Acknowledgments Thank you, first, to my wonderful book team. My agent, Suzanne Gluck, without whom this project definitely would not have gotten past chapter 1 and who tells me straight up when it''s not quite there yet. Ginny Smith is some kind of secret genius editor who got this turned into a real book when I wasn''t even looking. Thanks to her, Ann Godoff, and the whole team at Penguin for enormous support, genius title creation, and all sorts of other things. Huge thank you to Jenna Robins, who read everything first, rewrote most of it, made me sound like less of an economist, and without whose help I never would have gotten off the ground. Emily L. Seet, MD, was an incredible medical editor (although any mistakes remain very much my own). Emily Carmichael created lovely graphs with little guidance.

Jen Taylor provided invaluable contracting assistance. I am grateful to all my ladies, most of whom helpfully got pregnant at the same time and shared their stories (sometimes without knowing they''d be book fodder): Yael Aufgang, Jenny Farver, Hilary Friedman, Aude Gabory, Dwyer Gunn, Katie Kinzler, Claire Marmion, Divya Mathur, and, most especially, Jane Risen, Heather Caruso, Elena Zinchenko, and Tricia Patrick. Many colleagues and friends supported the idea and reality of this book at various stages. Including but by no means limited to: Judy Chevalier, John Friedman, Matt Gentzkow, Steve Levitt, Andras Ladanyi, Emir Kamenica, Matt Notowidigdo, Dave Nussbaum, Melina Stock, Andrei Shleifer, Nancy Zimmerman, and the More Dudes. Actually putting the time into writing this would not have been possible without the help of many, many people in running my household. Most important of all, Mardele Castel, who has been Penelope''s Madu since day one, who makes Penelope happy and her parents relaxed, and who, very simply, makes it all work. I''m lucky to have an incredibly supportive family. Thank you to the Shapiros: Joyce, Arvin, and Emily.

To the Fairs and Osters: Steve, Rebecca, John, and Andrea. And to my parents: I couldn''t ask for better ones; Penelope is lucky to have you as her mormor and Grandpa Ray. Mom, I hope you feel the ninety-six hours of labor was worth it. Finally, thank you to Jesse and Penelope, who, it goes without saying, were essential. You two make me happy every day. Penelope, you have the absolute best dad. I love you. Introduction In the fall of 2009 my husband, Jesse, and I decided to have a baby.

We were both economics professors at the University of Chicago. We''d been together since my junior year of college, and married almost five years. Jesse was close to getting tenure, and my work was going pretty well. My thirtieth birthday was around the corner. We''d always talked about having a family, and the discussion got steadily more serious. One morning in October we took a long run together and, finally, decided we were ready. Or, at the very least, we probably were not going to get any more ready. It took a bit of time, but about eighteen months later our daughter, Penelope, arrived.

I''d always worried that being pregnant would affect my work--people tell all kinds of stories about "pregnancy brain," and missing weeks (or months!) of work for morning sickness. As it happens, I was lucky and it didn''t seem to make much difference (actually having the baby was another story). But what I didn''t expect at all is how much I would put the tools of my job as an economist to use during my pregnancy. This may seem odd. Despite the occasional use of "Dr." in front of my name, I am not, in fact, a real doctor, let alone an obstetrician. If you have a traditional view of economics, you''re probably thinking of Ben Bernanke making Fed policy, or the guys creating financial derivatives at Goldman Sachs. You would not go to Alan Greenspan for pregnancy advice.

But here is the thing: the tools of economics turn out to be enormously useful in evaluating the quality of information in any situation. Economists'' core decision-making principles are applicable everywhere. Everywhere . And that includes the womb. When I got pregnant, I pretty quickly learned that there is a lot of information out there about pregnancy, and a lot of recommendations. But neither the information nor the recommendations were all good. The information was of varying quality, and the recommendations were often contradictory and occasionally infuriating. In the end, in an effort to get to the good information--to really figure out the truth--and to make the right decisions, I tackled the problem as I would any other, with economics.

At the University of Chicago I teach introductory microeconomics to first-year MBA students. My students would probably tell you the point of the class is to torture them with calculus. In fact, I have a slightly more lofty goal. I want to teach them decision making. Ultimately, this is what microeconomics is: decision science--a way to structure your thinking so you make good choices. I try to teach them that making good decisions--in business, and in life--requires two things. First, they need all the information about the decision--they need the right data. Second, they need to think about the right way to weigh the pluses and minuses of the decision (in class we call this costs and benefits ) for them personally.

The key is that even with the same data, this second part--this weighing of the pluses and minuses--may result in different decisions for different people. Individuals may value the same thing differently. For my students, the applications they care about most are business-related. They want to answer questions like, should I buy this company or not? I tell them to start with the numbers: How much money does this company make? How much do you expect it to make in the future? This is the data, the information part of the decision. Once they know that, they can weigh the pluses and minuses. Here is where they sometimes get tripped up. The plus of buying is, of course, the profits that they''ll make. The minus is that they have to give up the option to buy something else.

Maybe a better company. In the end, the decision rests on evaluating these pluses and minuses for them personally. They have to figure out what else they could do with the money. Making this decision correctly requires thinking hard about the alternative, and that''s not going to be the same for everyone. Of course, most of us don''t spend a lot of time purchasing companies. (To be fair, I''m not sure this is always what my students use my class for, either--I recently got an e-mail from a student saying that what he learned from my class was that he should stop drinking his beer if he wasn''t enjoying it. This actually is a good application of the principle of sunk costs, if not the primary focus of class.) But the concept of good decision making goes far beyond business.

In fact, once you internalize economic decision making, it comes up everywhere. When Jesse and I decided we should have a baby, I convinced him that we had to move out of our third-floor walk-up. Too many steps with a stroller, I declared. He agreed, as long as I was willing to do the house shopping. I got around to it sometime in February, in Chicago, and I trekked in the snow to fifteen or sixteen seemingly identical houses. When I finally found one that I liked (slightly) more than the others, the fun started. We had to make a decision about how much to offer for it. As I teach my students, we started with the data: we tried to figure out how much this particular house was worth in the market.

This wasn''t too difficult. The house had last sold in 2007, and we found the price listed online. All we had to do was figure out how much prices had changed in the last two years. We were right in the middle of a housing crisis--hard to miss, especially for an economist--so we knew prices had gone down. But by how much? If we wanted to know about price changes in Chicago overall we could have used something called the Case-Shiller index, a common measure of housing prices. But this was for the whole city--not just for our neighborhood. Could we do better? I found an online housing resource (Zillow.com) that provided simple graphs showing the changes in housing prices by neighborhood in Chicago.

All we had to do was take the old price, figure out the expected change, and come up with our new price. This was the data side of the decision. But we weren''t done. To make the right decision we still needed the pluses and minuses part. We needed to think about how much we liked this house relative to other houses. What we had figured out was the market price for the house--what we thought other people would want to pay, on average. But if we thought this house was really special, really perfect, and ideal for us in particular, we would probably want to bid more than we thought it was worth in the market--we''d be willing to pay something extra because our feelings about this house were so strong. There wasn''t any data to tell us about this second part of the decision; we just had to think about it.

In the end, we thought that, for us, this house seemed pretty similar to all the other ones. We bid the price we thought was correct for the house, and we didn''t get it. (Maybe it was the pricing memo we sent with our bid? Hard to say.) In the end, we bought another house we liked just as much. But this was just our personal situation. A few months later one of our friends fell in love with one particular house. He thought this house was a one-of-a-kind option, perfect for him and his family. When it came down to it, he paid a bit more than the data might have suggested.

It''s easy to see why that''s also the right dec.