2010
DOI: 10.3386/w15729
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Modeling College Major Choices using Elicited Measures of Expectations and Counterfactuals

Abstract: We examine differences in minority science graduation rates among University of California campuses when racial preferences were in place. Less-prepared minorities at higherranked campuses had lower persistence rates in science and took longer to graduate. We estimate a model of students college major choice where net returns of a science major differ across campuses and student preparation. We find less-prepared minority students at top-ranked campuses would have higher science graduation rates had they atten… Show more

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Cited by 65 publications
(58 citation statements)
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“…Similarly, Montmarquette, Cannings and Mahseredjian (2002) con…rm the importance of expected earnings on major choice and report signi…cant di¤erences in the marginal e¤ects of this variable by gender and race. In addition, Arcidiacono, Hotz and Kang (2010) propose that a substantial share of students would choose a di¤erent major if they made no error in their forecast of future earnings.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Similarly, Montmarquette, Cannings and Mahseredjian (2002) con…rm the importance of expected earnings on major choice and report signi…cant di¤erences in the marginal e¤ects of this variable by gender and race. In addition, Arcidiacono, Hotz and Kang (2010) propose that a substantial share of students would choose a di¤erent major if they made no error in their forecast of future earnings.…”
Section: Literature Reviewmentioning
confidence: 99%
“…3 The MLSFH has previously also been known as the Malawi Diffusion and Ideational Change Project (MDICP). models have addressed various decisions, such as contraception choice (Delavande, 2008a), portfolio allocation Rohwedder, 2011, Kezdi andWillis, 2011), fertility and sexual behavior (Shapira, 2010, education (Zafar, 2013, Arcidiacono et al, 2012, teacher career (van der Klaauw, 2012), committing a crime (Lochner, 2007), migration (McKenzie et al, 2013), strategies in games (Nyarko andSchotter, 2002, Bellemare et al, 2008), and the timing of Social Security claiming and retirement (van der Klaauw andWolpin, 2008, Hurd et al, 2004). We contribute to this line of work that combines choice data with data on subjective expectations to draw inferences on preferences.…”
Section: Introductionmentioning
confidence: 99%
“…At the same time the conditional logit is more restrictive than the multinomial logit in that it forces the coefficients of these variables to be the same across all choice options, which the multinomial logit does not (Andreß et al, 1997). Our application differs from such that use direct measures for X ij of majors not chosen by a person (Arcidiacono et al, 2012) in that variation in X ij was generated by interacting observed person characteristics with observed major characteristics (Shauman, 2006). viii Because computation of marginal effects hinges on fixed effects that are not estimated in the conditional (fixed-effects) logit model, we report coefficients as odds ratios.…”
Section: Diff Friends' Approvalmentioning
confidence: 99%