Economists Do It With Models

So I made something new for you:

A couple of years ago, I was told a story about how Milton Friedman would teach his Principles of Economics courses by walking into the classroom each day, opening the newspaper, and discussing the economics behind the stories that he saw. Now, I don’t know whether this tale is an urban legend or not, but the idea is quite solid, since it’s of crucial importance to tie back what is taught in the classroom to what students see out in the world. The downside of this approach, of course, is that the outside world isn’t organized for the purposes of providing examples in a logical order that students can easily follow and instructors can piece together into a syllabus in real time.

That’s where this document comes in- as an instructor and consumer of information myself, I spend a number of hours each day looking for interesting things that happen in the world that illustrate (or, in some cases, are at odds with) economic theory. Such an activity, with its high fixed cost and low marginal cost (when quantity is measured as number of instructors served), has the structure of a potential natural monopoly. In this case, however, the document has the features of a public good, since I’m making it open to everyone. Whoever said that public goods wouldn’t be provided by the free market? Oh right, (non behavioral) economists.

I hope that this database of Econ 101 examples (or, technically, spreadsheet, as some persnickety individuals have pointed out) is helpful to you! I will continue adding to it, hopefully almost daily, as I come across new things to talk about. There are a few features that I’ve put in to make the document easy to navigate and use:
– Sheets: The examples, not shockingly, are on the sheet labeled “Examples.” (This is the “Overview” sheet- see the tabs at the bottom left of the sheets to toggle between them.)
– Description/Comments: I’ve tried to provide enough information so that you don’t have to click through to the sources to understand what they are and whether you want to use them. In addition, I often give suggestions on how you can use the source in your classroom.
– Date: Where possible, I’ve added a date so that you can avoid sources that you feel are outdated. You can (reverse) sort by date and stop looking when you feel that the sources get too old. Note, however, that not every item has a date- such sources are obviously timeless!
– Date Added: You can (reverse) sort by date added in order to look at only those items that you haven’t already examined.
– Course: You can filter by course to only see items that are relevan to what you are teaching. Note, however, that some items are assigned to both Micro 101 and Macro 101, and this designation gets its own filter category.
– Tags: Created to help you find relevant items more easily, and I would search this column rather than trying to sort of filter, since a lot of items have multiple tags. When possible, I recommend scanning the items rather than relying solely on the tags so that you don’t miss anything relevant- for example, an item I’ve tagged as “price ceiling” when you’re looking for “price controls” or something like that.
– EDIWM Post: Where applicable, I’ve added links to posts on my web site that give more extensive discussion on the sources presented.
– Submitted By: For the most part, I am compiling this document myself with some help from a few econ friends. That said, I want to give proper credit where credit is due! In related news, email  econgirl at economistsdoitwithmodels.com if you have something relevant/fun to share, and be sure to let me know how you would like to be credited.

I hope this is helpful to you and makes economics more interesting to your students! You can also follow my Twitter- @jodiecongirl- in order to see most if not all of the items as I find them (as well as other econ stuff). If you want even more Econ 101 materials, be sure to check out the “Econ Classroom” section of my web site (listed below).

Happy Econ-ing,

Jodi Beggs
Lecturer, Northeastern University Department of Economics
www.economistsdoitwithmodels.com

My current plan is to update the database as I find new examples and then post a summary of new items once a week as a reminder. I also added a link to the document at the top of the Economics Classroom page for easy reference.

In a lot of ways, non-economists are in the best position to create economic humor, since their outsider status basically makes it so they are looking in on the world of economics and being like wtf? On the other hand, Zach Weinersmith might argue that the nature of economics makes relevant humor relatively challenging:

Nonetheless, I convinced Zach to give it a go at the AEA Annual Meeting Humor Session:

If that (or economics in general I suppose) hasn’t given you your fill of bad ad-hoc hypotheses, you can see much more at BAHfest.com.

You all seemed to like the conversation about the gender wage gap after Obama’s State of the Union address, so I put together something a bit more formal for the Boston Globe. (sidenote: I am strangely fascinated by the graphic used for that article- it’s very Beatles-esque trippy, as in “picture yourself in a bot on a money river” or something…I don’t know about you, but I’ve always wanted to fish for money with a red suction cup.) Here’s a more, let’s say, unfiltered version for those already somewhat familiar with the matter:

In terms of “equal pay for equal work,” literally speaking, men and women are in fact on pretty equal footing- about 96 cents on the dollar for non-married women versus men if I recall correctly, so better enforcement of narrowly-defined discrimination laws won’t do a whole lot to narrow the observed wage gap that has been defined as problematic. In other words, let’s cut it out with the 77-cents crap. From a fairness perspective, the matter is somewhat complicated, since women with families (and, by extension, women on average) do in fact work fewer hours than men and ask for more flexibility in terms of working hours and location. So what would be fair? Let’s say that a woman works 20 percent less than an equivalent male- one starting point for fairness might be that she earn 20 percent less than the man. But what if working 20 percent less means that she’s not as “on call” or “present” as the man? This could make her productivity, on an hours-adjusted basis, less than the man’s, in which case it could be considered fair to pay her more than 20 percent less.

The situation described above illustrates how an inflexible workplace that likely seems generally fair leaves women with family responsibilities behind in two ways. First, women who care for children can’t work as many hours as men who don’t have such responsibilities if it is mandated that those hours have to be between 8 a.m. and 6 p.m., for example, but they likely could if they could get proper credit for hours they spend working in the early mornings and evenings, perhaps from home. Second, inflexibility in terms of worker substitution makes it nearly impossible for a more “part time” employee to have the same productivity (even on an hours-adjusted basis) as one who gives over his entire existence to his employer. If workplaces enabled more flexible organizational practices, women would both be able to work more hours and be more productive in those hours, thereby alleviating much of the wage gap without what potential critics would refer to as special treatment. Also, men would get more flexibility too. (This is where you say “yay!”)

Claudia Goldin gives a very good example of how rigidity in the workplace that is not actually necessary has been done away with, to the benefit of women and their earnings:

Several changes in the pharmacy profession have been responsible for the increase of female to male earnings. The first is the decrease in self-ownership and the rise of large corporation and hospital employment. As corporate ownership and hospital employment increased, the portion of earnings that came from self-employment decreased. The ratio of the (time-adjusted) earnings of female to male pharmacists, in consequence, increased as the rents from ownership decreased and because men were disproportionately the owners.

The second change involves decreased costs to flexible employment in pharmacy. Pharmacists have become better substitutes for each other with the increased standardization of procedures and drugs. The extensive use of computer systems that track clients across pharmacies, insurance companies, and physicians mean that any licensed pharmacist knows a client’s needs as well as any other. If a pharmacist is assisting a customer and takes a break, another can seamlessly step in. In consequence, there is little change in productivity for short-hour workers and for those with labor force breaks. Other factors mentioned in the O*Net section are also of importance. For example, there is less need for interdependent teams in pharmacy and for extensive contact with other employees.

Female pharmacists have fairly high labor force participation rates and only a small fraction have substantial interruptions from employment. Rather than taking off time, female pharmacists with children go on part-time schedules. In fact, more than 40 percent of female pharmacists with children work part-time from the time they are in their early thirties to about 50 years old. Male pharmacists work around 45 hours a week, about nine hours more than the average female pharmacist.

Goldin also notes that similar changes have occurred in the field of obstetrics (i.e. delivering babies and such) once patients started accepting that anyone from a team of qualified doctors could deliver a baby under most circumstances, so having their particular obstetrician on call and available at all times was not absolutely crucial.

So why doesn’t this happen everywhere without the dreaded government intervention? In some industries, it might not logistically be possible. In those cases, I’m not sure what can be done other than making sure that people (not just women) know what they’re getting into when they make educational investments and career commitments. In other industries, well…how do I put this delicately…not everyone wants to compete with women with families for their jobs. These people aren’t necessarily being jerks, they’re just being rational- less competition for jobs means higher wages, although kind of in an inefficient rent-seeking fashion if changes that could make more people more productive are needlessly curtailed for the benefit of the incumbent workers. (before you judge too harshly, think about how you would respond if you were told that your employer wanted to enact policies that would make workers more interchangeable.)

I suppose that the second scenario could be legislated to some degree in the same sense that antitrust is legislated- on a complicated, case by case basis- but that sounds like a disaster waiting to happen. By process of elimination, I guess I would want to know what caused the industries that Goldin described above to change and have some smart people think about how similar mindsets and incentives could be brought to other industries. In any case, doesn’t it seem better to narrow the pay gap by enabling women to work as much and be as productive as men rather than by trying to just will it out of existence?

And here you thought it was my logo that was setting feminism back 50 years. =P

My sarcastic intro comments aside (obviously part of the problem for the specific event is that CSI is not supposed to be funny), I still think that this should exist on a larger scale…

You probably guessed that I am more of a Law and Order gal, since I’m obviously guilty of mixing my metaphors. I did try though- in the name of research, I bought the first season of the original CSI on Amazon, and, well…I just couldn’t get into it, and, other than what I included, there wasn’t anything obvious enough to pull from. Maybe because it wasn’t about statistics, maybe because it didn’t have Olivia Benson, who knows. Anyway…you can see by the list of sources at the end of the video, you just learned the basics of three academic papers and a reply in under ten minutes and you didn’t even feel the pain! In case you weren’t paying attention, here’s the overall trajectory of (some of) the research on the causal impact of police presence on crime:

• Early research (without the use of instrumental variables or natural experiments) either showed no relationship between police officers and crime or a positive association between police and crime. These findings are somewhat problematic due to potential for reverse causality (i.e. the crime increases could have called for an increase in police presence rather than the other way around). Some researchers tried to mitigate the reverse causality problem by looking at the relationship between yesterday’s police officers and today’s crime, but that didn’t really work for various reasons.
• In 1997, Steve Levitt published a paper that used election cycles as an instrumental variable to confirm that more police officers does in fact cause a reduction in crime. Election cycles work well as an instrument because they’re on a fixed schedule and therefore not subject to the whims of things that would affect crime levels, but are correlated with police presence for reasons similar to those outlined in the video. Unfortunately, said paper had a math error that he got called out on that kind of broke a bunch of his conclusions.
• Levitt published a reply to the aforementioned criticism that acknowledged the error and proposed using firefighters as an instrumental variable instead. Again, this seems valid because the data shows that increases in firefighters are correlated with increases in police officers, but it’s intuitively unreasonable that the number of firefighters would be correlated with things that other than police officers that affect crime rates. He again found a negative elasticity of crime with respect to police presence.
• A few years later, Jonathan Klick and Alex Tabarrok did a similar analysis that uses terrorism alert levels and got a similar result regarding the effect of police officers on crime. The logic there is the more police officers are mandated when terror alert levels are higher, but terrorism isn’t really related to factors that would affect more day-to-day crime instances, so it can be used for the causal analysis of police on crime.

I think that this sort of approach- i.e. showing rather than telling- is so powerful, and there definitely should be more teaching that is done in this way. (I might be a bit biased since Mathnet was an important part of my childhood. In related news, you’re welcome.) Granted, it’s harder- I’m pretty sure it took more effort to write the script (reproduced below in case you want it) than it did to write the list of bullet points above. But I’m convinced that it’s worth it, especially with some production value thrown into the mix. Okay, here’s the script, and I’m (not even kidding) going to go back to watching Law and Order now.

CSI: Regression Analysis

*intro screen – The following story is sort of fictional but sort of depicts actual people and events.*

Cop: You guys from Internal Affairs?

Econ 1: Nope, we’re from…the Program Evaluation squad. *David Caruso sunglasses moment, with music/regression graphic*

Cop: *completely breaking the dramatic moment* The what?

Econ 1: The Program Evaluation squad- you know, you catch criminals who cause crimes, we identify the culprits that cause…well, a bunch of stuff.

Cop: So…nerd detectives, got it. What brings you here?

Econ 1: Well, we’ve been enlisted to study the effect of police presence on crime rates, so your department seemed like a natural place to start.

Cop: And what happens to my job if I’m found to be useless?

Econ 2: We’re economists, so such policy questions are outside of our jurisdiction. So…we’re going to need your data on number of police officers and number of crimes committed over time.

Cop: I’m not exactly inclined to share data that could cost me my job.

Econ 2: Story of my life. Do I need to get the Captain involved?

Cop: No, let me find what you’re looking for.

Econ 1: *looks at data on paper and writes some numbers on white board, then inputs into computer* Hmmm…from what I see here, it actually appears that more police officers leads to MORE crime. Weird, right?

Cop: Wait, what? That can’t be right…you have to keep looking- after all, the first suspect is never the true culprit, right?

Econ 2: We’re looking into the matter, sir.

*later*

Econ 2: He’s right, you know.

Econ 1: About what?

Econ 2: There’s a problem with our case. Look- I found these forms that request additional officers into the department. what do you notice?

Econ 1: *looks at forms* That the reason listed for the request is…oh. Increased crime prevalence. So that means…

Econ 2: …that we can’t tell whether the police officers cause crime increases or if the crime increases cause more police officers to be hired.

Econ 1: So are we back to square one?

Econ 2: *looks at data* Mayyyyyyybe not… Look at this- crime increases aren’t the only reason that officers are requested.

Econ 1: There’s no reason given at all, actually. What’s up with that?

Econ 2: *at white board* I have a theory….*does some math* Yep, what I suspected- the requests match up with election cycles.

Econ 1: Why would that be?

Cop: Welllllll…I probably should be saying this, but the mayor likes us to beef up our presence before elections because it makes him look good.

Econ 2: Even though crime doesn’t follow election cycles?

Cop: *sadly* Yeah…I know it’s wrong, but…

Econ 1: *cuts off cop* Perfect.

Cop: Huh?

Econ 1: If we look at the part of the increase in police presence that has to do with election cycles and not crime sprees, we can use that data to estimate the causal effect of police on crime.

*Instrumental Variables start playing*

Cop: What the…?

Band: Sorry, thought you called for us.

Econ 2: *doing math* Ok, this seems to make more sense. Now I see a negative elasticity of crime with respect to police, especially for violent crimes.

Cop: *breathes sigh of relief*

Econ 1: *looks at math on board* Actuallyyyyyyy…you made a math error.

Econ 2: Where?

Econ 1: *points* There.

Econ 2: Ughhhhhhhhh…. *fixes mistake* Well, there goes my result.

Cop: Can’t you try something else?

Econ 2: Let me see… *searches around* How about firefighters? The number of firefighters is correlated with the number of police officers, but we certainly don’t get more firefighters when we have more crime…

*Instrumental Variables start playing*

*everyone glares*

Band: Again, I thought…

Econ 1: Okay, that seems to work, but we need a corroborating witness if we’re going to be convincing. What else affects the amount of police presence?

Cop: Well, we have to send out more cops when Homeland Security sets a high terrorism alert.

Econ 2: Am I the only one who finds it funny that when the government is helpful for research it’s rarely on purpose? Ok, do we have historical data on terrorism levels?

Cop; *runs in* Way ahead of you- after all, my ass is on the line here.

Econ 1: Unbiased and unmotivated research at its best, right here. *rolls eyes* *puts data in computer and on board* Bingo- another negative elasticity estimate.

Cop: Meaning…

Econ 2: Meaning that the data shows that, when police presence increases for reasons other than increases in crime, the increased presence leads to decreases in crime.

Cop: It’s good to feel useful.

Econ 1 and Econ 2: You can say that again.

*Dick Wolf-type credit thing – Executive Producer Charles Wheelan*

I suppose that should technically read “Correcting The State Of The Union Address,” since I make no claims as to my ability to fix all of the nonsense that is currently going in the U.S. Anyway, I of course had a number of (usually nitpicky) objections regarding President Obama’s State of the Union address last night, but I know by this point that people have to choose their battles. So here’s mine…from the speech:

Today, women make up about half our workforce. But they still make 77 cents for every dollar a man earns. That is wrong, and in 2014, it’s an embarrassment. A woman deserves equal pay for equal work. She deserves to have a baby without sacrificing her job. A mother deserves a day off to care for a sick child or sick parent without running into hardship – and you know what, a father does, too. It’s time to do away with workplace policies that belong in a “Mad Men” episode. This year, let’s all come together – Congress, the White House, and businesses from Wall Street to Main Street – to give every woman the opportunity she deserves. Because I firmly believe when women succeed, America succeeds.

I am soooooo sick of this statistic, since it basically suggests that a woman shows up at a workplace and her boss is like hey, you look like you might have ovaries, here’s $0.77 rather than $1. And that’s not what is actually happening. Yes, it is true that, on average (actually, comparing medians if you want to be technical), a woman in the U.S. earns 77 percent of what a man in the U.S. earns, but that figure doesn’t control for any relevant determinants of income- schooling, industry, tenure, etc. Therefore, I cringe whenever the “equal pay for equal work” line is trotted out, since “equal work” would imply that whoever is handing out this 77 percent figure did in fact run some sort of regression that would control for enough to get to a point where the comparison was at least close to equal. In the spirit of actually wanting to understand the gender pay disparity issue and not just quote a meaningless number, let’s look at some actual research from Claudia Goldin. Some helpful excerpts:

Men and women begin their employment with earnings that are fairly similar, both for full-time year-round workers and for all workers with controls for hours and weeks. In the case of the latter group, relative earnings are in the 90 percent range for the most recent cohorts even without any other controls. But these ratios soon decline and in some cases plummet to below the 70 percent level.

Translation: We’re basically at a place now where young men and women don’t differ substantially in their levels of education (in fact, I think women are actually outperforming in terms of educational attainment according to a number of metrics), so when comparing the initial situations of these young people, the divide is 90-some-odd cents on the dollar, not 77. And this is without taking into account the fact men pay be sorting into higher-paying jobs. That said, there seems to be a shift in gender disparity as people move on their lives that should be examined.

The main takeaway is that what is going on within occupations—even when there are 469 of them as in the case of the Census and ACS—is far more important to the gender gap in earnings than is the distribution of men and women by occupations. That is an extremely useful clue to what must be in the last chapter. If earnings gaps within occupations are more important than the distribution of individuals by occupations then looking at specific occupations should provide further evidence on how to equalize earnings by gender. Furthermore, it means that changing the gender mix of occupations will not do the trick.

Translation: Convincing men and women to enter the same occupations wouldn’t make the gender disparity go away, so let’s perhaps stop focusing on that so much as a potential solution.

The clear finding is that the occupations grouped as Business have the largest negative coefficients and that occupations grouped as Technology and Science have the smallest ones. That is, given age and time worked residual differences for Business occupations are large and residual differences in Technology and Science are small. In fact, for the “young” group (less than 45 years old) some Technology and Science occupations have positive coefficients.

Translation: The female “penalty” differs a lot by occupation, and in some cases there is no penalty and even a benefit to being female.

As I will later demonstrate using data on occupations in business and law, the impact of hours on the gender gap is large and goes far to explain much of the gender earnings gap. Individuals who work long hours in these occupations receive a disproportionate increase in earnings. That is, the elasticity of earnings with respect to hours worked is greater than one.

Translation: Within an occupation (in some cases), being a high earner (even on a per-hour basis) requires long hours and, as is shown in another part of the paper, working a particular schedule. This feature explains a lot of the gender discrepancy and is a result of women and men selecting into these situations at different rates, especially as women start caring for families.

The gender gap in annual earnings for the JDs and MBAs, although large by year 15, is almost entirely explained by various factors, such as hours worked, time out of the labor force, and years spent in part-time employment.

Translation: This is not an ovaries penalty story, at least not directly.

What, then, is the cause of the remaining pay gap? Quite simply the gap exists because hours of work in many occupations are worth more when given at particular moments and when the hours are more continuous. That is, in many occupations earnings have a nonlinear relationship with respect to hours. A flexible schedule often comes at a high price, particularly in the corporate, financial, and legal worlds.

Hopefully there is no translation needed here. The overall point of presenting this is that, in order to craft an actual solution to a problem, it’s crucially important to identify what is causing the problem. As a society, we seem to have decided that a gender pay differential is a problem. However, the lack of understanding of the nature and cause of the problem is going to prevent the problem from being solved. The information provided above suggests that any legislation of the “equal pay for equal work” form, for example, will be mostly ineffective, since observed differences in pay are in fact largely explained by inequalities in either job tenure or work quantity. In order to solve the problem, then, policymakers must look one step behind the curtain and think about how to mitigate the effects of differences in worker hours or tenure rather than just keep trotting out a well-worn sound bite to overshadow the real issue.

Econgirl out. *mic drop*

So, for those of you who weren’t able to attend the humor session at the AEA annual meeting in Boston this year, here’s some more from the event. First, the opening remarks, which explain who Caroline Postelle Clotfelter, the sessions’s namesake, is, and some commentary on the house band’s name. (The band footage wasn’t included due to a bad guitar feed, but they performed covers of economics songs that were submitted as part of Kim Holder’s Rock-o-nomix project.)

Next, James Tierney from Penn State gives the American Economic (Year In) Review:

I will be putting up more videos from the event, and you can see the full playlist here to make sure that you haven’t missed anything.