Ask the grumpies: If you were a supercommittee with superpowers where would you start reducing the federal government budget?

chacha1 asks

If you were the supercommittee, with actual governmental superpowers, where would you start with reducing the federal government’s budget so that we could actually start reducing the national debt without condemning the nation’s poor to starvation, homelessness, and/or death from preventable illnesses and workplace injuries?

Well, the answer to this would depend a lot on how much power said supercommittee had.  Like, does what we say become law?  Does it have to be voted on?  What happens when people protest?  And so on.

Here I’m going to assume that the committee has the power to force through legislation and people just have to lump it, but doesn’t have supernatural powers to change the hearts and behaviors of people.  We make the laws, they try to get around them.  They can’t vote us out.  In any case, some really easy cuts would be to go with evidence-based policy.

Note:  We may not actually *want* to reduce spending when times are bad because even just throwing money out of a plane over a city is better than reducing spending.  So I’ll assume that in those situations the money saved goes to feed kids, fix infrastructure, fund education, stimulate important research, and otherwise fix the economy in ways that are good for our long-term growth.

So easy things:

  1. Phase out the mortgage benefit– this benefit does not encourage homeownership, only overconsumption of houses
  2. Phase out the SS tax cap
  3. Completely eliminate ridiculous agricultural subsidies that are making us fat.
  4. Examine the corporate tax code– this is hard because there’s a lot to be cut, but there is a real worry that corporations will move things overseas, so it’s not just a slam-dunk.  I’m sure more educated folks than I have better ideas.
  5. Go with the Poterba policy recommendations for stream-lining the tax code so that there are fewer loopholes for extremely high earners (this is essentially expanding the alternative minimum tax system)
  6. Make stock earnings taxed as income (or otherwise make it so the Buffett tax hits people who own American stocks)
  7. Cut inefficient military spending, replace it with efficient military spending or infrastructure spending so as not to hurt communities dependent on the industry (possibly phasing out plants)
  8. I’m not so good at foreign policy, but there’s a lot that can be done to decrease our spending in this arena without jeopardizing our national security.  We need more focus on doing things with coalitions rather than unilaterally.  And we do need to help out more like with the Syrian refugee crisis.
  9. Cut foreign policy aid to Israel and possibly to Egypt.
  10. Cut some Medicare spending– allow Medicare better bargaining power, allow outcomes from experiments to influence policy, cut some doctor reimbursement (but not to Medicaid levels)
  11. Allow federal funds to fund abortions.
  12. Add a public option to health care with an eye towards eventually transitioning to single payer health care (this will actually cost money and we’ll have to pay more taxes but it is good for efficiency).

There’s probably a lot I’m forgetting.  In my work office I have a chart of government spending, but I don’t have one off the top of my head here.

When economists prefer tossing economic theory to being woke

I seriously do not understand how so many economists (white male etc.) think that “cultural differences” explain things that are easier explained by “different constraints.”

As if we’re not all rational actors, only the white guys are.  Everyone else is doing worse because they are worse.  They’re either low quality or have bad culture.  If everyone acted like a white guy, then everybody would be doing as well as white guys.  As if.

It’s like, do you not listen to your own theory? How is it that when someone who isn’t a rich white guy is involved, all of a sudden you become a poor quality sociologist (who doesn’t really understand sociology)?

How to write a power-point discussion (economics-specific)

The goal of a good discussion is to explain to the audience where the paper fits into the general social science/policy framework and to help the paper improve for the future.  The goal is not to destroy a paper but to improve it (see exception below).  Discussants are serving science!

  1. Frame question— why is it important?  (You can mention your own work here if applicable.)
  2. Briefly summarize paper.  If the presenter is great, you will be able to skip the summary or only go over what you see as the most important parts.  If the presenter is terrible, your audience will really appreciate figuring out what they just heard, so it’s good to be thorough on your slides if you don’t know a priori how good the presenter will be.  If applicable, here would be a great place to take the author’s work through a “sniff test”– Bridgette Madrian is one of the best discussants I’ve seen, and one of my favorite discussions of hers was where she took a person’s paper (on whether or not we need 70% of our income after retirement) and applied it to her own life with a spreadsheet and came to the conclusion that the paper’s thesis was plausible.  Sometimes discussants will call up experts in the industry to ask their qualitative opinion.  Really great discussants will sometimes replicate or extend with another dataset.  None of these things are necessary, but if they’re easy for you or an RA to do, they can really push you to be memorable (though being invited to discuss more papers is not necessarily something you want to do!).
  3. Constructively point out problems with the paper and suggest solutions (if any).  Don’t be a dick.  Frame these as questions to think about, how big a problem you think they are etc . Don’t use this part as a place to talk about why your work is awesome and theirs sucks.  If you do mention your work in this spot, use it only as a place to commiserate with standard problems and suggest solutions that could work for them.
  4. Extensions for the future, broader impact.  Here’s a place where you can talk up your own work if it is related and can speak to the paper you’re discussing.

How many slides do you want?  Fewer than the number of minutes you have to present.  It is better to go short than to go long.

Special cases:

  1.  The authors haven’t actually done anything yet:  Spent the majority of your time on why this is an interesting question and suggestions for future work.  (Also ok to use a chunk of your time talking about your own related work.)  Use the word “preliminary” a lot.
  2. The authors clearly haven’t addressed causality but causality needs to be addressed (or any other major elephant in the paper issue):  Spent the majority of your time on why this is an interesting question.  Talk about the problems of getting to causality and (if easy for you to do) what other authors have done and (if easy for you to do) the problems with what they’ve done (or if not problematic, then suggest these authors follow).  Gently mention that causality is something that these authors need to think about.  The audience will understand.  Then suggest future work (which will include really nailing down causality).
  3. You don’t get the paper to discuss until the night before at 3am:  Feel free to spend the entire time talking about your own work, or to come up with something off the cuff while they’re giving the presentation (it is AOK to note that you did not get the paper until the night before, but that should be the extent of your dickishness).
  4. The paper is poorly done and the results, if taken at face value, will do real harm to people, particularly those from marginalized groups:  In this case, it is ok to firmly and politely destroy the paper for shoddy craftsmanship.  You can do so in a professional manner in steps 2 and 3. You’re still not being a dick, but you don’t have to frame things as questions to think about but as real methodological problems.   It’s ok to throw around the terms “dangerous” and “needs stronger proof”.  It’s a shame that there are still guys (and the occasional woman) who write papers with sexist/racist agendas who ignore basic science in order to prove that wealthy white men are superior and deserve their privilege, but there are.  They shouldn’t be allowed to do bad science.

Academic readers– is this about right?  What things are the same or different in your discipline?  Any other tips?

How to do a powerpoint presentation (social sciences, economics)

I LOVE me some powerpoints.

Think about what you want your audience to take away.  Use the rule of 3 to emphasize those points (say what you’re going to say, say it, then tell people that you said it).  Depending on how much time you have you won’t be able to get through every point in the paper, so think about what subset you want to present, what slides you want to keep in case of questions but not actually present, and so on.

Use the powerpoint as a guide to remind you what to talk about, so brief bullets/phrases instead of full sentences.  Do not read off the slides.

Some people will only want to read your slides, some people will only want to listen to what you say.  Make sure that people who do one or the other will still get the gist of your presentation.

Make sure your fontsize is big enough that the people in the back can see it if they’re wearing glasses.   My heuristic is to not go below 28 point Calibri if it’s something I want them to read.  (Table notes can go smaller)

Graphs are often more compelling than regression output.  (But keep the regression output as a backup)

Don’t use fancy wipes/fade-outs/etc.  Anything that distracts without a purpose is useless.

Development economists, behavioral economists, psychologists, antrhopologists, etc. use a lot of photos/pictures/drawings and occasionally movies.  Do that if it is common in your field.  If it isn’t, then only sex it up like that if it helps improve understanding.

DO NOT USE PREZI.  Or if you do, use it like you would Powerpoint or Beemer.  You do not want to give members of your audience migraines.

I have often found it helpful to have different versions of the same information in the powerpoint that I can skip over depending on how pressed for time I am.  So I will have a pretty chart, regression output, and summary bullets (or two out of the three) and I will use combinations of one or two of these depending on how much time I have left.  It is also helpful to know which sections can be skipped without losing the main themes of the presentation.

Practice your talk.  Know how the talk is going to differ if questions are allowed vs. no questions being allowed.

It is better to go a little under than a little over.  It is better to skip parts than to talk so quickly nobody can understand you.

Join us next Tuesday for:  How to write a powerpoint discussion(!)

Academic readers– is this about right?  What things are the same or different in your discipline?  Any other tips?

Ask the grumpies: minimum wage

Mutant Supermodel asks:

what do economists think about raising the minimum wage? Is there a general consensus either way or is it as mixed up as it is on my Facebook feed?

#1 is not the economist but I’ll say that I support it right now, within reason.  What does #2 say?

Ok, the short of it is:  Economists are still divided on the topic of whether or not, and at what point, raising minimum wages decreases jobs or job growth.

Basic econ 101 theory says that if you raise the minimum wage, creating a wage floor, in a perfectly competitive market, then employment will go down because all the people who would have gotten jobs at lower wages will no longer get jobs.  (This is usually taught in the same chapter as why rent control is bad.)

That’s basic economic theory in a perfectly competitive market.  Market intervention hurts jobs.

HOWEVER, we don’t live in a textbook world.  So this is an empirical question.  And there are literally hundreds of papers exploring the effect of minimum wage increases in a non-experimental framework.  They find mixed results based on functional form.

The first major natural experiment on this topic is one by David Card and Alan Kruger.  They look at the effect of a minimum wage increase on employment of fast food workers at border counties in Pennsylvania and New Jersey.  They find that employment doesn’t change or actually *increases* in the state where the minimum wage goes up.  It doesn’t seem to be decreasing employment at all.

There are a number of potential explanations.  One that I’ve always liked (for its simplicity), but many prominent economists don’t share my like of is the thought that these fast food markets have monopsony power– which is like monopoly power, but what you have when you’re the only employer (or one of the only employers).  When employers have this kind of labor market power, they can keep wages artificially low because they can say either you work for us or you get nothing, because there’s no competitor to say you can work for me and I’ll give you a penny more (thus bidding up wages).  (You don’t need just one employer for there to be some monopsony power– just a small number of employers if they’re willing to collude, or for there to be other frictions in the labor market.)  When this happens, increasing the minimum wage would just reduce profits but wouldn’t negatively affect employment.

David Neumark is the big anti-Card and Kruger guy.  His work with coauthors argues that Card and Kruger’s survey data are inaccurate and that employment goes down based on administrative data.  Card and Kruger disagree.  There was some back and forth.  Cambridge-school economists tend to believe Card and Kruger.  Chicago-school economists tend to believe Neumark and Wascher.

More recently there’s been some work that reconciles all of the findings, suggesting that minimum wage increases don’t actually have a lot of effect in the short run.  People don’t get fired because the minimum wage increases, because firing people is bad for company morale (among other things).  HOWEVER, this newer work suggests that the minimum wage does depress job growth in the long-run.  As people leave (as they often do in minimum wage jobs), they’re not replaced  one-for-one.  There may also be an effect on overall growth by industry (with firms that can only afford minimum wage workers shrinking or going out of business), which would further disguise any negative effect on employment.

What’s the bottom-line?  Economists don’t know.  Most, if not all, economists will agree that there’s some point at which the minimum wage really does decrease employment rates.  (If minimum wage were $100/hr, most of us would stop sending our kids to daycare and fast food workers would be replaced entirely by machines.)  Many economists will also point out that, historically, real minimum wages have been much higher, particularly at times of economic growth (correlation not being causation, but slyly winking and nudging that direction), and some will even note that we’re subsidizing companies that don’t offer a minimum wage with foodstamps and other benefits.  Without those government subsidies, companies might be forced to offer living wages.

Personally, I think the minimum wage should be raised right now.  However, I’m not sure that it should be raised as much as some people are suggesting, particularly in some parts of the country where living costs tend to be lower.  (Just as a sniff test, I’m willing to hire people at $10/hr for standard minimum wage jobs that I’m not willing to hire at $12.50/hr– I’ll just do it myself at that price unless I already know the person is exceptionally competent.)  And then there’s all those exceptions to think about… should teenage wages be lower (and what does that do to adult unemployment?)?  Waitstaff positions?  And so on.  It’s a very complicated question full of many moving parts, and if economists can’t even agree on the direction, it’s hard to know what the magnitude is!

 

On budget constraints, endogeity, and interconnectedness: A deliberately controversial post

I was reading another mommy blog off a blog roll and came across an article talking about another article.  The original article made the argument, Fly-lady like, that if your life is a mess, then your bathroom floor is a mess, and to make your life less of a mess, you need to clean your bathroom floor because this is all interconnected.  Sort of a broken windows hypothesis for your life.

How do you know your life is a mess, asks the article?  The proof is whether or not the area behind your child’s car seat is sparkly clean.  Ignoring for the moment that that test says that all but the most OCD or wealthy enough to afford servants have lives that are messes, there are several logical and mechanical reasons that making a causal link from cleaning your house to cleaning your life doesn’t make sense.

Let’s start with the mechanical arguments.  As Laura Vanderkam is fond of noting, there are 168 hours in a week.  Every hour you spend cleaning behind the car seat is an hour you don’t spend organizing your paid work, your meals, your finances, your exercise routine, or anything else that people find worth organizing that makes them happier.  I’m guessing that area behind the car seat that is just going to get messy again ranks pretty low on most people’s priority list.  (Unless, of course an apple core got wedged there, then clean away!  But the example in the article didn’t include potential for rot or bad smells.)

Adding to the time-based mechanical arguments is research on willpower.  If cleaning is unpleasant, it takes willpower to do.  We have limited reserves of willpower that are replenished with sleep, rest, and food.  Willpower used on cleaning behind the car seat is willpower not used at work.  Or it is willpower to be replenished with sugar leading to unhealthiness.

Finally, even if there is a correlation between having a clean bathroom and feeling together with the rest of your life, that doesn’t mean that the clean bathroom *causes* you to have (or to feel like you have) the rest of your life together. There could be endogeneity.

Endogeneity comes in two flavors.

The first is reverse causality.  Here, feeling together would be the cause of the clean bathroom, not vice versa.  Maybe you have free time from being organized and good at delegating so you can clean the bathroom.  Maybe you’re so awesome at work and confident in yourself that you can easily hire a housecleaner.

The second source of endogeneity is omitted variables bias.  That means there is something else that causes both your bathroom to be clean and you feeling like you have your life together.  An omitted variable could be something like, being Martha Stewart.  Or having a really low sleep need and high reserves of will-power.  If you only need a few hours of sleep per night you have more time to do everything and to have a clean bathroom.  Or maybe having a partner who is supportive and enjoys cleaning– that could lead to both clean bathroom and the rest of life working.  (Just like having a partner who acts like an additional toddler rather than a caring and sharing adult can lead to messy bathrooms and unhappiness in other areas.)

 

Do you think that if you want to be perfect at one thing, you have to be perfect at everything?

Ask the grumpies: Time spent on housework by child status and gender

Laura Vanderkam asks:

Looking at the ATUS, how does having a kid affect how much time people devote to housework? Is this different for men and women? There are lots of different stories one could come up with: everyone does more housework because there’s more housework to be done. Everyone does less housework because there’s less available time to do it in. Mom does more and dad does less because they wind up conforming to traditional gender roles (and maybe mom winds up working less for pay, and so is the one around to do it). Maybe mom does a lot more and dad does a little more. So I’d love to know what the numbers actually show.

Lalalalala, Stata.  Ok, so I’m using the 2002-2012 ATUS here because I’m too lazy to download the 2013 one even though it’s now available.  In a bit I’ll show how things have changed if you limit to just 2011 and 2012.

How does having a kid affect how much time people devote to housework:

. ttest  bls_hhact_hwork, by(hh_child)

Two-sample t test with equal variances
——————————————————————————
Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
———+——————————————————————–
No |   72370     39.6372    .3054511    82.17145    39.03851    40.23588
Yes |   64590    44.33705    .3295443    83.75224    43.69114    44.98296
———+——————————————————————–
combined |  136960    41.85364    .2241497    82.95358    41.41431    42.29297
———+——————————————————————–
diff |           -4.699851    .4488466               -5.579582    -3.82012
——————————————————————————
diff = mean(No) – mean(Yes)                                   t = -10.4710
Ho: diff = 0                                     degrees of freedom =   136958

Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

Urgh, I can’t figure out how to make this pretty without making it a picture and I’m too lazy to do that (in word you make it courier new 9 or smaller and it’s all pretty).  Anyhow, this is saying that people with kids spend 44.33 minutes on housework and people without kids spend 39.63 minutes on housework during the reference day.  This is a difference of 4.7 minutes.  This difference (two-tailed is the one in the middle, since we didn’t have a prior about which direction it should go) is significant at the 5% level (also at the .0001% level).  So kids create housework.  (Which is no surprise, but the surprise is that people spend time doing housework– childcare is measured under a different variable.)

Note that theologyandgeometry reminded me that I’m supposed to be using sampling weights when I do this, and they do matter somewhat in the regression results.  Unfortunately, ttest doesn’t take weights.   The kludge is a pain in the rear in Stata 11 (which is what I have on my home computer), so I apologize, but you’re getting the unweighted results.

Next:  Is this different for men and women?

Let’s say I want to answer this question in one fell swoop.  I would do a regression with an interaction.  It would look something like this:

unweighted:
Housework_min = 18.96 + 37.47*Female – 1.04*hh_child + 8.21*(Femalehh_child)

I can’t get the standard errors to line up in wordpress, but the se for the intercept is 0.31, se for Female is 0.57, se for hh_child is 0.44, se for the interaction term is 0.82.   To see whether these coefficients are significant, you take the coeff and divide by the standard error to get the p-value.  If that number is bigger than 1.96, it is significant at the 5% level.  These coefficients are all significant.

weighted to take into account sampling weights:
Housework_min = 15.47 + 38.50*Female – 0.67*hh_child + 4.06*(Femalehh_child)

Here everything is significant at the 1% level except the main effect on hh_child is no longer significant even at the 10% level, with a se of 0.49.  So weights do matter.  Thanks for reminding me, theologyandgeometry!

Ok, so what does this regression *mean*?  Plug and chug, my dear Watson, plug and chug.

The way the dataset is coded, if you’re female, Female is coded as 1.  If you’re not female, then it is coded as 0 (it doesn’t allow for female and not female at the same time).  Similarly, hh_child is one if you have a child under age 18 in the household and 0 if you don’t.

So to answer: “how does having a kid affect how much time people devote to housework?” You would take [18.96 + 37.47*Female – 1.04*hh_child + 8.21*(Femalehh_child)] and plug in 1 for hh_child and then plug in 0 for hh_child.

[18.96 + 37.47*Female – 1.04 + 8.21*(Female)] – [18.96 + 37.47*Female – 0 + 0)]

The 18.96 drops out, the 37.47 drops out, and you’re left with -1.04 + 8.21*Female.

For women:  [-1.04 + 8.21*1] => having kids correlates with 7.17 minutes more housework

For men:  [-1.04 + 0]  => having kids correlates with 1.04 minutes less of housework

The savvy econometrician will note here that we’ve seen these numbers before– that -1.04 is the coefficient for the hh_child variable, and the 7.17 is what you get if you add that coefficient to the interaction term.

Doing the weighted version, you get:

For women: [-0.67+4.06*1] = having kids correlates with 3.39 minutes more housework

For men:  [-0.67+0] => having kids correlates with 0.67 minutes less of housework

Now, one concern is that there are a lot more single parent households with women heads than with men.  Let’s see what happens when we limit to married households with both spouses present only.

ttest  bls_hhact_hwork if married==1, by(hh_child)

Two-sample t test with equal variances
——————————————————————————
Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
———+——————————————————————–
0. No |   27875    40.94371    .5036533    84.08898    39.95653     41.9309
1. Yes |   40403    46.88803    .4207222    84.56725     46.0634    47.71265
———+——————————————————————–
combined |   68278    44.46122    .3230849    84.42228    43.82797    45.09446
———+——————————————————————–
diff |           -5.944315    .6569407               -7.231918   -4.656712
——————————————————————————
diff = mean(0. No) – mean(1. Yes)                             t =  -9.0485
Ho: diff = 0                                     degrees of freedom =    68276

Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

Having a child makes time spent on housework go up even more for two parent households than it does for everybody (about 6 minutes).  The difference is about one minute for unmarried households.  Maybe dads make a lot of mess.  More likely single moms don’t have time to do additional household chores while single people do have more time.  (Doing the interaction, this difference in the effect of having children between married and single couples is significant.)

Limiting to married couples only:

Housework_min = 14.23 + 51.44*Female +4.14*hh_child + 2.31*(Femalehh_child)

The interaction term is only marginally significant, and note a sign change on the hh_child coefficient.  Having a child affects married people by 4.14 +2.31*female.  Married men’s housework goes up by 4.14 minutes after having a child, but married women’s goes up by 6.45 minutes.

When you do it weighted, everything is significant at the 5% level.

Housework_min = 13.16+ 50.95*Female +2.51*hh_child + 3.39*(Femalehh_child)

Having a child affects married people by 2.51 + 3.39*female.  Married men’s housework goes up by 2.51 minutes after having a child, but a married woman’s goes up by 5.9 minutes.

Limiting to unmarried people only:

Housework_min = 22.29 + 28.89*Female – 5.41*hh_child + 6.34*(Femalehh_child)

All coefficients are significant.  Having a child affects unmarried people by -5.41 + 6.34*female.  Unmarried men’s housework goes down by 5.41 minutes and Unmarried women’s goes up by 6.34 minutes.  (Note that there are ~8,000 single men with kids and 16,000 single women with kids here, though I’m including married people whose spouses are absent in the “not married” category because we’re talking about housework.  It is more standard to include them in the married category when you’re looking at outcomes we care about like child well-being.)

Weighting the unmarried people regression:

Housework_min = 17.68 + 26.93*Female – 4.36*hh_child + 1.60*(Femalehh_child)

Here the interaction term is no longer significant, which suggests there isn’t a difference by gender in terms how how having a child affects housework.  Makes me wonder who the sampling frame is over- or under- sampling!  Here having a child affects unmarried people by -4.36 + 1.60*female.  Unmarried men’s housework goes down by 4.36 minutes when having a child and unmarried women’s also goes down (!) by 2.76 minutes.

There are other cuts that can be made… by age, by race, by ethnicity, by education, by work status etc.

I’m going to look now at the most recent years, 2011 and 2012.  Men are supposed to be more equal partners these days so…

. ttest  bls_hhact_hwork if year>2010, by(hh_child)

Two-sample t test with equal variances
——————————————————————————
Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
———+——————————————————————–
0. No |   13862    38.72681     .700486    82.47311    37.35376    40.09985
1. Yes |   11060    44.37197    .8187207    86.10202    42.76713    45.97681
———+——————————————————————–
combined |   24922    41.23204    .5330305    84.14795    40.18727    42.27682
———+——————————————————————–
diff |           -5.645164    1.072289               -7.746914   -3.543414
——————————————————————————
diff = mean(0. No) – mean(1. Yes)                             t =  -5.2646
Ho: diff = 0                                     degrees of freedom =    24920

Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

Having a child still increases the amount of housework done by around 5.6 minutes (so more than for the 10-year period).

Housework_min = 20.34 + 33.75*Female – 0.42*hh_child + 8.68*(Femalehh_child)

Here the coefficient on hh_child is nowhere near significant.  The interaction term is still significant, but having a child has no significant effect on minutes worked by itself, only as it interacts with gender.  Men no longer work less when they have a child.  But women still work more!  Results are pretty similar with the weights.

Limiting to married only provides:

Housework_min = 16.35 + 46.28*Female + 4.04*hh_child + 2.50*(Femalehh_child)

Now hh_child is significant, but the interaction term is no longer significant!  Everyone in a married couple works 4 min more (you could argue that women work 6 min more, but that difference is not significant) once they have children.  Again the weights matter, because with them, you get:

Housework_min = 15.06 + 44.79*Female + 1.55*hh_child + 6.22*(Femalehh_child)

With the weights, hh_child is back to being no longer significant and the interaction term is significant at the 10% level.   Married women work marginally significantly more than married women do upon birth of a child.

Limiting to the unmarried (and those with absent spouses) provides:

Housework_min = 22.77 + 27.25*Female – 3.89*hh_child + 7.72*(Femalehh_child)

These are all significant.  Having a child decreases the amount of housework for unmarried men by 4 minutes, but increases it for unmarried women by around 4 minutes.  (These results hold if I drop people who are married with spouse absent, so it’s not like truckers are driving this result.)

Putting the weights in again changes things:

Housework_min =18.15 + 26.06*Female – 3.04*hh_child + 1.41*(Femalehh_child)

Female is significant (as is the constant) but the other terms are not.  This argues that there’s really no difference once you have a kid in how much housework you do if unmarried, either by gender or not.  It could be that there’s not enough unmarried fathers in the sample to say much of anything once the weights are added (perhaps they over-sample single dads, who knows!  Well, presumably ATUS knows.)  Also I should note that their sampling weights seem to be based on 2006 methodology, so if things have changed, they could be introducing measurement error which might tend to bias towards not finding anything.

All in all, there’s less significance with only the last two years of the data, but the story is still very similar.

So, to summarize:  Having kids increases the amount of housework that people do each day by 5-6 minutes on average, but about 1 minute for single-parent households.  On average, having kids means more housework for women and less housework for men.  However, in dual-parent married households with both spouses present, having a child increases rather than decreases the amount of time spent on housework for men.  In households with only one parent present, women do more housework and men do less (though with weighting it seems they both do less).  Potential reasons for this difference could be that men outsource the housework or that they’re more likely to substitute childcare for housework (or that they put their kids to work and women don’t!).

Now, the variable I used above assumes marriage.  It turns out there’s a variable in the ATUS that also gets at whether or not there’s an unmarried partner in the household.

tab spousepres

Spouse or unmarried partner in |
household |      Freq.     Percent        Cum.
—————————————-+———————————–
1. Spouse present |     69,359       50.64       50.64
2. Unmarried partner present |      4,224        3.08       53.73
3. No spouse or unmarried partner prese |     63,377       46.27      100.00
—————————————-+———————————–
Total |    136,960      100.00

You would think that this shouldn’t change the results much.  Except it does.
. ttest  bls_hhact_hwork if spousepres==1 | spousepres==2, by(hh_child)
Two-sample t test with equal variances
——————————————————————————
Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
———+——————————————————————–

0. No |   30366    40.27857    .4782439    83.33803    39.34119    41.21595
1. Yes |   43217     46.9683    .4089027    85.00556    46.16684    47.76976
———+——————————————————————–
combined |   73583     44.2076    .3110836    84.38513    43.59788    44.81733
———+——————————————————————–
diff |           -6.689731    .6314013               -7.927276   -5.452187
——————————————————————————
diff = mean(0. No) – mean(1. Yes)                             t = -10.5951
Ho: diff = 0                                     degrees of freedom =    73581

Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

Having a child when you have a partner in the house increases housework by 6.7 min.

For cohabiters it’s an increase of 12 min!

. ttest  bls_hhact_hwork if spousepres==2, by(hh_child)

Two-sample t test with equal variances
——————————————————————————
Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
———+——————————————————————–

0. No |    2112    33.51657    1.586618    72.91542    30.40507    36.62807
1. Yes |    2112    46.34943    1.895052    87.08996    42.63307     50.0658
———+——————————————————————–
combined |    4224      39.933    1.239569    80.56248    37.50279    42.36321
———+——————————————————————–
diff |           -12.83286    2.471554               -17.67841   -7.987314
——————————————————————————
diff = mean(0. No) – mean(1. Yes)                             t =  -5.1922
Ho: diff = 0                                     degrees of freedom =     4222

Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

Married people spouse present:

Housework_min = 14.08 + 51.56*Female + 4.35*hh_child + 2.43*(Femalehh_child)

Everything significant at the 5% level.  (Results are similar with weighting)

Cohabiters:
Housework_min = 19.42 + 29.04*Female + 2.01*hh_child + 14.56*(Femalehh_child)
(results with weighting are pretty similar, with an even bigger interactive effect)

hh_child is not significant.  Note how much less housework cohabiting women do compared to married women!  (29.04 vs 51.56)  And look how much bigger that interaction of having a child is for cohabiting women– a child only adds 2.43 min (plus the 4.35 main effect that it adds to both parents) to married women, but it adds a full 14.56 minutes to cohabiting women (18.5 minutes in the weighted regression).  The story here is that cohabiters did less work and then were forced to be more traditional once a baby arrived.  With married women we’re probably seeing a lot of housewives increasing that female coeff.  There could also be differences in hiring out help between people who cohabit and people who are in more traditional marriages.  Or in how big the house/apartment is.  There are a lot of controls that could be put into these regressions (age, labor force status, etc.) if one wanted to try to get at causation instead of just the relationships.

Grumpy nation, how does this square with your experience, if applicable? And isn’t Stata awesome?