What Are We Weighting For?

Solon, Gary; Haider, Steven J.; Wooldridge, Jeffrey M.

doi:10.3368/jhr.50.2.301

Cited by 1,133 publications

(643 citation statements)

References 26 publications

Supporting

Mentioning

600

Contrasting

Unclassified

Order By: Relevance

“…Fourth, we re-estimated all models without sampling weights; differences in coefficient estimates in weighted versus unweighted models might reflect errors in model specification, incorporation of the survey sampling process, or both. 40 We did all analyses with Stata version 14. This study relied solely on public-use data so no ethical approval was sought for the study procedures.…”

Section: Statistical Analysesmentioning

confidence: 99%

Health consequences of the US Deferred Action for Childhood Arrivals (DACA) immigration programme: a quasi-experimental study

Venkataramani

Shah

O’Brien

et al. 2017

The Lancet Public Health

128

149

View full text Add to dashboard Cite

show abstract

Section: Statistical Analysesmentioning

confidence: 99%

Health consequences of the US Deferred Action for Childhood Arrivals (DACA) immigration programme: a quasi-experimental study

Venkataramani

Shah

O’Brien

et al. 2017

The Lancet Public Health

128

149

View full text Add to dashboard Cite

show abstract

“…For the univariate analysis therefore, we employed the sampling weighting scheme as recommended in the wider literature [64,65] and the Eurobarometer methodology [66]. Regarding the multivariate analysis, debate exists over whether such a weighting scheme should be used [64,67,68].…”

Section: Methodsologymentioning

confidence: 99%

Rethinking informal payments by patients in Europe: An institutional approach

Williams

Horodnic

2017

Health Policy

View full text Add to dashboard Cite

“…9 Nonetheless, we show in online appendix table A4 that the baseline results are robust to specifying a nonlinear conditional (FE) logit model that acknowledges the binary nature of the dependent variables, which takes the right hand side of equation (1) as its linear index. Another sensitivity analysis is presented in online appendix table A5, which shows that the preferred LPM estimates are robust to weighting by NCES-provided sampling weights, as suggested by Solon, Haider, and Wooldridge (2015).…”

Section: Identification Strategymentioning

confidence: 99%

Who believes in me? The effect of student–teacher demographic match on teacher expectations

Gershenson

Holt

Papageorge

2016

Economics of Education Review

434

242

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. Teachers are an important source of information for traditionally disadvantaged students. However, little is known about how teachers form expectations and whether their expectations are systematically biased. We investigate whether student-teacher demographic mismatch affects high school teachers' expectations for students' educational attainment. Using a student fixed effects strategy that exploits expectations data from two teachers per student, we find that non-black teachers of black students have significantly lower expectations than do black teachers. These effects are larger for black male students and math teachers. Our findings add to a growing literature on the role of limited information in perpetuating educational attainment gaps.JEL Classification: I24, D84, J15, J16

show abstract

What Are We Weighting For?

Cited by 1,133 publications

References 26 publications

Health consequences of the US Deferred Action for Childhood Arrivals (DACA) immigration programme: a quasi-experimental study

Health consequences of the US Deferred Action for Childhood Arrivals (DACA) immigration programme: a quasi-experimental study

Rethinking informal payments by patients in Europe: An institutional approach

Who believes in me? The effect of student–teacher demographic match on teacher expectations

Contact Info

Product

Resources

About