Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables

In this paper, we determine treatment effects when the treatment assignment is based on two or more cut-off points of covariates rather than on one cut-off point of one assignment variable. using methods that are referred to as multivariate regression discontinuity designs (MRDD). One major finding...
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Autor*in:	Albert Whata [verfasserIn] Charles Chimedza [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	multivariate regression discontinuity designs frontier regression discontinuity supplementary analyses credibility

Übergeordnetes Werk:	In: Stats - MDPI AG, 2020, 4(2021), 4, Seite 893-915
Übergeordnetes Werk:	volume:4 ; year:2021 ; number:4 ; pages:893-915

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/stats4040052

Katalog-ID:	DOAJ018777139

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topic_title	HA1-4737 Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables multivariate regression discontinuity designs frontier regression discontinuity supplementary analyses credibility
topic	misc HA1-4737 misc multivariate regression discontinuity designs misc frontier regression discontinuity misc supplementary analyses misc credibility misc Statistics
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title	Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables
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title_full	Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables
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title_sort	credibility of causal estimates from regression discontinuity designs with multiple assignment variables
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title_auth	Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables
abstract	In this paper, we determine treatment effects when the treatment assignment is based on two or more cut-off points of covariates rather than on one cut-off point of one assignment variable. using methods that are referred to as multivariate regression discontinuity designs (MRDD). One major finding of this paper is the discovery of new evidence that both matric points and household income have a huge impact on the probability of eligibility for funding from the National Student Financial Aid Scheme (NSFAS) to study for a bachelor’s degree program at universities in South Africa. This evidence will inform policymakers and educational practitioners on the effects of matric points and household income on the eligibility for NSFAS funding. The availability of the NSFAS grant impacts greatly students’ decisions to attend university or seek other opportunities elsewhere. Using the frontier MRDD analytical results, barely scoring matric points greater than or equal to 25 points compared to scoring matric points less than 25 for students whose household income is less than R350,000 (≈US$2500) increases the probability of eligibility for NSFAS funding by a significant 3.75 ( <i<p</i<-value = 0.0001 < 0.05) percentage points. Therefore, we have shown that the frontier MRDD can be employed to determine the causal effects of barely meeting the requirements of one assignment variable, among the subjects that either meet or fail to meet the requirements of the other assignment variable.
abstractGer	In this paper, we determine treatment effects when the treatment assignment is based on two or more cut-off points of covariates rather than on one cut-off point of one assignment variable. using methods that are referred to as multivariate regression discontinuity designs (MRDD). One major finding of this paper is the discovery of new evidence that both matric points and household income have a huge impact on the probability of eligibility for funding from the National Student Financial Aid Scheme (NSFAS) to study for a bachelor’s degree program at universities in South Africa. This evidence will inform policymakers and educational practitioners on the effects of matric points and household income on the eligibility for NSFAS funding. The availability of the NSFAS grant impacts greatly students’ decisions to attend university or seek other opportunities elsewhere. Using the frontier MRDD analytical results, barely scoring matric points greater than or equal to 25 points compared to scoring matric points less than 25 for students whose household income is less than R350,000 (≈US$2500) increases the probability of eligibility for NSFAS funding by a significant 3.75 ( <i<p</i<-value = 0.0001 < 0.05) percentage points. Therefore, we have shown that the frontier MRDD can be employed to determine the causal effects of barely meeting the requirements of one assignment variable, among the subjects that either meet or fail to meet the requirements of the other assignment variable.
abstract_unstemmed	In this paper, we determine treatment effects when the treatment assignment is based on two or more cut-off points of covariates rather than on one cut-off point of one assignment variable. using methods that are referred to as multivariate regression discontinuity designs (MRDD). One major finding of this paper is the discovery of new evidence that both matric points and household income have a huge impact on the probability of eligibility for funding from the National Student Financial Aid Scheme (NSFAS) to study for a bachelor’s degree program at universities in South Africa. This evidence will inform policymakers and educational practitioners on the effects of matric points and household income on the eligibility for NSFAS funding. The availability of the NSFAS grant impacts greatly students’ decisions to attend university or seek other opportunities elsewhere. Using the frontier MRDD analytical results, barely scoring matric points greater than or equal to 25 points compared to scoring matric points less than 25 for students whose household income is less than R350,000 (≈US$2500) increases the probability of eligibility for NSFAS funding by a significant 3.75 ( <i<p</i<-value = 0.0001 < 0.05) percentage points. Therefore, we have shown that the frontier MRDD can be employed to determine the causal effects of barely meeting the requirements of one assignment variable, among the subjects that either meet or fail to meet the requirements of the other assignment variable.
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title_short	Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables
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Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables

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