Understanding the impact of career academy attendance: an application of the principal stratification framework for causal effects accounting for partial compliance.
BACKGROUND
Results from MDRC's longitudinal, random-assignment evaluation of career-academy high schools reveal that several years after high-school completion, those randomized to receive the academy opportunity realized a $175 (11%) increase in monthly earnings, on average.


OBJECTIVE
In this paper, I investigate the impact of duration of actual academy enrollment, as nearly half of treatment group students either never enrolled or participated for only a portion of high school.


METHODS
I capitalize on data from this experimental evaluation and utilize a principal stratification framework and Bayesian inference to investigate the causal impact of academy participation.


METHODS
This analysis focuses on a sample of 1,306 students across seven sites in the MDRC evaluation.


METHODS
Participation is measured by number of years of academy enrollment, and the outcome of interest is average monthly earnings in the period of four to eight years after high school graduation.


RESULTS
I estimate an average causal effect of treatment assignment on subsequent monthly earnings of approximately $588 among males who remained enrolled in an academy throughout high school and more modest impacts among those who participated only partially.


CONCLUSIONS
Different from an instrumental variables approach to treatment non-compliance, which allows for the estimation of linear returns to treatment take-up, the more general framework of principal stratification allows for the consideration of non-linear returns, although at the expense of additional model-based assumptions.
Center for Education Policy Research, Harvard University, 50 Church Street, 4th Floor, Cambridge, MA 02138, USA. lindsay_page@gse.harvard.edu
Lindsay C
LC
Page
D000124:Achievement; D000293:Adolescent; D000465:Algorithms; D001499:Bayes Theorem; D002321:Career Choice; D004521:Educational Measurement / Q000379:methods; D004522:Educational Status; D005257:Fellowships and Scholarships / Q000191:economics* / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D017145:Models, Educational; D015233:Models, Statistical; D009010:Monte Carlo Method; D012574:Schools / Q000191:economics* / Q000706:statistics & numerical data; D013997:Time Factors; D014481:United States