Category: Uncategorized

I understand why the calendar adds an extra day every four years. The revolution of the earth around the sun is approximately 365 and one-quarter days. Every four years, that adds up to one additional day, plus some extra minutes. The modest rounding error in this calculation is offset by steps like dropping the extra day of leap year for years ending in “00.”

But my question is why February has only 28 days in other years. After all, January has 31 days and March has 31 days. If those two months each donated a day to February, then all three months could be 30 days long, three years out of four, and February could be 31 days in leap years. Every other month is either 30 or 31 days. Why does February only get 28 days?

The answer to such questions leads to a digression back into the history of calendars. In this case, Jonathan Hogeback writing at the Britannica website tells me, it seems to settle on the Roman king Numa Pompilius back around 700 BCE, before the start of the Roman Empire. The ancient Roman calendar of that time had a flaw: it didn’t have nearly enough days. As Hogeback writes:

The Gregorian calendar’s oldest ancestor, the first Roman calendar, had a glaring difference in structure from its later variants: it consisted of 10 months rather than 12. In order to fully sync the calendar with the lunar year, the Roman king Numa Pompilius added January and February to the original 10 months. The previous calendar had had 6 months of 30 days and 4 months of 31, for a total of 304 days. However, Numa wanted to avoid having even numbers in his calendar, as Roman superstition at the time held that even numbers were unlucky. He subtracted a day from each of the 30-day months to make them 29. The lunar year consists of 355 days (354.367 to be exact, but calling it 354 would have made the whole year unlucky!), which meant that he now had 56 days left to work with. In the end, at least 1 month out of the 12 needed to contain an even number of days. This is because of simple mathematical fact: the sum of any even amount (12 months) of odd numbers will always equal an even number—and he wanted the total to be odd. So Numa chose February, a month that would be host to Roman rituals honoring the dead, as the unlucky month to consist of 28 days.

This discussion does explain why February would be singled out, since it was the month of rituals honoring the dead. In Numa’s calendar, the 355-day year would be made up of 11 months that had the lucky odd numbers of 29 or 31 days, plus unlucky February.

The discussion also explains why months that start with the prefix “Oct-” or eight, “Nov” or nine, and “Dec-” or ten, are actually months 10, 11, and 12 in the calendar. Those names were originally part of a 10-month calendar year.

But questions remains unanswered: Why did the Romans of that time view odd numbers as lucky, compared with unlucky even numbers? I suppose that explaining any superstition is hard, but I’ve never seen a great explanation. In a Dartmouth course on “Geometry in Art and Architecture,” some course describes Pythagorean feelings about odd and even numbers. For those of you keeping score at home, Pythagoras lived about two centuries after Numa Pompilius. The Dartmouth course material summarizes aspects of “Pythagorean Number Symbolism”:

Odd numbers were considered masculine; even numbers feminine because they are weaker than the odd. When divided they have, unlike the odd, nothing in the center. Further, the odds are the master, because odd + even always give odd. And two evens can never produce an odd, while two odds produce an even. Since the birth of a son was considered more fortunate than birth of a daughter, odd numbers became associated with good luck.

Various mentions of the luckiness of odd numbers recur over time. A few centuries later in the first century BCE, the poet Virgil has the character Alphesiboeus (a shepherd who sings about love rituals) say in Eklogue VIII (from the A.S. Kline translation):

Bring Daphnis home, my song, bring him home from town.

First I tie three threads, in three different colours, around you

and pass your image three times round these altars:

the god himself delights in uneven numbers.

Bring Daphnis home, my song, bring him home from town.

Or leaping ahead a millenium-and-a-half, at the start of Act V of the The Merry Wives of Windsor, Shakespeare has Falstaff say:

Prithee, no more prattling. Go. I’ll hold. This
is the third time; I hope good luck lies in odd numbers.
 Away, go. They say there is divinity in odd
 numbers, either in nativity, chance, or death.
Away.

While I acknowledge this history of a belief in odd numbers, as a person born on an even day of an even month in an even year, I’m not predisposed to accept it. But it’s interesting that modern photographers have a guideline for composing photographs called the “rule of odds.” Rick Ohnsman at the Digital Photography School, for example, describes it this way:

This is where the rule of odds comes into play, a deceptively simple yet powerful tool in your photographic arsenal. It’s all about arranging your subjects in odd numbers to craft compositions that are naturally more pleasing to the eye. Unlike more static guidelines, the rule of odds offers a blend of structure and organic flow, making your images both aesthetically pleasing and impressively compelling.

The revised calendar of Numa Pompilius couldn’t last. With only 355 days, it didn’t reflect the actual period of the earth revolving around the sun, and thus led to further revisions which are a story in themselves.

But when you think about it, the question of February having 28 days all goes back to Numa Pompilius and the superstitions about odd numbers. The modern calendar has 365 days in a typical year. You might think that the obvious way to divide this up would be to start off with 12 months of 30 days, and then add five days. Indeed, the ancient Egyptians had a calendar of this type, with five “epagomenal” or “outside the calendar days added each year.

The preference over the last two millennia, at least since the time of Julius Caesar, is to have 12 months, with a few of them being a day longer. But even so, why not in a typical year have five months of 31 days, and the rest with 30? The “problem,” I think, is that most months would then have unlucky totals of an even number of days. By holding February to 28 days rather than 30, you can redistribute two days from February and have 31 days in January and March. Thus, you can have only four months with an even total of 30 days every year (“Thirty days hath September, April, June, and November …”), and seven months always with the luckier odd total of 31 days. In leap years, when February has 29 days, then eight months have an odd number of days. I think this makes February 29 a lucky day?

The fast-food chain Wendy’s finds itself in a kerfuffle over comments by its chief executive officer Kirk Tanner that it may shift to digital menu boards, which would in turn allow the firm to adjust prices by time of day. The usual suspects immediately asserted that Wendy’s was about to commit “surge pricing” by raising prices at peak meal hours; the company quickly responded that it was only going to use the mechanism for “discount pricing” at non-peak hours.

Of course, many restaurants have traditionally had “early bird” or “happy hour” specials for those eating in non-peak hours. Similarly, movies and shows often have matinee pricing, where tickets for shows in the afternoon are cheaper than those in the evening, or tickets for shows from Sunday through Thursday night are cheaper than on Friday or Saturday night. During the holiday shopping season, lots of retailers have cheaper prices for those who show up to buy during the opening hours of the store on certain dates, and more expensive prices for those who come later.

The trick for any seller, of course, is that it need to brand all such time-of-day or time-of-week variation as a “discount” for the cheaper times, which is laudable, rather than as a “surge” during the more expensive times, which would be condemned. In a similar spirit, gas stations and other retailers often list a “discount price” for those who pay cash, but never a “surge price” for those who pay with credit cards. Public transit systems often have a price “discount” for those who travel at off-peak hours, but never a “surge” price for those who travel at peak times. The Wendy’s CEO committed executive malpractice by not immediately emphasizing how the company was going to offer price discounts, not price surges.

A few years back, I wrote about dynamic pricing in a number of contexts: some historical episodes when Coca-Cola talked about raising or lowering prices at soft drink machines on hot days; when Disneyland or certain ski resorts charges higher prices at peak times than off-peak times; when prices for rideshare services like Uber go up in situations where quantity demanded of rides is high; when the price of electricity is adjusted up during periods of high demand; and when toll roads charge more when traffic is especially congested. There are of course complex issues across these cases. But the knee-jerk claim that prices should generally be constant across a wide range of conditions, including time-of-day and day-of-week, except that “discounts” are socially beneficial while “surges” are socially harmful, substitutes outrage-of-the-day rhetoric for any attempt at making meaningful distinctions.

Each year, investor extraordinaire Warren Buffett publishes a letter to the shareholders of Berkshire Hathaway, a personalized view of how he sees the previous year, the role of capitalism, and (this year) the investment strategies of his sister Bertie. But this year, he also admits that the company he has built has no future possibility of eye-popping growth, because of how large it has grown.

Think of it this way, say that you start off with an investment firm that is worth 0.1% of the net worth of the top 500 companies in the US. You do a superior job of investing that money, and double your share to 0.2%. You then double again and again and again to 0.4%, 0.8%, 1.6%, 3.2%, and 6.4%. Notice that each of these steps would make you a very successful investor. But notice also that each doubling gets harder, because each doubling requires a greater gain in the size of your firm relative to the market. Doubling from a small base is a lot easier that doubling from a large base. And Buffett is saying that his firm has become so large that future doublings are somewhere between hard and impossible. Here’s his comment from this year’s letter:

Our goal at Berkshire is simple: We want to own either all or a portion of businesses that enjoy good economics that are fundamental and enduring. Within capitalism, some businesses will flourish for a very long time while others will prove to be sinkholes. It’s harder than you would think to predict which will be the winners and losers. And those who tell you they know the answer are usually either self-delusional or snake-oil salesmen. At Berkshire, we particularly favor the rare enterprise that can deploy additional capital at high returns in the future. Owning only one of these companies – and simply sitting tight – can deliver wealth almost beyond measure. …

This combination of the two necessities I’ve described for acquiring businesses has for long been our goal in purchases and, for a while, we had an abundance of candidates to evaluate. If I missed one – and I missed plenty – another always came along.

Those days are long behind us; size did us in, though increased competition for purchases was also a factor. Berkshire now has – by far – the largest GAAP net worth recorded by any American business. Record operating income and a strong stock market led to a yearend figure of $561 billion. The total GAAP net worth for the other 499 S&P companies – a who’s who of American business – was $8.9 trillion in 2022. (The 2023 number for the S&P has not yet been tallied but is
unlikely to materially exceed $9.5 trillion.)

By this measure, Berkshire now occupies nearly 6% of the universe in which it operates. Doubling our huge base is simply not possible within, say, a five-year period … There remain only a handful of companies in this country capable of truly moving the needle at Berkshire, and they have been endlessly picked over by us and by others. Some we can value; some we can’t. And, if we can, they have to be attractively priced. Outside the U.S., there are essentially no candidates that are meaningful options for capital deployment at Berkshire. All in all, we have no possibility of eye-popping performance.

Nevertheless, managing Berkshire is mostly fun and always interesting. On the positive side, after 59 years of assemblage, the company now owns either a portion or 100% of various businesses that, on a weighted basis, have somewhat better prospects than exist at most large American companies. By both luck and pluck, a few huge winners have emerged from a great many dozens of decisions. And we now have a small cadre of long-time managers who never muse about going elsewhere and who regard 65 as just another birthday …

With that focus, and with our present mix of businesses, Berkshire should do a bit better than the average American corporation and, more important, should also operate with materially less risk of permanent loss of capital. Anything beyond “slightly better,” though, is wishful thinking.

Pointing out that it’s easier to have fast growth rate from a tiny base than from a larger base is a lesson worth remembering in many contexts. As one example, the economy of China had very rapid growth for some decades, but starting from an exceptionally low base. There are multiple reasons for China’s current economic woes, but one unavoidable issue is that when you get bigger, growth rates get harder to achieve.

In public opinion polls, one of the primary concerns about rising levels of immigration is the extent to which it might increase crime rates. But several puzzles arise here. The evidence that immigration systematically leads to an increase in local crime rates turns out to be meager. However, Olivier Marie and Paolo Pinotti explore these issues “Immigration and Crime: An International Perspective” in the Winter 2024 issue of the Journal of Economic Perspectives (38:1, 181-200).

(Full disclosure: I’ve been Managing Editor of the JEP since 1986. All articles in JEP from the most recent issue back to the first have been freely available online for the last decade or so, compliments of the publisher, the American Economic Association.)

As a starting point, it turns out that when you ask people about their concerns over immigration, in many countries crime is a bigger concern than jobs. In the graph the dark line (the “45-degree line”) shows equal concern about these two issues, like in the United States. But most countries are above that line, showing that more natives in those countries express concerns over crime than over unemployment as a consequence of immigration.

At some level, it wouldn’t be surprising if immigration did lead to a rise in crime, given that immigrants are more likely to be young men with a lower education level than is common in their destination country, and thus limited job prospects. This group tends to be higher-crime. And there is some evidence supporting this belief: the authors note that across a sample of 30 countries, the the share of foreigners in the prison population is about double their share of the than overall population. Interestingly, the United States is a major exception to this pattern: here, the share of foreigners in the prison population is about half the share in the overall population. Of course, to some extent this comparison will reflect that the US has a larger share of its native-born population in prison than most countries.

However, trying to figure out how much immigrants increase the crime rate is a harder question. For example, it’s possible that immigrants might tend to move to places with more job opportunities, thus tending to hold down the crime rate, or that the cost to immigrants of getting involved with the criminal justice system might be higher. It’s also possible that when immigrants become criminals, they to some extent crowd out native-born criminals–which could mean that the overall crime rate doesn’t rise. A researcher would like to have a random selection of different levels of immigrants arriving at a random selection of locations, but this social experiment seems unlikely to be carried out.

As Marie and Pinotti explain, the standard approach in this research literature happens in two parts. First, it’s well-known that immigrants have a tendency to locate where other previous immigrants from their country have located. Thus, one can look at where previous waves of immigrants have located and do a projection of where future immigrants will go. The second step is to look at whether immigration across different areas is higher or lower than expected, and to calculate whether these unexpectedly higher or lower rates of immigration are correlated with crime.

This “shift-share” approach, as it is called, is used across a number of areas of economics. It has various built-in assumptions, and the migration and crime data across areas has its own measurement problems. Marie and Pinotti walk through these issues. But the bottom line is that when you apply this approach to data from the United States or various European countries, there doesn’t appear to be any effect of unexpectedly higher or lower immigration on crime rates in a given area.

Of course, immigrants are not a homogenous group. Those with legal status, for example, will find it easier to get jobs. Thus, there is evidence from the US and other countries that when there is an amnesty for illegal immigrants that makes it easier for them to hold jobs, crime rates tend to fall–which in turn suggests that before the amnesty, crime rates for this group was higher. Also, a number of countries have rules that make it harder for immigrants who are asylum-seekers to hold jobs. Evidence from the UK suggests that areas which were randomly chosen to receive immigrant asylum-seekers did have higher crime rates. Thus, the job market prospects of immigrants seem to affect their crime rates.

Marie and Pinotti point out several studies of situations when immigration became an especially salient political issue: a 2009 referendum in Switzerland about whether minarets could be built on new mosques, and a situation in Chile where the number of foreign-born residents with work permits tripled from 2010 to 2017. They note that media coverage of these issues tended to raise public fears of crime by immigrants, although the evidence of actual higher crime rates was lacking. There’s of course no doubt that some immigrants, being human, will commit crimes. But there’s good reason to question how important the risk of higher crime rates should be when thinking about benefits and costs of immigration policy.

The Congressional Budget Office has published The Demographic Outlook: 2024 to 2054 (January 2024), which offers some recent history and projections of how the US population is evolving. Here are three snapshots:

The Role of Immigration in Total US Population Growth

The black line shows projected US population growth since 2004, with firm data up through 2020, partial data to the present, and projections up through 2020. The dark green bars show “births minus deaths”: that is, births have been outnumbering deaths, adding to overall population growth, but the gap between the two has been shrinking, and by about 2040 births are projected to equal deaths, and the drop below deaths. The light green bars show immigration. The spike in the last few years is notable. Here, as the CBO emphasizes, the future projections have a high degree of uncertainty, because they depend on policy choices. If immigration fell to zero, then on these projections, the US population would be declining in absolute numbers by about 2040.

The Aging of the US Population

Back around 1950, there were six Americans in the traditionally working-age population from 25 to 64 for every American who was 65 and older. The ratio fallss over time, but when the “baby boom” generation that was born starting in 1946 started turning 65 in 2010, you can see the drop-off in the ratio become more severe for about 25 years, before it more-or-less levels out again. We’re now at a ratio of 2.9 25-64 year-olds for every person over age 65, headed for a ratio of 2.2 by 2054. Many of the fundamental financial issues for Social Security and Medicare stem from this this shift. But in addition, it’s also a measure of how many elderly American in the future will have many fewer children and grandchildren, and so the quantity of care and support for older American provided by family members seems sure to decline. We are shifting from a country of playgrounds and K-12 schools to a country of accessible walkways and elder-care centers.

Fertility Rates

The shifts in fertility rates (births per woman) in the last half-century are remarkable. In the decades leading up to the Great Recession from 2007-09, it looked as if fertility rates for US women were gradually rising. From 1974 up to the Great Recession, teenage fertility rates for women aged 14-29 fell slightly, but this was more than offset by rising fertility rates for the women aged 30-49. Now, these two lines are criss-crossing: that is, the number of children born to women over-30 is exceeding the number born to women under-30. This is mainly because the fertility rate for the under-30 women has fallen so sharply. Of course, this decline in births is mirrored in how the gap between births and deaths is evolving in the figure above. Again, the future predictions are subject to considerable uncertainty. The “baby boom” itself was not predicted, and fertility rates had been low in the 1930s.

Back in 2002, the private investment firm called Hicks Muse that owned the Vlasic Pickle Company sought to purchase the Claussen Pickle Company. The Federal Trade Commission blocked the merger. In the press release announcing the action, the FTC said:

According to the FTC’s complaint, Hicks Muse’s proposed acquisition of Claussen would eliminate competition and the unique rivalry between the two national pickle brands. Claussen is the dominant producer of refrigerated pickles and Vlasic serves as the primary price constraint on Claussen. Together, the companies would have a monopoly share of the refrigerated pickle market in the United States. The complaint alleges that the effect of the proposed acquisition, if consummated, may be to lessen competition substantially and lead to increases in prices or a reduction in competitive vigor. The complaint also alleges that Vlasic is the leading seller of premium shelf-stable pickles and that Vlasic’s shelf-stable pickles also operate as a competitive constraint on Claussen. Finally, the Commission contends that entry into the refrigerated pickles market by a competitor is likely to be neither sufficient nor timely enough to alleviate the likely anticompetitive effects of the transaction as proposed.

I have no deep insight into the entry and exit dynamics of the pickle industry, although it does seem to me that when the two biggest producers in a market seek to merge, the antitrust authorities are right to take a close look. But setting aside the narrow issue of gains to pickle consumers, the case led to a broader enrichment of our cultural landscape in the form of a poem by Thomas B. Leary, who served as a member of the FTC at the time. The poem was delivered at a meeting of alumni of the FTC on December 18, 2002. It’s called:

“The Spell of the Gherkin”

(With apologies to “The Spell of the Yukon” by 
Robert Service.)

“There are strange things done in the midnight sun
By the men who moil for gold;
The Arctic trails have their secret tales
That would make your blood run cold . . .”

So begins a story of grit and glory:
The Cremation of Sam McGee.
I remember when, as a boy of ten,
T’was the epitome of poetry.

We here unveil a gentler tale,
Which still will stir the blood,
Where heroes try, in coat and tie,
To serve the public good.

* * *

There are strange things done in Washington
When companies are sold.
And paper trails tell lurid tales
Of price hikes to unfold.

It is not nice to raise the price
When rivals have disappeared.
The problem, though, is how will we know
Before the deal has cleared.

The cases we face are all over the place
But, the strangest I’ve seen so far
Was the time we took a good long look
At pickles in a jar.

Now, you may say in a scornful way:
“Who cares what the parties claim?
A nickle’s a nickle and a pickle’s a pickle;
They’re all exactly the same!”

But, you see, they’re not. Some like them hot
And some like them cold and clear.
We had to say: “What will you pay
For one, if the other grows dear?”

We sacrificed leisure in order to measure
Elasticity of demand.
As we carefully counted, the evidence mounted.
The pickles, it seems, had been scanned!

On these occasions, regression equations
Are never considered a bore.
The pluses and minuses cleared out the sinuses,
And thrilled us all to the core.

“The Spell of the Yukon,” indeed! The next time I read
Those poems I loved long ago,
About the quest for gold in the bitter cold
And wolves that howl in the snow – –

I’ll say: “My lad, you’ve never had
A moment so sublime
As that shining hour when market power
Was checked in the nick of time!”

* * *

Today, throughout this favored land
The sun is shining strong.
The bands are playing everywhere,
As children skip along.
Because those pickles, those luscious pickles
Still are sold for a song.

Tom Leary
(With a last-minute nod to the
next-best poem in the language.)

When people think about what an economy produces, they tend to think in terms of solid objects: cars, appliances, clothes, houses, food. But US consumers are in the midst of a long-term shift away from consuming goods and toward consuming services. Here’s an illustrative figure from the Congressional Budget Office (The Budget and Economic Outlook: 2024 to 2034, February 2024).

Here are a few ruminations:

1) The decline is fairly rapid, from 36% of consumer spending going to goods in 2000 to only about 30% at present–and if the CBO projections are to be believed, a continuing trend for the next decade.

2) The US economy is not mostly composed of material objects. It’s much more services than goods.

3) In practical terms, what does this shift mean? Major categories of services include health care, education, entertainment and tourism, finance, and the like. As my wife and I have paid college tuition bills for multiple children over the years, we say that it’s the equivalent of buying a very nice car and driving it off a cliff each year. But we choose the college tuition, although our cars are more than a decade old. More generally, we try to choose (without being in any way extreme about it) to consume experiences rather than to accumulate stuff.

4) The distinction between goods and services remains meaningful: for example, issues of production, supply chains, and how they are provided to ultimate consumers are systematically different. But it’s also worth remembering that even within the category of “goods,” there are a lot of embedded services. When someone buys a car or a smartphone or a pair of jeans or a cart full of groceries, the physical objects they are buying include a substantial dose of underlying service activities like research, design, marketing, finance, management, and transportation. In that sense, the fact that services are 70% of consumption surely underestimates their importance in an high-income economy.

The size of an economy is typically measured by Gross Domestic Product, which sums up all the monetary transactions in an economy. Non-monetary actions like household production is not covered by GDP: to use the old classroom example, if two stay-at-home parents hired each other to do all the household tasks, then GDP would go up, but if they work in their own homes, GDP is lower. Government statisticians have estimated that if the value of household production was included in GDP, the size of the US economy would be about one-fifth bigger.

But of course, household production feeds back into the monetary transactions part of the economy in many ways. Raising children today will help make Social Security solvent a few decades from now. Providing care to someone with a disability or recovering from illness can help them enter or re-enter the workforce. In a number of paid jobs, like teachers and health care workers, some employees will provide a quality of care far higher than others, without receiving monetary compensation for the higher lifetime earnings of students or the quicker return to health of patients.

Nancy Folbre tackles many of these issues in “Care Provision and the Boundaries of Production,” in the Winter 2024 issue of the Journal of Economic Perspectives. (Full disclosure: I’ve been the Managing Editor of JEP since 1986, and so may be predisposed to find the articles published there of interest. Since 2011, all articles published in JEP, including all archives back to the first issue, have been freely available online courtesy of the publisher, the American Economic Association.)

As Folbre emphasizes, neither the costs of care nor the broader benefits of care are well-captured by a focus on monetary transactions. I can’t do justice to the full argument here, but here are a couple of comments to ponder.

As Folbre points out, we socialize the economic benefits of children through programs like Social Security and more generally through taxes paid by future workers, but we rely heavily on nonmonetary private provision of care for raising children. She writes:

The US Social Security system taxes employees to finance health and retirement benefits for retirees. These benefits, linked to the earnings history of recipients and their spouses, offer no credit for the time and money put into raising future taxpayers. As a result, the net benefits of Social Security are significantly higher for individuals who devote relatively little time or money to children, even though single-earner married couples reap higher net benefits than dual-earner or single-parent households. US public policies socialize the economic benefits of children far more extensively than the costs (Folbre and Wolf 2013). Parents—defined in terms of contribution to childrearing rather than biology—create a significant fiscal externality by raising the next generation of taxpayers (Wolf et al. 2011). While empirical research has not yet distinguished between the total contributions of mothers and fathers or impact of greater life expectancy (and thus greater take-up of Social Security and Medicare) among women, mothers clearly make larger fiscal contributions through this channel than either childless individuals or noncustodial parents who fail to provide much support for their biological children.

Folbre on the role of care provided by teachers:

However, teachers are obviously not rewarded for their individual contributions to their students’ lifetime skills, just as parents are not rewarded for their value added to their children. Likewise, nurses and doctors are not rewarded for their individual contributions to patients’ lifetime health, and social workers are not rewarded for their individual contributions to social welfare. Industry wage differentials’ net of personal characteristics partly reflect cross-industry differences in ability to capture value-added: Unlike employees in care services, employees in business services generate measurable and significant revenues for their employers, are more easily paid for performance, and earn significantly more …

Folbre on the arguments about the subjective satisfaction of care to providers, rather than recognizing the social gains from such care.

Rather than treating care provision merely as a source of subjective satisfaction, we could recognize its moral valence and productive contributions. Rather than valuing human capital as an input into market output, we could value market output as an input into the improvement of human capabilities. Rather than fighting over the distribution of costs and benefits, we could emphasize the gains from well-designed social insurance programs. We cannot accomplish these goals without expanding the boundaries of production to look inside households and beyond market dynamics. Concerns about the negative consequences of inadequate care provision in the United States are growing. As one example, a coalition of large corporations, small businesses, entrepreneurs, and investors formed a Care Economy Business Council in 2021 (https://timesupnow.org/care-economy-business-council) to “shift the cultural narrative about who is responsible for care, encourage and enforce equitable practices to support caregivers, and advocate for key public policy interventions.”

In some countries, like Norway, your income tax forms are public information, so any one can look up what anyone else earns. In a US context, income is mostly considered to be private information, unless you are a public employee or an executive at a public company. Would it be a good thing to have greater disclosure of what workers are paid? Zoë Cullen tackles this question in “Is Pay Transparency Good?” in the just-published Winter 2024 issue of the  Journal of Economic Perspectives (38:1, 153-80).

(Full disclosure: I’ve worked as Managing Editor of JEP since 1986. Since 2011, all articles in going back to the first issue have been freely available online courtesy of the publisher, the American Economic Association.)

One reason against making pay public, at least in a US context without a tradition of doing so, is that it can put a sharp stake through employee morale. Most workers believe they are above-average. By definition, most workers will not be paid above-average. Lower earners will feel they deserve more. Higher earners will be believe that the gap over lower earners isn’t big enough. From this view, it’s better to keep pay behind the scenes.

But this secrecy can be a curtain for discrimination. Cullen mentions the famous Lilly Ledbetter case from the late 1990s, in which Ledbetter was one of 16 “area managers” at a company, doing the same job and with roughly similar levels of experience. However, she was the only woman in the job, and until a male colleague sent her a message, she did not know (and had no way of knowing) that she had been substantially underpaid compared to her male co-workers for years. In the last few decades, concern over this situation has led many US states and high-income countries to pass laws that seek to provide more information about pay in general, but typically in a way that continues to provide some secrecy about individual pay.

Cullen usefully identifies three types of pay transparency: horizontal pay transparency, which provides information of what people in the same job category at the same company are making (the Ledbetter situation); vertical pay transparency, which pressures employers to reveal the pay gap across different levels of seniority in a firm (like the pay raise you would get if you were promoted); and cross-firm pay transparency, in which companies are encouraged to put into public view what they are paying for various jobs (for example, by requiring that job ads include a salary range). She draws upon a wide range of empirical studies of the pay transparency laws and rules that have been passed, and finds that the effects are quite different.

It turns out that horizontal pay transparency has two issues. One is the morale issue mentioned above: in many places, workers get snippy about what their co-workers earn. The other issue is that horizontal pay transparency changes the dynamics when an employer and employee are negotiating over pay. When an employer knows that the ultimate pay decision will be known to all other workers, the employer has a strong incentive to push back against a pay raise for one employee, because the employer knows that it will lead to almost immediate pressure from all other employees. Cullen writes:

In the cases where transparency achieved greater pay equalization between men and women … the reduction in pay gap was accompanied by an overall reduction in wages. Economic theory offers an explanation. Horizontal pay transparency between coworkers within a firm created spillovers between negotiations; specifically, a $1 raise for one worker became more costly due to renegotiations with other workers who have the expectation of equal pay, causing employers to bargain more aggressively with each worker. Moreover, when wages were not equalized under horizontal transparency, research has shown that workers paid visibly less than some of their peers (which can be the majority of workers) felt disgruntled and exerted less effort.

In contrast, vertical and cross-firm pay transparency do not have these effects. As Cullen explains (with empirical studies to back it up):

In contrast, vertical pay transparency and cross-firm pay transparency, while less equipped to hold specific organizations accountable for discrimination, have proven capable of raising productivity and raising wages by reducing information frictions in the labor market. Vertical pay transparency increases workers’ information about what they could earn if they were to be promoted. Because employees typically underestimate the steepness of financial rewards from promotion, vertical transparency raises expectations about potential earnings and has proven to boost effort and productivity in meritocratic environments. Cross-firm pay transparency, achieved through salary benchmarks like Glassdoor or salary ranges in job posts, informs prospective candidates about which employers pay more than others and leads applicants, especially those underpaid, to redirect their search toward higher paying firms and more favorable pay negotiations. Cross-firm pay transparency policies have also informed firms what their competitors are paying, increasing competition and putting upward pressure on wages. These pay transparency policies shine the light outward, away from coworkers under the same employer, toward vertical and cross-firm pay differences.

Perhaps one lesson here is that when it comes to higher pay, most of us are not competing against our immediate coworkers in the same job at the same firm; instead, we are competing for the next promotion, or competing against our counterparts at other firms.

For a sense of the available experience and evidence on these points, I’ll append here a table from Cullen on the different types of pay transparency laws enacted across different countries and US states:

There was a time when the electricity for running server farms was an afterthought, but in many place, that time is already past. The International Energy Agency has a discussion of the issue in its recent report, Electricity
2024: Analysis and forecast to 2026 (January 2024, pp. 31-36). 

The light blue and dark blue bars show the rise in electricity from 2022 to 2026 needed for server farms for the US, EU, and China, together with Denmark and Ireland. The expected rise is substantial. But focus instead on the orange diamonds, which show the share of total electricity for the country or region as a whole. For the US and the EU, server farms are projected to be 5-6% of total electricity consumption by 2026. For Denmark, the projection for 2026 is about is 20% of total electricity consumption; for Ireland, it’s 32%.

Ireland’s situation is unique: it has both a welcoming business climate for foreign investment and a geographically advantageous position between the US and EU economies. But its experience also illustrates that data centers tend to be concentrated in a few locations, often locations of tech firms, financial centers, and government. For the US, the IEA report notes:

In the United States, the largest data centre hubs are located in California, Texas and Virginia. In the case of Virginia, their economy was dominated in 2021 by the data centre sector expansion, attracting 62% of all of the state’s new investments and providing more than 5 000 new jobs. Northern Virginia is the largest data centre market in the country, collecting USD 1 billion in local tax revenues per year, with growth trending higher as companies, such as Amazon’s planned USD 35 billion expansion by 2040, continue to increase their investment in the state.

Of course, there are potential innovations with some promise for holding down the increase in this level of electricity use–but the fundamental drivers behind the rising demand for data servers seem likely to keep rising. Here’s one vivid example from the IEA report:

Market trends, including the fast incorporation of AI into software programming across a variety of sectors, increase the overall electricity demand of data centres. Search tools like Google could see a tenfold increase of their electricity demand in the case of fully implementing AI in it. When comparing the average electricity demand of a typical Google search (0.3 Wh of electricity) to OpenAI’s ChatGPT (2.9 Wh per request), and considering 9 billion searches daily, this would require almost 10 TWh of additional electricity in a year.

Providing the additional electricity for data centers will be a challenge, but my guess is that, when it comes to the everyday problems of business and government as well as the big-picture problems of national and global policy-making, additional data-processing capacity is a (relatively) cheap investment.