For whom the reductions count: A quantile regression analysis of class size and peer effects on scholastic achievement

By J. Levin. 2001

195 citations recorded by the source corpus.

Subfields: Demography, Education

View Paper: https://www.semanticscholar.org/paper/c169b92218b912cf74a4f4c049802cf8115a18cd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. I. Sebastian Buhai
2. Lingjie Ma
3. Mo‐Yin S. Tam
4. R. Winter‐Ebmer
5. Gilbert W. Bassett Jr.
6. Keith Knight
7. Craig Reed
8. Keming Yu

In Education

1. Lingjie Ma
2. Mo‐Yin S. Tam
3. R. Winter‐Ebmer
4. Gilbert W. Bassett Jr.
5. Keith Knight
6. Craig Reed
7. Keming Yu
8. I. Sebastian Buhai

Citations in the corpus

• Quantile Regression: Overview and Selected Applications ∗ (2004, methodology): “The authors reconsider an application by Levin (2001) investigating the effect of class size on student performance in Dutch primary schools.”
• A partially collapsed Gibbs sampler for Bayesian quantile regression (2009, background): “For example, Levin (2001) studies a panel survey of the performance of Dutch school children and finds some evidence of positive peer effects in the lower tail of the achievement distribution.”
• Estimating and Testing a Quantile Regression Model with Interactive Effects (2012, unknown): “results obtained from: ordinary least squares (OLS), the least squares version of the fixed effects estimator (LSFE), the classical instrumental variable estimation for a model with fixed effects (LSIVFE), the estimator introduced in proposition 1 (LSIE) which we consider to be a version of the CCE ”
• Peer effects in Austrian schools (2005, background): “Levin (2001) found stronger effects for weaker students in the Netherlands.”
• Quantile models and estimators for data analysis (2002, methodology): “Recently, quantile regression has been used by Eide and Showalter (1998) to consider how student scores changed over time as a function of school quality, and Levin (2001) considered peer e¤ects and class size.”
• Quantile regression methods for recursive structural equation models (2004, methodology): “Empirically, researchers have taken the assigned class size, Krueger (1997); school enrollment, Akerheilm (1995), Iacovou (2001), Levin (2001); and grade enrollment, Angrist and Lavy (1999); as instrumental variables for actual class size in either continuous or non-continuous forms.”