demonstrate that there was statistically significant mean year schooling difference between high value and traditional market channel participants. High-value market channels participants paid 685ETB for each quintal of avocado, while traditional market channels participants paid 380.96ETB. T-test result (14.68) confirms that there was a statistically significant mean price difference between high-value market channels and traditional market channel. Furthermore, the average market supply of avocados by the high-value market channels and traditional market channels participants were 9 and 2.6 quintals, respectively. The t-test value (19.57) reveals that at the 1% level of significance, there was a statistically significant mean quantity of avocado supply difference between high-value market channels participants and those in the traditional market channels participants. High-value market channels participants and traditional market channels participants had mean marketing experience of 7.28 and 7.77 years, respectively (Table 2). The t-test result shows that the market experience of the household head was statistically insignificant, indicating that the participants in the traditional and high-value market channels had comparable distributions of market experience. The average distance from the homes of traditional market channel participants to the high-value market was 27.57 kilometers, compared to 17.6 kilometers for high-value market participants. The t-test result (-6.89) reveals that There was statistically significant difference in distance between traditional market channels participants and high value market channels participants home to the high value market channels at 1% significance level. The t-test result revealed that there was no statistically significant difference between traditional and high-value market participants in terms of mean extension contact. Table 3 Characteristics of sample respondents Variables High-value market channels participants (N = 168) Traditional Market channels participants (N = 221) Continuous variables Mean Mean t-value Age 35.53 42.23 4.393*** Family size 5.0 6.42 3.605*** Quantity of Avocado sold 9.0 2.6 1957*** Price (Birr/quintal) 685 380.96 14.68** Distance to market 17.6 27.57 6.89*** Education status 7.95 2.55 23. 28*** Market experience 7.28 7.77 1.18 Extension contact 4.93 4.81 0.449 Dummy Variables Frequency percentage Frequency percentage Chi square Sex Male 157 93.4 183 82.8 4.783*** Female 11 6.6 38 17.1 Market information Yes 165 98.2 62 45.9 13.902*** No 3 1.8 73 54.1 Credit access Yes 33 19.6 16 7.2 10.145*** No 135 80.4 205 92.8 *** and** indicates statistically significant at 1% and 5% probability level respectively. The result of the survey showed that out of the high-value market channel participants 157 (93.4%) were male while only 11(6.6%) of them were female. Out of the traditional market channels participants 183 (82.8%) were male while 38 (17.1%) were female. Chi-square test (χ2 = 4.783) indicates that, sex was statistically significant at 1% shows that male headed households were more participating in high value market channels (Table 2). Regarding to the market information, 54.1% of the traditional market channel participants did not access any market information while 45.9% of the respondents accessed market information. The result of the survey revealed that about 98.2% of sampled high value market channel participant farmers accessed market related information. The chi-square test (χ2 = 13.902) showed that, there was significant market information difference between the high-value and traditional market channel participants. According to the survey results, only 7.2% of participants in the traditional market channel used credit access, whereas 92.8% of them unutilized. Furthermore, the survey's result shows that among respondents from the high-value market channel, 80.4% did not use credit while 19.6% did (Table 2). The chi-square test (10.145) reveals a statistically significant difference between high value market and traditional market channels participant farmers in terms of access to credit. Econometric results This section discusses econometric analysis used to assess how participation in high-value market channels impact food security of households. It displays the findings of the propensity score estimation, defining common support region, matching algorithm selection, matching quality testing, calculating average treatment effect on treated, and finally sensitivity analysis. Estimation of propensity score The logistic regression model was used to estimate the propensity score of the high value market channel participants and traditional market channel participants (Table 3). The logistic regression model is applied when the choice variable is dichotomous. For this study, the dependent variable was the participation of the household in high value market channels which takes a value of 1 if the household engaged in high value market channels and 0 otherwise. After matching, the distribution of covariates across the two groups should not differ in a systematic way, therefore the pseudoR 2 should be quite low (Aku et al., 2018 ). In this study, the pseudo-R 2 value is 0.2354 which is fairly low and it indicates high value market channels participants and traditional market channels participant do not have much distinct in overall characteristics and hence the matching between them becomes easier. Eleven explanatory variables were used to estimate the determinants of household participation in high value markets channels. Of these variables age of household, sex of the household, education level of the household head, market information and distance to the high value market channels were the factors that influence household’s participation in high market channels. Among these variables, five of them such as sex, education level, market information were found to be positively determining households’ probability of participation in high value market channels, whereas distance to the high value market channels negatively determining participation high value market channels Age of household head The age of the household has a considerable impact on households' participation in high-value market channels at a 1% probability level, as expected (Table 3). As farmers get one year older, the likelihood that they will participate in a high-value market channels falls by a factor of 0.26, holding all other factors equal. This finding supported by Hernandez et al., ( 2007 ) that demonstrated a negative link between farmer age and the decision to participate in high-value market channels. Education level of household head The result in Table 3 shows that at a 5% level of significance, the educational level of the household head positively and significantly influenced the households' participation in high-value market channels. Keeping all other factors equal, the likelihood that farmers will participate in the high-value market channel increases by a factor of 1.47 as their educational level rises by one school year. From the study's finding, it may be inferred that farmers with greater education are more likely to sell through high-value market channels. In other words, if the farmers are less educated, the likelihood of selling via traditional channels increases. This finding is consistent with a prior study by Ismail et al., ( 2013 ), who found that farmers who sell through high-value market channels have greater educational levels than farmers who sell through traditional market channels. Quantity of avocado Supplied to Market The volume of avocados supplied to the market has a favorable and significant impact on the chance of participating in the high-value market channel with a 5% level of relevance (Table 3). When producers supply one quintal more of avocados to the market while holding all other factors equal, the probability of participating in the high-value market channel rises by a factor of 3.88. Based on this finding, households that supplied a considerable amount of avocados accessed high-value market channels more frequently than those that supplied less of fruit. This result is consistent with that of Muthini (2015), who discovered that farmers with more mango trees than those with fewer mango trees were more likely to sell to the export market. Distance to the high-value market At a 5% significance level, distance to the high-value market had a negative and substantial impact on participation in the high-value avocado market channels. As farmers' distance from residences home to the high-value market increases by one kilometer while holding all other variables constant, the odds in favor of participating in the high-value market channel decrease by a factor of 0.87. Farmers who are located far from the high-value markets may decide to sell to the traditional market channels, in their villages, rather than selling to high-value markets in far-off markets that increase transaction costs. This finding is in line with Shiimi et al., ( 2012 ) who found that participation in high-value markets was negatively impacted by distance to market. Price of avocado The study's findings demonstrate that at a 5% level of significance, the farmers' high-value market channel participation is significantly and favorably correlated with avocado price. When the price of avocado rises by one birr, the likelihood of participating in the high-value avocado market channel rises by 0.98 factor, keeping other independent variables at their mean levels. The variables' positive and significant correlation suggests that producers are increasingly choosing high-value market channels as the price of avocados grows there. The result is in line with those of Alene et al., ( 2008 ), who discovered a beneficial link between price and market activity. Access to market information At a 5% level of significance, access to market information had a favorable and significant impact on participating high value market channels. When the farmer gained access to market information, the likelihood of participating in the high-value avocado market channels increased by factors of 14.8, keeping other independent variables at their mean levels. This result concurs with studies by Megersa etal., ( 2020 ), who discovered that people with access to market information are more likely to shop in the agri-food sector. Table 3 Determinants of Avocado farmers’ participation in high value market channel Variables Coefficient P-value Odd ratio Sex of household head 0.608 0.516 1.838 Age of household head -1.332 0.001 0.263*** Educational status 0.387 0.044 1.473** Family size -0.224 0.266 0.798 Extension contact 0 .660 0.527 1.936 Credit access 0.689 0.577 0.501 Avocado Marketing experience 0.092 0.506 0.911 Quantity of avocados sold 1.358 0.018 3.888** Price of avocado 0.010 0.016 0 .989** Market information -0.132 0.022 9.875** Distance to market 2.695 0.026 14.808** Number of observation = 389 Prob > ch2 = 0.0000 Log likelihood = --188.2688 LR Ch2 (11) = 162.73 Pseudo R 2 = 0.2354 *** and ** significant at 1and 5% significance level respectively Matching quality analysis Table 4 below shows the distribution of propensity score for all households. As shown in the table, the propensity scores vary between 0.0104–0.9985 for all households with mean score of 0.4318. Whereas the score vary between 0.0826–0.9985 for high value market channels participants household with mean score of 0.5946. The common support then lies between 0.0826–0.9364. This means that household whose propensity score less than minimum (0.0826) and larger than maximum (0.9364) are not considered for matching purpose. Based on this procedure, 39 households (6 household from high value market channels participant group and 33 households from traditional market channels participant group were discarded from the study in impact assessment. Table 4 Distribution of estimated propensity scores Categories Obs Mean Std. Dev. Min Max Total households 389 0.4318 0.2663 0.0104 0.9985 High value channel market participant 168 0.5946 0.2279 0.0826 0.9985 Traditional market channel participant 221 0.3081 0.2236 0.0104 0.9364 Figure 2 below shows the calculated propensity scores for both traditional and high-value market channels participants. The bottom half of the graph pertains to participants in the traditional market channels, while the top half of the graph shows the distribution of propensity scores for high-value market channels participants. On the y-axis are the score densities. The expected output, a trustworthy indicator of this, falls solely between the range of 0 and 1. It demonstrates that the distribution of the anticipated likelihood of participation has an acceptable amount of overlap. Choice of matching algorism After determining the common support region, several matching estimators (algorithms) were tested to match households in the common support region that were participants in traditional market channels with high value market participants. Three factors—namely, the equal mean test (balancing test), pseudo R 2 , and size of the matched sample were taken into consideration while deciding on the matching algorithm. It is ideal to use matching algorithms that balance all explanatory variables across groups (resulting in insignificant mean differences between participants in high-value market channels and those in traditional market channels), have low pseudo-R 2 values, and provide large sample sizes (Dehejia and Wahba, 2002). In this matching at nearest neighbor of neighborhood 5, 11 variables were insignificant in mean difference, relatively have low pseudo-R 2 (0.016), and comparatively have large matches sample size (350). Based on those criteria, nearest neighbor of neighborhood 5 was found to be best estimator for this study. Therefore, impact analysis procedure was followed and discussed by using nearest neighbor of neighborhood 5. Table 5 Performance criteria of matching algorisms Matching Algorism Performance criteria Balancing test* Pseudo R 2 Matched sample size Nearest Neighbor Neighbor 1 10 0.038 350 Neighbor 2 11 0.027 350 Neighbor 3 11 0.028 350 Neighbor 4 11 0.027 350 Neighbor 5 11 0.016 350 Radius 0.01 5 0.218 350 0.1 5 0.218 350 0.25 5 0.218 350 0.5 5 0.218 350 Caliper 0.01 11 0.016 306 0.1 10 0.038 350 0.25 10 0.038 350 0.5 10 0.038 350 Kernel 0.01 11 0.012 306 0.1 11 0.024 350 0.25 10 0.024 350 0.5 11 0.065 350 Testing of the matching quality (effect analysis) After selecting best performing matching algorism which satisfies prior identified performance criteria, balance of propensity score and explanatory variables was checked by the selected matching algorism (nearest neighbor of neighborhood 5 in this case).The standard bias difference between identified explanatory variables before matching was in the range of 1.8%-128.0% in absolute value (Table 6). But after matching, the remaining standardized error differences between explanatory variables lay between 2.1%- 13.9% in absolute value which is below the critical level of 20% suggested by Rosenbaum and Rubin ( 1983 ). Table 6 Testing of the matching quality (effect analysis) Covariate Sample Mean % bias % bias reduction P value Treated Control pscore Unmatched 0.59468 0.30812 126.9 0.000 Matched 0.58078 0.57905 0.8 99.4 0.998 SOHH Unmatched 0.93452 0.83258 32.1 0.002 Matched 0.9321 0.96049 -8.9 72.1 0.389 AOHH Unmatched 50.351 53.697 -25.9 0.013 Matched 50.574 50.01 4.4 83.1 0.688 ELOHH Unmatched 5.1964 3.6697 36.0 0.000 Matched 5.1667 4.837 7.8 78.4 0.665 FSIADE Unmatched 4.012 3.9186 5.6 0.588 Matched 3.9862 3.8659 7.2 9.8 | 0.676 AME Unmatched 18.73 18.625 1.8 0.855 Matched 18.713 18.836 -2.1 -17.2 0.825 AS Unmatched 24.543 16.525 128.0 0.000 Matched 23.953 24.288 -5.4 95.8 0.629 TLU Unmatched 3.9171 2.8777 53.6 0.000 Matched 3.9205 4.0954 -9.0 83.2 0.393 MI Unmatched 0.72024 0.70588 3.2 0.828 Matched 0. 72222 0.75185 -6.5 -106.4 0.755 CU Unmatched 0.52976 0.50226 5.5 0.765 Matched 0.53086 0.51852 2.5 55.1 0.479 FOECPM Unmatched 5.1369 3.8009 48.4 0.000 Matched 5.1667 4.784 13.9 71.4 0.384 DTHVM Unmatched 29.536 32.882 -17.4 0.097 Matched 29.824 31.823 -10.4 40.3 0.284 The covariate balance before and after result showed that nearest neighbor of neighborhood 5 has low R 2 and insignificant likelihood ratio indicating that high market channels participant household and traditional market channel participant household had same distribution after matching (Table 7).These results indicate that the matching procedure is able to balance the characteristics in the treated and the matched comparison groups. Hence, these results can be used to assess the impact of formal market participant having similar observed characteristics. This enables to compare observed outcomes for high value market channel participant with those of traditional market channel participant group sharing a common support. Table 7 Indicators of covariate balances before and after matching Test indicator Before matching After matching Pseudo R 2 0.2354 0.016 LR χ 2 (P value) 125.23(0.0000) 7.13(0.896) Estimation of average treatment effect on the treated (ATT) The ATT result revealed that high value market channels had a positive and significant effect on food security of smallholder avocado farmers in the study area. The participants in the high-value market channel consumed 2910.65 kcal, whereas the participants in the traditional market channels consumed 2279.91 kcal.The finding of this study is consistent with earlier studies that discovered participation in high value markets has potential for raising food security (Reardon, 2015 ; Wiggins, 2014 ; Mmbando et al., 2015 ). Table 6 Results of average treatment effect on the treated Outcome variable High value market channels participants Traditional market channels participants Difference (ATT) Weekly calorie intake 2910.65 2279.91 493.71*** Sensitivity analysis Sensitivity analysis revealed that even after allowing high value market channel participant and traditional market channels participant households to have different probabilities of getting treated by up to 200% (2) in terms of unobserved variables, the conclusion for the effect of high value market channel remained unchanged. This indicates that, the p-critical values are significant for all outcome variables estimated at different levels of the critical value of γ, indicating that the study has taken into account significant covariates that impact both participation and outcome variables. The impact estimates (ATT) of this study are therefore insensitive to unobserved selection bias and are a direct result of the high value market channels participation. Table 8 Result of sensitivity analysis using Rosenbaum bounding approach Outcome variables e γ =1 e γ =1.25 e γ =1.5 e γ =1.75 e γ =2 Food security 0.00000 0.00000 1.3e-15 4.9e-13 4.0e-11 Conclusion and policy implications This study examined avocado producers' participation in high-value market channels and its impact on food security in the Sidama region of Ethiopia. This study employed a cross-sectional research design. The primary and secondary data were used to gather the necessary data for the study. The primary data were collected from 389 avocado producing households using a semi structured questionnaire. Secondary data were collected from journals, annual reports, websites and different published and unpublished materials. Descriptive statistics, inferential statistics, and propensity score matching models were used to analyze the data. The PSM result revealed that the households were participation in high value market channels consumed 2910.65 kcal, whereas the households were participating in the traditional market channels consumed 2279.91 kcal. It was concluded that high-value market channel's participation has a favorable and considerable impact on the food security of the households. However, the logit result showed that factors such as the household head's age, level of education, the quantity and price of avocados sold, market information, and distance from the high-value market all had an impact on rural households' participation in high-value market channels. Given the substantial contributions farmers' participation in the high-value avocado market channels makes to household food security, concerned bodies in Ethiopia should encourage more households to participate in the high-value market channels by raising awareness among other traditional market participant farmers. Declarations Acknowledgments The authors would like to thank the experts’ agriculture office of the district for their patience and support to get the required supplementary data. The authors also want to express their gratitude to the participants for their enthusiastic readiness to take part in this study. Author contributions: First author contributed to research proposal writing, data collection, and supervision. The Second author assisted data cleaning and feeding. Third author contributed data analysis and article writing. All authors read and approved final manuscript Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. 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Barron., (2018). A review of trends, constraints and opportunities of smallholder irrigation in East Africa. Global Food Security. 17: 196-212. Ngema, P.Z, Sibanda, M, Musemwa, L., (2018). Household food security status and its determinants in Maphumulo local municipality, South Africa. Sustainability, 10, 3307. [CrossRef] Oduniyi, O.S., Antwi, M.A. and Mukwevho, A.N. (2021). Assessing emerging beef farmers’ participation in high-value market and its impact on cattle sales in South Africa. Int. J. Agril. Res. Innov. Tech. 11(2): 27-36. https://doi.org/10.3329/ijarit.v11i2.57252outhern Ethiopia Reardon T. (2015). The hidden middle: the quiet revolution in the midstream of agrifood value chains in developing countries. Oxf Rev Econ Policy.;31(1):45–63. https://doi.org/10.1093/oxrep/grv011. Regasa, M.S., M. Nones and D. Adeba., (2021). A review on land use and land cover change in Ethiopian Basins. Land, Vol. 10. 10.3390/land10060585 Romero Granja, C., Wollni, M., (2018). Dynamics of smallholder participation in horticultural export chains: evidence from Ecuador. Agric. Econ. 49 (2), 225-235. Rosenbaum PR, Rubin DB. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1):41–55. https://doi.org/10.1093/biomet/70.1.41. Rosenbaum, P. R., (2002). Observational Studies (2nd Ed.).New York, NY: Springer Sharp, K., Ludi, E., & Gebreselassie, S., (2007). Commercialization of Farming In Ethiopia: Which Pathways? Ethiopian Journal of Economics, 16, 39–54. Shiimi, T, Taljaard, T. P. R., & Jordaan, H., (2012). Transaction cost and cattle farmers’ choice of marketing channel in North Central Namibia. Agrekon , 51 (1), 42-58. Smale M, Diakité L, Keita N., (2012). Millet transactions in market fairs, millet diversity and farmer welfare in Mail. Environ Dev Econ, 17(5):523–46. Tamirat G, Muluken P., (2018). Analysis of apple fruit value chain in southern Ethiopia; the Case of Chencha District. Greener J Plant Breeding Crop Sci. 2018; 6(3):26–34., https://doi.org/10.15580/GJPBCS, 3.100218043 Tarekegn, K., Asado, A., Gafaro, T., & Shitaye, Y., (2020). Value chain analysis of banana in Bench Maji and Sheka Zones of Southern Ethiopia. Cogent Food & Agriculture, 6(1), 1785103. https://doi.org/10.1080/ 23311932.2020.1785103 Warsanga, W. B., & Evans, E. A., (2018). Welfare impact of wheat farmers’ participation in the value chain in Tanzania. Modern Economy, 9(4), 853. https://doi.org/ 10.4236/me.2018.94055 Warsanga, W. B., & Evans, E. A., (2018). Welfare impact of wheat farmers participation in the value chain in Tanzania. Modern Economy, 9(4), 853. https://doi.org/ 10.4236/me.2018.94055 Wiggins S. African agricultural development: Lessons and challenges. J Agric Econ. 2014; 65(3):529–56. https://doi.org/10.1111/1477-9552.12075. Wooldridge JM. (2010). Econometric analysis of cross-section and panel data. Cambridge: MIT press. Wordofa, M.G., J.Y. Hassen, G.S; Endris, C.S. Aweke; D.K. Moges and D.T. Rorisa., (2021). Adoption of improved agricultural technology and its impact on household income: A propensity score matching estimation in Eastern Ethiopia. Agric. Food Security. Vol. 10. 10.1186/s40066-020-00278-2. Zegeye, M.B., A.H. Fikrie and A.B. Assefa., (2022). Impact of agricultural technology adoption on wheat productivity: Evidence from North Shewa Zone, Amhara Region, Ethiopia. Cogent Econ. Finance, Vol. 10. 10.1080/23322039.2022. 2101223 Cite Share Download PDF Status: Under Review Version 1 posted First submitted to journal 27 Jun, 2023 Editor assigned by journal 27 Jun, 2023 Submission checks completed at journal 27 Jun, 2023 Editor invited by journal 27 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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In the sub-Saharan Africa, smallholder agriculture has long dominated the economy and will continue to play a crucial role for the foreseeable future (Gollin, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite agriculture being the main source of income in Africa, it is inadequate to feed the continent's expanding population (African Union, 2013).The sector's limited market integration and subsistence status, however, continue to be significant obstacles.\u003c/p\u003e \u003cp\u003eThe agricultural sector also dominates the Ethiopian economy, contributing 34.1% of GDP, 79% of export revenues, 79% of the labor force, and 70% of the raw materials used in industries (Asrat et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Endalew et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gebremariam et al., 2021; Wordofa et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zegeye et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Agriculture in the country is mostly dependent on rainfall, carried out on a small scale, and has limited access to technology, extension assistance, market information, and financial access, all of which have lowered agricultural productivity (Kifle et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nakawuka et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Indeed, the country's agricultural production growth has lagged behind the pace of population expansion (Regasa et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHorticulture farming has been identified as one of the agricultural sub-sectors that is expanding quickly and a potential driver of poverty reduction for low-income smallholder households (Mkindi, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Barrett et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Horticultural crops refer to fruits, vegetables, ornamental and medicinal plants (Amao, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Avocado is one of the horticultural crops with high economic value.\u003c/p\u003e \u003cp\u003eAvocado (Persea Americana Mill.) is originated in Mexico (Muhammad, 2015. After pineapple, avocado is the fruit that is traded the most, it makes up around 25% of all tropical fruits. Avocado has nutritional benefits such as potassium, unsaturated fatty acids, proteins, and fat-soluble vitamins that are uncommon in other fruits (Duarte et al., 2017). The fruit is also utilized as a raw ingredient in the pharmaceutical and cosmetic industries (Duarte et al., 2017). Africa has a strong interest in avocado cultivation and marketing. South Africa, Ethiopia, Cameroon, Rwanda, and Kenya continue to be the continent's top producers of avocados (FAO, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Ethiopia, avocado is grown on a total of 20,908.00 hectares, with a production of 104,492 tonnes. The output of avocados in Ethiopia has historically ranged from 13,888 tonnes in 2001 to 104,492 tonnes in 2019 (FAOSTAT, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Approximately 36% of the nation's annual avocado production comes from the Sidama region (CSA, 2021).\u003c/p\u003e \u003cp\u003eLinking Smallholder farmers to high value market may offer the opportunity to produce and sell high-value products, translating their vertically-coordinated relationships into premium prices and letting them capture a bigger share of the price paid by final consumers (Hussein \u0026amp; Suttie, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; De Janvry \u0026amp; Sadoulet, 2020; Kilelu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Indeed, there is evidence in that participation of small-scale farmers in high-value markets presents opportunities to improve their productivity, income, food security, and reduce poverty (Belay, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharp et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Barrett et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mmbando et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn current days, small-holder farmers have more options to participate in high-value markets due to the booming supermarkets and agro-processing industries, which may help them earn more money and have more reliable access to food (Hernandez et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, for the majority of smallholders in developing regions, sustained participation in high-value markets remains difficult. Recent empirical evidences shows that smallholders frequently quit high value market upon entry due to substantial post-entry transaction risks (Anderson et al., 2015, Michelson, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Lambrecht and Regasa, 2018, Romero and Wollni, 2018). According to Mossie et al., (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) some issues such as poor infrastructure, grading systems, insufficient market information and communication between farmers, traders and consumers pose a significant hindrance to high value market accessibility.\u003c/p\u003e \u003cp\u003eEthiopia has been implementing agricultural development lead industrialization (ADLI) policy since the early 1990s. In order to attain such a development goal, there should be a market oriented production, which ensures marketable surplus as well as efficient and effective marketing system that will enable farmers to gain the actual profit from what they produce. Given reliable market, an increase in market participation in turn makes it easy for farmers to shift into commercial farming, increasing economic growth (Jari and Fraser, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe marketing channels used by farmers to sell their avocados in the study area can be split into two groups: high-value market channels and traditional market channels. Traditional market channels represented by Village markets, whilst, the high-value market channel represented by processor (Yirgalem agro-processing industry, Hawassa juice house and supermarkets).\u003c/p\u003e \u003cp\u003eThere are few research on the impact of smallholder farmers' participation in high-value markets on food security in Ethiopia. Many studies have emphasized on factors determining smallholders participation in high value market (Tamirat et al., 2018; Getahun et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Girmalem et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kassa et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mengesha et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tarekegn et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Biggeri et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gebremedhin et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Habtewold et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kyaw et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Warsanga \u0026amp; Evans, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Despite their undeniable importance, these studies have methodological limitations since they only look at the factors that influence smallholders\u0026rsquo; participation in the high value market, neglecting the importance of this for food security. Thus, this study aimed to fill a knowledge gap in the area by investigating the factors influencing smallholder avocado farmers' participation in a high-value market channel and how it impacts households' food security in Aleta Chuko district, Sidama Region of Ethiopia.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the study area\u003c/h2\u003e \u003cp\u003eThis study was conducted in Aleta Chuko district, Sidama Region, Ethiopia. It is situated in the Sidama region, 62 kilometers south of Hawassa, the Sidama region's capital, and 330 kilometers south of Addis Ababa. Its precise location ranges from 38004'E to 38024'E and 6046'N to 7001'N. The district is divided administratively into 26 rural and 5 urban Kebeles. The Aleta Chuko district has a total population of 209,886, of which 102,215 (48.7%) are men and 107,671 (51.3%) (CSA, 2021). A rough estimate of the district's land area is 32.2 square kilometers. The area has lowland agro ecological zones and varies in altitude from 1400 to 2000 meters above sea level (CSA, 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eResearch design\u003c/h2\u003e \u003cp\u003eCross-sectional data were used in this study. It refers to the type of data in which the data required for analysis need to be collected at a single point in time from the sample to represent population. It is convenient for the qualitative and quantitative descriptive study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData source and methods of collection\u003c/h2\u003e \u003cp\u003eBoth the primary and secondary data were collected for this study. Primary data was collected from avocado producers who are participated in traditional and high value market channel. Primary data collected through checklists, semi-structured questionnaires, and field observations. Prior to the survey, the questionnaire was pre-tested on 14 farmers (7 traditional and 7 high value market channel participants) to evaluate the appropriateness of the design and clarity of the questions, relevance of the questions and to estimate the time required for an interview. Three enumerators who completed a first degree and have knowledge of the local culture, and language of the community were recruited to conduct the interview. They were given appropriate training including field practices, in order to make them understand the objectives of the study, the contents of the interview schedule, how to approach the respondents and conduct interview. The secondary data used for this study, obtained from a review of many sources, including academic journal articles, books, government reports and research works published by various researchers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSampling techniques\u003c/h2\u003e \u003cp\u003eThe purpose of this study was to analyze factors affecting smallholder farmers\u0026rsquo; participation in high value market and its impact on food security. So, smallholder avocado producer farmers in Aleta chucko district are the target population of this study. Multi-stage sampling techniques were used to draw representative sample. In the first stage, Aleta chuko district was purposefully chosen from 36 districts in the Sidama region for its significant potential for avocado production. In the second stage, four Kebeles from 12 avocado producing kebeles were chosen randomly with the help of districts\u0026rsquo; agriculture and natural resource office experts. In the third stage, 389 sample avocado producers were drawn from the 14,748 total avocado producers of the chosen kebeles using Yemane's (1967) formula at 5%level of error. We applied this formula to determine sample size because it is the most appropriate formula when the study population size is known [51]. Consequently, it has been widely used by many recent studies [52\u0026ndash;54] in determining the sample size for their studies. Finally, the sample size was distributed using the probability proportional to the sample size.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$n= \\frac{N}{1+N\\left({e}^{2}\\right)}----------------------------\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, n\u0026thinsp;=\u0026thinsp;sample size, N\u0026thinsp;=\u0026thinsp;total avocado producer households, and e\u0026thinsp;=\u0026thinsp;is level of precision (0.05).\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$n= \\frac{\\text{14,748}}{1+\\text{14,748}({0.05}^{)2}}=389 ------------------------\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMethod of data analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eMeasuring food security\u003c/h2\u003e \u003cp\u003eDue to the intricacy of the food security concept, choosing an acceptable food security indicator is the most difficult problem (Hendricks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This is due to the fact that none of the metrics adequately represent the idea of food security. As a result, one of the indicators from (Lele et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which divided indicators into eight groups based on the underlying data source, was employed in the current investigation. These might all be put to use in different ways. Individual or household recollection, national observations, market observations, prevalence and depth of undernourishment, anthropometric measurements, breastfeeding and sanitation, clinical data, composite indices, and multidimensional measures are the sources of data used to create the indicators. Among the aforementioned indicators, individual or household recall indicators are regarded as the simplest technique to collect pertinent information from household using survey questionnaires. One of the most crucial aspects of household food security is the total quantity of calories consumed by each member of the home for each food item (Berry et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this study, it was calculated how many calories were consumed differently by the treatment and control groups. Interviewees were asked to provide information about the types and quantities of food that their households had consumed in the seven days prior to the survey. The processes that were involved in translating the family's physical food consumption into calories consumed were as follows. For each food item consumed, local measuring units were first transformed into a standard unit of measurement. Second, the national food composition table created by the Ethiopian Health and Nutrition Research Institute was used to convert each food item ingested to calories (EHNRI, 2022). Third, the total number of food calories consumed was multiplied by 24 hours to get daily quantities. Using a conversion factor for adult equivalent, the total food calories per household were changed to an adult equivalent (AE) unit (Stock et al., 1997). The calculated average kilocalorie (kcal) demand per adult household equivalent per day was compared to the minimum subsistence kcal requirement for Ethiopia, which was defined by FDRE (EHNRI, 2022) at 2200 kcal.\u003c/p\u003e \u003cp\u003eAs a result, the study employed 2200 kcal as a precise cutoff point to classify households as either food secure or food insecure. Finally, the household was classified as food secure if their physical food consumption in kcal was greater than or equal to 2200 kcal/day/AE, whereas a household was classified as food insecure if it consumed less than 2200 kcal/day/AE. Using a generally used model of effect evaluation, such as that in (Seng, 2016, and Ngema et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), it is possible to evaluate how market entry affects household food security.\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;βX\u0026thinsp;+\u0026thinsp;γI\u0026lowast;+ε and Y\u0026thinsp;=\u0026thinsp;1, Yi\u0026lowast; \u0026gt; 0; 0, Yl\u0026lowast; \u0026le; 0\u003c/p\u003e \u003cp\u003eWhere Y is the household\u0026rsquo;s HDDs per capita, β represents the coefficient, and X is a vector of household and farm characteristics and other factors expected to affect household food security. \u0026ldquo;I\u0026rdquo; is a dummy for high-value market participation, γ is the coefficient capturing the effect of high-value market participation on household food security, and ε stands for random errors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were edited, coded, entered, and cleaned to make it ready for analysis. After doing this, data analysis techniques such as descriptive statistics, inferential statistics, and econometric models were used. To give summary statistics of quantitative data related to the socio-demographic, economic, and institutional features of sample households, descriptive statistics including percentage, frequency, and mean were employed. To determine whether there were any statistically significant differences in the observations between the high-value market channel and traditional market channel participants, inferential statistics including the t-test and Chi-square test were performed. The impact of farmers\u0026rsquo; participation in the high-value market channel on food security was analyzed using the propensity score matching (PSM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSpecification of propensity score matching (PSM) model\u003c/h2\u003e \u003cp\u003eThe following three expected biases make it difficult to estimate the impact of treatment on outcomes: (1) the selection of observables as a result of sampling bias, (2) the choice of a comparison group in the presence of externalities, and (3) the choice of an unobservable as a result of differences between the treated and control groups in the distribution of their unobserved characteristics (Wooldridge, 2012). The coefficients on the control variables in basic regression or logistic models would be the same for participants and non-participants. Due to this constraint, the majority of studies in the literature used the PSM model to assess how treatments affected outcomes (Smale et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rosenbaum et al., 1983). The PSM method enhances regression's capacity to generate accurate causal estimates due to its non-parametric approach to the balance of variables between the treated and control groups (Connife et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). According to Caliendo and Kopeinig (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), the implementation of PSM involves six steps. These are: PSM estimation; choosing a matching algorithm, checking for overlap (common support); matching quality test, impact estimation, and sensitivity analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEstimating the propensity score\u003c/h2\u003e \u003cp\u003eThis involves the identification of the probability of participation in high value market. When estimating the propensity score, two choices have to be made. The first one concerns the model to be used for the estimating the probability of participation in high value market channel, and the second one is about the variables to be included in that model (Caliendo and Kopeinig, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). For binary dependent variables the models that are used to estimate the probability of participation against non-participation households are logit or probit models. However, the choice between the two is not as problematic as they provide the same result (Gujarati and Porter, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn practice, a model (Logit or Probit for binary treatments) is estimated in which participation in treatment is explained by a number of pre-treatment variables. Predictions from this estimation are then used to construct the propensity score, which runs from 0 to 1 (Rosenbaum et al., 1983; Aku et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study used the Logit model to estimate the propensity score even though both models produce results that are essentially equivalent. Participation in the high-value market channel, which has a value of 1 if a farmer is high value market channels participant and 0 traditional market channels participant, served as the dependent variable for the logit model's estimation. Rosenbaum and Rubin claim (Rosenbaum et al., 1983), the logit model can be specified as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Pi=\\frac{{e}^{zi}}{{ 1+e}^{zi}}---------------------------------$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Pi\\)\u003c/span\u003e\u003c/span\u003e is the probability of participation in high-value market channel\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$zi={\\beta }\\text{o} +\\sum \\beta ixi+ui----------------------------$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e= 1, 2, 3, - --, n, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }\\text{o}\\)\u003c/span\u003e\u003c/span\u003e = intercept, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta i\\)\u003c/span\u003e\u003c/span\u003e =regression coefficients to be estimated \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(ui\\)\u003c/span\u003e\u003c/span\u003e = a disturbance term, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(xi\\)\u003c/span\u003e\u003c/span\u003e =variables\u003c/p\u003e \u003cp\u003eThe probability that a household belongs to the traditional market channel is:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$1-p=\\frac{1}{{ 1+e}^{zi}}--------------------------------$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThen the odds ratio can be written as\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\frac{Pi}{1-p} = \\frac{{ 1+e}^{zi}}{{ 1+e}^{-zi}}---------------------------------$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe left-hand side of Eq.\u0026nbsp;(6) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{Pi}{1-p}\\)\u003c/span\u003e\u003c/span\u003eis simply the odds ratio in favor of participation in the high-value market channels. It is the ratio of the probability that the household would participate in the high-value market channel to the probability that it would not participate in the high-value market channels. Lastly, by taking the natural log of Eq.\u0026nbsp;(4) the log of odds ratio can be written as:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\text{Li}=\\text{ln}\\left(\\frac{pi}{1-pi}\\right)=\\text{ln}\\left({e}^{{\\beta }\\text{o}+{\\sum }_{j}^{n}={1}^{\\beta jXij}}\\right)=Zi------------------$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$zi={\\beta }\\text{o}+{\\sum }_{j}^{n}={1}^{\\beta jxji+\\epsilon i}-------------------------$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{L}\\text{i}\\)\u003c/span\u003e\u003c/span\u003e is the log of the odds ratio in favor of participation in the high value market channels, which is not only linear in\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Xji\\)\u003c/span\u003e\u003c/span\u003e, but also linear in the parameters. Predictor variables that affect the selection process, participation in high-value market channels, and the outcome of interest should be included in the matching theory in the propensity score created by the logit model (Bryson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Jalan and Ravallion, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Bergstra et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Eleven independent variables were proposed and utilized in the model as a result of this recommendation. These factors were found, and it is thought that they control whether households participate in high-value market channels or traditional market channels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe common support region determination\u003c/h2\u003e \u003cp\u003eThe conditional independence assumption (CIA) and the common support condition (CSC) must both be satisfied for the PSM method's results to be considered legitimate (Caliendo et al., 2008). The Confoundedness Assumption (CIA), which asserts that a treatment must satisfy the requirement of being exogenous, contends that any systematic difference in results between a treatment group and a control group with the same values for characteristic X can be attributed to the treatment. The common support or overlap requirement indicates that there is enough similarity between the traits of the treated and untreated units to identify suitable matches (or common support). Households for which there is no match are removed since there is no foundation for comparison. It is done by eliminating observations whose propensity scores are lower than the minimum and greater than the maximum of the participant and control groups, respectively (Caliendo and Kopeinig, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eChoosing matching algorithm\u003c/h2\u003e \u003cp\u003eThere most widely used matching algorithms are (NNM), kernel-based matching (KBM), radius-based matching (RBM), caliber-based matching (KBM), and Kernel-based matching (KBM) (Caliendo \u003cem\u003eet al.\u003c/em\u003e, 2008; Caliendo and Kopeinig, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Among them, a matching estimator that bears low pseudo R\u003csup\u003e2\u003c/sup\u003e, results in large matched samples and insignificant explanatory variables after matching should be chosen among them (Dehejia and Wahba, 2002).Therefore, NNM, KBM, RBM, CBM, and KBM estimators were utilized in this work.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTesting the matching quality\u003c/h2\u003e \u003cp\u003eThe matching quality of the best matching algorism should be tested for its quality using different parameters. The basic idea is to compare the situation before and after matching and check if there are any differences after conditioning on the propensity score. The matching quality of the best matching algorism identified in step three should be tested for its quality using different parameters. Chi-square test for the joint significance of variables, bias reduction after matching and number of covariates results in significant differences between two groups should be checked as a criterion of matching quality (Caliendo and Kopeinig, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImpact estimation\u003c/h2\u003e \u003cp\u003eAfter matching quality tests, impact estimation needs to be conducted using the average treatment effect on the treated (ATT). It is the mean outcome difference between intervention participants and non-participants matched by PSM.\u003c/p\u003e \u003cp\u003eHence, the ATT for the individual can be defined as the difference between the expected outcome variable; (food security based on our study with and without participating high value market channels).The ATT can then be calculated as follows after determining the propensity scores:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$ATT=E p\\left(X\\right) \\left\\{\\right(E (Y │D=1, P(X)-E (Y0│D=0, P\\left(X\\right)\\left)\\right\\}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, ATT represents Average Treatment effect on the treated group. The symbol \u0026ldquo;│\u0026rdquo; stands for conditional on Ep(X) denotes the expectation with respect to the distribution of propensity score in the entire population and D denotes intervention participation indicator which is equal to one (1) if a farmer participated in the intervention and zero (0) if otherwise. The estimation of ATT clearly depends on the characteristics of the two groups: treated and control for (Y1│D\u0026thinsp;=\u0026thinsp;1) and (Y0│D\u0026thinsp;=\u0026thinsp;0)} respectively as explained above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eSensitivity analysis is final step in PSM application. An assumption of the matching method is based on Conditional Independence (CIA), which states that the researchers should observe all variables that are at the same time influencing the participation decision and outcome variables (food security). However, this assumption is non-testable since the data are uninformative about the distribution of the untreated outcome for treated groups and vice versa (Becker and Caliendo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The estimation of treatment effects with matching estimators is based on the selection of observables assumption. As a result, if there are unobserved variables that affect assignment into treatment and the outcome variable simultaneously, a hidden bias might arise which invalidate the CIA and it results in biased estimates of ATTs (Rosenbaum, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Since estimates are not robust against hidden biases, it is important to test the robustness of results to depart from the identifying assumption. However, it is impossible to estimate the magnitude of selection bias with non-experimental data. To address such problem, in this study, sensitivity analysis was applied accordingly (Rosenbaum, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eHypothesis, variable description and expected sign\u003c/h2\u003e \u003cp\u003eFinding the factors that influence avocado producers to participate in high value market channels and how this affects their food security is important. It is also necessary to investigate the relationships between these factors and the dependent variables to determine which factors have a significant impact. In light of this, the following dependent, independent, and outcome variables were identified and hypothesized for this investigation (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDependent Variable\u003c/h2\u003e \u003cp\u003eParticipation in the high-value market, which has a value of 1 for high-value market channel participants and 0 for the traditional market channel participants, is the study's dependent variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOutcome variable\u003c/h2\u003e \u003cp\u003eFood security, as evaluated by household food intake in calories, is the study's outcome variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Variables\u003c/h2\u003e \u003cp\u003eThe independent variables which are expected to influence avocado households\u0026rsquo; participation in high-value market channels are presented below.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSex of the household head\u003c/h2\u003e \u003cp\u003eThis is a dummy variable which takes a value 1 if the household head is male and 0 otherwise). Male household heads have been reported to have a better tendency in searching market alternative for the sale of avocado than female household heads. Male households are participated in the vegetable market more than females and females are disadvantaged in marketing because of unequal distribution of resources as well as cultural barriers (Banchamlak \u0026amp; Akalu, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, sex of household head was hypothesized to influence farmers participation in high value market positively\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eAge of the household head\u003c/h2\u003e \u003cp\u003eThis is a continuous variable and defined as the number of year of household head age. In this study, Age of household\u0026rsquo;s head is expected to be negatively affect high value market participation since the older farmers are more likely to participate in village market. Previous study by Edosa, (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that age has negative effect in market participation decision. Thus it was expected to be negatively correlated with high market channel participation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eEducation level of household head\u003c/h2\u003e \u003cp\u003eEducational level of the household head is continuous variable measured in a number of years spent in formal school. Household heads with more years of formal education expected to have a higher ability to accept new ideas and innovations, and therefore would be more willing to sale avocado through high value market channel. Better educated farmers tend to be more innovative and are therefore more likely to participate in modern market channels (Habtamu, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Banchamlak and Akalu, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) Thus, education level of household head was hypothesized to influence farmers\u0026rsquo; participation in high value market positively.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003eFamily size\u003c/h2\u003e \u003cp\u003eThis variable is a continuous explanatory variable and measured in adult equivalent ratio of the household members. It is assumed that household with more adult household members produce large amount of avocado with expected quality and supply to the high value market. The involvement of smallholders in in the value chain market influenced by family size of the household (Kassa et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).Therefore, this variable was hypothesized to have positive influence on high value market participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eQuantity of Avocado produced\u003c/h2\u003e \u003cp\u003eIt is a continuous variable measured in quintals. High avocado production was hypothesized to have a positive effect on high value market participation. Farmers producing small quantities are likely to sell their products to traditional market within a village rather than selling to modern market. According to Chalwe (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) an increase in output is a motivation to produce and sell more and produce more that ultimately increases income Therefore, it was hypothesized that quantity of avocado produced would have positively relationship with high value market participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003ePrice of avocado\u003c/h2\u003e \u003cp\u003eThis is a continuous variable measured in price of avocado in the current price in birr per quintal. When the price of the product is promising, farmers are motivated to sell their product to a particular market chains. This makes the supply to be directly related with a price offer. For example, Warsanga et al., (2018) found that supermarkets paid horticultural suppliers higher than what they got in traditional market. Therefore, this variable was hypothesized to have positive relationships with high value market participation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eMarket information\u003c/h2\u003e \u003cp\u003eIt is dummy variable that takes a value of 1 if the farmer\u0026rsquo;s obtained market information and zero otherwise. According to Jari and Fraser (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) as a result access to market information is a positively correlate with the market participation of farmer. A more access to market information could contribute to an increase in smallholder farmers\u0026rsquo; participation in modern high value markets. Therefore, access to market information hypothesized to influence the high value market participation positively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eAvocado marketing Experience\u003c/h2\u003e \u003cp\u003eThis is a continuous variable measured in number of years. A household with better experience in avocado marketing is expected to participate more in high value market channel than the one with the less experienced. Oduniyi et al., (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found beef farmers market experience positively determine farmers participation in high value market channel. Therefore, higher avocado marketing experience anticipated to have a positive relationship with high value market participation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFrequency of extension contact\u003c/h3\u003e\n\u003cp\u003eThis refers to the number of contacts that the respondent made with extension agents. Here, the frequency of contact between the extension agent and the farmers is hypothesized to be the potential force, which accelerates the effective dissemination of adequate agricultural and market information to the farmers. Previous study by Tarekegn et al., (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that access to extension service has positive and significant role on the farmers participation in high value chain market. As per this study, it was hypothesized that frequency of extension contact influence farmers participation in high value market positively.\u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003eAccess to credit service\u003c/h2\u003e \u003cp\u003eIt is measured in terms of whether respondent farmers accessed or not credit service. It is Dummy variable which takes value of 1 if the farmer has access to credit in terms of availability of credit sources and possibility of getting credit and 0 otherwise. Farmers who have access to credit may overcome their financial constraints and therefore buy inputs (Hagos et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Farmers without cash and no access to credit will find it very difficult to attain and adopt new technologies. Thus, in this study, it was expected that access to credit increase the probability of participation in high value market positively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eDistance to the high-value market\u003c/h2\u003e \u003cp\u003eThis variable was measured by kilometers from the production area to the high value market. The closer they are to the high value market; the more likely it is that, the farmer will receive valuable information and choose the channel to supply avocado. On the other hand, if the distance to the high value market increases, the transportation cost will also increase and the farmers obligated to sell their avocado to traditional village market. The studies by Girmalem et al., (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Mengesha et al., (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that the distance to the market has the negative influence on the farmer\u0026rsquo;s participation in market. Therefore, it was hypothesized that the modern market is far from the farm the farmer more selling to traditional market chain and then the income of farmer become low. Thus, distance to the high value market was expected to have negative relationships with high value market.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable description and their expected signs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected sign\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of the household head \u003cem\u003e(1\u0026thinsp;=\u0026thinsp;Male, 0\u0026thinsp;=\u0026thinsp;Female)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of the household head \u003cem\u003e(Years)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level of household head \u003cem\u003e(Year of schooling)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily size (\u003cem\u003eADE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantity of Avocado sold \u003cem\u003e(Quintal)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrice of avocado/quintal\u003cem\u003e(Birr)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket information \u003cem\u003e(1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvocado marketing Experience \u003cem\u003e(Years)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of extension contact \u003cem\u003e(Number of days )\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to credit service \u003cem\u003e(1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to the high-value market \u003cem\u003e(KM)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of sample households\u003c/h2\u003e \u003cp\u003eThe mean ages of the sampled respondents who participated in the high-value market channels and the traditional market channels were 35.53 and 42.23 years, respectively (Table\u0026nbsp;1). The t-test (-4.393) result shows that there was a statistically significant mean age difference between farmers participating in the high-value market channel and those in the traditional market channels at a 1% level of significance. Study\u0026rsquo;s finding revealed that the typical family sizes of high-value market channels and traditional market channels participants were 5.0 and 6.42, respectively. The t-test (-3.605) demonstrates that, at the 1% level of significance, there was a statistically significant difference in the mean family size between high-value market participants and traditional market participants.\u003c/p\u003e \u003cp\u003eRegarding the level of education in the households, farmers who participated in the high-value market channels had a greater mean number of school years (7.95) than farmers who participated in the traditional market channels, who had a mean of 2.55 school years. The t-test score (23.283) demonstrate that there was statistically significant mean year schooling difference between high value and traditional market channel participants. High-value market channels participants paid 685ETB for each quintal of avocado, while traditional market channels participants paid 380.96ETB. T-test result (14.68) confirms that there was a statistically significant mean price difference between high-value market channels and traditional market channel.\u003c/p\u003e \u003cp\u003eFurthermore, the average market supply of avocados by the high-value market channels and traditional market channels participants were 9 and 2.6 quintals, respectively. The t-test value (19.57) reveals that at the 1% level of significance, there was a statistically significant mean quantity of avocado supply difference between high-value market channels participants and those in the traditional market channels participants. High-value market channels participants and traditional market channels participants had mean marketing experience of 7.28 and 7.77 years, respectively (Table\u0026nbsp;2). The t-test result shows that the market experience of the household head was statistically insignificant, indicating that the participants in the traditional and high-value market channels had comparable distributions of market experience.\u003c/p\u003e \u003cp\u003eThe average distance from the homes of traditional market channel participants to the high-value market was 27.57 kilometers, compared to 17.6 kilometers for high-value market participants. The t-test result (-6.89) reveals that There was statistically significant difference in distance between traditional market channels participants and high value market channels participants home to the high value market channels at 1% significance level. The t-test result revealed that there was no statistically significant difference between traditional and high-value market participants in terms of mean extension contact.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of sample respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHigh-value market channels participants\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;168)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTraditional Market channels participants (N\u0026thinsp;=\u0026thinsp;221)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eContinuous variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e35.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e42.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.393***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.605***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQuantity of Avocado sold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1957***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrice (Birr/quintal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e380.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.68**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDistance to market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e27.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.89***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23. 28***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarket experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExtension contact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDummy Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFrequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003epercentage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFrequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003epercentage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eChi square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.783***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.902***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCredit access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.145***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*** and** indicates statistically significant at 1% and 5% probability level respectively.\u003c/p\u003e \u003cp\u003eThe result of the survey showed that out of the high-value market channel participants 157 (93.4%) were male while only 11(6.6%) of them were female. Out of the traditional market channels participants 183 (82.8%) were male while 38 (17.1%) were female. Chi-square test (χ2\u0026thinsp;=\u0026thinsp;4.783) indicates that, sex was statistically significant at 1% shows that male headed households were more participating in high value market channels (Table\u0026nbsp;2). Regarding to the market information, 54.1% of the traditional market channel participants did not access any market information while 45.9% of the respondents accessed market information.\u003c/p\u003e \u003cp\u003eThe result of the survey revealed that about 98.2% of sampled high value market channel participant farmers accessed market related information. The chi-square test (χ2\u0026thinsp;=\u0026thinsp;13.902) showed that, there was significant market information difference between the high-value and traditional market channel participants. According to the survey results, only 7.2% of participants in the traditional market channel used credit access, whereas 92.8% of them unutilized. Furthermore, the survey's result shows that among respondents from the high-value market channel, 80.4% did not use credit while 19.6% did (Table\u0026nbsp;2). The chi-square test (10.145) reveals a statistically significant difference between high value market and traditional market channels participant farmers in terms of access to credit.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEconometric results\u003c/h3\u003e\n\u003cp\u003eThis section discusses econometric analysis used to assess how participation in high-value market channels impact food security of households. It displays the findings of the propensity score estimation, defining common support region, matching algorithm selection, matching quality testing, calculating average treatment effect on treated, and finally sensitivity analysis.\u003c/p\u003e\n\u003ch3\u003eEstimation of propensity score\u003c/h3\u003e\n\u003cp\u003eThe logistic regression model was used to estimate the propensity score of the high value market channel participants and traditional market channel participants (Table\u0026nbsp;3). The logistic regression model is applied when the choice variable is dichotomous. For this study, the dependent variable was the participation of the household in high value market channels which takes a value of 1 if the household engaged in high value market channels and 0 otherwise.\u003c/p\u003e \u003cp\u003eAfter matching, the distribution of covariates across the two groups should not differ in a systematic way, therefore the pseudoR\u003csup\u003e2\u003c/sup\u003e should be quite low (Aku et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this study, the pseudo-R\u003csup\u003e2\u003c/sup\u003e value is 0.2354 which is fairly low and it indicates high value market channels participants and traditional market channels participant do not have much distinct in overall characteristics and hence the matching between them becomes easier. Eleven explanatory variables were used to estimate the determinants of household participation in high value markets channels. Of these variables age of household, sex of the household, education level of the household head, market information and distance to the high value market channels were the factors that influence household\u0026rsquo;s participation in high market channels. Among these variables, five of them such as sex, education level, market information were found to be positively determining households\u0026rsquo; probability of participation in high value market channels, whereas distance to the high value market channels negatively determining participation high value market channels\u003c/p\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003eAge of household head\u003c/h2\u003e \u003cp\u003eThe age of the household has a considerable impact on households' participation in high-value market channels at a 1% probability level, as expected (Table\u0026nbsp;3). As farmers get one year older, the likelihood that they will participate in a high-value market channels falls by a factor of 0.26, holding all other factors equal. This finding supported by Hernandez et al., (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) that demonstrated a negative link between farmer age and the decision to participate in high-value market channels.\u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003eEducation level of household head\u003c/h2\u003e \u003cp\u003eThe result in Table\u0026nbsp;3 shows that at a 5% level of significance, the educational level of the household head positively and significantly influenced the households' participation in high-value market channels. Keeping all other factors equal, the likelihood that farmers will participate in the high-value market channel increases by a factor of 1.47 as their educational level rises by one school year. From the study's finding, it may be inferred that farmers with greater education are more likely to sell through high-value market channels. In other words, if the farmers are less educated, the likelihood of selling via traditional channels increases. This finding is consistent with a prior study by Ismail et al., (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), who found that farmers who sell through high-value market channels have greater educational levels than farmers who sell through traditional market channels.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003eQuantity of avocado Supplied to Market\u003c/h2\u003e \u003cp\u003eThe volume of avocados supplied to the market has a favorable and significant impact on the chance of participating in the high-value market channel with a 5% level of relevance (Table\u0026nbsp;3). When producers supply one quintal more of avocados to the market while holding all other factors equal, the probability of participating in the high-value market channel rises by a factor of 3.88. Based on this finding, households that supplied a considerable amount of avocados accessed high-value market channels more frequently than those that supplied less of fruit. This result is consistent with that of Muthini (2015), who discovered that farmers with more mango trees than those with fewer mango trees were more likely to sell to the export market.\u003c/p\u003e \u003cdiv id=\"Sec39\" class=\"Section3\"\u003e \u003ch2\u003eDistance to the high-value market\u003c/h2\u003e \u003cp\u003eAt a 5% significance level, distance to the high-value market had a negative and substantial impact on participation in the high-value avocado market channels. As farmers' distance from residences home to the high-value market increases by one kilometer while holding all other variables constant, the odds in favor of participating in the high-value market channel decrease by a factor of 0.87. Farmers who are located far from the high-value markets may decide to sell to the traditional market channels, in their villages, rather than selling to high-value markets in far-off markets that increase transaction costs. This finding is in line with Shiimi et al., (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) who found that participation in high-value markets was negatively impacted by distance to market.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrice of avocado\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study's findings demonstrate that at a 5% level of significance, the farmers' high-value market channel participation is significantly and favorably correlated with avocado price. When the price of avocado rises by one birr, the likelihood of participating in the high-value avocado market channel rises by 0.98 factor, keeping other independent variables at their mean levels. The variables' positive and significant correlation suggests that producers are increasingly choosing high-value market channels as the price of avocados grows there. The result is in line with those of Alene et al., (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), who discovered a beneficial link between price and market activity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAccess to market information\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAt a 5% level of significance, access to market information had a favorable and significant impact on participating high value market channels. When the farmer gained access to market information, the likelihood of participating in the high-value avocado market channels increased by factors of 14.8, keeping other independent variables at their mean levels. This result concurs with studies by Megersa etal., (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who discovered that people with access to market information are more likely to shop in the agri-food sector.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeterminants of Avocado farmers\u0026rsquo; participation in high value market channel\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdd ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.263***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.473**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExtension contact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 .660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCredit access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAvocado Marketing experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQuantity of avocados sold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.888**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrice of avocado\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 .989**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarket information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.875**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDistance to market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.808**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of observation\u0026thinsp;=\u0026thinsp;389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;ch2 = 0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLog likelihood = --188.2688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLR Ch2 (11) = 162.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e = 0.2354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*** and ** significant at 1and 5% significance level respectively\u003c/p\u003e \u003cp\u003e \u003cb\u003eMatching quality analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;4 below shows the distribution of propensity score for all households. As shown in the table, the propensity scores vary between 0.0104\u0026ndash;0.9985 for all households with mean score of 0.4318. Whereas the score vary between 0.0826\u0026ndash;0.9985 for high value market channels participants household with mean score of 0.5946. The common support then lies between 0.0826\u0026ndash;0.9364. This means that household whose propensity score less than minimum (0.0826) and larger than maximum (0.9364) are not considered for matching purpose. Based on this procedure, 39 households (6 household from high value market channels participant group and 33 households from traditional market channels participant group were discarded from the study in impact assessment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of estimated propensity scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh value channel market participant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional market channel participant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 2 below shows the calculated propensity scores for both traditional and high-value market channels participants. The bottom half of the graph pertains to participants in the traditional market channels, while the top half of the graph shows the distribution of propensity scores for high-value market channels participants. On the y-axis are the score densities. The expected output, a trustworthy indicator of this, falls solely between the range of 0 and 1. It demonstrates that the distribution of the anticipated likelihood of participation has an acceptable amount of overlap.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eChoice of matching algorism\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter determining the common support region, several matching estimators (algorithms) were tested to match households in the common support region that were participants in traditional market channels with high value market participants. Three factors\u0026mdash;namely, the equal mean test (balancing test), pseudo R\u003csup\u003e2\u003c/sup\u003e, and size of the matched sample were taken into consideration while deciding on the matching algorithm. It is ideal to use matching algorithms that balance all explanatory variables across groups (resulting in insignificant mean differences between participants in high-value market channels and those in traditional market channels), have low pseudo-R\u003csup\u003e2\u003c/sup\u003e values, and provide large sample sizes (Dehejia and Wahba, 2002). In this matching at nearest neighbor of neighborhood 5, 11 variables were insignificant in mean difference, relatively have low pseudo-R\u003csup\u003e2\u003c/sup\u003e (0.016), and comparatively have large matches sample size (350).\u003c/p\u003e \u003cp\u003eBased on those criteria, nearest neighbor of neighborhood 5 was found to be best estimator for this study. Therefore, impact analysis procedure was followed and discussed by using nearest neighbor of neighborhood 5.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance criteria of matching algorisms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMatching Algorism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePerformance criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalancing test*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMatched sample size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNearest Neighbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaliper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKernel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTesting of the matching quality (effect analysis)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter selecting best performing matching algorism which satisfies prior identified performance criteria, balance of propensity score and explanatory variables was checked by the selected matching algorism (nearest neighbor of neighborhood 5 in this case).The standard bias difference between identified explanatory variables before matching was in the range of 1.8%-128.0% in absolute value (Table\u0026nbsp;6). But after matching, the remaining standardized error differences between explanatory variables lay between 2.1%- 13.9% in absolute value which is below the critical level of 20% suggested by Rosenbaum and Rubin (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1983\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTesting of the matching quality (effect analysis)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% bias reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epscore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSOHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAOHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eELOHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFSIADE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.8 |\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 72222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-106.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFOECPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDTHVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe covariate balance before and after result showed that nearest neighbor of neighborhood 5 has low R\u003csup\u003e2\u003c/sup\u003e and insignificant likelihood ratio indicating that high market channels participant household and traditional market channel participant household had same distribution after matching (Table\u0026nbsp;7).These results indicate that the matching procedure is able to balance the characteristics in the treated and the matched comparison groups. Hence, these results can be used to assess the impact of formal market participant having similar observed characteristics. This enables to compare observed outcomes for high value market channel participant with those of traditional market channel participant group sharing a common support.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndicators of covariate balances before and after matching\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBefore matching\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter matching\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR χ\u003csup\u003e2\u003c/sup\u003e (P value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.23(0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.13(0.896)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEstimation of average treatment effect on the treated (ATT)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe ATT result revealed that high value market channels had a positive and significant effect on food security of smallholder avocado farmers in the study area. The participants in the high-value market channel consumed 2910.65 kcal, whereas the participants in the traditional market channels consumed 2279.91 kcal.The finding of this study is consistent with earlier studies that discovered participation in high value markets has potential for raising food security (Reardon, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wiggins, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mmbando et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of average treatment effect on the treated\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh value market channels participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraditional market channels participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifference (ATT)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly calorie intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2910.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2279.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e493.71***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSensitivity analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSensitivity analysis revealed that even after allowing high value market channel participant and traditional market channels participant households to have different probabilities of getting treated by up to 200% (2) in terms of unobserved variables, the conclusion for the effect of high value market channel remained unchanged. This indicates that, the p-critical values are significant for all outcome variables estimated at different levels of the critical value of γ, indicating that the study has taken into account significant covariates that impact both participation and outcome variables. The impact estimates (ATT) of this study are therefore insensitive to unobserved selection bias and are a direct result of the high value market channels participation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of sensitivity analysis using Rosenbaum bounding approach\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ee \u003csup\u003eγ\u003c/sup\u003e=1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ee \u003csup\u003eγ\u003c/sup\u003e=1.25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ee \u003csup\u003eγ\u003c/sup\u003e=1.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ee \u003csup\u003eγ\u003c/sup\u003e=1.75\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ee \u003csup\u003eγ\u003c/sup\u003e=2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3e-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9e-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0e-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion and policy implications","content":"\u003cp\u003eThis study examined avocado producers' participation in high-value market channels and its impact on food security in the Sidama region of Ethiopia. This study employed a cross-sectional research design. The primary and secondary data were used to gather the necessary data for the study. The primary data were collected from 389 avocado producing households using a semi structured questionnaire. Secondary data were collected from journals, annual reports, websites and different published and unpublished materials. Descriptive statistics, inferential statistics, and propensity score matching models were used to analyze the data. The PSM result revealed that the households were participation in high value market channels consumed 2910.65 kcal, whereas the households were participating in the traditional market channels consumed 2279.91 kcal. It was concluded that high-value market channel's participation has a favorable and considerable impact on the food security of the households. However, the logit result showed that factors such as the household head's age, level of education, the quantity and price of avocados sold, market information, and distance from the high-value market all had an impact on rural households' participation in high-value market channels.\u003c/p\u003e \u003cp\u003eGiven the substantial contributions farmers' participation in the high-value avocado market channels makes to household food security, concerned bodies in Ethiopia should encourage more households to participate in the high-value market channels by raising awareness among other traditional market participant farmers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the experts\u0026rsquo; agriculture office of the district for their patience and support to get the required supplementary data. The authors also want to express their gratitude to the participants for their enthusiastic readiness to take part in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst author contributed to research proposal writing, data collection, and supervision. The Second author assisted data cleaning and feeding. Third author contributed data analysis and article writing. \u0026nbsp; All authors read and approved final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during this investigation are accessible upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that we do not have competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo fund\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbate, T. 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Finance, Vol. 10. 10.1080/23322039.2022. 2101223\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-innovation-and-entrepreneurship","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jiae","sideBox":"Learn more about [Journal of Innovation and Entrepreneurship](http://innovation-entrepreneurship.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jiae/default.aspx","title":"Journal of Innovation and Entrepreneurship","twitterHandle":"@Springernomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Avocado, Participation in high-value market channels, Impact, Food security, Propensity score matching model","lastPublishedDoi":"10.21203/rs.3.rs-3124184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3124184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFood insecurity is an enduring, critical challenge in Ethiopia. Linking farmers to high-value markets continue to be a viable option for breaking the food insecurity. Many studies have emphasized on factors determining smallholders\u0026rsquo; participation in high value markets. Despite their undeniable importance, these studies have methodological limitations since they neglected the importance of this for food security. Thus, this study aimed to fill a knowledge gap in the area by investigating the factors influencing smallholder avocado farmers\u0026rsquo; participation in a high-value market channels and how it impacts households' food security in the study area. This study employed a cross-sectional research design and multistage sampling techniques to achieve the objectives of the study. Both primary and secondary data were collected. The primary data were collected from randomly selected 389 avocado producers using a semi structured questionnaire. Secondary data were collected from journals, annual reports, websites and different published and unpublished materials. Descriptive statistics, inferential statistics, and propensity score matching model were used to analyze the data. The result of the binary logit model revealed that the participation of avocado producers in a high-value market channels was influenced by age, educational status, the quantity of avocados sold, and price of avocado in quintal and market information. The ATT estimation of PSM model indicated that the participation in high-value market channels had a positive and significant influence on the food security in the study area. Given the substantial contributions participation in the high-value market channels to food security, concerned bodies in Ethiopia should encourage more households to participate in the high-value market channels.\u003c/p\u003e","manuscriptTitle":"Farmers’ participation decision in high value market and its effect on food security of smallholder avocado producers in Sidama region, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-05 20:52:43","doi":"10.21203/rs.3.rs-3124184/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"submitted","content":"","date":"2023-06-28T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-28T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-27T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-06-27T23:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-innovation-and-entrepreneurship","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jiae","sideBox":"Learn more about [Journal of Innovation and Entrepreneurship](http://innovation-entrepreneurship.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jiae/default.aspx","title":"Journal of Innovation and Entrepreneurship","twitterHandle":"@Springernomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac22d819-5ded-49d5-8271-9574a666a890","owner":[],"postedDate":"July 5th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2023-07-05T20:52:43+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-05 20:52:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3124184","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3124184","identity":"rs-3124184","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}