House Prices and Birth Rates: The Impact of the Real Estate Market on the Decision to Have a Baby

This project investigates how changes in Metropolitan Statistical Area (MSA)- level housing prices affect household fertility decisions. Recognizing that housing is a major cost associated with child rearing, and assuming that children are normal goods, we hypothesize that an increase in real estate prices will have a negative price effect on current period fertility. This applies to both potential first-time homeowners and current homeowners who might upgrade to a bigger house with the addition of a child. On the other hand, for current homeowners, an increase in MSA-level house prices will increase home equity, leading to a positive effect on birth rates. Controlling for MSA fixed effects, trends, and time-varying conditions, our analysis finds that indeed, short-term increases in house prices lead to a decline in births among non-owners and a net increase among owners. Our estimates suggest that a $10,000 increase in house prices leads to a 2.1 percent increase in births among home owners, and a 0.4 percent decrease among non-owners. At the mean U.S. home ownership rate, our estimates imply that the net effect of a$10,000 increase in house prices is a 0.8 percent increase in births. Given underlying differences in home ownership rates, the predicted net effect of house price changes varies across demographic groups. Our paper provides evidence that homeowners use some of their increased housing wealth, coming from increases in local area house prices, to fund their childbearing goals. In addition, we find that changes in house prices exert a larger effect on current period birth rates than do changes in unemployment rates.

BagBoosting for tumor classification with gene expression data

Motivation: Microarray experiments are expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. They create a need for class prediction tools, which can deal with a large number of highly correlated input variables, perform feature selection and provide class probability estimates that serve as a quantification of the predictive uncertainty. A very promising solution is to combine the two ensemble schemes bagging and boosting to a novel algorithm called BagBoosting. Results: When bagging is used as a module in boosting, the resulting classifier consistently improves the predictive performance and the probability estimates of both bagging and boosting on real and simulated gene expression data. This quasi-guaranteed improvement can be obtained by simply making a bigger computing effort. The advantageous predictive potential is also confirmed by comparing BagBoosting to several established class prediction tools for microarray data. Availability: Software for the modified boosting algorithms, for benchmark studies and for the simulation of microarray data are available as an R package under GNU public license at http://stat.ethz.ch/~dettling/bagboost.htm

Reverse-Polarity Activity-Based Protein Profiling

Reverse-polarity activity-based protein profiling (RP-ABPP) is a chemical proteomics approach that uses clickable, nucleophilic hydrazine probes to capture and identify protein-bound electrophiles in cells. The RP-ABPP approach is used to characterize the structure and function of reactive electrophilic PTMs and the proteins that harbor them, which may uncover unknown or novel functions of proteins in an endogenous setting. RP-ABPP has demonstrated utility as a versatile method to monitor metabolic regulation of electrophilic cofactors, as was done with the pyruvoyl cofactor in S-adenosyl-L- methionine decarboxylase (AMD1) and discover novel types of electrophilic modifications on proteins in human cells, as was done with the glyoxylyl modification on secernin-3 (SCRN3). These cofactors cannot be predicted by sequence and as such this area is relatively undeveloped. RP-ABPP is the only global unbiased approach to discover these electrophiles. Here, the utility of these experiments is described and a detailed protocol is provided for de novo discovery, quantitation, and global profiling of electrophilic functionality of proteins through the use of nitrogenous nucleophilic probes deployed directly to living cells in culture

Essays on the Economics of Women, Work and Family

This dissertation explores recent trends in female labor supply and fertility, focusing on the impact of two modern phenomena that affected women's work and family formation decisions: the introduction of residential high speed Internet and the recent housing boom and bust cycle. The first chapter provides an introduction and discussion. The second chapter investigates the impact of home Internet usage on women's labor supply. From shopping to telecommuting, home high speed Internet has affected where, when and how individuals conduct numerous activities, and many of these changes could plausibly alter labor supply decisions. Utilizing exogenous variation in Internet usage induced by supply-side constraints to residential broadband Internet access, I find that married women who use the Internet at home are more likely to participate in the labor force. In the third chapter, I explore the potential mechanisms explaining this increase in labor supply by examining data on Internet usage, telework and time use. I find that telework, job search and time saved in home production are all plausible mechanisms through which Internet usage affects female labor supply, and the ability to engage in part-time telework through an employer is the greatest contributor to the estimated effects. In the fourth chapter, coauthored with Melissa Kearney, we examine the effect of the recent housing boom and bust cycle on fertility decisions. Recognizing that housing is a major cost associated with child rearing, and assuming that children are normal goods, we hypothesize that an increase in house prices will have a negative price effect on current period fertility. This applies to both potential first-time homeowners and current homeowners who might upgrade to a bigger house with the addition of a child. On the other hand, for current homeowners, an increase in house prices will increase home equity, leading to a positive effect on birth rates. Employing data on Metropolitan Statistical Area (MSA)-level house prices, MSA-group level fertility rates, and MSA-group level home ownership rates, we find that indeed, short-term increases in house prices lead to a decline in births among non-owners and a net increase among owners

Boosting for tumor classification with gene expression data

Motivation: Microarray experiments generate large datasets with expression values for thousands of genes but not more than a few dozens of samples. Accurate supervised classification of tissue samples in such high-dimensional problems is difficult but often crucial for successful diagnosis and treatment. A promising way to meet this challenge is by using boosting in conjunction with decision trees. Results: We demonstrate that the generic boosting algorithm needs some modification to become an accurate classifier in the context of gene expression data. In particular, we present a feature preselection method, a more robust boosting procedure and a new approach for multi-categorical problems. This allows for slight to drastic increase in performance and yields competitive results on several publicly available datasets. Availability: Software for the modified boosting algorithms as well as for decision trees is available for free in R at http://stat.ethz.ch/~dettling/boosting.html Contact: [email protected] * To whom correspondence should be addresse

Boosting for tumor classification with gene expression data

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch

Supervised clustering of genes

BACKGROUND: We focus on microarray data where experiments monitor gene expression in different tissues and where each experiment is equipped with an additional response variable such as a cancer type. Although the number of measured genes is in the thousands, it is assumed that only a few marker components of gene subsets determine the type of a tissue. Here we present a new method for finding such groups of genes by directly incorporating the response variables into the grouping process, yielding a supervised clustering algorithm for genes. RESULTS: An empirical study on eight publicly available microarray datasets shows that our algorithm identifies gene clusters with excellent predictive potential, often superior to classification with state-of-the-art methods based on single genes. Permutation tests and bootstrapping provide evidence that the output is reasonably stable and more than a noise artifact. CONCLUSIONS: In contrast to other methods such as hierarchical clustering, our algorithm identifies several gene clusters whose expression levels clearly distinguish the different tissue types. The identification of such gene clusters is potentially useful for medical diagnostics and may at the same time reveal insights into functional genomics