scholarly journals Minimum Wage Changes across Provinces in China: Average Treatment Effects on Employment and Investment Decisions

2021 ◽  
Vol 14 (1) ◽  
pp. 22
Author(s):  
Ji Luo ◽  
Daniel J. Henderson
Keyword(s):  
Adverse Effects ◽  
Minimum Wage ◽  
Investment Decisions ◽  
Household Finance ◽  
Average Treatment Effect ◽  
Bank Account ◽  
Level Of Education ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
The Impact

We exploit data from the China Household Finance Survey to examine the impact of changes in the minimum wage on employment and investment decisions. We are able to non-parametrically identify the average treatment effect on the treated via exogenous variation in the minimum wage across provinces. We find that changes in the minimum wage had no adverse effects on employment (in terms of days worked per month or hours worked per work day) but found evidence that changes in the minimum wage impacted the percentage of families that had a bank account, a family in a rural area owned their home, and whether families (whose highest level of education was primary school) planned to purchase a home.

The Effect of Extension Services and Credit on Agricultural Production in Bolivia, Peru, and Colombia

2021 ◽  
Author(s):  
Mateus C. R. Neves ◽  
Felipe De Figueiredo Silva ◽  
Carlos Otávio Freitas
Keyword(s):  
Travel Time ◽  
Treatment Effect ◽  
Agricultural Production ◽  
Treatment Effects ◽  
Geographic Information ◽  
Average Treatment Effect ◽  
Extension Services ◽  
Average Treatment ◽  
Andean Countries ◽  
Average Treatment Effects

In this paper we estimate the average treatment effect from access to extension services and credit on agricultural production in selected Andean countries (Bolivia, Peru, and Colombia). More specifically, we want to identify the effect of accessibility, here represented as travel time to the nearest area with 1,500 or more inhabitants per square kilometer or at least 50,000 inhabitants, on the likelihood of accessing extension and credit. To estimate the treatment effect and identify the effect of accessibility on these variables, we use data from the Colombian and Bolivian Agricultural Censuses of 2013 and 2014, respectively; a national agricultural survey from 2017 for Peru; and geographic information on travel time. We find that the average treatment effect for extension is higher compared to that of credit for farms in Bolivia and Peru, and lower for Colombia. The average treatment effects of extension and credit for Peruvian farms are $2,387.45 and $3,583.42 respectively. The average treatment effect for extension and credit are $941.92 and $668.69, respectively, while in Colombia are $1,365.98 and $1,192.51, respectively. We also find that accessibility and the likelihood of accessing these services are nonlinearly related. Results indicate that higher likelihood is associated with lower travel time, especially in the analysis of credit.

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.

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 Too LATE for Natural Experiments: A Critique of Local Average Treatment Effects Using the Example of Angrist and Evans (1998)

2019 ◽  
Author(s):  
Stefan Öberg
Keyword(s):  
Indirect Effect ◽  
Instrumental Variable ◽  
Natural Experiment ◽  
Average Treatment Effect ◽  
Natural Experiments ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Average Treatment Effects ◽  
Local Average Treatment Effects ◽  
Local Average

There has been a fundamental flaw in the conceptual design of many natural experiments used in the economics literature, particularly among studies aiming to estimate a local average treatment effect (LATE). When we use an instrumental variable (IV) to estimate a LATE, the IV only has an indirect effect on the treatment of interest. Such IVs do not work as intended and will produce severely biased and/or uninterpretable results. This comment demonstrates that the LATE does not work as previously thought and explains why using the natural experiment proposed by Angrist and Evans (1998) as the example.

scholarly journals Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness

2020 ◽  
Vol 8 (1) ◽  
pp. 249-271
Author(s):  
Nathan Corder ◽  
Shu Yang
Keyword(s):  
Multiple Imputation ◽  
Treatment Effects ◽  
Missing Values ◽  
Missing At Random ◽  
Average Treatment Effect ◽  
Asymptotically Normal ◽  
Average Treatment ◽  
Accuracy And Precision ◽  
Causal Treatment ◽  
Average Treatment Effects

Abstract The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Alternatively, fractional imputation, proposed by Kim 2011, has been implemented to handling missing values in regression context. In this article, we develop fractional imputation methods for estimating the average treatment effects with confounders missing at random. We show that the fractional imputation estimator of the average treatment effect is asymptotically normal, which permits a consistent variance estimate. Via simulation study, we compare fractional imputation’s accuracy and precision with that of multiple imputation.

scholarly journals The impact of climate smart agriculture on household welfare in smallholder integrated crop–livestock farming systems: evidence from Zimbabwe

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Angeline Mujeyi ◽  
Maxwell Mudhara ◽  
Munyaradzi Mutenje
Keyword(s):  
Food Security ◽  
Treatment Effects ◽  
Positive Impact ◽  
Arable Land ◽  
Household Head ◽  
Full Potential ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Smart Agriculture ◽  
The Impact

Abstract Background Agriculture contributes significantly to the welfare of smallholder farmers, but it has become highly susceptible to climate change, due to its reliance on the increasingly erratic rainfall patterns. Climate Smart Agriculture (CSA) offers important opportunities for enhancing food security and incomes through increased agriculture productivity. Technology evaluation through impact studies provides information on the effect of CSA on farmer welfare, thereby highlighting its potential in optimizing agriculture productivity. This paper analyses the impact of CSA adoption on food security and income of households, using cross-sectional survey data collected from 386 households across four districts in Zimbabwe. The analysis was done using the endogenous switching regression model which controls for selection bias and unobserved heterogeneity, a commonly used method in adoption impact analysis. Results The study found several agricultural and socio-economic factors which affect adoption and food security. The econometric results show that the status of soil fertility in fields, distance to input and output markets, ownership of communication assets, and Total Livestock Units (TLU) have a significant impact on the decision of farmers to adopt CSA. The Average Treatment Effects on the Treated (ATT) and Average Treatment Effects on the Untreated (ATU) were found to be positive and significant for adopters and non-adopters, indicating that CSA adoption has had a significantly positive impact on the welfare of the farmers. An analysis of the outcomes revealed that the characteristics of farmers and farms, as well as market factors, significantly affect the welfare of households. The household income, with reference to the adoption of CSA, was significantly affected by factors such as the education of household head, labour size, TLU, and asset index. Food security was influenced by factors such as the education of household head, TLU, access to sanitation, and arable land size. Conclusions The study concludes by giving policy recommendations centred on the access to inputs, sanitation, and encouraging investing in assets and TLU. The findings indicate that the adoption of CSA has a positive impact on the welfare of farmers. To exploit the full potential of these technologies, the study suggests that access to timely weather forecasts must be ensured, that sanitation must be promoted, and that incentives must be provided for agricultural input agro-dealers to decentralize to rural areas.

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.

scholarly journals Impact of a Contract Farming Scheme on Income, Food Security, and Nutrition among Maize Farmers in North Western, Nigeria

2021 ◽  
Author(s):  
Ibrahim Hussaini Yusuf ◽  
Garba Sakinatu Umar ◽  
Wahab Munir Jamiu
Keyword(s):  
Food Security ◽  
Simple Random Sampling ◽  
Study Data ◽  
Average Treatment Effect ◽  
Contract Farming ◽  
Maize Production ◽  
Average Treatment ◽  
Total Labor ◽  
Food Security And Nutrition ◽  
The Impact

Background: The study examined the impact of a contract farming scheme on the farmers’ income, food security, and nutrition. Methods: Simple random sampling was used to select 100 respondents for the study. Data were analyzed using descriptive and inferential statistics as well the Propensity Score Matching technique. Results: The major determinants of participation in contract farming included commercialization index, distance from the collection center, and total labor available in the household. The average treatment effect on the treated, the average effect of the treatment, and the average treatment on the untreated shows that contract farming will enhance the income from Maize production by ₦50234.8 ($131.79)/hectare, ₦37170.8 ($97.53)/hectare, and ₦28809.8 ($75.59)/hectare respectively. Conclusion: Contract farming participation can affect farming households negatively if food security concerns are not considered into the contract farming agreements.

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