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ISSN : 1225-8504(Print)
ISSN : 2287-8165(Online)
Journal of the Korean Society of International Agriculture Vol.34 No.3 pp.211-220
DOI : https://doi.org/10.12719/KSIA.2022.34.3.211

Assessing the Impact of Outgrower Scheme on Smallholder Farmers’ Income in Tanzania: The Case of Small-scale Tea Producers in Mufindi District

Dismas Jerome Kimaro, Dae Seob Lee†
Kangwon National University, Graduate School, Department of Agricultural and Resource Economics
Corresponding author (Phone) +82-33-250-8275 (E-mail) ldaeseob@kangwon.ac.kr
July 13, 2022 August 22, 2022 September 1, 2022

Abstract


Enhancing income for small-scale farmers in developing countries, is one of the major concerns for governments and many stakeholders. This is attributed by the fact that, a majority of smallscale farmers in the region are characterized by low income earners caused by agronomic related challenges, such as low productivity of input factors. In addressing this challenge, the purpose of this study was to assess the impact of outgrower scheme on its members’ income, using the propensity score matching approach. Through this approach the study assessed cross-sectional data, collected from small-scale tea farmers in the Mufindi district, founded in Tanzania. Results of the assessment on the impact of outgrower scheme on its members, suggest that the scheme has a negative and insignificant impact on its members’ income. This implies that, the outgrower scheme does not meet its intended objective of raising the income of its members, due to failure of improving quality as well as quantity of tea produced in the study area.



탄자니아 계약재배 확대 정책이 소농 소득에 미치는 영향 분석: Mufindi 지역의 소규모 차 생산자를 중심으로

디스마스 제롬 키마로, 이 대섭†
강원대학교 대학원 농업자원경제학과

초록


    INTRODUCTION

    Enhancing income stability for small-scale farmers is one of the major concerns for many Sub-Saharan Countries and Tanzania in particular. This is attributed by the important roles played by the subsector for the country’s economy. The subsector dominates in the country’s economy which employs over 80% of the population that are engaging in agriculture-related activities (MOA, 2017). Small-scale farmers are also reported to produce over 85% of food crops in Tanzania from their total cultivated area of 5.1 million ha (Chivaghula, 2018). Studies reported the presence of over 3.7 million rural-based small-scale farmers in Tanzania (FAO, 2015). The average agricultural land per farm household turned out to be between 0.3 and 0.9 ha (Chivaghula, 2018).

    However, despite its crucial role in the economy, smallscale farmers are characterized as marginalized part of the population which often lives in an isolated and unfriendly environment areas. This is caused by the fact that, the agricultural sector for many years has been experiencing agronomic challenges that lead to low productivity, small share to the total production and poor livelihood among its participants. The common agronomic challenges experienced by Tanzania’s small-scale farmers are mentioned to be the lack of appropriate farming inputs and extension training and services, inadequate access to financial services, unstable markets for their products and challenging transportation, and communication and infrastructures.

    In Tanzania, the government and other stakeholders have implemented various policies addressing the existing challenges. One of the interventions implemented in the country is the adoption of outgrowers scheme framework. This framework is sometimes known as contract farming with an agreement between the farmers and the buyers. These arrangements sometimes go beyond selling and purchasing of the produced agricultural commodities, rather a buyer is also extending its commitments to provision of farming inputs and technical assistance to member farmers (Eaton and Shepherd, 2001;Abdulai and Al-hassan, 2016). In that perspective, outgrowers scheme can be regarded as a manner to provide stable markets to smallholder farmers and also an opportunity to allow private companies to join in the production process and also control the amount produced (Key and Runsten, 1999;Bellemare, 2012).

    Tea cultivation in Tanzania was firstly started as one of the experimental crops under the German settlers in 1904 at Rungwe district in the Southern highlands of the country. Following its successful grow, commercial production started in 1926 in the areas around Usambara Mountain and Rungwe district, and later in 1929, production extended to districts of Mufindi and Tukuyu (Carr et al. 1992). Small-scale tea production activities in the country was not active until the country gained its political independence in the early 1960s. During that time period, most of the small-scale farm operations were monitored and supervised by the Ministry of Agriculture through the Tanzania tea authority. The authority was primarily responsible for technical assistants such as planting and processing for the smallholders. As a result of increasing government commitments and foreign agricultural aid to the tea producing activities, planted areas for the smallholders were reported to increase rapidly from 2,665ha in 1970 to 8,994 ha in 1980 (Nagu, 1986). In recent years, tea production area is approximately 22,000 ha in eight districts of the country such as Mufindi, Njombe, Rungwe, Lushoto, Korogwe, Muheza Bukoba and Muleba.

    Small-scale tea producers are living under challenging environment characterized by low income and poverty. A study conducted by IDH in 2014 reported that there were approximately 1,800 small-scale tea famers with an average of 2.5 ha in Mufindi district. Small-scale farmers at Mufindi were characterized by low income with an average household earning of USD1.45 per day which is below the country’s average earning of USD5.90 per day. Low income and associated challenges in the area were caused by low tea productivity which led to average production of 1.2 Kg/ha, which is about a half of the private tea producers in the area (Waarts et al., 2016). From these findings, outgrowers scheme was utilized to address many existing challenges experienced in the study area to enhance income of small-scale tea farmers in Mufindi district. Therefore, under the unpredicted impact of the outgrowers scheme framework, it is necessary to review and assess the ongoing operations of tea outgrower scheme for the proper management in the future.

    The outgrowers scheme framework in Tanzania was firstly introduced to Kilombero sugarcane plantations in Kilombero district in the early 1960s when small-scale farmers began to cultivate sugarcane along with their main crops such as maize and millet (Sulle, 2017). Recently, with the sugarcane plantations, the framework has extended to other crops and products such as tea, poultry and dairy products. However, the impacts of outgrowers scheme on its members’ income is still controversial as some studies found positive impacts in short-run while other studies concluded otherwise. The main objective of the study aims at contributing to the prevailing debate by assessing the impact of this framework on the small-scale tea farmers in Mufindi district. The region is composed with rural based population that depends on agriculture and agricultural activities for their livelihoods. The region is also one of the eight districts producing teas in Tanzania which makes it suitable to be selected for the purpose of the study. The detailed objectives are to find out socioeconomic characteristics of smallholder tea farmers in Mufindi district and to investigate the factors influencing smallholder farmers’ income and participation in outgrowers scheme in the region.

    METHODOLOGY

    Theoretical Background and Data Collection

    This study was conducted under the consideration of theory of production. Under the theory of production, producers are assumed to be rational with their intention of maximizing profit. Producers profit can be firstly assessed through maximization of income which is obtained by considering output produced under the given price level. Maximum production is obtained through an efficient combination of factor inputs to produce a desired level of output.

    Data for this study were randomly collected using survey questionnaire from smallholder farmers (both outgrowers and non- outgrowers) surround the Unilever Tea Tanzania (UTT) Company. These small-holder farmers are distributed in 11 villages out of 19 villages cultivating tea within the study area. A total of 330 individual data for both outgrowers (150 respondents) and non-outgrowers (180 respondents) were collected from the selected 11 villages in the study area. Through using sampling, the study selected 11 villages which are Ikwega, Mninga, Udumuka, Sawala, Ihomasa, Kasanga, Kibao, Mtwango, Ifupia, Luhanga and Lufuna and the 330 farmers participated in this study.

    This study employed two models which are logit model and Propensity Score Matching (PSM) to assess factors influence farmers in joining the outgrowers scheme and the impact of outgrowers scheme to smallholder farmers income respectively. The use of logit model was attributed by its capacity of estimating the probability of an event to occur or not to occur on dependent variable with a binary outcome while the use of PSM was attributed by its capacity of minimizing the selection bias under the non-experimental studies.

    The binary logit model was used to identify the factors on socioeconomic characteristics of smallholder farmers, which influence participation in the scheme. Binary logit models are usually used to assess dependent variable with the two binary outcomes like the case of this study that is whether to participate or not to participate in the scheme. Logit model is used under the scenario suggests that the probability of dependent variable changes in nonlinear relationship with the change in independent variables despite the proportions change cannot lie outside the range from 0 to 1 (Bewick et al. 2005). In this regards, as the dependent variable having two binary outcome, then the decision of participating in the scheme takes the value of 1 and the decision of not participating in the scheme takes the value of 0.

    Odds of a farmer’s decision to join the scheme are calculated by finding the ratio of the probability a farmer joining the scheme to the probability that she/he will not join the scheme. Under this circumstance, the impact of any independent influencing factors for a farmer to join the scheme using logistic regression are explained in terms of odds (Park, 2013). However, under the scenario of extreme independent variable, the probability of a farmer joining the scheme will lies out 0 and 1 and thus requires the use of natural logarithm to transform the odds as a linear function of the independent variables (Peng, Lee & Ingersoll, 2002). Then, the logit model for assessing the socioeconomic factors influence farmers participation in the outgrower scheme is specified as:

    Log P i / ( 1 P i ) = β 0 + i = 1 11 ( α j X j ) + ε i
    (1)

    Where

    • Pi is the probability that a farmer (i) will participate in an outgrowers scheme

    • β0 = constant term

    • αj = coefficient of explanatory variable Xj, (where j = 1, 2, … 11)

    • Xj = Regressors variables

    • εi = error term

    The model specification was done through considering the theoretical and empirical reviews. Under the theoretical review three variables were adopted which are farm size, fertilizer uses and herbicides uses while under the empirical reviews the remaining eight variables were adopted (Tran et al., 2020;Akuriba & Tangonyire, 2020 and Abdulai & Al-hassan, 2016). The variables used in the logit model are further defined in the Table 1.

    Further, on estimating the impact of outgrowers scheme to its members, the study employed Propensity Score Matching (PSM) as a result of presence of imbalance in characteristics of observational data. PSM through developing a propensity score is used to match and compare individuals with balanced characteristics in control and treated groups (Rosenbaum and Rubin, 1985; Garrido et al., 2014). The use of PSM is advocated in measuring the impact of the program effects by adjusting the selection bias that may be caused by non-random treatment assignments which are likely to happen in observational studies (Jalan and Ravallion, 2003;Maertens and Swinnen, 2009). Presence of selection bias especially in observational studies make it difficult to simply compare the effects between treated and untreated groups since, even in absence of the treatment, these two groups can differ due to various reasons such as the presence of confounding variables (Caliendo and Kopeining, 2005;2008).

    One of the famous models developed in measuring the impact by propensity score matching is the Roy-Rubin model. Through using the Roy-Rubin model, the analysis for assessing the impact of the program to the treated individual requires the speculations about how this individual would have acted if she/he would have not received the treatment (Caliendo and Kopeinig, 2008). The Roy-Rubin model focuses on assessing individual’s treatment and potential outcomes (Moe and Lee, 2021). These outcomes are assessed with regards of the binary treatment indicator Yi equals to 1 if individual i receives treatment and zero otherwise. This model can be summarizes as:

    ITE i  = Y i  (1) - Y i  (0)
    (2)

    Where

    ITE is an individual treatment effect of the population, Y (1) is an outcome of treated group and Y (0) is an outcome untreated group.

    However, estimating ITE under observational studies become impossible due to the existence of counterfactual outcome. The counterfactual outcome exists due to the fact that it is only possible to observe only one of the potential outcomes for each individual i. Under this scenario, the recommended approach is to deal with the entire population as stipulated by Caliendo & Kopeinig (2008). One of the population parameters in measuring the impact of the treatment to the individuals is the approach proposed by Rosenbaum and Rubin. This approach intends to estimate the average treatment effect on the treated (ATT), which focuses explicitly on the effects on those for whom the program is actually intended (Caliendo and Kopeinig, 2008). ATT is given as follows:

    ATT = E[Y(1)/D = 1] - E[Y(0)/D = 1]
    (3)

    The value of ATT is the difference between the expected outcome for participants and non-participants for the intended individuals. However, estimating average treatment effect on treated (ATT) in equation 3 is impossible due to the fact that the counterfactual outcome (E[Y(0)/ D=1]) cannot be observed under the non-randomness studies. Therefore, the counterfactual outcome (E[Y(0)/D=1]) can be replaced with the expected outcome of the control group having the same characteristics as the treated group but didn’t involve in the policy (Tran et al., 2020). Thus, the ATT can be estimated as:

    ATT = EX|D = 1[E{Y(1)|D = 1, X}- E{Y(0)|D = 0,X}]
    (4)

    Where

    • ATT is the Average Treatment Effect on the Treated

    • X is a set of social characteristics that can affect household decision to participate in outgrowers scheme and/or expected outcome of those households.

    • E{Y(1)|D = 1, X} is the expected outcome of household that participate in outgrowers scheme given the set of social characteristics.

    • E{Y(0)|D = 0,X} is the expected outcome of control group given the set of social characteristics.

    After estimating the propensity score, choosing an algorithm that uses in matching the treated and control group were done. Through this process, the households under the treated group are matched with the households under the control group based on the propensity score estimated. After choosing the algorithm, the next step is to assess the matching quality so as to minimize the selection bias and the last is the estimation of the average treatment effect on the treated for income variable by comparison with treated to untreated groups after matching process completes.

    Data for the study are randomly collected using a survey questionnaire distributed to small-scale tea producers in 11 villages of the study area. A total of 330 individual data for both outgrowers (150 respondents) and non-outgrowers (180 respondents) are collected. The collected data are analyzed by using empirical techniques and econometric analysis. Empirical analysis is employed to establish social-economic characteristics of small-scale tea farmers in the study area while econometric analysis is adapted to assess the determinants of participating in the scheme and the impact on small-scale tea farmers’ income from the scheme.

    EMPIRICAL ANALYSIS

    Socioeconomic Characteristics of Tea Small-Scale Farmers at Mufindi

    The analysis for social economic characteristics of small-scale tea farmers at Mufindi district were conducted and summarized on the Table 2 and 3. The Table 2 depicts the social economic characteristics of small-scale farmers at the Mufindi district with dummy variables in nature while nominal characteristics are analyzed and presented in the Table 3.

    The results of the socioeconomic characteristics of the smallholder tea farmers at Mufindi district indicate that most of the households in the study area are led by male. In addition, small-scale tea activities in the district are mainly conducted by aged farmers. The results further show that the marriage rate in the study area is high while the level of education turns out to be low at the primary level. Furthermore, the results show that there is a high provision of extension services, high rate of non-farm activities, low rate of access to financial services for smallholders, and high birth rate within the districts.

    These results imply that policies on financial services can be recommended to develop a favorable environment to attract to younger generation and women engaging in the sector. Also, efforts to enable farmers’ children to attend higher level education is highly required to transform the rural lives through innovative agriculture taught in the higher level of educational institutions. Furthermore, strategies to offer financial services to smallholder farmers should be kept in place to rise farmers’ assets for sustainable farming operations. Moreover, further efforts should be put in place to maintain provision of extension training to farmers and more education on investing in non-farm activities given the challenges of unpredictability of the agriculture sector in Tanzania due to various reasons such as high dependency on rain feeding.

    Factors Determining the Participation in the Schemes

    The results of the binary logit regression analysis for determining participation in outgrowers scheme are summarized in the Table 4 below. These results are obtained from the logit regression on the dependent variable named as treat against the listed independent variables by using the robust command to correct the heteroscedasticity.

    The results show that the variables of age, years of schooling, farming experience, distance from UTT, fertilizer use(kg/ha) and extension training are statistically significant at 5% level. It implies that these variables can statistically be influenced on the decision of small-scale tea farmers in the study area to join the Scheme. In addition, age, years of schooling and distance from UTT have a negative effect on the decision to join the scheme while years of experience, fertilizer use and extension training have positively influenced on farmers’ decision to join the scheme.

    As summarized, age of a farmer had a significant negative effect on the decision of a farmer to join the scheme. The negative sign to the age variable indicated that older farmers were less likely to join the scheme compared to young farmers. In marginal effect, an increase in age by one year decreased the probability of an individual joining the outgrower scheme by 0.57 percent. This can be attributed by the fact that older farmers are less likely to adopt new changes and thus less likely to adopt new introduced policy. The other reason is that the possibility of older farmers to have already accumulated enough capital to operate their farming activities such as capital for purchasing their own farming inputs thus, may not found the necessity of joining the scheme which advocate mostly on the provision of farming inputs such as fertilizers.

    Furthermore, the results of the analysis revealed that the level of education had significant negative influence on the decision of joining the outgrower scheme. This result means that farmers with high level of education in the study area were less likely to join the outgrower scheme compared to those with low level of education. In marginal effect the result indicates that as a year of schooling increased by 1 year, it reduces the probability of farmers to join the outgrower scheme by 4.9 percent. These results were attributed by the fact that farmers with high level of education can easily access important agronomy information such as input availability, inputs prices, commodity prices and market availability from different platforms preferably internet compared to farmers with low level of education. The same result was obtained by the work of Abdulai &Al-hassan (2016) in assessing the contact farming in Ghana.

    On the other hand, the study found that the farming experience had a positive and significant influence in joining the outgrowers scheme. The results showed that an increase in farming experience by 1 year led to an increase in probability of joining the outgrower scheme by 9.7 percent. The possible reason for the positive influence of farming experience to join the outgrower scheme is that experienced farmers have faced challenges of working on their own and to work alone especially on market and farming inputs issues, thus found it necessary to seek assistance through joining the scheme. The positive influence of farming experience to join the outgrower scheme has also been found by previous works such as Can et al., (2020) and Akuriba & Tangonyire, (2020).

    Distance of household’s farms from the Unilever Tea Tanzania (UTT) Company had the negative influence in joining the scheme. The UTT Company is the implementing agency of the scheme and the provider of market and necessary farming inputs to small-scale farmers. Results shows that the furthest household’s farms are less likely to join the scheme. The marginal effect suggests that an increase in 1Km from the UTT Company decreased the probability of joining the scheme by 0.22 percent. This can be attributed by the facts that the furthest household’s farm can experience higher cost in transporting its produced tea leaf to the UTT company and other reason may be difficulty in getting important information that would sensitize farmers decision.

    Furthermore, extension training was also found to have positive and significant influence on joining the scheme. This implies that farmers who received extension training were more likely to join to the scheme. The marginal effect indicated that farmers who received the scheme had increased probability of 42.7 percent of joining the scheme and vice versa. This result might be supported by the fact that most small-scale farmers in Tanzania has low education on good farming practices thus most of them require extension training to improve their productivity. Under such situation, farmers may find joining the scheme as the easy way to access reliable extension training.

    Further results indicate that household size had a negative and insignificant effect on the decision. The marginal effect indicates that an increase in 1 person at the household had decreased the probability to join the scheme by 0.78 percent. This result implies that household with large number has low probability of joining the scheme. This might be attributed by the fact that household with large number of individuals may found to be okay to use rudimentary farming tools as they believed on source of labor in the house thus less interest of joining the scheme.

    Additionally, farm size was found to have a positive and insignificant influence. The positive marginal effect of this variable indicates that as farm size increase by 1ha, the probability of joining the scheme increases by 0.035 percent. A positive sign of the farm size implies that smallscale tea farmers with large farms have high interest of joining the scheme in order to access the benefits offered by the scheme including access to farming inputs and ready market of the produced product.

    Lastly, gender variable was a dummy assigned as 1 for male headed household and 0 for female headed household. The regression results indicate that male headed household had a positive and insignificant influence over a female respondent. Marginal effects showed that if the head of the household was a male, it increased the probability of joining the scheme by 4.3 percent.

    Impacts of Outgrowers Scheme on Rural Small- Scale Farmers’ Income

    The Impact of the scheme to smallholder farmers was assessed by using the Propensity Score Matching (PSM) technique. A propensity score is a single score that represents the probability of receiving a treatment, conditional on a set of observed covariates (Garrido et al., 2014). The propensity score is established by identifying the optimal number of blocks and to test balancing property of the propensity score. Number of blocks are estimated to ensure the mean propensity score, which is insignificant for treated and untreated observations in each block by using the t test. Since the propensity score estimated is not different from each block, balancing property in the stated covariates is satisfied.

    After confirming that balancing property is satisfied, the following step can be an assessment of the quality of matching procedure. The good matching algorithm ensures that there are no differences between the covariates in the treated and untreated group after matching (Caliendo and Kopeinig, 2008). This study employs three matching methods such as the nearest neighbor matching (NNM), Caliper and Radius Matching (CRM) and Kernel Matching (KM). The summary of results of balancing tests for the NNM, Radius and KBM are presented in the Table 5.

    Results indicate that six variables have significant difference in the mean value between treated and control groups. However, after matching, all variables become statistically insignificant implying that the covariates within the treated and control group are well matched and balancing property is satisfied. Estimated impact of the scheme on the smallscale farmers’ income compared to the income of non-participants in the scheme is presented on the Table 6.

    The results show insignificant negative impact of the outgrowers scheme to smallholder farmers’ income. This means that the framework has statistically no impact on its members due to traditional operationalization of the farming activities even after adopting the scheme, inefficient implementation of the scheme by the government, etc. In this situation, members’ income has not changed after the scheme since most of the small-scale farmers have faced similar challenges related to quality control and productivity. These challenges were obtained as a result of failure to introduce new technologies which improves efficiency in the implementation of farming activities which in turn would improve output and quality of tea produced. Furthermore, extension services provided by the scheme were not enough in terms of quality and quantity as well to ensure members attain their maximum tea production. This implies that the strategic scheme implemented by both the government and the firm has not obtained its initial objectives which has been to increase small-scale farmers’ income to the level of the national average. In this case, much efforts are needed to be implemented with the innovative strategies to enhance quantity and quality of the tea products so as to increase their income.

    CONCLUDING REMARKS

    This study analyzed the impact of the outgrowers scheme on small-scale farmers’ income in Mufindi district of Tanzania. The study used the propensity score matching approach to analyze 330 data collected from 11 villages in the Mufindi district. The study found the scheme to have negative though insignificant impact on small-scale farmers’ income in the study area. The insignificant impact showed that both members and non-members have relatively no change in income by the scheme. This implies that the scheme has much rooms to improve by ensuring the improvement of quantity and quality of its members’ products through adaptation of improved farming technologies and invest in value addition technologies. Without innovative intervention to replace traditional farming techniques in the society, the framework will be as failure to reach intended objective of transforming people’s lives and eventually would perish as one of the strategy to improve agriculture sector in the country. The need of improvement on the operationalization of the scheme is attributed by the importance of the framework in developing countries and in specific Sub-Saharan Africa in offering non-monetary benefits to farmers such as agronomic training to its members, availability of factors inputs credit arrangements which ensures timely access to inputs even if farmers have no fund at the time of needs. All these benefits are highly required in the region given the fact that level of investment to the agriculture sector by the government remains low which in turn keeping the productivity to remain low and rate of poverty to be higher.

    적 요

    1. 본 연구는 탄자니아 무핀디 지역의 계약재배에 대한 효 과를 로짓 모형과 성향점수일치 접근법을 활용하여 분석, 특 히 소규모 차 재배 농가의 소득에 미치는 영향을 검증. 본 연 구는 탄자니아 무핀디 지역(Mufindi) 내 11개 차 재배마을에 서 수집한 횡단면 자료를 활용.

    2. 분석 결과 탄자니아 정부의 계약재배 정책이 소규모 차 생산 농가의 소득에 부정적인 영향을 미치는 것으로 나타났으 며, 정부정책 방향이 품질 제고에 초점을 두고 있음에도 불구 하고 해당 농산물의 가격이 낮게 형성되고 있는 것과 연관성 이 있다고 해석될 수 있음.

    3. 더불어 연구 결과를 토대로 소규모 생산 농가의 소득 증 대를 위한 정부의 정책은 기술개발 및 보급, 교육훈련, 시장의 안정성 등 다양한 분야를 대상으로 복합적인 형태로 추진되어 야 긍정적인 효과가 나타날 수 있는 시사점이 도출됨.

    Figure

    Table

    Definition of Variables.

    Socioeconomic Characteristics of Small-Scale Farmers in Mufindi District.

    Other Socioeconomic Characteristics.

    Results of the Binary Logit Regression.

    Covariates on Balanced Test Across Treated and Control Groups after Matching.

    Estimated Impact of the Scheme on Income.

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