Good, thank you for having me, and I've enjoyed it as well.
Now, the downside to not having a manuscript when you speak
is then when the students come and say, I missed the day
or I wasn't paying attention.
Can I have your notes?
I say, ah, notes, I don't have any notes,
which runs into some problems sometimes.
I have to say, well, go to your friend.
He probably has better notes than I've got.
So this is a paper that's joint with Thomas Stratman, who
is actually a German-trained economist.
Actually, his college was here in Berlin,
but then he went into his PhD at the University of Maryland.
He teaches now at George Mason.
I've written with him quite a bit.
And we've added a third co-author to this paper,
Sven Nielsen, who is a PhD student in economics
here in Germany.
So we added Sven to this paper.
Sven was sort of crucial in working out
some of the foreign data here, the European data.
And so this paper is, in some ways,
a third paper in a series.
It's a paper that we sort of hope
fills in some of the gap of the two earlier papers.
We started talking about those papers today in class,
but we'll give a little bit of background
for those of you who aren't here or as review for the others.
So the general research question here
is, does risky sexual behavior respond to incentives?
Or another way to put it is, is there
a downward-sloping demand curve for risky sex?
Oh, and I should stop and say, if you have questions,
certainly you can ask them at the end.
But I also sort of prefer the American style,
where you're rude and break in.
So break in whenever you've got questions or comments,
and we'll address them as we go on.
That may be easier.
So anyway, is there a downward-sloping demand
curve for risky sex?
If you go and talk to non-economists,
that is, I call them regular people, humans,
they'll say, rational choice models don't apply here.
We're not fully ready to accept that rational choice models
apply anywhere, but if they do, they certainly
don't apply here.
Risky sex choices are driven by biology.
They're driven by psychology.
And in fact, some people will go even so far as to say,
the whole decision, the whole idea
that somebody engages in risky sex is irrational.
And my response to them is, you must
be a lot of fun at a party, right?
No, but people will have this kind of response.
They'll say, this is just not an appropriate place
to have these models, right?
Maybe if you want to talk about firm behavior
and firm corporate governance mechanisms and things
like that, maybe we can analyze those
in a rational choice model.
But certainly not this, right?
In fact, there are a number of areas
like this where people are just very
resistant to economic thinking.
When you talk about the use of drugs
or you talk about a situation like obesity,
they say these very areas are places where people
are making stupid decisions.
So for the fact that you would come at these with a framework
where you're assuming people are making rational decisions,
it just doesn't make any sense, you dumb economist, right?
If instead, then you walk down the hall
and talk to the non-humans, the economists, and you say, well,
hey, does risky sex behavior respond to incentives?
They say, of course it does, right?
They say, that's a dumb question, right?
Is it behavior?
Yes.
Well, then it responds to incentives, OK?
So how does abortion relate to all this?
Well, as we talked about before, and it is fairly obvious to people,
one of the big costs of risky sex is unwanted pregnancy, OK?
And so if we have an abortion option,
this lowers the cost of risky sex.
In some cases, some economists have actually
referred to abortion as insurance, right?
There's a nice paper, I don't know if it was ever published,
but certainly an NBER working paper by Phil Levine and Doug Stager,
two very good applied econometricians, where the whole title of the paper
is abortion as insurance, right?
And that's a good way to think about it.
And so what ends up happening is, if we liberalize abortion laws,
that is, we make abortion more easily available, or legally available,
or we reduce restrictions on abortions,
what does that mean?
It means the marginal cost of sex goes down, right?
The marginal cost of risky sex goes down.
And what does that mean?
Well, if the marginal cost goes down and the benefit doesn't change,
that means people are going to demand, people
are going to engage in more risky sex.
If you want to think of it another way in a completely compatible model,
another way to think about it is the relative costs of condoms goes up,
right?
So if we think of condoms as having a cost, both a financial cost
and a cost in terms of reducing enjoyment from sex,
if now there's another pregnancy control mechanism out there,
condoms become relatively less attractive, OK?
And so you can either think of this as abortion liberalization
induces people to engage in more risky sex,
or you can think of it as it induces people to engage in the same amount of sex
but with a less fastidious condom use or some combination of the two.
We actually, in this paper and in any of the three papers,
cannot distinguish between those two hypotheses.
I've often told people when I present these papers,
in between the Journal of Law, Economics, and Organization
paper and this paper, I spent about 18 months
trying to get a hold of condom data, right?
Turns out when you write letters to every single condom manufacturer
in the Western world, they're not all that happy to give you
data on how many condoms they sell in given markets, right?
I would love to have that data.
If I had that data, I could rerun all these studies and say, what happens?
Is it that condom sales go down?
Is it that condom sales stay the same?
Is it that it's some combination?
We just can't answer that question, and so there aren't good condom data.
But we can talk more about that if people are interested in it.
So if we want to test this idea of a downward sloping demand curve in this
context, we run into a couple of problems immediately.
The first problem is the data on risky sex are just bad, right?
The first place that you would presume to go would be to surveys.
The survey data that's available in this area are just terrible, right?
Turns out the handful of surveys that do ask questions related to risky sex,
at least in the US and any place else that I found that asks anything close to
this question, they have big response rate problems.
Meaning if we have a survey of 30,000 people in the US,
maybe 10% of them answer this question, right?
Did you use a condom the last time you had sex?
So you have really big data limitations in that sense.
It also turns out in the US context, the surveys that ask this question
only ask the question every couple of years, right?
So you have big gaps, both in terms of who responds and
in terms of when the question is asked.
Furthermore, even if we had really good survey data,
there's reasons to think that we should be skeptical of it, right?
One can think intuitively.
How would you answer?
How would somebody answer if somebody came up to you and said, hey,
how often did you have risky sex in the last six months?
Well, do I remember?
I don't know.
This is an area where people don't have strong incentives
to be particularly careful in answering this question accurately.
Second of all, even if you can remember,
you might be embarrassed as to zero or about 20.
Both of those answers on either extremes are kind of embarrassing,
depending on what kind of person you are, right?
So we'd be really suspicious of survey data in this context.
What was in our first paper, what was sort of our big innovation here,
our big innovation was to use sexually transmitted infection data
as our proxy for risky sex, particularly gonorrhea data.
Gonorrhea is very strongly related to risky sex.
In fact, your likelihood on a given random risky sexual encounter,
your likelihood of transmitting gonorrhea is about three times as
big as your likelihood of actually getting someone pregnant, right?
So if you think, well, click, why aren't you looking at pregnancy rates and
things like that?
Well, it turns out that gonorrhea actually is a better marker for
risky sex than pregnancy rates.
Secondly, other people have looked at pregnancy rates, and
there's some other problems with those, right?
So gonorrhea is really nice in this context, maybe not in all contexts,
but in this context is really nice.
The other thing that's good is that the dynamics of gonorrhea are pretty simple.
I mentioned this earlier today, but
basic idea is unlike things like herpes or HIV or a handful of other STIs.
If you get them, you might not know for months or even a year that you've got it.
With gonorrhea, it's fairly quick, right?
Within a couple of weeks, most people manifest some pain in their urinary
tract, it's actually, as I hear, actually it's apparently quite painful.
And so you will sort of naturally go to the doctor or go to a clinic and
things like that.
So we can be pretty sure that there aren't a whole lot of infections that
are out there in the world, but aren't showing up in the data, right?
Furthermore, this effect, once you get diagnosed,
the cure for gonorrhea is quite quick and easy.
You get antibiotics.
In the US nowadays, you get cipro,
which is the same antibiotic you get for some colds and things like that.
So it's a really simple medicine.
You get it, it's cured, you can no longer re-infect people, right?
So you have really nice, simple dynamics that don't lead to any problems in a
linear regression.
And then lastly, gonorrhea is better than a couple of other sexually transmitted
diseases that one could use.
A number of the other sexually transmitted diseases have more complicated
dynamics.
They actually require more modeling assumptions to get right, so
that's a problem.
Syphilis, which is another sexually transmitted disease that we've got lots
of data on in the modern time in the US and actually in throughout Europe,
has become sort of a gay disease.
So it wouldn't make sense to look at that as our proxy of risky sex in this
context, okay?
So gonorrhea is pretty good to use for this, and
we think it's sort of a nice innovation here.
So what's our existing evidence sort of pre this paper?
Well, we have this paper in 2003 in the Journal of Legal Studies,
which looks at the legalization as sort of our shock to the cost of risky sex.
In 1969, you have California legalizing by court order.
In 1970, you have four states, most importantly New York State,
legalizing by the legislature.
And then in 1973, you have this really surprising Supreme Court case,
which took everyone by surprise.
It took the general public by surprise.
It took legal scholars by surprise in the US with Roe v.
Wade, which legalized abortion everywhere in the US.
What did we find?
To make a long story short, well, gonorrhea goes up by about 20% when
those early states legalize relative to their pre-legalization baseline and
relative to what changes are occurring in the non-legalizing states.
And then in 1973, all the other states catch up, right?
They basically pop up by about 20% to eliminate that gap.
Okay, so that's kind of interesting.
But as we sort of discussed this morning, it's not perfect.
Why isn't it perfect?
Well, no design is perfect, but this one has some very obvious problems.
One is, what if what we're picking up there is not a true change in gonorrhea,
it's merely a change in diagnosis rates.
Well, now, I've sort of asserted to you that everybody who gets gonorrhea,
it hurts when they go to the bathroom, they go to the doctor,
they show up in the data set.
Clearly, that's an exaggeration.
There are some people who just say, it'll go away, right?
And never go to the doctor and don't show up in the data set.
That is, no data are perfect.
Even if we've got reasons to think this is pretty good data.
Why is that a problem?
Well, it generates measurement error.
Why is measurement error a problem in this?
Well, because we can tell a very strong story as to why the measurement error
might be actually correlated with the abortion legalization.
I mentioned a little bit of this at the end of class today.
But the story is, when I go for an abortion to a reputable clinic,
a reputable hospital, a reputable doctor, the good ones provide STD testing, right?
And so if we're going from a world where pre-legalization, you have a lot of women,
for example, going and getting illegal abortions at places that aren't run by
doctors or at places that are not very well-run kind of thing.
Or I just wasn't getting abortions at all, right?
But I maybe had gonorrhea, I just didn't know it.
And then all of a sudden, now, abortions legalized, I go forget an abortion.
I now get tested, I now get diagnosed.
And maybe what's happening is behavior's not changing,
underlying gonorrhea rates aren't changing, merely diagnosis rates are going up.
I can't rule that story out, right?
We make some attempts in the journal Legal Studies article, but
those attempts are not perfect, okay?
So that's one great big worry that I've got.
What's another great big worry that I've got?
Well, what if it's the case somebody even brought this up?
They said, look, I seem to recall in my hazy recollection of U.S.
history, the late 1960s in California are known as the summer of love, right?
This is all just changing sexual revolution.
Maybe what's happening is the whole state is becoming more
libertine, right, so to speak.
So everybody's having risky sex all over the place in California.
And this is exactly coinciding with when the court makes its decision.
And so you have these two trends going in the same direction, and
so you can't distinguish one from the other.
My response to you is, I can't rule that out, right?
I can't rule that out at all.
All I can say is, is if you look at the legal history,
people in California are surprised, right?
So the timing doesn't seem to be completely endogenous
to this underlying social movement, but I don't know that for sure, right?
I do know that 1973 is really surprising to everybody, and
I do know the sexual revolution never quite made it to Iowa, right?
So you can't tell the same story for these other shocks.
But still, I can't rule this ad ex ante.
This is known in general as the endogeneity problem, right?
I'm acting as if these legalizations are true shocks, but they may not be.
They may be endogenous to underlying social forces.
That's the second big problem that I've got here, okay?
Luckily, in this 2008 Journal of Law, Economics, and Organization paper,
I think that we can make some improvements to both of those worrisome areas.
What does this paper do?
Well, this exploits subsequent laws,
which basically put restrictions on when young women can get abortions, right?
So Roe v Wade in 1973 guarantees the right of every woman to get an abortion,
but it leaves open the question as to whether or
not states can put some restrictions on young women's,
you know, women under the age of 18's ability to get abortions.
They can't stop them from getting abortions, but they can do things according
to the court like tell the young woman she needs to get her parents' permission,
or she at least needs to tell her parents.
Now, as you can imagine, if a state passes this law,
this is like increasing the cost of abortion, right?
You know, perhaps my parents didn't know that I was sexually active,
so by telling them I've given them information that I presumably wanted to keep secret,
that's a cost.
Maybe my parents are anti-abortion, and so by telling them, hey,
I want to get to an abortion, that may generate psychic costs, right,
tension in the family, it may even generate physical costs if they sort
of are violent toward me, mostly the father, that's the general view, right?
So these laws presumably increase the cost of abortion for this group.
Why are these laws, from my perspective, very helpful, not normatively,
but helpful from an empirical strategy?
Well, the nice thing here is these laws affect one group in the state,
but not another group in the state.
So what we can do is, if we assume the sexual behavior of 20-year-old women
in a state is pretty comparable to the sexual behavior of 17-year-old women
in that state, except for this law, we've got really nice counterfactuals here.
We can control for these sort of nonlinear trends that might be going
on in the background, right, where just the attitudes in the state are changing,
that changes sexual behavior, and that also changes the legal, the law, right?
So what's the standard story you would tell here?
The standard story you would tell here is, well, what's going
on is the state is becoming more conservative.
As the state becomes more conservative, people are being more protective
of their daughters, their daughters are having less risky sex,
they are also telling their politicians, hey, pass these conservative laws,
and what's going on is this pattern is generating what you're finding, but it's not causal.
Well, if we can use 20-year-old women as a nice counterfactual for 17-year-old women,
that story no longer runs through.
Why is it?
Well, because the story then would have to be,
even though we're becoming more conservative,
this conservatism isn't affecting the behavior of 20-year-old women,
but it is affecting the behavior of 17-year-old women.
I can't rule out that story, but it's becoming a much more specific story.
It's much less plausible, arguably, okay?
What else can we do in that paper?
Well, the other thing that we can do in that paper that's really nice
from our perspective is a lot of these laws get thrown out over time, okay?
So the legislature passes it, two years later the court says, no,
this is not consistent with our Constitution, throw it out.
Three years after that, we pass it again.
Four years later, courts throw it out.
We have a lot of on-off, on-off, on-off for the laws.
Why is this valuable?
It's valuable for two reasons at least.
If you believe that these underlying social beliefs are affecting what voters are demanding
of their politicians, that is, that's the story of endogeneity you're telling.
Okay, I can't rule that out.
But it's much harder to say that what voters want is influencing what the court is deciding, right?
State court, you know, Supreme Court justices in general don't need to stand for election, right?
They can do what they want, okay?
And so it's much less plausible that these court actions
to throw these laws out are endogenous, okay?
And again, in general, if you're going on, off, on-off, you've got more nice variation.
The second reason why this is really nice is go back to that diagnosis story.
That is, behavior doesn't change.
People just get diagnosed more when they get abortions than when abortions aren't available.
Tell that story in this context.
What that story says in this context is teenage girls were out getting abortions.
They were getting diagnosed with sexually transmitted diseases.
That was showing up in the data.
Now the abortion restriction comes into place.
They stop going for abortions.
But their behavior doesn't change.
But the data changes.
Why? Because they're not getting abortions.
They're not getting tested.
Therefore, the gonorrhoea is not showing up.
Now all of a sudden, two years later, state court throws out the law.
Now all of a sudden, everybody can go get abortions again, right?
What should we expect?
Well, you now had two years where people were getting gonorrhea and not getting tested,
not getting cured.
You should have this huge explosion in gonorrhea.
It shouldn't just go back to baseline.
It should go above baseline.
In fact, we don't find that.
We find it goes back to baseline.
So I'm pretty sure diagnosis is not driving our results since we can do this in this J. Leo paper.
What do we find?
We find results that are exactly comparable to the earlier paper, right?
What do we find specifically?
We find that gonorrhoea rates come down by 20% when these laws go into effect.
Interestingly enough, we find some racial heterogeneity.
This seems to happen among whites and Hispanics, but not blacks, yes.
So I guess, intuitively, we could sit down and we could write out a model of disease spread
where we sort of show this.
But intuitively, I don't think it's too hard, right?
At a minimum, if I'm carrying the disease for a longer period now, right,
because I'm not getting diagnosed, even if the body naturally, you know, you get over a cold, right?
You get over gonorrhea.
You don't get over it in three months, actually.
It actually takes quite a bit longer, right?
But you would get over it eventually.
And so maybe if it's a two-year period, you're not going to say, well, now all of a sudden,
my girlfriend can get an abortion that turns out she has gonorrhea.
Now I get tested.
It's the same case that I got two years ago.
No, that's not strictly speaking what I'm saying.
What I'm saying is it's got to be the case that there's more of a stock of gonorrhea if,
let's say, on average, people had previously been treated after two months,
and now the disease lingers for six months, right?
You've got to have some increase, and we're not finding that there, okay?
We could put parameters on that, and we could probably bound the effect.
That might be interesting, but we haven't done that.
But intuitively, I think we're okay.
Anyway, we have this neat, or not neat, but it's interesting, potential racial heterogeneity.
We seem to find the effect among whites and Hispanics, but we don't find it among blacks.
We puzzle over this in the paper.
We can go into this if people are interested in, but since it's the old paper,
I won't spend a lot of time on it.
I think we have a decent explanation for that, but it is something interesting to keep in mind.
Okay, so again, back to our concerns.
We've got big endogeneity concerns, but again, the idea that in this particular case,
these laws get thrown out, so they're presumably less endogenous,
if it's the court that's doing a lot of action versus the legislature.
Also, we've got sort of this nice within-state control of the older, not old women,
but sort of 20-year-old women, so to speak.
And it turns out that our results are the same now that we can look at this.
We also have this diagnosis issue that we've been talking about.
In the first paper, we did this comparison I talked about earlier that, you know,
we compared the effect among men and among women, whereas if it's clearly just just diagnosis,
we should see the effect concentrated among women, but not among men.
But that's not perfect because maybe the women, once they learn they have gonorrhea,
go and tell the men, and so then they go get tested.
So, you know, I think that's unlikely based on my reading of what the public health people say
who claim that a partner reports are very, very low despite doctors urging
to go tell all your partners it actually doesn't seem to happen, but I can't rule it out, right?
I'm not, you know, if you stick me on the position of put a gun to my head and say, you know,
do you really trust the public health people, I would have to say no, right?
Just as generically I don't trust them, so I shouldn't trust them specifically here
where it helps me, right?
The Jay Leo paper, we sort of have a nice, you know, the thing that we just talked about, right?
We don't see this ricochet.
We don't see an increase from baseline after these laws get thrown out.
We just see a nice natural return to baseline, okay?
But, and this is a point I was making through much of today, never believe a paper or even papers.
We're off to a good start.
Why? Well, because we've got two flawed research designs,
but they're both flawed in different ways, and they're both generating comparable effects.
In both instances we're finding sort of limitations on abortions or the getting rid
of a limitation on abortion is generating something on the order of 20% effects, right?
Furthermore, you know, if we were showing sort of comparable results with the same design,
that's not really new information.
These are slightly different designs.
Furthermore, if we're showing same results over the same sample,
that's not really new information either, but this is two different samples, right?
The one sample is running from 1963 to basically 1980 or 1975 depending on the specification.
Here in the Jay Leo paper we're looking at more recent times, 1980 basically through 2000, right?
So different time periods covered, and we're still getting the same result.
As an intuitive matter for the students, you know, as you have different designs
and different data samples as they continue to generate comparable results,
we're starting to get more and more confidence, right?
But you always like to see more.
You always like to see what social scientists often refer to as replication.
Now when social scientists talk about replication, they use that word kind of loosely.
What do I mean by that?
Well, if I'm a physicist or I'm a chemist, right, and I do an experiment and I get a certain set of results,
another chemist or another physicist might do a paper where they replicate my results.
That is, they do exactly the same thing that I did and then publish that.
They say, hey, I did the exact same thing he did and I got the same results, right?
In social science, you know, we don't do that.
It wouldn't be very interesting.
The reason it's interesting in science is because you're running experiments
to generate a new data sample, right?
If you did the exact same thing an economist did, you'd be using the same data, you know,
running the same regressions.
You know, it would be kind of scary if you didn't get the same results, right?
It's like, you know, computer spirits saying, ah, today we'll give them these results,
tomorrow we'll give them other results.
That's, you know, silly.
Now I say that and it's kind of funny.
It does turn out that from time to time, people will try to replicate inexactly that way
in social science and it turns out you get different results.
And usually it's the fact that, you know, somebody messed up on coding or something like that.
So that's valuable.
But it's not as valuable in social science as it is in real science.
When we talk about social science replication, what we usually mean is you kind of do what
they did.
You look at the same kind of issue but in a different sample or in a slightly different
way and do you get roughly comparable results, right?
And so that's a way to view this paper where it's, again, Stratman and me.
Then we had this guy Sven Nielsen.
And so what do we do?
Well, we ask the following question.
We say, well, maybe Americans are rational with respect to risky sex but Europeans,
we're not so sure if you're rational with respect to risky sex.
And not just Europeans.
We've got sort of in Europe and sort of more broadly than that.
Turns out if you look across the developed world,
gonorrhea data are still pretty high quality and still pretty well available.
Why is that?
Well, certainly by the 1980s, the WHO, the World Health Organization,
basically encouraged most of the developed world to collect STD data and sort of provided
some guidelines.
Even with those guidelines, there are some countries that are worse than others in their
collection rates.
We have a whole appendix in the paper if you're interested but, you know,
Spain is terrible at collecting its STD data.
But most of the rest of Europe is pretty good.
And, you know, there's details in the appendix if you're interested.
What can we do in the European context?
Well, there's different kinds of policy variation, right?
So if you look across Europe and these other countries in this time period,
it turns out that there are actually a couple different abortion regimes.
There's certainly countries that have abortion on request.
That is, hey, I'd like to have an abortion.
Okay, sure.
Come at three.
We'll give you an abortion, right?
There's no sort of hoops to jump through.
It's no problem.
There are some countries at some years in this time period that
basically required the woman or the woman's doctor to make some argument that
if this woman doesn't get an abortion, it's going to generate a hardship,
either an economic hardship or an emotional hardship, right?
Now, in the U.S. context, when states had laws like this for a very brief period,
a lot of the states just kind of ignored them.
Maybe all you need to do is get a note from your doctor.
The doctor says, I'll be really sad if I have to have this baby.
Okay, give you abortion.
In the European context, some of the countries seem to actually take this rule seriously.
So it actually is sort of a distinct rule.
And there are some countries that still, and certainly in the course of the sample,
basically required a credible claim that if this woman doesn't have an abortion,
she is going to suffer physically, maybe even die, right?
And so the countries that have had this rule almost uniformly take it seriously, right?
This is not just a rubber stamp.
This is, you know, this doctor is actually certifying
that this woman is in grave danger.
And that's when you can get the abortion.
And then there are some countries in this sample time period
that just completely prohibit abortion.
What does the sample look like and how did we generate it?
Well, we basically have 41 countries for those 20 years.
Which countries?
Western Europe, Eastern Europe, Canada, Israel,
in the U.S., and sort of the former Soviet countries.
Why those 41 countries?
Well, those are the ones that internationally,
we could be reasonably certain that the gonorrhino data were at least decent, right?
That is, that these countries had sort of fairly high reporting rates,
followed WHO guidelines, those sorts of things, okay?
So what's our empirical design?
Well, we run weighted least squares.
Basically, countries with larger populations get higher weighting in the regressions
for both technical reasons and representativeness reasons.
We include country fixed effects.
What does that mean?
Countries are just different in their baseline levels of gonorrhea.
You know, Italy has low gonorrhea levels.
The, you know, the Eastern Europe has high gonorrhea levels.
And that seems to be unrelated to any other explanatory variable.
They're just different, right?
So we'd like to pull that out of the data.
There are also year fixed effects.
What's going on here?
Well, in different years, the WHO changed guidelines, right?
So, you know, here, you know, we're going to really make sure you're reporting everything.
This year, we're not being as, you know, as careful.
What else do we include?
Well, we include these country specific trends,
at least in some of the specifications, right?
So what you may be worried about is, you know, maybe the trend in gonorrhea,
actually in all these countries, more or less, is a downward trend.
But you may be worried that in some countries,
it's downward really steeply in other countries.
It's sort of downward in a more, you know, in a slower trend, right?
So you'd like to pull that effect out of the data.
Make sure that's not infecting your causal estimates.
We include a bunch of covariates, sort of things like income measures,
things like inflation measures.
You know, we have at least some kind of arguments for most of those covariates.
But really, these are nuisance parameters.
We just want to pull their effects out of the data,
make sure that's not driving any of our results.
And we cluster standard errors at the country level.
We haven't gotten to this.
For those of you who are coming to the class,
we'll probably talk about this somewhat tomorrow.
But one way to think about it is,
the gonorrhea rates in Germany in 1990
are not independent of the gonorrhea rates in Germany in 1989, right?
There tends to be some inertia in these sorts of things.
And so if you don't recognize that inertia,
if you basically say,
look, I've got 20 observations from each of these countries,
well, yeah, but they're not 20 independent observations.
So you like to take account of that.
Well, we'll talk, I mean, if people are interested,
we can talk about it today,
but we'll certainly talk about it in a more technical way tomorrow.
So that's the basic setup.
What do we find?
What's the punchline?
And then we'll talk about the weaknesses.
Well, we find that when a country moves to abortion allowed on request,
you see an increase in gonorrhea of about 40%, 45%, right?
So it spikes up.
These are all relative to a regime where it's either prohibited
or you have to show that the mother's life was in danger.
So if you move from that regime to abortion on request,
you see about a 45% increase in gonorrhea rates.
What happens if you move to this intermediate regime
where you need to show some psychological or economic duress?
You see a comparable result, about 38%.
So yes, abortion on request seems to generate a bigger effect
than the other one, as we could maybe predict.
But the difference between these two results
is not statistically significant.
And so maybe that's a bit worrisome,
the fact that we don't see a much bigger movement
for the bigger liberalization than for the smaller liberalization.
But at least on the point estimates,
we're finding sort of the right ordering.
These results are all significant,
statistically significant at better than the 5% level.
And we get the order right for which liberalization
generates a bigger effect.
But again, the difference between these coefficients
is not significant.
So take that for what you will.
Now one of the things that you might want to do,
and we talked about this a bit today,
is you might want to see how stable are your results.
When you look at just certain parts of the sample.
So if we exclude Eastern Europe and the former Soviet countries,
our results stay the same.
Now you might say, well, why do you pick on those guys?
What's wrong with those guys?
Well, we don't know in the gonorrhea context,
but we know that a lot of Eastern European data,
and certainly a lot of the Soviet data under communist rule,
was all just made up.
So the GDP numbers that Soviet Union was putting out,
for example, were always made up.
They would make things up to make themselves look better.
And so we had this worry that maybe they were doing
the same thing with some of their health data.
Seems to not be driving our results if they were doing that.
What if we only have focus on the countries
that actually change their law?
There are a bunch of countries in here
throughout the sample actually don't change.
What if we focus attention just on the ones that change,
since they're the ones that drive the causal estimate?
If we focus just on the changers,
the results actually stay comparable,
maybe even they get a little bit better.
So those things are good.
What are the shortcomings?
What are the concerns?
Well, we again have our old worries
about simultaneity, endogeneity, and measurement error.
Right?
And we don't have the tools available to us in this paper
that we used in the other papers to look at this, right?
So we can't do this comparison of the effect on men and women.
WHO guidelines do not require that the countries report
separately by sex.
So we don't have that data, right?
We don't have the situation where European courts
or some of these courts sort of throw these things out
after a while, right?
So we can't look at what happens when the courts
get rid of these things.
So we take a slightly different approach
with a certain motivation.
We look at other diseases that shouldn't be affected, right?
What you may be worried about is maybe there's
some different background trend going on
where people are becoming more, having higher health care demand,
or being more interested in their health over time.
And what's going on is we're just picking up
these background trends in terms of people's interest in health.
And we're seeing it just so happens that these trends
are related to the law changes, right?
So if that story is true, we should probably
see increases in a lot of different diseases.
If I'm just more interested in health,
I have more money to go spend on health,
or the country spends more on health.
They shouldn't just pick up more sexually
transmitted diseases like gonorrhea.
They should pick up lots of other diseases, right?
So if we look at this, first of all,
if we look at malaria and tuberculosis,
two things that we've got data on for all these countries,
you don't really see the same kind of change.
We look at syphilis, and again, you might say,
well, syphilis is a sexually transmitted disease.
But remember, by this time period,
it's a gay sexually transmitted disease, right?
And so homosexual couples aren't worried
about unintended pregnancy.
And so this mechanism shouldn't be working.
So we look at syphilis, no real change, okay?
So is this great as a way to rule out these worries?
No, it's not perfect, right?
But it's another approach that we hadn't done
in the other papers, and so it's another way, right?
It's another way to sort of fill in holes
about stuff that you may be worried about, okay?
Now, you know, it seems kind of funny
that this is the slide that's sort of toward the end.
And I say, well, you had me sit here for half an hour,
and you told me this whole story,
and I still don't know why this is important.
Well, now I'll tell you why this is important, right?
It's important for a couple of different reasons.
One, for the economists, you know, if we think of ourselves
as, you know, having gotten the call from Gary Becker
in the 1960s to go forth and conquer the rest of the world
with our tools, go on the academic crusade
where we sweep through the land with economics
and take it all over, you know, we want to extend our tools
into these areas that people think they don't apply to.
You know, here's one, right?
We're adding more evidence that even in an area
like risky sex, even in an area like teen risky sex,
even in an area like Europe where we're not so sure
they're all that rational,
steer we're still finding sensitivity to incentives.
We're finding forward-looking behavior, right?
That's important.
I think that's important from a social science standpoint.
That's important from a, you know, how universal are our rules,
are our models, are our framework way of thinking.
I think that's important.
It's not important from a policy standpoint, so to speak,
but it's certainly important from our, you know,
way we view the way people act, the way people think,
the way people make decisions.
And that's good.
But probably the most important point from a policy standpoint
is we can use this information to improve our models,
our epidemiological models of disease spread.
One of the things I noticed back when we did that first paper
in the journal of Legal Studies,
you know, we were finding a 25% increase, right?
That's big.
And so I said to myself, it's got to be the case
that the public health authorities in the U.S. noticed this.
So I went back through all the public health reports at that time.
The Centers for Disease Control in the U.S.
puts out surveillance reports for every area of health you can think of.
So they have a whole division that's dedicated to sexually transmitted diseases.
So I went back to their reports from the 1970s.
And right there in the reports, they said,
there's this big spike in gonorrhea.
We don't know what's going on.
It must be sexual revolution.
It must be changing morality, right?
Well, no, at least part of it seems to be a change in cost,
a change in price, something that's easily predictable,
something that's easily modelable, right?
And so what does that mean?
Well, if you're a public health person,
if you're an epidemiologist, right, you can improve your models.
Why is that valuable?
Well, resources are scarce.
If we're going to have interventions, we can't have them everywhere.
And we can't have huge interventions everywhere.
But now we can maybe target them better, right?
That's why we have these disease models,
so that we can presumably then have some interventions to fight the diseases.
Here, if you have a better model, you can have better interventions.
That's really important, right?
A lot of people certainly in law schools, you know, I teach in law schools,
you know, economists never ask you this question,
but in law schools, it's almost the first question.
They say, what's the import of your results for how we should think about abortion?
My response is, I don't know, right?
You know, at the end of the day, what have I shown?
I've shown an effect, a behavioral effect of abortion.
You know, is this good or is this bad?
It's hard to say, right?
You may say, well, gonorrhea is bad for sure.
And I think even economists who are completely subjective,
not willing to call anything good or bad,
even economists, if you push them, they'll say, yeah, gonorrhea is probably bad, right?
But notice what the gonorrhea is proxying for.
It's proxying for risky sex.
Risky sex is good, right?
It presumably enters people's utility function as a positive.
So can I say, is this is good or bad?
I can't, right?
I can simply say it's an effect.
And then they push you further and they say, no, you can't cheat like that, right?
You've got to be able to make a normative statement.
I say, no, I'm unwilling to, right?
That's not what my tools allow me to do, right?
But presumably, other people who are willing to make those normative trade-offs
can take these results and sort of make decisions accordingly.
Funny story, I mean, we're sort of at the end of what I'll talk about,
but we'll take lots of questions.
But funny story, if we get back to this improving models of disease spread,
it was really shocking to me when I found that the CDC didn't recognize this.
So none of what I did is I did a lot more reading in public health,
trying to look at what models public health people use
for the spread of communicable diseases, for example.
And almost 201, these diseases, I'm sorry, these models, these epidemiological models,
basically start from the point that behavior is static, right?
Now, the behavior of the disease might have all sorts of interesting non-linearities,
and that's what they spend their time modeling.
But they basically assume that the behavior of the people
who harbor the diseases is static.
It's not changing.
It's uninteresting to add to the model, right?
As an economist, this is just striking you as ridiculous, right?
Of course, people respond to costs and benefits.
Of course, you could improve your model by taking this into account.
And yet, almost none of the public health models ever do, right?
Interesting side note.
I mentioned this at lunch today, but not everybody was there.
So an example of this is the following.
In the early 1990s, Dick Posner, the very famous American judge and legal scholar,
was really interested in communicable diseases.
He wrote a book on the topic.
He wrote a couple of articles on the topic.
And you're saying, well, that's not surprising.
He's written a book and a couple of articles on every topic in the world, right?
So this was his communicable disease period, right?
And at the time, he gave a talk at NYU Law School,
or maybe it had been a generic talk for the whole university, right?
But it was at NYU, and it was a very well-attended talk,
as his talks usually are, because he's so famous.
And in the course of his talk, he made the following statement.
Now, remember, this is the early 1990s.
This is sort of the height of the AIDS epidemic and the AIDS scare in the U.S.,
and the world more generally, but the U.S. for sure.
Posner says the following.
He says, you know, all the people who are thinking and worrying
about the public health consequences of AIDS keep telling everybody,
go out and get tested, right?
There are billboards in every city that says, go out and get tested.
We got tested.
You should get tested.
You need to know, right?
And Posner says, you know, I've been thinking.
And if we have a really simple model of behavior,
this could be a really bad idea of getting everybody tested.
And the idea was the following.
He said, look, imagine you don't know that you have AIDS, right?
And again, this is the early 90s when we have no real drugs
that sort of can extend the life of somebody when they get AIDS.
This is more or less a death sentence.
He says, imagine you know, or I'm sorry,
imagine you don't know that you have AIDS, right?
You're going to be really careful,
because you're worried about everybody else who has AIDS.
And so you're careful in partner selection.
You're careful in condom use, et cetera, et cetera.
Now imagine you get tested and find out you have AIDS, right?
How does your behavior change?
Well, you're not going to die twice from it, right?
And so you're now not going to be careful
in partner selection on the margin.
You're going to be less careful in partner selection.
You're going to be less fastidious in condom use on the margin, right?
And this could really lead to a worsening of AIDS.
NYU, I don't know if you know American University systems.
NYU is generally known as a fairly liberal place
in the U.S. sense of the term.
Posner gets booed off the stage, right?
People just are up in arms.
Are you ready to go and grab him and shake him?
And you know, how can you say such heretical things, et cetera, et cetera, right?
A couple of years later, a very important economist,
he's a development economist at Harvard, Michael Kramer, has the same idea.
I don't know if he knew what Posner had said.
He might have had the idea on his own.
But he tests it.
He tests it using data in Southeast Asia, right?
So in Southeast Asia, some of the countries started mandating AIDS testing
for the prostitutes and whatnot, and some countries didn't.
So we got data on disease rates and things like that in Southeast Asia.
And he found a la Posner, right?
And sort of contrary to maybe people starting assumptions.
Once they started doing AIDS testing in these countries in Southeast Asia,
risky sex went way up, right?
And so Posner was right.
What's the import of this?
Well, it sort of suggests that there are many areas where we are missing the boat, right?
We're missing the boat in these models and in these interventions for diseases in public health,
simply because we're not listening to the economists, right?
And we should always listen to the economists put behavioral models in these things.
And we can probably improve a lot of these policies.
So that's why this paper is important.
And that's why this kind of paper is important.
So what's the conclusion?
Again, the conclusion, not so much from this paper, right?
I think of the three, even if you don't recognize that this is the third paper in a series,
of the three, this is the weakest of the papers for sure,
but its weaknesses are different than the other two.
So take this as the conclusion about this whole enterprise, right?
About this whole set of papers.
The conclusion is now we have evidence of the risky sex and abortion effect across
different countries, across different time periods,
across different levels of data aggregation, right?
Do we look at the whole gonorrhea rate?
Do we look at it by sex?
Do we look at it by race?
Do we look at it by age?
We're finding it in all of these.
Different types of law changes, right?
You might all be able to tell me an endogeneity story for each of these papers,
but now you need to tell an endogeneity story that's consistent across these papers.
Maybe you can do it, and I can't rule it out,
but it becomes a much more complicated story, right?
So if you're saying, well, it's all about politics in the US,
well, the politics in Europe are way different, right?
So is it the same thing that's generating the results here in Europe?
Probably not, right?
Again, this is never believed, even a series of papers,
certainly not a series of papers done by the same person,
but I think we've got some decent evidence here, okay?
Any questions?