TEACHER TEAM-BASED INCENTIVES: EVIDENCE FROM A NATURAL EXPERIMENT IN A CHINESE MIDDLE SCHOOL

2021 ◽  
pp. 1-37
Author(s):  
KAIWEN LEONG ◽  
HUAILU LI ◽  
DAN LI ◽  
YUCHEN XIE
Keyword(s):  
Middle School ◽  
Social Science ◽  
Treatment Effect ◽  
Natural Experiment ◽  
Causal Effect ◽  
Average Treatment Effect ◽  
Incentive Scheme ◽  
Average Treatment ◽  
Standard Deviations ◽  
Team Incentive

Using a natural experiment, we examine the causal effect of a team incentive scheme on teachers in a Chinese middle school that intended to help the school’s students improve in their weak subjects. The scheme was successful, the average treatment effect is positively significant in math and total scores. The most improvement observed in top students’ weak subjects. The top students weak in math, English and social science improved in those subjects by 0.12, 0.10, 0.16 standard deviations, respectively. Students at the bottom 20% of the testing distribution also improved in Chinese and math.

scholarly journals Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community

2019 ◽  
Vol 16 (13) ◽  
pp. 2314 ◽  
Author(s):  
Hai-Yan Yu ◽  
Jing-Jing Chen ◽  
Jying-Nan Wang ◽  
Ya-Ling Chiu ◽  
Hang Qiu ◽  
...  
Keyword(s):  
Health Services ◽  
Gini Coefficient ◽  
Treatment Effect ◽  
Causal Effect ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
The Gini Coefficient ◽  
Health Community ◽  
Level 1 ◽  
The City

Inequality of health services for different specialty categories not only occurs in different areas in the world, but also happens in the online service platform. In the online health community (OHC), health services often display inequality for different specialty categories, including both online views and medical consultations for offline registered services. Moreover, how the city-level factors impact the inequality of health services in OHC is still unknown. We designed a causal inference study with data on distributions of serviced patients and online views in over 100 distinct specialty categories on one of the largest OHCs in China. To derive the causal effect of the city-levels (two levels inducing 1 and 0) on the Gini coefficient, we matched the focus cases in cities with rich healthcare resources with the potential control cities. For each of the specialty categories, we first estimated the average treatment effect of the specialty category’s Gini coefficient (SCGini) with the balanced covariates. For the Gini coefficient of online views, the average treatment effect of level-1 cities is 0.573, which is 0.016 higher than that of the matched group. Similarly, for the Gini coefficient of serviced patients, the average treatment effect of level-1 cities is 0.470, which is 0.029 higher than that of the matched group. The results support the argument that the total Gini coefficient of the doctors in OHCs shows that the inequality in health services is still very serious. This study contributes to the development of a theoretically grounded understanding of the causal effect of city-level factors on the inequality of health services in an online to offline health service setting. In the future, heterogeneous results should be considered for distinct groups of doctors who provide different combinations of online contributions and online attendance.

Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable

2013 ◽  
Vol 21 (4) ◽  
pp. 492-506 ◽  
Author(s):  
Peter M. Aronow ◽  
Allison Carnegie
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Causal Effect ◽  
Average Treatment Effect ◽  
Treatment Variable ◽  
Estimation Strategy ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Receive Treatment ◽  
Local Average

Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, researchers may instead be interested in the ATE for the entire population of interest. In this article, we develop a simple reweighting method for estimating the ATE, shedding light on the identification challenge posed in moving from the LATE to the ATE. We apply our method to two published experiments in political science in which we demonstrate that the LATE has the potential to substantively differ from the ATE.

scholarly journals Intangible Capital and Leverage

2020 ◽  
pp. 1-24 ◽  
Author(s):  
Philipp Horsch ◽  
Philip Longoni ◽  
David Oesch
Keyword(s):  
Treatment Effect ◽  
Causal Effect ◽  
Average Treatment Effect ◽  
Intangible Capital ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Local Average

We investigate the causal effect of intangible capital on leverage. To address endogeneity, we exploit patent invalidations by a U.S. court in which judges are randomly assigned to cases. Differences in judge leniency provide exogenous variation in the probability that firms’ patents are invalidated. Using this probability as an instrument for exogenous losses in intangible capital, we find a patent invalidation leads to a 14.1% reduction in leverage, suggesting that intangible capital causally supports leverage. This local average treatment effect is stronger in firms that use patents as loan collateral and in less creditworthy as well as smaller firms.

scholarly journals Identification of the Differential Effect of City-level on the Gini Coefficient of Healthcare Service Delivery in Online Health Community

2019 ◽  
Author(s):  
Haiyan Yu ◽  
Jingjing Chen ◽  
Jying-Nan Wang ◽  
Ya-Ling Chiu ◽  
Hang Qiu ◽  
...  
Keyword(s):  
Health Service ◽  
Gini Coefficient ◽  
Treatment Effect ◽  
Causal Effect ◽  
Average Treatment Effect ◽  
Online Health Community ◽  
Average Treatment ◽  
The Gini Coefficient ◽  
Health Community ◽  
Level 1

Inequality of health service for different specialty categories not only occurs in different areas inequality of health service for different specialty categories in the world, but also happens in the online service platform. In the online health community (OHC), health service was often of inequality for different specialty categories, including both online views and medical consultation for offline registered service. Moreover, how the factor city-level impacts the inequality of health service in OHC is still unknown.   We designed a causal inference study with data on distributions of serviced patients and online views in over 100 distinct specialty categories on one largest OHC in China. To derive the causal effect of the city-levels (two levels inducing 1 and 0) on the Gini coefficient, we matched the focus cases in cities of rich healthcare resources with the potential control cities. For the Gini coefficient of serviced patients in over 100 specialty categories, the average treatment effect of level-1 cities is 0.470, which is 0.029 higher than that of the matched group. Similarly, for the Gini coefficient of online views, the average treatment effect of Level-1 cities is 0.573, which is 0.016 higher than that of the matched group. For each of the specialty categories, we also estimated the average treatment effect the specialty category’s Gini coefficient ( ) with the balanced covariates. The results support the argument that the total Gini coefficient of all the doctors in OHC shows that the inequality of health service is still very serious. This study contributes to the development of the theoretically grounded understanding of the causal effect of city-level on the inequality of health service in an online to offline healthcare service setting.

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.

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