James' World 2

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Saving money is an essential personal finance habit you want to build the right way. Having a healthy savings base achieves several things: It lessens your reliance on debt if an unexpected bill arises. And, it can help you build the discipline necessary to achieve your goals.

When used well, saving money can help you build a healthy financial foundation. However, there are also common mistakes you want to avoid.

Avoiding these mistakes ensures you’re maximizing your gains and not leaving money on the table. Here’s a look at some of the most common oopsies and ways to avoid them.

I used to have a brick-and-mortar bank, and I loved the customer service. Every time I walked in, I felt like I was on an episode of Cheers. Yet, a harsh reality hit every month when I received my statement. I was only earning pennies.

That wasn’t going to build momentum. So, I switched to a high-yield savings account with an online bank. I earn an APY that helps me reach my goals quicker.

Here’s a comparison: If I deposit $10,000 into a Chase savings account earning 0.01% APY for a year, I’m earning a dime. That’s not optimal. However, by switching to Newtek Bank, I earn an APY of 4.20% APY. Over the course of the year, I’m earning $428.92. That’s over a $400 difference in one year. And that will grow incrementally the longer I save.

Therefore, if you’re keeping your money in a low-interest account, it pays to shop around. Use this Bankrate tool to find the best high-yield savings accounts for you:

Now, the thing about these savings accounts is that they come with variable interest rates. It means they could change at any time due to Federal Reserve or bank policy. So make sure to pay close attention to rates on occasion, and if they dip, use this tool to help you find a better option.

Along with being proactive about shopping around for savings rates, there’s another critical factor you’ll need to consider.

Placing too much money in a savings account

The smart rule of thumb is to save at least six months of living expenses in an emergency fund. That way, if you experience a job loss or a surprise medical diagnosis, you can cover your bills without relying on debt.

To determine your emergency fund, add up all your essential monthly expenses, such as mortgage payments, debts, utilities, prescriptions, and household items. Once you have that total, set a savings goal to reach this amount using a high-yield savings account.

After you reach that goal, you’ll want to adjust your strategy aside from other short-term savings goals you might have. Why? Savings can help build a healthy financial foundation, but investing builds wealth. Say you have an extra $40,000 you keep in a high-yield savings account for 20 years instead of investing it and earning an average annual return of around 8%.

The difference between these strategies is significant—almost $70,000. Remember that savings protect wealth, but investing builds it. You’ll need both to achieve your long-term goals, so adjust accordingly once you reach your emergency target and short-term goals. And speaking of goals…

Not saving with a purpose

Many people save because they know it’s necessary, but there’s no direction beyond that intention. This can be problematic as aimless saving could lead to aimless spending. When you don’t have a clear objective, that money can appear as “extra” cash, making it easier to justify impulse purchases.

Meanwhile, saving with a purpose means creating “sinking funds” for an expressed purpose. By assigning every dollar a job in your savings plan, you create a mental barrier against spending it.

Think of it this way: If you’re earmarking money for a dream vacation in a year, you’re less likely to splurge on an impulse purchase because you have a bigger goal in mind. Having a target keeps you focused, so a higher reward awaits you in the future.

Which savings account is right for your goals? Here’s a guide that can help:

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(Mistake) Sitting your money in a low APY account

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Many Ukrainians — even those born after the country gained independence from Moscow’s rule in 1991 — grew up with much of the same mythology of the Great Patriotic War. Ukraine, which was under German occupation for most of that war, lost some 10 million people. Mila’s surviving grandparents, like mine, celebrated every anniversary of that war’s end but almost never talked about what they had experienced. After the war, the Soviet authorities sent thousands of Ukrainians to the gulag for suspected collaboration with the Germans — in many cases, as what amounted to punishment for surviving the occupation. Ukrainians never forgot that injury. Both of those World War II stories — of the heroism of Ukrainians and of the cruelty of Moscow — inform the way Ukrainians think about the war they are fighting now.

Newer works of history reframe the period as two sides of a coin: German and Soviet occupations of Ukraine, two empires that aimed to enslave Ukrainians — Germany during World War II, the Soviet Union before and after. And yet, the number four has continued to loom large in collective memory. Now Ukraine’s patriotic war, against Russia, has crossed that threshold, with no end in sight. Russia’s offensive appeared to speed up in December. In February, Ukraine recaptured ground, in its most successful counteroffensive in more than two years. But on the whole, the front line has remained largely static for more than three years. Russia’s apparently overwhelming superiority in manpower and military resources didn’t bring about a swift victory, but neither have the resolve of the Ukrainian people and the Western aid they have received proved enough to stop Russia’s aggression.

Whatever lies ahead feels as if it will last forever. Ukrainians have organized their lives accordingly. They are living this war in their work, their social lives, their waking and sleeping hours. It is a fundamental orientation of time, values and social relations that will define many future generations of Ukrainian life.

By any measure, Ukraine is a profoundly different country now than it was four years ago. At the start of the full-scale invasion, excluding regions that were already occupied by Russia, it had a population of perhaps 36 million people, according to Tymofii Brik, a sociologist and the rector of the Kyiv School of Economics. (Other estimates tend to be higher.) Since then, Brik says, six million have been displaced inside the country and some four million — mostly women and children — have left Ukraine. More than 100,000 Ukrainians, troops and civilians, are estimated to have been killed. Millions of people live under occupation in areas Russia controls.

When people were fleeing the Russian offensive in the winter of 2022, squeezing onto overcrowded train cars headed west, few imagined that the war would go on for a long time. Either Russia’s tremendous military might or the West’s firm resolve would dictate a fast resolution, it seemed. But four years after that — and 13 months into the presidency of Donald Trump, who promised to bring the war to an end within 24 hours of his inauguration — there is no safe home for Ukrainian war refugees to return to. And there is less and less reason even to think about it: The people who stayed in Western Europe have adapted to their new homes, and to the separation from those they left behind.

“What kind of relationship can we have, with them over there and me back here?” Taras Viazovchenko said when I asked him about the state of his marriage. He got his wife and two children out of Irpin, one of the Kyiv suburbs, then under Russian occupation, on March 3, 2022. The wife and kids live in Switzerland now. He has visited once. “She’s built a life there,” he said. “The kids speak French to each other, and I don’t understand.”

Like many Ukrainians who remained in the country, Viazovchenko has lived several different lives in the past four years — lives that he has shared with his parents and some of his friends, but not with his wife and kids. Before the full-scale invasion, Viazovchenko was a yoga instructor and a member of the Irpin City Council, a position he still holds. During the weeks in 2022 when part of Irpin was occupied, he spent every day helping people escape the town. When Russian troops retreated from the Kyiv region, Viazovchenko joined the effort to identify the bodies of people killed in Irpin and neighboring Bucha, which has become synonymous with Russian war crimes.

People killed during the occupation had been buried in private yards, in group graves, in town parks, often after their bodies were left for days wherever the killing had occurred. Viazovchenko and others exhumed the bodies, interviewed loved ones and witnesses, and tried to match remains to descriptions. After several months of this work, Viazovchenko became obsessed. He and his colleagues had been able to identify more than 400 bodies, but several dozen remained. Viazovchenko couldn’t sleep. He couldn’t think of anything else. He kept unzipping the black bags in which the bodies were kept — or what remained of them after several months in morgues that didn’t consistently have electricity.

It took the intervention of visiting mental health professionals for Viazovchenko to get help. He worked on setting up therapy centers for survivors of Russian aggression in different parts of Ukraine. And then last year, at the age of 46, he enlisted. He thinks that everyone should.

To be clear, not everyone agrees. After an initial wave of volunteers immediately after the full-scale invasion, the Ukrainian armed forces have struggled to conscript enough people. People who enlisted four years ago and who are still physically able to serve have been unable to leave the service. Meanwhile, enlistment officers stage daily raids in Ukrainian cities, apprehending potential conscripts and delivering them to military bases. Some escape. At the same time, on this visit in particular, I heard many stories of people who either chose to enlist or submitted to a conscription raid and found peace in the service — and in no longer trying to evade it. Viazovchenko thinks this is as it should be, and that those who cannot serve at the front should join the war effort in the rear. He complained that, after several years of pooling money for the war effort, parents’ groups have resumed collections for gifts and flowers for teachers. That strikes him as frivolous, as does any pretense of peacetime life. As an example of proper, realistic adjustment, he cited the schools of Kharkiv, many of which have permanently moved to underground bunkers.

Underground schools have become symbols of Ukrainian unbreakability, along with warming tents set up in the shadow of unheated high rises. I visited the Kyiv School of Economics, a small, ambitious private university that has managed to draw some outstanding academic talent from both Ukraine and the West. Brik, the rector, excitedly led me to the basement, where the university has created several classrooms, complete with whiteboards. The school schedules only as many classes as can simultaneously convene in the bunker, so that whenever the air-raid alarm sounds, as it does on most days, classes can move down below. Then Brik showed me something else he was proud of: a classroom equipped for a vocational training program, this one in soldering — a skill newly in demand in the growing drone industry.

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Ms. Tereshkova is a Ukrainian photographer and filmmaker based in Berlin.

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“When a clown moves into a palace, he doesn’t become a king, the palace instead becomes a circus. — Turkish proverb,”

 

Hmmmmm…History is repeating itself yet again!

 

Isaiah 59:14

New Living Translation

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You might find these videos enlightening!

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A. R. Bernard: one of many

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Below the surface, Greenland’s ice appears to be churning up, a process one scientist described as akin to a “boiling pot of pasta”

Hidden underneath the surface of Greenland’s ice, there are strange, vast structures known as “plumes.” But for years, scientists didn’t know how these curious forms came to be—until now. A new study published in the journal Cryosphere reveals that the structures may have been caused by thermal convection in a process similar to the churning of the hot rock in Earth’s mantle.

Convection is caused by temperature differences within a material. Hot material rises, and cool material falls—driving a cycling, or convection. The same phenomenon appears to occur in ice, too, indicating that parts of the ice sheet may be softer than scientists realized. That’s important for one crucial reason: we know that Greenland’s ice sheet is rapidly melting. And as the climate warms, scientists are racing to understand how exactly it will melt and how fast.

“We typically think of ice as a solid material, so the discovery that parts of the Greenland ice sheet actually undergo thermal convection, resembling a boiling pot of pasta, is as wild as it is fascinating,” said Andreas Born, a professor of Earth science at the University of Bergen in Norway, in a statement.

Although the findings do not necessarily mean that the ice sheet will melt faster, they could offer clues as to how it may melt. And that knowledge is critical—the sheet is more than 650,000 square miles in size; by one estimate, if all of it melts, it will raise the planet’s sea levels by a whopping 24 feet.

“Our discovery could be key to reducing uncertainties in models of future ice sheet mass balance and sea-level rise,” Born said in the same statement.

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The chief justice’s opinion hinged on a legal principle called the “major questions doctrine” — the same doctrine that was used repeatedly to block the Biden administration’s regulations and orders.

As Justice Gorsuch explained in his concurring opinion, the doctrine means, “When executive branch officials claim Congress has granted them an extraordinary power, they must identify clear statutory authority for it.”

In other words, relying on broad and vague statutory language, such as the International Emergency Economic Powers Act’s grant of authority to presidents to “regulate” importation when he or she declares an emergency isn’t precise enough to sweep away the Constitution’s explicit language granting taxing authority to Congress.

Other justices, including Kagan, Sotomayor and Jackson, had an even simpler explanation for blocking the tariffs. As Kagan wrote in her concurring opinion, “Ordinary principles of statutory interpretation lead to the same result.”

It’s not that words like “regulate” and “importation” aren’t precise enough to grant the president extraordinary powers. Instead, as Kagan wrote, “IEEPA’s key phrase — the one the government relies on — says nothing about imposing tariffs or taxes.”

And since the statute says nothing about tariffs or taxes, then the Trump administration can’t use it to prop up the president’s lawless scheme.

The majority’s reasoning alone makes the tariff case extraordinarily important.

For years presidents of both parties have been using broad and vague language in federal statutes as a pretext for engaging in lawmaking in place of Congress.

The expansion of presidential power, which has accelerated exponentially under Trump, has placed our republican form of government under strain. When presidents yank power from Congress, they begin to assume the role of an elected monarch — the exact opposite of the framers’ intent.

Gorsuch explained this masterfully in his concurrence. “For those who think it important for the nation to impose more tariffs,” he wrote, “I understand that today’s decision will be disappointing. All I can offer them is that most major decisions affecting the rights and responsibilities of the American people (including the duty to pay taxes and tariffs) are funneled through the legislative process for a reason.”

The legislative process can be slow and frustrating, Gorsuch explained, but

through that process, the nation can tap the combined wisdom of the people’s elected representatives, not just that of one faction or man. There, deliberation tempers impulse, and compromise hammers disagreements into workable solutions. And because laws must earn such broad support to survive the legislative process, they tend to endure, allowing ordinary people to plan their lives in ways they cannot when the rules shift from day to day.

In a series of interim decisions, the Trump administration recently enjoyed a temporary winning streak at the Supreme Court, but the judicial tide seems to be turning. Combined with its recent decision in Trump v. Illinois, which refused to stay a lower-court ruling blocking Trump’s deployment of the National Guard in Illinois, the Supreme Court has defied two of the administration’s most dangerous, most authoritarian ambitions.

It also appears set to defy Trump yet again in another ruling soon. In oral arguments in Trump v. Cook — a case challenging the president’s decision to fire Lisa Cook, a member of the Federal Reserve’s board of governors — a clear majority of the court seemed skeptical of his actions.

The court will also hear arguments in Trump v. Barbara — the case challenging Trump’s executive order abolishing birthright citizenship as we know it — in April, and the omens are not good for him in that case, either.

Perhaps the most important aspect of the court’s decision is how it may help restore faith in how courts make decisions. The crisis in American democracy isn’t simply a product of the Trump administration’s overreach, it’s also a product of deep public cynicism about government institutions. Trump owes at least some of his appeal to that cynicism. If all that matters is power, then why not pick the man who exercises that power to its fullest?

As a result, millions of Americans wonder, do principles matter at all? Or is all of politics merely a matter of gaining and wielding power, supporting your friends and crushing your enemies?

The tariff decision is a reminder that principles do still matter, that at least one branch of government is not in thrall to the president, and that we can rely on reason and precedent to decide cases rather than simply counting Republican and Democratic appointees.

It is important that Roberts anchored his majority opinion in three cases that struck down the policies of Democratic presidents — Biden v. Nebraska, West Virginia v. EPA (involving environmental regulations), and National Federation of Independent Business v. OSHA (Covid vaccine mandate). That sent a clear signal that presidents of both parties are held to the same standard.

And when you combine the tariff case with Trump v. Illinois, alongside the Trump administration’s terrible record at the Supreme Court during his first term (where he had the worst record of any president at least since Franklin D. Roosevelt), it’s clear that the conservative-dominated judiciary bears little resemblance to the sycophantic Republican Congress.

That doesn’t mean the court has gotten everything right. I still have profound disagreements with its decision to grant expansive presidential immunity in Trump v. United States. That case remains dangerously wrong. I also have qualms about its applications of the unitary executive theory, but the measure of a functioning branch of government isn’t whether I always agree with its decisions (and we should all be thankful for that).

Instead, I measure judicial integrity differently. I ask whether judges are acting in good faith, honestly applying their legal philosophies to the questions before them, regardless of their partisan or ideological affiliation.

There’s also a third, less obvious way in which the court’s decision helps preserve the Republic — by limiting opportunities for corruption.

By assuming vast powers of taxation, Trump made himself the focal point for an enormous amount of lobbying and trading favors. In January, for example, Politico reported that the 20 largest lobbying firms raked in nearly $824 million in revenue in the first year of Trump’s second term, a sharp increase from $595 million in Biden’s last year.

The administration has sent a message, loudly and clearly — almost anything is for sale, at the right price. And as ProPublica reported last April, politically connected people and companies were already benefiting from what appears to be targeted relief from Trump’s tariffs.

The Wall Street Journal’s editorial board called the administration’s opaque process for granting exemptions “the Beltway Swamp’s dream.”

The case is a victory for the Constitution and the rule of law, but there are still causes for concern. Trump is furious. He said he was “ashamed of certain members of the court” and said they were “very unpatriotic and disloyal to our Constitution.”

Those are dangerous words from a dangerous man.

There were also dissents, of course. Justice Brett Kavanaugh wrote one, as did Justice Clarence Thomas. Kavanaugh wrote the principal dissent, which both Thomas and Justice Samuel Alito joined, and his argument was straightforward. When the statute granted Congress the power to regulate importation, the word “regulate” encompasses the power to tariff. “Like quotas and embargoes,” Kavanaugh wrote, “tariffs are a traditional and common tool to regulate importation.”

The most notable portion of his dissent came later in his opinion, when he accurately noted that “numerous” other statutes grant the president the authority to impose tariffs.

Kavanaugh is correct. Other statutes do grant tariff authorizations to the president, and we should expect the administration to try to reconstruct as much of his tariff authority as he can through different means. (Biden did much the same thing in response to the Supreme Court’s student loan decision.)

But Trump’s most powerful tool has been taken away. He’ll have trouble doing tariffs the hard way when he loses the easy way. This is not an administration that is known for its legal competence.

In addition, as Roberts notes in his opinion, those other statutes “contain various combinations of procedural prerequisites, required agency determinations and limits on the duration, amount and scope of the tariffs they authorize.”

Now the ball is on Trump’s side of the net. The court has challenged him on perhaps the signature economic policy of his second term, and he is not taking it well. The threat of Trump defying the Supreme Court hovers over every decision he doesn’t like. On Friday he announced that he would impose a new 10 percent tariff on imports through different legal authorities — a move that will no doubt also be contested in court. Then on Saturday he upped it to 15 percent.

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Jonno Rattman for The New York Times

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When you walk into a doctor’s office, you assume something so basic that it barely needs articulation: your doctor has touched a body before. They have studied anatomy, seen organs, and learned the difference between pain that radiates and pain that pulses. They have developed this knowledge, you assume, not only through reading but years of hands-on experience and training.

Now imagine discovering that this doctor has never encountered a body at all. Instead, they have merely read millions of patient reports and learned, in exquisite detail, how a diagnosis typically “sounds.” Their explanations would still feel persuasive, even comforting. The cadence would be right, the vocabulary impeccable, the formulations reassuringly familiar. And yet the moment you learned what their knowledge was actually made of—patterns in text rather than contact with the world—something essential would dissolve.

Every day, many of us turn to tools such as OpenAI’s ChatGPT for medical advice, legal guidance, psychological insight, educational tutoring or judgments about what is true and what is not. And on some level, we know that these large language models (LLMs) are imitating an understanding of the world that they don’t actually have—even if their fluency can make that easy to forget.

But is an LLM’s reasoning anything like human judgment, or is it merely generating the linguistic silhouette of reasoning? As a scientist who studies human judgment and the dynamics of information, I recently set out with my colleagues to address this surprisingly underexplored question. We compared how LLMs and people responded when asked to make judgments across a handful of tests that have been studied for decades in psychology and neuroscience. We didn’t expect these systems to “think” like people, but we believed it would be valuable to understand how they actually differ from humans to help people evaluate how and when to use these tools.

In one experiment, we presented 50 people and six LLMs with several news sources, then asked them to rate the source’s credibility and justify their rating. Past research shows that when a person encounters a questionable headline, several things typically happen. First, the person checks the headline against what they already know about the world: whether it fits with basic facts, past events or personal experience. Second, the reader brings in expectations about the source itself, such as whether it comes from an outlet with a history of careful reporting or one known for exaggeration or bias. Third, the person considers whether the claim makes sense as part of a broader chain of events, whether it could realistically have happened and whether it aligns with how similar situations usually unfold.

Large language models cannot do the same thing. To see what they do instead, we asked leading models to evaluate the reliability of news headlines following a specific procedure. The LLMs were instructed to state the criteria they were using to judge credibility and to justify their final judgment. We observed that even when models reached similar conclusions to those of human participants, their justifications consistently reflected patterns drawn from language (such as how often a particular combination of words coincides and in what contexts) rather than references to external facts, prior events, or experience, which were factors that humans drew upon.

In other experiments, we compared humans’ and LLMs’ reasoning around moral dilemmas. Humans draw on norms, social expectations, emotional responses, and culturally shaped intuitions about harm and fairness to think about morality. As one example, when people evaluate morality, they often use causal reasoning. They consider how one event leads to another, why timing matters, and how things might have turned out differently if something had changed along the way. People imagine various situations through counterfactuals in which they ask, “What if this had been different?”

We found that a language model reproduced this form of deliberation fairly well: The model provides statements that mirror the vocabulary of care, duty or rights. It will present causal language based on patterns in language, including “if-then” counterfactuals. But importantly, the model is not actually imagining anything or engaging in any deliberation, just reproducing patterns in how people talk or write about these counterfactuals. The result can sound like causal reasoning, but the process behind it is pattern completion, not an understanding of how events actually produce outcomes in the world.

Across all the tasks we have studied, a consistent pattern emerges. Large language models can often match human responses, but for reasons that bear no resemblance to human reasoning. Where a human judges, a model correlates. Where a human evaluates, a model predicts. When a human engages with the world, a model engages with a distribution of words. Their architecture makes them extraordinarily good at reproducing patterns found in text. It does not give them access to the world those words refer to.

And yet, because human judgments are also expressed through language, the model’s answers often end up resembling human answers on the surface. This gap between what models seem to be doing and what they actually are doing is what my colleagues and I call epistemia: when the simulation of knowledge becomes indistinguishable, to the observer, from knowledge itself. Epistemia is a name for a flaw in how people interpret these models, in which linguistic plausibility is taken as a surrogate for truth. This happens because the model is fluent, and fluency is something human readers are primed to trust.

The danger here is subtle. It is not primarily that models are often wrong—people can be, too. The deeper issue is that the model cannot know when it is hallucinating, because it cannot represent truth in the first place. It cannot form beliefs, revise them or check its output against the world. It cannot distinguish a reliable claim from an unreliable one except by analogy to prior linguistic patterns. In short, it cannot do what judgment is fundamentally for.

People are already using these systems in contexts in which it is necessary to distinguish between plausibility and truth, such as law, medicine, and psychology. A model can generate a paragraph that sounds like a diagnosis, a legal analysis or a moral argument. But sound is not substance. The simulation is not the thing simulated.

None of this implies that large language models should be rejected. They are extraordinarily powerful tools when used for what they are: engines of linguistic automation, not engines of understanding. They excel at drafting, summarizing, recombining, and exploring ideas. But when we ask them to judge, we quietly redefine what judgment becomes—shifting it from a relationship between a mind and the world to a relationship between a prompt and a probability distribution.

What should a reader do with this knowledge? Do not fear these systems, but seek a clearer understanding of what these systems can and cannot do. Remember that smoothness is not insight, and eloquence is not evidence of understanding. Treat large language models as sophisticated linguistic instruments that require human oversight precisely because they lack access to the domain that judgment ultimately depends on: the world itself.

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Engineering majors earn some of the highest salaries right after college — and they’re still near the top years later.

That’s according to the latest findings from the Federal Reserve Bank of New York, based on 2024 U.S. Census data, the most recent available.

The analysis breaks down annual earnings for college graduates by major and reflects the income of full-time workers whose highest degree is a bachelor’s, excluding currently enrolled students. Various engineering degrees have consistently ranked among the top-paying fields in recent years.

The highest-paying majors for workers ages 22 to 27 are computer engineering, computer science, and chemical engineering, with recent graduates earning median early-career salaries of $85,000 or more. That’s well above the U.S  median personal income of just over $45,000, according to U.S. Census Bureau data. 

However, engineering accounts for about 6% of bachelor’s degrees awarded nationwide, representing a relatively small share of all college graduates, according to the most recent data from the National Center for Education Statistics.

Here are the 18 majors that lead to the highest salaries for workers ages 22 to 27:

1. Computer engineering: $90,000
2. Computer science: $87,000
3. Chemical engineering: $85,000
4. Aerospace engineering: $85,000
5. Industrial engineering: $83,000
6. Electrical engineering: $82,000
7. Mechanical engineering: $80,000
8. Construction services: $75,000
9. Civil engineering: $75,000
10. General engineering: $75,000
11. Miscellaneous engineering: $75,000
12. Economics: $72,000
13. Business analytics: $72,000
14. Finance: $70,000
15. Mathematics: $70,000
16. Nursing: $70,000
17. Mathematics: $70,000
18. Finance: $70,000

Engineering graduates remain in strong demand for their mix of mathematical skills and technical expertise, which are valuable across a wide range of industries, data from the National Association of Colleges and Employers shows.

While artificial intelligence is reshaping how the technical work is done, employment in many engineering fields is still projected to grow over the next decade, according to the Bureau of Labor Statistics.

Engineering also tends to pay well over time. Among graduates ages 35 to 45, every engineering major reports median pay of at least $100,000. Here are all of the majors with mid-career median earnings of $100,000 or more, according to the New York Fed.

• Chemical engineering: $135,000
• Computer engineering: $131,000
• Aerospace engineering: $130,000
• Electrical engineering: $123,000
• Computer science: $120,000
• Mechanical engineering: $120,000
• Construction services: $120,000
• Civil engineering: $115,000
• Economics: $115,000
• Finance: $112,000
• Business analytics: $109,000
• General engineering: $105,000
• Miscellaneous engineering: $105,000
• Physics: $105,000
• Engineering technologies: $104,000
• Industrial engineering: $100,000
• Mathematics: $100,000
• Information systems: $100,000
• Marketing: $100,000
• Biochemistry: $100,000
• Political science: $100,000

In contrast, education and arts majors tend to earn significantly less overall. By ages 35 to 45, six fields of education majors report median earnings below $60,000, placing them among the lower-paid fields in the study

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