Peer Effects in Medical School

Using data on the universe of students who graduated from U.S. medical schools between 1996 and 1998, we examine whether the abilities and specialty preferences of a medical school class affect a student's academic achievement in medical school and his choice of specialty. We mitigate the selection problem by including school-specific fixed effects, and show that this method yields an upper bound on peer effects for our data. We estimate positive peer effects that disappear when school-specific fixed effects are added to control for the endogeneity of a peer group. We find no evidence that peer effects are stronger for blacks, that peer groups are formed along racial lines, or that students with relatively low ability benefit more from their peers than students with relatively high ability. However, we do find some evidence that peer groups form along gender lines.

Living Rationally Under the Volcano? An Empirical Analysis of Heavy Drinking and Smoking

This study investigates whether models of forward-looking behavior explain the observed patterns of heavy drinking and smoking of men in late middle age in the Health and Retirement Study better than myopic models. We develop and estimate a sequence of nested models which differ by their degree of forward-looking behavior. We also study models which allow for heterogeneity in discounting, and thus test whether certain types of individuals are more likely to show forward-looking behavior than other types. Our empirical findings suggest that forward-looking models with an annual discount factor of approximately 0.78 fit the data the best. These models also dominate other behavioral models based on out-of-sample predictions using data of men aged 70 and over. Myopic models predict rates of smoking and drinking for old individuals which are significantly larger than those found in the data on elderly men.

Modeling College Major Choices Using Elicited Measures of Expectations and Counterfactuals

The choice of a college major plays a critical role in determining the future earnings of college graduates. Students make their college major decisions in part due to the future earnings streams associated with the different majors. We survey students about what their expected earnings would be both in the major they have chosen and in counterfactual majors. We also elicit students' subjective assessments of their abilities in chosen and counterfactual majors. We estimate a model of college major choice that incorporates these subjective expectations and assessments. We show that both expected earnings and students' abilities in the different majors are important determinants of student's choice of a college major. We also show that students' forecast errors with respect to expected earnings in different majors is potentially important, with our estimates suggesting that 7.5% of students would switch majors if they made no forecast errors.choice of college major, subjective expectations

Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration

Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of high-dimensional dynamic models based on sieves and establish results for the: (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the model's parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated.

Peer Effects, Learning, and Physician Specialty Choice

Sixty percent of medical students switch specialties between the first and fourth year of medical school. These changes have a profound impact on the students' future earnings because there are substantial income differences between specialties. In 1997, for example, the mean income ranged from $323,000 in orthopedic surgery to$133,000 in psychiatry. In this paper we use a unique data set that contains the universe of students who graduated from a U.S. medical school between 1996 and 1998 to examine factors that influence specialty choices. There are possible several reasons why so many students switch specialties. One possibility is that switching occurs because residency positions in high-income specialties such as orthopedic surgery and dermatology are rationed, based in part on a student's performance during medical school. As students learn about their own performance during medical school and learn about the rationing rule -- students who perform well in medical school have a higher probability of entering competitive, high-income specialties -- they adjust their specialty choice accordingly. Another possibility is that the ability and specialty preferences of a student's peer group affects his own performance and specialty choice. Finally, schools might exert their own influence on a student s performance and specialty choice. Empirical Model We divide the specialty decision into two parts. A student arrives at medical school aware of his ability, as measured by the Medical College Admission Test (MCAT) score, and his preferred specialty. Medical students take the Step 1 National Board of Medical Examiners test after their second year of school. In the first part of the model, first-year medical students forecast their performance on the board exam based on their ability, the average ability of their medical school classmates (the ability peer effect), their current preferred specialty, and the school they attend. The first-year specialty preference is included to account for the possibility that students with similar levels of unobserved ability will select the same specialty, and students who plan to enter competitive specialties might work harder in order to increase the likelihood of entering the specialty. The error term from the regression of the board score on the forecasting variables is interpreted as a `performance shock' -- new information a student receives regarding his or her performance in school. In the second part of the model, the probability that a fourth-year student chooses a high-paying specialty is assumed to be a function of their predicted performance on the board exam, the performance shock, their first-year specialty preference, the first-year specialty preferences of their classmates (the specialty preference peer effect), and the school they attend. Data The sample consists of 37,000 medical students who graduated from one of the 127 U.S. medical schools in 1996, 1997, or 1998. We have information on students' ability prior to medical school, their performance during medical school, and their specialty preferences at the beginning and end of medical school. A student's pre-matriculation ability is represented by their test score on the biology, chemistry, and reading components of the MCAT, a uniform exam given to all medical school applicants. The Step 1 board score, also a uniform national exam, measures a student's performance in medical school. Students are surveyed by the Association of American Medical Colleges in their first and fourth years of medical school and asked to indicate their preferred specialty. Because we have the universe of U.S. medical students, we can measure ability and preference peer effects: the average pre-matriculation ability of each school's students and the proportion of each school's first-year students who prefer each specialty. Results Among first-year students, there are no substantial differences in MCAT scores between specialties. By the fourth year, however, students with high board scores are more likely to be in high-income specialties. Students who receive a positive performance shock -- a higher board score than they expected -- are much more likely to switch to a high-income specialty relative to other students. Women are more likely than men to choose a low-income specialty in the fourth year, conditional on their board score and initial specialty preference. Peer effects are an important determinant of board scores and specialty choices, but these effects disappear when we include school-specific fixed effects.

Living Rationally Under the Volcano? Heavy Drinking and Smoking Among the Elderly

Most rational addiction models focus on how drinking and smoking are made when young. Yet, the costs of drinking and smoking generally come later in life. We focus on the decisions of the elderly where individuals know their propensity for addiction but are uncertain about their future earnings and helath status. Using data from the Health and Retirement Survey, we estimate a dynamic stochastic model of heavy drinking and smoking of the elderly. Individuals make decisions not only based upon the current effects of heavy drinking and smoking, but also the future effects of drinking and smoking on earnings, health, and mortality. We are especially interested in the identification of the discount factor. We show how the likelihood function varies with the discount factor and also how behavior decisions vary from not estimating a dyanmic model. In particular, we find that the dynamic model forecasts more drinking and smoking as well as individuals living longer. This is because individuals know when, and when not to, engage in heavy drinking and smoking behavior.rational addition, dynamic discrete choice, economics of the elderly

University differences in the graduation minorities in STEM fields: evidence from California

We examine differences in minority science graduation rates among University of California campuses when racial preferences were in place. Less-prepared minorities at higher-ranked 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 attended lower-ranked campuses. Better matching of science students to universities by preparation and providing information about students' prospects in different major-university combinations could increase minority science graduation

Modeling College Major Choices using Elicited Measures of Expectations and Counterfactuals

The choice of a college major plays a critical role in determining the future earnings of college graduates. Students make their college major decisions in part due to the future earnings streams associated with the different majors. We survey students about what their expected earnings would be both in the major they have chosen and in counterfactual majors. We also elicit students’ subjective assessments of their abilities in chosen and counterfactual majors. We estimate a model of college major choice that incorporates these subjective expectations and assessments. We show that both expected earnings and students’ abilities in the different majors are important determinants of student’s choice of a college major. We also show that students’ forecast errors with respect to expected earnings in different majors is potentially important, with our estimates suggesting that 7.5% of students would switch majors if they made no forecast errors.

Search Discrimination, Human Capital Accumulation and Intergenerational Mobility

I develop a matching model with statistical discrimination and intergenerational mobility. Workers make both observable and unobservable investments before entering the market which affect their future productivity. Firms can search for workers based upon observable characteristics, be it observable investments, age, or some exogenous characteristic such as race. Multiple equilibria within each market can exist, one in which workers make unobservable investments and many firms search and one in which workers do not make unobservable investments and few firms search. Hence, two groups of workers that differ on an observable, exogenous characteristic (say, race) can be in two different equilibria. An equal opportunity law, where firms must search equally hard across the exogenous characteristic can remove the discriminatory outcome in one generation. However, if parents' investments decisions affect the investment decisions of their children, policies which remove the statistical discrimination by pooling the labor markets across the exogenous characteristic will still lead to unequal results in the short run. An affirmative action program which subsidizes investment for the group previously in the bad equilibrium will receive support for a time due to the positive externalities such investments have on the search options for the population. The program will receive more support the more individual there are who would not make the investment without the subsidy; there are no benefits to subsidizing workers who would make the investment anyway. Empirical predictions on the relationship between experience, education, and wages also result.