Adoption of rural bank credit programs among smallholder farmers in Ghana: an average treatment effect estimation of rates of exposure and adoption and their determinants

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arnold Missiame ◽  
Patrick Irungu ◽  
Rose Adhiambo Nyikal ◽  
Grace Darko Appiah-Kubi
Keyword(s):  
Treatment Effect ◽  
Smallholder Farmers ◽  
Household Head ◽  
Sampling Technique ◽  
Average Treatment Effect ◽  
Bank Credit ◽  
Content Type ◽  
Average Treatment ◽  
Credit Programs ◽  
Effect Estimation

PurposeThe study aims to estimate the rates of exposure to, and adoption of, rural bank credit programs by smallholder farmers in rural Ghana and the factors responsible for those rates.Design/methodology/approachThe study used a random sample of 300 smallholder farmers in the Fanteakwa District of Ghana, obtained through the multistage sampling technique. The study also employed the average treatment effects approach to estimate the average treatment effect of farmers’ exposure to rural bank credit programs, on their adoption of such programs.FindingsThe actual adoption rate is approximately 41%, and the potential, conditional on the whole population being aware of rural bank credit programs, is approximately 61%. Accordingly, there is a gap of about 20% in the adoption of rural bank credit programs, and is due to the incomplete exposure of smallholder farmers to the rural bank credit programs. Age of the household head, access to extension services, membership in farmer-based organizations and active savings accounts with a rural bank are the major contributors to smallholder farmer exposure to and the adoption of rural bank credit programs.Originality/valueThe current study is the first of its kind to be conducted in Ghana on rural bank credit programs. It takes into account the extent to which smallholder farmers are exposed to such credit programs and how it influences their decisions to access or adopt.

scholarly journals High-dimensional regression adjustments in randomized experiments

2016 ◽  
Vol 113 (45) ◽  
pp. 12673-12678 ◽  
Author(s):  
Stefan Wager ◽  
Wenfei Du ◽  
Jonathan Taylor ◽  
Robert J. Tibshirani
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
High Dimensional ◽  
Randomized Experiments ◽  
Finite Sample ◽  
Simple Method ◽  
Average Treatment ◽  
Population Average ◽  
High Dimensional Regression ◽  
Effect Estimation

We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample–unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks.

Impact of the production of underutilized vegetables on the livelihood of farmers in Southwestern Nigeria

2017 ◽  
Vol 44 (12) ◽  
pp. 1669-1682
Author(s):  
Oluwatosin Adejoke Oyedele ◽  
Kemisola O. Adenegan
Keyword(s):  
Treatment Effect ◽  
Nutritive Value ◽  
Average Treatment Effect ◽  
Current Status ◽  
Special Focus ◽  
Vegetable Production ◽  
Southwestern Nigeria ◽  
Content Type ◽  
Average Treatment ◽  
The Impact

Purpose African indigenous vegetables have high nutritive value which contains high levels of minerals. The current status of indigenous vegetable production in developing countries shows that these crops are “under-recognized” and “underutilized” with respect to nutritional value and opportunities for food security. The purpose of this paper is to examine the impact of the production of underutilized vegetables on the livelihood of farmers in South Western Nigeria. Design/methodology/approach The population for the study includes all the vegetable farmers in South Western Nigeria with a special focus on farmers’ groups formed by the NICANVEG project in Osun, Oyo, Ondo and Ekiti states. Descriptive statistics and propensity score matching (PSM) was used to analyze the objective. Findings Perceptions on individual household income reveal that the majority of participating respondents perceived higher production and harvesting density. This is due to the fact that harvesting is done by cutting the stems of the vegetables. The probability score shows that the dependent variables have an average effect of 44.6 percent on the probability of farmers participating in NICANVEG project. The PSM results reveal that average treatment effect on the treated is ₦269,254.87. Average treatment on the untreated is ₦11,990.63 while average treatment effect is ₦139,336.43. The total income of the participants from all the various livelihood strategies is increased by 29.73 percent because of their participation in the NICANVEG project. Originality/value This work has not been carried out by any other person before. This work will add to the existing knowledge on the impact of evaluation in agricultural economics.

scholarly journals Regression-adjusted average treatment effect estimates in stratified randomized experiments

Biometrika ◽  
2020 ◽  
Vol 107 (4) ◽  
pp. 935-948
Author(s):  
Hanzhong Liu ◽  
Yuehan Yang
Keyword(s):  
Treatment Effect ◽  
Asymptotic Variance ◽  
Average Treatment Effect ◽  
Randomized Experiments ◽  
Asymptotically Normal ◽  
Variance Estimators ◽  
Average Treatment ◽  
Effect Estimation ◽  
Regression Adjustment ◽  
And Control

Summary Linear regression is often used in the analysis of randomized experiments to improve treatment effect estimation by adjusting for imbalances of covariates in the treatment and control groups. This article proposes a randomization-based inference framework for regression adjustment in stratified randomized experiments. We re-establish, under mild conditions, the finite-population central limit theorem for a stratified experiment, and we prove that both the stratified difference-in-means estimator and the regression-adjusted average treatment effect estimator are consistent and asymptotically normal; the asymptotic variance of the latter is no greater and typically less than that of the former. We also provide conservative variance estimators that can be used to construct large-sample confidence intervals for the average treatment effect.

scholarly journals Impact of Training of Small-Scale Dairy Farmers on Milk Production and Income in Ethiopia

2020 ◽  
Vol 24 (3) ◽  
pp. 1-8
Author(s):  
Wongel Getachew Seble ◽  
Kubota Satoko ◽  
Kanayama Toshihisa ◽  
Tiana Navalona Randrianantoandro ◽  
Hiroichi Kono
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Milk Production ◽  
Treatment Effect ◽  
Smallholder Farmers ◽  
Average Treatment Effect ◽  
Dairy Farmers ◽  
Average Treatment ◽  
Management Condition ◽  
Smallholder Dairy

This paper examined dairy husbandry training impact on milk production and milk income under smallholder farmers’ management condition. A cross-sectional survey was conducted in two districts in Ethiopia and the data was collected from a total of 180 smallholder dairy farmers (60 of the participants were trained on dairy husbandry practices). Propensity Score Matching (PSM) technique was employed to construct suitable comparable group and to calculate the average treatment effect on the treated sample. The average treatment effect on the treated shows that dairy husbandry training increased milk production, volume of milk processed and milk income by about 21.7%, 56.5% and 22.5% respectively. This study confirms that training on dairy husbandry plays great role to bring change in dairy technology adoption which further enhance milk production and milk income under smallholder farmers’ management condition. Keywords: milk income; milk production; Ethiopia; propensity score matching; smallholder dairy farmers, training

Do farmer and credit specific characteristics matter in microfinance programmes’ participation? Evidence from smallholder farmers in Ada west and east districts

2019 ◽  
Vol 79 (3) ◽  
pp. 353-370 ◽  
Author(s):  
Emmanuel Tetteh Jumpah ◽  
Yaw Osei-Asare ◽  
Emmanuel Kodjo Tetteh
Keyword(s):  
Logistic Regression ◽  
Interest Rate ◽  
Regression Model ◽  
Urban Areas ◽  
Logistic Regression Model ◽  
Smallholder Farmers ◽  
Household Head ◽  
Sampling Technique ◽  
Content Type ◽  
Factors Affecting

Purpose Users of smallholder farmer microfinance are able to make enough returns to repay credits advanced to them. However, they are in dire need of financial capital such that they are inconsiderate of farmer- and credit-specific characteristics when participating in a microfinance programme. This study analyses perceptions of stakeholders regarding select farmer and credit characteristics within the microfinance industry. The study identifies and analyses the factors that influence participation in a microfinance programme by farmers using the logistic regression model. The purpose of this paper is to widen the knowledge base of rural agricultural finance, including factors that influence participation in microfinance intervention(s) thereof. Design/methodology/approach A total of 104 participants and 120 non-participant farmers in microfinance programmes were interviewed using a semi-structured questionnaire by applying the multistage sampling technique. The paper applied the logistic regression model in which farmer- and credit-specific characteristics were used to estimate the probabilities of participation. Findings The logistic regression results showed that distance, interest rate, experience, membership of farmer-based organisation, number of dependants, household, gender and age were statistically significant farmer- and credit-specific characteristics that influence participation in microfinance programmes. Interest rate and distance exact negative significance influence on participation, whereas membership of farmer-based organisations, experience, gender, household head and age influence participation positively. Reduction in the interest rate and expansion of microfinance to very remote areas rather than locations in urban areas are crucial in terms of improving participation. Research limitations/implications The paper used data from only farmers so there is a limit to which the results can be generalised for all microfinance users. It may be relevant to undertake a study that considers non-farm enterprises. Practical implications This paper brings to light the need to develop well-structured microfinance facilities that meet the specific needs of the rural poor in transitioning economies while taking into consideration critical factors affecting participation before the establishment of such programmes. Originality/value This paper provides empirical evidence to show that farmer- and credit-specific characteristics are essential to ensure participation and success of microfinance programmes thereof.

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