DOUBLE MAJORS: ONE FOR ME, ONE FOR THE PARENTS?

I. INTRODUCTION At least a quarter of college students have more than one undergraduate major (2003 National Survey of College Graduates), and the share of college students choosing more than one major is increasing at a very fast rate (Lewin 2002). It has been postulated that parents inadvertently drive their children to have more than one major, that is, students major in one field to satisfy their own interests, and in another field that meets parents' approval. (1) Other explanations for double majors include students majoring in one field associated with their professional specialty and another that reflects a very different interest (like an Engineering major who is also majoring in French), or students hedging their chances in the labor market by preparing to work in more than field. However, evidence for all these explanations remains anecdotal (Gomstyn 2003; Lewin 2002), and there is little systematic evidence on how students choose the composition of their double majors. This article provides, to the best of my knowledge, the first direct evidence on how students, conditional on having a double major, choose the composition of majors. (2) Students choose a college major (or pair of majors) in order to influence the occurrence of choice-specific outcomes that enter their utility function. These outcomes include, for example, being able to successfully complete a field of study, gaining parents' approval, finding a job upon graduation, enjoying coursework or earnings at the job. As these outcomes are uncertain at the time the student makes his choice, he has a belief distribution of the probability for the occurrence of these outcomes conditional on each major in his choice set. Therefore, a student uses both his preferences and subjective beliefs in choosing his college major(s). The researcher usually only observes the major(s) that the student chooses, and has to make nonverifiable assumptions on expectations/beliefs to infer the parameters of the utility function (preferences). The basic difficulty is that observed choices may be consistent with several combinations of expectations and preferences, and the list of underlying assumptions on expectations may not be valid (see Manski 1993, for a discussion of this inference problem in the context of how students infer returns to schooling). A solution to this identification problem is to use additional data on expectations (Manski 2004), and that is precisely what I do. I survey a group of 69 Northwestern University students pursuing double majors and elicit their subjective beliefs about major-specific outcomes. In my relatively homogenous sample, there is substantial heterogeneity in beliefs for outcomes within a major as well as across majors, indicative that there exists tremendous heterogeneity in beliefs in the population of college students. Analysis of beliefs for the same outcome across majors suggests that students have sensible beliefs about the occurrence of outcomes conditional on major. For example, the belief distribution of reconciling work and family at jobs available in Literature and Fine Arts first order stochastically dominates the corresponding distribution in Natural Sciences (in which most pre-med students major). Comparison of beliefs of being able to graduate with a grade point average (GPA) of at least 3.5 and expected income at the age of 30 with objective measures reveals that students are aware of differences across majors. I find that students believe that their parents are more likely to approve majors associated with high social status and returns in the labor market. For example, the mean belief of gaining parents' approval for majoring in Engineering is 0.87 (on a scale of 0-1) compared with 0.59 for Literature and Fine Arts. The subjective data elicited from the students is employed directly in a structural model of double major choice. (3) Although I assume that students are forward-looking and care about future outcomes when making their choices, I do not have data needed to estimate a dynamic model. …