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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MDPI AG
2021-11-01
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| Series: | Stats |
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| Online Access: | https://www.mdpi.com/2571-905X/4/4/52 |
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| author | Albert Whata Charles Chimedza |
| author_facet | Albert Whata Charles Chimedza |
| author_sort | Albert Whata |
| collection | DOAJ |
| description | 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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| issn | 2571-905X |
| language | English |
| last_indexed | 2024-03-10T03:05:07Z |
| publishDate | 2021-11-01 |
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| spelling | doaj.art-bd17345632924162a05a8c856763fa932023-11-23T10:35:00ZengMDPI AGStats2571-905X2021-11-014489391510.3390/stats4040052Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment VariablesAlbert Whata0Charles Chimedza1School of Natural and Applied Sciences, Sol Plaatje University, Kimberley 8301, South AfricaSchool of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg 2050, South AfricaIn 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.https://www.mdpi.com/2571-905X/4/4/52multivariate regression discontinuity designsfrontier regression discontinuitysupplementary analysescredibility |
| spellingShingle | Albert Whata Charles Chimedza Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables Stats multivariate regression discontinuity designs frontier regression discontinuity supplementary analyses credibility |
| title | Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables |
| title_full | Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables |
| title_fullStr | Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables |
| title_full_unstemmed | Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables |
| title_short | Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables |
| title_sort | credibility of causal estimates from regression discontinuity designs with multiple assignment variables |
| topic | multivariate regression discontinuity designs frontier regression discontinuity supplementary analyses credibility |
| url | https://www.mdpi.com/2571-905X/4/4/52 |
| work_keys_str_mv | AT albertwhata credibilityofcausalestimatesfromregressiondiscontinuitydesignswithmultipleassignmentvariables AT charleschimedza credibilityofcausalestimatesfromregressiondiscontinuitydesignswithmultipleassignmentvariables |