- Christine Mulhern
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There is an emerging consensus that teachers impact multiple student outcomes, but it remains unclear how to summarize these multiple dimensions of teacher effectiveness into simple metrics that can be used for research or personnel decisions. Here, we discuss the implications of estimating teacher effects in a multidimensional empirical Bayes framework and illustrate how to appropriately use these noisy estimates to assess the dimensionality and predictive power of the true teacher effects. Empirically, our principal components analysis indicates that the multiple dimensions can be efficiently summarized by a small number of measures; for example, one dimension explains over half the variation in the teacher effects on all the dimensions we observe. Summary measures based on the first principal component lead to similar rankings of teachers as summary measures weighting short-term effects by their prediction of long-term outcomes. We conclude by discussing the practical implications of using summary measures of effectiveness and, specifically, how to ensure that the policy implementation is fair when different sets of measures are observed for different teachers.
We use high frequency internet search data to study in real time how US households sought out online learning resources as schools closed due to the Covid-19 pandemic. By April 2020, nationwide search intensity for both school- and parent-centered online learning resources had roughly doubled relative to baseline. Areas of the country with higher income, better internet access and fewer rural schools saw substantially larger increases in search intensity. The pandemic will likely widen achievement gaps along these dimensions given schools' and parents' differing engagement with online resources to compensate for lost school-based learning time. Accounting for such differences and promoting more equitable access to online learning could improve the effectiveness of education policy responses to the pandemic. The public availability of internet search data allows our analyses to be updated when schools reopen and to be replicated in other countries.
Family and social networks are widely believed to influence important life decisions but identifying their causal effects is notoriously difficult. Using admissions thresholds that directly affect older but not younger siblings’ college options, we present evidence from the United States, Chile, Sweden and Croatia that older siblings’ college and major choices can significantly influence their younger siblings’ college and major choices. On the extensive margin, an older sibling’s enrollment in a better college increases a younger sibling’s probability of enrolling in college at all, especially for families with low predicted probabilities of enrollment. On the intensive margin, an older sibling’s choice of college or major increases the probability that a younger sibling applies to and enrolls in that same college or major. Spillovers in major choice are stronger when older siblings enroll and succeed in more selective and higher-earning majors. The observed spillovers are not well-explained by price, income, proximity or legacy effects, but are most consistent with older siblings transmitting otherwise unavailable information about the college experience and its potential returns. The importance of such personally salient information may partly explain persistent differences in college-going rates by geography, income, and other determinants of social networks.
We study within-family spillovers in college enrollment to show college-going behavior is transmissible between peers. Because siblings’ test scores are weakly correlated, we exploit college-speciﬁc admissions thresholds that directly affect older but not younger siblings’ college options. Older siblings’ admissibility substantially increases their own four-year college enrollment rate and quality of college attended. Their improved college choices in turn raise younger siblings’ college enrollment rate and quality of college chosen, particularly for families with low predicted probabilities of college enrollment. Some younger siblings follow their older sibling to the same campus but many upgrade by choosing other colleges. The observed spillovers are not well-explained by price, income, proximity or legacy effects, but are most consistent with older siblings transmitting otherwise unavailable information about the college experience and its potential returns. The importance of such personally salient information may partly explain persistent differences in college-going rates by income, geography and other characteristics that deﬁne a community.