Li Yining, Chinese reformer, helped re-establish the Chinese stock market
Padma Desai, Indian reformer and scholar of planning
Rebecca Blank, labor economist, UW chancellor, acting US Secretary of Commerce
Harry Markowitz, won Nobel for “pioneering work in the theory of financial economics” (finding the risk-return optimal frontier for a portfolio)
Not all the biggest names, but all important enough that I knew of them despite not working in their subfields and, unfortunately, not having met them personally.
Let me know if I’m currently missing anyone, though let’s hope the list doesn’t get much longer by the end of 2023.
If a scientific finding is really true and important, we should be able to reproduce it- different researchers can investigate and confirm it, rather than just taking one researcher at their word.
Economics has not traditionally been very good at this, but we’re moving in the right direction. It is becoming increasingly common for researchers to voluntarily post their data and code, as well as for journals (like the AEA journals) to require them to:
This has certainly been the trend with my own research; if you look at my first 10 papers (all published prior to 2018) I don’t currently share data for any of them, though I hope to go back and add it some day. But of my most recent 10 empirical papers, half share data.
This sharing allows other researchers to easily go back and check that the work is accurate. This could mean simply checking that it is “reproducable”, i.e., that running the original code on the original data produces the results that the authors said. Or it could mean the more ambitious “replicability”, i.e., you could tackle the same question with different data and still find basically the same answer. Economics does generally does well at reproducability when code is shared, but just ok at replication.
Of course, even when data and code are shared, you still need people to actually do the double-checking research; this is still relatively rare because it is harder to publish replications than original research. But more replication journals are opening, and there are now several projects funding replications. The trends are all in the right direction to establish real, robust findings, with one exception- the rise of restricted data.
Traditionally most economics research has been done using publicly available datasets like the Current Population Survey. But an increasing proportion, perhaps a majority of research at top journals, is now done using restricted datasets (there’s a great graph on this I can’t find but see section 3.3 here). These datasets legally can’t be shared publicly, either due to privacy concerns,licensing agreements, or both. But journals almost always still publish these articles and give them an exemption to the data sharing requirement. One the one hand it makes sense not to ignore this potentially valuable research when there are solid legal reasons the data can’t be shared. But it does mean we can’t be as confident that the data has been analyzed correctly, or that it even really exists.
One potential solution is to find people who have access to the same restricted dataset and have them do a replication study. This is what the Institute for Replication just started doing. They posted a list of 100+ papers that use restricted data that they would like to replicate. They are offering $5000 for replications of most of the papers, so I think it is worthwhile for academics to look and see if you already have access to relevant datasets, or if you study similar enough things that it is worth jumping through the hoops to get data access.
For everyone else, this is just one more reason not put too much trust in any one paper you read now, but to recognize that the field as a whole is getting better and more trustworthy over time. We will be more likely to catch the mistakes, purge the frauds, and put forward more robust results that at least bear a passing resemblance to what science can and should be.
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:
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 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:
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:
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.
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.
Industry
Long Run Mean
End 2022
AERO
12.14
19.49
AGRIC
10.75
9.64
AUTOS
9.65
17.52
BANKS
10.38
10.46
BEER
15.23
35.70
BLDMT
12.00
15.41
BOOKS
12.95
17.60
BOXES
12.18
10.69
BUSSV
12.07
13.03
CHEMS
12.40
19.26
CHIPS
10.48
17.47
CLTHS
11.45
10.94
CNSTR
8.98
4.58
COAL
8.04
2.92
DRUGS
1.14
8.01
ELCEQ
10.78
17.85
FABPR
10.28
19.40
FIN
11.16
12.97
FOOD
14.30
25.03
FUN
9.10
21.06
GOLD
3.18
-5.95
GUNS
11.50
5.05
HARDW
7.96
19.16
HLTH
11.91
6.09
HSHLD
12.60
20.15
INSUR
10.95
16.33
LABEQ
13.46
25.18
MACH
12.51
20.27
MEALS
13.83
19.19
MEDEQ
6.81
27.64
MINES
8.06
16.27
OIL
6.96
9.00
OTHER
12.20
27.68
PAPER
12.50
16.69
PERSV
12.86
-0.65
RLEST
8.13
-0.30
RTAIL
12.26
8.58
RUBBR
12.11
12.81
SHIPS
9.79
17.42
SMOKE
11.74
17.79
SODA
12.38
32.09
SOFTW
8.21
-2.85
STEEL
8.18
4.30
TELCM
6.75
9.58
TOYS
9.18
-1.32
TRANS
11.25
13.11
TXTLS
9.43
-49.00
UTIL
12.34
17.41
WHLSL
11.08
13.13
Mean Industry Median
10.52
12.73
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.
Last week South Carolina Governor McMaster signed a bill repealing almost all Certificate of Need (CON) laws in the state. If you want to open or expand a health care facility in South Carolina, you can now do so faster, cheaper, and with more certainty.
This is a bigger deal than West Virginia’s reform earlier this year because it applies to almost all types of facilities, and applies to both new facilities and expansions of existing facilities. Only two parts of the CON system remain: a 3-year sunset where hospitals still need special permission to add beds, and a permanent restriction on nursing homes (why? see my recent post on why states hate nursing homes).
As is often the case, this reform took years to enact. I wrote last year about a repeal bill passing the SC Senate; it didn’t make it through the House then, but did this time. As I said then:
This seems like good news; here at EWED we’ve previouslywritten about some of the costs of CON. I’ve written several academic papers measuring the effects of CON, finding for instance that it leads to higher health care spending. I aimed to summarize the academic literature on CON in an accessible way in this article focused on CON in North Carolina.
CON makes for strange bedfellows. Generally the main supporter of CON is the state hospital association, while the laws are opposed by economists, libertarians, Federal antitrust regulators, doctors trying to grow their practices, and most normal people who actually know they exist. CON has persisted in most states because the hospitals are especially powerful in state politics and because CON is a bigger issue for them than for most groups that oppose it. But whenever the issue becomes salient, the widespread desire for change has a real chance to overcome one special interest group fighting for the status quo. Covid may have provided that spark, as people saw full hospitals and wondered why state governments were making it harder to add hospital beds.
Why did reform succeed this time in South Carolina? From where I sit in Rhode Island I can only guess, but here are my guesses. First, the reform side really had their stuff together. See this nice page from SC think tank Palmetto Promise on why to repeal CON, and this paper from Matt Mitchell that does a comprehensive review of the literature on CON and explains what it means for South Carolina. Legislative supporters like Senator Wes Climer just kept pushing.
Second, the biggest opponent of CON reform is usually the state hospital association, but in this case they did not formally oppose repeal. Why not? Here I’m really speculating, but in general it has been faster-growing states that repeal CON. Population growth makes it obvious that new facilities are needed, and it means that existing facilities are thinking about how to grow to take advantage of new opportunities, rather than thinking about lobbying to maintain their share of a static or shrinking pie. You can see some hospital CEOs say they don’t mind repeal in this article (where I’m also quoted). South Carolina has been growing at a decent clip, as is Florida, which also almost-entirely repealed CON in 2019. On this theory, the next big CON reform would happen in a fast-growingCON state like Montana, Delaware, North Carolina, Georgia, or Tennessee. If I had to pick one, I’d say North Carolina.
Update: Apparently Montana already repealed all non-nursing home CON in 2021 and I missed it!
When music rights make the news, it’s generally because a superstar’s entire catalog is selling for hundreds of millions of dollars. That may be why I always assumed that buying music rights would be difficult and expensive- that you’d both have to know the right people to even hear about potential deals, and have to be quite rich to afford them.
But this week I found out about Royalty Exchange, a site that currently lists hundreds of music rights for sale. They certainly appear to make the process of finding and buying rights, and collecting royalties, easy (I haven’t bought any yet so can’t say for sure). They currently list songs and partial catalogs from all sorts of artists you’ve heard of:
When I say I find many listings to be surprisingly cheap, I mean this relative to the hundred million dollar deals you hear about. Of those that offer a list price (as opposed to simply asking for offers), the vast majority are over $10,000, and many are over $100,000. Overall I’d put it in the “luxury car” bucket- expensive enough that its a bad idea for a normal middle-class person to buy one, but cheap enough that they could if they really wanted to. It’s a bit of a better idea than a luxury car, since its more investment than consumption. But if I actually bought the Flogging Molly catalog like I want to, I’d be taking an unnecessary risk by putting a large proportion of my net worth in a single investment. Their music is great and I think it will maintain its popularity, but if I’m wrong and people stop listening to it I’d lose out. So, for most people it’s a bad idea in the same way that putting half your retirement account into a single company’s stock is bad idea. But I’m surprised its even possible.
Why are these rights so affordable? Sometimes, of course, its because the artist isn’t that popular. But why are the rights to songs and musicians that are household names affordable? It seems to mainly be because the rights have been sliced and diced so that you’re only buying a small piece of them. Consider Miley Cyrus above. First of all its only the rights to one of her songs (admittedly a hit song). Second, you’re only buying the rights for ten years (lifetime rights are sometimes available but naturally they cost more). Finally, you’re only buying some of the rights, in this case the right to get paid when someone publicly performs the song (but not when someone streams it or buys a copy):
Even given all that though, I’m surprised how cheap the rights are. I expected that people would overpay for them because they like an artist, or for the bragging rights. But the yields seem pretty reasonable, often over 10%. Yields could rise or fall over time as an artist becomes more or less popular, or as the economics of the music industry change, but current prices generally seem justified by the income stream. I look forward to having enough money that this could make sense as an investment for me; I expect I might in 10 or 20 years, but maybe some of you are already there.
The cheapest listing from an artist I’ve heard of, Busta Rhymes (only performance rights, only certain tracks)
Predicting elections is hard. Poll aggregators and prediction markets can help. Many of the usual suspects like FiveThiryEight and PredictIt aren’t covering Sunday’s election in Turkey, partly due to their ownissues, and partly because US organizations often ignore foreign elections. But we do have several good predictors to consider, and they all list opposition candidate Kiliçdaroglu as a slight favorite.
Polymarket is most optimistic for the opposition, giving them a 67% chance. British betting site Smarkets gives them a 61% chance. Play-money site Manifold Markets gives them 56%. Finally, no-money prediction site Metaculus gives a 60% chance that the opposition wins, and a 79% chance that Erdogan leaves office if he loses the election. I’m not sure how the count the Swift Centre, a small closed panel of forecasters, but they are the exception in seeing Erdogan as a slight favorite.
My economist’s instinct is to trust the real-money markets more here, although Manifold and Metaculus outperformed them in the 2022 US midterms. The usual bias is to predict a win for the candidate you like more (which for Westerners on these markets means betting against Erdogan), and have real money on the line can help counteract this. On the other hand, some might use betting markets as a hedge and bet on the outcome they don’t want. In this case the betting markets are slightly more favorable to the opposition, but the gap is small.
Of course, the biggest real-money markets are those that don’t ask directly about the election: the markets for Turkish stocks and bonds. These have generally performed well in the past year as the opposition’s chances have risen, which may indicate that markets think a new Prime Minister with more conventional economic views will get inflation under control.
Last weekend fellow Temple University economics PhD Adam Ozimek hosted the inaugural #EconTwitterIRL conference. He managed to get 100+ people, including many big names, to come to his bowling alley / arcade in Lancaster, PA.
The overall demographic of Econ Twitter people appears to be youngish professionals, mostly male, surprisingly social and normal-looking (surprising to me because I retain the ’90s-era stereotype that people who write a lot online are nerds who don’t want to talk to anyone IRL).
Adam opened with a history of EconTwitter, which to him is not just about Twitter, but is anywhere where communities of people write about economics online. This starts with the comment sections of the earliest blogs, like Brad DeLong’s, in the early 2000’s. Then in the late 2000’s many commenters start their own blogs, like Karl Smith at Modeled Behavior. In the 2010’s Econ Twitter comes into its own. It may persist or a new forum might take over, but either way the discussion and community will live on.
While it was cool to see a live recording of Odd Lots, and a panel on innovation with MacArthur Genius Heidi Williams, my favorite panel was the one on immigration, because it saw the most serious disagreement. Garett Jones and Daniel Di Martino argued for reforms to the immigration system that would move it away from a focus on family reunification and toward a focus on skills and other indications (like country of origin) that immigrants would benefit the US economy. In contrast, Leah Boustan argued that the current system has worked well, including for assimilation and economic growth, and we should be wary of making big changes to it. Moderator Cardiff Garcia pointed out the oddity of the economists from George Mason and the Manhattan Institute arguing for a “socialist” system where the government determines what the economy needs when it comes to immigration, while the Princeton economist argues against. Garett Jones noted that the rest of his department at Mason disagree with him, but he’s glad to have the freedom to disagree.
While the panel saw intense disagreement about what the ideal system looks like, all panelists shared a frustration with parts of the current system that seem to pointlessly slow or prevent high-skill immigration. Some of this is bureaucracy slowing the process for immigrants who are legally allowed already. Some is politicians refusing to make the smallest, simplest, most common-sense fixes unless they are part of a comprehensive immigration reform that hits their big priority. The big priorities differ by party, but the commitment to holding simple fixes hostage is bipartisan.
Hopefully discussions like this can start to change things. That might sound naive or idealistic, but on an earlier panel Matt Yglesias noted that we should be both impressed and slightly scared of how aware Capitol Hill staffers are about the opinions of Econ Twitter.
Source. Got 2nd at trivia as part of team Acemoglu et al (actual Acemoglu not included).
The magic of all this is that you never know what can come from a post. You might make a friend, make an enemy, get a job, lose a job, influence public policy, get a job in the White House… even make (or lose) a million dollars. So we keep poasting, and once in a while see the results IRL.
Economist Writing Every Day 