One of the oldest theories in economics is the idea of compensating differentials. A job represents not just a certain amount of money per hour, but a whole package of positive and negative things. Jobs have more or less stability, flexibility, fun, room to grow, danger… and non-cash benefits like health insurance. The idea of compensating differentials is that, all else equal, jobs that are good on these other margins can pay lower cash wages and still attract workers (thus, the danger of doing what you love). On the other hand, jobs that are bad on these other margins need high wages if they want to hire anyone (thus, the deadliest catch)
I think this theory makes perfect sense, and we see evidence for it in many places. But when it comes to health insurance, everything looks backwards. A job that offers employer-provided health insurance is better to most employees than one that doesn’t, so by compensating differentials it should be able to offer lower wages. There’s just one problem: US data shows that jobs offering health insurance also offer significantly higher wages. The 2018 Current Population Survey shows that workers with employer-provided health insurance had average wages of $33/hr, compared to $24/hr for those without employer insurance.
All the economists are thinking now: that’s not a problem, compensating differentials is an “all else equal” claim, but not all else is equal here. The jobs with health insurance pay higher wages because they are trying to attract higher-skilled workers than the jobs that don’t offer insurance.
That’s what I thought too. It is true that jobs with insurance hire quite different workers on average:
The problem is, once we control for all the observable ways that insured workers differ, we still find that their wages are significantly higher than workers who don’t get employer-provided insurance. Like, 10-20% higher. That’s after controlling for: year, sex, education, age, race, marital status, state of residence, health, union membership, firm size, whether the firm offers a pension, whether the employee is paid hourly, and usual hours worked. I’ve thrown in every possibly-relevant control variable I can think of and employer-provided health insurance always still predicts significantly higher wages. Of course, there are limits to what we get to observe about people using surveys; I don’t get any direct measures of worker productivity. Possibly the workers who get insurance are more skilled in ways I don’t observe.
We can try to account for these unobserved differences by following the same person from one job to another. When someone switches jobs, they could have health insurance in both jobs, neither, only the new, or only the old. What happens to the wages of people in each of these situations? It turns out that gaining health insurance in a new job on average brings the biggest increase in wages:
What could be going on here? One possibility is that health insurance makes people healthier, which improves their productivity, which improves their wages. But we control for health status and still find this effect. The real mystery is that papers that study mandatory expansions of health insurance (like the ACA employer mandate and prior state-level mandates) tend to find that they lower wages. Why would employer-provided health insurance lower wages when it is broadly mandated, but raise wages for individuals who choose to switch to a job that offers it?
My current theory is that “efficiency benefits” are offered alongside “efficiency wages”. The idea of efficiency wages is that some firms pay above-market wages as a way of reducing turnover. Workers won’t want to leave if they know their current job pays above-market, and so the company saves money on hiring and training. But this only works if other firms aren’t doing it. The positive correlation of wages and insurance could be because the same firms that pay “efficiency wages” are more likely to pay “efficiency benefits”- offering unusually good benefits as a way to hold on to employees.
I still feel like these results are puzzling and that I haven’t fully solved the puzzle. This post summarizes a currently-unpublished paper that Anna Chorniy and I have been working on for a long time and that I’ll be presenting at WVU tomorrow. We welcome comments that could help solve this puzzle either on the empirical side (“just control for X”) or the theoretical side (“compensating differentials are being overwhelmed here by X”).
I recently bought a used desktop computer for what seemed like next to nothing. $240 for a machine more powerful than my much-more-expensive 2019 MacBook Pro, most notably due to its 32GB of RAM. Desktops have always been cheaper than equivalently powerful laptops, Windows computers cheaper than Macs, and used computers cheaper than new, so this isn’t totally shocking. But the extent of the difference still surprised me. For instance, buying a new desktop from Dell with similar specs to the used one I just got would cost $1399.
So why is the used discount so big right now? My guess is that its one more knock-on effect of work-from-home. Remote work has been the most persistent change from Covid, and there’s been a huge decline in the demand for office space, with occupancy rates still half of pre-Covid levels.
This means that offices are on sale relative to their pre-Covid prices. Office REITs are down 37% over the past year even after the Covid-induced drop of the previous two years. So it makes sense that all sorts of office equipment is on sale too. Offices tend to be full of employer-owned desktop computers, but when employees work from home they typically use their own machine or a company laptop. That means less demand for office desktops going forward, and a big overhang of existing office desktops that are being under-used. Employers realizing this may just sell them off cheap. Several things about the refurbished desktop I bought, such as its Windows Pro software, indicate that it used to be in an office.
Currently, we have software that can write software. What about physical machines that can produce physical machines? Indeed, what about machines that can produce other machines without human direction?
First of all, machines-building machines (MBM) still require resources: energy, transportation, time, and other inputs. A well-programmed machine that self-replicates quickly can grow in number exponentially. But where would the machines get the resources that enable self-replication? They’d have to purchase them (or conquer the world sci-fi style). Where would a machine get the resources to make purchases of necessary inputs? The same place that everyone else gets them.
I keep reading about how inflation has peaked (even peaked many months ago) and so any minute now the Fed will relent on raising interest rates, and will in fact start reducing them. Every data point that seems to support an early Fed pivot and a gentle “soft landing” for the economy is greeted with optimistic verbiage and a rip higher in stocks.
Except – – other meaningful data points regularly appear which show that inflation (especially core inflation) is remaining stubbornly high. The Personal Consumption Expenditures (PCE) Index is the Fed’s preferred way to track core inflation. It did peak in early 2022, and is falling, but very slowly and fitfully. Just when it seems like it is about to cascade downward, along comes another uptick. The latest report for 02/24/23 showed the PCE index (excluding the volatile categories of food and energy) increasing 0.6 percent during the month of January, which translated to a 4.7 percent year-on-year gain. That was considerably higher than the 0.4 percent monthly gain (4.3 percent year-on-year) that economists expected.
The chart below illustrates the chronic tendency of the economists at the Fed to lowball the estimates of future inflation. Each of the ten bars depicts quarterly projections of what inflation would be for 2023, starting back in September 2020 (first, green bar). No one in the craziness of 2020 could be held particularly responsible back then for accurately projecting 2023 conditions. But the Fed embarrassed themselves badly into late 2021 by airily dismissing inflation as “transitory”, due mainly to supply chain constraints that would quickly pass. (See towards the middle of the chart, yellow Sept 2021 and blue Dec 2021 bars projecting a mere 2.2% inflation for 2023.)
Only as of December 2022 did estimates of inflation jump up to 3.1% for 2023, and that estimate will surely get revised upward even further.
Many factors probably went into this systematic failure on the part of the Fed economists. There are probably political reasons for erring on the rosy optimistic side, which I will not speculate on here.
One factor in particular was mentioned in the Minutes of the Jan 31/Feb 1 Fed meeting that I thought was significant:
A few participants remarked that some business contacts appeared keen to retain workers even in the face of slowing demand for output because of their recent experiences of labor shortages and hiring challenges.
Jeremy LaKosh notes regarding this feature, “If true across the economy, the idea of keeping employees for fear of facing the labor force shortage would represent a fundamental shift in the employment market. This shift would make it harder for wage increases to mitigate towards historical norms and keep upward pressure on prices.”
This all rings true to my anecdotal observations. In bygone days, when business slowed down, factories would lay off or furlough workers, with the expectation on all sides that they would call the workers back (and the workers would come back) when conditions improved. However, employers have had to struggle so hard this past year to find willing/able workers, that employers are loath to let them go, lest they never get them back. I have read that even though homebuilders are not sure they can sell the houses they are building, they are so worried about losing workers that they are keeping them on the payroll, building away.
Other inflation data points show big decreases in prices for goods (and energy), but not for services. Wages, of course, are the big driver for service costs.
So the inflation story in 2023 seems to come down largely to a labor shortage. This is a large topic cannot be fully addressed here. I will mention one factor for which I have anecdotal support, that the enormous benefits (stimulus money plus enhanced unemployment) paid out during 2020-2021 set up a large number of baby boomers to leave the workforce early and permanently. Studies show that this is a major factor in the drop in workforce participation rate post-Covid. Maybe some of those folks had not planned ahead of time for such early retirement, but they got a taste of the good life (NOT getting up and going to work every day) in 2020-2021 along with the extra cash to pad their savings, and so they decided to just not return to work. That exodus of trained and presumably productive workers has left a hole in the labor force which now manifests as a labor shortage, which drives up wages and therefore inflation and therefore interest rates, which will eventually crater the economy enough that struggling firms will finally lay off enough workers to mitigate wage gains.
I wonder if this unhappy scenario could be staved off with increased legal migration of targeted skilled workers from other countries to alleviate the labor shortage. Dunno, just a thought.
The US government is great at collecting data, but not so good at sharing it in easy-to-use ways. When people try to access these datasets they either get discouraged and give up, or spend hours getting the data into a usable form. One of the crazy things about this is all the duplicated effort- hundreds of people might end up spending hours cleaning the data in mostly the same way. Ideally the government would just post a better version of the data on their official page. But barring that, researchers and other “data heroes” can provide a huge public service by publicly posting datasets that they have already cleaned up- and some have done so.
That’s what I said in December when I added a data page to my website that highlights some of these “most improved datasets”. Now I’m adding the Behavioral Risk Factor Surveillance Survey. The BRFSS has been collected by the Centers for Disease Control since the 1980s. It now surveys 400,000 Americans each year on health-related topics including alcohol and drug use, health status, chronic disease, health care use, height and weight, diet, and exercise, along with demographics and geography. It’s a great survey that is underused because the CDC only offers it in XPT and ASC formats. So I offer it in Stata DTA and Excel CSV formats here.
Let me know what dataset you’d like to see improved next.
It is a pleasure to be able to report on a successful microfinance outfit that helps the poorest of the poor. I heard a talk recently from Dale Stanton-Hoyle, CEO of the Five Talents organization. (He is as nice in person as he looks in this photo).
This group was birthed at Truro Anglican Church, in Fairfax, Virginia. An Anglican bishop from Tanzania noted that he had many thousands of people under his care who were suffering so much from hunger and other concomitants of poverty that they had little inclination or energy to listen to elevating spiritual messages. As he put it, “An empty stomach has no ears.”
Inspired by Jesus’ parable of the talents, where servants were each entrusted with some large sum of money (expressed in “talents”) and were expected to multiply that money productively, a group was formed in 1998 to help people living in the most extreme poverty to build productive enterprises.
Their approach would be classified as micro-credit, which nowadays is well-known and well-regarded approach. The modern stream of micro-credit, which is a subset of microfinance, has its roots In the Grameen Bank of Bangladesh, founded by micro-finance pioneer Mohammed Yunus in the 1970s.
A micro-enterprise development organization that helps the world’s most vulnerable families escape poverty. Partnering with local churches around the world, we train men and women, mostly women living in extreme poverty, to form savings groups, take out loans, and build their own businesses. It may seem surprising, but even those living in extreme poverty can save a little each week, start a tiny business, and fulfill their God-given potential.
In general, Five Talents does not give handouts. They support a limited number of full-time trainers, who in turn train local volunteer trainers, who do most of the actual organizing and leading. They found that when Western sources provided the initial seed capital, the money was not valued as much, and the loan payback rates were unsustainably low, around 60% or so.
So their model is to form a group of 20 or more people, and have them save their own money for at least six months. This develops tremendous accountability for borrowed funds. You are borrowing precious money from your group of friends and associates, and they all have a stake in helping your business succeed so you can repay it.
During those initial 6 to 12 months, the organization provides training: first, basic literacy (many are illiterate) and math skills which are essential for running a small business. Then, they provide training for more specific business planning and operation. This graphic depicts the process:
A typical loan might be $30-$150. This might be used to buy a goat to raise, or some beans to sell in the market. The local people can be creative in coming up with enterprises. The speaker told of a woman who was stuck in a refugee camp, who had been beaten up by life and was bitter and hopeless. All she could see were wretched poor people, and not much else. But the trainer persisted in asking her, “But what has God blessed you with?” The subsequent conversation went something like this: “Well there is this large river nearby. And…there are unemployed men in the camp who used to have skilled jobs. I could probably pay some of them to make me a dugout canoe, then I could ferry people across the river for a fee. And…there are all these ragged children running around underfoot… I could probably buy them some fishing gear and pay them to catch me fish in the river, that I could sell in the market.” So this insightful local person was able to identify two completely new business ideas that the trainer had not thought of.
Some 80% of their participants are women. These women get a huge boost in self-confidence and community status, as well as income and food for their families.
Five Talents typically operates in concert with the local Anglican church in a country, which gives them some credibility and support and structure to start with. They are currently active in nine countries, mainly in central and eastern Africa along with Bolivia and Myanmar. They aim for countries with largest numbers of people living in extreme poverty. There is a wide range of development among so-called Third World countries. Many African countries already have a nascent middle class economy, so Five Talents directs its effort elsewhere.
According to their tracking, they have developed some 95,000 businesses so far, with a total of 1.4 million family members supported. They currently train about 10,000 people a year, and hope to increase that to 20,000 people. As with most development NGOs, the ultimate holy grail is to have your development project become independent and self-sustaining. Happily, Five Talents reports a great deal of success in getting groups to become self-funding after about one and a half years.
We have six weekly contributors here at EWED and I try to read every single post. I don’t always read them the same day that they are published. Being subscribed is convenient because I can let my count of unread emails accumulate as a reminder of what I’ve yet to read.
Shortly after my fourth child was born over the summer, I understandably got quite behind in my reading. I think that I had as many as twelve unread posts. I would try to catchup on the days that I stayed home with the children. After all, they don’t require constant monitoring and often go do their own thing. Then, without fail, every time that I pull out my phone to catch up on some choice econ content, the kids would get needy. They’d start whining, fighting, or otherwise suddenly start accosting me for one thing or another – even if they were fine just moments before. It’s as if my phone was the signal that I clearly had nothing to do and that I should be interacting with them. Don’t get me wrong, I like interacting with my kids. But, don’t they know that I’m a professional living in the 21st century? Don’t they know that there is a lot of good educational and intellectually stimulating content on my phone and that I am not merely zoning out and wasting my time?
No. They do not.
I began to realize that it didn’t matter what I was doing on my phone, the kids were not happy about it.
I have fond childhood memories of my dad smoking a pipe and reading the newspaper. I remember how he’d cross his legs and I remember how he’d lift me up and down with them. I less well remember my dad playing his Game Boy. That was entertaining for a while, but I remember feeling more socially disconnected from him at those times. Maybe my kids feel the same way. It doesn’t matter to them that I try to read news articles on my phone (the same content as a newspaper). They see me on a 1-player device.
So, one day I printed out about a dozen accumulated EWED blog posts as double-sided and stapled articles on real-life paper.
The kids were copacetic, going about their business. They were fed, watered, changed, and had toys and drawing accoutrement. I sat down with my stack of papers in a prominent rocking chair and started reading. You know what my kids did in response? Not a darn thing! I had found the secret. I couldn’t comment on the posts or share them digitally. But that’s a small price to pay for getting some peaceful reading time. My kids didn’t care that I wasn’t giving them attention. Reading is something they know about. They read or are read to every day. ‘Dad’s reading’ is a totally understandable and sympathetic activity. ‘Dad’s on his phone’ is not a sympathetic activity. After all, they don’t have phones.
They even had a role to play. As I’d finish reading the blog posts, I’d toss the stapled pages across the room. It was their job to throw those away in the garbage can. It became a game where there were these sheets of paper that I cared about, then examined , and then discarded… like yesterday’s news. They’d even argue some over who got to run the next consumed story across the house to the garbage can (sorry fellow bloggers).
If you’re waiting for the other shoe to drop, then I’ve got nothing for you. It turns out that this works for us. My working hypothesis is that kids often don’t want parents to give them attention in particular. Rather, they want to feel a sense of connection by being involved, or sharing experiences. Even if it’s not at the same time. Our kids want to do the things that we do. They love to mimic. My kids are almost never allowed to play games or do nearly anything on our phones. So, me being on my phone in their presence serves to create distance between us. Reading a book or some paper in their presence? That puts us on the same page.
Since early in graduate school I’ve kept a running list of ideas for economics papers I’d like to write and publish some day. I’ve written many of the papers I planned to, and been scooped on others, but the list just keeps growing. As I begin to change my priorities post-tenure, I decided it was time to publicly share many of my ideas to see if anyone else wants to run with them. So I added an ideas page to my website:
Steal My Paper Ideas! I have more ideas than time. The real problem is that publishing papers makes the list bigger, not smaller; each paper I do gives me the idea for more than one new paper. I also don’t have my own PhD students to give them to, and don’t especially need credit for more publications. So feel free to take these and run with them, just put me in the acknowledgements, and let me know when you publish so I can take the idea off this page.
Here’s one set of example ideas:
State Health Insurance Mandates: Most of my early work was on these laws, but many questions remain unanswered. States have passed over a hundred different types of mandated benefits, but the vast majority have zero papers focused on them. Many likely effects of the laws have also never been studied for any mandate or combination of mandates. Do they actually reduce uncompensated hospital care, as Summers (1989) predicts? Do mandates cause higher deductibles and copays, less coverage of non-mandated care, or narrower networks? How do mandates affect the income and employment of relevant providers? Can mandates be used as an instrument to determine the effectiveness of a treatment? On the identification side, redoing older papers using a dataset like MEPS-IC where self-insured firms can be used as a control would be a major advance.
You can find more ideas on the full page; I plan to update to add more ideas as I have them and to remove ideas once someone writes the paper.
Thanks to a conversation with Jojo Lee for the idea of publicly posting my paper ideas. I especially encourage people to share this list with early-stage PhD students. It would also be great to see other tenured professors post the ideas they have no immediate plans to work on; I’m sure plenty of people are sitting on better ideas than mine with no plans to actually act on them.
The US government is great at collecting data, but not so good at sharing it in easy-to-use ways. When people try to access these datasets they either get discouraged and give up, or spend hours getting the data into a usable form. One of the crazy things about this is all the duplicated effort- hundreds of people might end up spending hours cleaning the data in mostly the same way. Ideally the government would just post a better version of the data on their official page. But barring that, researchers and other “data heroes” can provide a huge public service by publicly posting datasets that they have already cleaned up- and some have done so.
I just added a data page to my website that highlights some of these “most improved datasets”:
the IPUMS versions of the American Community Survey, Current Population Survey, and Medical Expenditure Panel Survey
I hope to keep adding to this page as I find other good sources of unofficial/improved data, and as I create them (one of my post-tenure goals). See the page for more detail on these datasets, and comment here if you know of existing improved datasets worth adding, or if you know of needlessly terrible datasets you think someone should clean up.
Economist Writing Every Day 