This is a guest post by Hebron Girma Belete, written with Mahelet G. Fikru.

Last updated: October 23, 2026 · This is a living Q&A. We add new questions as students ask them.

A quick note before you read: these answers are a guide, not the final word. They come from our own experience as students, applicants, and mentors. Programs, deadlines, and scholarship rules change every year, so always check the official website of each university and scholarship before you apply.

This is Part 2 of Grad School Application 101, our three-part Q&A guide for first-time master’s and PhD applicants. The questions come from students in our monthly mentorship group. The answers are ours.

Part 1, Grad School Application 101, Part 1: How to Plan and Build Your Profile Before You Apply, covers what to do months in advance. This part covers the application itself: how to apply intentionally instead of randomly. Part 3, Grad School Application 101, Part 3: What to Do After You Apply to Grad School, covers what comes after you submit.

Have a question that is not on this list? Ask us in the comments. We respond to every comment and add new questions to this directory.

The short answer

Apply strategically, not randomly. Research each program, confirm that you meet its eligibility requirements, tailor your Statement of Purpose and CV to it, and submit a complete application, including optional materials, before the deadline.

Contents

1. Researching programs and checking eligibility

2. Deadlines: US vs. Europe and rolling admissions

3. Scholarships and admission: which do you apply for first?

4. Your Statement of Purpose and CV

5. Submitting a complete application

1. Researching programs and checking eligibility

How do I know which schools are realistic for me?

Filter programs by whether they offer your field and, if you need funding, whether they offer scholarships. Make a shortlist of strong matches, then apply strategically within that list. If you are applying in the US, Grad School Finder can help you start: it suggests a shortlist of 10 programs based on your degree, field, and profile, including those more likely to offer funding.

Once you have a potential program, reach out to students who are currently enrolled in it or have graduated recently. You can often find them on the program’s website or on LinkedIn. Ask a few short questions about funding, coursework, and what helped their application. They can tell you things the official website does not.

How do I check whether I am eligible?

Read each program’s and scholarship’s eligibility requirements before you start an application: the academic background required, minimum grades, language scores, work experience, and supporting documents. Requirements differ from one program to the next.

Work experience is a good example: Chevening asks for at least 2,800 hours, about two years of full-time work, while the Swedish government’s scholarship for global professionals asks applicants from many countries for at least 3,000 hours plus leadership experience. Other programs set no minimum work requirement at all. If you do not meet a firm requirement, you may not be able to submit the application.

What if my transcript shows courses that are unrelated to the program?

First, check the program’s specific entry requirements. If you meet them, address the change directly in your Statement of Purpose: give clear reasons for the change in direction, and show how your past experience still adds value to the field you want to study. If you do not meet the requirements but are very interested in the program, contact the admissions office and ask whether your background could qualify.

2. Deadlines: US vs. Europe and rolling admissions

What is the difference between applying to graduate programs in the US and in Europe?

US programs are more standardized. Many have fixed deadlines around the end of the year. European programs, including Erasmus programs and other international scholarship programs, have more varied timelines, and some universities admit students on a rolling basis. Scholarship and document requirements also differ, including which language tests and scores are accepted (see Part 1). Both are highly competitive, so plan well in advance.

What does “rolling admissions” mean?

The program reviews applications as they arrive, rather than waiting for a single deadline. Spots and funding can fill up before the final date, so applying early improves your chances. Programs that admit on a rolling basis usually state this on their admissions pages, so check each program and apply early.

3. Scholarships and admission: which do you apply for first?

Do I apply for the scholarship and university admission at the same time?

It depends on the program and the scholarship. This is one of the questions we are asked most often, because the order varies, and following the wrong order can cost you the scholarship. Most programs and scholarships follow one of three setups:

• One application for both. For Erasmus Mundus master’s programs, you generally apply for admission and the scholarship through the same application, submitted to the program by its deadline.

• Admission first, then the scholarship. In Sweden, you typically apply for university admission first and then submit a separate scholarship application, such as the Swedish Institute Scholarship for Global Professionals. If you miss the admission deadline, you usually cannot apply for the scholarship.

• Scholarship first, then the university. Some scholarships, often government-funded, select scholars first, and the university application comes afterward. In some cases, the scholarship program helps place you at a university.

Before you start, find out which setup applies to each program on your list. Read the scholarship’s official page and write down both deadlines, since admission and scholarship deadlines are often weeks apart. If the page is unclear, email the program or scholarship office and ask.

4. Your Statement of Purpose and CV

Can I use the same Statement of Purpose for every program?

No. A generic Statement of Purpose is one of the most common mistakes we see. Tailor it to each program, each department, and even to the specific professors you want to work with (if relevant). Your statement should tell a coherent story that connects your background, research interests, and future goals.

Not sure whether a program wants a personal statement or a statement of purpose? Read Are you writing the wrong essay for your grad school application?

How specific should my Statement of Purpose be?

Fully customized. If you apply to a financial economics program, your story should center on finance. If you apply to a sustainability program, your story should emphasize your environmental and policy interests. One document cannot do both jobs.

What should my CV highlight?

Align your CV with the program’s focus. Put your relevant coursework, research experience, and skills where the committee can see them quickly. Keep it clear, concise, and uncluttered. Include only your most relevant experiences, skills, and achievements, and leave out anything that does not support your application.

If you are applying in Europe, consider preparing a Europass CV. It is a standard CV format widely used for European applications, and some universities and scholarship programs specifically require it. Not all institutions do, so always check the requirements of each program you apply to. You can create one for free with the official Europass CV editor.

How do I compete when so many international students apply?

Be realistic about the competition, and be strategic. Read the scholarship criteria carefully, apply to several programs and scholarships simultaneously, and make your materials as specific as possible. Showing genuine, specific research interest in your materials helps you stand out. Prepare and scan your documents ahead of time so you are not rushing when deadlines arrive.

Highlight your experiences in your CV and Statement of Purpose, because they are what distinguish your application from others. Learn to turn what may look like a disadvantage into a strength. Your background, challenges, and experiences give you perspectives other applicants may not have. Reflect on what they taught you, how they shaped your goals, and why they make you a strong candidate, and tell that story clearly.

5. Submitting a complete application

Should I submit optional application materials?

Yes, always. When a document is listed as optional or preferred, submitting it shows commitment and thoroughness. Optional materials are your chance to shine and to stand out from other applicants. This matters more than it may seem: when two applicants have similar achievements, the extra documents one of them submits can be what gets them selected. Never leave required documents incomplete, either. An incomplete application is a common, avoidable mistake.

Should I use AI in my college application?

You can use AI to help you, but not to write your application for you. Admissions committees read hundreds of statements, and AI-polished writing tends to sound the same: confident claims with no evidence behind them. What makes your application stand out is the part only you can provide: your own experience, reasoning, and goals.

Be aware that some programs and scholarships now ask applicants directly whether they used AI, and some screen applications for AI-generated writing.

This is one of the most common questions we get, and it deserves more than a paragraph. For more advice on how to use AI to strengthen your CV, and why it cannot replace real evidence of your skills, read our post with AI Wide Open: When Everyone Sounds Qualified, What Counts as Evidence?

How can I avoid or reduce graduate application fees?

Attend the university’s information webinars. Many programs offer application fee waivers to webinar attendees. You can also email the university directly to request a fee waiver, especially if you meet the minimum eligibility requirements.

Who should I contact with questions, and should I cold email professors?

It depends on your question.

Questions about the application process: For technical or logistical questions, such as required materials, where to submit documents, or problems with the submission portal, contact the admissions office directly.

Questions about the program: For questions about eligibility or about the program itself, such as its content, structure, or field-specific requirements, contact the department directly. Many departments have a graduate coordinator who handles these questions.

Research assistant positions: If you find a posting where a professor is hiring a research assistant (RA) and lists an email address, you can email that professor with questions about the position. To learn how to stand out when applying, read What Professors Actually Look for in a Research Assistant and How to Stand Out.

Professors whose research matches yours: If you have strong research experience related to a professor’s work, apply to the program first, then reach out to ask about positions. Professors generally will not consider you if you have not applied or are not eligible to apply, because the university has to admit you before you can work for a professor. In general, do not send generic cold emails.

Your next steps

1. Confirm that you meet all eligibility requirements before you start an application.

2. Add every deadline to your tracker, and apply early to rolling-admission programs.

3. Write a separate Statement of Purpose and CV for each program.

4. Submit all optional materials.

5. Attend university webinars and ask about application fee waivers.

Continue the series

Previous: Grad School Application 101, Part 1: How to Plan and Build Your Profile Before You Apply, on choosing your field, building your profile, language tests, and scholarships.

Next: Grad School Application 101, Part 3: What to Do After You Apply to Grad School, on tracking your application, interviews, contacting professors, and finding more funding.

If you are looking for US graduate programs, try Grad School Finder, a free tool for subscribers. It suggests programs tailored to your degree, field, and profile, including those that are likely to offer funding.

Ask your question

This directory grows with your questions. Ask your Grad School-related questions in the comments below. We respond to every comment and add your questions here, so the next student can find them too.

These answers are a guide, not the final word. Always confirm details with the official university and scholarship websites.

About the authors

Hebron Girma Belete is an Erasmus Mundus Master’s student in Economic Policies for the Global Bifurcation, specializing in the digital economy, with experience working in international organizations

Mahelet G. Fikru is a professor of economics at Missouri S&T, with 15 years of experience teaching and mentoring students. She writes The Grad School Path, a section of her newsletter with practical advice on applying to graduate programs.

Imagine two students applying for the same internship.

Both submit polished résumés. Both describe themselves as analytical thinkers with strong problem-solving skills. Both used AI to refine their applications and match their experience to the job description.

One has completed a data analysis project and can explain the decisions behind it, the mistakes they made, and how they interpreted the results. The other has used AI to describe a limited classroom exercise, making it sound far more advanced.

Their applications may look equally convincing. If a recruiter or an AI screening system judges them mainly on what is written, how reliably can it tell the difference?

When AI can make any application sound polished, written claims no longer serve as reliable evidence of skill. The fix is to separate what applicants say they can do from what they can show and explain.

This matters for every student preparing for college admissions or the job market, because AI use is now routine. In a 2025 survey of 1,041 full-time UK undergraduates, 92 percent reported using an AI tool, and 88 percent had used generative AI for assessments. Job seekers are no different: about two-thirds of job candidates say they use AI when applying for jobs. As polished writing becomes easy to produce, a convincing application loses value as a standalone signal of competence.

The problem is not that students use AI. It is that application systems may keep rewarding polished presentations without asking for the evidence that supports the claims.

What Is the Difference Between a Claim and a Capability?

A claim is what an applicant says they can do. A capability is what they can show and explain.

For years, applicants have been told to use strong action verbs, include keywords, quantify achievements, and tailor each résumé. AI makes all of this easier. A student who struggles to communicate their experience can now get help organizing their experience and clearly describing their contributions. That is a genuine benefit.

But the same technology can make an unsupported claim sound credible. Consider this sentence:

“I used data-driven insights to improve operational efficiency.”

It sounds professional, yet it leaves the important questions open. What data did the applicant analyze? What did they find? What decision followed? Did the process improve, and how do they know? AI can polish the sentence without answering any of them.

A student should not have to be an exceptional writer to prove they are a capable analyst. Equally, an institution should not assume a polished description proves analytical ability. The challenge is to make the underlying evidence visible without turning every application into an exam.

How Does AI Change Résumé Screening for Applicants and Employers?

When applicants use AI to write, and employers use AI to screen, the application language converges and stops distinguishing candidates.

Students are not the only ones optimizing. Employers use applicant tracking systems (ATS) and AI screening tools to sort applications and flag qualifications, and some universities use AI in parts of the admissions process, though practices vary widely. In a 2025 Resume.org survey, 57 percent of companies used AI to support hiring, and 79 percent of those used it to review résumés.

Applicants emphasize the language they believe evaluators value. Institutions rely on structured signals to process applications at scale. AI makes it easier both to produce those signals and to process them.

The trouble comes when the signal replaces the quality it was meant to represent. If every résumé uses the same vocabulary about leadership, adaptability, and critical thinking, those words say little about how candidates differ. Hiring managers already see this: in Resume Genius’s 2026 survey of 1,000 U.S. hiring managers, 77 percent said many résumés appear AI-generated, even as most said résumés had become more polished and better tailored. If institutions respond with more automated screening, applicants have even more reason to optimize their language. Both sides become more efficient without becoming better at identifying ability.

This outcome is not inevitable. Screening systems differ, and the risk depends on what a system measures, how its outputs are used, and whether meaningful human review is in place. But it raises a design question: when the usual signals become easy to manufacture, what should replace them?

How Can Applications Be Evaluated in the AI Era? The Claim–Evidence–Evaluation–Review Framework

The Claim–Evidence–Evaluation–Review framework treats an application as a set of claims to be communicated and checked, rather than a document to be optimized. It has four steps.

1. Claim: What is the applicant saying they can do?

A student might claim strong analytical skills, leadership experience, or the ability to do independent research. These claims should relate to the opportunity, not simply repeat qualities listed in a job description or admissions guide.

2. Evidence: What supports the claim?

Evidence can be a project, a work sample, a documented contribution, an achievement, or a detailed account of how the applicant approached a problem. A student applying for a data-related internship, for example, could describe cleaning a dataset, comparing approaches, and explaining why they chose one method over another.

What matters is not how sophisticated the project is, but whether the student can explain what they did and what they learned. AI can help communicate that experience. It should not invent the project, inflate the student’s contribution, or supply conclusions that the student cannot defend.

3. Evaluation: How does the institution assess the evidence?

The method should fit the capability being assessed. An employer hiring someone to interpret data will learn more from a work sample or a discussion of a past project than from résumé language. A university interested in intellectual curiosity should create chances to see a student’s reasoning, not rely only on style.

Research on hiring supports this. A 2022 re-analysis of hiring research found that structured interviews were the strongest predictor of job performance, and that job-specific methods such as job knowledge tests and work sample tests ranked above general cognitive ability tests.

No method is perfect. Work samples can disadvantage applicants with fewer resources, interviews can introduce bias, and extra assessments add burden. Methods should be proportionate, relevant, and accessible.

4. Review: What happens when the assessment is wrong?

An automated recommendation should not be assumed correct. This is not a hypothetical concern: in the Resume.org survey, only 26 percent of companies said a human reviews every rejection AI makes, and 57 percent of companies using AI worried it could screen out qualified applicants. Institutions should define when human review is needed, who is responsible for it, and how applicants can raise concerns. A human somewhere in the workflow is not enough; someone must have the authority, information, and responsibility to question a result.

How Should Students Use AI on Résumés and Applications?

Use AI to communicate real experience, not to upgrade it. Students cannot redesign the systems that evaluate them, but before submitting an application, they can ask themselves three questions.

Can I back up every important claim?

If I call myself a strong problem solver, can I describe a real problem I worked through and the decisions I made?

Can I explain my contribution without the application in front of me?

If someone asks about a project, can I describe my role, the difficulties I ran into, and what I learned?

Has AI improved my communication, or changed the substance of my experience?

If the tool made my work sound more impressive than it was, I should revise the description rather than keep the flattering version.

Students should also weigh the privacy implications of uploading personal documents or confidential information to AI tools.

These questions will not guarantee an interview or an admission offer. They do help students build applications that accurately represent their abilities and prepare them to discuss their work with confidence.

The Bottom Line

AI has made it easy to sound qualified, so sounding qualified no longer proves much. The Claim–Evidence–Evaluation–Review framework shifts attention from how an application reads to what supports it. For students, the goal is not an elaborate portfolio. It is making sure the claims they emphasize are relevant, specific, and defensible.

Frequently Asked Questions

Is it wrong for students to use AI on their résumés or applications?

No. AI can help students organize their experience and describe it clearly, which is a real benefit for those who struggle with professional writing. The problem starts when AI changes the substance, making a small classroom exercise sound like advanced work. The test is whether you can explain and defend every claim.

Can employers tell whether an application was written with AI?

The better question is whether the application’s claims are supported by evidence. Employers learn more from a work sample, a walkthrough of a past project, or a short discussion of decisions the applicant made than from trying to judge who wrote the sentences.

What counts as evidence of a skill on an application?

Evidence is anything that shows what you actually did: a project, a work sample, a documented contribution, or a detailed account of how you approached a problem. It does not need to be sophisticated. What matters is that you can explain your choices, your mistakes, and what you learned.

Should a human review decisions made by AI screening tools?

Yes, and the review has to be real. Institutions should define when human review occurs, who is responsible for it, and how an applicant can request that a decision be reconsidered. That person needs the authority and information to overturn a result.

About the Authors

Zohre Shirazi is the founder and editor of AI Wide Open, exploring where AI creates real value beyond the hype, from everyday workflows to the way businesses operate.

Mahelet G. Fikru is a professor of economics at Missouri S&T with over 15 years of experience teaching college students. She writes The Grad School Path, a section of her newsletter with practical advice on applying to graduate programs.

This is a guest post by Hebron Girma Belete, written with Mahelet G. Fikru.

Last updated: October 19, 2026 · This is a living Q&A. We add new questions as students ask them.

A quick note before you read: these answers are a guide, not the final word. They come from our own experience as students, applicants, and mentors. Programs, deadlines, and scholarship rules change every year, so always check the official website of each university and scholarship before you apply.

Every month, we meet with students in our mentorship group on Zoom. Many of them are applying to master’s and PhD programs in the US and Europe for the first time, and the same questions keep coming up. So we organized them into Grad School Application 101, a three-part Q&A guide that follows the application journey from start to finish:

• Part 1: Before you apply (this post): choosing your field, building your profile, standardized tests, scholarships, and getting organized

• Part 2: While you apply: Grad School Application 101, Part 2: How to Apply to Grad School Strategically

• Part 3: After you apply: Grad School Application 101, Part 3: What to Do After You Apply to Grad School

Think of this post as a directory. The questions come from students. The answers are ours. When a new question comes up, we add it here.

Have a question that is not on this list? Ask us in the comments. We respond to every comment and add new questions to this directory.

The short answer

Start months before applications open. Decide what career you want and which field leads there, then build experience that fits that field. Prepare for standardized tests, shortlist scholarships, and gather your documents early. Most application steps take a few weeks. A relevant profile takes months to build.

Contents

1. Choosing your field and programs

2. Building your profile: experience, internships, and LinkedIn

3. Standardized tests: IELTS, TOEFL, and the GRE

4. Scholarships

5. Getting organized: documents and an application tracker

1. Choosing your field and programs

Is grad school worth it for me?

Grad school is worth it when what it gives you outweighs what you give up to attend. Economists call what you give up the opportunity cost, and it is different for each person. So the real question is not “Is grad school worth it?” but “Is grad school worth it for me, right now?” Read the full answer in Is Grad School Worth It? Start With Opportunity Cost, Not the Application Essay.

How do I choose which graduate programs to apply to?

Start with your academic background, your research interests, and your career goals. Then, research programs carefully before you commit. If you studied economics, accounting, or another social science, you have more options than you think: applied economics, financial economics, development economics, energy economics, an MA in Sustainability, or an MA in Environmental Policy.

Choose programs that match the courses you took and the experience you have. Doing this research first saves you time and money.

Should I apply to many different fields to increase my chances?

No. Narrow your focus within one field, for example, financial economics or development economics. When your focus is clear, it is easier to match with the right programs and the right professors. Applying across unrelated fields makes your story confusing.

Can I get into a graduate program if my background is in a different field?

Yes, but you have to explain the transition. Use your Statement of Purpose to say why you are moving into the new field, how your prior experience is relevant, and how it supports your academic goals. A different background can even be an advantage, since many professors do interdisciplinary research that connects to other fields. For example, someone with an economics background may fit well with faculty who study finance. A non-traditional background works in your favor when you frame it well.

2. Building your profile: experience, internships, and LinkedIn

Start here early. Most application steps can be done in a few weeks, but relevant experience and a professional network take months to build.

How do I build a profile that fits the program I want?

Choose activities connected to the field you want to study. If you are interested in finance, for example, look for volunteering, internships, or other work in the financial sector. Each experience gives you concrete material to include in your CV and Statement of Purpose, and it shows the admissions committee that your interest is genuine. It also helps with scholarships, since some require a set number of years of work experience.

How should I use LinkedIn for graduate school and academic opportunities?

Build your LinkedIn profile intentionally. Post relevant content, connect with researchers and professors, and use your profile to signal your research interests and professional goals. An active, well-structured profile makes you visible to potential supervisors and collaborators.

3. Standardized tests: IELTS, TOEFL, and the GRE

Many applicants need two kinds of standardized tests. English proficiency tests (IELTS and TOEFL) show that you can study in English. The GRE is not an English test. It measures verbal reasoning, quantitative reasoning, and analytical writing, and some programs use it to compare applicants.

Do I need IELTS or TOEFL?

Most programs taught in English require proof of English proficiency, and many accept either the IELTS or the TOEFL. Check each program’s page for the tests it accepts, the minimum overall score, and any minimum section scores.

Can I apply without IELTS or TOEFL if my degree was taught in English?

Sometimes. Some programs accept a Medium of Instruction (MOI) certificate. This is an official letter from your university confirming that your undergraduate degree was taught in English. Depending on the program, you may be able to apply without taking IELTS or TOEFL. If a program does not mention MOI, you can contact the admissions office to request a waiver. Even so, we recommend taking the test if you can. A strong score helps your application stand out.

Do I need the GRE?

It depends on the program. Many fully funded US programs, especially in economics and STEM fields, ask for GRE scores, while some have made the GRE optional. Most European master’s programs do not require it, but some do. Check each program’s requirements before you register.

When should I take these tests?

Early. Book your test date so your scores are ready well before your first deadline, with enough time to retake the test if you need a higher score.

4. Scholarships

Which scholarships should I look at for a master’s or PhD abroad?

There are many scholarships for graduate study abroad. Below are a few examples of well-known programs, listed to show the range of options, not as recommendations. Each has its own eligibility rules, priorities, and deadlines, and some funds only support master’s studies, so read the official criteria and decide which ones fit you.

For example:

• Erasmus Mundus Joint Masters: joint master’s programs taught at universities in several countries

• Chevening Scholarships: one-year master’s study in the UK

• DAAD Scholarships: master’s and PhD study in Germany

• Mastercard Foundation Scholars Program: offered through partner universities, for young Africans

• France Excellence Eiffel Scholarship Program: master’s and PhD studies in France; the French university nominates candidates

• Swedish Institute Scholarships for Global Professionals: master’s studies in Sweden, for citizens of eligible countries

• Commonwealth Scholarships: master’s and PhD study in the UK, for residents of eligible Commonwealth countries

This is not a complete list, a ranking, or a recommendation. Some European universities also run their own scholarship programs with their own deadlines and rules.

How do I know which scholarship is the best fit for me?

Look for programs whose priorities match your academic background and research interests. Some of these scholarships may focus on education and align with the UN Sustainable Development Goals (SDGs) or development more broadly. If your interests are in development or SDG-related topics, frame your profile and research interests around that. Relevance to the program’s goals is a key selection criterion.

Some programs also weigh social impact heavily. For example, the Mastercard Foundation Scholars Program and the Swedish Institute Scholarships for Global Professionals both describe social impact as part of their goals.

As a student from a developing country, how can I position myself for programs at Western universities?

Be intentional and confident. Many funding bodies prioritize research relevant to developing countries and the Global South, so connect your research interests to that context when it fits. Present your experience clearly, and do not undersell yourself. Imposter syndrome is real, but your profile deserves to be presented with confidence.

5. Getting organized: documents and an application tracker

What documents should I prepare in advance?

Gather and scan the documents most programs ask for: transcripts, degree certificates, your passport, test scores, and an up-to-date CV. Ask your recommenders early, so they have time to write strong letters. Having everything ready means you are not rushing when deadlines arrive.

How do I keep track of my applications?

Create a spreadsheet in Excel or Google Sheets with one row per program. Track the university, program, deadline, requirements (tests, documents, work experience), scholarship options, application fee or fee waiver, and the status of your application. Update it as you complete each step. You will keep using it after you apply.

Your next steps

1. Write down the career you want and the field that leads there.

2. Pick one or two activities that build experience in that field.

3. Update your LinkedIn profile and connect with researchers in your field.

4. Book your IELTS or TOEFL date, and the GRE if your programs require it.

5. Shortlist scholarships and note their requirements.

6. Start your application tracker and scan your documents.

Continue the series

Next: Grad School Application 101, Part 2: How to Apply to Grad School Strategically, on checking eligibility, meeting deadlines, and tailoring your Statement of Purpose and CV.

Then: Grad School Application 101, Part 3: What to Do After You Apply to Grad School, on tracking your application, interviews, contacting professors, and finding more funding.

If you are looking for US graduate programs, try Grad School Finder, a free tool for subscribers. It suggests programs tailored to your degree, field, and profile, including those that are likely to offer funding.

Ask your question

This directory grows with your questions. Ask your Grad School-related questions in the comments below. We respond to every comment and add your questions here, so the next student can find them too.

These answers are a guide, not the final word. Always confirm details with the official university and scholarship websites.

About the authors

Hebron Girma Belete is an Erasmus Mundus Master’s student in Economic Policies for the Global Bifurcation, specializing in the digital economy, with experience working in international organizations

Mahelet G. Fikru is a professor of economics at Missouri S&T, with 15 years of experience teaching and mentoring students. She writes The Grad School Path, a section of her newsletter with practical advice on applying to graduate programs.

The Finder is free for subscribers. If you are not subscribed yet, subscribe for free and request access with the same email.

Disclaimer: The Finder is only an initial step in your search. It is not an admission decision, an approval, or a recommendation of any program.

Why I built the Grad School Finder

Every application cycle, students ask me the same first question: Where should I even apply? Most start with rankings, open dozens of tabs, and spend weeks on schools that were never a realistic fit for their background. Application fees add up quickly when that happens.

In my last post, Is Grad School Worth It?, I argued that the decision to go to grad school starts with opportunity cost, not the essay. The Finder is the practical next step. It draws on several public data sources to give you a starting list built around your degree, grades, and experience, so your time and money go toward universities that match your background.

Who is the Grad School Finder for?

Students in any country who plan to apply to a master's or PhD program in the United States, including those switching fields. It is most useful at the start of your search, before you spend money on application fees.

What students are saying

Beckham Berhanu, one of the first students to use it, put it simply:

"The grad school finder saves a lot of time."

He also gave me two suggestions I am taking seriously: link directly to the specific program or scholarship page instead of the general university website, and add a Go/No-Go recommendation based on an applicant's needs, such as funding, standardized tests, and years of experience.

Kibreab Wudu Zegeye, who read the opportunity cost post, tried it soon after:

“I checked on it and it works perfect. The matching is truly great.”

He also asked whether it could cover Europe and Australia, since U.S. requirements like the GRE and IELTS hold many applicants back.

How do I get access to the Grad School Finder?

1. For subscribers, enter your email at gradfinder-mgfikru.netlify.app.

2. Wait for approval. I check each request against the subscriber list and approve it within 24 hours.

3. Sign in with the code sent to your email.

Each approval gives you 7 days of access, starting from your first search, and up to 10 searches a week. When your 7 days are up, you can ask for another 7 days from the same screen.

What information do I need to enter?

Have these ready before you start:

• Where you live: your country.

• Your bachelor's degree: your major, the year you graduated (or expect to), your CGPA out of 4.0, and whether the degree was taught in English.

• Your master's degree, if you have one.

• Work experience: organization and dates. Leave the end date empty if it is your current job.

• What you want: degree level (master's or PhD), the field you want to study, and where in the United States. The field is filled in from your major. Change it if you are switching fields.

Then click Show my programs.

How do I read my results?

You will see three things:

1. Every U.S. university that awards your degree. Schools are sorted into three groups based on how your CGPA and experience compare with each university. Within each group, the tool also shows which schools are more likely to offer funding, based on estimates derived from several public data sources.

2. A shortlist of 10. These are the universities that best match your background, and the ones to look into first. For each one, you will see its admission fit, funding chance, and links to the university website.

3. An application checklist. For each item, you tell the tool where you are, so you can see what is still missing.

If you are switching fields, the tool will remind you that many programs accept career changers but expect prerequisite courses, especially in math or statistics. If no university reported awarding your degree at that level, try turning on related programs or choosing a broader field.

What doesn't the Grad School Finder do?

The Finder is an initial check, not advice or a prediction of admission or funding. It works only from what you enter and from public data. Before you apply, read the program's page, confirm the deadline, and check what the application requires.

Two reminders from my own experience:

• Apply first, then reach out to professors. Once your application is in, a brief, direct email to a faculty member whose work fits yours is appropriate. I explain what professors look for in What Professors Actually Look for in a Research Assistant.

• For international applicants: after admission, you will need to demonstrate that you can cover any costs not covered by your funding to obtain your I-20 for the student visa. Plan for this early.

Where to go next

Your shortlist is the starting point. For the next steps, these posts go deeper:

• Deciding whether to go: Is Grad School Worth It? Start With Opportunity Cost, Not the Application Essay

• Writing your essay: Are You Writing the Wrong Essay for Your Grad School Application? explains the difference between a personal statement and a statement of purpose.

• Working with professors: What Professors Actually Look for in a Research Assistant and How to Stand Out

Frequently asked questions

Is the Grad School Finder free?

Yes. It is free for M G Fikru newsletter subscribers, and subscribing is free. Not a subscriber yet? Subscribe first, then request access.

Does it cover master's and PhD programs?

Yes. You choose the degree level when you enter your profile.

Does it cover universities outside the United States?

No. It covers only U.S. programs, but you can use it from any country.

Where does the data come from?

The list of universities comes from College Navigator, the U.S. Department of Education's college search website. The funding chance is calculated from several other public data sources.

How many searches can I run?

Up to 10 a week, during each 7-day access period.

How long does approval take?

Usually within 24 hours. I check each request against the subscriber list.

Is the Finder a recommendation or an admission decision?

No. It is an initial step to help you build a starting list. Always confirm details on each program's own page.

Try it and tell me what you find

Open the Grad School Finder, run your first search, and tell me in the comments what surprised you in your shortlist. Suggestions from students are how this tool can get better.

If you know someone applying to grad school this cycle, send them this post. A good shortlist early in the process saves weeks of searching and a lot of application fees.

I am Mahelet G. Fikru, a professor at Missouri S&T with more than 15 years of experience teaching and mentoring students. I write The Grad School Path to share practical advice on applying to master's and PhD programs.

Last updated: October 18, 2026.

The question behind the question

Many students come to me with the same question: How do I stand out in my grad school application? The first thing that comes to my mind is a different question. Are you ready for it? Do you want it? Have you thought it through? Or does it simply sound like the natural next step because you have all A's?

Many students assume that strong grades lead naturally to graduate school. They do not. Good grades show you can succeed in coursework. They do not show that you want two to six more years of study, or that you are motivated by research. Those are separate decisions, and they deserve separate thinking.

Are you sending mixed signals?

Most students I meet are trying to do both at once after graduation. They turn on the "Open to Work" sign on LinkedIn while also preparing graduate applications. I understand the instinct to keep options open, but it often sends mixed signals. Which one is it: a job until you are admitted, or graduate school until you find a good job?

Graduate programs want students who are eager to be there, especially those motivated toward research. Employers also want the full you, not a split version who spends half the workday searching for graduate program openings. Both sides can usually tell the difference.

What is opportunity cost, and why is yours unique?

Opportunity cost is the value of the best alternative you give up when you make a choice. For graduate school, it includes the salary you would have earned, the work experience you would have gained, and the years themselves. Tuition is only part of the cost.

That cost varies from person to person. A student with a strong job offer in a field that rewards experience gives up a lot by enrolling. A student whose career goal requires a research degree, such as a university faculty position, gives up much less because the job path itself runs through the degree.

The benefits vary too. In a study of U.S. graduates, Altonji and Zhong (2021, Journal of Labor Economics) find that earnings gains from advanced degrees differ widely across fields and even depend on a person's undergraduate major. In their estimates, a few master's fields show negative rates of return. The same degree can be a strong investment for one person and a weak one for another.

Three questions to ask before you apply

1. What specific goal requires this degree? If you cannot name one, pause.

2. What would I give up by enrolling now? List the job, the salary, and the experience.

3. Would I still choose it if no one were watching? Choose it for your goals, not for the signal it sends.

If you answer these honestly, the application essay becomes easier to write, because you will know exactly why you are applying. For the essay itself, see my earlier post, Are you writing the wrong essay for your grad school application?, and for research roles, What Professors Actually Look for in a Research Assistant.

A free tool for your next step

If you have thought it through and decided graduate school is right for you, I built a free tool to help with what comes next. Grad School Finder lists the U.S. universities that award the master’s or PhD you want, and suggests a shortlist of 10 to look into first, with an application checklist.

It is designed for students planning to apply to master's or PhD programs in the United States, and it is free for subscribers to this newsletter. Try Grad School Finder here.

Grad School Finder is an initial check, not advice. Always confirm requirements, funding, and deadlines on each university’s website.

If you are weighing this decision right now, tell me in the comments what you are deciding between.

Know someone applying to grad school? Share this post with them. If it resonated, please like and restack it so it reaches more students weighing the same decision.

Read more on grad school and research careers:

• Are you writing the wrong essay for your grad school application?

• What Professors Actually Look for in a Research Assistant

• How to Better Guide Student Researchers in the AI Age

What does the research say about AI and student learning?

The clearest evidence comes from a field experiment involving nearly 1,000 high school math students. Bastani et al. (2025, PNAS) found that access to a standard ChatGPT-style tool increased performance on practice problems by 48 percent. But when access was removed, those students scored 17 percent lower on the exam than students who never had it. A tutor version designed to give hints rather than answers increased practice performance by 127 percent without the exam penalty.

The difference was not the technology. It was who did the thinking.

Survey research points the same way. In a study of 666 participants, Gerlich (2025, Societies) found that frequent AI use was associated with lower critical-thinking scores, a link explained by cognitive offloading, which means handing mental work to the tool.

I see this in my own classroom. In my environmental economics course, about 18 percent of first-week reflections in 2022 listed the same combination of environmental problems that ChatGPT typically returns. By 2023, that share was 48 percent, and it was still 45 percent in 2025. I wrote about those three years of data in Teaching Economics in the Age of AI.

How I use AI as an economics professor

I use AI every week, for tasks where my judgment stays in charge.

Weekly reminder emails. I gave an AI tool my syllabus. Every Friday, it drafts a reminder email for students covering what is due that weekend, what is coming next week, and what to look forward to. I review the draft and then post it to our learning management system. The AI saves me time on formatting. I still decide what students need to hear.

Interactive worksheets. I also use AI to build interactive worksheets where students shift a market, adjust inputs, and see a concept respond, while they still do the reasoning. I collect them in Let's Do Classwork!

In both cases, AI handles the routine work, and the thinking stays with a person.

A simple test: who did the thinking?

Before you use AI on an assignment, ask one question: Is this the step I am supposed to learn?

• Use AI to explain a concept in a different way, quiz yourself, or check your reasoning after you have tried.

• Do not use AI to produce the answer to a problem you are expected to solve yourself.

Using AI to understand why a firm maximizes profit where marginal revenue equals marginal cost supports learning. Using AI to solve the assigned profit-maximization problem replaces the need to solve it. For a classroom example of this line in practice, see They All Got A's. None of Them Learned Anything.

Frequently asked questions

Does using ChatGPT make students worse at thinking?

Not by itself. Students lose skills when AI completes the reasoning a task was meant to build. In a field experiment with nearly 1,000 students, answer-giving AI lowered exam scores by 17 percent, while a hint-based tutor did not.

What is cognitive offloading?

Cognitive offloading means handing mental work to a tool. Some offloading is useful, like using a calculator. It becomes a problem when the offloaded step is the skill you are supposed to learn.

How can students use AI without becoming lazy thinkers?

Ask one question first: is this the step I am supposed to learn? If yes, attempt it yourself, then use AI to explain, quiz you, or check your work.

Should professors ban AI in their courses?

In my experience, redesigning assignments works better than banning AI. Students do the reasoning in class, and AI handles routine tasks or gives feedback.

Educators: What is one task you hand to AI, and one you never will? Share it in the comments.

Know someone confused about AI use in education? Share this post with them, whether a student, a parent, or a fellow educator. If it resonated, please like and restack it so it reaches more people asking the same question.

Read more on teaching and learning with AI:

• They All Got A's. None of Them Learned Anything.

• Teaching in the AI Age: Why Flipped Classrooms Are Working Better Than Ever

• Turning AI answers into a learning tool starts with intentional assignment redesign

Mahelet G. Fikru is a professor of economics at Missouri University of Science and Technology with 15 years of teaching and research experience.

A note that traveled further than I expected

On June 24, I posted a short note on Substack:

Most of my notes are read by a couple of hundred people, mostly readers who already know my work. This one was different. It was shown 1,698 times, well above my average. Of the roughly 1,400 people who saw it, nearly nine in ten were not subscribers or followers. It drew 63 likes, 10 restacks, 11 replies, and nine new subscribers.

Who saw the note: nearly nine in ten viewers had never subscribed to or followed my work. Source: Substack note statistics.

What I keep coming back to, though, is not the numbers. It is the replies. People did not simply agree. They added to the idea, pushed on it, disagreed with it, and in a few cases put it better than I had. This post is my attempt to take those replies seriously.

What readers told me

Jiajie Zhang, a cognitive scientist and author of The Cognitive Revolution, wrote: “The age of content scarcity is over. The real value of education is shifting from knowledge transmission to judgment cultivation.” I borrowed his first sentence for the title of this post, because I have not found a better way to say it.

Darja Koneva agreed that “judgment is becoming even more valuable.” Her point was that AI can support analysis and generate ideas, but it cannot fully replace “the experience, mentorship, and contextual understanding that great educators bring.”

Andrew Errant remembered his best professors as the ones who “challenged me as my ideas were evolving, pushing me to be more deliberate with every sentence.” Not the ones who delivered the clearest lectures, but the ones who pushed back while the thinking was still being formed.

Alyssa Fu Ward, PhD, noted that people can do a great deal on their own with AI, “but there’s something to be said for expertise to just put you on a different starting plane.” Everyday Human Rights, written by two educators, made a similar point: an expert still brings “discretion.”

Juliet, who writes The Future We Design, took the idea one step further: “The question isn’t just whether students can evaluate information. It’s whether they understand how institutions determine what counts as knowledge in the first place.”

Don Southerton said simply that AI “has accelerated my learning.” I believe that. For a motivated learner with good judgment, AI is an extraordinary tool.

And a few readers did not fully agree.

• tablefor1drinksfor2 wrote that “judgements are byproducts of subjective interpretation and can often be bias.” What serves students better, they argued, is curiosity and questioning, and helping students “reach their own conclusions,” using AI “as a research partner not the qualified answer.”

• Dont stop the beat. argued that “abundance of knowledge and its effective understanding and utilisation is education,” and that universities that encourage self-directed thought, or apprenticeships, teach more than teachers do.

I have learned a lot by thinking more carefully about these two points; disagreements are the most useful replies of all.

An economist’s way to see it: information and judgment are complements

In economics, two goods are complements when they are used together: cars and gasoline, printers and ink, coffee and cups. When the price of one falls, people use more of it, and demand for its complement rises.

Now think about information and judgment as complements.

For most of history, information was expensive. You needed a library, a textbook, a teacher, a subscription, or a lot of time. A university’s value was partly that it gave you access to information you could not easily get anywhere else.

Generative AI has pushed the cost of producing information, explanations, summaries, and fluent paragraphs close to zero. A student can get a clear explanation of any concept in my course in seconds. They can get five versions of it, in five styles, at five reading levels.

Information and judgment are complements: when the cost of content approaches zero, value shifts toward judgment.

When the price of one input falls, the value does not disappear. It moves to the complement. If everyone can produce an answer, the scarce thing becomes knowing which answer is right, which one fits the situation, what it leaves out, and what to do next. That is judgment.

AI may make one part of education abundant: the delivery of content. That makes the other part, the formation of judgment, more valuable, not less.

The evidence points in the same direction. In a field experiment with nearly 1,000 high school math students, Bastani and colleagues (2025, PNAS) found that students with unrestricted access to a ChatGPT-style tutor performed 48% better on practice problems. When the access was removed, they scored 17% worse on the exam than students who never had it. The tool supplied answers. It did not build the judgment the exam required.

What judgment actually looks like: four skills

“Judgment” can sound vague, so let me be concrete. In my note, I described four things. I still think they are the right four.

1. Deciding what matters. Every question has more information around it than anyone can use. Judgment is choosing the few facts, assumptions, or data points that actually drive the answer. In economics, this is the difference between listing every factor that affects a price and identifying the two that explain most of the change.

2. Noticing what is missing. AI is very good at producing a complete-looking answer. It is much less reliable at telling you what it left out. Judgment is asking: whose costs are not in this analysis? What happens in the long run? Which group was not in the sample?

3. Seeing what is wrong. A fluent argument can still rest on a bad assumption, a misread number, or a leap in logic. Judgment is the ability to read a polished paragraph and say, “This step does not follow.”

4. Explaining why. This is the part that turns an opinion into judgment. It is not enough to say an argument is weak. You have to show the reasoning, so someone else can check it, challenge it, or learn from it.

None of these is about producing content. All four are about evaluating it. That is exactly the work AI has made more important.

What this means for teaching: make judgment the outcome

If judgment is a scarce skill, then it has to be the thing we design for and assess in the classroom. This is the idea behind the last part of LIFT, the framework I use in my own courses. The “T” stands for Thinking as the Outcome. When demonstrated reasoning is what we grade, AI’s ability to produce fluent answers becomes far less disruptive, because fluent answers are not what we are grading.

In practice, that shifts a few things:

• Grade the reasoning, not just the result. Ask students to explain why they chose one approach over another, what they considered and rejected, and where they are unsure.

• Ask students to find what is missing. Give them an AI-generated answer and ask them to critique it: what does it assume, what does it leave out, and where would it fail?

• Make disagreement part of the assignment. Ask students to take a position and then respond to the strongest objection to it.

• Watch thinking change. Some of the most revealing student work shows a mind changing halfway through. That is much harder to fake than a finished product.

None of this requires banning AI. It requires being clear about what we are actually trying to teach. I have written more about this in “How to Test for Real Understanding When AI Makes Every Answer Sound Right.”

What this means for students and lifelong learners

If you are a student, here is the uncomfortable part. AI can make your work look finished before your thinking is. It is easy to confuse the two. I saw exactly this in my own class, which I wrote about in “They All Got A’s. None of Them Learned Anything.”

A few habits help:

• Decide before you ask AI. Write down what you think the answer is, even roughly, before you ask AI. Then compare. The gap is where your learning is. Soulful Learning with AI has developed a trademarked framework exactly for this.

• Ask what is missing. After you get an answer from AI, ask yourself, not the tool, what it did not consider.

• Be able to defend every choice. If you cannot explain why a sentence, a number, or an assumption is in your work, it is not really yours yet.

• Look for people who push back. Andrew Errant’s professors who “challenged [him] as [his] ideas were evolving” describe the most valuable thing a learning community offers. Seek it out, in class, in office hours, or in the comments.

Used this way, AI does accelerate learning, just as Don Southerton said. It becomes a sparring partner for your judgment rather than a substitute for it.

This is not an anti-AI argument. I use AI in my work, and my students will use it throughout their careers. Nor is it an argument that content no longer matters. Good information still matters enormously. It is just no longer the bottleneck, and when something stops being the bottleneck, the attention and the value move to whatever is.

Frequently asked questions

What does “the age of content scarcity is over” mean?

It means information, explanations, and fluent writing are no longer hard to get. Generative AI produces them almost instantly and at almost no cost. What remains scarce is the judgment to decide which information matters, what is missing, and what is wrong.

Why is judgment more valuable in the age of AI?

Because information and judgment are complements. When AI makes information nearly free, people use far more of it, and the ability to evaluate it becomes the bottleneck. Value moves to the scarce complement.

Can AI replace teachers?

AI can replace much of the content delivery. It cannot fully take responsibility for judging a student’s reasoning, explaining why an argument fails, or mentoring a student while their ideas are still forming. Those are the parts of teaching that become more important.

How can teachers assess judgment instead of AI-generated answers?

Grade the reasoning rather than the final product: ask students to justify choices, critique an AI-generated answer, respond to the strongest objection to their position, and show how their thinking changed.

Should professors ban AI in class to protect judgment?

I do not believe so. Banning AI mostly shifts where students use it. Designing assignments where reasoning is the thing being graded does more to build judgment than any ban.

The scarce thing

As my readers put it: The age of content scarcity is over. Judgment is what is scarce now.

I would love to hear where you disagree. That, after all, is the point. Leave a comment, and if this changed how you think about teaching or learning with AI, restack it or share it with a colleague.

Related reading from M G Fikru:

• My University Prepared Me for the Pandemic. Not for AI. (the LIFT framework)

• They All Got A’s. None of Them Learned Anything.

• How to Test for Real Understanding When AI Makes Every Answer Sound Right

• AI in Higher Education: What Institutions Should Do Next

Mahelet G. Fikru is a Professor at Missouri University of Science and Technology, where she has taught economics for nearly fifteen years. She writes about energy, natural resources, technology, and how AI is changing learning.

Quick answer

Economists writing on Substack under their real names include Nobel laureate Paul Krugman, Justin Wolfers of the University of Michigan, John H. Cochrane of the Hoover Institution, former Federal Reserve economist Claudia Sahm, Branko Milanovic, Deirdre Nansen McCloskey, and Mariana Mazzucato of UCL. This directory lists 50 named economists active on Substack in 2026, grouped by their fields of study, with a link and a one-line description for each. I update it as the list grows.

Why I built this list

I have been thinking about who is actually shaping the way economics is discussed in public, not just in academic journals. So I started paying attention and collecting data on economists who are actively showing up and sharing their work on Substack.

Economists study very different parts of the economy — labor markets, macroeconomic policy, financial systems, environmental protection, energy markets, agricultural systems, and more. Yet, to the public, “economist” often sounds like a single voice. It is not.

Economics shapes policy. Economic thinking also shapes the day-to-day decisions we make, and how we understand the world around us — prices, jobs, energy, wars, and technology. Who communicates economic ideas and how often they show up in public spaces matter. Visibility influences which ideas gain traction.

One of my goals is to help my undergraduate students see the different types of economists and the variety of topics we study.

How this list was formed

This database is not a ranking, and it is not static. It is a dataset of economists who consistently engage with the public through Substack. An economist is included if they:

• clearly identify as an economist on Substack

• use their real name (not anonymous)

• have posted on Substack in 2026

The list started with my own reading and your recommendations. In September 2026, I also searched Substack for writers whose profiles mention “economist” or “economics” and added everyone who met these three criteria.

What stands out is not just what these economists say but that they show up.

What are the different types of economists?

Quick answer: Economists differ along four dimensions: the methods they use, how they think about the economy, what they study, and where they apply their ideas. No single dimension defines an economist.

When students ask how to differentiate economists, I tell them to stop looking for one defining feature. I break it down into four dimensions:

• The methods they use — for example, someone using econometrics or data analysis to study inflation

• How they think about the economy — for example, approaching a problem from a free-market perspective or a behavioral lens

• What they study — for example, focusing on energy markets, labor, or financial systems

• Where they apply their ideas — for example, working in academia, advising governments, or working in the corporate sector

Not all economists do the same job. The directory below is organized by the third dimension, which is what they study.

The directory: named economists on Substack

Macroeconomics and economic policy

• Paul Krugman — Nobel laureate, professor at the CUNY Graduate Center, and former New York Times columnist; writes notes on economics.

• Justin Wolfers — Professor of economics and public policy at the University of Michigan, senior fellow at the Brookings Institution, and author of an introductory economics textbook; writes Platypus Economics.

• John H. Cochrane — Economist at the Hoover Institution; writes The Grumpy Economist.

• Claudia Sahm — Former Federal Reserve and White House economist and founder of Sahm Consulting; writes Stay-At-Home Macro (SAHM) on macroeconomics and economic policy.

• Ernie Tedeschi — Chief Economist at Stripe, nonresident senior fellow at The Budget Lab at Yale, and former Chief Economist at the White House Council of Economic Advisers.

• Laurence Kotlikoff — Boston University economist and author of Money Magic; writes Economics Matters.

• Josh Hendrickson — Professor and Chair of the Department of Economics at the University of Mississippi; writes Economic Forces.

• Luca Fornaro — Researcher at CREI and professor at the Barcelona School of Economics; writes Macroeconomics from Europe.

• Spyros Andreopoulos — Economist and consultant who formerly conducted monetary policy analysis at the European Central Bank; writes Thin Ice Macroeconomics on macro and markets.

• Anthony Chan — Former Chase Chief Economist with a PhD in economics; writes The People’s Economist.

• Anirban Basu — CEO of Sage Policy Group and chief economist to a number of organizations; writes Sage Economics.

• Jim Paulsen — PhD economist and former chief investment strategist; writes Paulsen Perspectives on the economy and financial markets.

• Kathryn Anne Edwards — Economist and co-host of Optimist Economy, a podcast on how to make the U.S. economy better.

• David Peez — Practitioner economist and New Zealand–based business owner; writes The Rebel Economist on economic policy through a Modern Monetary Theory lens.

• Andrew Jonelis — Economics professor focused on economic development and institutions; Force in the Game covers macroeconomics, political economy, and geoeconomics.

• Michael Testa — Economist and founder of Nature Economy Jobs; Bloomsbury Macro covers macro, AI, and markets.

• Abdullah Al Bahrani — Award-winning economist and professor; Decode Econ explains economic news and what it means for readers.

• Chris Clarke — Assistant professor at Washington State University; writes on the economy, public policy, and current events.

• Carlos Chavez — Peruvian economist.

• David Owen and Marchel Alexandrovich — Co-write Saltmarsh Economics on Bank of England and ECB policy, the UK and European economies, and climate change. Owen is the former Chief European Economist at Jefferies; Alexandrovich is a former Senior Economist at Jefferies.

• Michael J. Hicks — Eldon R. Lindsey Professor of Economics and Director of the School of Business at Shenandoah University; writes The Country Economist on economic and policy analysis for overlooked regions, including recent work on data centers and housing.

• Nicolás Cachanosky — Associate Professor of Economics at The University of Texas at El Paso; writes Economic Order on monetary policy and institutions, with a focus on Argentina’s central bank reform, the IMF, and economic freedom.

• Jacob Vigdor — Economist, professor of public policy and governance at the University of Washington, and research associate at the National Bureau of Economic Research; writes Jacob’s Substack, offering even-handed takes on partisan issues.

Labor markets

• Gad Levanon — Chief Economist at the Burning Glass Institute; writes Labor Matters on labor markets and economic insights.

• Guy Berger — Workforce economist in residence at Guild, senior advisor on labor markets at Access/Macro, and senior fellow at the Burning Glass Institute; writes MacroMostly.

Finance, housing, and household decisions

• Arpit Gupta — Associate Professor of Finance at NYU Stern; Arpitrage covers finance, real estate, and urban economics.

• Claes Bäckman — Economist working on AI, housing, mortgages, and savings decisions.

• Carly Urban — Economics professor who studies financial education and decision-making.

• Hans Stegeman — Chief Economist at Triodos Bank; writes System Economics.

Environment, energy, climate, and agriculture

• Matthew E. Kahn — Provost Professor of Economics at the University of Southern California and Visiting Fellow at the Hoover Institution; writes on environmental and urban economics.

• Richard Tol — Economist writing Tol Tales on climate change, energy, and the environment.

• Greg Ibendahl — Agricultural economist tracking U.S. row crops and farm input costs; writes Agricultural Economics.

• Thomas Fridrich — Writes Resource Economist on power and energy markets, including monthly analyses of German day-ahead electricity prices and battery storage spreads.

• Aaron Smith — Gordon Rausser Distinguished Chair and Professor of Agricultural and Resource Economics; writes Ag Data News on agriculture, energy, and environmental economics, using public data to tackle questions like climate damages, corn-based jet fuel, and California’s dairy and fuel policy.

• Mahelet G. Fikru — Professor of Economics at Missouri University of Science and Technology; writes on energy, natural resources, technology, and sustainability.

Econometrics, data, and machine learning

• Scott Cunningham — Professor at Baylor University and author of Causal Inference: The Mixtape; teaches causal inference, econometrics, and applied empirical research.

• Erdener Emin Eker — PhD candidate in economics researching machine learning; curates a monthly digest of new ML and AI research from more than 100 economics journals.

• Austin Denteh — Applied econometrician at Davidson College working on health, education, and development economics; writes Methods Matter, on putting econometric theory into practice.

Economics education and higher education

• Jay Akridge and David Hummels — Co-write Finding Equilibrium, a weekly, data-driven newsletter on the future of higher education from two economists and veteran academic leaders; Akridge is a Purdue University faculty member and former administrator.

• Jadrian Wooten — Economics educator and researcher at Virginia Tech; Monday Morning Economist explains economics through current events and pop culture.

• John Kruggel — Director of the JMU Center for Economics and lecturer at James Madison University; writes Teaching Econ in Stereo on economic education.

• Jordan Peeples — PhD economist from the University of Pennsylvania; uses economics research to answer popular questions.

• Cameron Belt — Economist and former regional rideshare executive at Uber and Lyft; Economics For... makes economic thinking practical for different roles.

• Jessica Hoel — Associate Professor of Economics at Colorado College; writes The Liberal Arts Ledger, which uses basic economics to explain how liberal arts colleges finance and organize themselves.

Inequality, political economy, history, and hidden markets

• Branko Milanovic — Economist; writes Global Inequality and More 3.0.

• Deirdre Nansen McCloskey — Economist and historian.

• Mariana Mazzucato — Professor at University College London and founding director of the UCL Institute for Innovation & Public Purpose; author of The Entrepreneurial State and Mission Economy.

• Jostein Hauge — Political economist and assistant professor at the University of Cambridge, author of The Future of the Factory; writes Global Currents.

• Michael Prunka — Writes Enough on hunger, inequality, and the economics of everyday life.

• Varun Gupta — The “Knotty” Economist, on illicit markets from counterfeiting and corruption to smuggling and cybercrime.

Monthly editions

• March 2026 — the first names on the list

• April 2026 — more names, including recommendations from readers

Frequently asked questions

Which economists write on Substack?

Named economists on Substack include Paul Krugman, Justin Wolfers, John H. Cochrane, Claudia Sahm, Laurence Kotlikoff, Branko Milanovic, Deirdre Nansen McCloskey, Mariana Mazzucato, Scott Cunningham, and Matthew E. Kahn. The full list of 50, with what each one studies, is in the directory above.

Are there economics newsletters written by actual economists?

Yes. Everyone in this directory writes under their real name. Many are university faculty. Others are current or former economists at the Federal Reserve, the European Central Bank, the White House Council of Economic Advisers, or private companies.

Which economists on Substack write about labor markets?

Gad Levanon of the Burning Glass Institute writes Labor Matters, and Guy Berger writes MacroMostly.

Which economists on Substack write about climate, energy, and agriculture?

Matthew E. Kahn writes on environmental and urban economics, Richard Tol on climate change, energy, and the environment, Greg Ibendahl on agricultural economics, Thomas Fridrich on power and energy markets, David Owen and Marchel Alexandrovich on climate change, and I write on energy, natural resources, technology, and sustainability.

Which economists on Substack write about teaching economics?

Jadrian Wooten, John Kruggel, Jay Akridge, David Hummels, and Jessica Hoel write about economic education and the future of higher education.

How can I suggest an economist for this list?

If you know an economist, share their link in the comments. If you are an economist who uses your real name on Substack, share your work in the comments section.

The U.S. has correctly diagnosed its rare earth problems: a lack of midstream processing and concentrated sourcing. The nation has moved with real speed and real money to address them. The proposed solution is to create a national champion through equity, debt, guaranteed demand, and price protection, all at once. While this approach may temporarily solve the capacity gap, it recreates the concentration risk by shifting dependency from a single foreign country to a single domestic company.

Quick Takeaways

• The U.S. holds substantial known rare earth reserves and has mine-level production, but is missing midstream capacity to process them into compounds and metals.

• The U.S. is a net importer of rare earth compounds and metals, with roughly 71% of imports coming from China.

• The U.S. faces two distinct problems in the rare-earth space, and each requires a different policy fix.

• In 2025, the U.S. government’s response to these problems was to turn one company, MP Materials, into a “national champion”.

• Better alternatives exist: targeted research funding for innovative processing technologies spread across multiple firms, investment in workforce development and recycling, and binding partnerships with allied countries.

What are Rare Earths?

Rare earth elements (REEs) are a group of 17 metallic elements extracted from the earth’s crust that serve as essential inputs across high-technology, energy, industrial, and defense applications.

Their industrial applications are wide-ranging: they are incorporated into consumer electronics such as smartphones, laptops, headphones, and hard disk drives; they are essential components in electric vehicle motors and magnetic resonance imaging (MRI) systems; and they are required inputs for jet engine components and missile guidance systems.

What distinguishes rare earths from most other industrial inputs is the limited availability of substitutes. In most of their current applications, REEs cannot be readily replaced by alternative materials because, in many production processes, viable substitutes do not yet exist at comparable cost or efficacy.

This near-irreplaceability is a central feature of the economic significance of REEs, and it underlies much of the policy attention directed toward securing their supply.

The Rare Earth Value Chain: Five Stages

The conversion of REEs from raw ore into a usable industrial input (for example, the magnet in your smartphone) proceeds through approximately five stages of production, each typically undertaken at a distinct location.

1. Mining: The process begins with the extraction of rare earth-bearing ore, a practice that occurs at sites such as the Mountain Pass mine in California and is sometimes referred to as an upstream activity.

2. Concentration: The extracted ore is then crushed and processed into a concentrate, a step in the upstream operation.

3. Separation: The concentrate is subsequently chemically separated into individual rare earth oxides (such as neodymium-praseodymium (NdPr) oxide). This is a midstream process that, for ore from Mountain Pass, also occurs on-site.

4. Metal conversion: The separated oxide is then converted into metal form at a dedicated facility, such as MP Materials’ Independence facility in Fort Worth, Texas.

5. Alloying and magnet-making: Finally, the metal is alloyed and processed into a finished magnet — the downstream stage of the value chain — as is expected to occur at the planned MP Materials’ 10X facility

How Import-Dependent is the U.S.?

The United States has long been a net importer of rare earth compounds and metals. For example, between 2016 and 2020, the U.S. imported, on net, 100% of the rare earth compounds and metals consumed. During 2021–2022, net import reliance was at least 95%, while in 2023 it was at least 90%.

Furthermore, U.S. REE imports are heavily concentrated. Between 2021 and 2024, 71% of U.S. rare-earth compound and metal imports came from China, with Malaysia supplying another 13%, and Japan and Estonia supplying 5% each.

Why is the U.S. import-dependent?

It isn’t because the U.S. lacks known rare earth deposits.

The U.S. holds substantial known rare earth reserves and has mine-level production of concentrates, but is not processing them into compounds and metals.

According to the USGS, the United States ranks sixth globally in rare earth reserves, with an estimated 1.9 million metric tons, accounting for 3% of global reserves. Most of this reserve is located at the Mountain Pass Mine in California, where the company reported about 1.69 million metric tons of total reserves as of December 31, 2023.

Beyond Mountain Pass, the U.S. has several other known but largely undeveloped deposits, which the USGS has identified as advanced exploration sites. Examples include Bear Lodge, Wyoming; Bokan Mountain, Alaska; Elk Creek, Nebraska; and Round Top, Texas.

REE mine production in 2023 was 41,600 metric tons of mineral concentrate, representing 11% of the world's mine production, making the US rank third after China (255,000 metric tons) and Burma (43,000 metric tons).

In the same year, 2023, U.S. consumption of REE compounds and metals was estimated at 10,100 metric tons, while compound and metal production totaled only 250 metric tons.

So, in 2023, the U.S had high import reliance on compounds and metals (>90%), due to low production of these midstream products (only 250 metric tons), while having huge reserves of 1.9 million metric tons and modest mine production. Most of this mine production in concentrate form is exported abroad for further processing and then reimported in compound and metal forms.

This is because, since the 1990s, the U.S. has been steadily losing the midstream industry: the factories, processes, metallurgists and engineers, and processing facilities themselves. Instead, we were relying on imports of REE compounds and metals, most notably from China, which has built its technical capabilities to process rare earths over the same period.

The reason domestic separation and processing cannot compete is straightforwardly economic: at prevailing global prices, U.S. companies cannot recover their costs. Those prices are set by low-cost producers — chiefly China, whose rare earth sector is state-supported and has had decades of investment behind it.

Framing the Economic Problem Correctly

Before getting to what was actually done about this, it is worth being precise about what the problem even is. Because the U.S. importing a lot of rare earths by itself isn’t a problem.

The U.S. is a high-import country generally, and has been for a long time. Importing something you don’t have a comparative advantage in producing is well-established economics: other countries can make it more cheaply, and it’s a better use of everyone’s resources to trade rather than produce it domestically at a loss. There’s nothing alarming about that principle on its own.

The actual problem here is two separate things layered on top of each other:

1. A missing midstream industry. Domestic separation and processing capacity have nearly disappeared.

2. Concentrated sourcing. 71% of what the U.S. imports comes from a single country, namely China.

These are different problems, and they call for different fixes. Conflating them — treating “build more capacity” as automatically the same thing as “reduce concentration risk” — is where a lot of the current U.S. policy goes wrong.

Fixing the Capacity Gap

The barrier to entry here is cost.

The sustainable fix is to make domestic processing cost-competitive by improving the technical efficiency of separation and refining, and by investing in technologies that improve the ore-to-metal ratio. Rebuilding this capability takes time: it requires expertise, a trained workforce, and specialized equipment that is no longer in sufficient supply, which in turn requires real R&D investment in that equipment.

That points to a specific toolkit:

• Targeted research and process-innovation funding aimed at separation and metallurgical efficiency, not blanket subsidies or general tax breaks, which tend to be inefficient.

• Public-private partnerships or national lab investment in the development of improved solvent extraction and separation technology.

• Investment in metallurgical engineering training, since much of that domestic expertise has eroded over the past several decades.

The goal should be to help an industry get on its feet and enable the private sector to develop better processes and innovative technologies, not for the government to take a piece of it or to keep supporting it for multiple years without tying that support to performance. Support should taper off once the industry reaches an organic comparative advantage, and it can reasonably favor eco-friendly innovation with extra incentives along the way.

Insulating producers from market prices doesn’t hold up as a long-term solution for two reasons. First, global prices move a lot and are uncertain — you want the supply side to be resilient to that, not shielded from it. Second, insulation removes the incentive to invest in cost-reducing processes. If the government cushions the midstream against low prices, producers have no reason to keep investing in the technology that would bring their costs down on their own.

Fixing the Concentration Risk

Even if the U.S. had all the midstream capacity it needed, that capacity shouldn’t rest on a single entity — whether that’s one country or one company.

This is a different problem from the first one. Fully price-competitive domestic processing would still leave the U.S. structurally reliant on a single dominant supplier unless sourcing is deliberately diversified. You don’t solve concentration risk by making one domestic producer bigger and richer; you solve it by multiplying the number of independent supply nodes, so that a disruption at any single one doesn’t take the whole system down.

The fix here looks different from the capacity fix:

• Spread the same targeted federal support (the tech and process-innovation funding) across multiple companies and multiple stages of the supply chain, including promising startups and recycling efforts, instead of concentrating it in one entity, a national champion.

• Binding agreements with a different set of allied countries that hold independent reserves and processing potential.

• Strategic stockpiling as a bridge while diversified capacity comes online — not as a permanent substitute for it.

A country sourcing a critical input from several independent suppliers can absorb the loss of one or two of them. A country sourcing it from a single dominant supplier can’t: if that supply is disrupted, every downstream industry that needs the material gets hit at once, with no alternative source to absorb the shortfall. That’s both an economic and a social problem, and it’s exactly the kind of thing markets alone aren’t well-suited to fix. It does merit policy intervention, but the form that intervention takes matters just as much as the decision to intervene.

The U.S. Policy Response: A National Champion in MP Materials

Given the U.S.’s two REE problems, the missing midstream and concentrated sourcing, the approach that has been taken is to pick a national champion and back it with a wide range of support.

MP Materials is a publicly traded company that owns and operates the Mountain Pass mine and processing facility — the only rare earth mining and processing site in the United States. It is vertically integrated: mining, concentration, and separation happen at Mountain Pass, and metal, alloy, and magnet production happen at its Independence facility in Texas. Additional large-scale magnet manufacturing is planned at the 10X facility in Texas.

Mountain Pass has a history worth knowing. It dominated world rare earth production from the 1960s through the 1980s, before Chinese state-subsidized output undercut its costs, and the site went dormant in the early 2000s. It was acquired by MP Materials from bankruptcy in 2017, when it resumed mining and concentrate production (upstream).

In 2023, MP Materials commenced midstream operations, including NdPr oxide among other processed REEs. In January 2025, the Independence facility started commercial production of NdPr metal and trial production of magnets. By December 2025, the Independence facility started producing commercial magnets, making MP Materials a fully integrated company.

The national-champion package, announced in July 2025, deployed four tools at once:

• A $400 million preferred-equity stake — a 15% share — making the Department of Defense MP’s largest shareholder.

• A $150 million Department of Defense loan for heavy-rare-earth separation capacity at Mountain Pass.

• A 10-year guaranteed offtake for the 10X magnet facility, under which the government committed to ensuring 100% of the magnets it produces get sold, split between defense and commercial buyers.

• A 10-year NdPr price floor of $110/kg, signed in July 2025, insulates MP from Chinese price competition. Since payments began in the fourth quarter of 2025, MP has collected $110.9 million in cumulative price-support payments from the U.S. government. That price floor covers NdPr content in concentrate, oxide, and metal form, but excludes support for the finished magnet.

The national-champion deal’s effect, as captured by the data

The immediate impact of the national-champion policy is straightforward: domestic REE processing capacity doubled, and ore exports fell by more than half as MP absorbed more concentrate at home instead of shipping it abroad. The capacity gap appears to be starting to resolve.

But the U.S. still imported a meaningful share of its compounds and metals that year because demand outpaced what the single facility driving these gains could supply. And as demand grows, which it will, as the nation invests in AI, the digital economy, and energy technologies, one facility alone won’t be enough to supply the rare earths the economy needs.

In fact, the national champion policy, by its own design, has not and cannot solve the concentration risk problem.

The U.S. may still end up sourcing its midstream output from a single site operated by a single company. Swap “China” for “MP Materials” in the sentence “the U.S. depends on one supplier for 71% of its rare earths”. That’s not a side effect or unintended effect of the policy; it is the direct, mechanical result of choosing a national-champion strategy over a diversification strategy. You simply cannot fix a concentration problem by concentrating harder.

Relying on a single entity, domestic or foreign, to solve a national problem is, at best, a fragile strategy. Decisions at a single company should not be able to sway national statistics. The fix is to encourage competition and remove barriers to entry so that new separators and alloyers can come online, broaden the base, and create the competition that a single national-champion deal can’t.

Let’s look at how the numbers moved once the national-champion package took effect in mid-2025 and MP’s midstream capacity came online.

By 2025, U.S. REE mine production had reached 51,000 metric tons of concentrate, an increase from levels recorded during 2021-2024. Compare this against the domestic production of 8,900 metric tons of REE compounds and metal in 2025, representing a doubling of production capacity from the previous two years.

The export side tells the same story from the other direction. In 2024, the U.S. exported 37,500 metric tons of rare-earth ores and compounds — raw material shipped abroad because domestic processing couldn’t absorb it. By 2025, those exports had fallen to 14,000 metric tons, as MP’s own processing capacity started absorbing more of that concentrate at home.

Yet in 2025, the U.S. still imported roughly 21,000 metric tons of compounds and about 1,450 metric tons of metals to meet growing demand.

In 2025, net import reliance for REE compounds and metals stood at 67% — still high, but a major improvement from the 100% import reliance recorded from 2016 to 2020.

The 2024 improvement predates the national champion deal

Here’s the detail that matters most for evaluating the national champion policy: the 2024 drop in import reliance (53% net import reliance) happened before the July 2025 “MP as national champion” deal was even signed.

It was MP’s own Stage II/III capital ramp-up and capacity that it had already been building.

MP Materials’ own reported numbers show the ramp-up in Q4:2024: the company’s rare earth concentrate production rose from 10,766 metric tons in Q3: 2023 to 13,742 metric tons in Q3:2024, a 28% increase, while its NdPr product output jumped from 50 metric tons in Q3: 2023 to 478 metric tons in Q3:2024.

Looking at the national ratios between 2023 and 2025 makes this even clearer.

1. The import-to-production ratio of REE compounds and metals was the lowest in 2024, even lower than the 2025 value.

2. The import-to-consumption ratio declined from 1.1 over three consecutive years (2021-2023) to 0.9 in 2024, then to 0.8 in 2025.

3. The production-to-consumption ratio was the highest in 2024, exceeding the 2025 value.

Furthermore, from 2023 to 2024, national production of REE compounds and metals jumped from 800 to 4,300 metric tons, a 438% increase, reflecting the midstream capacity MP brought online in 2023. The net import reliance was 53% in 2024, the lowest amount since 2015.

Looking at how the data was already moving in the right direction in 2024, without the national-champion policy, raises questions:

Was the deal actually necessary, and would MP have continued to perform well without it? Or did MP do well in 2024 in anticipation of the deal?

And does improving one company’s bottom line count as national policy, or is that a separate outcome from solving the country’s rare earth problem?

What’s Wrong with the National-Champion Approach

At its core, a national-champion approach creates a government-backed monopoly, with weaker incentives for cost-cutting and innovation.

• It just relocates concentration. The supply risk moves from China to a single company. A mine failure, an accident, or any disruption at one of MP’s facilities reproduces the exact same vulnerability, just from a different source.

• It solves the capacity gap by re-creating a concentration risk. Mid-stream capacity increases due to the national champion, but it’s concentrated entirely in a single entity, MP Materials.

• It raises the barrier to entry for everyone else. The price floor works for exactly one company. Every other potential U.S. entrant, such as a competing miner, a separation startup, or a magnet manufacturer trying to raise private capital, still faces the real market price, roughly half of MP’s guaranteed price floor.

• Price floors blunt the incentive to innovate. Extending the same protection to every incumbent and new entrant would require enormous public funds, and taking equity stakes across the board — as DoD did with MP — would amount to a form of nationalization. A guaranteed price also removes the pressure to improve efficiency: firms keep producing with high-cost techniques because the floor already covers their costs, wasting both company resources and public funds. The NdPr price floor for MP protects only one recipient and raises the effective cost of entry for everyone else.

• It creates a moral-hazard trap. Once the government is an equity stakeholder in the firm it’s subsidizing, it has an incentive to keep subsidizing that firm regardless of whether it is the efficient long-run winner, because withdrawing the subsidy would mean writing down the government’s own stake. That is a trap a healthy competitive-entry policy wouldn’t create.

• It crowds out private capital. An investor weighing whether to fund rare earth midstream capacity now has to reckon with competing domestically against a government-backed entity, even if global prices improve. Policy should crowd in private finance, not push it out.

What Better Strategies Look Like

None of this means the government should sit out of rare earths policy.

The market failure here is real and merits some intervention (not just a national-champion approach). There are at least three types of market failures at play here, and their combination justifies some form of government intervention.

1. Supply security, or the security benefit of not depending on a single entity, is an impure public good that benefits a majority of industries in the economy, but no single firm captures that value, so the market underprovides it.

2. U.S. producers face global prices that are uncertain, less transparent, and often decoupled from their rivals’ true cost, so the price signal itself is distorted, facilitating an information asymmetry problem.

3. Barriers to entry imply that building domestic separation and processing capacity requires years of upfront investment before it pays off. No single firm can rationally bear that risk when competing against an incumbent that is state-subsidized and has already recovered its costs.

The question here is the choice of instrument, and there’s a whole set of alternatives that would address the same two problems without concentrating the fix in one company. A few alternatives, other than the ones we already discussed above, include:

• Loans, rather than equity, for expanding existing producers and helping new entrants get built. Loans are repaid, while equity stakes create an incentive for the government to maintain its support.

• Support for recycling and the circular economy, not just mining, presents an underused capacity for REE sourcing.

Every one of these tools does real work toward closing the capacity gap and spreading out the concentration risk. These alternatives build resilience by relying on multiple competing sources of supply, whereas the MP deal creates a monopoly.

The capacity problem and the concentration problem are both real and distinct problems, and both deserve a national policy response. But treating them as the same problem and solving both with the same company is how you end up trading one dependency for another.

About the Authors

Mahelet G. Fikru is an economist and educator whose work focuses on critical minerals and the societal challenges of the clean energy transition. Her Substack examines how innovation and technology shape markets and society.

Bonnie L. Thornton is a writer covering critical minerals, with a particular focus on rare earth elements. Her work on Substack explores the industry from multiple perspectives, including geopolitics, environmental impacts, policy, and the role of rare earths in the energy transition.

Let’s Do Classwork! is a free collection of interactive economics exercises. I built them from the problem sets I use in my applied microeconomics courses at Missouri University of Science and Technology. Each lab lets students change inputs, see the result right away, and check their answers. The labs cover supply and demand, marginal analysis, elasticity, cost curves, game theory, market power, environmental economics, and U.S. pollution abatement cost data. Undergraduates, graduate students, and self-learners can use them to review a concept before an exam or to prepare for graduate coursework.

At a glance: 11 free interactive labs (seven microeconomics labs plus a four-part environmental economics lab)

· Levels: undergraduate, graduate, and general audience

· Created by Mahelet G. Fikru, Professor of Economics, Missouri S&T

· Last updated September 2026.

Find a lab by level

• Undergraduate review: The Efficiency Lab

• Graduate review: the Equilibrium, Nash, Elasticity, Cost, and Market Power Labs

• No economics background needed: The Workforce Lab

• Environmental and resource economics: The Environmental Economist Lab

1. The Equilibrium Lab: Supply and Demand, Profit Maximization, and Risk

This interactive exercise covers three fundamental economic concepts in one handout: shifting a market to see how supply and demand respond to changing conditions, chasing a profit-maximizing output level to find where a firm should produce, and pricing two risky investments to compare returns against risk. Click through each one, adjust the inputs, and see the concept play out in real time instead of just reading about it.

Concepts covered: market equilibrium, shifts in supply and demand, profit-maximizing output, expected return and risk.

Recommended for graduate students who need to review concepts in microeconomics.

2. The Nash Lab: Game Theory and the Principal–Agent Problem

This interactive exercise covers the agency problem between managers and owners — why managers acting on shareholders' behalf don't always choose what's best for the firm, and how strengthening monitoring changes the payoffs enough to shift the equilibrium outcome.

Concepts covered: Nash equilibrium, payoff matrices, the principal–agent problem, monitoring and incentives.

Recommended for graduate students who need to review concepts in game theory.

3. The Efficiency Lab: Marginal Analysis, Consumer and Producer Surplus, and Taxes

This interactive exercise covers the efficiency of markets — finding the optimal input level where marginal benefit meets marginal cost, deriving market equilibrium from supply and demand, and measuring the net benefits a market creates through consumer and producer surplus, including how a per-unit tax changes that split.

Concepts covered: marginal benefit and marginal cost, market equilibrium, consumer surplus, producer surplus, tax incidence.

Recommended for undergraduate students who need to review marginal analysis and market surplus.

4. The Workforce Lab: Data Center and Computing Jobs (NAICS 518)

An interactive look at who works in Sector 518 (computing infrastructure, data processing, hosting, and related services) and how that's shifted over time. Explore employment broken down by sex and by education level from 2010 to 2025, including a widening gender gap. Since data center jobs fall within this sector, tracking Sector 518 is one of the few ways to see AI-driven data center employment show up in the numbers, even though it isn't broken out on its own.

Concepts covered: employment trends, the gender gap in tech employment, education levels, how AI and data center jobs show up in industry data.

Data: U.S. Census Bureau, LEHD Quarterly Workforce Indicators (QWI).

Recommended for anyone with no formal training on economics or data analysis.

5. The Elasticity Lab: Price, Cross-Price, and Income Elasticity of Demand

This interactive exercise covers price, cross-price, and income elasticity of demand — calculating and interpreting each measure, along with how elasticity connects to total revenue (TR) and marginal revenue (MR).

Concepts covered: price elasticity, cross-price elasticity, income elasticity, total revenue, marginal revenue.

Recommended for graduate students in applied microeconomics who have a good command of analytical skills and a solid understanding of the demand-supply model.

6. The Cost Lab: Short-Run and Long-Run Cost Curves

This interactive exercise covers the structure of short-run production costs, deriving the relationships between total, fixed, and variable costs; using marginal cost to find how the cost of the next unit changes, and distinguishing accounting profit from economic profit through an opportunity-cost decision, including a look at how the long-run cost curve differs from the short-run one.

Concepts covered: total, fixed, and variable cost; marginal cost; opportunity cost; economic vs. accounting profit; long-run average cost.

Recommended for graduate students who need to review cost curve relationships and the accounting-vs-economic-profit distinction.

7. The Market Power Lab: From Perfect Competition to Monopoly

This interactive lab walks students from perfect competition to monopoly through guided stations. Students derive equilibria, fill in calculation tables, build an Excel model, and get instant feedback as they go.

Concepts covered: perfect competition, monopoly pricing, deadweight loss, building the model in Excel.

It is recommended for graduate applied microeconomics as a hands-on refresher on imperfect competition.

8. The Environmental Economist Lab

A. Externalities

A steel mill pollutes the air and never pays for the damage. Solve the competitive and efficient equilibria, add marginal damage cost to get marginal social cost, and measure the deadweight loss of a negative externality.

B. The Commons Lab

Public goods and common-pool resources side by side: a climate abatement game between two emitting blocs, a shared wetland buffer solved with the Samuelson condition, and a groundwater aquifer facing the tragedy of the commons. Shows why the free-rider problem and the tragedy of the commons come from the same root: non-excludability.

C. Nuclear Generation in the US

Who works in U.S. nuclear power plants (NAICS 221113), by state and by sex, quarter by quarter from 2007 to 2025. Data: U.S. Census Bureau, LEHD Quarterly Workforce Indicators.

D. Abatement Cost Atlas: U.S. Manufacturing Pollution Abatement Costs by State and Industry (2005)

How much did U.S. manufacturers spend to reduce pollution, and where did that money go? In 2005, plants spent about $20.7 billion on pollution abatement operating costs and $5.9 billion on abatement equipment. This interactive atlas turns the raw Excel tables from the Pollution Abatement Costs and Expenditures (PACE) survey into a map and filterable charts. Compare spending by state and manufacturing sector, and break it down by activity (treatment, prevention, recycling, disposal), by media (air, water, solid waste), and by cost category (labor, energy, materials, contract work, depreciation). Data: U.S. Census Bureau, PACE 2005 survey.

More coming soon

This is the start of a series. More classroom exercises will turn into these interactive pieces and land here as they’re made. Try them out, and share what’s confusing, what’s fun, and what you’d want to see documented next. I built these labs with the help of AI tools, as part of the broader shift I describe in AI in higher education: What institutions should do next.

Check back here again for more updates.

Frequently asked questions

What is Let’s Do Classwork?

Let’s Do Classwork! is a free set of interactive economics labs created by Mahelet G. Fikru, Professor at Missouri University of Science and Technology. It currently includes seven microeconomics labs and a four-part Environmental Economist Lab, each built from exercises she uses in her own courses.

Are these economics exercises free?

Yes. Every lab is free to use, with no sign-up required.

Where can I practice supply and demand problems online?

The Equilibrium Lab and the Efficiency Lab let you shift supply and demand, solve for equilibrium price and quantity, and calculate consumer surplus, producer surplus, and the effect of a per-unit tax, with instant feedback.

How do you calculate the deadweight loss of a negative externality?

Find the efficient quantity where marginal benefit equals marginal social cost (private cost plus marginal damage). The deadweight loss is the triangle between marginal social cost and demand, from the efficient quantity out to the competitive quantity. The Externalities lab walks through a full worked example.

What is the difference between the free-rider problem and the tragedy of the commons?

Both come from non-excludability. With a public good, people contribute too little, so it is under-provided. With a common-pool resource, people use too much, so it is run down. The Commons Lab works through both.

How much do U.S. manufacturers spend on pollution abatement?

In 2005, U.S. manufacturing plants with 20 or more employees spent about $20.7 billion on pollution abatement operating costs and $5.9 billion on abatement capital expenditures, according to the U.S. Census Bureau’s Pollution Abatement Costs and Expenditures (PACE) survey. Chemical manufacturing had the highest operating costs ($5.2 billion), followed by petroleum and coal products and primary metals. Texas, California, and Louisiana spent the most among states. The Abatement Cost Atlas breaks these figures down by state, sector, activity, and media.

Who are the labs for?

Each lab is labeled by level. Most target graduate applied microeconomics, the Efficiency Lab targets undergraduates, and the Workforce Lab needs no economics background.

Can instructors use these labs in class?

Yes. Instructors can link to any lab from a syllabus or learning management system. Feedback on how you use them is welcome in the comments.

How often are new labs added?

New labs are added as more classroom exercises are converted into interactive versions. Subscribe to get each new lab when it is published.

About the author

Mahelet G. Fikru is a Professor at Missouri University of Science and Technology, where she teaches applied microeconomics courses. Her research covers energy transition economics, critical minerals, and public policy. She writes M G Fikru, a newsletter on energy, natural resources, technology, and sustainability.

Short answer: A statement of purpose looks forward: your academic background, skills, and study plan. A personal statement looks back: your journey, values, and what shaped you. My rule of thumb is that an SOP is 80–90% professional and 10–20% personal, and the personal statement flips that ratio.

If you’re applying to grad school, confused about personal statements and statements of purpose, and English isn’t your first language, this post is for you.

I often get emails from undergrads, asking exactly this. Most want a second look at their personal statement or statement of purpose.

Students applying to multiple schools get confused fast. A personal statement. A statement of purpose. What’s the difference? And why do schools even ask for this? Isn’t a transcript enough?

This post provides a short guide on what grad schools actually want from each essay and how to write both without sounding like everyone else.

1. What actually separates a personal statement from a statement of purpose?

2. Why do schools ask for them?

3. What are the dos and don’ts?

What’s the difference between a personal statement and a statement of purpose?

A Statement of Purpose (SOP) covers your academic background, your technical skills, and your study plan. What’s your end goal? Why this program? How does it fit your background? It looks forward. Most research-based programs ask for one. In short, it answers two questions: what do you want to do, and how do you plan to do it?

Cornell is a good example. Their guidance on writing an SOP is specific.

Include your research experience — the project, your exact role, and what you learned. Include relevant professional or internship experience. Explain why you’re applying. Name your academic interests and the skills you bring.

They also want you to name specific faculty whose work overlaps with yours. That tells them how closely you’ve actually researched the program, not just the field.

A Personal Statement is more personal, as the name suggests. It’s your journey. Your passion. Your interests. And why did you choose this specific path? This is the part of your story no one else can duplicate — even someone with your exact GPA and your exact skills. It looks back, not forward. Your identity, your values, how you got here. This is the most common ask for scholarship and graduate applications in the UK, and it’s becoming more common across continental Europe too.

Cornell requires both documents for most graduate programs. They’re explicit that the two should complement, not repeat, each other. The personal statement covers your background and what shaped you. The SOP stays focused on your academic interests and professional goals.

UCLA works the same way. Most departments ask for both a Statement of Purpose and a personal statement. UCLA’s official prompt asks you to describe educational, personal, cultural, economic, or social experiences relevant to your academic journey. Any challenges? Any opportunities?

Separately, it asks you to describe how your background contributes to the university’s mission of community outreach, volunteer work, or research that addresses inequality. In short: the ways you’ve shown up for something bigger than your own résumé.

There’s overlap between the two, of course.

My rule of thumb: think of the SOP as roughly 80-90% professional — your background, your skills, your goals — and 10-20% personal. The personal statement flips that ratio.

Not every school asks for both. Some ask for only one. Don’t assume you need to write both — read the instructions carefully first.

Why do grad schools ask for this?

For the SOP, schools need proof that you have the appropriate training and skills. Not just to do the program, but to succeed in it.

Cornell says this directly: they use the SOP to understand your academic goals and judge whether you’re a fit for that specific program. A great GPA or test score only proves you can handle the coursework. It doesn’t prove that your goals align with what the program is actually trying to do. That alignment is what the SOP is testing.

For the personal statement, schools want to know you have the drive to make it through. Whatever shaped your path — that’s what they’re trying to measure. Grades measure aptitude. This essay measures something grades can’t show.

A quick checklist for both

✅ Do:

• Read the exact term in the application instructions. A statement of purpose and a personal statement are not interchangeable. Schools notice when you’ve reused one for the other

• Check for specific prompts. Address each one directly, point by point

• Say something new — information not already covered elsewhere in your application

• Write your own first draft. Not AI

• Read the program’s AI policy before you use AI for any part of the essay. More programs have explicit rules now. If AI is allowed, use it to check grammar and consistency after your own draft is done

• Once you have a first draft, try this prompt in ChatGPT or your favorite AI to refine it without losing your voice:

Without changing my voice, my content, or my substance, edit this personal statement (or statement of purpose) for my graduate school application. You may correct grammar, spelling, and inconsistencies directly. Do not rewrite my sentences, change my tone, or add any experience, detail, or claim I did not write myself. I’ll give you the application instructions. Check that I’ve addressed everything the university asked for, point by point. If something required is missing, or if there’s a major issue with structure or content, flag it for me to fix myself. Do not fix it yourself — just tell me what’s wrong and where.

• Stick close to the word count. Asked for 1,200 words? Aim for 1,150–1,200. Not 800. Not 1,300

• Ask someone you trust to read it. Ask for honest feedback, not a polite “looks good”

❌ Don’t:

• Don’t repeat the same idea in different words just to fill space

• Don’t exceed the word or page limit

• Don’t make things up

• Don’t submit something mostly written by AI. Schools can increasingly tell — and it defeats the purpose of the essay

• Don’t write generic or vague statements; mention specific items or instances

• Don’t send the same essay to every school; customize for each

• Don’t use big quotes or fancy vocabulary if it is not natural to you

Final thoughts

If you’re applying to grad school and want a second pair of eyes or just honest, genuine feedback, like this post and drop a comment below. I read and respond to every comment.

For similar topics, see:

• What Professors Actually Look for in a Research Assistant and How to Stand Out

• How to Better Guide Student Researchers in the AI Age

In my case, I spent about a year receiving training on online teaching. I completed two formal training programs and earned two certificates. The institution purchased platforms such as Zoom and Panopto. We received guidance on best practices, what to do and what not to do, and even clarification on issues that many faculty were uncertain about, such as whether students could be required to turn on their cameras and what legal considerations applied.

The message was clear: online education represented a major disruption, and the institution had a responsibility to help educators adapt.

My experience with AI has been different.

When AI arrived, everyone responded differently

When generative AI entered the classroom, there was no comparable institutional response. The guidance I received was limited. Faculty were told to use AI detectors, often without institutions purchasing those tools or providing training on their strengths and limitations. We were told to discuss AI at the department level and do what made sense for our disciplines.

In practice, the message often felt like "figure it out."

As a result, I observed a wide range of responses among educators. Some reacted in fear and focused on preventing the use of AI whenever possible. Others largely ignored it and continued teaching as usual, relying heavily on take-home assignments and assessments even as students increasingly submitted those same questions to ChatGPT and received fluent answers. Some faculty embraced AI and began using it to help generate assignments, quizzes, and exams. Many institutions adopted traffic-light policies—green for unrestricted AI use, yellow for limited use, and red for no AI use.

While these approaches provide a starting point, they do not amount to a coherent institutional strategy.

Higher education needs a more structured approach while still allowing flexibility at the departmental and individual faculty levels.

Step 1: Understand AI’s impact on student learning

The first step is understanding what AI is actually doing to learning within our institutions.

Universities should systematically document AI’s impact by gathering information from both faculty and students. Educators should be asked how AI has affected their teaching, challenged their assessments, and influenced their pedagogical decisions. Students should be asked how AI affects their learning, what concerns they have, how they use these tools, and what they expect from their instructors.

Before developing solutions, institutions need a clear understanding of the problems they are trying to solve.

For example, are students completing assignments without understanding the underlying concepts? Are traditional assessments still measuring what educators believe they are measuring? Which learning objectives are most vulnerable to disruption? Where is AI creating opportunities for deeper learning, and where is it creating risks?

Without answering these questions, institutions are responding to AI based largely on assumptions.

Step 2: Move from awareness to action

Understanding the problem is only the first step. Once institutions have documented how AI is affecting learning, they need a framework for responding to it—a framework for action. In my view, that response should focus on three areas:

1. Keep learning objectives at the center

2. Invest in educators

3. Build frameworks, not rulebooks

I am not advocating for rigid mandates or one-size-fits-all policies. Different disciplines have different learning goals, and faculty need flexibility to design courses that fit their contexts. However, institutions can provide a common framework that helps guide decision-making.

Keep learning objectives at the center

One possible starting point is learning objectives.

If the purpose of an assessment is to evaluate a particular learning objective, educators should be encouraged to ask whether the current design still allows that objective to be achieved in an environment where AI is readily available. If AI use prevents students from demonstrating the intended learning, then the assessment may need to be redesigned.

The focus should not be on whether AI is present. The focus should be on whether learning is occurring.

This type of framework would not dictate specific assignments or teaching methods. Instead, it would provide a shared principle that departments and faculty can adapt to their own needs.

Invest in educators

Institutions must invest in educators if they expect meaningful change. During the transition to online learning, universities invested resources, training, and time. Faculty were given opportunities to learn new approaches and experiment with new teaching methods. Incentives were aligned with institutional expectations.

A similar investment is needed today.

If universities expect faculty to redesign courses, rethink assessments, and adapt to rapidly evolving technologies, they should provide the training, resources, examples, and support necessary to do so. Faculty should be given opportunities to experiment, learn from one another, and develop approaches that work in their own classrooms. One example from my own teaching is Let’s Do Classwork!, a free set of interactive economics labs I built from my classroom exercises with the help of AI.

Educators should not be left alone to navigate these changes.

Build frameworks, not rulebooks

What is needed is not a detailed rulebook for AI. The technology will continue to evolve, and today’s solutions may not fit tomorrow’s challenges. Instead, institutions need broad frameworks that help educators make decisions when new technologies emerge.

The goal is to ensure that learning remains at the center of education, even as the technologies surrounding it continue to change.

Key takeaways

During the transition to online learning, institutions invested in helping educators adapt. In the AI era, many educators have been left to navigate the changes on their own.

Universities do not need all the answers. AI is evolving too quickly for any institution to develop a perfect response. But institutions can begin by building systems that help educators respond to change. In my view, understanding AI’s impact on learning, investing in educators, and creating adaptable frameworks are important first steps.

The goal is not to manage AI. The goal is to ensure that learning remains at the heart of education.

Related: The Age of Content Scarcity Is Over. Judgment Is What’s Scarce Now.

You don’t need a textbook to think like an economist. You need to notice what’s already around you.

I spent part of this summer in Addis Ababa, and I found economics everywhere—in an empty bike lane, in a city where charcoal and modern energy systems coexist, and in a financial system that has moved from bank branches to mobile phones in just a few years.

These everyday observations reveal some of the most fundamental questions in economics: What do we produce? How do we produce it? And who benefits from it?

You don’t always need data or tables to see economic transformation. Sometimes you just need to pay attention to what people build, what they use, and what they choose.

1. A brand-new bike lane, just no bikes on it yet

Addis Ababa has new parks now—real ones, landscaped, with dedicated bike lanes running through them.

Walking along the Arada Park, I didn’t see a single bicycle.

That’s an economics lesson hiding in plain sight.

Economists often frame resource allocation around three questions:

• What should we produce?
  The city decided to produce more public infrastructure. Arada Park was inaugurated by Prime Minister Abiy Ahmed on April 30, 2026 — 40 hectares, with a 5-kilometer dedicated bike lane, a 5-kilometer running track, and a 6-kilometer pedestrian walkway, built alongside the renovation of more than 700 existing homes on the site. I was walking on infrastructure that was at most 10 to 11 weeks old. That's a supply-side investment intended to improve urban mobility and quality of life.

• How should we produce it?
  The infrastructure was built quickly through public investment. Besides the infrastructure, a functioning cycling system also requires complementary investments—affordable bicycles, safer road designs beyond the park, secure parking, maintenance, traffic enforcement, and public awareness.

• For whom is it produced?
  Bike lanes create value only if people actually use them. Today, many residents may not own bicycles, may perceive cycling as unsafe, or simply may not view biking as a practical way to commute. The intended beneficiaries have not yet become active users.

The key insight is that building infrastructure creates supply, but it does not instantly create demand.

In this case, the supply arrived. But demand hasn’t caught up yet.

Demand for cycling depends on complementary capital, incentives, habits, expectations, and social norms—all of which evolve much more slowly than concrete and asphalt.

Governments can build a bike lane in months. They cannot build bike ownership, a safe cycling culture, or commuter confidence on the same timeline.

The empty bike lane shouldn’t necessarily be viewed as a failure. It is the beginning of a natural adjustment process. The more interesting question is not what the bike lane looks like today, but whether usage will steadily increase over the next two or three years.

Public infrastructure succeeds when people change their behavior because of it, not just when it’s built.

2. Two different systems, running at the same time

A new park on one side. Corrugated rooftops on the other.

Both are real. Both are visible from the same spot — this stretch of Kebena Riverside Park was inaugurated on June 28, 2026, part of an 87.6-hectare, 10.5-kilometer riverside project that's one of eight such projects covering roughly 48 kilometers across the city.

The same contrast appears in Addis’s food culture.

Restaurants and cafés are everywhere now, serving beautifully plated, photo-ready food you’d expect in any global city. It’s not just that there are more restaurants—the menu itself has changed. Hummus, fusion small plates, latte art, cold brew, imported ingredients like olive oil and tahini—not long ago, many of these were difficult to find in Addis. Today, they are increasingly common.

This is more than a food trend. It is an economic signal.

The three fundamental questions of economics help explain what is happening.

• What should we produce?

  As incomes rise and an urban middle class expands, consumer demand changes. People begin to seek not only basic necessities, but also variety, convenience, and new experiences. Businesses respond by producing and importing new goods and services. Injera and wat have not disappeared; they have been joined by new choices because more consumers now have the purchasing power to demand them.

• How should we produce it?

  New demand creates new markets and new production systems. Restaurants invest in modern kitchens, specialized skills, new supply chains, and imported inputs. The economy adapts because entrepreneurs see opportunities as consumer preferences change.

• For whom is it produced?

  This is where the contrast becomes most visible. The new cafés, restaurants, and lifestyle services are evidence of rising incomes—but they also reveal who has benefited most from economic transformation. Economic growth does not reach all households at the same speed. Some consumers are choosing between specialty coffee and global cuisine, while others are still making choices based on affordability and access.

The same uneven transition appears in Ethiopia’s energy system.

At my mother’s house, coffee still begins the traditional way—charcoal glowing in a small metal stove, smoke rising before the jebena ever goes on.

This is the economic lesson: Ethiopia is not moving from charcoal to electricity in a straight line. It is running multiple energy systems simultaneously.

Economists often describe this as energy stacking rather than energy switching. As incomes rise, households frequently add modern energy sources without immediately abandoning traditional ones. Different energy sources continue to coexist because choices are shaped by income, reliability, infrastructure, and cultural practices.

A household with steady access to electricity may adopt electric appliances and cleaner cooking technologies. Another household may continue using charcoal because it remains affordable, available, and familiar. Or just in case there is a power outage.

This is why measuring Ethiopia’s energy transition only through solar panels, electric vehicles, or grid expansion misses an important part of the story. The transition is not happening in one economy—it is happening across multiple realities within the same city.

Development is rarely a clean replacement of old systems with new ones. It is a process of gradual transformation in which new opportunities emerge alongside older ways of living.

3. A financial system that didn’t exist five years ago

People in Addis are online in a way that’s easy to miss until you’re looking for it.

Walk down a quieter street, and you’ll often spot someone filming—almost certainly for TikTok—using the empty space and good light the same way creators do everywhere else. Attention has become an economic resource, one that can be created, traded, and monetized.

The same digital transformation is happening in finance.

Telebirr — Ethiopia's mobile money platform — launched in May 2021. It reached 50 million users within four years, and Ethio Telecom had projected 62.5 million users by June 2026.

Five years ago, sending money, paying at a shop, or settling a utility bill by phone—without ever visiting a bank branch—was simply not an option for most Ethiopians. Today, for millions of people, it’s routine.

Again, the three fundamental questions of economics help explain why this matters.

• What should we produce?

  The economy is no longer producing only physical goods and services. It is increasingly producing digital financial services: mobile payments, electronic transfers, digital wallets, and online commerce. Financial services themselves have become an important output of the economy.

• How should we produce it?

  Instead of relying on costly physical bank branches, transactions are increasingly delivered through digital platforms. Mobile money dramatically reduces the time, travel, paperwork, and administrative costs associated with making payments. Economists describe these savings as reductions in transaction costs—one of the most important drivers of economic development.

  Digital finance doesn’t simply replace cash; it makes economic exchange faster, cheaper, and more accessible.

• For whom is it produced?

  Perhaps the greatest benefit is inclusion. Mobile money extends financial services to households and small businesses that previously had limited access to formal banking. A smartphone can now perform functions that once required a physical bank branch, expanding participation in the formal economy.

Development is not simply about building more infrastructure or adopting more technology. It is about steadily lowering the cost of participating in markets and expanding people’s choices, and mobile money does exactly that.

Why is this worth your five minutes?

Economics is often presented as graphs, equations, and theory. But the most important economic ideas are already visible in everyday life.

Three ordinary things in this short walk through Addis — a bike lane, a charcoal stove, a mobile banking app — answer the same three questions that shape every economy: what we produce, how we produce it, and who benefits. Seeing the pattern here helps you see it anywhere.

What’s the version of this on your own street? Tell me in the comments — I’d like to know what you’re noticing.

When generative AI arrived, I got nothing.

Read more

Virginia’s new data center tax — 1.1 cents per kilowatt-hour, effective July 1, 2026 — is the first state-level energy consumption tax on data centers in the United States. This post breaks down what the tax is designed to do, how it compares to the $1.6–$2 billion in existing tax incentives, and whether the economics support the policy.

Key Facts

• 1.1¢/kWh — New energy consumption tax on Virginia data centers, effective July 1, 2026

• $600 million/year — Expected state revenue from the tax

• $1.6–$2 billion/year — Equipment tax exemptions still in place through 2035

• 26% — Share of Virginia’s electricity consumed by data centers in 2023

• 371 operating data centers in Virginia; 471 more planned

• First state-level energy consumption tax on data centers in the US

On June 22, 2026, Virginia made history.

Starting July 1, the state will impose a 1.1 cent per kilowatt-hour tax on electricity consumed by data centers — the first state-level energy consumption tax of its kind in the United States. Virginia expects to collect $600 million per year.

The timing is deliberate. Virginia is home to 371 operating data centers and 471 more in the pipeline. In 2023, those facilities consumed 26% of the state’s total electricity supply — roughly 35.9 million MWh out of 138 million MWh sold statewide. The grid is under pressure, electric prices are rising, and the state is responding.

But here is what makes this policy interesting: Virginia is not pulling back the incentives that built the data center industry here in the first place. The sales and use tax exemptions on equipment and infrastructure — worth $1.6 to $2 billion per year and locked in through 2035 — remain fully intact. Those exemptions waive the 5.3% to 7% sales tax on equipment purchased inside Virginia, and the equivalent use tax on purchases made online or out of state.

Virginia is adding a stick. It is keeping the carrot. Understanding what that combination actually does requires looking at the numbers carefully.

Virginia’s Data Centers and Electricity Prices

Virginia’s energy portfolio runs on 38% natural gas, 29% nuclear, 17% renewables, and about 8% coal. It is a net electricity importer, part of the PJM interconnection that covers much of the mid-Atlantic and parts of the Midwest.

The state operates under a vertically integrated utility model: the utility owns the power plants, the transmission lines, and the distribution infrastructure. Consumers have no choice of provider. Rates require regulatory approval — but not all costs do. Wholesale fuel costs can be passed through to customers without rate approval, and that distinction matters enormously when demand spikes.

The data shows what has happened. In March 2025, the commercial electricity rate in Virginia was 8.84 cents/kWh. By March 2026, it had climbed to 10.16 cents/kWh, representing a 15% increase. Residential customers saw their rate rise from 14.89 to 17.05 cents/kWh over the same period.

Across every sector, prices rose sharply. The Virginia average moved from 10.75 cents/kWh in March 2025 to 12.23 cents/kWh in March 2026.

The mechanism is straightforward. When data center demand surges, expensive generators that had been sitting idle have to start producing. Those incremental fuel costs pass directly to consumers. Virginia’s overall average and sector-specific electric rates are still below the US average, but the gap is closing fast.

How Virginia’s Data Center Energy Tax Works

Before analyzing the policy, the design of the tax needs to be clear: this is a consumption tax, not a carbon tax.

That distinction carries real consequences. A data center running entirely on renewable energy pays the same 1.1 cent/kWh tax as one running on coal. The tax creates no incentive to shift toward cleaner energy sources. Its purpose is different: to reduce the quantity of energy data centers consume, and generate state revenue in the process.

In standard economic models, taxing energy consumption pushes firms to reduce their energy use. The critical question is how they respond. They can cut operations — running the centers less — or they can invest in energy efficiency, achieving the same output with lower energy intensity. The second response is the better outcome: the centers keep running, the grid gets some relief, and efficiency improves. The first response is costly.

So the question here is whether this tax is large enough to make efficiency investment the rational choice.

Is Virginia’s 1.1 Cents Per kWh Energy Tax High Enough to Change Behavior?

Economic theory is specific on this point. If the tax is too low relative to the cost of efficiency upgrades, firms will absorb it as a routine operating expense and change nothing. If the tax is high enough that the savings from reduced energy use exceed the cost of the upgrades, firms will invest. The tax has to cross that threshold to change behavior.

Consider the math. Data centers in Virginia likely face the commercial rate of approximately 10.16 cents/kWh. A 1.1 cent tax could add up to 10.8% to their energy bill if they do nothing. That is a meaningful number — not a rounding error.

The following figure illustrates why the tax may encourage some efficiency investments. If the data center were able to reduce electricity consumption, say by 10%, both its electricity bill and tax liability would fall, essentially returning costs closer to (or even below) their pre-tax level.

Whether a 10.8% increase in energy costs is large enough to trigger efficiency investment depends on the actual cost of upgrades relative to the resulting tax savings. That is an empirical question, and we do not yet know to what extent the tax rate would shift the industry’s response towards efficiency or other meaningful reductions in energy usage.

Another way to look at the tax burden (the stick) is to set it against the full incentive picture (the carrot). The state projects collecting $600 million per year from the energy tax. The equipment and infrastructure tax exemptions are worth $1.6 to $2 billion per year. The energy tax offsets between 30% and 38% of the value of those exemptions. Put differently, the stick is worth roughly one-third of the carrot.

After accounting for energy tax payments, is the net value of equipment tax exemptions sufficient to keep Virginia competitive enough that data centers are unlikely to leave? The answer is likely yes, but the ultimate effect remains to be seen as both existing and prospective data centers weigh the state's carrot-and-stick approach in their location and investment decisions.

Two additional features of this tax deserve attention.

First, it is a discriminatory tax in the technical economic sense — it applies exclusively to data centers, not to other commercial consumers paying the same 10.16 cent base rate. This is an intentional policy design. The state is targeting the specific sector that has placed disproportionate pressure on the grid. That is a defensible choice that effectively creates a two-tier structure within the commercial rate class.

This energy tax also appears to be the first tax of its kind in the United States — a state-level energy consumption tax applied specifically to data centers.

Second, because this is a consumption tax and not a carbon tax, decarbonization is not the primary goal. A data center that cuts its energy use through efficiency improvements — regardless of whether that energy came from natural gas or solar — lowers its tax liability. The incentive is to use less energy, not necessarily to use cleaner energy. Those are related goals, but not the same goal.

The Open Question: Will Virginia’s Data Center Tax Drive Efficiency or Just Become a Cost of Business?

Virginia’s $600 million revenue projection may be the most revealing number in this policy. A state that expects to collect $600 million per year while continuing to provide sales tax exemptions worth up to $2 billion annually may not expect a swift, widespread behavioral change. If every major data center invested heavily in efficiency and dramatically reduced consumption, the tax revenue would fall well short of that figure.

The projection suggests Virginia is anticipating something between the two extremes: some efficiency investment, and some absorption of the tax as a cost of doing business.

Whether 1.1 cents per kWh proves to be the right signal — enough to move the industry toward genuine efficiency, without being so high that it disrupts the investment environment Virginia has spent years building — is the question this policy will answer over the next several years.

The data will follow. Virginia has set the price. Now we watch what the market does with it.

About this post

I am an economics professor at Missouri University of Science and Technology, and I study the economic impact and public perception of data centers. If you are interested in the employment impact of data centers, explore Sector 518 jobs in Virginia and other states in The Workforce Lab, a free interactive tool built from U.S. Census Bureau data.

The Problem

By Mahelet G Fikru

I teach principles of microeconomics using a Cengage textbook. Cengage has built-in interactive homework exercises — multiple choice questions, problem sets, activities — all delivered through their digital platform. Students complete them at home, get immediate feedback, and receive a grade. I thought it was a clean, efficient system. My students thought so too.

What I did not anticipate was how quickly they would find a workaround.

Some students took a screenshot of the problem and uploaded it directly to ChatGPT. Others copy-pasted the question text. Either way, the AI returned an answer — and for multiple choice questions, it was almost always the right one. Students submitted that answer, the platform registered it as correct, and they earned full marks. Most of them got A’s on the homework.

A few weeks later, I gave a closed-book in-class test using the same types of questions. The scores were strikingly low.

What stayed with me from that experience was not the test scores. It was what happened before the test. Some students came to me after completing the homework, holding their A, and asked me why that particular answer was correct. They had the right answer and no idea why it was right. A few told me they had tried to work backwards from the AI output — they took the answer and tried to figure out what it meant — but the logic still wasn’t landing.

They were not cheating, at least not in the way I had understood that word. They were not hiding what they had done. They told me openly, matter-of-factly, as though it were an obvious strategy. And honestly, I could not fully disagree with them. The homework was designed to produce a correct answer. They produced a correct answer. The system rewarded them for it.

The problem was that the assessment was not designed to make them learn. It was designed to produce an output, and AI had made producing that output effortless. The A was real. The learning was not.

That was not a pleasant moment for me as an educator. But it was an important one. It forced me to ask a question I had not seriously asked before: What was the homework actually for?

The Solution

By Devika Toprani, Soulful Learning with AI

What Mahelet describes is one of the clearest illustrations I have seen of what I call the missing cognitive layer. Not missing from the student. Missing from the learning design.

Her students were not confused about economics because they were incapable. They were confused because the sequence gave them the answer before their brains had formed any question. And that is a structural problem. Not a moral one.

There is a name for what happens cognitively when an answer arrives before any internal framework exists to receive it. Automation bias. The documented human tendency to defer to an AI output because it arrived first... and because it arrived confidently (Skitka et al., 1999; Endsley, 2016). The student does not evaluate the answer.

They accept it. Then they submit it. Then they earn full marks. And the gap in their understanding stays invisible.

Invisible gaps do not get fixed. That is the part that does not show up in the gradebook.

The Somagraphic Learning™ framework is built around a three-stage sequence: Attempt → Map → Refine (Toprani, 2026). You can see it in the infographic above and explored in detail on my website. It was designed precisely for this moment. Not as a policy response to AI use. As a cognitive intervention that changes what the learner brings into the encounter with AI.

🖊️ Attempt: Write or sketch what you already think about the question. Messy is expected. Wrong is fine. Incompleteness is the point. Research on productive failure shows that effortful initial retrieval, even when incorrect, strengthens subsequent encoding (Kapur, 2016). The attempt is not a warm-up. It is the first layer of learning.

🗺️ Map: Connect two ideas. Force a relationship. Use the sentence: [this] affects [that] because [your reason]. Even one directional relationship, written in the learner’s own words, changes everything that follows. It creates what I call a cognitive artifact. A timestamped, externalized record of the learner’s own reasoning. Before any AI output exists. That artifact becomes the learner’s reference point.

✨ Refine: Now AI enters. But it enters a different room. The learner is no longer open-handed, ready to receive whatever the machine offers. They are holding their own structure. They are comparing. Questioning. The gap they identified in the Map stage becomes the question they take to AI. That is the difference between generating answers and refining understanding.

Mahelet’s students would have arrived at ChatGPT with something that could not be faked: a directional claim of their own. Something like “demand falls when price increases because consumers have a fixed budget and substitute options.” That one sentence. That one, because it would have changed everything about how they read AI’s response. They would have had something to compare it against.

The Map stage produces what no detection tool, no proctoring system, and no AI literacy curriculum currently on the market produces. Verifiable pre-AI evidence of original human reasoning. Not surveilled. Not inferred. Visible. Dateable. Theirs.

This is also why I built Map Before Machine™ as a deployable tool, not a concept to aspire to. It is printable in under a minute. LMS-ready on Canvas, Moodle, and any comparable platform. It requires no app, no subscription, no curriculum redesign. Ten minutes. A pen. Paper. That is the entire barrier to entry. Because if the solution is not that accessible, it will never reach the students who need it most.

What Mahelet named was the real question educators are circling right now without quite landing on it. What was the homework actually for? Was it for producing a correct answer? Or for building a mind that could recognize one?

The Somagraphic Learning™ framework says: the sequence is the pedagogy. When AI enters, is the design decision that everything else depends on.

The A+ grade was real. The learning was not. And there is a structure that changes that…

Have you seen something similar in your classroom or organization? We would love to hear from you in the comments!

Related: The Age of Content Scarcity Is Over. Judgment Is What’s Scarce Now.

About the authors

Mahelet G. Fikru is an economist and educator who has taught a variety of economics courses for nearly fifteen years. She writes about education, technology, and economics on her Substack, M G Fikru.

Devika Toprani is an independent researcher, educator, and EdTech Futurist with 5+ years of multidisciplinary experience across three countries. She is the founder of the Somagraphic Learning™, a USPTO trademarked, IP-protected, human-first visual cognitive framework that reduces automation bias in AI-integrated learning. She writes at Soulful Learning with AI on Substack.

References

Endsley, M. R. (2016). From here to autonomy: Lessons learned from human-automation research. Human Factors, 59(1), 5–27. https://doi.org/10.1177/0018720816681350

Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289–299. https://doi.org/10.1080/00461520.2016.1155457

Skitka, L. J., Mosier, K. L., & Burdick, M. (1999). Does automation bias decision-making? International Journal of Human-Computer Studies, 51(5), 991–1006. https://doi.org/10.1006/ijhc.1999.0252

Toprani, D. (2026, May 8). Somagraphic Learning™ Framework: A human-first, AI-supported visual cognitive approach (Version 3). OSF Preprints. https://osf.io/preprints/edarxiv/fnk7z_v4

A recent study by Morton, Prakash, and Dolšak in Environmental Research Letters on public support for deep sea mining (DSM) in the US shows exactly that.

DSM is often presented as an alternative to land-based mining. Why look for alternatives?

One reason is economic. As demand for critical minerals continues to increase, many existing mineral deposits are becoming harder and more expensive to extract as ore grades decline. In other words, companies must process more material to obtain the same amount of minerals, thereby increasing their costs.

There is also a geopolitical reason. The US relies heavily on imports of critical minerals, particularly from China. As a result, access to minerals is increasingly framed not only as an economic issue, but also as a national security concern. Should China decide to block the US from accessing the minerals it controls, the US would lose a source of raw materials used in consumer, energy, and defense technology, including computers, electric cars, aircraft, and munitions. Minerals from the deep sea could provide a new source of those raw materials.

DSM could occur in Exclusive Economic Zones (EEZs), where marine resources are located within a country’s waters, or in international waters. EEZs are the nearshore waters that coastal countries control. International waters lie beyond those borders, making up most of the world’s oceans.

What researchers asked and what they found

The researchers gave study participants a simple task:

The International Seabed Authority is debating regulations on deep sea mining. Indicate your level of support for deep sea mining.

Researchers mentioned the Clarion Clipperton Zone in the Pacific Ocean as an example, stating that this project alone could provide enough minerals to make one billion electric vehicles. This zone is located between Hawaii and Mexico in international waters, where mineral-rich rocks lie on the abyssal plains.

They wanted to understand whether there is support for deep sea mining in international waters, and whether the degree of support depends on how the issue is framed. And does the public respond differently from people working in environmental NGOs (ENGOs)?

The study explores whether support for DSM changes depending on how benefits and costs are presented. The researchers used three framings:

1. DSM can reduce the need for land-based mining, which affects the local water supply

2. DSM reduces reliance on China because Chinese companies control most of the land-based mines (national security framing)

3. DSM can disturb fisheries that serve as a food source

They also included a control group.

The researchers administered an online survey to 1,142 individuals from the general public and 278 ENGO professionals. After exposure to the framings, respondents were asked to indicate their support for DSM on a 1–5 scale (1 = no support, 5 = strong support).

The two groups looked very different.

ENGO respondents showed low overall support for DSM, with an average support level of 1.97. The general public was closer to indifferent, with an average of 2.95.

The most important result:

Among the general public, the national security framing increased support for DSM relative to the control group. This means people are more likely to tolerate uncertainties around new types of mining, such as DSM, when framed with information about the benefits of DSM to reduce reliance on imports from China or Chinese-owned mines.

But among ENGO professionals, none of the framings changed support levels. Their DSM support remained consistently low across all conditions.

Should the public be part of the DSM conversation?

Some may ask whether public support even matters for deep sea mining in international waters. Should the public be part of the conversation at all?

Theories in environmental economics suggest that the answer is yes. People often value environmental resources even when they do not directly use them. Many individuals derive satisfaction simply from knowing that marine ecosystems, species, and habitats are preserved. This is known as existence value. Others value preservation because it allows future generations to enjoy these resources, a concept often referred to as bequest value.

The absence of a market does not imply the absence of value. Marine ecosystems may generate economic value even when they are never harvested, visited, or directly used. John V. Krutilla (1967), an American environmental economist, argued that natural environments possess economic value simply through their existence and preservation, even when they are never directly used by people.

From this perspective, the public has standing in the DSM debate. Decisions about mining in international waters should not be based solely on the value of extracted minerals. They should also consider the value society places on preserving deep ocean ecosystems that may otherwise be overlooked by markets.

A caution regarding geopolitical framing

The study also offers an important caution. Because the environmental impacts of DSM on marine ecosystems remain uncertain and may emerge only over long time horizons, public opinion can be shaped by other narratives before the scientific evidence is fully established.

The authors find that geopolitical and national security framing increases public support for DSM. This suggests that policymakers and industry actors may be able to move public opinion from indifference toward support by emphasizing mineral security and dependence on foreign suppliers.

This does not mean the geopolitical concerns are unimportant. However, it highlights the need to rapidly generate credible evidence on the environmental impacts of DSM. Otherwise, public opinion may become driven primarily by geopolitical considerations while environmental uncertainties remain unresolved or receive insufficient attention.

In deep sea mining, media coverage matters

This study points to the risk of media outlets covering deep sea mining in incomplete or misleading ways.

Public perception may not have the power to make or break the DSM industry. Still, an informed public can support wise decision-making about shared ecosystems and resources, such as those found on the international seabed. After all, the resources of the international seabed are defined by the UN Convention on the Law of the Sea as the “common heritage” of humankind.

There are direct ways the public can play a role in DSM decision-making. For example, US public comment periods on proposed deep sea mining projects (including some in international waters) allow individuals to submit statements. The government is required to consider and respond to those comments, as long as they’re fact-based and unique (that is, not an exact copy of someone else’s comment). An informed public is better-prepared for such opportunities to respond to deep sea mining proposals.

So, if framing shapes public perception of the DSM industry, the media has a responsibility to use framing with care, to foster an informed public. As the authors of the study write, “over-reliance on security framing risks sidelining important equity and environmental concerns.” Deep sea mining’s potential environmental and social harms are at least as important as its national security benefits.

National security arguments also run the risk of veering into propaganda, which is perhaps why they’re so heavily relied on by governments, including the current Trump administration. Naturally, balanced coverage can’t be expected in media releases by governments, NGOs, or private companies. These actors are not “the media”, and have specific agendas to promote, offering marketing material or propaganda rather than true news.

From independent news outlets, however, the public deserves a nuanced view of deep sea mining’s potential costs and benefits. Context is also valuable here. For example, geopolitical tensions can shift or resolve in a matter of years, while the deep sea will likely take decades or longer to recover (even partially) from the environmental impacts of mining. It is the media’s job to make the public aware of the full picture.

Your turn

To what extent would you support deep sea mining? Please provide in the comments section why or what factors you would consider.

About the authors

Mahelet G. Fikru is an economist and educator whose work focuses on critical minerals and the societal challenges of the clean energy transition. Her Substack examines how innovation and technology shape markets and society.

Elyse Hauser is an environmental journalist whose work focuses on issues that impact aquatic ecosystems, especially the deep sea. Her Substack covers essential deep sea mining news.

Most discussions about electric vehicles focus on driving range, charging time, and price. But there is another question quietly emerging in the background of clean vehicles:

Do consumers really care where the minerals inside an EV battery come from?

Our recent study suggests the answer is yes — to some extent.

Using a national U.S. survey of 1,200 respondents, the study examined whether Americans value electric vehicles differently depending on where the lithium in the battery is mined and refined. Specifically, the study tested whether consumers prefer lithium sourced and processed in the United States rather than abroad.

The findings suggest that although consumers primarily care about the performance and price of the final product, they also place measurable value on the upstream supply chains supporting clean technologies.

What the data shows

We showed 1,200 survey respondents two different types of electric vehicles and asked them to make hypothetical choices between the offered EVs and a default gasoline vehicle.

The EVs differed in:

• driving range,

• charging time,

• amount of tax credits available from state and federal governments,

• percentage of lithium mined in the U.S.,

• whether the lithium was refined in the U.S. or abroad,

• and the EV price premium relative to a comparable gasoline vehicle.

The results show that about 36% of the time, respondents chose the gasoline vehicle, either because they opposed purchasing the EVs shown or did not want to pay the higher price premium associated with them.

The results also show that consumers primarily care about the price premium. About 47% of respondents said price was the dominant factor affecting their choice, mainly to avoid paying more for the EV. Another 26% said driving range was the most important factor. Only a small percentage said they primarily focused on where the lithium was mined (6%) or refined (3%).

This suggests that price remains the main concern for potential EV buyers, followed by vehicle performance.

Consumers value domestic lithium sourcing

After respondents completed the choice tasks, we estimated how much they would be willing to pay for domestic sourcing while holding other factors constant.

We found that respondents were willing to pay about $29 more for every one-percentage-point increase in lithium mined domestically. They were also willing to pay about $1,613 more for EVs using lithium refined in the United States rather than abroad.

Although these values are smaller than what consumers would pay for improved driving range or reduced charging time, the findings are still notable because mineral sourcing does not directly improve vehicle performance. Domestic lithium content does not make the EV drive farther or charge faster.

Yet consumers still placed some value on it.

This mirrors broader consumer research showing that people increasingly care about where products are made and where their inputs originate.

But information changed responses

The most interesting result was not simply that consumers valued domestic lithium sourcing.

It was that their valuation changed depending on the information they received.

Before completing the EV choice tasks, respondents were randomly shown different types of information, including:

• emissions from lithium refining,

• economic benefits from lithium mining, such as jobs and local tax revenue.

When respondents were shown positive information about the economic benefits of mining, willingness to pay for domestic lithium mining doubled, rising to about $59 per percentage-point increase in domestic lithium. Willingness to pay for U.S.-based refining also increased to $2,226.

This suggests that information about mining’s economic benefits can strengthen consumer support for domestic sourcing.

But when respondents were shown environmental concerns associated with lithium refining, their valuation for domestic sourcing weakened slightly. They were willing to pay about $25 for each percentage-point increase in lithium mined in the U.S., and about $1,609 for domestic lithium refining.

Interestingly, negative environmental framing reduced preferences for domestic sourcing, but did not fully reverse them. Consumers still showed positive willingness to pay for domestic mining and refining even under the negative-framing condition.

Overall, the research shows that domestic sourcing matters for consumers — and it matters more when people receive positive information about mining. However, traditional EV attributes still dominate decision-making.

This suggests that supply-chain transparency may complement — rather than replace — concerns about performance and affordability.

Beyond tailpipe emissions

For years, debates about EVs focused mainly on tailpipe emissions.

But as the transition to clean vehicles accelerates, attention is shifting upstream:

• where minerals are extracted,

• who processes them,

• how communities are affected,

• and who captures the economic value.

Policies such as the pre-2025 Clean Vehicle Credit (under the Inflation Reduction Act) sought to encourage domestic sourcing by linking EV tax credits to mineral-origin requirements. Our findings suggest that such financial incentives (although currently defunct) may align with at least some consumer preferences — especially when consumers understand the economic implications of domestic mining and refining.

At the same time, the results also highlight a challenge for policymakers and industry.

Consumers do not evaluate domestic mining in a vacuum. Their preferences shift depending on whether the conversation emphasizes jobs and regional development, or pollution and environmental risks.

That means the future of critical battery minerals may depend not only on geology and technology, but also on public trust, transparency, and how the trade-offs of the energy transition are communicated.

Your turn

Suppose you are about to buy an electric vehicle. Which factor would matter most to you? Why? I am curious to hear how people think about the trade-offs. Are there other factors you would consider before buying an EV?

Or would you not buy an EV at all? Use the comments section to tell me why you answered the way you did.

Mining communities are endowed with non-fuel mineral deposits that are key for a diverse set of manufacturing industries, including electronics, transportation, and energy technology. Copper, iron ore, and aluminum are widely used across applications, while nickel and lithium are essential for battery systems. States like Arizona (copper), Nevada (copper), and Michigan (iron ore) rank among the top 10 in the value of non-fuel mineral production.

Despite this resource base, the U.S. has historically been a net importer of both raw and refined materials. Demand continues to outpace the rate at which minerals can be profitably extracted and refined. More recently, national priorities have shifted toward reducing reliance on imports and building a resilient domestic extraction and refining industry. These goals have elevated domestic mining as a strategy for mineral sourcing. Alternatives such as recycling and recovering used metals are also being advocated, though to a lesser extent.

Local Tradeoffs

Domestic mining inevitably involves (re)opening operations in local communities, bringing both opportunities and social challenges.

On one hand, mining projects can create jobs, improve infrastructure, and generate local tax revenue.

On the other hand, they may introduce disruptions—air and noise pollution, competition for scarce resources such as water, changes in land use, and a transient (and sometimes unwelcome) labor force.

When perceived risks outweigh benefits, communities often mobilize opposition that can delay or block projects.

In contrast, smaller or rural communities may welcome mining projects because the benefits carry more weight. Where alternative economic opportunities are limited, mining may be seen as one of the few viable paths for development. This has sometimes led to a YIMBY dynamic—Yes-In-My-Backyard—where proximity to a proposed site increases support.

The Same Pattern, Different Locations

This pattern mirrors conditions in many mineral-rich countries in the Global South, where resource endowments position them primarily as suppliers of raw, unrefined materials (e.g., the Democratic Republic of Congo). These countries typically do not engage in higher-value refining or manufacturing and therefore capture only a small share of the value generated along the global value chain. In some cases, mining sites are owned by foreign entities through foreign direct investment, meaning profits are repatriated rather than retained locally. The result is limited economic gain alongside full exposure to the external costs of extraction.

A similar dynamic can be observed in U.S. mining towns. These communities are often sparsely populated, economically less diversified, and tied to operations owned by out-of-state corporations or investors who capture most of the economic profit. High-value activities—refining and advanced manufacturing—tend to occur elsewhere or abroad.

For example, in 2024, Michigan exported all 8,000 tons of nickel produced at the Eagle Mine in concentrate form to smelters in Canada and other countries.

Closing the Value Gap

Mining communities, in the U.S. or elsewhere, need not remain confined to the lowest-value stage of extraction while higher-value activities occur elsewhere.

One way to close this gap is to co-locate mining with advanced manufacturing, allowing host communities to participate further along the value chain. Another is to strengthen the ability of mineral-rich communities to negotiate higher royalties and secure direct monetary or non-monetary benefits from mining projects.

These approaches could allow communities to capture a greater share of the economic value while fostering broader support for domestic extraction.

Your Turn

If mining happens locally, should value creation stay local too? Or is it enough for communities to host extraction while the higher-value gains happen elsewhere? Let me know your thoughts in the comments.

In March, I started paying attention to economists who are actively writing on Substack. April continues that effort. Thanks to everyone who reached out and made recommendations.

For the year 2026, my list includes economists who were active on Substack. So I searched using the following criteria:

• Substack writers who clearly identify as economists

• Those who posted an article on Substack

• Those who used their real names

One of my goals in documenting this database is to help my undergraduate students see the different types of economists and witness the variety of topics we study. If you know an economist, share their link in the comments. If you are an economist who uses your real name on Substack, share your work in the comments section.

How I explained this to my students

When students ask how to differentiate economists, I tell them to stop looking for one defining feature.

I break it down into four dimensions:

• the methods they use
→ for example, someone using econometrics or data analysis to study inflation

• how they think about the economy
→ for example, approaching a problem from a free-market perspective or a behavioral lens

• what they study
→ for example, focusing on energy markets, labor, or financial systems

• where they apply their ideas
→ for example, working in academia, advising governments, or working in the corporate sector

No single dimension defines an economist.

Named Economists Active on Substack

• Claudia Sahm (last post April 27, 2026)

• Scott Cunningham (last post April 29, 2026)

• Arpit Gupta (last post April 5, 2026)

• Carlos Chavez (last post April 2, 2026)

• Carly Urban (last post April 15, 2026)

• Peter Coy (last post April 21, 2026)

• Andrew Jonelis (last post April 5, 2026)

• Varun Gupta (last post April 24, 2026)

• Claes Backman (last post April 16, 2026)

• Ernie Tedeschi (last post April 21, 2026)

• Djoann Fal (last post April 7, 2026)

• Ken Yeung (last post April 28, 2026)

• Mariana Mazzucato (last post April 2, 2026)

⸻

(More to come as this database grows.)