Non-governmental Organisations and Democracy: An Empirical Analysis

2015 ◽  
Vol 21 (4) ◽  
pp. 489-496 ◽  
Author(s):  
Tania Masi
Keyword(s):  
Developing Countries ◽  
Propensity Score ◽  
United Nations ◽  
Propensity Score Matching ◽  
Empirical Analysis ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Government Accountability ◽  
The United Nations ◽  
The Impact

AbstractThe United Nations established in 2005 the United Nations Democracy Fund (UNDEF), whose objective is to support projects submitted by national NGOs aimed at increasing government accountability. The purpose of this paper is to investigate the impact of NGOs activity on democracy exploiting the UNDEF database. An empirical analysis based on a propensity score matching (PSM) method is implemented on a sample of 102 developing countries. The findings indicate that the average treatment effect on the treated (ATT) is positive and significant only when countries receive UNDEF-funded NGOs projects for three rounds or more. In this case the Polity IV indicator improves by an average of 1.08 points with respect to the level of 2005.

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 mobile money access on internal remittances, consumption expenditure and household welfare in Ghana

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Priscilla Twumasi Baffour ◽  
Wassiuw Abdul Rahaman ◽  
Ibrahim Mohammed
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Consumption Expenditure ◽  
Content Type ◽  
Mobile Money ◽  
Money Transfer ◽  
Per Capita ◽  
Transfer Services

PurposeThe purpose of this study is to examine the impact of mobile money access on internal remittances received, per capita consumption expenditure and welfare of household in Ghana.Design/methodology/approachThe study used data from the latest round of the Ghana Living Standards Survey (GLSS 7) and employed the propensity score matching technique to estimate average treatment effect between users and non-users of mobile money transfer services.FindingsThe study finds that using mobile money is welfare enhancing, particularly for poor households and the channel by which it impacts on welfare is through higher internal remittances received and per capita expenditure. The results from the average treatment effect indicate that mobile money users receive significantly higher remittances and consequently spend averagely higher on consumption than non-users.Research limitations/implicationsAlthough the data employed in this study is limited to one country, the findings support the financial inclusion role and developmental impact of mobile money transfer services. Hence, mobile money transfer services should be promoted and facilitated by the telecommunication and financial sector regulators.Originality/valueIn addition to making original contribution to the literature on the welfare impact of mobile money, the study's use of the propensity score matching is unique.

scholarly journals Estimating Treatment Effects of Unit-Based Pricing of Household Solid Waste Disposal

2018 ◽  
Vol 48 (1) ◽  
pp. 21-43
Author(s):  
Christopher Wright ◽  
John M. Halstead ◽  
Ju-Chin Huang
Keyword(s):  
Propensity Score ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Propensity Score Matching ◽  
Waste Disposal ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Solid Waste Disposal ◽  
Annual Reduction

Propensity score matching is used to estimate treatment effects when data are observational. Results presented in this study demonstrate the use of propensity score matching to evaluate the average treatment effect of unit-based pricing of household trash for reducing municipal solid waste disposal. Average treatment effect of the treated for 34 New Hampshire communities range from an annual reduction of 631 pounds per household to 823 pounds per household. This represents an annual reduction of 42 percent to 54 percent from an average of 1530 pounds per household if a town did not adopt municipal solid waste user fees.

Performance of propensity score matching to estimate causal effects in small samples

2020 ◽  
Vol 29 (3) ◽  
pp. 644-658 ◽  
Author(s):  
Anais Andrillon ◽  
Romain Pirracchio ◽  
Sylvie Chevret
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Treatment Effect ◽  
Marrow Transplant ◽  
Small Sample ◽  
Average Treatment Effect ◽  
Sensitivity Analyses ◽  
Small Samples ◽  
Allogeneic Bone ◽  
Matching Methods

Propensity score (PS) matching is a very popular causal estimator usually used to estimate the average treatment effect on the treated (ATT) from observational data. However, opting for this estimator may raise some efficiency issues when the sample size is limited. Therefore, we aimed to evaluate the performance of propensity score matching in this context. We started with a motivating example based on a cohort of 66 children with sickle cell anemia who received either allogeneic bone-marrow transplant or chronic transfusion. We found substantial differences in the ATT estimate according to the model selected for propensity score estimation and subsequent matching. Then, we assessed the performance of the different propensity score matching methods and post-matching analyses to estimate the ATT using a simulation study. Although all selected propensity score matching methods were based of previous recommendations, we found important discrepancies in the estimation of treatment effect between them, underlining the importance of thorough sensitivity analyses when using propensity score matching in the context of small sample sizes.

Team Incentives for Education in Developing Countries: A Randomized Field Experiment in Benin

2014 ◽  
Vol 6 (4) ◽  
pp. 90-109 ◽  
Author(s):  
Moussa P. Blimpo
Keyword(s):  
Developing Countries ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
The Other ◽  
Standard Errors ◽  
Incentive Structure ◽  
Monetary Rewards ◽  
Randomized Field Experiment ◽  
The Impact ◽  
Rule Out

I examine the impact of student incentives in Benin, using three different designs that can be implemented relatively cheaply and with administrative data. The first design is a standard incentive structure where students receive monetary rewards for reaching a performance target. In the other two designs, teams of four students receive incentives based on either their performance level as a group or in a team tournament scheme. I find a large and similar average treatment effect across designs, ranging from 0.27 to 0.34 standard deviations (Standard errors do not allow to rule out that the three designs are equally effective). (JEL C93, D82, I21, I28, O15)

Fairtrade and Workers’ Welfare: A Case Study of the Soccer Industry in Pakistan

2019 ◽  
Vol 55 (4) ◽  
pp. 536-551
Author(s):  
Javeria Younas Abbasi ◽  
Musleh Ud Din
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Treatment Effect ◽  
Labor Laws ◽  
The South ◽  
Football Industry ◽  
The Impact

The resurgence of the debate on standardization of labor laws via trade has brought into focus the importance of labeling programs, which are seen as a desirable avenue to remedy weak labor laws in the “South.” This study aims to quantify the impact of the Fairtrade labeling program on the welfare position of workers in the football industry. Propensity Score Matching (PSM) is used to calculate the treatment effect of the program. The study finds no treatment effect of labeling upon the welfare condition of workers. It concludes that the overall program has failed to make an impact upon the lives of workers.

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

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