scholarly journals A Quasi-Experimental Study of Medicaid Expansion and Urban Mortality in the American Northeast

2021 ◽  
Vol 9 ◽  
Author(s):  
Cyrus Ayubcha ◽  
Pedram Pouladvand ◽  
Soussan Ayubcha
Keyword(s):  
Experimental Study ◽  
Treatment Effect ◽  
Control Method ◽  
Mortality Rates ◽  
Average Treatment Effect ◽  
Medicaid Expansion ◽  
Synthetic Control ◽  
Urban Settings ◽  
Average Treatment ◽  
Quasi Experimental

Objectives: To investigate the association of state-level Medicaid expansion and non-elderly mortality rates from 1999 to 2018 in Northeastern urban settings.Methods: This quasi-experimental study utilized a synthetic control method to assess the association of Medicaid expansion on non-elderly urban mortality rates [1999–2018]. Counties encompassing the largest cities in the Northeastern Megalopolis (Washington D.C., Baltimore, Philadelphia, New York City, and Boston) were selected as treatment units (n = 5 cities, 3,543,302 individuals in 2018). Cities in states without Medicaid expansion were utilized as control units (n = 17 cities, 12,713,768 individuals in 2018).Results: Across all cities, there was a significant reduction in the neoplasm (Population-Adjusted Average Treatment Effect = −1.37 [95% CI −2.73, −0.42]) and all-cause (Population-Adjusted Average Treatment Effect = −2.57 [95%CI −8.46, −0.58]) mortality rate. Washington D.C. encountered the largest reductions in mortality (Average Treatment Effect on All-Cause Medical Mortality = −5.40 monthly deaths per 100,000 individuals [95% CI −12.50, −3.34], −18.84% [95% CI −43.64%, −11.67%] reduction, p = < 0.001; Average Treatment Effect on Neoplasm Mortality = −1.95 monthly deaths per 100,000 individuals [95% CI −3.04, −0.98], −21.88% [95% CI −34.10%, −10.99%] reduction, p = 0.002). Reductions in all-cause medical mortality and neoplasm mortality rates were similarly observed in other cities.Conclusion: Significant reductions in urban mortality rates were associated with Medicaid expansion. Our study suggests that Medicaid expansion saved lives in the observed urban settings.

scholarly journals Evaluación de impacto académico y social en egresados universitarios

2019 ◽  
Vol 30 (3) ◽  
pp. 695-712
Author(s):  
Gabriel González ◽  
Luisa Díez-Echavarría ◽  
Elkin Zapa ◽  
Danilo Eusse
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Educación Superior ◽  
Educacion Superior ◽  
Average Treatment

Las instituciones de educación superior deben formar a sus estudiantes según requerimientos del contexto en que se desenvuelven, ya que, sobre la base de su desempeño, es donde se medirá si las políticas de desarrollo socioeconómico son efectivas. Para lograrlo, es necesario identificar el impacto de esa educación en sus egresados, y hacer los ajustes necesarios que generen mejora continua. El objetivo de este artículo es estimar el impacto académico y social de egresados del Instituto Tecnológico Metropolitano – Medellín, a través de un análisis multivariado y la estimación del modelo Average Treatment Effect (ATE). Se encontró que la educación ofrecida a esta población ha generado un impacto académico, asociado a los estudios de actualización, y dos impactos sociales, asociados a la situación laboral y al nivel de ingresos percibidos por los egresados. Se recomienda usar esta metodología en otras instituciones, ya que suele arrojar resultados más informativos y precisos que los estudios tradicionales de caracterización, y se puede medir el efecto de cualquier estrategia.

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.

OLS and 2SLS in Randomized and Conditionally Randomized Experiments

2018 ◽  
Vol 238 (3-4) ◽  
pp. 243-293 ◽  
Author(s):  
Jason Ansel ◽  
Han Hong ◽  
and Jessie Li
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Randomized Experiments ◽  
Adaptive Randomization ◽  
Working Paper ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Effect Parameter ◽  
Multiple Treatments ◽  
Local Average

Abstract We investigate estimation and inference of the (local) average treatment effect parameter when a binary instrumental variable is generated by a randomized or conditionally randomized experiment. Under i.i.d. sampling, we show that adding covariates and their interactions with the instrument will weakly improve estimation precision of the (local) average treatment effect, but the robust OLS (2SLS) standard errors will no longer be valid. We provide an analytic correction that is easy to implement and demonstrate through Monte Carlo simulations and an empirical application the interacted estimator’s efficiency gains over the unadjusted estimator and the uninteracted covariate adjusted estimator. We also generalize our results to covariate adaptive randomization where the treatment assignment is not i.i.d., thus extending the recent contributions of Bugni, F., I.A. Canay, A.M. Shaikh (2017a), Inference Under Covariate-Adaptive Randomization. Working Paper and Bugni, F., I.A. Canay, A.M. Shaikh (2017b), Inference Under Covariate-Adaptive Randomization with Multiple Treatments. Working Paper to allow for the case of non-compliance.

A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments

2013 ◽  
Vol 1 (1) ◽  
pp. 135-154 ◽  
Author(s):  
Peter M. Aronow ◽  
Joel A. Middleton
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Covariate Adjustment ◽  
Randomized Experiments ◽  
Treatment Assignment ◽  
Unbiased Estimators ◽  
Average Treatment

AbstractWe derive a class of design-based estimators for the average treatment effect that are unbiased whenever the treatment assignment process is known. We generalize these estimators to include unbiased covariate adjustment using any model for outcomes that the analyst chooses. We then provide expressions and conservative estimators for the variance of the proposed estimators.

scholarly journals Optimal Allocation of Interviews to Baseline and Endline Surveys in Place-Based Randomized Trials and Quasi-Experiments

2018 ◽  
Vol 42 (4) ◽  
pp. 391-422 ◽  
Author(s):  
Donald P. Green ◽  
Winston Lin ◽  
Claudia Gerber
Keyword(s):  
Random Sample ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Optimal Allocation ◽  
Average Treatment Effect ◽  
Baseline Survey ◽  
Baseline Measure ◽  
Panel Surveys ◽  
Planning Stage ◽  
Average Treatment

Background: Many place-based randomized trials and quasi-experiments use a pair of cross-section surveys, rather than panel surveys, to estimate the average treatment effect of an intervention. In these studies, a random sample of individuals in each geographic cluster is selected for a baseline (preintervention) survey, and an independent random sample is selected for an endline (postintervention) survey. Objective: This design raises the question, given a fixed budget, how should a researcher allocate resources between the baseline and endline surveys to maximize the precision of the estimated average treatment effect? Results: We formalize this allocation problem and show that although the optimal share of interviews allocated to the baseline survey is always less than one-half, it is an increasing function of the total number of interviews per cluster, the cluster-level correlation between the baseline measure and the endline outcome, and the intracluster correlation coefficient. An example using multicountry survey data from Africa illustrates how the optimal allocation formulas can be combined with data to inform decisions at the planning stage. Another example uses data from a digital political advertising experiment in Texas to explore how precision would have varied with alternative allocations.

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