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 Adoption of Conservation Practices: Its Impact on Input Use and Performance in the Northern Region of Ghana

2018 ◽  
Vol 11 (5) ◽  
pp. 149
Author(s):  
Mavis Boimah ◽  
Akwasi Mensah-Bonsu ◽  
Yaw Osei-Asare ◽  
Daniel B. Sarpong
Keyword(s):  
Treatment Effect ◽  
Environmental Sustainability ◽  
Technical Assistance ◽  
Negative Impact ◽  
Maize Yield ◽  
Average Treatment Effect ◽  
Inorganic Fertilizer ◽  
Switching Regression ◽  
Average Treatment ◽  
The Impact

Conservation Agriculture (CA) is promoted worldwide on the basis of its contribution to economic, social, and environmental sustainability of agricultural production. In Ghana, despite the increasing interest in the promotion of CA and its practices, its rate of adoption is still low, mainly due to the conflicting evidences regarding its effectiveness. This paper contributes to the numerous debates by examining the impact of CA practices on hired labour, rates of inorganic fertilizers applied by adopters, maize yield, and profit of adopters. Using a cross-sectional data, a multinomial endogenous switching regression (MESR) model was employed to compute the Average Treatment Effect (ATE) and Average Treatment Effect on Treated (ATET) for yield, hired labour, inorganic fertilizer rate, and profit of adopters of CA practices. The study reveals that CA practices impact positively on hired labour employed on the farm, but have a negative impact on profits of adopters. No impact whatsoever of adoption of CA practices is observed on maize yield and also inorganic fertilizer application rates. Technical assistance, and training of farmers on strategies that minimize costs of production must be intensified to raise profits of adopters.

Propensity score specification for optimal estimation of average treatment effect with binary response

2020 ◽  
Vol 29 (12) ◽  
pp. 3623-3640
Author(s):  
John A Craycroft ◽  
Jiapeng Huang ◽  
Maiying Kong
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Propensity Scores ◽  
Optimal Estimation ◽  
Theoretical Explanation ◽  
Average Treatment Effect ◽  
Adaptive Lasso ◽  
Confounding Bias ◽  
Average Treatment ◽  
The Impact

Propensity score methods are commonly used in statistical analyses of observational data to reduce the impact of confounding bias in estimations of average treatment effect. While the propensity score is defined as the conditional probability of a subject being in the treatment group given that subject’s covariates, the most precise estimation of average treatment effect results from specifying the propensity score as a function of true confounders and predictors only. This property has been demonstrated via simulation in multiple prior research articles. However, we have seen no theoretical explanation as to why this should be so. This paper provides that theoretical proof. Furthermore, this paper presents a method for performing the necessary variable selection by means of elastic net regression, and then estimating the propensity scores so as to obtain optimal estimates of average treatment effect. The proposed method is compared against two other recently introduced methods, outcome-adaptive lasso and covariate balancing propensity score. Extensive simulation analyses are employed to determine the circumstances under which each method appears most effective. We applied the proposed methods to examine the effect of pre-cardiac surgery coagulation indicator on mortality based on a linked dataset from a retrospective review of 1390 patient medical records at Jewish Hospital (Louisville, KY) with the Society of Thoracic Surgeons database.

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 A Text-As-Data Approach for Using Open-Ended Responses as Manipulation Checks

2020 ◽  
Author(s):  
Jeffrey Ziegler
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Continuous Measure ◽  
Causal Inferences ◽  
Online Surveys ◽  
Manipulation Check ◽  
Potential Variation ◽  
Average Treatment ◽  
The Impact

Participants that complete online surveys and experiments may be inattentive, which can hinder researchers’ ability to draw substantive or causal inferences. As such, many practitioners include multiple factual or informational closed-ended manipulation checks to identify low-attention respondents. However, closed-ended manipulation checks are either correct or incorrect, which allows participants to more easily guess and it reduces the potential variation in attention between respondents. In response to these shortcomings, I develop an automatic and standardized methodology to measure attention that relies on the text that respondents provide in an open-ended manipulation check. There are multiple benefits to this approach. First, it provides a continuous measure of attention, which allows for greater variation between respondents. Second, it reduces the reliance on subjective, paid humans to analyze open-ended responses. Last, I outline how to diagnose the impact of inattentive workers on the overall results, including how to assess the average treatment effect of those respondents that likely received the treatment. I provide easy-to-use software in R to implement these suggestions for open-ended manipulation checks.

Child labor and family structure: the role of divorce

2018 ◽  
Vol 45 (10) ◽  
pp. 1453-1468
Author(s):  
Shirley Pereira de Mesquita ◽  
Wallace Patrick Santos de Farias Souza
Keyword(s):  
Family Structure ◽  
Child Labor ◽  
Treatment Effect ◽  
Single Mother ◽  
Average Treatment Effect ◽  
Empirical Approach ◽  
Content Type ◽  
The Impact ◽  
Single Mother Families

Purpose The purpose of this paper is to investigate the role of family structure on child labor by comparing children of nuclear families headed by the father with children of single-mother families headed by the divorced mother. Design/methodology/approach This paper uses data from Brazilian urban areas provided by the Brazilian Demographic Census of 2010. The empirical approach consists of the estimation of three treatment effect models: the Average Treatment Effect, IV Treatment Effect and Two-Stage Estimator proposed by Lewbel (2012). Findings The main findings show that children of single-mother families headed by divorced mothers are more likely to work, compared to children living with both parents. This paper found evidence of a direct effect of family structure parents’ determinant on child participation in labor. The main hypothesis is that the absence of the father paired with exposure to family stress arising from marital dissolution is an indicator toward child labor. Practical implications This study implies that in order to combat child labor effectively, it is important to understand deeply its several causes and consider ruptures in family structure, such as divorce, as one of these factors. In addition, location and family’s characteristics also play a role on the decision of child labor. For instance, boys living at metropolis areas have less chance to work. Family’s head education and non-work income affects positively the child well-being by reducing the probability of child labor. On the other hand, the number of siblings increases the chance of child labor. Finally, the results of this study suggest policies to raise awareness among parents about the negative effects of child labor on children during both childhood and adulthood, and that social policies need to act beyond legislation and enforcement, but including family mobilization. Originality/value This paper estimates the impact of family structure on child labor using an empirical approach to deal with the endogeneity problem of the treatment.

scholarly journals Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness

2020 ◽  
Vol 8 (1) ◽  
pp. 182-208
Author(s):  
Nick Huntington-Klein
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Weighted Average ◽  
Small Sample ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Heterogeneous Effects ◽  
The Impact ◽  
Local Average

AbstractIn Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may vary across the sample. In this case, IV produces a local average treatment effect (LATE), and if monotonicity does not hold, then no effect of interest is identified. In this paper, I calculate the weighted average of treatment effects that is identified under general first-stage effect heterogeneity, which is generally not the average treatment effect among those affected by the instrument. I then describe a simple set of data-driven approaches to modeling variation in the effect of the instrument. These approaches identify a Super-Local Average Treatment Effect (SLATE) that weights treatment effects by the corresponding instrument effect more heavily than LATE. Even when first-stage heterogeneity is poorly modeled, these approaches considerably reduce the impact of small-sample bias compared to standard IV and unbiased weak-instrument IV methods, and can also make results more robust to violations of monotonicity. In application to a published study with a strong instrument, the preferred approach reduces error by about 19% in small (N ≈ 1, 000) subsamples, and by about 13% in larger (N ≈ 33, 000) subsamples.

scholarly journals Impact Visualisation in Educational Interventions

2017 ◽  
Author(s):  
ZhiMin Xiao ◽  
Steve Higgins ◽  
Adetayo Kasim
Keyword(s):  
Variable Selection ◽  
Treatment Effect ◽  
Effect Size ◽  
Evidence Synthesis ◽  
Educational Interventions ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
The Impact ◽  
Analytical Approaches ◽  
Size Estimates

Reporting of research data analysis often resorts to numerical summaries, such as effect size estimates in Randomised Controlled Trials (RCTs). Summary statistics are helpful and important for evidence synthesis and decision making. However, they can be unstable and inconsistent due to diversity in research designs and variability in analytical specifications. They also mask the dynamics of individual responses to a certain intervention by focusing on average treatment effect on the treated, even though the variation in impact may be crucial information for policy makers. To establish stability and consistency of impact estimates and to reveal the dynamics of individual responses in RCTs, we conduct variable selection, harness the power of noise, implement Cumulative Quantile Analysis (CQA), and devise umbrella plots of loss and gain in this study, using real datasets from over 30 educational interventions funded by the Education Endowment Foundation (EEF) in England. For the purpose of comparison, which is essential in data visualisation, all the aforementioned methods are built upon multiple analytical approaches. We show that the importance of an intervention can be ordered through variable selection, and that the power of noise or the bias induced by inappropriate variables, can be utilised to assess the stability of an impact estimate. We also demonstrate that estimates of average treatment effect cannot fully capture the impact of an intervention on sub-groups of participants with varying levels of attainment at baseline, not to mention individual responses to the intervention. Using CQA and umbrella plots, we are able to supplement what common effect size estimates lack in educational interventions. We argue that the impact of an intervention is often more complex than the average treatment effect suggests, and that until a summary is more informative and able to speak directly to the eye, evidence-based policy and practice cannot be fully achieved.

scholarly journals A Text-As-Data Approach for Using Open-Ended Responses as Manipulation Checks

2021 ◽  
pp. 1-9
Author(s):  
Jeffrey Ziegler
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Continuous Measure ◽  
Causal Inferences ◽  
Online Surveys ◽  
Manipulation Check ◽  
Potential Variation ◽  
Average Treatment ◽  
The Impact

Abstract Participants that complete online surveys and experiments may be inattentive, which can hinder researchers’ ability to draw substantive or causal inferences. As such, many practitioners include multiple factual or instructional closed-ended manipulation checks to identify low-attention respondents. However, closed-ended manipulation checks are either correct or incorrect, which allows participants to more easily guess and it reduces the potential variation in attention between respondents. In response to these shortcomings, I develop an automatic and standardized methodology to measure attention that relies on the text that respondents provide in an open-ended manipulation check. There are multiple benefits to this approach. First, it provides a continuous measure of attention, which allows for greater variation between respondents. Second, it reduces the reliance on subjective, paid humans to analyze open-ended responses. Last, I outline how to diagnose the impact of inattentive workers on the overall results, including how to assess the average treatment effect of those respondents that likely received the treatment. I provide easy-to-use software in R to implement these suggestions for open-ended manipulation checks.

Quantile treatment effect and double robust estimators

2017 ◽  
Vol 44 (4) ◽  
pp. 585-604 ◽  
Author(s):  
Francesco Caracciolo ◽  
Marilena Furno
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Short Term ◽  
Content Type ◽  
Double Robust ◽  
Monte Carlo Studies ◽  
And Control ◽  
The Impact

Purpose Several approaches have been proposed to evaluate treatment effect, relying on matching methods propensity score, quantile regression, influence function, bootstrap and various combinations of the above. This paper considers two of these approaches to define the quantile double robust (DR) estimator: the inverse propensity score weights, to compare potential output of treated and untreated groups; the Machado and Mata quantile decomposition approach to compute the unconditional quantiles within each group – treated and control. Two Monte Carlo studies and an empirical application for the Italian job labor market conclude the analysis. The paper aims to discuss these issue. Design/methodology/approach The DR estimator is extended to analyze the tails of the distribution comparing treated and untreated groups, thus defining the quantile based DR estimator. It allows us to measure the treatment effect along the entire outcome distribution. Such a detailed analysis uncovers the presence of heterogeneous impacts of the treatment along the outcome distribution. The computation of the treatment effect at the quantiles, points out variations in the impact of treatment along the outcome distributions. Indeed it is often the case that the impact in the tails sizably differs from the average treatment effect. Findings Two Monte Carlo studies show that away from average, the quantile DR estimator can be profitably implemented. In the real data example, the nationwide results are compared with the analysis at a regional level. While at the median and at the upper quartile the nationwide impact is similar to the regional impacts, at the first quartile – the lower incomes – the nationwide effect is close to the North-Center impact but undervalues the impact in the South. Originality/value The computation of the treatment effect at various quantiles allows to point out discrepancies between treatment and control along the entire outcome distributions. The discrepancy in the tails may differ from the divergence between the average values. Treatment can be more effective at the lower/higher quantiles. The simulations show the performance at the quartiles of quantile DR estimator. In a wage equation comparing long and short term contracts, this estimator shows the presence of an heterogeneous impact of short term contracts. Their impact changes depending on the income level, the outcome quantiles, and on the geographical region.

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