The Cost of Raising a Child, Revisited

Last week my post was about a new article I have with Scott Winship on the “cost of thriving” today versus 1985. That paper has gotten quite a bit of coverage, including in the Wall Street Journal, which is great but also means you are going to get some pushback. Much of it comes in the form of “it just doesn’t feel like the numbers are right” (see Alex Tabarrok on this point), and that was the conclusion to the WSJ piece too.

Here’s a response of that nature from Mish Talk: “There’s no way a single person is better off today, especially a single parent with two kids based on child tax credits that will not come close to meeting daycare needs.”

He mentions daycare costs, but never comes back to it in the post (it’s mostly about housing costs). Daycare costs are undoubtedly an important cost for families with young children (though since Cass’ COTI is about married couples with one earner, they may not be as relevant). And in the CPI-U, daycare and preschool costs only getting a weight of 0.5%. Surely that’s not reality for the families that actually do pay daycare costs! If only there was an index that applied to the costs of raising children.

In fact, there already is. Since 1960, the USDA has been keeping track of the cost of raising a child. Daycare costs are definitely given much more weight: 16% of the expenditures on children got to child care and education. And much of that USDA index (recently updated by Brookings) looks similar to what COTI includes: housing, food, transportation, health care, education, but also clothing and daycare. I wrote about it in a post last year and compared that cost to various measures of income (including single-earner families and median weekly earnings). But what if we compared it to Oren Cass’ preferred measure of income, males 25 and older working full-time? Here’s the chart.

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Wives Slightly Out-earning Husbands Is No Longer Weird

As we have gone through our education and training and changed jobs, my wife and I have been in every sort of relative income situation, with each one sometimes vastly or slightly out-earning the other. Currently she slightly out-earns me, which I thought was unusual, as I remembered this graph from Bertrand, Kamenica and Pan in the QJE 2015:

Ungated source: Bertrand Pan Kamenica 2013

The paper argues that the big jump down at 50% is driven by gender norms:

this pattern is best explained by gender identity norms, which induce an aversion to a situation where the wife earns more than her husband. We present evidence that this aversion also impacts marriage formation, the wife’s labor force participation, the wife’s income conditional on working, marriage satisfaction, likelihood of divorce, and the division of home production. Within marriage markets, when a randomly chosen woman becomes more likely to earn more than a randomly chosen man, marriage rates decline. In couples where the wife’s potential income is likely to exceed the husband’s, the wife is less likely to be in the labor force and earns less than her potential if she does work. In couples where the wife earns more than the husband, the wife spends more time on household chores; moreover, those couples are less satisfied with their marriage and are more likely to divorce.

But when I went to look up the paper to show my wife the figures, I found that the effect it highlights may no longer be so large.  Natalia Zinovyeva and Maryna Tverdostup show in their 2021 AEJ paper that the jump down in wives’ income at 50% is quite small, and is largely driven by couples who have the same industry and occupation:

They created the figure above using SIPP/SSA/IRS Completed Gold Standard Files, 1990–2004. I’d be interested in an analysis with more recent data. Much of their paper uses more detailed Finnish data to test the mechanism for the remaining jump down at 50%. They conclude that gender norms are not a major driver of the discontinuity:

We argue that the discontinuity to the right of 0.5 can emerge if some couples tend toward earnings equalization or convergence. To test this hypothesis, we exploit the rich employer-employee–linked data from Finland. We find overwhelming support in favor of the idea that the discontinuity is caused by earnings equalization in self-employed couples and earnings convergence among spouses working together. We show that the discontinuity is not generated by selective couple formation or separation and it arises only among self-employed and coworking couples, who account for 15 percent of the population.

Self-employed couples are responsible for most observations with spouses reporting identical earnings. When couples start being self-employed, both sides of the distribution tend to equalize earnings, perhaps because earnings equalization helps couples to reduce income tax payments, facilitate accounting, or avoid unnecessary within-family negotiations. Large spikes emerge not only at 0.5 but also at other round shares signaling the prevalence of ad hoc rules for entrepreneurial income sharing in couples. Self-employment is associated with a fall of household earnings below the level predicted by individuals’ predetermined characteristics, but this drop is mainly due to a decrease in male earnings, with women being relatively better off.

In the case of couples who work together in the same firm, there is a compression of the earnings distribution toward 0.5 both on the right and on the left of 0.5. As a result, there is an increase both in the share of couples where men slightly outearn their wives and in the share of couples where women slightly outearn their husbands. Since the former group is larger, earnings compression leads to a detection of a discontinuity.

So, concerns about relative earnings aren’t causing trouble for women in the labor market. But do they cause trouble at home? Perhaps yes, but if so its not in a gendered way and not driven by the 50% threshold:

Separation rates do not exhibit any discontinuity around the 0.5 threshold of relative earnings. Instead, the relationship between the probability of separation and the relative earnings distribution exhibits a U-shape, with higher separation rates among couples with large earnings differentials either in favor of the husband or in favor of the wife.

The American Family Is Thriving, Even if They Only Have One Male Earner (But Most Don’t)

62 weeks. That’s how long the median male worker would need to work in a year to support a family in 2022, according to the calculations of Oren Cass for the American Compass Cost-of-Thriving Index released this year. Not only is 62 weeks longer than the baseline year of 1985 (when it took about 40 weeks, according to COTI), but there is a big problem: there aren’t 62 weeks in year. It is, by this calculation, impossible for a single male earner to support a family.

Is this true? In our new AEI paper, Scott Winship and I strongly disagree. First, we challenge the 62-week figure. With a few reasonable corrections to Cass’ COTI, we show that it is indeed possible for a median male earner to support a family. It takes 42 weeks, not 62 as reported in COTI.

But wait, there’s more. Much more. In our paper, we provide a range of reasonable estimates for how the cost of thriving has changed since 1985. In the COTI calculation, the standard of living for a single-earner family has fallen by 36 percent since 1985. In our most optimistic estimate, the standard of living has risen by 53 percent. The chart below summarizes our various alternative versions of COTI. How do we get such radically different results? Is this all a numbers game?

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Chocolate Prices Will Shoot Up

I write about various topics, usually with at least some loose connection to economics. Sometimes these are fairly macro issues, other times there are specific, actionable observations. For instance, back in March of 2021, we inferred from the critical shortages of semiconductors that car manufacturing would be severely crimped, likely leading to big price increases in cars.  Our post “Chip Shortages Shutting Down Auto Assembly Lines; Buy Your Car Now Or Else” came out just in time (red arrow below) to alert the readership here:

https://fred.stlouisfed.org/series/CUSR0000SETA02 – – Consumer Price Index for All Urban Consumers: Used Cars and Trucks in U.S. City Average

Chocolate Prices

But now, a price increase of more ubiquitous import looms. Most of us were not in the market for cars in March of 2021, but some 81% of us eat chocolate, with the average American consuming about 9.5 pounds a year. Indeed, 50% “cannot live without it every day.”

And so, it is with a heavy heart that I bring warning of a rise in the price of chocolate. Back in pandemic lockdown, I was bored and speculated a few bucks in cocoa futures, as tracked by the NIB exchange traded fund. My shares went up, and then down, and I sold out to limit losses (which was a good move at the time), and moved onto other investments.

Imagine my surprise when I randomly checked on NIB this week and saw the price ramp-up in the past few months:

Source: Seeking Alpha

A quick internet search led to a CNBC article which confirmed my worst fears:

“The cocoa market has experienced a remarkable surge in prices … This season marks the second consecutive deficit, with cocoa ending stocks expected to dwindle to unusually low levels,” S&P Global Commodity Insights’ Principal Research Analyst Sergey Chetvertakov told CNBC in an email.

…Chetvertakov added that the arrival of the El Niño weather phenomenon is forecast to bring lower than average rainfall and powerful Harmattan winds to West Africa where cocoa is largely grown. Côte d’Ivoire and Ghana account for more than 60% of the world’s cocoa production

The price of cocoa will feed into the price of consumer chocolate products, especially dark chocolate which has more actual cocoa content. And the price of sweets generally will rise on the back of sugar prices, which stand at 11-year highs, driven again largely by weather.

There is still time to stock up ahead of the hoarders…

Intro to Textual Indices: Ngrams & Newspapers

There have been a lot of popular papers in the past decade or so that make use of textual analysis. A fun one is “The Mainstreaming of Marx” by Magness & Makovi. They use Google Ngram to analyze the popularity of people mentioned in books and determine when Karl Marx became popular.  “Measuring Economic Policy Uncertainty” by Baker, Bloom, & Davis is one of my favorites. They use set theory to detect terms in newspapers that denote economic policy uncertainty. In this post, I’m just going to describe practical differences between the two data sources and how the interpretations differ.

Ngram

Ngram measures takes a term and measures how popular that term is in its corpus of book text, which is about 6% of all books ever written (in English, anyway). Because popularity is expressed as a percent, we can make direct popularity level comparisons among words. For example: “Cafe” & “Coffee Shop”. In the figure below, we can see that the word “cafe” was more popular in books until very recently.

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A Surprisingly Good Year for Homebuilders

The Federal Reserve has been increasing interest rates at the fastest pace since the 1980’s, from near-zero rates in March of last year to over 5% today. This has led to rapid slowdowns in interest-rate sensitive sectors like housing, cars, and startups. Because most people finance their home buying, higher interest rates mean higher monthly payments for a house at a given price. Since many people were already buying houses near the highest monthly payment banks would allow them to, higher interest rates mean they need to buy cheaper houses or just stay out of the market and rent. This is especially true as the interest expense on mortgages has tripled in two years:

Source: Jeff Weniger

You’d think this would be bad news for homebuilders, and for most of 2022 markets agreed: homebuilder stocks fell 36% from the beginning of 2022 to September 2022 after the Fed started raising rates in March. But homebuilder stocks have recovered since September, with some major names like D.R. Horton and Lennar hitting all time highs. Why?

I bought homebuilder stocks in January but I have to say even I wasn’t expecting such a fast recovery (if I had, I would have bought a lot more). I was buying because they were cheap on a price to earnings basis and temporarily out of fashion; I love stocks that are priced like they’re in a secular decline to bankruptcy when its clear they are actually just having a bad cycle and will recover when it turns. But I thought I’d have to wait years for falling interest rates and a recovering housing market for this to happen. Instead these are up 20-100% in 6 months. Why?

The big thing I missed was that high interest rates have hit their competition harder, reducing supply as well as demand. Who is the competition for homebuilders? Existing homeowners. Homeowners with the “golden handcuffs” of a 3% mortgage who don’t want to move if it means switching to a 7% mortgage. I’m seeing this personally in Rhode Island- I’d kind of like a house with a bigger yard on a quieter street, but there are only 5 houses for sale in my whole school district. Between that and interest rates, we’re staying put. But for people who really need to move, new homes are making up a record proportion of the available inventory:

Source: Jeff Weniger

This situation seems likely to persist for at least months, and possibly years. The Fed paused its rate hikes yesterday for the first time since last March, but indicated that more hikes may lie ahead. I’m tempted to take the win and sell homebuilder stocks, but they still have price to earnings ratios under 10, and the “golden handcuffs” on their competition seem likely to stay on for at least another year.

“Good Money at the Time”

On summer vacation, I recently visited Mount Rushmore. It’s amazing structure, and the story of its construction is as impressive as the monument itself. Much of the story you learn when visiting is the story of its creation. As an economist, of course seeing the following display with wage data got me very excited:

While the sign says that laborers made 30 cents per hour, searching online it appears that 50 cents was more common. More skilled workers, such as assistant sculptors, made $1.50 per hour. These were, as the sign says, “good wages” for that time. In the economy generally, production workers made around 50 cents per hour our as well around that time period, and most of the construction of Rushmore was during the Depression (some of the workers were WPA funded), so having any job, much less one that paid pre-Depression wages, was certainly a good one.

How does this compare to wages today? This is always a tricky question, as I have documented on this blog several times before, but the most straight forward approach (and good first approximation) is a simple CPI inflation adjustment. Using 1929 as the baseline year, when construction was in full swing, 30 cents an hour is roughly $5 today, 50 cents per hour is close to $9, and $1.50 would be about $26.50. That doesn’t sound too bad!

The best comparison I like to use is BLS’s average hourly earnings for private production and non-supervisory workers. Averages aren’t perfect, but this measure excludes management occupations that will be distorting the average. In May 2023, that wage was $28.75 per hour. So the average worker today earns 3-6 times as much per hour as these “good paying jobs” in the late 1920s and the Depression. And, as the Rushmore signage notes, these jobs were seasonal. Their off-season jobs probably paid even less.

The wage of the assistant sculptor does compare well with average wages today, but that pay was unusual for the time and was likely a highly skilled worker. The only record I can find of anyone making that much at Rushmore was Lincoln Borglum, the son of the main sculptor Gutzon Borglum. Lincoln oversaw the completion of the project after Gutzon’s death, and it was only in later years on the project that his pay was increased to $1.50 per hour.

For the typical laborer on Rushmore, having a good job was indeed good to have, but the wages pale in comparison to a typical worker today.

Historical Price to Earnings Ratios By Industry

Getting long-run historical PE ratios of US stocks by industry seems like the kind of thing that should be easy, but is not. At least, I searched for an hour on Google, ChatGPT, and Bing AI to no avail.

I eventually got monthly median PEs for the Fama French 49 industries back to 1970 from a proprietary database. I share two key stats here: the average of median monthly industry PE 1970-2022, and the most recent data point from late 2022.

IndustryLong Run MeanEnd 2022
AERO12.1419.49
AGRIC10.759.64
AUTOS9.6517.52
BANKS10.3810.46
BEER15.2335.70
BLDMT12.0015.41
BOOKS12.9517.60
BOXES12.1810.69
BUSSV12.0713.03
CHEMS12.4019.26
CHIPS10.4817.47
CLTHS11.4510.94
CNSTR8.984.58
COAL8.042.92
DRUGS1.148.01
ELCEQ10.7817.85
FABPR10.2819.40
FIN11.1612.97
FOOD14.3025.03
FUN9.1021.06
GOLD3.18-5.95
GUNS11.505.05
HARDW7.9619.16
HLTH11.916.09
HSHLD12.6020.15
INSUR10.9516.33
LABEQ13.4625.18
MACH12.5120.27
MEALS13.8319.19
MEDEQ6.8127.64
MINES8.0616.27
OIL6.969.00
OTHER12.2027.68
PAPER12.5016.69
PERSV12.86-0.65
RLEST8.13-0.30
RTAIL12.268.58
RUBBR12.1112.81
SHIPS9.7917.42
SMOKE11.7417.79
SODA12.3832.09
SOFTW8.21-2.85
STEEL8.184.30
TELCM6.759.58
TOYS9.18-1.32
TRANS11.2513.11
TXTLS9.43-49.00
UTIL12.3417.41
WHLSL11.0813.13
Mean Industry Median10.5212.73

One obvious idea for what to do with this is to invest in industries that are well below their historical price, and avoid industries that are above it (not investment advice). Looking just at current PEs is ok, but a stock with a PE of 8 isn’t necessarily a good value if its in an industry that typically has PEs of 6.

By this metric, what looks overvalued? Money-losing industries (negative current earnings): Gold, Personal Services, Real Estate, Software, Toys, and Textiles. Making money but valuations 19+ above historical average: Medical Equipment, Beer, Soda. Most undervalued relative to history: Guns, Health, Coal, Construction, Steel, Retail (all 3+ below the historical average).

Of course, I don’t recommend blindly investing in these “undervalued” industries- not just for legal reasons, but because sometimes the market prices them low for a reason- that earnings are expected to fall. The industry may be in secular decline due to new types of competition (coal, steel, retail). Or investors may expect it to get hit with a big cyclical decline in an upcoming recession or rotation from the Covid goods/manufacturing economy back to services (guns, construction, steel, retail). Health services (as opposed to drugs and medical equipment) stands out here as the sector where I don’t see what is driving it to trade at barely half of its usual PE.

I’d still like to get data on long run market-cap weighted mean PE by industry, as opposed to the medians I show here. The best public page I found is Aswath Damodaran’s data page, which has a wide variety of statistics back to about 1999. Some of the current PEs he calculates are quite different from those in my source, another reason to tread carefully here. I’m not sure how much of this is mean vs median and how much is driven by different classification of which stocks fit in which industry category.

This gets at a big question for anyone trying to actually trade on this- do you buy single stocks, or industry ETFs? Industry ETFs make sense in principle (since we’re talking about industry level PEs overall) and also add built-in diversification. But the PE for the ETF’s basket of stocks likely differs from that of the industry as a whole. It would make more sense to compare the ETF’s current PE to its own historical PE, but most industry ETFs have very short track records (nothing close to the 53 years I show here). PE is also far from the only valuation metric worth considering.

All this gets complex fast but I hope the historical PE ratio by industry makes for a helpful start.

Disinformation Is Real, And It Is a Concern

Two recent essays push back against the concept of “disinformation” in thoughtful but, I believe, ultimately incorrect ways.

Martin Gurri is primarily concerned with government trying to stamp out what it views as disinformation. I am concerned about that too, but there are ways for private actors to correct bad information too.

Dan Klein (my friend and professor in grad school) argues that most labeling of “disinformation” or “misinformation” is not really about information, but instead about knowledge. I agree that sometimes this is true. But sometimes it is not true. Sometimes we really are talking about information. And sometimes the information is about extremely important topics.

As I search through my own Twitter history for these terms, I see that there is overwhelmingly one period of time and one piece of information that I used them for: the total number of deaths in the United States in 2020. If you can think way back to the fall and winter of 2020/early 2021, you might recall that we were just finishing up the first year of the pandemic, and we were also going through one of the worst periods in the pandemic. Vaccines were now starting to become widely available as we got into 2021, and people were starting to make person decisions about whether to “get the jab.”

The number of total deaths in 2020 was an important number. There was still a lot of uncertainty about exactly how bad the pandemic was, or (to a small but vocal minority) whether the pandemic was even “real.” The data was crucial to this debate. Of course, once we have the data, we must interpret it. This is one of Klein’s main points, and a good one. But if we aren’t starting from a common baseline of true information, there is really no point in discussions based on interpretations of those different apparent realities. We will, by definition, be “talking past each other.”

So what were people saying about total deaths in 2020 during this moment of importance in late 2020/early 2021?

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New Paper with Evidence that ChatGPT Hallucinates Nonexistent Citations

I posted a new working paper with systematic evidence for false citations when ChatGPT (GPT-3.5) writes about academic literature.

Buchanan, Joy and Shapoval, Olga, GPT-3.5 Hallucinates Nonexistent Citations: Evidence from Economics (June 3, 2023). Available at SSRN: https://ssrn.com/abstract=4467968 or http://dx.doi.org/10.2139/ssrn.4467968

Abstract: We create a set of prompts from every Journal of Economic Literature (JEL) topic to test the ability of a GPT-3.5 large language model (LLM) to write about economic concepts. For general summaries, ChatGPT can perform well. However, more than 30% of the citations suggested by ChatGPT do not exist. Furthermore, we demonstrate that the ability of the LLM to deliver accurate information declines as the question becomes more specific. This paper provides evidence that, although GPT has become a useful input to research production, fact-checking the output remains important.

Figure 2 in the paper shows the trend that the proportion of real citations goes down as the prompt becomes more specific. This idea has been noticed by other people, but I don’t think it has been documented quantitatively before.

We asked ChatGPT to cover a wide range of topics within economics. For every JEL category, we constructed three prompts with increasing specificity.

Level 1: The first prompt, using A here as an example, was “Please provide a summary of work in JEL category A, in less than 10 sentences, and include citations from published papers.”

Level 2: The second prompt was about a topic within the JEL category that was well-known. An example for JEL category Q is, “In less than 10 sentences, summarize the work related to the Technological Change in developing countries in economics, and include citations from published papers.”

Level 3: We used the word “explain” instead of “summarize” in the prompt, asking about a more specific topic related to the JEL category. For L we asked, “In less than 10 sentences, explain the change in the car industry with the rising supply of electric vehicles and include citations from published papers as a list. include author, year in parentheses, and journal for the citations.”

The paper is only 5 pages long, but we include over 30 pages in the appendix of the GPT responses to our prompts. If you are an economist who has not yet played with ChatGPT, then you might find it useful to scan this appendix and get a sense of what GPT “knows” about varies fields of economics.

If SSRN isn’t working for you, here is Also a Google Drive link to the working paper: https://drive.google.com/file/d/1Ly23RMBlim58a7CbmLwNL_odHSNRjC1L/view?usp=sharing

Previous iterations of this idea on EWED:

https://economistwritingeveryday.com/2023/04/17/chatgpt-as-intern/ Mike’s thoughts on what the critter is good for.

https://economistwritingeveryday.com/2023/01/21/chatgpt-cites-economics-papers-that-do-not-exist/  This is one of our top posts for traffic in 2023, since this is a topic of interest to the public.  That was January of 2023 and here we are in June today. It’s very possible that this problem will be fixed soon. We can log this bug now to serve as a benchmark of progress.

A check in and comparison with Bing: