Using unique longitudinal probabilistic expectations data from the Berea Panel Study, which cover both college and early post-college periods, we examine young adults' beliefs about their future incomes. We introduce two new approaches to testing whether, ex ante, agents exhibit Rational Expectations. We show that taking into account the additional information about higher moments of individual belief distributions contained in probabilistic expectations data reveals violations of Rational Expectations that are not detected by existing mean-based tests. Empirically, we find that our subjects underestimate the level of uncertainty they face about future incomes.

While a large literature is interested in the relationship between family and labor supply outcomes, little is known about the expectations of these objects at earlier stages. We examine these expectations, taking advantage of unique data from the Berea Panel Study. In addition to characterizing expectations, starting during college, the data details outcomes for ten years after graduation. On average, both male and female college students are well-informed about the future gender gap in labor supply. Gender differences in beliefs about this future gap are primarily explained by gender differences in beliefs about how future family outcomes are related to future labor supply. Methodological contributions come from an approach for addressing measurement error in survey questions and the recognition that expectations data, along with longitudinal data, can potentially help address endogeneity issues arising in the estimation of the causal effect of family on labor supply. 

The choices, and hence outcomes, of students, consumers, or investors depend on their beliefs. Inaccurate beliefs lead to poor choices and undesirable outcomes. Recent attempts to elicit probabilistic expectations that capture the full distribution describing agents’ beliefs provide an opportunity to study those beliefs. We propose a novel but intuitive measure of the average ex post accuracy of a group of economic agents’ beliefs, and show how a decomposition of that measure captures different sources of inaccuracy: failure of rational expectations, subjective uncertainty, and aggregate shocks. We illustrate with application to the income expectations of college students and recent graduates in the BEREA panel survey. 

An important feature of post-secondary schooling is the experimentation that accompanies sequential decision-making. Specifically, by entering college, a student gains the option to decide at a future time whether it is optimal to remain in college or to drop out, after resolving uncertainty that existed at entrance about factors that affect the return to college. This paper uses data from the Berea Panel Study to quantify the value of this option. The unique nature of the data allows us to make a distinction between “actual” option values and “perceived” option values and to examine the accuracy of students’ perceptions. We find that the average perceived option value is 65% smaller than the average actual option value ($8,670 versus $25,040). A further investigation suggests that this understatement is not due to misperceptions about how much uncertainty is resolved during college, but, rather, because of overoptimism at entrance about the returns to college. In terms of policy implications related to college entrance, we do not find evidence that students understate the overall value of college, which depends on the sum of the option value and expectations at entrance about the returns to college.

We use unique data from the Berea Panel Study to characterize how much earnings uncertainty is present for students at college entrance and how quickly this uncertainty is resolved. We characterize uncertainty using survey questions that elicit the entire distribution describing one's beliefs about future earnings. Taking advantage of the longitudinal nature of the expectations data, we find that roughly two‐thirds of the income uncertainty present at the time of entrance remains at the end of college. Taking advantage of a variety of additional survey questions, we provide evidence about how the resolution of income uncertainty is influenced by factors such as college GPA and college major, and also examine why much income uncertainty remains unresolved at the end of college. This paper also contributes to a literature interested in understanding the relative importance of uncertainty and heterogeneity in determining observed earnings distributions. 

In our evaluation of the housing expenditure share cap, a macroprudential policy, we discover the importance of modeling space. In a spatial model, the equilibrium features income-based spatial sorting where a household competes with households of their own income type for residential space. As a result, the cap policy causes a larger drop in housing demand, and consequently a larger reduction in equilibrium housing prices, for constrained low-income families than for unconstrained high-income families. Depending on the assumption on households' preference, this mechanism leads to a smaller increase or even a modest decrease in welfare inequality in a spatial model than in a spaceless model.

What is the contribution of demographic changes to house prices? We answer this question by analyzing various channels through which changes related to demographics may affect aggregate housing demand and supply differently. Specifically, we focus on the changes in life expectancy, international immigration, urbanization, and fertility. As these changes are sustainable and predictable, understanding the role they play in the real estate markets is important for predicting future house prices. We develop and estimate a general equilibrium supply-demand model to evaluate the contribution of these factors and find that they can account for 40.54% of the observed house price growth from 1970 to 2010. Adopting the projection of these four factors provided by the Census and the United Nations, we use our model to predict house prices through 2050. We find that the house price will keep growing from 2010 to 2050 by 4.42% to 18.85%, depending on urbanization rates and the level of immigration.

We develop a simple spatial equilibrium model with the peer group effect and local public finance to analyze the implications of housing policies such as public housing and housing voucher programs, and education policies such as school finance consolidation. The calibrated model can match several stylized facts of the labor market and the housing market in the United States. Our counterfactual policy analyses suggest that public housing and housing voucher programs have similar welfare implications on the household level. However, within a household, the public housing program tends to benefit the children more than the parents, while the housing voucher program delivers the opposite result. Combining the school finance consolidation policy with the public housing program could improve the well-being of children from poor households without hurting other households’ welfare. Some policies’ short-run welfare implications can deviate significantly from their long-run counterparts when all choices are optimized.

This paper uses the Euler equation and novel data from Berea College students on their consumption expenditures during and after college, desired borrowing amounts, beliefs about post-college earnings, and elicited risk-aversion and time preference parameters to determine their consumption value of college attendance.  Estimates suggest an average annual consumption value of college as high as $15,110 with considerable heterogeneity across students.  Accounting for these benefits raises the average expected return to college by as much as 18% and substantially lowers the estimated willingness-to-pay for higher student loan limits.

The decisions of economic agents are often affected by their beliefs about relevant future outcomes. An accurate characterization of these beliefs is crucial for estimating decision rules and conducting counterfactual analysis. While the traditional approach for characterizing beliefs typically involves imposing some form of Rational Expectations (RE), a recent burgeoning literature on the direct elicitation of subjective expectations is motivated by the appeal of relaxing this assumption. Since expectations data are often unavailable in standard datasets, I propose an alternative method to jointly identify and estimate individuals' beliefs and decision rules without either the RE assumption or expectations data. Specifically, I consider learning models in which individuals use signals to update their beliefs about an unknown permanent factor and repeatedly make decisions based on these beliefs. The econometrician observes individuals’ decisions and the signals they receive at each period. Identification builds on an assumption that is both intuitively appealing and standard in the literature: The posterior mean of the distribution describing beliefs is the same as the prior mean whenever the signal equals the prior mean. If individuals' decisions only depend on the mean of the distribution describing beliefs in a time-invariant fashion, this assumption implies that the prior mean for individuals who do not change decisions in two consecutive periods equals the signal they receive between periods. Using data from the Berea Panel Study, I demonstrate the empirical importance of relaxing the RE assumption by applying my method to estimate the relationship between college students’ study time and their beliefs about learning ability as measured by the ratio of semester GPA to study time. I find that high expectations about own learning ability have a negative effect on students’ study effort. The RE assumption is rejected at a 10% level for students who spent less than 2 hours per day studying in high school. These students over-estimate their learning ability in college. Incorrectly imposing the RE assumption would lead to a much more negative estimate of the effect of beliefs about learning ability on college study time. 

Unique data from the Berea Panel Study provides new evidence about fertility outcomes before age 30 and beliefs about these outcomes elicited soon after college graduation. Comparing outcomes and beliefs yields a measure of belief accuracy. Individuals who are unmarried and not in relationships at age 24 are extremely optimistic about the probability of having children, while married individuals have very accurate beliefs. Novel attractiveness measures are central for understanding fertility beliefs and outcomes for females but not for males. Marriage is a mechanism that is relevant for understanding differences in beliefs, outcomes, and misperceptions across relationship and attractiveness groups.

We examine the initial post-college geographic location decisions of students from hometowns in the Appalachian region that often lack substantial high-skilled job opportunities, focusing on the role of non-pecuniary considerations. Novel survey questions allow us to measure the full non-pecuniary benefits of each relevant geographic location, in dollar equivalents. A new specification test is designed and implemented to provide evidence about the quality of these non-pecuniary measures. Supplementing perceived location choice probabilities and expectations about pecuniary factors with our new non-pecuniary measures allows us to estimate a stylized model of location choice and obtain a comprehensive understanding of the importance of pecuniary and non-pecuniary factors. We compare our approach to alternative expectations-based approaches. We also combine the non-pecuniary measures with realized location and earnings outcomes to characterize inequality in overall welfare.