Over the past months, Popular by Design (PbD) has published several guest essays, and they convinced me that this should be a larger part of the project. The newsletter is also turning into the public face of a research lab I'm starting at Notre Dame, with new postdoctoral and research associates joining soon. I want PbD to become a venue for serious policy writing by researchers and practitioners, so I'm making it official: if you have something that fits, I'd like to consider it. This post doubles as our standing guide for contributors; if you're reading it months from now, it still applies.

Earlier this month, I turned on paid memberships and promised that every essay and all other substantive content would stay free. The point of this project is to influence the public conversation, and a paywall would work against that. Supporters ($120/year) keep the writing open for everyone and get invitations to our reader meetups, online and in person. Founding Members ($1,000/year) join a smaller circle that gets private conversations with me each year and helps shape what I write and convene. The money funds the editing and buys the time it takes to keep doing this well while I have a full-time teaching job.

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We want your piece that falls between the academic article and the op-ed: too evidence-heavy for an op-ed, too timely or policy-relevant to wait on peer review. Immigration and public opinion remain the center of gravity, but the newsletter is also opening up to population change, housing, AI, and the politics of policy design. The goal is the same across all of them: policies that actually work, made popular by design. If you study land use, demographic decline, or how AI is changing institutions, and you can write about it for a general reader, I want to hear from you. Co-authored pieces are welcome, especially when they pair a researcher with a practitioner. Most guest essays run between 2,000 and 4,000 words. The main test: would a thoughtful reader of any persuasion be glad they spent ten minutes on it?

Three main formats we look for:

1. A case study or practitioner essay. One policy, program, election, event, or live debate, here or abroad: what is actually going on and what we should learn from it. These pieces are especially valuable when written by people close to the subject, whether they run a program or analyze the field. Examples: Aubrey Westfall on how Canada lets its provinces select their own immigrants, built on her own field interviews; and Gil Guerra on why another Mariel is less likely than Washington assumes, an immigration analyst weighing the evidence on a live policy question.

2. An evidence review. What the literature actually shows on a contested question, explained for non-specialists, with a clear position taken no matter where it lands politically. Example: Hanno Hilbig on whether mainstream parties gain by moving right on immigration.

3. An original empirical finding. A novel result from your own administrative data, survey, or sharpened re-analysis, not published anywhere else. The data should be open or shareable so the result is replicable; we can provide a template, and we will redo any charts in our house style.

House style, briefly:

• Plain, pragmatic, and cross-ideological. Take the other side’s best case seriously before you answer it.

• Dense hyperlinks (20 or more), always to the primary source.

• Specific numbers with named sources. No “studies show.”

• Every factual claim has to survive a fact-check before publishing.

• AI tools are fine for research, editing, and writing (!) as long as the argument is yours, you verified it, and you’re willing to sign onto it.

What we don’t run: Vibes-only hot takes that add no new evidence or argument, jargon-heavy theory, or anything already published or under review elsewhere. If a smart non-academic policy person cannot follow the argument, this is the wrong outlet for it.

How it works: If you have an essay that fits Popular by Design, send a pitch to submissions@popularbydesign.org (a paragraph on what you’d write, plus a line on why you’re the person to write it). The readership here includes some of the best policy folks in the United States and beyond, and a guest essay is a direct way to put your argument in front of them.

Accepted pieces get one light developmental edit and one copyedit, with every change yours to approve. We commission a custom cover image and write a short note introducing you and your work. You keep the copyright and can republish anywhere with a credit line; we only ask that the piece run here first.

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Is it morally justifiable to deport people from a liberal democracy? If you hesitated just now even for a moment, or if your answer was anything short of a resounding yes, this essay is for you.

Without any need for throat-clearing, my own answer is absolutely. Moreover, it’s possible liberal democracy even requires the real possibility of deportation. My sense is that a large number of pro-immigration advocates and normie liberals, including many of my fellow migration scholars, simply carry an implicit assumption that a just immigration system is one where no one—not a single person regardless of anything—ever gets deported. What I’ll try to convince you about here is that this assumption is wrong, and I think it has become one of the obstacles to building the immigration policy most of us actually want.

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This spring, I argued in The Atlantic that the old norm of opposing “only” illegal immigration, insincere as it often was, served as the political shield protecting legal immigration, and that its collapse should worry everyone who wants more of the latter. This essay takes that argument one step further, to the part hardly anyone on my side wants to defend, and argues for a narrow but essential proposition: in a liberal democracy, some people who lack a legal right to remain need to be removed, including by force, and to third countries, when nothing else works.

That sentence sounds harsher than it is because immigration debates have trained us to hear “deportation” as either unnecessary cruelty or restrictionist toughness. But the better way to think about deportation is to consider it, just like incarceration or even capital punishment, as one of the legitimate policy instruments our democratic governments have to enforce its laws and to maintain order. If our asylum procedures are meaningful, some claims will fail. And, if court review matters, some appeals will be exhausted. In the end, if we want to ask citizens to trust a generous immigration system, their government must be able to carry out the system’s own decisions.

I am not going to propose a detailed enforcement framework here, and the essay is deliberately agnostic about how many people should be removed and how. My goal here is simply to establish a baseline that everyone can stand on, so that we can have a productive conversation about enforcement design instead of the current bickering between “deport them all” and “deportations are illegitimate.”

The best case against deportation

Since I’m trying to provide the liberal case in favor of deportations here, I should steelman the opposite view first. Don’t get me wrong—deportations are pretty bad, to say the least.

When government agents deport migrants, they commit coercive state violence against people who are, in the vast majority of cases, guilty of nothing beyond wanting a better life. Deporting people costs serious money that could be spent on almost anything more constructive.1 It inflicts direct, lasting harm on the deported, their employers, friends, and families, including many citizens. It can violate international law when it deports people to persecution, while migration adjudication decisions are made under genuine uncertainty, with trauma, bad translation, missing documents, and uneven access to lawyers, all raising the odds of error. On top of all that, sometimes there is no functioning state to deport anyone to, or no willing state to accept anyone. In the end, deportation is clearly among the most severe things a modern liberal democratic state can do to a person outside its criminal law.

Some scholars and advocates take these premises to a radical conclusion, so what follows is no strawman. In the UCLA Law Review, legal scholar Angélica Cházaro argues that deportation is an indefensible act of violence whose abolition should be the horizon of pro-immigrant advocacy. Others scholars have mapped out what lawyering toward that horizon looks like.

I have no doubts this position is coherent and sincerely held. It’s also likely more influential than its explicit adherents suggest: for every scholar who writes “abolish deportation,” there are many more advocates and academics who would never sign the slogan but treat every actual removal as morally suspect. And a still probably larger group, including many of my more moderate and libertarian colleagues, simply keeps deportation out of sight and out of mind. For them, the topic feels vaguely discrediting, so their papers and policy proposals mostly route around it. I should admit I have been guilty of this myself. My own book barely discusses the need for deportations and enforcement at all, even though everything it argues about trust and order plainly depends on them. Writing this essay is my attempt to correct that.

I saw this worldview on display at the Council for European Studies conference in Dublin this June (a rare big conference which I actually enjoyed), where panels on migration kept running in two different directions, with some very heated disputes. Part of the room insisted that liberal democracies cannot survive without clear membership and public order, while many other researchers and advocates talked about removal as something morally contaminated, something decent states just do not do anymore. What struck me most was how rarely that second camp would say what should actually happen when a person’s asylum claim has failed. The question usually just hung there until the panel moved on to the next hard case.

The structure of this evasion is familiar from another debate. Prison abolitionists have built a serious intellectual movement on the observation that prisons are brutal, expensive, and unequally applied. Those observations are true as far as they go. But the movement has always struggled with the question of what happens to people who commit serious harm, and the most common answer is that in a just society, with poverty and desperation addressed, people would rarely commit such harm in the first place. Just last month, days before winning her New York congressional primary, democratic socialist and prison abolitionist Darializa Avila Chevalier was asked repeatedly in an interview what should happen to someone who kills another person; she never directly answered, saying she was talking about “the distance between the world we want to see and the world that we’re at.”

Many pro-immigration advocates hold the mirror-image view: in a just immigration system, with barriers low or gone, there would be few immigration laws left to break, so no one would need to be deported. Both moves relocate the hard case to a utopia where it no longer arises. Both leave the actual institution, the one operating in the world we have, without a principled account of its hardest and most defining task.

Of course, illegal immigration is literally not a murder. My friend Bryan Caplan made a version of the argument just last month: everyone who has ever driven has broken the speed limit, and “if you think it’s OK to break a law against driving 56 mph in the desert, you should think that it’s OK to break a law against mowing grass for money.” Unauthorized presence, in this telling, is a victimless regulatory violation, which makes deportation a grotesquely disproportionate punishment.

I hear Bryan and other libertarians who make this argument. But we live in a democracy, for better or worse, and that’s just not how most voters who have a say in who gets elected next think. And “victimless” is doing a lot of work in that sentence. In the United States the description is at least half right, since unauthorized workers pay federal taxes toward benefits they can never claim, but the costs of their presence land close to home: the schooling and local services their families use are funded by states and towns, a mismatch the National Academies documented in its landmark fiscal study. In Europe’s far more generous welfare states, where even people ordered to leave remain entitled to accommodation and benefits, the fiscal ledger is harder still to wave away.2 Besides, the current state of the law in the United States and other developed democracies is that illegal aliens, once ordered removed, must by statute actually be deported, while speeding—which is enforced sometimes even when it’s not absolutely necessary—will normally get you nothing worse than a ticket.

Political scientists Matthew Wright, Morris Levy, and Jack Citrin have also shown that Americans evaluating illegal immigration switch from weighing individual immigrants’ characteristics to categorical moral judgments rooted in the rule of law, and Levy and Wright’s book argues that civic fairness is the dominant frame through which Americans judge the issue. Voters treat unauthorized entry as a breach of the social contract, and a movement that keeps comparing it to a traffic ticket mostly convinces them that it has no plans to enforce anything.

So even the best case against deportation still falls short of the conclusion many advocates draw from it. Yes, the high economic and moral costs of deportation are an overwhelming argument for a fair and just deportation process: full hearings, competent interpretation, access to counsel (which, by the way, taxpayers would also have to pay for), and aggressive error correction to ensure no false positives. They make a strong, if less convincing, argument for making removal as rare as possible, prioritizing the worst offenders, and subjecting decisions to review. But the best case against deportation does not support the conclusion that no one should ever be deported, just as the best case against the horrifying conditions across America’s domestic carceral systems fails to prove that no one should ever be separated from society.

My test for anyone who hesitates is this single hypothetical case. Let’s say someone applies for asylum and receives everything the process can offer: proper notice, an interpreter, a lawyer, a full hearing, and an appeal. They lose at every stage; their country of origin is safe for them and willing to take them back, no legal protection applies, and the government even offers to pay for the flight home. They still refuse to leave. If you’re saying that the liberal state cannot remove even this person, then losing an immigration case has no consequence at all,3 and all those hearings were an expensive ritual of pretend law. Even Joseph Carens, the philosopher who did more than anyone to make the ethical case for open borders, writes plainly that nothing in his argument “denies a government’s moral and legal right to prevent entry in the first place and to deport those who settle without authorization, so long as these expulsions take place at a relatively early stage of residence.”

Germany: what happens when no one is deported

Germany is worth extended attention because it is a wealthy liberal democracy with elaborate legal protections and visible enforcement failure, and because Americans, who currently associate deportation with televised raids and daily arrest quotas, tend to find its situation genuinely unbelievable when they learn about it. On my research visit there last year, I talked to politicians and activists across the spectrum, and the mainstream center-right (CDU) kept telling me the same exact surprising thing: It is nearly impossible to deport anyone from Germany. Many people whose asylum applications were rejected years ago, they complained, still live in accommodation financed by taxpayers, and the authorities cannot do much about it. I was skeptical, since complaining about weak enforcement is what right-wing politicians do everywhere. Then I checked the numbers, and they were mostly right.

To an outside observer, this may all sound like a bureaucratic joke. At the end of 2025, about 232,000 people in Germany were legally required to leave the country, and about 82 percent of them held a “Duldung,” an official certificate of toleration: the state has ordered you to leave and simultaneously certifies that it will not, for now, make you. Over the whole of 2025, Germany deported fewer than 23,000 people, while more than 34,000 planned deportations were called off or otherwise never carried out, usually because the person could not be produced on the day. More planned removals fell through than succeeded, and just as the CDU claims, many of those ordered to leave remain entitled to state-financed accommodation and benefits while they stay, sometimes at reduced levels. Note that such an arrangement is no kindness to the “tolerated” either: years of insecure status and shifting work restrictions, with no way to plan a life.

Of course, if you talk to left-wing pro-immigration activists, they would tell you a different interpretation of these exact numbers. German deportations have actually risen for five consecutive years, voluntary departures exceed forced removals, and most people with a Duldung cannot lawfully be removed right now, for reasons that range from missing travel documents and unresolved identity to family and medical circumstances. Germany also keeps converting the stock into legal residents through a recent “opportunity to stay” law for the long-settled.

But even a state that deports more every year has built a durable category holding a quarter million people at a time, suspended, often for years, between a legal decision and any genuine resolution, whether departure or secure status. Carrying out removals is “fundamentally the responsibility of the Länder,” the sixteen states, whose practices diverge so much that the government’s own Expert Council on Integration and Migration diagnosed a “manifest coherence problem”: identical cases end differently depending on which local office holds the file. The local official who must put a family on a plane absorbs the newspaper story, while the costs of never enforcing anything land on no one in particular.

This is all no longer hypothetical. After an Afghan man who was legally required to leave the country killed a two-year-old and an adult in Aschaffenburg in January 2025, Friedrich Merz built his five-point enforcement plan around this enforcement gap and pushed a nonbinding motion through the Bundestag with AfD votes, shattering a postwar taboo. Meanwhile, the removals that dominate headlines remain almost ceremonial: two heavily publicized flights of convicted offenders to Afghanistan and one man returned to Syria, around a hundred people in total, against a stock of 232,000. When mainstream government looks simultaneously cruel and ineffective, the parties that promise to be merely cruel do not stay at 20 percent forever. The AfD now polls ahead of the CDU, pushing 30 percent.

None of this paralysis is the price a liberal democracy must pay for being humane. Canada, everyone’s favorite immigration success story, closed about 23,000 removal cases in 2025, up from about 15,000 in 2023, more than four in five of them involving failed refugee claimants. Most of these people left unescorted after a final decision, with force reserved for a small minority, and the government openly funds its border agency to sustain 20,000 removals a year to protect the integrity of the asylum system. A wealthy democracy can clearly run one of the world’s most generous immigration systems and still make sure that a final no is usually followed by an actual departure.

USA: large-scale enforcement without credibility

The United States has the opposite pathology, and I watched it up close. In November 2025, I was still living in Charlotte, North Carolina, when Border Patrol commander Gregory Bovino brought his agents to town for what the government called Operation Charlotte’s Web. In five days, agents made over 250 arrests, of which, according to an internal DHS document obtained by CBS News, fewer than a third involved people classified as “criminal aliens.” On the Monday after the operation began, more than 30,000 students, a fifth of the district, did not show up to Charlotte-Mecklenburg schools. Many businesses had to close for weeks. Even Republicans flinched: former North Carolina Gov. Pat McCrory told The Daily Beast that his party “had the upper hand on immigration, as long as they were going after the criminals and the gangs,” and was losing it “because of the apparent disjointed implementation of arrest.”4

Unlike Germany, America has been removing people at scale: by December 2025, the administration claimed more than 605,000 deportations in under a year, plus far larger numbers it counts as self-deportations.5 But I should be honest that the two countries have more in common than the cable news versions suggest. America has its own Duldung-shaped stock: roughly 1.5 million people sit on ICE’s docket with final removal orders that have never been executed, a heterogeneous pile that ranges from absconders to people the law itself currently protects, stalled by many of the same things that stall Germany, from countries that refuse to take their nationals back to missing travel documents and limited detention and flight capacity. And the process behind those orders manages to be slow and thin at the same time: the immigration court backlog stands at about 3.2 million pending cases, asylum cases take four years or more to decide, most people in those proceedings have no lawyer, and in fiscal 2025 roughly 63 percent of removal orders in newly initiated cases were issued in absentia, to people who were not in the courtroom. In the end, in both Germany and the United States, the visible lesson for citizens is pretty much the same: the immigration system’s formal decisions do not describe what actually happens.

Our American discourse has its own version of the no-deportation assumption, and it hides inside an apparently moderate position: the idea that enforcement is legitimate only against “criminals.” When Janet Murguía of the National Council of La Raza famously dubbed Barack Obama “deporter-in-chief” in 2014, his administration had just set the modern record of more than 438,000 removals in a single year. The label stuck precisely because most of those removed were not dangerous.

Today’s critics of the Trump administration lean on the same premise from the other direction, arguing the crackdown is illegitimate because agents arrest gardeners instead of gang members. Prioritizing serious offenders is correct triage, and I share the revulsion at what replaced it. But notice what the premise implies if you take it as a principle rather than a priority: that violating immigration law as such may never, even after a full process, carry the consequence the law prescribes. Every legal system runs on discretion, but a categorical bar on enforcement essentially repeals the law it claims to soften.

Obama’s own enforcement leadership understood the distinction; in his final year in office, over 90 percent of interior removals involved people with serious criminal convictions, yet the system retained the stated capacity to remove others with final orders. Losing that capacity does not produce a more humane equilibrium; it produces the German scenario, in which a quarter of a million people live suspended between a legal decision and its consequence. Many of the tolerated would surely rank limbo above deportation, even if what they actually want is secure status. But a liberal democracy is still a democracy, and its immigration rules are ultimately up to the citizens and their representatives.

Deportations do work

OK, now you may say: fine, deportations may be legitimate in principle, but they do not work, so all this cruelty buys nothing. Or to be precise, deportations do not help deter people from coming illegally.

This reasoning usually comes in two steps. Step one says that restricting legal immigration cannot work because it just displaces the same people into illegal channels. Step two says that enforcement and deportations cannot work either, because push factors decide everything. People who risk their lives to cross the border are pushed out by the conditions of their home countries, and nothing the U.S. government does will influence their decisions.

Neither step survives contact with the evidence. Hein de Haas, Mathias Czaika, and their colleagues, who have studied migration policy effects as systematically as anyone, conclude that these policies are “generally effective”: restrictions genuinely shrink the flows they target, and the documented deflection into irregular channels is only partial. In a related European study, Czaika and Mogens Hobolth found that a 10 percent increase in asylum and visa rejections raised irregular migration by only 2–4 and 4–7 percent respectively, far from one-for-one displacement. Immigration is hardly the only domain where smart people make this mistake—the fixed-quantity way of thinking goes well beyond, into debates over prohibitions of almost anything.

So, whether you like it or not, immigration policy does change who comes, how many, and through which door, and the last two years demonstrated it at scale. Border Patrol encounters at the southern border fell from over 2 million in fiscal 2023 to under 240,000 in fiscal 2025, the lowest annual level in more than 50 years, with 2025’s monthly totals among the lowest ever recorded. Economists at Brookings and AEI project that net migration turned negative in 2025 for the first time in roughly half a century, with people leaving in response to the enforcement climate possibly outnumbering the people formally removed. Yes, the decline began in January 2024 under Biden and Mexico’s stepped-up enforcement did much of the early work, but no one can look at these numbers and still claim with a straight face that enforcement has no deterrent effect on unauthorized migration.

And deterrence is only a part of the case anyway. Criminologists have long distinguished between deterring crime and simply incapacitating the person committing it, and deportation runs through the same channels. Like incarceration, it works even when it scares no one else: it ends a stay the courts have already ruled unlawful and stops the local costs I mentioned above. Even if the deterrent effect were somehow close to zero, removals would still be worth carrying out, just like any other final judgment.

People respond to credible rules, which is exactly why the rules themselves need to be worth enforcing. We absolutely should be mindful that every deterred crossing or a deportee is a person who never gets what would probably have been the largest income gain of their life, and for people fleeing persecution the loss can be safety itself. What justifies paying that price is what it buys, a system trusted enough to keep admitting people, ideally more of them, through the front door.6

Deportations are what democracy owes citizens

The deepest reason someone needs to be deportable in a liberal democracy is about what democratic government owes the people who authorize it. A liberal democracy is a self-governing political community: citizens bear the local costs of policy, vote governments out when policies fail, and (indirectly) make binding rules for a bounded territory, including rules about who may enter and stay. A rule that can never be enforced against anyone is an aspiration rather than a law, and voters can tell the difference. So no one in particular has to be deported on any given day, but deportation has to remain possible in principle, with the actual number settled by good process rather than by paralysis or spectacle.

In my own recent research, I argued that protection for humanitarian migrants stays politically durable only on a foundation of trust in the broader system, including its enforcement. How much removal performance itself feeds that trust is exactly the kind of question my field should be testing instead of avoiding. A government that asks citizens to accept immigration while visibly declining to enforce its own final decisions is asking for trust it has not earned, and the immigration thermostat eventually kicks in: when voters sense the system is out of control, they push policy back in the opposite direction, usually harder than anyone intended.

None of this dictates where people go, and the destination is where enforcement earns or forfeits its legitimacy: in the normal case, the country of origin or a third country under an agreement that upholds basic human rights, and never a maximum-security prison run by a third authoritarian country. The legal baseline is worth stating plainly here, because pro-immigration advocates tend to keep blurring it, and annoyingly so. The Universal Declaration of Human Rights guarantees the right to leave any country and the right to seek asylum from persecution; it contains no right to enter the country of your choosing, and no right against removal once a lawful process has said no. To the extent any of this is enforceable at all, international law’s hard limits, above all the ban on returning anyone to persecution or torture, govern where and how a removal may happen; they do not make removal itself illegitimate.

The obligation runs to noncitizens too, and this is where my argument departs from the restrictionist one. A liberal democracy owes every person in removal proceedings a serious hearing, competent interpretation, accurate information about their country of origin, protection from return to persecution, and treatment that never becomes degrading. The people most betrayed by a collapsed distinction between lawful removal and arbitrary force are the noncitizens with strong claims, whose cases drown in a system nobody trusts.

What follows for policy deserves its own essay, and I have already sketched the general formula in Reason: administrative capacity, the binding constraint in Germany as much as in America, that delivers faster protection for those who qualify and faster removal for clearly ineligible cases, and enforcement predictable enough that the rules feel real in everyday life for regular citizens and noncitizens alike. The goal of this all should be a deportation system that is credible, bounded, and boring.

Serious pro-immigration advocates already know much of this, whatever they think of my framing. The American Immigration Council’s blueprint for rebuilding the asylum system concedes that even with every one of its reforms implemented, people without meritorious claims “will still be deemed ineligible for relief and ordered deported.” And as Dara Lind, whose chronicling of American enforcement failures I trust more than almost anyone’s, put it at that blueprint’s launch: “The problem isn’t that too many people are seeking asylum. The problem is that the U.S. hasn’t done the work to handle those who come. That’s what creates backlash.”

Whoever governs after Trump, and whichever mainstream coalitions survive in Europe, will inherit the same choice: own enforcement and make it lawful, or keep ceding it to people who enjoy it. The backlash to this administration’s methods will tempt Democrats to define themselves by the opposite of everything ICE does, just as humanitarian pressure tempts German officials to leave every file untouched. Both instincts feel humane, and both keep producing the politics we currently have.

So my ask to my skeptical readers—assuming you all are still here—is a rather small one. What I want from pro-immigration folks is a plain concession that in a liberal democracy with any immigration rules worth having, the number of justified deportations is not zero. I don’t want you to cheer for masked agents or endorse anyone’s removal targets to grant it. But once we say that deportations are legitimate and needed out loud, we can finally argue about the questions that matter: how many, after what process, and what to do when removal would be unlawful. A liberal democracy that cannot say no will not be believed when it says yes.

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Huge thanks to Mike Riggs and for their helpful suggestions on the draft.

OK, folks, real talk. Just so that we’re all on the same page, I’m an American nationalist, too. Not just a patriot in the sense of being proud of America, but a nationalist. And today I’m finally doing something about it.

By nationalist, I mean that we, American citizens, are one unique people, that the people who govern and the people being governed should belong to the same community, and that the American government should serve the common interests of this community. When necessary, it should serve those interests over both narrower domestic interests and foreign interests. Yes, especially as an immigrant, I also think America is superior1 to other countries in the ways that matter most for human progress. And, yes, I can believe all of that while still valuing human beings everywhere, regardless of origin.

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But long before I was a nationalist and started writing about immigration politics, I was writing about globalization and cosmopolitanism. In my undergraduate thesis, supervised by the late Ronald Inglehart, known for his ideas on postmaterialist values, I tried to measure how many people saw themselves as citizens of the world and why supranational identities varied across countries. I was young and naive enough, and living in Europe at the time, to think the future might be cosmopolitan (and even European). I was also eager enough to turn that hope into a dataset to prove globalization is good and inevitable.

That position still makes some moral sense to me. All humans are part of the same family, or at least close enough that any decent person, regardless of their religious beliefs, should feel the force of that idea. Borders are morally arbitrary in many ways, and the accident of birthplace remains the largest determinant of life chances in the world, more so than race, gender, or anything else. Much of my work on immigration still begins with that discomfort and with the fact that immigration empowers people to escape this unfortunate reality and contribute to this world.

Yet the more I studied public opinion and democratic politics, the harder it became to believe that cosmopolitanism (just like socialism) could do what its admirers wanted it to do. True, identifying with humanity as a whole can and probably should guide moral reflection for some people in positions of power. But no one has yet used it to build a durable democratic community, a welfare state, a public school system, or a legitimate and capable government that ordinary people feel belongs to them. In the end, most politics still runs through countries, whether we like it or not.

This is why I have gradually become more sympathetic to the idea of nationalism, or at least to American nationalism of the classical-liberal variety, certainly more than my younger self would have expected. Of course, we also know that nationalism, just like anything in large doses, can be dangerous and easily abused. But nationalism is also the political form through which modern democracy and equal citizenship have actually been built. In fact, liberal nationalism is arguably the most inclusive political identity that works at scale right now.

As for the something I’m finally doing about it: it comes at the end of the essay, where this newsletter becomes a bigger project and you get an invitation to become a part of it. But my argument on the virtues of US nationalism comes first, because the invitation depends on it.

What nationalism is and can do

Many folks I respect will still reject the label or even the idea itself. and , for instance, have made a forceful case against nationalism, and they have good reasons for doing so. If nationalism means ethnic hierarchy, coercive cultural uniformity, trade protectionism, or a state empowered to decide who counts as a “real” American, I oppose it too.

If you wish, you can call the good parts liberalism, patriotism, or equal citizenship. But I care much less about the label than about the ideas underneath it. Alex has also argued that American identity is based on a broad creed, not religion or ancestry. That is pretty much what I and many others mean by American liberal nationalism, too, whether you like the label or not.

So, forget the meager defenses of nationalism by self-proclaimed white nationalists, right-wing populists, or self-styled dual national philosophers with obvious foreign policy agendas. My personal favorite conceptual and empirical account of nationalism comes from Andreas Wimmer because it starts with nationalism as a principle of political legitimacy. The nation-state provided the ideological basis for democracy and the provision of public goods because these institutions could be justified in the name of a people of equal citizens, held together by a shared purpose and mutual obligation.

Yael Tamir, whose work is among the best-known liberal defenses of nationalism, makes a related point in a more personal register. People need meaning and continuity alongside liberty and opportunity. Liberalism supplies rights and freedoms, while nationalism supplies belonging and obligation. A good, successful society needs both.

That is also exactly the part of nationalism that many of my cosmopolitan and libertarian friends underrate. The welfare state, or if you’re skeptical, state capacity more generally, does not run on universal benevolence or market mechanisms alone. Public schools and other services command support because people see other citizens as part of a shared project. This kind of solidarity is imperfect and morally partial, but it is also how large-scale generosity usually becomes politically durable.

My own empirical research pushed me in the same direction from a different angle. In my PhD dissertation and, later in In Our Interest, I show that most voters are closer to what I have called altruistic nationalists than to pure selfish homo economicus or principled humanitarians. They genuinely care about others, but they weigh the well-being of compatriots first when judging policy.

We know from most other research that people are pretty groupish. But even I was still surprised by how stark this was in the data. In both UK and US surveys I ran with an incentivized charity choice game, only 10 percent of respondents decided to donate money to global charities over domestic charities or their personal pocketbook. Importantly, this held even among left-wing voters often wrongly accused of being cosmopolitan.

That sounds limiting if you want universal solidarity, but it helps explain why democratic politics works at all. International cooperation is indispensable, but no global organization can command democratic legitimacy or popular loyalty. The nation-state remains the primary site where democratic accountability and public trust converge. Of course, I should be frank that this is a compromise for some of us: politics built on national solidarity will always do less for the world’s poorest than pure cosmopolitanism promises on paper. But a promise that cannot survive an election delivers even less.

The beauty of American nationalism

The American experiment is about to turn 250 years old. That is a long run for a constitutional republic built on the repeated expansion of citizenship across the continent. And this is all beautiful.

Some readers will cringe at that sentence. But the flag, the oath, the Declaration, and the stubborn idea that people from everywhere can become part of one political people are not empty words and rituals. They are part of what made my own American life and success possible. And I do not feel embarrassed to say that. And neither should you, dear reader.

Policies that ask people to trust one another, make sacrifices, and wait for results need a shared we behind them. recently argued that America needs liberal nationalism back. I think he is right because the more realistic choice is between better and worse forms of nationalism: a liberal nationalism that treats newcomers and minorities as potential co-authors of the national project, or a narrower nationalism that treats them as permanent threats. A country with no shared national story will usually become more fragmented and vulnerable to even narrower forms of identity politics and conflict.

The American version specifically has an unusual promise. The United States is not the only civic nation, and it doesn’t always live up to its own principles. Still, as an immigrant who has lived in several countries, I find it hard to avoid the emotional conclusion that there is no better country for human life and progress.

Yes, American institutions sometimes run badly. Our immigration system needs serious reform, housing policy fails in many places, and the country regularly makes its own advantages harder to use. These shortcomings are worth fixing precisely because they keep Americans from the opportunity the country already offers. But never underestimate how much is working: America still has the size, the markets, and, more unusually, a national identity that almost anyone can adopt. Those are all the right ingredients for renewal.

This is one reason American debates about progress have so much force. Americans expect the country to solve problems and get better. In many places I’ve lived, the failure to build enough housing or even ensure people are not dying from the heat can feel like ordinary decline. Here, it feels like an offense against the country’s promise. ’s distinction between progress and abundance is useful here: abundance starts with making it easier to build, while progress also depends on culture and ambition. A better liberal nationalism can acknowledge exclusion and broken promises while still insisting that the American project is worth fighting for. That better version is not automatic by any means, but it is still available if we choose it collectively.2

Immigration needs nationalism, and nationalism needs immigration

This brings me back to the argument that started Popular by Design. The original manifesto promised writing about immigration policies that work: policies that are evidence-based, demonstrably beneficial, and built to earn public support. A few years earlier, in one of the first public essays where I laid out the argument that became my book, I argued that immigration policy needs more enlightened nationalism. But now I see that the argument goes both ways.

Immigration needs nationalism because voters need to see a national benefit. Most voters will not support freer immigration because economists tell them it increases global welfare or because advocates tell them that borders are morally arbitrary. They support immigration when they can see how it serves the country they belong to: by filling real labor needs, strengthening universities, reuniting families under rules they trust, and making the country more dynamic.3 Pro-immigration politics that sound embarrassed by the national interest often fail because they ask voters to treat their attachment to compatriots as a moral defect. A more durable pro-immigration politics starts from the fact that voters care about their country and then shows how better immigration policy can serve their country.

But nationalism also needs immigration because a liberal nation that stops adding new members slowly becomes less liberal. Membership begins to look like ancestry. The more a country treats the national community as closed, the more its creed becomes decorative. America especially cannot defend a civic identity while treating newcomers as contaminants.

Days before the country's 250th birthday, the Supreme Court reaffirmed birthright citizenship, rejecting the idea that a child born on American soil could be denied membership because of their parents' immigration status. Indeed, a confident nation can afford to be generous because it expects to absorb newcomers and be improved by them, while an anxious one treats every arrival as a referendum on its survival. We absolutely can and should decide who can join us and under what conditions.4 But the phrase “nation of immigrants” is more than a sentimental slogan. It is a theory of national strength.

The same logic applies to other important issues

Immigration is the clearest case for me. The deeper principle is that good policy has to be built for imperfectly informed humans who care about their communities and judge policy by visible consequences. National interest is often the bridge between substantive merit and democratic legitimacy. People need to see how a policy makes their country more capable, more prosperous, more trustworthy, or more open to the people who can join and strengthen it.

Housing, not to mention newly controversial heating and cooling issues, is another useful example. For a long time, many supporters of new housing described opposition as the product of selfish homeowners or exclusionary racism. But housing politics also runs through people’s ideas about community, aesthetics, and whether change will make the place they live better or worse. and have been especially good in showing how local veto points turn ordinary family needs into a national housing problem. Recent work on the symbolic politics of housing and sociotropic aesthetic judgments points in this direction. If people can see housing growth as improving their communities, the politics can change.

Artificial intelligence is another case where the public argument is already too crude. I came to the AI debates almost by accident, after using the tools in my own research, and found a messier story than either panic or accelerationism allows. has made the interesting case that large language models may push public opinion toward more expert-aligned information, partly reversing the fragmentation of social media. ’s approach to AI policy is useful here because he takes transformative possibilities seriously while keeping a high bar for policy claims made under uncertainty. I am less sure than either of them, but good AI politics will require institutions and publics capable of deciding what should be accelerated or constrained, especially as we weigh the more consequential aspects of AI, such as job disruption, alignment, and catastrophic misuse.

Population decline is the third case, and probably one of the biggest issues facing humanity alongside AI now. Dean Spears and Michael Geruso argue in After the Spike that the future challenge may be too few people to sustain the progress America and the world need. Fewer people means fewer workers, fewer caregivers, fewer ideas, and fewer chances for the lucky accidents that drive progress.

Immigration cannot solve global fertility decline by itself. Despite my weakness for housing theories of everything, permitting reform cannot solve it on its own, either. AI could offset some labor scarcity and help people do more with less, but a country that treats children and national membership as afterthoughts should not expect robots to rescue it from demographic and institutional decline. All these questions point back to the same political fact: progress depends on people who trust one another enough to build, adapt, welcome, and take risks together.

What Popular by Design is for: A new chapter

When I launched Popular by Design, I described it as a newsletter about immigration policies that work. Almost a year in, that remains true. Immigration and public opinion will stay at the center because they are the topics I know best and because immigration is the best test of the broader idea.

But the broader idea was always there. Good policy has to work substantively and politically. It has to account for trade-offs, limited information, group attachment, institutional incentives, and the fact that voters often judge policies by whether the benefits are visible to people like them.

Over the next year, I want Popular by Design to keep making that argument in a wider set of cases without becoming a generic politics newsletter. The center will remain migration, public opinion, and policy design. Around that center, I want to write more about population change, AI, housing, and the other areas where progress depends on turning good ideas into policies that ordinary people can understand and support. Behind the scenes, this newsletter is also becoming the public face of a research lab I’m starting at Notre Dame. More on that soon.

If Popular by Design is doing its job right, readers should come away with a better map of politics: where voters are wiser than elites think, where voters are wrong, where experts are useful, and where policy design can change the incentives so good ideas survive contact with democratic consent. Liberal nationalism fits the project because it lets us be ambitious without pretending that politics can float above membership, trust, and country.

So today, I am turning on paid memberships for Popular by Design. As I promised, every essay and all other substantive content will always remain free. That is central to the project because the point is to influence the public conversation, not to hide the core argument from people who cannot pay.

By now, you should have a clearer sense of what I am trying to do here. Popular by Design will remain rooted in immigration, but the community around it is for people who think politics works better when ideals are attached to institutions, national interest is treated as a democratic constraint rather than an embarrassment, and policy claims are judged by their consequences. If that way of understanding politics is useful to you and you want to be part of our community, paid membership is the way to help make it sustainable.

Popular by Design is now almost a year old, and it grew during my sabbatical, when I had more time to write, edit, and build the audience. Next month, I am getting back into the normal life of teaching, research, and university service obligations. If the newsletter is going to stay serious, publish regularly, and host useful conversations, it needs a base of recurring support.

There are two tiers at launch. Supporters contribute $15/month or $120/year. They help keep the essays free and receive invitations to reader meet-ups online and in person, where thoughtful folks can trade ideas and build connections around immigration, public opinion, and policy design.

Founding Members contribute $1,000/year, recurring annually. They join a smaller circle that gets two hour-long conversations with me each membership year, helps shape topics, and gives the project an informal kitchen cabinet. If your institution would benefit from a deeper engagement with these ideas through a book talk, workshop, or consulting engagement, reach out directly.

The money funds continuity: editing, guest posts, occasional reader events, and the time required to keep doing this well, while I have a full-time job. I am asking readers who care about this pragmatic policy community to help keep it open and improve it. I’ll be honest, though: giving folks the version they’d rather not hear has come at a price, from Bluesky curses and public calls to fire me to a few genuine death threats, ugly enough to end up in the news. I have thick skin and a steady job, so I can take it, but knowing there are real people behind this work, and not only the loud ones telling me to shut up, is a big part of what keeps me doing it.

If you want a public conversation where immigration is treated as a policy problem instead of a tribal signal, where patriotism is open enough to welcome newcomers, where AI and population are discussed with curiosity and discipline, and where progress is judged by whether policies actually work, I hope you will become a paid member. If money is genuinely a constraint, email me. I want students, early-career researchers, practitioners outside the United States, and thoughtful readers who cannot pay to stay in the conversation.

The United States of America, the country I became part of, is still the best bet for human progress. But no country keeps that status by inertia. It needs better policies, better institutions, and, yes, a better nationalism: confident enough to love what America is, honest enough to improve it, and open enough to keep adding the people who will make it stronger.

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Huge thanks to all the folks who read and commented on this essay: , , , , , , and .

Update: Just as this went out, the Supreme Court upheld birthright citizenship in a 6-3 decision (Trump v. Barbara), striking down President Trump's executive order and holding that the Fourteenth Amendment guarantees citizenship to nearly everyone born on U.S. soil.

This month I went to Ireland for the first time, splitting two weeks between Dublin and Belfast for two conveniently timed political science conferences. To make sure this was not just a nice European vacation (before the heat wave), I took one of those “both-sides” walking tours of “the Troubles” through the peace walls and murals, standing in the streets where neighbors were killing each other just a few decades ago. I teach courses on war and conflict, and “the Troubles” is one thing I always assign to my students, to show how even a relatively wealthy democratic society slid into something close to civil war. It is all a useful corrective to the comfortable assumption that prosperity completely inoculates a country against political violence.

Now for the good news: not every academic conference is a slog. I had two on this trip, the Conference of Europeanists (CES) in Dublin and the inaugural European Political Science Society (EPSS) meeting in Belfast, and both were genuinely good. I came away impressed by the venues and the quality of the presentations, which is not something I say lightly. Regular readers know how little I enjoyed my last big conference, the 2026 ISA convention in Columbus, where I sat through (mostly absent) tenured scholars reading bad arguments off their slides. These were a different experience entirely, and a reminder that both human and AI slop is avoidable when people care. Another benefit of a good conference: in Dublin I caught a presentation by Aubrey Westfall on Canadian immigration federalism that genuinely blew me away, and I asked her on the spot to write it up for this newsletter, which she did.

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There was one EPSS panel that deserves its own paragraph. I sat in on a session where people shared how they use AI in their research workflows, which was practical and mostly civil. But you could feel the polarization in the room, and at a conference for quantitative political scientists of all places. A vocal share of the audience was deeply skeptical of the recent AI advances, citing the environmental footprint, equity, and European technological sovereignty, with a few proposing that Europe build its own large language models to rival OpenAI and Anthropic (bless their souls). I admire the ambition. But we, academics, are people who, collectively, have not yet figured out how to run a journal submission system that works, so I am not holding my breath.

In case you missed it: On June 1, I (or Codex?) published Pangram Policing is the New Grammar Nazism, my defense of the possibilities of AI writing against the rise of detector-policing. After that came How Canada (Un)made My Thinking on Immigration, on how a points-based system and broad elite consensus still produced the steepest immigration backlash its pollsters have recorded in half a century, and Why Evidence-based Policymaking Is Overrated.

That last one turned into the thread of my whole month. Most of my time online went to a running fight over degrowth and air-conditioning denial, which the Wall Street Journal picked up as an op-ed on Europe’s strange war on cooling. It is the same argument as the evidence-based piece: a comfortable expert class keeps moralizing against policies that are demonstrably beneficial and popular, whether that means asylum work bans or keeping people alive in a heat wave.

We also just ran the guest post I teased above: Aubrey Westfall of Wheaton College on whether local governments should pick their own immigrants, drawn from her own fieldwork interviewing the Local Immigration Partnership directors who run those programs. If you want to write something yourself, DM me.

Here are the June links (linking does not imply endorsement):

• The National Constitution Center has a calm, non-partisan rundown of the Supreme Court’s TPS decision and the companion asylum ruling. In Mullin v. Doe, the Court held 6-3 that it cannot review how DHS ended Temporary Protected Status for roughly 350,000 Haitians and 6,000 Syrians, who now stand to lose work authorization and protection from removal.

• has an honest ten-years-on retrospective on Brexit, conceding the economic case was oversold and that sovereignty over immigration was the actual driver. His line that “constituencies are created who are perpetually disappointed” is the salience problem in miniature, and a good companion to my running argument that you cannot wish immigration off the agenda.

• Hiroko Yoda, in a guest post for about what defines Japanese national identity, argues that belonging in Japan runs on shared culture and conduct, “how you read the air,” more than on creed or bloodline. I think this squares pretty well with my running claim that Japan is uncannily normal, and it complicates the lazy story that Japan is simply closed by ethnicity.

  • It pairs well with Ryoko Yamamoto‘s new JEMS article, “Fashioning homogeneity”, on how Japan keeps a self-image of homogeneity while quietly importing workers, a process she calls decoupling.

• has a sharp takedown of the claim that immigration researchers are ideologically biased. Borjas and Breznau found that researcher ideology predicts findings, but other researchers show the effect appears only in one preferred specification and vanishes across most reasonable alternatives. As usual, the study accusing the field of doing something shady turns out to be the one doing it. Note that this decidedly doesn’t imply immigration research is not biased!

• lays out the strongest version of the skills-and-culture case against betting on immigration, adapted from his UATX debate with . As someone who is probably somewhere in-between Jones and Caplan on the issue, I disagree with much of it, including the very deep cultural-persistence pessimism, but it is the cleanest steelman of selective immigration in one place, and worth reading precisely because he takes the tradeoffs seriously.

• Mihnea Cuibus at Oxford’s Migration Observatory has a clean evidence review on why asylum seekers come to the UK. The finding worth sitting with: countries that deported a larger share of refused applicants saw no subsequent drop in applications. Deterrence through enforcement is a weaker lever than its advocates assume, which is exactly the inconvenient kind of evidence my recent piece on honest judgment says we should take seriously.

• has a thoughtful essay on why right and left see two different worlds. His point is subtle: the two sides often accept the same verifiable facts but filter them through different interpretive frameworks, the “pseudo-environments” that decide which facts register and what they mean. Borrowing Walter Lippmann’s line that “we define first and then see,” he captures why misperceptions are so sticky, which fits my long-running point that they are downstream of identity and rarely move when you correct them.

• wrote a visual follow-up that turns my AI-writing spectrum into a two-dimensional chart. In the Pangram piece I put writing on a single provenance spectrum, from where provenance matters most to where only the content does. Archie rebuilds it on two different axes: how much of the work the AI did, from a one-shot prompt down to just fixing typos, and how mechanical or ideas-led the writing is, from admin emails to novels. It seems like Archie (and other folks I deeply respect like , , and ) strongly disagree on both the usefulness and morality of AI writing, but having a chart like that is a genuinely useful upgrade that helps us see what exactly we disagree about (Archie’s yellow edge cases are my clear green yeses). It also makes me genuinely happy that nobody is trying to defend the human authenticity of admin emails :)

One last thing before I go. Big things are coming on the Fourth of July, for the world and for Popular by Design, and I will have much more to say very soon. Stay tuned!

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I have a new op-ed (ungated) in the Wall Street Journal. (I didn’t pick the headline, but “Air Conditioning, Scourge of the French Left” is hard to beat, and Chad Crowe’s illustration of a café standoff over an unplugged window unit is pretty neat.)

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The short version: as Europe bakes, the boldest plans to cool people come from the populist right, while much of the left treats air conditioning as a selfish indulgence and an ecological menace. Cooling people in a deadly heat wave is about as demonstrably beneficial as policy gets, which makes this one of the rare debates where the far right simply has the better of the argument.

I did not plan to become an air-conditioning expert this month. What started as one cranky take turned into a whole week of near-total fixation, and it pairs unexpectedly well with my last piece on why evidence-based policymaking is overrated. Basically, the same voices who spent the pandemic insisting we “follow the science” are now reasoning from a narrow read of climate models to the conclusion that you should just sweat it out.

A few things the op-ed only gestures at. First, this is all bigger than spoiled American tourists missing their comfort. We Americans fail at plenty of things the rest of the rich world finds baffling, from public transit to gun violence, so the point is not that the US has it all figured out. It is that almost everywhere cooling is normal, from Japan to the Americas, people look at Europe with the same sincere puzzlement: how does a continent that gets so much else right decide that a machine invented in 1902 is the one thing it cannot bring itself to use?

Second, the death toll, grim as it is, is probably the smaller part of the story. keeps making the point that deaths averted are likely a minority of the well-being lost to a lack of cooling. Heat that wrecks your sleep, your concentration, and your ability to work or study is an enormous and largely invisible cost. When someone shrugs off a heat wave as “a week of discomfort,” what they are shrugging off is billions in lost productivity and health, every summer, borne by the people least able to escape it.

Third, the debate is also missing the people best placed to settle it. It has been left largely to sustainability advocates reasoning from their picture of climate change toward conclusions about how the rest of us should live, while the quantitative social scientists who could actually measure what cooling does to human welfare, and what the public wants, have mostly stayed out. I went looking for good survey data on how Europeans feel about air conditioning and found almost nothing. What little I have come across even hints that support for cooling may run stronger on the political right, which would be its own puzzle worth taking seriously. So I will probably end up fielding some of these questions myself.

And this is finally why I keep circling back to it. People ask why I am so allergic to degrowth, and Europe’s response to the heat is the clearest answer I have. Air conditioning is just one instance of a much larger instinct: that wanting to be comfortable is a little shameful, that the rich world should consume less and endure more, that even a cool room counts as a kind of vice. The trouble is that heat waves are happening, and they will keep happening, more often and more severely. You cannot wish that away with an appeal to sobriety, and no amount of environmental concern changes the fact that a cool room is one of the cheapest and most reliable ways we have to keep people alive and well. And, as I mention in the piece, you do not have to like Marine Le Pen, or agree with her or other populists on immigration, to admit she has this one right.

As always, I’d love to hear what you think I’m getting wrong, especially if you live somewhere this fight is live. And if anyone knows of good survey data on how people outside the US actually feel about cooling, I’d be grateful to see it.

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As more US states and cities demand a say over who settles within their borders, it helps to know that Canada has been doing a version of this for thirty years. Today's guest essay by Aubrey Westfall, a political scientist at Wheaton College, draws on her own interviews with the people who run Canada’s local immigration programs to show how provinces and towns select immigrants to fit local needs. She’s just as good on the harder part: how the federal government keeps that patchwork from splintering into conflict. I hope you all enjoy the read!

In 1996, the Winnipeg Jets left for Phoenix. For many Manitobans, losing their NHL team to a Sun Belt city was more than a sports heartbreak; it confirmed a quieter fear that their province was being left behind, its people and economic life drifting toward Toronto, Montreal, and Vancouver. Out of that anxiety came an unlikely experiment. Rather than wait for Ottawa to direct newcomers its way, Manitoba set out to choose its own immigrants, and pioneered a model that has since reshaped how the whole country decides who gets to come.

That model is immigration federalism: provinces, and increasingly local communities, selecting and admitting immigrants to fit their own labor needs instead of leaving the choice to the national government. Canada has spent three decades building it, and its experience speaks directly to a question now pressing on the United States and Europe: can you give regions meaningful control over immigration without fracturing the country or feeding the backlash now reshaping democratic politics? Canada’s answer is a qualified yes. The conditions that make it work, and the one that makes it fragile, are what the rest of us should be studying.

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Canada is the quintessential country of immigration. Its population is made up of over 450 ethnicities, and 23% of the population is foreign born (compared to 15% in the US). Canada’s robust economic immigration is facilitated by a points-based system that determines eligibility based on a prospective migrant’s human capital, and their potential to meet current and emerging market needs. Points are awarded for skills, education, language, work experience, age, and other characteristics. Prospective migrants exceeding a certain threshold are invited to apply for a visa. Such a system is intended to clearly match immigration levels to Canada’s demonstrable needs. It is also intended to be transparent, flexible, and responsive, as governments set the criteria for awarding points, and regularly review and update the criteria and the planned levels of migration. The system assumes an admitted individual responds to the domestic labor market to find appropriately skilled work, wherever the jobs may be.

And yet, at the turn of the millennium, the vast majority of immigrants in Canada disproportionately settled in just three major cities in Ontario, Quebec, and British Columbia, many in jobs mismatched to their skills. During the 1990s, the other provinces and territories began to recognize that the national immigration policy was not working for them or the newcomers, and that they were missing out on a critical influx of human capital.

Where immigration federalism began

For Manitoba, the message of losing out was driven home when their beloved hockey team moved due to financial shortfalls. Devastated by the loss, and driven by a desire to grow Manitoba’s economy, a group of businessmen formed the Business Council of Manitoba (BCM). The BCM worked with other stakeholders to build immigration capacity, and as a result, Manitoba became a forerunner in developing the first of Canada’s Provincial Nominee Programs (PNPs). Manitoba’s innovation worked—migration numbers started to rise shortly after the implementation of the PNP, and now it receives a proportionate share of Canada’s immigrants. Encouraged by the population and economic growth, the Winnipeg Jets returned to Manitoba in 2011.

More broadly, the PNPs are intended to facilitate the settlement of migrants outside of the three largest Canadian cities, and to help employers address their workforce needs in more context-sensitive ways. Through a PNP, provincial governments can nominate and select a potential economic immigrant, either from the candidate pool in the federal government’s system, or their own provincial selection systems, which are designed to reflect provincial priorities and labor shortages.

Historically, the vast majority of the candidates nominated by the provinces are invited to apply for a visa by Immigration, Refugees and Citizenship Canada (IRCC - the federal department responsible for immigration). Between 1998 and 2009, PNPs were introduced in every province (excluding Quebec, which had already made its own immigration arrangements based on its special status as a minority nation) and every territory (excluding Nunavut). By 2019, provincial nominees accounted for 35% of all new economic migrants to Canada and the PNPs became the single largest economic immigration stream into Canada.

More place-based policy innovations

The PNPs achieved their objective of getting more immigrants to land in different parts of Canada. They also demonstrated how delegating the management of migration to lower-level governments can better respond to diverse needs and capacities. Even better, according to a government report, around 89% of those who immigrate on PNP schemes tend to stay in their province for the first year, and 86% of those are still there five years later. However, there is significant variation across provinces. Those provinces that need immigration the most—especially the Atlantic provinces—suffer the lowest five-year retention rates (59% on average).

In response, the Atlantic Immigration Program (AIP), which started as a pilot in 2017 and became a permanent program in 2022, was developed as an employer-driven program. The Atlantic provincial governments designate employers to participate in the program, and the employer commits to intercultural competency training and enhanced settlement support for newcomers by connecting with service providers. The program then connects employers with qualified immigrants from all over the world. Once the employer makes a job offer, the province endorses the candidate to IRCC. By requiring local businesses to invest in the success of their newcomer employees, the AIP makes the immigration process a community enterprise, and as a result, improves long term retention of newcomers.

For many within Canada, the existing provincial and region-based programs still didn’t go far enough. Those in rural, northern, and francophone communities advocated for a distinct policy focus to meet their unique needs. In response, the IRCC issued Ministerial Instructions to quickly introduce a series of employer-driven pilot programs within selected communities, built on the example of the AIP. The pilots are centered on the community or municipality more than the province, as the community identifies their labor needs, designates employers for the program, and recommends the migrant for permanent residency through the program. These pilots are ongoing (and could be stopped at any time), but early assessments indicate success at attracting migrants to these less conventional destinations, as demand outstrips allocated spaces.

Avoiding the risks of immigration federalism

The layering of fragmented immigration programs and pilots at varied levels of government creates a very complex system. There are over 100 pathways of migration into Canada for skilled workers, business people, and families, all with potentially moving targets. Just like in the US, this complexity undermines the transparency of the points-based system and makes it challenging for immigrants to navigate. At the same time, the sophistication of the system allows it to better serve the purposes of the Canadian government and meet varied needs within the country.

Still, fragmented systems come with risk. Where the central government allows provincial governments to assert control over key policy areas, it effectively establishes the institutions and competencies of a “mini-state,” prepared to function more independently. This has the potential to undermine national unity and increase conflict between levels of government, even to the point of secession. This possibility became reality for Canada with the announcement of an Alberta referendum on October 19, 2026, focusing on ten questions relating to immigration, constitutional amendments, and independence. The Albertan premier partly blames provincial budget deficits on federal immigration policy, even as her government maintains an active “Alberta is Calling” program to attract skilled tradespeople.

Even with the threat of the Alberta referendum, and despite the fragmented nature of Canada’s immigration federalism, there is still remarkable national coherence in the general approach to immigration. Xhardez and Tanguay analyze the design, requirements, and distribution of provincial nominations within PNPs, and note policy variation within prevalent employment-based PNP streams, which require prearranged work. The emphasis on employability aligns with federal policy that requires skilled work experience, or targets selection for specific in-demand sectors and occupations.

Why it all doesn’t fall apart

I saw similar trans-provincial convergence in interviews with directors of Local Immigration Partnerships (LIPs), which are bodies intended to integrate the inclusion of newcomers in the general community planning agenda. I was told about distinct programs intended to serve the particular needs of their community. Examples include targeted programs to welcome Ukrainians in rural Ontario, collaborations with the police department in Medicine Hat (AB), music programs aligned with London’s (ON) designation as a UNESCO city of Music, even distributing vitamin D to newcomers in the north. The activities vary, but the ethos driving the activities and the vocabulary used to describe the work were the same. As one LIP director in Ontario put it, “The bottom line for all the local immigration partnerships is creating a more welcoming community, a more inclusive community. Whatever the political system they have, this is the goal…I haven’t seen many differences… every community is unique, every LIP is different, but I think the objective is the same.”

How is interprovincial agreement in the management of migration achieved, given the robust opportunities for immigration policy fragmentation? Analysis of my interviews offers three explanations.

Federal funding. Among those working directly in migration, such as the LIPs, service providers, and the IRCC, federal funding is perhaps the most powerful coordinating tool. The Canadian government provides extensive funding to support the many actors in the immigration sector. In July of 2025, the government announced an allocation of $3.2 billion over three years to more than 520 organizations intended to improve the integration of newcomers. Recipients include service providers and LIPs, both of which are almost entirely reliant on federal funds. The amount of funding is tied to the landing rates of immigrants, and is also determined by federal strategy. High reliance on federal funding allows the government to define the programmatic direction and eligibility, which manifests in parallelism and congruity in the migration sector across provincial or regional entities.

Formal and informal collaboration. Consistent collaboration, within and across provinces, and between the federal, provincial, and local actors, supports coherent shared governance. Much of the collaboration within the migration sector is informal, built on relationships and a desire to share and implement best practices. However, collaboration is also encouraged through formal federally-supported structures. The federal government supported the development of a LIP secretariat as a coordinated planning body for all LIPS. The federal government also sponsors annual professional conferences for those who work in the migration sector. There are ministerial, provincial-municipal, and municipal advisory “tables” (working groups) that promote dialogue and information sharing among stakeholders at all levels of government.

Low politicization of immigration. Immigration in Canada is bureaucratized, at least in part because the system is so complex. And, because immigration is managed as a public policy embedded within formal structures of multilevel governance, the immigration sector is relatively depoliticized. The federally-funded actors within the immigration sector are required to remain non-partisan, which is not extremely difficult, because immigration has not been a partisan issue for the last few decades. There is partisan consensus about the desirability of immigration, at least at the symbolic level, and even if a party is not ideologically committed to immigration, there are strong electoral reasons for parties to maintain pro-immigrant and pro-minority messaging. Until recently, the majority of Canadians supported the size of immigrant flows, which made it less politically salient. As a result, it has not been used to support divisive politics, notwithstanding the very recent case out of Alberta.

These three factors go a long way in explaining how immigration federalism has been implemented in Canada without succumbing to the serious risk of fragmentation.

What can other countries learn from Canada?

The Canadian case demonstrates that immigration federalism can work to serve place-based interests in countries with multi-level systems serving regions with distinctive needs. Insistence that a common border requires a centralized immigration policy that exclusively governs all migration into a country ignores the obvious reality of diverse economies, cultures, and polities within a country, and how they can benefit differently from immigration. There are risks associated with decentralizing powers over immigration, but if the federal government financially supports a multi-level migration sector, and assists in coordinating intergovernmental networks, it can create efficiencies and push back against forces of fragmentation.

The development of Canada’s immigration federalism is a feature of Canada’s willingness to experiment and fail. The LIPs began as a regional effort in southern Ontario, and have since spanned the nation. The directors at LIPs shared a willingness to discuss both successes and failures in an effort to better inform others about what to adopt and avoid. The stable financial support from the federal government allows them to be honest—they do not have to oversell their successes to private donors. PNPs were adopted gradually over a decade because the early adopters of the PNPs were watched by other provinces, who took cues from policy successes to tailor their own PNP agreements.

The pilot programs implemented with Ministerial Instructions allows for the design and implementation of small initiatives without time-consuming Parliamentary review. If the programs succeed, they can go through a process of regularization, with alterations to fix identified problems. Any multi-level system could embrace their ability to use lower-level systems as a type of “laboratory” to explore policy options, but systems of collaboration and review should be implemented to facilitate learning.

In my view, the most difficult element to replicate, and the most fragile one, is the political and social consensus around the desirability of immigration. In contexts where immigration is hotly contested, or where political distrust is high, experimentation and policy flexibility may not be democratically viable, at least not in the short term. International policy analysts like have identified a persistent representation gap, where there is a disconnect between public attitudes about immigration and the immigration policies passed by governments. The far right has built a base of support in Europe and the United States on this very issue. Mainstream parties have responded by moving right on immigration, with mixed results.

and others in this newsletter argue that another solution could be found in smart policy implementation that effectively demonstrates the benefits of immigration to the national population. Place-based migration policies are an underutilized way to build trust in the system by making the contribution of immigrants clear and directly applicable to the needs of individual communities.

For the US in particular, the Canadian example demonstrates the viability of a state-based visa program. American states are institutionally capable of administering such a system; however, the current American immigrant integration sector is typified by infrequent and ad hoc collaborations. In order for a state-based visa system to achieve policy coherence and operational efficiency without compromising national consensus, more formal structures of intra- and interstate collaboration must be developed.

Crucially, this structural coordination must extend to funding; the current federal system of competitive grant programs for NGOs—which remain heavily reliant on private donations—results in a piecemeal approach that does not serve the national interest. In order to promote equity and systematic unity, the federal government should develop a consistent federal budget line to provide high-level strategic direction while continuously supporting service provision and community inclusion at the local level. Instating a national or hybrid federal-state program modeled after Canada’s LIPs would help communities coordinate resources and develop coherent strategic plans.

In the absence of political will at the federal level, Manitoba’s experience reminds us of the power of community-level policy entrepreneurs. States can take the lead by funding and organizing regional initiatives. Ultimately, as the development of Canadian immigration federalism proves, good policy ideas are contagious.

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Three years ago, the United States declared the end of the pandemic emergency, and the country exhaled. You, dear reader, have probably filed the whole thing away by now. I’m sorry, but I just cannot let it go, and reading Macedo and Lee’s excellent “In Covid’s Wake” on my way back from Ireland last week reminded me why. The virus, of course, was not the fault of any of us in particular.1 But how we reasoned about it as a democratic society was absolutely our fault. The so-called experts spent years telling the rest of us to “follow the science,” then claimed a certainty the science never gave them, dressed value judgments up as technical ones, told plenty of noble lies for our own good, and waved away every cost that sat outside their own narrow expertise. We were drowning in data, but what we lacked was honest judgment.

This knee-jerk evidence-based mindset is not just a pandemic thing, and it shows up wherever we treat one kind of data as the only kind that counts. In 2018, a top medical journal published a randomized controlled trial (RCT) finding that parachutes did nothing to prevent death or injury when people jumped from an aircraft. The catch was that the planes were parked on the ground, and the mean jump altitude was about half a meter. The whole study, of course, was a joke. The authors were not against experiments as such, but they were mocking the common reflex among their colleagues that treats RCTs as the only respectable form of knowledge, even about a claim you could check by looking out the window of a plane.2

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I have spent much of my professional life contributing to and asking for better evidence in immigration debates, usually to the quiet exasperation of people on my own side, so I am not about to start sneering at data and acting on “vibes.” Good policymaking does need evidence, cost-benefit analysis, and careful counterfactual thinking. But it also needs humility and better judgment about what kind of evidence a given question can actually require, if any.

There are some policies that are so obviously good that we should not need a perfect study to try them, like legally allowing more housing where demand is high, keeping reliable low-carbon power online when the substitute is fossil fuel, or increasing visas for foreign top talent that everyone says they want. Reasonable people can argue about details and trade-offs. But on questions like these, the case for action does not depend on a perfect RCT, and the burden of proof should not be infinite.

There are also policies so obviously bad that we should not need a study to stop them, like asylum seeker work bans. Seriously, you do not need a randomized trial, or any hard evidence for that matter, to predict what happens when you forbid a willing adult in a legal limbo to work, pay to shelter them instead, and then point to their idleness as proof the system is broken. Most of the hardest calls in policy look more like this than some great mystery requiring a well-designed experiment.

What “evidence” actually means

One rung down from the parachute sits dental floss. I personally don’t enjoy flossing, but most of us can probably feel the difference after flossing out a big meal, and our dentists can see it at the next cleaning. Yet when some journalists seized on a review that found little high-quality randomized evidence for flossing, a run of headlines announced there was no evidence it works at all. As the historian of science Naomi Oreskes points out, that was a misreading. We should be “broad-minded about evidence,” she argues, counting professional experience and ordinary observation, especially where a clean long-term trial is impractical or will never be funded.

The same confusion, though with far higher stakes, ran through the COVID pandemic. In their recent (highly-recommended) book on the topic, Stephen Macedo and Frances Lee make a startling case: much of the relevant knowledge on how to deal with a respiratory pandemic was already there, but governments across the West largely set it aside. Before 2020, the dominant pandemic preparedness plans were skeptical of sweeping measures like lockdowns and prolonged school closures, warning that the evidence for them was weak and the human and economic costs high. In the panic of early 2020, those governments scrapped that guidance almost overnight and then projected a confidence that science never supported.

It helps to sort things by the kind of feedback they give, and to what end. A parachute gives the simplest kind: the benefit of staying alive is immediate, individual, and impossible to miss, so a trial would only confirm what anyone can already see. A new drug or vaccine gives the opposite: the benefits and costs may be genuine, but they are also much more diverse and often invisible. The infection that never arrives is easy to confuse with luck or the body healing on its own, which is exactly why a randomized trial with placebos is essential and why modern medicine runs on them.

Most government policy sits in between. You can usually tell whether a policy points toward gain or loss long before you can put a clean number on it, and the effects run through labor markets, prices, and politics all at once, so no single trial can isolate them, and often none can be run at all. But you cannot settle it just by looking, the way you can with a parachute, and you cannot settle it with one clean experiment, the way you can with a drug. What is left is judgment based on cumulative evidence, comparisons across places and times, and honest cost-benefit reasoning under uncertainty, all stated openly instead of disguised as settled science.

Another casualty of our actually existing knee-jerk evidence-based policy is the prior question of what even counts as relevant evidence, and whose expertise gets to decide. Here, Macedo and Lee are devastating. As they document, a niche cohort of public health and infectious disease experts was suddenly treated as the only legitimate authority on a crisis that touched every part of life, and their lens was narrow by design: fixed on minimizing infections, it pushed almost every other effect, economic and non-economic alike, off the table as someone else’s department.

Francis Collins, who ran the National Institutes of Health, admitted that the “public health mindset” he shared led him to “attach infinite value to stopping the disease” and “zero value to whether this actually totally disrupts people’s lives, ruins the economy, and has many kids kept out of school.” In Britain, the chief medical officer, Chris Whitty, told the official Covid inquiry that adding economic or social experts to the government’s advisory group would have made it too “unwieldy.” Stopping the virus became the only goal that counted as following the science, and the costs any honest policy has to weigh were ruled out of scope.

The economists already had this fight

Development economists have been arguing about this for 20 years. The “credibility revolution” taught social science to distrust sloppy causal claims and to prize identification, which was a genuine advance. But the economist argues that some of its champions then performed a strange trick: having demanded the most stringent possible evidence for a narrow estimate inside a paper, they would turn around and accept sweeping, system-level claims built on those estimates “with complete and total gullibility.” He calls it the credulity revolution. An experiment can be internally airtight and still tell you almost nothing about whether a program will work at a national scale in another country.

The complaint comes from the center of the field. Angus Deaton and Nancy Cartwright, hardly enemies of quantification, argue that randomized trials earn their keep only “as part of a cumulative program” alongside theory and mechanism. Demanding external validity from a single trial “expects too much of an RCT while undervaluing its contribution.”

Pritchett offers a blunter smell test for his own field of economic growth: if rich countries do not have more of some fashionable factor than poor ones, we should be suspicious of claims that it explains development. No country has ever run a randomized trial on its way to wealth. Poland did not climb out of communism and into prosperity because of a well-run cash-transfer experiment; it did so through messy, large-scale changes in markets, institutions, and politics that no trial could have tested in advance. The method should fit the question. When it does not, more rigor on the wrong question is just a costlier way to be confidently beside the point.

When the trial earns its keep

Don’t get me wrong—I like my RCTs and have carried out some myself. Randomized studies are essential when intuition gets ahead of knowledge. The best case for a trial is the mirror image of the parachute: sometimes the answer is not obvious at all, the intuition almost everyone shares turns out to be wrong, and the only way to find out is to run the experiment and pay its cost. When you cannot see the effect immediately just by looking, you absolutely have to measure it carefully and systematically.

Cash transfers in rich countries can be an interesting example here. In poor countries, the evidence that handing people money improves their lives is about as strong as social science gets, with randomized studies finding large gains in income, assets, food security, and even lower infant mortality. When economists first proposed simply handing poor people cash, the worry was that they would fritter it away on alcohol and other temptations, which is why aid so often came as food or as cash with strings attached. The trials found that worry was largely unfounded: across dozens of studies, cash did not raise spending on alcohol or tobacco and often reduced it, because people in poverty turn out to be good judges of what they need.

Now, since it’s getting increasingly obvious to more folks that cash transfers clearly work, you might assume the same logic carries over to a rich country: if you give a struggling American a few hundred dollars a month, their life gets measurably better. At least it did seem obvious to the researchers who believed in it. But then they ran the trials.

As details, a run of careful American studies, including an OpenResearch experiment that gave people $1,000 a month for three years, found no sustained improvement in health, employment, stress, or children’s outcomes. Piper, who went in expecting cash to help more, called the evidence “shocking.” The developing-country findings were sound, but they did not transfer cleanly to this context, which is exactly that external validity problem Angus Deaton was so worried about. Here the trial earned every dollar it cost, because the stakes were high, the intuition was strong, but it was ultimately wrong. The problem, of course, is telling a parachute from a medical trial before you decide whether the study is worth running.

Even then, the right tool is not always an experiment, and sometimes it is not available at all. Some of the most important things we know rest on theory and modeling more than on any single trial. No one has run, or could run, a randomized study on whether a country should open itself to free trade. The case here rests on the theory of comparative advantage, worked out two centuries ago and refined by mountains of non-experimental evidence since. No one randomized nations into democracy and dictatorship to learn which produces better lives. We reason about questions like these from theory and values, and we lean on thought experiments and formal models, the kind of computational and thought simulations social scientists build precisely because some questions can never be put to an experimental test.

When everyone is wrong at once

I’m not at all an expert on the pandemic. But it is a very important case that still shapes public trust for the worse. We saw the uncertainty in the evidence already. But the deeper failure was the refusal to admit it. Officials at every level kept repeating that “we know what works against Covid-19,” as Macedo and Lee document, “even as it became increasingly evident that policymakers were improvising and did not, in fact, know with certainty what worked.” Certainty was claimed where it did not exist. Science can tell you what a policy is likely to do. But it cannot tell you what you ought to value, and pretending otherwise spends the credibility you need the next time.

The critics often made the mirror-image mistake, treating the absence of a clean trial as proof that a measure was worthless, which is the flossing error scaled up to a national emergency. Macedo and Lee fault both reflexes at once: it was wrong, they write, “to mock and censor mask skeptics, as it was to insist with certainty that masks do not work.” And underneath the shouting sat the trade-off nobody wanted to name. The studies on masking measured “only one side of the equation,” they point out, and said “nothing about the costs of masking to children’s learning, communication, socialization, and psychological well-being,” while school closures “most hurt poor kids.” What both errors share is a refusal to say the honest thing: the evidence was incomplete, and the choices imposed heavy costs on people who never got a vote.

The “follow the science” failure took a disagreement about values, about how much weight to put on the schooling of the young against the safety of the old, on liberty against caution, and dressed it up as a technical dispute the data had already settled. Immigration runs on the same move constantly. A great deal of what looks like an argument about evidence is actually an argument about what a government owes its own citizens against what it owes foreigners. No experiment can tell you how much to weigh a citizen’s wages against a stranger’s safety, or whether a more diverse society is better than a more cohesive one.

These are all questions of value, and if people simply do not want a certain outcome such as a more diverse society, no clean estimate that a policy would deliver it will change their minds. I have made this case at length: the most useful thing evidence can do here is discipline a values argument by telling us what a given policy will cost and produce. It cannot make the values argument disappear, and recasting a value conflict as a scientific one mostly just hides what people are fighting about.

makes a closely related point in his new essay on how political tribes construct rival realities. Political disagreement often runs through rival systems of interpretation that decide which facts matter, what counts as representative, and who belongs in the story as victim, villain, or hero. That is why appeals to “the evidence” so often disappoint. The fight is partly over facts, but also over the frame that tells people what the facts mean.

Demonstrable benefits

No randomized trial will also ever tell you everything you want to know about admitting a productive, skilled worker who pays taxes, fills a documented shortage, starts a company, or treats patients in a town that cannot recruit a doctor. The feedback here runs through a variety of institutions, and you cannot hold the rest of the world fixed while you turn the dial. Yet the basic direction is not at all mysterious. A policymaker weighing whether to route more eligible scientists through a specialized visa or to clear a needless skilled-worker backlog will never have clean experimental evidence that settles the whole question, and waiting for it just means letting a bad default persist.

This is why skilled immigration is so popular: its benefits are intuitive and visible without anyone reading an econometrics paper. About 80 percent of American voters support high-skilled immigration across party lines, and policymakers who wanted to act on that could streamline the uncapped O-1A visa for extraordinary-ability workers tomorrow, without a single new law. This is what I mean by demonstrably beneficial: a policy whose contribution to the country ordinary people can see in practical terms, explicitly and straightforwardly serving the national interest. The persuasive framing is already baked into the policy itself, so you do not need a campaign to explain it.

But setting immigration policy, of course, is not the same thing as choosing parachutes. “A skilled worker who pays taxes and fills a documented shortage is an asset to the country” is a claim about as close to observational saturation as social facts get. “This particular visa reform will produce that benefit at this particular magnitude” is a genuine empirical question that does need identification, and it is the kind of thing my colleagues spend careers trying to estimate. Treating the second claim as if it were as self-evident as the first is the same error the parachute authors mocked, just aimed in a friendlier direction. As I have argued before, “immigration” in the abstract has no clean effect waiting to be discovered; specific policies admitting specific people under specific rules do.

Not every visible benefit is as fraught as immigration. Since we were already talking about flossing, consider… the idea of a Japanese toilet. You only have to use a heated washlet once or twice to know it beats what most regular bathrooms offer, and nobody needs a randomized trial measuring cleanliness or satisfaction to settle the matter, as just argued. I also bought one recently after spending some time in Japan and have opinions.3 I am fairly confident that installing washlets at scale, in hotels and airports, would be a good policy to have for a lot of American institutions, and just as sure it does not need to be evidence-based in any serious sense. The holdup here is not a shortage of evidence. It’s our cultural habits, building codes, and the electrical wiring most American bathrooms were never built for.

Demonstrable harms

The same logic runs in reverse, and it produces what may be the clearest self-defeating immigration policy in the rich world: the ban on letting asylum seekers work while their claims are being processed. In many countries, that exclusion lasts half a year or more, and in practice it can stretch much longer.

Before considering any of the politics, let’s think about what this does to a person. A government accepts an asylum claim for consideration, pays to shelter the applicant, then forbids the one activity that would let them support themselves, build a record, and start to belong, which is work. Months of forced idleness drain savings, erode skills, and corrode the habits and confidence that make someone employable, and the damage outlasts the ban by years. You do not need a clever instrument, or a randomized trial, to see that this is a bad trade.

Then there is the fraught politics of the issue. The ban manufactures the exact image that anti-immigration politicians point to as proof the system is broken: able-bodied newcomers idle in taxpayer-funded hotels, or selling fruit at stoplights because the formal economy is closed to them. That visible dependence can sour voters on immigration more broadly, including the skilled pathways voters otherwise like.

Whenever I argue that immigration policy has to be intuitively beneficial to stay popular, someone asks what a non-beneficial pro-immigration policy would even look like. This is what it looks like, and no amount of aggregate evidence about the long-run fiscal contribution of refugees will make a voter unsee the visible disorder of the asylum crisis in the streets. The honest fix is a policy that stops generating the visible failure in the first place.

Against evidence theater

It helps to see why the maximalist version of evidence-based policy keeps disappointing its champions. Pushing every chip onto “the science,” and deferring to whatever the experts currently agree on, still does not deliver a policy, because the hardest questions are not the ones science was built to answer. Macedo and Lee make the case for Covid: scientists, they write, “with their narrow bases of expertise, should not make policy,” because the choices turn on values and trade-offs no study can weigh. The same gap shows up on climate, where the economist argues that the loud framings, “no big deal” and “existential threat,” are both probably wrong, and that responsible policy has to weigh trade-offs and judge which scenarios are plausible, something no appeal to the consensus can settle. And it shows up on immigration, where “the evidence” gets invoked by both sides to settle what is at bottom a fight over what we owe one another.

So what does this look like in practice, for someone who wants to take evidence seriously without hiding behind it? The following questions help:

• What kind of claim is actually on the table: an empirical one that data can settle or a normative one no regression will ever resolve?

• What kind of feedback does the policy produce, and would a clean study even be valid and available before the decision has to be made?

• And what does it cost to gather the evidence, what does it cost to wait, and who pays for the delay?

When the mechanism is clear and strong, the downside is reversible, and the people who would benefit cannot afford to wait, the honest move is to act now and keep studying as you go. And when the disagreement is at bottom about values, experts should say so, instead of laundering it through “the science.”

From evidence-based policy to honest democratic judgment

Every study has a price, and so does every delay. The writer recently named a failure mode he calls the tyranny of the edge case in piece, where any conceivable harm to anyone, however rare, becomes a reason for nobody to act, and the demand for one more study is among its favorite instruments. That demand almost never falls evenly: the bar rises for the reform someone dislikes and disappears for the status quo, which usually rests on no trial at all.

An isolated demand for rigor, aimed only at the conclusion you would rather not reach, is one of the most effective ways to block action while sounding admirably careful about the evidence. Running a trial means spending money, burning time, and sometimes withholding a promising policy from the people in the control group, while every month spent waiting is a month the current rule stays in force. You can usually weigh this informally: nobody needs a randomized trial to see that paying people to sit idle, as the asylum bans do, and then resenting them for it is a bad trade.

The discipline that keeps all of this honest is a willingness to say what would change your mind. If I claim the asylum work ban is obviously indefensible, I owe you the conditions under which I would drop the claim: solid evidence that it deters fraudulent applications at a scale worth years of lost earnings and delayed integration for people who would otherwise work. I have not seen that evidence, but I would genuinely look at it. Naming the finding that would move you is what separates a considered judgment from a convenient one, and it is a test the loudest demanders of “evidence-based policy” too rarely apply to themselves.

Don’t get me wrong—none of this is a license for vibes. The parachute study is funny because obviously nobody needs a randomized trial at altitude, but most of the choices that matter look less like a parachute and more like our immigration system: the evidence is partial and the feedback is slow or invisible. Evidence is indispensable for those choices. But it still cannot make the hard judgment for you, and a good judgment will not survive long in a democracy unless the policy behind it can show its value in the world ordinary citizens of any political persuasion actually live in.

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A year ago, I published a book arguing that democracies can make immigration popular by making it demonstrably beneficial. In the months since, the United States ran something close to the opposite experiment. Mass deportations and a “border” enforcement surge reached even in decidedly non-border cities like Charlotte, NC where I was living at the time. It became a year-long case study in what happens when a policy many voters backed in the abstract becomes, in practice, hard to see as serving their interests. And it came right on the heels of the Biden years, when an administration that wanted to be humane lost control of the border and paid for it politically. In barely two years, the country had produced two opposite failures and a fast lesson in how quickly immigration politics can swing.

But the test I was most worried about did not come from Washington. It came from Canada. The book leans on Canada as its clearest case of immigration done right, and I had said, more than once, that if Canadian support for immigration ever collapsed, it would invalidate part of my thesis. Over the past two years, it came closer to that than I thought possible. So I want to spend this publication anniversary explaining what happened and why the backlash against immigration in Canada still fits the argument I made in In Our Interest, and recalibrate a bit. In short, Canada’s “backlash” has been much more about speed, housing, and temporary flows than about crime or ethnic identity, while the selective core of the system remained uncontested.

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Is there a popular backlash against immigration in Canada?

Of course, I never claimed in the book that Canadian opinion could never turn. But a swing this fast, in the country I held up as the model, cuts against the grain of my argument, and I owe readers an honest account before I move to the defense.

Drawing on Environics survey data going back to the 1970s, my book showed that the share of Canadians who think immigration is too high had fallen for decades, from around 70 percent in the early 1990s to under 30 percent by 2020. All consistent with the idea that Canada achieved a high-immigration system that most people actually like by running it as a selective, orderly program whose benefits were visible to ordinary people. This is what I meant by demonstrably beneficial: a policy whose payoff you can grasp without a degree in economics, not a claim that all immigration is automatically good. My last data point sat near the bottom of that long decline. It was, in hindsight, almost the worst possible place to stop drawing the line.

Because here is the picture now. Agreement that immigration is too high jumped 17 points in 2023, then another 14 points in 2024, reaching 58 percent, the first clear majority since the late 1990s and the steepest climb in the half-century where Environics has asked the question. The line I published as evidence that immigration can be made durable turned and shot upward almost the moment the book came out. I would be lying if I said that it did not sting.

After nearly every talk I give on the book, a hand goes up, and someone asks, “What is going on with Canada?” The vibes are bad, and they are bad in places that did not feel that way before. For instance, in October Alberta is set to hold a province-wide referendum carrying five explicit anti-immigration questions, a ballot move with no real precedent in modern Canadian politics.1 You could line the chart’s spike up with that and tell a clean story: Canada has finally joined the rest of the developed world in a right-wing immigration backlash, and the fuss I made about demonstrably beneficial policy turned out to be wishful thinking.

What actually did and did not happen

As I show in the book, this is usually what we see across the world: there are people who can be labelled “committed nativists” and “committed cosmopolitans,” each around 10 percent of the public, who rarely budge on immigration. What usually moves is the large middle. I call this majority of voters altruistic nationalists: they care about newcomers but weigh their own compatriots first, and they back immigration when they can see it working for the country.

Over the last few years, Canada’s middle majority have not seen immigration working as well as it used to. Canada scaled temporary residents and international students faster than housing and services could absorb them, the textbook condition under which the diffuse and often invisible benefits of immigration shrinks while its concentrated costs swell. Even so, a majority of Canadians still say immigration is good for the economy, and when Environics asks the people who want less why, they point overwhelmingly to housing and the cost of living, not to crime or culture.

The book actually flagged this risk in the Canada chapter, noting that the post-pandemic surge in international students, plus a housing shortage, had already begun to produce dissatisfaction. What I got wrong was the speed of the public confidence decline: I assumed a system this good would keep absorbing shocks without a serious political reaction.

But notice, too, what the most recent opposition actually targets. Pierre Poilievre and the Conservatives ran hard against temporary residents and students while leaving the skilled, points-based core of the system alone; even the country’s most restrictionist federal party, the People’s Party, has proposed capping permanent admissions at 100,000 to 150,000 a year, a sharp cut from today’s pace but a level that would still admit several times what most other rich democracies do. The argument in Canada has effectively been about how much and how fast, while the principle of selecting immigrants for national contribution goes essentially unchallenged.

Even at its angriest, the backlash never turned Canada into a restrictionist country. Gallup still ranks it the most migrant-accepting society in the world, and even after the recent cuts, Canada is on track for roughly 395,000 permanent admissions in 2025, near one percent of the population a year, more than double the United States’ rate per capita and above the OECD average. Even if every one of Alberta’s referendum questions passed, they could not override the federal totals. A Canadian who complains of “too many immigrants” is still open to an extraordinary number of immigrants. A Canadian who says there is too much immigration and, let’s say, a Japanese voter who says the same thing are not asking for remotely the same world.

The dial has also already started to move back. Faced with the backlash, Ottawa did the both responsive and responsible thing: it cut permanent-resident targets from 500,000 to 365,000 by 2027, imposed the first-ever caps on temporary residents and international students, and set out to shrink the temporary population to about 5 percent of the country’s population.

Opinion is already tracking the policy. Environics’ 2025 reading shows the “too many” number leveling off at 56 percent, the first year in three it stopped climbing, and the people who still hold that view increasingly describe it as a failure of government management, not as a problem with immigrants.

The refinements: thermostatic politics, credibility, and salience

The whole recent Canadian sequence is what thermostatic politics looks like: while deep attitudes barely move,2 the dial of “too much” or “too little” adjusts to whatever the government is actually doing in response to those attitudes. Overshoot what the public can absorb, and the dial swings; correct the policy, and the dial swings back. I gestured at this in the book. But I should have built a chapter on it, because it turns out to be the engine of the whole story.

The same mechanism just ran in the opposite direction south of the border. As enforcement escalated from the border into workplaces and neighborhoods, the share of Americans who told Gallup they wanted less immigration fell from 55 percent in 2024 to 30 percent in 2025, one of the sharpest reversals on the question in decades. Same thermostat, opposite setting. As I argued in a guest piece for ’s Substack, competence builds trust, and chaos invites dissatisfaction, whether the chaos is restrictionist or expansionist.

The other refinement is about the importance of credibility. Demonstrable benefit only holds support when voters find it credible, and credibility, as Canadian housing politics has shown, is much easier to lose than to build. Once a community sees an acute housing shortage that can plausibly be blamed on a surge in arrivals, even a system that had been working stops being seen as beneficial. The visible benefit and the credible attribution have to travel together.

The same point came through more sharply from the American side. Consider what happened to the Harris campaign’s late attempt to look tough on the border. In her September 2024 Douglas, Arizona speech, she promised to “bring back the border security bill that Donald Trump tanked” and to “do more to secure our border to reduce illegal border crossings,” language that would have read as a Republican script a few cycles earlier. It did not move the needle. Voters continued to trust Trump on the border by wide margins. No matter what Harris said by then, voters had little reason to treat the pivot as credible after the previous Democratic administration had presided over visible border disorder. Their skepticism made total sense.

This all now points us back to the actual policy. Demonstrable benefit and credibility are the same project seen from two angles: a policy earns credibility by being implemented and producing benefits people can see, which is why you cannot shortcut it with a pivot. A promise of orderly, beneficial immigration is worth very little until voters have watched you keep it. And it is the credibility of mainstream parties and politicians, specifically, that matters most: the way trust in the center-left or center-right to handle immigration well is built up over multiple terms and then squandered in one episode of total dysfunction.

The third refinement is issue salience. For a long time, Canada came unusually close to what I still think is the best political outcome for immigration: a mostly boring, technical policy area nobody cares about, closer to something like “international measurement standards” than to a culture-war battlefield. Voters cared about whether the system worked, but they did not organize their politics around it. That is much harder to sustain once immigration becomes one of the main things people blame for housing shortages, crowded services, or a general loss of control.

That is why Canada now worries me more than it did when I finished the book. Once a quiet technical issue becomes a top-line political issue, the margin for error shrinks. Every backlog, administrative failure, or local pressure point starts to stand in for the system as a whole. And because immigration opponents tend to care more intensely about reducing immigration than supporters care about expanding it, the politics can move quickly once the issue rises on the public agenda.

​​Where the project goes next

So where does my book and research go from here? Three directions, concretely.

First, the empirics of demonstrable benefit, and the credibility that follows from it. I want to know what voters actually have in mind when they say a policy is nationally beneficial: which features they register, which they ignore, and how long a government has to deliver before its promises start to seem credible. The book basically asserted the construct, and the next step is testing it rigorously, which is among the first questions I want to take up through the immigration lab I am starting at Notre Dame.

Second, issue salience and ownership. I want to understand when immigration becomes one of the issues voters use to judge whether a government is competent, and who they then trust to handle it. In work I am now doing with James Dennison, we’re trying to figure out how that dynamic travels beyond immigration as well: when an issue becomes politically available, who gets to own it, and how mainstream parties can keep hard policy problems from becoming permanent trust problems.

Third, humanitarian design, which is the hardest case for the argument and the part of the book I most want to extend. The way to protect humanitarian immigration is to build public trust first, through visible benefits and shared responsibility. That is the case I made in a recent policy brief, and the next step is testing which combinations of sponsorship, faster decisions, and credible limits actually hold up politically.

In Our Interest was an academic book by design (I had to finish my dissertation and get an academic job, folks!), and I am proud of the research behind it. But its argument deserves a more direct public life too, which is part of what this newsletter is for: the same case, in a register that meets readers where they actually are.

A year ago, the question under every other question, in rooms from an Oxford lecture hall to a retiree center in Charlotte, was whether any of this is realistic, or whether making immigration popular is a fantasy that collapses the moment real politics arrives. After the year we just had, the year that bent my favorite chart the wrong way and then began to bend it back, my answer is steadier than when I started. Immigration support becomes durable when people can see it is working, and the work of showing them is never finished. That is still, for everyone involved, in our interest.

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Many thanks to more than a thousand3 of great folks who read, reviewed, argued with, or hosted the book over the past year. If you haven’t had a chance to check it out yet, for the full argument and the evidence behind it, the book is still the place to go. For the next stage of the project, this newsletter is where I am taking it. And if you want a say what should come next, tell me in the comments. You can find the rest of the year’s talks, reviews, and podcast conversations on my media page.

The latest AI-writing scandal arrived, fittingly, with the Pope. On May 25, 2026, the Vatican published Pope Leo XIV’s first encyclical, Magnifica Humanitas, dated May 15, on safeguarding the human person in the age of artificial intelligence. Within days, people were feeding it to Pangram, the detector that has suddenly become the respectable instrument of literary and academic suspicion. Some portions were apparently flagged as AI-assisted, and the accusation was easy to understand: the Pope, or at least the Vatican, had used AI to write about AI.

My reaction was basically: OK, and then what? Encyclicals already pass through institutional drafting, staff work, consultation, revision, translation, and committee-like smoothing. If some Vatican official used Claude to turn Pope Leo’s ideas into prose, the relevant questions would still be whether the document is accurate, thoughtful, and worth reading. But I should admit I was wrong.

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In Part II of my AI series, I wrote that AI writing detectors were bad and would probably remain bad. Pangram changed my mind: it has third-party evidence behind it, claims a very low false-positive rate, and has become the detector people reach for when they suspect undisclosed AI writing. Kelsey Piper recently wrote about Pangram Labs’ claims that several prize-winning short stories were AI-generated or substantially AI-assisted, and The Atlantic’s Matteo Wong has now written about Pangram’s growing power in schools, publishing, journalism, and the AI-writing accusation economy.

A working detector makes the ethics more urgent because it tempts people to treat provenance as a verdict. That is why the joke about using Pangram to filter out human-written content is sharper than it first sounds: in plenty of settings, AI-assisted writing may be more readable and more useful than unaided human prose. If the data underneath are sound, the human sentence-level struggle contributes little.

I should also be upfront that I am an interested party here. I am that infamous AI professor who proudly writes with the help of AI. An essay arguing that AI detection can become status policing is conveniently a defense of my own practice, so readers should ask whether I am drawing the line in a way that flatters me.

The provenance spectrum

AI-writing ethics starts with the promise the writer made. A writer can make a promise to a teacher, editor, reader, institution, or recipient. The ethical question depends on that promise before it depends on a detector score.

Let’s start where the skeptics are right. A student assignment that explicitly bans AI is the obvious case. A creative writing contest that promises to recognize new human writers is another. A condolence note belongs in a different category from an exam, but if someone grieving expects words from you, outsourcing the emotional act to a machine feels like a betrayal.

Some decisions also require accountable human judgment. If I am deciding whether someone gets a scholarship or grant, provenance matters because the applicant is owed my judgment. AI can help organize evidence or check consistency, but the evaluative act has to remain mine.

Consequences matter too. The more influential the decision, and the more it depends on personal judgment, the stronger the case for knowing who or what made the call. Human discretion can also be worse than AI discretion: a committee can be biased or arbitrary, and a well-designed AI system may eventually make some decisions more consistent.

There is also a special rule for first-person claims. When I write “I think” or “I feel,” that conviction should actually be mine. AI can help me phrase it, pressure-test it, or make it less awkward. It cannot supply the conviction itself.

Research and journalism sit closer to the middle. A byline is a promise that the author stands behind the claims, evidence, and judgment calls. It has never meant that the author personally typed every sentence without help from search engines, copy editors, co-authors, translators, or now LLMs. If my name is on an argument, the argument has to be mine; the prose can be assisted.

Much technical writing belongs closer to the content-matters side. If I ask AI to describe a chart, write a methods paragraph, or translate a regression result into normal English, the important question is whether the output is correct. I still have to verify the numbers and own the final text. Accuracy and accountability carry the moral weight.

At the far end are administrative messages where almost nobody cares about the human act of writing. If a department asks you to send a polite note confirming a committee meeting, use AI freely. The relevant standard is whether the note is true and clear.

A single AI score cannot answer the ethical question. The same level of AI assistance can be harmless in an administrative email, useful in a technical report, questionable in a personal essay, and disqualifying in a no-AI classroom assignment. Context is the whole point, even when the underlying detection is accurate.

Detection has a spectrum too

The ethics of detection should follow the ethics of use. If a teacher has told students to write without AI on a particular assignment, a detector can be part of an academic-integrity process. A Pangram score should never be the only evidence, especially given the stakes for students.

Creative contests face a similar problem. Piper’s argument about the Commonwealth Short Story Prize should be taken seriously because fiction prizes are partly about human craft. If a prize is rewarding a human writer’s voice, a fully AI-generated submission violates the premise. The organizer can allow AI, ban AI, or create a separate category. Trust alone will not settle the problem.

Peer review is harder. Seth Lazar gave the strongest version of the pro-detection case in response to my earlier Pangram post: AI-generated research output can become a denial-of-service attack on peer review. The cost of producing plausible-looking papers collapses, while the obligation to read them remains expensive. In that context, a detector may help preserve scarce review capacity.

The peer-review case still depends on the goal. If the goal is to catch students violating an explicit rule, provenance is the target. If the goal is to protect reviewers from worthless submissions, provenance is only a proxy: a detector estimates the probability that a text is AI, never the probability that it is bad. The real target is bad work: hallucinated data, fake citations, nonexistent methods, and papers with no question worth answering. A detector might help triage some of that, but someone still has to check the actual claims.

My worry is that we will police the em dashes while ignoring the hallucinated data underneath them. That would be a very academic way to lose the plot: exquisite attention to the surface marker, little attention to whether the thing says anything true.

Why disclosure mostly fails

The obvious compromise is disclosure. Let people use AI, require them to say so, and let readers decide how much it matters. That sounds attractive because it treats AI assistance as information and lowers the moral temperature.

I argued in Part II of the AI series that disclosure norms collapse under the incentives they create. The detector half of that argument now needs revision because Pangram appears to work much better than I expected. The disclosure half still looks right to me.

The more ethically questionable the AI use is, the stronger the incentive to hide it. A student who used AI after promising to write unaided, a contest entrant who submitted machine-written fiction to a human-writing prize, or a researcher who used AI to paper over fake citations has every reason to stay silent.

The people most likely to disclose are the ones using AI in low-stakes ways: cleaning up a paragraph, translating a chart, or turning rough notes into readable prose they still own. Those are also the cases where disclosure matters least. The likely equilibrium is a world full of ritual acknowledgments about harmless AI assistance, while the genuinely deceptive cases remain hidden until someone investigates them.

Disclosure can still help when the disclosure itself explains the work, as it does here. Editors, teachers, prize committees, and people exercising institutional authority should also be clear about the rules they enforce. But if the whole system depends on honest confession, it will punish the conscientious and leave the strategic users alone.

The new grammar policing

I know the phrase “grammar Nazism” is abrasive, and I mean something specific by it. I was born in the Soviet Union, and Russian elite culture can be intensely sensitive to grammar, pronunciation, stress patterns, and the small status markers embedded in speech. In practice, grammar correction often doubled as social sorting: the wrong school, the wrong region, the wrong family background, or the wrong kind of education could leak through the way you spoke.

America has its own version of this. Academic English is full of status signals masquerading as standards. The right kind of fluency makes you sound smart before anyone checks whether you are right, and the wrong accent or idiom can mark you as unserious before your argument gets a hearing.

AI detection is turning this old habit into a new technical ritual. The same people who once policed grammar now police “AI tells”: em dashes, smooth transitions, generic metaphors, oddly balanced paragraphs, prose that seems a little too clean. Sometimes they are right. AI writing does have recognizable patterns, which is why I have a style guide full of them.

If someone reads a piece, learns something new, and then makes the conversation about one suspicious phrase, the Pangrammar instinct has wasted everyone’s time. A reader’s attention should go first to the claim, the evidence, and the payoff, with style policing saved for cases where the prose actually blocks understanding or signals deception.

This status dynamic is very familiar. The detector score gives a scientific-looking license to dismiss work without reading it carefully. The people who benefit are usually the incumbent writers and credentialed gatekeepers who can turn a judgment call into a score. The accusation becomes especially convenient against lower-status writers and people who do not write English well but can now use AI to translate, draft, and reach an English-language audience. Too polished looks fake. Too awkward looks low quality. Either way, the gatekeeper wins.

The moral contamination logic makes the problem worse. Once AI involvement is treated like impurity, any trace of assistance becomes enough to condemn the whole work. That is a strange standard for a world where human writing has always been socially produced by editors, reviewers, co-authors, translators, and the sentence you read yesterday.

The funniest possible equilibrium is already here. AI tools write prose that is too clear, detectors punish the clarity, and then new “humanizer” tools rewrite the prose to look more awkward. TIME recently described people inserting mistakes and oddities to avoid sounding AI-generated. This is Grammarly in reverse: make the writing worse so it looks more authentic.

What to do instead

I am arguing for detector modesty. Pangram should only be used where provenance is part of the agreement: exams with explicit no-AI rules, contests that promise human craft, or institutional settings where the source of the text is part of the job. The institutional rule should be written before the score is consulted: define what AI use would violate the promise and what appeal process follows a high score.

In many domains, the standard should be quite simple: if you put your name on the work, you own it. You own the facts, the claims, the errors, the taste, the structure, and the judgment. If AI helped you produce an accurate technical summary, good. If AI helped you produce nonsense faster, that is on you.

Because attention is scarce, people will still rely on shortcuts. They will trust names they know, journals they respect, editors with a track record, friends who have read the work, and institutions that have something to lose if they publish junk. That is imperfect and often unfair. Outsiders and newcomers pay a price when reputation becomes the filter. But at least reputation is accountable over time. If a journal, prize, professor, or writer keeps endorsing bad work, people can notice.

A Pangram score is different. It gives a quick guess about textual provenance and invites us to stop reading before we have asked what the text is doing. Pangram seems to work, so the question is no longer whether we can detect AI. The question is what we should do with that information. Use it when provenance is part of the bargain and the stakes justify an investigation. Treat it as a prompt for judgment, never as a substitute for judgment.

If the work is fake, wrong, plagiarized, emotionally fraudulent, or a violation of a clear rule, say that and act accordingly. If the work is accurate, useful, and owned by the person whose name is on it, the fact that Codex, Claude, or ChatGPT helped assemble the sentences is a weak basis for scandal. The scandal would be building a culture where everyone learns to make writing worse so it can pass as human.

One last disclosure, since the whole piece is about this question: the essay above was written entirely in Codex from several hours of my dictated thoughts, earlier posts, saved style instructions, and recent social media exchanges. The cover image and spectrum chart were also produced by Codex. This was not a one-shot prompt. We went through more than a dozen iterations, mostly refining the argument and the chart. Yes, I am using Codex more than Claude Code now. Yes, I read the draft before publishing but did not line-edit the prose at all. By my own chart, this essay sits on the content-matters side of the spectrum, and I stand behind it.

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Some good news first. I am opening Popular by Design to occasional guest contributors, with formal guidelines coming soon. The first two posts were a perfect reminder of why this is worth doing: asked whether intervention would cause a migration crisis in Cuba, and explained why mainstream parties seem to lose either way when they accommodate the far right. Gil’s piece asks what a Cuba intervention debate misses when it treats migration as an afterthought. Hanno’s piece asks what mainstream parties actually gain or lose when they borrow far-right immigration positions. Both pieces say something new and practical about the problems facing our politics.

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May was a lighter month for my own writing after the April sprint, which was probably healthy. I still managed to publish my Washington Post op-ed on why high-skilled immigration is a winning policy that needs a champion, plus a Popular by Design essay on why LinkedIn is doing what Bluesky was supposed to do. I also recently spent two days in DC at the Teaching Progress workshop, hosted by at the JHU Bloomberg Center, where faculty reworked syllabi around the question of how to teach about progress, especially now in the era of AI. I went in with my migration and conflict courses and came out with a clearer understanding of what I need to change. After a stretch of academic conferences that felt more draining than useful, this one felt like a breath of fresh air. I actually learned something new and met interesting people in a cool setting.

Here are the May links (linking does not imply endorsement):

• argues that Trump closed the representation gap on immigration. I agree with the broad thrust that Trump’s immigration politics are more popular than many smart people want to admit. My main criticism is that the usual Gallup increase/decrease item is a shaky way to diagnose representation: it mixes what people want from immigration, what politicians in power and opposition are doing, and thermostatic reactions to current events. I would also separate Trump’s popular moves on asylum and border security from his much less popular moves against legal immigration, green cards, travel, and high-skilled migration. Bundling all of that as “Trump’s immigration policy” hides too much of the real politics.

• at Cato reports on the DHS quitting granting green cards almost entirely. USCIS now says adjustment of status should be granted only in “extraordinary circumstances” and that most applicants should use consular processing abroad. It is still unclear if the memo will be implemented as written, including forcing many people with pending green-card applications to leave the United States and apply abroad. But crazy stuff nonetheless.

• Tara Watson, Matthew Wich, and Johnny Willing at Brookings give the broader talent-pipeline version of the same concern in a long report on how the Trump administration is eroding high-skilled immigration. They project a 29% decline in new F-1 student visa issuances in 2025 and estimate a green-card backlog of roughly 1.2 million. Their useful point is that the pipeline is sequential: international students become OPT workers, some become H-1B workers, and some eventually become permanent residents. Breaking one step does not stay confined to that step.

• The American Immigration Council released “Restoring Credibility and Humanity”, an enforcement framework built around compliance, safety, proportionality, and accountability. As I have been saying before, there is very little programmatic work on enforcement from the left, so this is exactly the kind of thing the pro-immigration side needs more of: a concrete alternative to the mass deportation agenda that doesn’t just brush off prevalent people’s concern about legality and order.

• uses recent CBO projections to show that aging is in the future for the United States too. In his zero-net-migration scenario, the 20-64 population falls by 20.5 million by 2056 while the 65-plus population rises by 21.9 million, and the ratio of working-age adults to people over 65 falls from 3.1 to 2.1. This is the immigration-demography link in its least sentimental form: even countries that avoid near-term depopulation still age, and aging changes what labor, care, and growth require.

• pushes back on a too-simple fertility narrative in “The (Fake) Long Decline of Fertility”. His target is the viral story that fertility has just been sliding down the same long trend line for centuries. Instead, he separates long-run forces like child mortality, norms, and the cost of children from the more recent post-2007 decline, where he emphasizes partnership, sex, contraception, and digital technology.

As before, if you want me to write more about one of these or other related topics (or now if you want to write something yourself!), let me know. And if you came across something I missed this month, send it my way.

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Today’s post is a guest essay by Hanno Hilbig, a political scientist at UC Davis who does some of the most interesting work on political behavior. I’m excited to publish his assessment of the accommodation literature, which has become especially controversial lately. Too often, the debate gets flattened into either “moving right on immigration never works” or “mainstream parties should do it.” Hanno’s piece gives the more nuanced useful answer: accommodation can move some voters, but it also creates hard trade-offs around credibility, base defection, and issue salience.

In May 2025, Britain’s Keir Starmer warned of an “island of strangers” and rolled out a sweeping immigration white paper. A few months earlier, Friedrich Merz had pushed a five-point migration motion through Germany’s parliament with the votes of the far-right Alternative for Germany (AfD). Both leaders reached for a familiar playbook: move closer to restrictive immigration voters, reduce the appeal of the radical right, and hope to gain back voters drifting away from the mainstream.

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The temptation is easy to understand, as mainstream parties often do not align with their own voters on the issue of immigration. As documents, large representation gaps exist on cultural issues across Europe, with immigration among the clearest examples. In many countries, majorities of voters favor lower immigration, while mainstream politicians, including those in center-right parties, hold more permissive views. Such a gap looks like an opening, and so far the radical right has often been the party to exploit this gap.

Whether this kind of mainstream “accommodation” wins voters back remains an open empirical question. The research has not settled it yet. Existing evidence suggests that restrictive shifts can attract some anti-immigration or radical-right voters. But those gains can be offset by losses among pro-immigration voters and the mainstream party’s own base. Accommodation can also make immigration more salient, look less credible than the radical right, and make once-fringe positions seem more normal.

Parties usually turn to accommodation only when they are already losing. That is part of why it is so hard to judge: the studies finding it rarely works are looking at parties that were sinking anyway. Accommodation can win some voters back, but only when the shift looks credible and the party keeps its own base. When it can’t, the party just ends up boosting the rival it set out to beat, which is why mainstream parties so often lose either way.

Accommodation as spatial voting

So what is accommodation? In practice, accommodation usually involves one or more elements: policy movement, cooperation with other parties on legislation, and rhetorical signals. Merz’s case involved substantive movement on migration and a controversy over the norm not to cooperate with the radical-right AfD party. Starmer’s case combined an official policy white paper with rhetoric that made immigration highly salient.

The basic case for accommodation stems from what political scientists call “spatial voting.” In the simplest version, it means that we can imagine that voters and parties sit on a certain policy line. Voters prefer the party closest to them. Parties can convert some voters by moving toward them but may lose voters that they are moving away from. If voters want more restrictive immigration policy than mainstream parties currently offer, this creates an opening for radical-right parties. In turn, spatial voting predicts that a mainstream party may win back some voters by moving toward those voters in policy terms. Since mainstream politicians are relatively distant from some voters in terms of immigration positions, this suggests an opportunity to regain voters by closing the representation gap.

But several processes are not captured in a simple spatial model. First, accommodation on issues like immigration can make these issues more salient or important to voters. This could then benefit radical-right parties who “own” the issue of immigration.

Second, accommodative positions by mainstream parties may not be seen as credible. Voters may consider such positions to be “cheap talk.” This could be the case when accommodation is proposed by some party officials but criticized by others. It could further occur when accommodative rhetoric directly contradicts a long track record of policy positions of the party that proposes it.

Third, accommodation may reduce the stigma around radical-right parties or illiberal political ideas. This can happen because mainstream cues signal that positions, parties, or forms of rhetoric previously treated as outside ordinary democratic competition are now acceptable options for ordinary voters. There is a tradeoff between short-term electoral gains and long-term normalization of radical positions.

What We Should Expect

My discussion focuses on programmatic accommodation: a mainstream party’s substantive movement toward more restrictive positions on immigration, asylum, border control, integration, or immigrant welfare.

The spatial-voting expectation. The pro-accommodation case starts with a “representation gap.” If mainstream parties are more liberal on immigration than many voters, radical-right parties can attract voters who are to the right of the mainstream. Recent work supports this possibility. Guenther (2025) argues that representation gaps are especially large on cultural issues and that right-wing populists fill those gaps. In Germany, the immigration gap is especially relevant to AfD support among voters who consider immigration important. Alizade (2025) makes a related point: concerns about immigrant crime can create a mismatch between left voters and left parties, pushing some voters toward the center right.

This logic gives accommodation its strongest theoretical foundation. If some voters are close to mainstream parties on most issues but more restrictive on immigration, a mainstream party might gain by reducing that distance. In this view, accommodation is a response to underrepresentation rather than an attempt to pander to radical-right voters.

Salience and issue ownership. Spatial voting is useful, but it leaves out several things that matter in campaigns. Movement on immigration can make immigration more central. That matters because radical-right parties often benefit when politics is organized around immigration, national identity, or crime.

Immigration was already highly salient when Starmer made his 2025 pivot. In May 2025, Ipsos found that 49% of Britons named immigration as one of the biggest issues facing the country, the highest level since the 2016 Brexit referendum. At the EU level, immigration was also among the top concerns in recent Eurobarometer data.

High salience does not necessarily doom accommodation. If immigration is already unavoidable, a mainstream party may prefer to compete on it rather than ignore it. But if accommodation makes immigration more salient, it can change the payoff of the strategy. Issue ownership means that voters see one party as more competent or authentic on an issue. If voters see the radical right as more credible on immigration, a mainstream party may end up making the radical right’s strongest issue more important.

Credibility and policy delivery. Accommodation also requires credibility. A mainstream party with a long record of pro-immigration policies and visible internal divisions on the issue may struggle to convince voters that its pivot is real and durable. The problem is especially severe for rhetorical accommodation. Voters may hear a restrictive speech and conclude that the party is merely opportunistic, especially if the party’s record points in a different direction.

A U.S. example is Kamala Harris’s 2024 pivot on border security. Harris emphasized border security and tougher asylum rules while still supporting comprehensive immigration reform. Substantively, this moved her campaign toward a more restrictive border position. Politically, it also created a credibility problem, because the shift came after years in which the Biden administration was associated with a more permissive border and asylum posture. Voters may therefore have perceived the move as late electoral repositioning rather than a durable commitment.

Policy change may be more credible than slogans, but delivery creates its own problems. Visible change can keep immigration in the news. Invisible change may not be noticed. Failed delivery can reinforce the perception that mainstream parties talk a lot but do little.

Where the Lost Voters Go

Similar vote movements can have different consequences across party systems. In a bloc-based proportional system, a social-democratic party might lose pro-immigration voters to Greens, liberals, or other left parties while gaining anti-immigration voters from the radical right. If these voters remain inside the left bloc, accommodation may improve the bloc’s chance of governing even if the main party loses some supporters. This is the logic behind the more favorable Danish case in Hjorth and Larsen (2022).

But this logic does not always apply. If alienated voters abstain or move across blocs, losses are not contained. In a majoritarian system, a voter leaving Labour for the Greens or Liberal Democrats may still hurt Labour’s ability to win seats, even if Labour gains some voters who favor more restrictive immigration policies. Radical-right parties can also respond. They can emphasize mainstream parties’ lack of credibility, shift the debate to stricter positions, or argue that their own ideas have been vindicated.

Accommodation may also change what counts as legitimate politics. The concern is not only that mainstream parties lose or gain voters in the short run. It is also that their cues reduce the stigma around radical-right parties or illiberal ideas. Evidence for this first-stage effect appears in several settings. Valentim et al. (2025) show that anti-immigrant statements by mainstream-right politicians erode anti-prejudice norms more than similar statements by radical-right politicians. Daur (2025) shows that mainstream legitimization can increase the perceived legitimacy of pariah parties. Ekholm et al. (2022) show that mainstream recognition can increase sympathy toward the Sweden Democrats.

A U.S. example is the erosion of the distinction between legal and illegal immigration. On some accounts, Trump did not create the underlying restrictionist constituency, but he recognized it and pushed it beyond prior boundaries. The result was not only a tougher stance on illegal immigration, but a broader weakening of the old norm that kept legal immigration politically protected. This is the normalization mechanism in a different setting: once the boundary of acceptable restrictionism moves, later political entrepreneurs can push farther at lower reputational cost.

So, the net effect is hard to predict. Accommodation can work through several channels at once. It can persuade voters who see the mainstream party’s shift as credible. It can make immigration more salient for voters who trust the radical right more on the issue. It can also fail if voters view the pivot as opportunistic.

What Does the Evidence Say?

Researchers have made progress on the relationship between accommodation and electoral returns. But the evidence is genuinely hard to read, for a simple reason: parties rarely accommodate at random. They usually move right on immigration when radical-right parties are already gaining, when immigration is already salient, or when public opinion has shifted. If radical-right support then changes, it is hard to know whether accommodation caused the change or simply followed the same trend.

The cross-national evidence is mixed and does not support a pro-accommodation story. Krause et al. (2023) find little evidence that mainstream accommodation reduces radical-right support, and some evidence that it may increase movement to the radical right. Dahlstrom and Sundell (2012) find, in observational evidence from Swedish municipalities, that tougher average mainstream positions are associated with higher Sweden Democrat support. Down and Han (2020) do not directly measure legitimacy, but their voter-level evidence is consistent with a legitimization channel: restrictive mainstream positioning is associated with more radical-right voting among voters who do not already see the radical right as competent on immigration.

Other studies are more favorable. Spoon and Klüver (2020) find that mainstream-left parties benefit electorally from going tougher on immigration, while mainstream-right accommodation is largely null. Thesen (2025) reanalyzes part of this literature with controls for immigration news and radical-right media visibility, and finds that once those media variables are included, accommodation appears to hinder radical-right success.1

The strongest causal evidence comes from designs that separate accommodation from the trends that usually precede it. Conjoint designs ask respondents to choose between profiles whose attributes are randomly varied. Survey experiments randomly assign information about party positions. Unexpected-event designs use real political shocks that occur during survey fieldwork. These designs are narrower than cross-national studies, but they are closer to the central question: whether a mainstream party’s restrictive movement changes vote choice, vote intention, or propensity to vote.

Chou et al. (2021) study German voters using a conjoint design that varies candidate positions on immigration and other issues. Restrictive mainstream positions can attract some AfD voters, which supports the spatial argument. But the same positions can alienate mainstream-party voters. The net party-level effect depends on whether gains among the target voters exceed losses elsewhere.

Hjorth and Larsen (2022) study the Danish Social Democrats after the 2019 election, when the party’s immigration position was plausibly ambiguous to voters. Their survey experiment told some respondents that the party had taken an accommodative immigration position and others that it had taken an adversarial one. The study finds that accommodation can attract anti-immigration and Danish People’s Party voters. Losses among pro-immigration voters were partly contained because many of those voters remained inside the left bloc. This is the clearest case in which accommodation looks electorally useful, but it also shows why institutional context matters.

Kollberg (2026) separates positional accommodation from populist rhetoric in a pre-registered factorial survey experiment. The central result is that position matters more than populist style. Accommodation increases propensity to vote among right-leaning voters but repels left and centrist voters. Again, the spatial mechanism is present, but so is the offsetting-loss mechanism.

An important recent non-experimental case is the preprint by Turnbull-Dugarte et al. (2025). They use the timing of British Election Study fieldwork around Starmer’s May 2025 speech. Some respondents were interviewed just before the speech and others just after it, so the episode works like an unexpected-event treatment. That makes the design unusually close to the question here: what happens after a real social-democratic accommodation episode? It is still not a pure test of policy delivery, since the treatment combines rhetoric, policy announcement, and media attention. The paper finds that exposure made Labour look more anti-immigration and right-leaning, reduced Labour support, and did not reduce Reform UK support.

Overall, the strongest causal designs suggest that accommodation can move some anti-immigration or radical-right voters, but these gains are often offset by losses among centrist and pro-immigration voters. Experiments also hold constant many parts of real campaigns: media attention, elite conflict, credibility, and radical-right responses. That makes the evidence useful but incomplete, and how much you trust it comes down to how much weight you put on a clean causal estimate versus the realism of an actual campaign.

Interlude: What Researchers Should Do Next

Before concluding, I would like to add a few thoughts on what ambitious grad students and other folks who care about this issue can do next. The main evidence gap is pretty clear at this point. We need more designs in which mainstream accommodation is the shock and both mainstream and radical-right electoral outcomes are observed in real elections. The key challenge is to separate accommodation from the political trends that usually precede it, such as rising immigration salience, radical-right growth, or shifts in public opinion.

Cross-national research will remain valuable, but even the best versions face the same problem. Parties accommodate when the political environment is already changing, and that endogeneity is difficult to solve with aggregate data alone.

One promising direction is subnational or candidate-level research. Close elections between candidates with clear programmatic differences on immigration, asylum, policing, or integration could provide useful evidence, especially when the office has visible policy authority. MPs, mayors, and local party branches also sometimes take positions that differ from their national party. These cases create more variation than national party-level studies, although national party brands may still drown out local differences.

Another promising direction is unexpected-event designs. Survey fieldwork sometimes overlaps with high-profile party pivots or policy shocks. These designs capture real-world accommodation without asking voters to imagine a hypothetical campaign. But they depend on the case that happens to occur. They cannot tell us what the effect would have been if the pivot had been more credible, or if issue salience had been held constant.

What Accommodation Can and Cannot Do for You

The spatial voting argument remains the strongest case for accommodation. If mainstream parties are more liberal on immigration than many voters (which they often are!), a more restrictive position can reduce the distance between the mainstream party and those voters. The evidence reviewed above suggests that this works. Accommodation can make mainstream parties more attractive to some anti-immigration voters. But a spatial gain among some voters is not the same as a positive net gain after factoring in the “side effects” of accommodation.

Recent arguments that “mimicking” the radical right is electorally self-defeating capture an important concern. Accommodation can make immigration more salient, activate an issue on which the radical right is often viewed as more credible, alienate centrist and pro-immigration supporters, appear opportunistic when it conflicts with a party’s prior record, and weaken the boundary between mainstream and radical-right positions.

But the broader conclusion that accommodation cannot work is too strong. The evidence points to a more conditional claim: accommodation can win some voters – but the shift has to be credible, voters must be persuadable, and losses elsewhere must be contained. The Danish case is the one most often associated with these favorable conditions.

The skeptical view is that these conditions are demanding and may often not hold. That skepticism is important, but it does not make the strategy irrational from the perspective of parties that choose it. Mainstream parties often accommodate from a position of weakness, after immigration is already salient and after some voters have already begun to defect. Mainstream parties may therefore choose accommodation even when they know it is risky. If defeat already looks likely, accommodation is less a path back to power than a bet to slow the bleeding. And that is the deeper bind: by the time accommodation looks tempting, a mainstream party is rarely choosing between winning and losing. It is choosing between two ways of losing, and betting that accommodation is the slower one.

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Nearly 80 percent of American voters support high-skilled immigration, across party lines. That is more popular than nuclear power, building apartments, or deregulation. And yet not a single major political figure in either party is willing to champion it.

Republicans know the country needs foreign scientists and engineers. Most will say so privately. But they refuse to say it publicly, terrified of being outflanked on their right.

The left has a different version of the same cowardice: the abundance crowd wants more housing, more energy, more growth, but has quietly decided immigration is too dangerous to include in their pitch.

The piece proposes two concrete moves that do not require Congress: promote and expand the O-1A extraordinary-ability visa, which has no cap and no lottery, and fix how the government sets wages for foreign workers.

The Labor Department’s public comment window on the proposed prevailing wage rule closes May 26. If you work in research, tech, or immigration, submit a comment before it’s too late.

I’d love to hear what readers think I’m getting wrong or what’s missing from the argument. After all, if this were that easy and obvious, some Republican or Democrat would already be trying it much more forcefully.

P.S. As a fun tidbit for my readers, this is actually the first cold pitch I ever landed. That is, I just literally emailed a WaPo editor with the pitch and the piece, and they were like—yep, let’s publish it. This never happened before quite like that.

Probably non-coincidentally, this is also the most revised and re-revised 1,000 words of my career. Thanks to all the folks who have read and edited the piece for pushing me over and over again and making it all possible.

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I’m excited to share our first guest post on Popular by Design from . Gil does some of the best migration research around at the , grounded in hard demographic data, and it shows in his feel for what actually drives people to move. I found his read on the potential Cuban migration persuasive and constructive without being complacent. Follow Gil and his new work at Points of Entry.

Speaking on Face the Nation this past Sunday, former Secretary of Defense Robert Gates argued that the greatest national security risk posed by Cuba to the United States is another massive refugee outflow along the lines of the 1980 Mariel Boatlift. This sentiment has been approvingly echoed by established experts as an inevitable contingency the United States will have to deal with if it does intervene on the island.1

While no one can predict the future with certainty, this point seems intuitive at first glance. Cuba has a well-established history of weaponizing migration toward the United States, and the Syrian Civil War has ingrained the association between violent conflict and mass immigration in people’s minds.

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But on a closer look, the specifics that govern how large a Cuban departure wave can actually get all point in a generally conservative direction. Four features of the present moment should make us rethink the conventional wisdom and expectations around another Cuban migration crisis.

The people most likely to leave a crisis have largely already left. Migration waves recruit disproportionately from the young, the working-age, and the mobile, and Cuba has spent four years exporting precisely that cohort. The regime conceals the magnitude (it counts emigrants as residents until they have been gone two years) but an independent reconstruction by the demographer Juan Carlos Albizu-Campos puts the actual resident population near 8.62 million and total departures since 2021 at roughly 1.79 million, a figure consistent with Cuban arrival counts in the United States and elsewhere.

Composition matters more than the total here. An estimated 77 percent of recent Cuban migrants are between 15 and 49, a slight majority of them women. The Cubans who remain on the island today are older, sicker, poorer, and likelier to stay. A recent estimate I authored for the Niskanen Center caps a weaponized scenario at 60–75 percent of a Mariel-equivalent event on these grounds alone.

The regime has reduced incentives to encourage irregular migration. Cuba has a long record of wielding migration as a coercive instrument before, and part of the reason it worked according to political scientist Kelly Greenhill is because this type of coercion played on American humanitarian concerns and domestic political pressures over the treatment of Cuban migrants.

Whether you agree with the administration’s positions or not, it’s hard to argue that they are overly concerned with migrant wellbeing or popular approval. In our present state of affairs, any attempt to facilitate (let alone actively encourage) irregular migration would almost certainly be treated as casus belli by a White House seemingly itching for an excuse to intervene.

The calculus might change in the event of American military action on the island, but as I’ll argue in a forthcoming paper for Florida International University, we haven’t seen a migratory surge out of Venezuela following Operation Absolute Resolve, and Venezuela has a much larger population and geography that is better suited for mass migration.

Migrants would have only one feasible route, and the United States should be prepared for it. During both the 1980 Mariel boatlift and the 1994 Balsero crisis, the United States largely had to scramble to mount an adequate response under the pressure of a migrant wave that was already underway. In the present day, the Coast Guard appears to be running its standing maritime interdiction program (Operation Vigilant Sentry) at an elevated level. The most recent public disclosure from 2024 put our assets in the area at roughly eighty cutters against a baseline of thirteen. It’s possible, but unlikely, that the administration has wound this down given how telegraphed its willingness to confront Cuba has been so far.

While recent migration waves from Cuba have come by other means, at present the sea is the only theater where a mass migration event could take place. The 2021-2024 spike in Cuban encounters came from Cuban flights through countries like Nicaragua followed by travel on foot to the southern border. Nicaragua revoked visa-free access for Cubans in February 2026, and Mexico has stepped up interior enforcement to stop migrants from reaching the border.

Thus, migrants determined to leave Cuba for the U.S. will have to take their chances via the Florida Straits, where chances of interception are high and where their ability to leave at all will have to clear difficult hurdles like vessel quality and availability and favorable weather conditions. The fact that the dire conditions Cubans are currently experiencing on the island haven’t led to more irregular migration attempts suggests that Cubans are pricing in these factors in their decisions on whether to try to leave, which is the subject of the final constraint.

Cuban migrants have no reason to expect their journey will end well. Migration responds to expected outcomes, not to push factors alone, and the expected outcome for a Cuban reaching the United States in 2026 is an unpleasant detention and removal.

Every pathway that structured the last wave is closed: CHNV parole has been terminated, CBP One has been shut down, the Cuban Family Reunification Parole program was also terminated (but is in active litigation), a travel ban is in force, USCIS processing for Cuban nationals is paused, and the Cuban Adjustment Act is functionally inert.

A prospective migrant weighing the crossing confronts a harder route and a near-certain unfavorable result at the end of it, and that calculus dampens the pull even when the push is acute. The underlying factor that convinced many Cubans to undertake the treacherous maritime route to the United States during previous migrant crises was the “Wet foot, dry foot” policy, which guaranteed them a reasonable chance to pursue legal residency if they could reach the United States. No equivalent to this exists today, and there is no reason to expect one to appear in the near future.

What should the U.S. do? No one can be fully sure of whether a migrant wave will occur after a hypothetical conflict in Cuba or how large such a wave would be. But the administration can do two things now to ensure that minimal harm befalls ordinary Cubans seeking to leave the island if a military conflict does trigger a significant exodus.

The first is for White House officials and civil and military planners to craft a more detailed and long-term migration mitigation strategy on the island. The plan that exists now seems to be a holding pen: the head of SOUTHCOM has testified it would support DHS in a Cuban migrant crisis by standing up a camp at Guantánamo Bay. This answers where an outflow goes but not what happens once migrants get there. A camp without pre-set screening criteria, release standards, and a defined endpoint makes the base a ticking detention problem. Contingency planning here is prudent, and seemingly incomplete.

The second is that the administration, in partnership with Congress and with the help of the South Florida delegation in particular, should be negotiating third-country humanitarian arrangements for Cubans now, while flows are low, rather than improvising them under the pressure of a wave. Brazil, Uruguay, and Spain are already informally absorbing the redirected Cuban flow. Structured agreements that include specifics like resettlement slots, orderly processing, and burden-sharing in exchange for diplomatic and economic considerations would convert an unmanaged diversion into a managed one and free Washington from the hard choice of taking fleeing migrants ourselves or leaving them trapped on the island. The time to build that channel is before the boats are in the water.

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For a brief moment about a year ago, it really did look like Bluesky might work. Researchers and left-of-center intellectuals were flooding in, swapping starter packs, reassembling what felt like a nostalgic reunion of old Twitter. Then everyone arrived, and the center could not hold. It turns out that people can disagree even when they are all on the left, and that without strong social norms of free speech and civil conduct, humans—including university professors with allegedly high IQ—remain tribal and quick to pile on. Meanwhile, LinkedIn (yes, LinkedIn!) has quietly been doing the job Bluesky was supposed to do.1

Like most researchers and public intellectuals I know, I had a LinkedIn account for years that I barely used: I would accept the occasional connection request and otherwise ignore the platform. At some point I noticed that people were actually talking on it, often substantively, in a way I had previously expected only from old Twitter. So, for the last year or so, I've been cross-posting essentially the same content to Twitter, Bluesky, and LinkedIn. At this point, the pattern is consistent enough to feel like an A/B test. Pieces that read as pro-immigration get cheers on Bluesky and silence on X; pieces that read as "anti-immigration" get the reverse. Pieces that carry the most nuance get nothing on either platform. But LinkedIn has been the exception.

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It is not hard to specify what functional public-facing research discourse should look like in theory. You post something new and informative that you care about, like a working paper on a policy-relevant topic. People who find it interesting or useful say so and add nuance. People who disagree explain why in a respectful, substantive way. People who find it uninteresting ignore it. Nobody calls you names, impugns your motives, or turns a technical dispute into an accusation unrelated to your argument. If they do, they are in the minority, and they get called out.

These principles once described Academic Twitter. On Bluesky, they have mostly collapsed. Bluesky has fundamentally failed as a venue for public-facing research and, more importantly, as the channel through which serious research reaches policymakers, journalists, and the broader public. The platform that has quietly taken over that function is LinkedIn. If a lot of sane people left Bluesky or X for LinkedIn tomorrow, or at least started reposting their content there, the research internet would be better for it, and so would the rest of the internet.2

What the public square was actually for

Public-facing research had a simple social media function for most of the last decade. It was a cheap pipeline from research to policy, to journalism, and to the reading public. Old Twitter did this job unevenly but at scale through the famous posting-to-policy pipeline, as well put by . It was where a congressional staffer bumped into a political scientist’s thread and turned it into a briefing, where you learned about a working paper before it was published, where a fight over whether an RCT had identified the right thing played out in real time for an audience that partly understood and partly just absorbed the norms of how serious researchers talk.

When Elon Musk bought Twitter and rebranded it as X, the pipeline broke: the algorithm got more chaotic, external links got suppressed, spam and reply-guy slop proliferated, and blue-check monetization created a new economy of rage-farming accounts. The new regime did loosen some of Old Twitter’s speech restrictions around heterodox positions on public health, gender, and race, and for a minority of researchers on those topics, post-Musk X is genuinely freer than its predecessor. But the net effect was negative on balance, and a large plurality of social scientists, think-tank writers, and left-of-center journalists migrated to Bluesky over 2024-2025.

Why Bluesky failed

The short version is that most of what people used to blame on Twitter’s algorithm turned out to be problems with users and norms. Bluesky removed the engagement-maximizing amplification but, through aggressive self-selection during the Musk-era migration, kept a left-of-center user base disproportionately invested in policing speech and unwilling to treat ideological deviation as anything other than a moral failure. The predictable result is that any post touching a politicized topic (migration, AI, racial disparities, or whatever gets politicized next) draws volume rather than argument. For most researchers most of the time, the rational response is to say less rather than more, and the deterrent scales faster than the discourse.

This is not just something right-wingers complain about. As argues from the center-left perspective, Bluesky’s discursive norms are almost designed to repel outsiders. also argues that Bluesky functions less as a political movement than as a tribal affiliation with a narrow demographic profile; and observes that progressive commentators on Bluesky, having lost their mainstream audience, now spend much of their energy trying to cancel each other.

I have often experienced this all firsthand: academics respond to even my less controversial Bluesky posts by email, because they don’t want to risk getting piled on for saying anything in public. The numbers bear out this diagnosis: Bluesky peaked at around 40 million registered users by late 2025. Pew’s 2025 survey finds just 4 percent of U.S. adults have ever tried Bluesky.

Why LinkedIn

LinkedIn is not a perfect platform, and I will get to the downsides in a moment, but the case for it rests on a handful of things that have quietly become decisive for anyone doing public-facing research.

Scale and distribution. LinkedIn reports 1.2 billion registered users globally, roughly 30 times Bluesky’s total user base and orders of magnitude higher than its daily active count. A reasonable skeptic asks why an account number should translate into actual reach, since having a profile is not the same as reading a feed. Two things answer that. First, LinkedIn’s algorithm explicitly pushes posts with early engagement outward to your 2nd- and 3rd-degree connections, to followers of relevant topics and hashtags, and to professionals in the same industry, regardless of whether they follow you directly. A strong LinkedIn post travels to strangers in a way a Bluesky post simply cannot.

That said, LinkedIn engagement is notoriously hard to measure externally, because LinkedIn closed its public API in 2015 and restricts analytics data to approved Marketing Developer Platform partners, which is one reason the migration of research-adjacent discourse to LinkedIn has been largely invisible to researchers who expect to measure platforms the way they measured Twitter.

Audience mix. Think of LinkedIn as a kind of Switzerland of the internet: higher-status people from different camps show up under their real names and engage with each other because the professional cost of behaving badly is real. This is also the point most researchers have not fully absorbed, as I can attest, and it explains the apparent paradox of LinkedIn being the dominant platform for research-adjacent discourse without most researchers noticing. Bluesky is full of other researchers, the journalists who cover them, and activists.

The people on LinkedIn are the people we should be trying to reach: policymakers, congressional staffers, civil servants, industry analysts, foundation program officers, and journalists at general-interest outlets. A 2025 Avoq survey of DC policy insiders found that 81 percent of Democrats, 84 percent of Republicans, and 78 percent of MAGA-aligned respondents use LinkedIn. Good representative data on LinkedIn compared to other platforms is notoriously hard to find, but this looks like a bipartisan footprint no other platform comes close to matching. Researchers have not clocked the shift because the people reading their LinkedIn posts are not the people they spend time with online; they are the people their work is supposed to reach.

Format favors substance. LinkedIn’s format (longer posts, real names tied to real careers, a less snarky default register) does a lot of the work of civilizing discourse without needing heavy moderation, because when the poster is visibly accountable to an employer and a professional reputation, the median comment tone shifts correspondingly, and bad-faith quote-dunking becomes rarer. There are also no anonymous accounts and almost no sub-tweeting; the median post reads more like a memo than a hot take.

People can still disagree or criticize you heavily if you post something provocative, but they are much less likely to do it in a mindless or righteous way. In some ways, LinkedIn feels like an academic conference: people are civil, sometimes too nice, and not always willing to criticize a colleague openly. That conference-style politeness can smooth over real disagreement, but it is a much better failure mode than ad hominems and pile-ons.

Discussion that actually moves understanding. The clearest evidence I have for all of this is my own cross-posting experience. I have often shared the same piece, including the more controversial ones, simultaneously on Bluesky, X, and LinkedIn, and the pattern has been remarkably consistent. On Bluesky, the reaction is usually either silence or a small pile-on when the piece challenges prevailing consensus, and substantive engagement is rare. On X, responses are a mix of real engagement and the usual ratio of slop, bad-faith screenshotting, and reply guys.

On LinkedIn, the pushback I get is both the most civil and the most productive: named professionals who actually work on the topic, often from perspectives I don’t share, who write multi-paragraph responses that engage with the argument rather than perform outrage about it. This holds even for pieces and takes I expected to trigger the most hostility, because people disagreeing under their own name with their employer looking over their shoulder have strong incentives to be reasonable.

No, LinkedIn is still not perfect

A pragmatic case for LinkedIn has to be honest about what the platform does poorly. I’ve seen anonymous accounts on X complain that LinkedIn has been quietly suppressing links to right-coded publications for some time. I haven’t been able to verify this, but if you write from the political right, or many of your sources are outlets that LinkedIn’s filters treat as low-quality, your reach may be cut in ways that are not transparent. Even if this is true, for a left-leaning researcher writing about migration or AI, this is a non-issue, while for a right-of-center one writing about the same topics, Bluesky’s ideological environment is more hostile, and LinkedIn’s algorithm is not neutral either.

The interface is also genuinely clunky. The composer is awkward, threading is mediocre, search is bad, and basic features that X and Bluesky get right out of the box are either missing or buried. This is a fair complaint, but it is also a complaint that gets answered as the platform’s serious-discussion userbase grows: the more prominent intellectuals show up expecting a real public square, the more pressure LinkedIn faces to build the tools they need (I’m looking at you, ).

And yes, LinkedIn has its cringe: the AI-generated inspirational posts, the three-emoji bullet lists, the humblebrags about how humbling it was to be invited to speak somewhere, the AI-generated summaries of papers the poster obviously has not read. None of this is enjoyable, but it is easy to filter past, and the cost of ignoring the slop is low compared to the cost of staying on a platform that actively punishes substantive engagement.3

Come build the bridge

Every sane researcher who migrates to LinkedIn takes one more useful voice with them. The tipping threshold is probably not high: a few hundred visible researchers moving their primary public-facing work to LinkedIn, as many have already been doing quietly, would shift the center of gravity enough to make the migration self-sustaining.

If you are skeptical, you don’t have to abandon Bluesky (or X for that matter) tomorrow. Just start cross-posting whatever you’d normally write there onto LinkedIn too for a couple of months, and watch the difference in who shows up in your replies. The on-ramp is low-friction: post a short summary or excerpt of your latest piece with the link, tag a few people whose work it actually engages with, and see what comes back over a week or two. Based on two years of doing exactly this, my bet is that the LinkedIn version will draw substantive engagement from people whose opinions actually matter to your work, while the Bluesky version fades into silence or a small pile-on when you’re even slightly challenging the prevailing consensus. You won’t need another essay to convince you.

I am on LinkedIn here. Follow me, post your research and writing, and let the audience decide. The public square gets built wherever serious people choose to show up. Right now, that place is LinkedIn.

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Quick personal news before we get to the links. As this newsletter goes out, I'll be on my way to the Borders and Belonging conference at IAST in Toulouse, one of the more exciting migration gatherings in Europe this year. If you're there, come say hi. On the way, I've been trying to visit every Notre Dame cathedral I can reach, on the theory that this is what most of my non-American friends assume my employer looks like anyway.

At the actual University of Notre Dame, we just hosted the 2026 Midwest Migration Conference using a new workshop-format without presentation slides, gathering the new and existing work on the botched reception of Venezuelan asylum seekers in Chicago, the effects of anti-immigration policies, and the difference between various Chinese communities in Paris. Let me know if you want to participate next year.

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Looking further out, I’ll be speaking at the Progress Conference 2026 in Berkeley on October 8-10 about immigration as a key progress issue. It’s one of my favorite conferences out there, both because the attendees are unusually thoughtful and motivated to make things better despite their disagreements, and because it’s one of the few places where people treat policy design as a genuine problem to be solved rather than an afterthought. If you’re going, come find me. And if you don’t, try to sign up and come anyway.

April was busy. At Migration Policy Institute, Caitlyn Yates and I traced 15 years of UK immigration policy swings and the salience cycles that drive them: salience rises, labor visas get cut, shortages follow, and visas quietly reopen. At The Atlantic, I published “How the Left Accidentally Bolstered the Nativist Right” (their title, not mine), on what we lost when the “I only oppose illegal immigration” norm collapsed. In the Frankfurter Allgemeine Zeitung, I explained why the German migration debate has gone off the rails, specifically how “well-intentioned disinformation” from the center-left feeds the far right. I released a Keough School policy brief, “To Protect Humanitarian Immigration, Build Public Trust First”, on how the sequential logic of my case applies to refugee and asylum policy.1 And, thanks to The Chronicle of Higher Education, I now have “AI Is a Better Researcher Than You” as a headline attached to my name forever.

OK, let’s move on to something a bit awkward. After two rounds of online pile-ons this spring, I’ve received some earnest private feedback from colleagues, with some worried about my mental health and some others worried about me becoming a “right-wing crank” for lack of a better word, all suggesting I should dial back how much I post on social media (I’m talking about short-form takes rather than long-form essays). I want to take that feedback seriously, so I’d like to also hear from the people actually reading this newsletter and following me on social media. Conditional on being factually accurate (which I still strive for believe it or not):

Here are the April links (linking does not imply endorsement):

• launched In Development this month, a new magazine on what works and what doesn’t in global development. If you liked her job market papers roundup and her thinking on evidence-based policy, this is the natural next step. The inaugural piece, a guest essay by GiveDirectly co-founder Paul Niehaus on how evidence supported the organization’s journey to delivering $1 billion in unconditional cash transfers, is worth reading in full. Immigration is a huge part of global development, so I’m glad Lauren is doing this. There is also a rumor that their next piece will be exactly on immigration.

• Amy Nice and Paola Sapienza have a new Hoover Institution conversation on “Unlocking Global Talent: The J-1 and O-1A Visas”. It’s the clearest thing I’ve seen on the practical alternatives to the H-1B lottery for companies trying to hire international STEM talent. Amy is one of the sharpest practitioners working on legal immigration in Washington, and she’s unusually good at explaining the mechanics to non-specialists.

• at No Idle Sitting argues Trump’s H-1B lottery policy kind of works. The $100,000 fee is clumsy and imprecise, but it does effectively raise the bar on employer-sponsored immigration in roughly the direction selection-based systems should go. Take our wins where we can get them.

• We had some minor disagreements with lately, but when he’s right, he’s right. His new piece on the “culture crutch” is a banger. The problem with “culture” as an explanation is that it’s the most conceptually overstretched term still allowed in polite scholarly society. Whenever it’s actually useful, more precise concepts (norms, institutions, prices) usually do the work better.

• on selection, not origin, driving immigrant welfare use. When you control for pathway type, country-of-origin effects go away. The policy lever worth pulling is how people come (i.e., for needed work), not where they come from.

• My friends and co-authors James Dennison and Andrew Geddes have a new book forthcoming soon, What Europeans Think about Immigration and Why It Matters (Oxford). It maps decades of European survey data and offers a proper framework for why immigration becomes politically salient when it does. I pre-ordered mine. Anyone serious about European immigration politics should.

• at The Argument on whether a liberal society can do affirmative action, makes a point that generalizes well beyond DEI. As I’ve been beating this drum on immigration, the problem with unpopular policies isn’t usually that they’re sold poorly. It’s often that the policies themselves need to be different. Reframing only gets you so far when the underlying design is the thing voters are rejecting.

• at Stanford has a must-read piece on how AI is already 10x-ing academic research. His line I keep repeating: “stop waiting for permission. The tools are here.”

• at New Instruments asks who actually answered this survey in a world where people routinely delegate tasks to AI. Survey research has to shift from detecting AI-generated responses to redesigning instruments that make authentic participation easier than delegation. Yamil was creatively using AI in polls before it was cool, and he’s worth following if you want to make sense of where public opinion research is headed.

• Besides the aforementioned Progress Conference 2026, is still taking applications for the BBI fellowship (deadline: June 1). I’ll be advising and speaking on the human talent and potential track there. Highly recommended for any academic (or non-academic writer for that matter) seeking more serious public engagement.

As before, if you want me to write more about one of these or other related topics, let me know. Or if you agree that I should stay away from social media for some time before I turn into a complete crank, this is your chance to speak up, too :)

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Originally published in September 2025, this was the first long-form essay I wrote for Popular by Design, and the one that jump-started it. I only had a few hundred subscribers at the time. I still think it’s one of the best things I’ve written, since getting humanitarian immigration right is especially hard. I hope you enjoy it.

Despite all its supposed potential, immigration is deeply unpopular today. Refugee and asylum immigration is even more so, because humanitarian appeals don’t resonate much with voters. Most want to see clear benefits for their own country, not just compassion for strangers abroad. That’s why expanding refugee admissions is politically much harder than skilled or labor migration.1

The premise of this newsletter is that making meaningful progress on immigration requires more than just better messaging—it requires better policy. So I wanted to begin with one of the hardest cases and write about a possible solution for making humanitarian immigration more popular and sustainable. What I learned while working on this piece is that one doesn’t have to be a bleeding-heart liberal to support refugees.

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Enter programs for private or community sponsorship of refugees for permanent resettlement. The model was first launched in Canada in 1979 and is now being considered or piloted in other countries, including the United States. This policy innovation directly addresses a common skeptical retort in political debates about immigration and humanitarian obligations: “Why don’t you house them yourself?” This question, often used by skeptics to imply hypocrisy among pro-immigration advocates, points to the real and perceived costs of resettlement borne by taxpayers.

But the simple truth is that many people would gladly help refugees with their own money and resources—they just cannot legally do so. Outside of Canada, in most countries around the world—rich or poor, democracies or autocracies—only governments decide who gets to immigrate or resettle there and how, regardless of how generous their populations may be. This issue cuts across ideology—orthodox congregations can’t bring in culturally similar believers, while humanitarians can’t help families in danger even if they want to do it on their own dime.

Community sponsorship aims to change that. It gives willing individuals and private organizations a legal way to act on their motivations to help migrants, share the financial and social costs of resettlement, and show tangible benefits of migration to their communities. Just as importantly, unlike other pro-immigration policies, it creates a durable constituency of both conservative and liberal citizens with a direct stake in immigration and refugee protection. While this is a hard counterfactual to prove, I’m increasingly convinced that had Canada not pioneered sponsorship 45 years ago, it would have resettled far fewer refugees, and its immigration politics would be far more contentious.

There are many good overviews of what community sponsorship is, along with case studies and policy assessments. What I want to do here, in the spirit of Popular by Design’s mission, is something I haven’t seen anyone do yet: assess the potential of sponsorship to generate a more pro-immigration political consensus, both in theory and in practice, and with an open mind for its possible limits. Below, I outline the longest-standing Canadian PSR program, what we can learn from its successes and shortcomings, why it hasn’t spread more widely, the major criticisms, and the available polls on how this idea is received globally. I conclude with a discussion of America’s short-lived Welcome Corps sponsorship program (launched in 2023 but abruptly halted by the second Trump administration), and how better policy design could set it up for greater success if or (hopefully) when it resumes.

What is community sponsorship, and how does it operate?

Community sponsorship is a set of policies that let individuals, community groups, and nonprofit organizations sponsor specific refugees for resettlement in their country, in addition to or otherwise independently of traditional government resettlement. Sponsors cover housing and basic needs, provide social connections, and help with integration, for a defined period, typically twelve months after arrival.

Canada runs the longest-standing and most developed system. Since 1979, hundreds of thousands of regular Canadians have helped resettle around 400,000 privately sponsored refugees with the help of more than 200 local and faith-based groups, all in addition to government-assisted arrivals. In recent years, a slight majority of resettled refugees have come via private sponsorship, and federal targets now plan for more private than government-assisted admissions. Here is the basic breakdown of the current version of Canada’s Private Sponsorship of Refugees (PSR) program:

• Who can sponsor: Small groups of five or more Canadian citizens or permanent residents (“G5s”), Community Sponsors (local organizations such as cultural associations, schools, or municipalities), and Sponsorship Agreement Holders (“SAHs”) which are established charities, faith-based communities, or nonprofits previously approved by the government. SAHs also educate and support sponsors and the sponsored, and help resolve issues that arise.

• Who can be sponsored: Canadian sponsors may “name” a person abroad who meets Canada’s refugee definition. For sponsorships by G5s or Community Sponsors (but not SAHs), the person must also generally already be recognized as a refugee by UNHCR or a foreign state.2 Because global resettlement slots are scarce (UNHCR projects about 2.5 million refugees in need of resettlement in 2026, a fraction of the 30+ million recognized refugees worldwide), the eligible pool is rather constrained. In practice, the vast majority of named cases are distant relatives or close friends of people in Canada.

• What’s required from sponsors: Sponsors commit to 12 months of support: start-up funds, income support, housing, and hands-on help with school, work, and language. Government guidance suggests budgeting about 26,700 CAD for a family of three (minimum, varies by location and in-kind support).

• What happens to those sponsored: Resettled refugees arrive as permanent residents, receive federally funded interim health coverage, and after the sponsorship year, can access regular provincial benefits like all other residents.

• What the government still does: It sets and manages annual admissions targets (currently 21,000–26,000 for 2025, with new PSR applications paused until December 2025 to reduce backlogs), vets applications, conducts security and medical screening, issues visas and permanent residence, and monitors compliance across all resettlement streams. The federal and provincial governments are responsible for healthcare coverage from the time of arrival and for other benefits that accrue to permanent residents.

The system is now considered a global model that has inspired adaptations in at least 14 other countries while securing financial and other support pledges from dozens of organizations. In 2016, together with UNHCR and a range of non-profit partners, the Government of Canada launched the Global Refugee Sponsorship Initiative to promote community sponsorship as a complementary pathway for resettlement around the world. Since 2013, Canada has also been running a “mixed” stream, the Blended Visa Office-Referred (BVOR) program, where sponsors are matched to UNHCR-referred (rather than named) refugees and costs are shared with the government. Many countries have modeled their sponsorship schemes on either this matching approach or the traditional naming approach, with varying parameters.

In Australia, for example, the sponsorship programs can involve business support, but are explicitly counted within the same annual Humanitarian Program quota. In the United States, the Welcome Corps sponsors only provide the first 90 days of core services with arrivals entering as refugees and applying for permanent residency after one year. In Italy, the “Humanitarian Corridors” program allows only organizations (not individuals) to sponsor people on humanitarian visas, so there is no guaranteed permanent residency on arrival.

Why community sponsorship wins more support than resettlement or asylum

Although Canada’s program has occasionally been criticized over sponsor–refugee matching, long wait times, and tension with government quotas, it has not caused any significant right-wing backlash. The same is not true of humanitarian immigration generally—and asylum in particular—which often raises concerns about border chaos, arguably a major driver of recent populist resurgence worldwide. Even in Canada, the right of foreigners to claim asylum at the border is much more controversial than either government-assisted or privately sponsored resettlement or foreign aid.

The political promise of community sponsorship lies exactly in how it channels citizens’ both altruistic and somewhat parochial impulses—helping people you can identify with—into a structured way to resettle vulnerable populations from abroad while maximizing integration success and minimizing the concerns of skeptics. By providing individuals and organizations with a legal and effective way to help, community sponsorship makes larger refugee resettlement more politically durable in otherwise hostile anti-immigration environments.

First, it allows willing citizens to act upon their humanitarian beliefs beyond helping migrants who are already here or voting for a preferred party and leaving refugee protection solely to politicians and bureaucrats. The act of communal sponsorship builds lasting civic networks and constituencies of people invested in resettlement and immigrant success more generally. Research from Canada and other countries shows that sponsors overwhelmingly report positive experiences and stronger ties to their communities.

Second, it appeals to people’s conservative intuitions of localism, faith, and control, especially when “naming” the sponsored refugees is allowed. It is not a coincidence that Canada’s private sponsorship program roots lie in church-based aid and local civic voluntarism. Faith communities were already running settlement ministries and pressing the state to share responsibility, and then stepped in as enthusiastic yet “reluctant partners” during the late-1970s resettlement of Southeast Asian refugees. According to a recent survey of sponsorship organizations in Canada, 60% of them still belong to a religious organization, while 22% focus on another particular non-religious ethnic community or group.

Third, community sponsorship explicitly addresses common public fears. Because sponsors shoulder much of the cost and responsibility, perceived fiscal burdens are lower. Because sponsorship groups tend to be deeply involved in helping refugees they sponsor—finding housing, connecting newcomers to schools and jobs—social cohesion and integration outcomes should be stronger. While no randomized trials exist, observational studies generally find better integration outcomes in employment and income for privately sponsored individuals compared to government-assisted refugees, which is only partly explained by selection bias. A recent study by the Canadian government found that after one year 75% of privately sponsored refugees had employment earnings vs 37% of government-assisted, and social assistance receipt was 16% vs 93%, with advantages persisting over several years.

To my surprise, however, despite nearly half a century of Canada’s private sponsorship program and its recent global proliferation, direct public opinion evidence on the topic is scant. The only report I was able to find on public attitudes and sponsorship found high support but mostly relied on indirect or qualitative evidence (e.g., more positive general immigration attitudes among people who have participated or live in high-sponsorship areas). After further investigation, which took me much longer than I care to admit, I was able to locate a few relevant surveys that straightforwardly ask people about their support for sponsorship programs.

Here are the key reports and their highlights:

• In Canada, a vast majority are aware of the private resettlement program (which is impressive given the generally low political knowledge in public opinion). A clear majority—especially those who are aware—view it favorably. According to the 2018 and 2021 Environics surveys, about 3-7% say they have been directly involved, 15–25% say they personally know a sponsor, and about the same share say they would like to participate in the future. A 2017 McGill survey, which explicitly asked whether private sponsorship or government resettlement works better, found that significantly more respondents chose the former (41% vs. 6%, with the rest unsure).

• A 2021 Environics poll found that, among the small minority who view private sponsorship negatively (13-16%), reasons cluster around how the program is administered (taxpayer burden, insufficient resources) or unfavorable views of refugees (concerns about integration or competition for resources). Although these skeptics were not asked about other policies, it is reasonable to assume they have similar or stronger concerns about traditional government resettlement.

• In Germany, a 2016 More in Common survey conducted during the Syrian crisis found 45% in favor of introducing a sponsorship program, with about one-third opposed. These levels exceeded general positivity toward “refugees” at the time. Forty percent also reported donating or volunteering to help refugees, and 22% said they would be willing to participate in a sponsorship program.

• In the United Kingdom, a 2021 More in Common survey found 48% support and 34% opposition to accepting more (Afghan) refugees via community sponsorship. Net support was 14 points higher than for general resettlement, driven mainly by lower opposition among socially conservative and anti-immigration segments of the population.

• In Poland, a 2024 CMR Ipsos survey found 31-39% support for introducing a sponsorship program—the only case I saw where opposition exceeded support somewhat. Even so, community sponsorship was more popular than traditional government-led resettlement. An earlier survey from May 2022, fielded shortly after the start of the war in Ukraine by the same research team, reported much higher support numbers.

• In the United States, a 2023 YouGov survey at the launch of Welcome Corps showed 60% overall support, including 76% of Democrats and 53% of Republicans. Given heightened border salience and a thermostatic cooling on immigration during the Biden administration, these are notable figures. About one in four Americans also expressed interest in personally sponsoring a refugee in the coming years.

Bottom line: Community sponsorship is broadly popular—either absolute majorities or strong pluralities support it across various contexts—and it tends to outscore government-only resettlement and many other humanitarian policies.

Why hasn’t it caught on more? The major bottlenecks and limits of sponsorship

If community sponsorship works so well, why hasn’t it spread more widely? Despite my constant reminder to immigration advocates that humanitarian intentions are rarer than they assume, my sense is that the answer is probably not a lack of willing citizens. The Canadian surveys I described earlier show that a small but meaningful share of the public already participated (about 3-7%), or would like to participate (another 5–15%) if given the chance. This aligns well with my own surveys and incentivized experiments: while most people understandably prioritize their own or their country’s well-being, at least 10% in rich democracies display pronounced humanitarian motivations and are willing to benefit foreigners even at a personal cost. Even if we take a very conservative cap of 5% of the working-age population as the potential pool of sponsors, that is still a large number. Extrapolated to the United States and other rich democracies, this implies millions of potential sponsors. In short, public enthusiasm seems sufficient.

The bottleneck is the government’s resolve and capacity. Policy innovation in immigration is slow, especially when leaders want clear proof of success before scaling. Even Canada’s famous points-based skilled migration system took years to become a global practice. And sponsorship requires more than goodwill—it demands real administrative capacity.

Governments must vet sponsors, screen refugees, issue visas, arrange travel, monitor cases, and step in if failures occur. Many countries lack the bureaucratic infrastructure or trust in civil society to manage this. The start-up costs of building sponsor networks, training groups, and supporting them through the process are significant. Philanthropic seed funding has increased recently but remains modest, and officials rarely see enough upside to overcome inertia.

Even if some of these bottlenecks ease, community sponsorship is clearly not going to solve the world’s displacement crises alone. There are over 35 million refugees worldwide, with 2–3 million designated as urgent resettlement cases, and each year, only a fraction are resettled anywhere. If every wealthy nation decided to adopt the Canadian sponsorship model tomorrow, total numbers would still be in the hundreds of thousands per year, not millions.

Moreover, community sponsorship does not address the messy, politically toxic issue of spontaneous border crossings and asylum claims. Sponsorship is simply not designed for these scenarios—it is orderly and selective, which is the opposite of chaotic inflows. Some development economists—and now even The Economist—argue the asylum system is outdated and should be rebuilt around protection and legal work in proximate host countries, fewer camps, and more legal pathways with regional processing to deter dangerous journeys. In that reimagined setup, community sponsorship could serve as one of the channels to redirect some of the would-be asylum seekers into managed programs supported by citizens. But realizing this would require policy shifts far beyond sponsorship itself.

Interlude: the successful, yet short-lived, case of U.S. Welcome Corps

The recent two-year U.S. sponsorship experience illustrates both the appeal and fragility of sponsorship. The Welcome Corps, launched in 2023 as a pilot within the federal refugee admissions program, invited Americans to form groups and directly sponsor refugees for the first time. Several observers even called it a “revolution” in the U.S. refugee admissions or even immigration policy in general. The response was remarkable: more than 160,000 people across every state registered interest within two years. The most engaged states ranged from Minnesota and California to Texas and Indiana, showing geographic and political diversity.

Public opinion matched this enthusiasm. A YouGov poll found 60% of Americans supported the idea, including 76% of Democrats and 53% of Republicans. For a pro-immigration policy initiated by a Democratic administration to secure majority Republican support in 2023 was striking.

At the same time, the program did not generate any evident backlash. Some anti-immigration groups raised alarms about potential fraud and weaker vetting (which they do about pretty much all immigration programs), but a review by the Niskanen Center found those concerns unsubstantiated. Refugees underwent the same security screening as in other resettlement channels, and sponsors themselves were background-checked and trained. No major scandals occurred: refugees were vetted, sponsors were supported by intermediary nonprofits, and cases proceeded smoothly.

The U.S. case demonstrates the political potential of sponsorship: grassroots enthusiasm, broad partisan reach, and no visible backlash. It is not proof of long-term success, but it shows how strongly the model resonates with American civic culture. The Welcome Corps ended only because refugee admissions overall were paused by the second Trump administration in early 2025—not because of any explicit opposition to the program itself. If and when revived, it would likely continue to draw bipartisan interest.

Learning from Canada, the U.S. pilot, and other countries, we can try to identify some key design principles that make a community sponsorship program both more sustainable and scalable, from rigorous participant vetting to well-funded administration. I will write about these, as well as possible extensions to the program, in a separate post in the future. For now, I want to highlight two features that I find especially important for the program’s political success (notably absent in the initial version of the U.S. Welcome Corps program): naming and additionality.

Naming and additionality: the key sponsorship principles and the debates around them

As with any reasonable policy compromise, community sponsorship programs and their key principles have also been debated and criticized on both the left and the right. Let’s start with the aforementioned naming principle, which essentially allows sponsors in Canada to pick specific refugees (at least among those who qualify for resettlement by law). This principle raises obvious fairness questions: Are those refugees the neediest, or just the best connected? These concerns have led some left-leaning analysts to criticize the naming feature of private sponsorship as inequitable, since it tends to prioritize refugees who have family or friends abroad.

Although I have found relatively little explicit criticism from the Canadian right focused on the program itself, the concerns I did find are almost a mirror image. In particular, some worry that private sponsorship could become a sneaky backdoor for increasing lower-skilled immigration, in relative or absolute terms. Because sponsors usually name their relatives or co-ethnic friends, the program might be used to bring in people who would not qualify under stricter points-based streams. The most troubling aspect for these critics is that sponsorship leads to permanent resettlement, meaning those brought in—and their descendants—may draw on taxpayer-funded benefits if they contribute less in taxes than they consume. Given Sweden’s disappointing experience in improving fiscal outcomes for humanitarian migrants and their families despite strong integration efforts, this critique should not be easily dismissed.

As some rightly argue, however, one of the Canadian program’s strengths compared to its many offshoots is precisely that sponsors are allowed (though not required) to nominate specific refugees. Equity and human capital concerns aside, naming taps into the strongest motivations people have to sponsor in the first place. Individuals and groups are more committed when the person they welcome is not a stranger, but someone they already know, or someone with whom they feel a direct cultural or religious connection. Prior relationships often bring shared language and customs, which can ease integration. Besides, sponsors can also nominate people they do not already know, enabling creative uses such as campus sponsorship for refugee students or partnerships focused on sexual and gender minority refugees.

At the same time, matching-only streams like the aforementioned BVOR program have struggled to mobilize and retain large numbers of sponsors. After completing a matched case, many groups end up seeking channels that let them name specific people to help their relatives or friends. The U.S. Welcome Corps, for example, saw faster uptake after adding a possibility for naming in the second phase of the program, underscoring how the ability to nominate specific people can drive participation. In short, naming makes the program work politically by sustaining civic engagement over decades, even if it may complicate the purist ideals of impartial humanitarian protection or skill selection.

But the most serious structural critique of the program relates to the additionality principle or the lack thereof. Does sponsorship actually increase protection for vulnerable people, or does it substitute for government action? In 1979, when the program started during the Indochinese resettlement, the federal government made an explicit one-for-one pledge (one government-assisted admission for each privately sponsored case). The pledge was discontinued soon after as backlogs grew. Today, the government sets separate targets for government-assisted and privately sponsored streams, and allocations can shift between them from year to year.

This raises the familiar “crowd-out” concern: if volunteers sponsor 10,000 refugees, a cost-conscious government might reduce its own intake by a similar amount, yielding no net increase. The risk is debated and hard to prove, but in some years PSR admissions have exceeded GARs, which sponsors cited as contradicting their additionality expectations—even though additionality is not part of the PSR official program theory anymore.

From a political perspective, however, even a pure substitution arguably can have an upside: if taxpayers see that enthusiastic citizens are handling more refugees, it might reduce backlash and keep overall support higher than if the government tried to do it all. It also effectively addresses the most salient conservative critiques of the program. Still, for community sponsorship to reach its full potential, it ideally needs to complement, even imperfectly, not fully supplement, government resettlement.

Clear government commitments can prevent this—whether through pledges that private sponsorship will not reduce overall quotas, or even through formulas that increase official resettlement proportionally. Transparency is also essential: if citizens can see that their efforts genuinely expand the total number of refugees welcomed, more will step up. Creative mechanisms could reinforce this link, such as tying sponsor contributions directly to funding additional government-assisted arrivals. However it is achieved, additionality, even when it is only partial, is the key to unlocking sponsorship’s promise: mobilizing private compassion to help vulnerable populations beyond voting or charitable donations.

So, how can sponsorship make our immigration politics better?

Despite current challenges and limits, I believe that community sponsorship of refugees has a bright future. Its track record in Canada shows it can make refugee resettlement more popular and politically sustainable, even where traditional humanitarian policies face hostility. Programs that empower citizens to welcome refugees consistently score higher approval than almost any other immigration initiative. They harness grassroots goodwill that would otherwise go untapped. And they tangibly benefit not only refugees, who get a chance at a new life in a supportive environment, but also hosts, who often find new purpose and social ties, and their communities, which gain integrated workers amid population decline.

In an age of polarized politics, community sponsorship uniquely appeals to a broad demographic and manages to bring unlikely allies together—church groups and LGBT nonprofits, veteran groups and humanitarian agencies, liberals and conservatives, small towns and big cities. This coalition-building effect is invaluable for the long-term sustainability of refugee protection. It is much harder to demonize “refugees” in the abstract when your neighbors, co-workers, or your parents’ church are personally helping someone settle nearby.

In the more immediate future, we are likely to see continued scaling up country by country. The new Global Refugee Sponsorship Initiative has been advising governments, and roughly 14 countries have launched some version since 2016. Most remain pilot-sized and have resettled only a few thousand families, though separate family-reunion pathways for Ukrainians brought in tens of thousands under a similar sponsorship principle.

The real game-changer would be the U.S. fully embracing community sponsorship with naming alongside its government program. If the U.S. activated hundreds of thousands—if not millions—of willing sponsors, or even reached Canadian-level per capita rates, we could be talking about hundreds of thousands of refugees resettled annually via private means. Even if those numbers are aspirational, they illustrate significant untapped capacity. High-income countries collectively host only a small fraction of the world’s refugees today, but by empowering their own citizens to sponsor refugees, they could increase that share in a politically sustainable way.

Community sponsorship will not solve the refugee crisis on its own, and it will not replace the need for robust government action and international cooperation. But it will give tens of thousands of people a safe new home who would not have had one otherwise. In a world where so much of the immigration debate is abstract and mistrustful, community sponsorship offers a concrete and intuitively positive story: regular people working together on something compassionate and constructive, with visible results that many can admire even if they choose not to participate. That is a useful antidote to cynicism and a reason to think that, while community sponsorship may not transform global numbers overnight, it can improve our immigration politics over the long run, making it more open and humane by design.

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Many thanks to Gabriella D’Avino, Ania Kwadrans, Biftu Yousuf, and BBI fellows for their help and comments on this piece.

Select bibliography

• Global Refugee Sponsorship Initiative

• Fratzke, S., Kainz, L., Beirens, H., Dorst, E., & Bolter, J. (2019). Refugee sponsorship programmes: A global state of play and opportunities for investment. Migration Policy Institute.

• Labman, S., & Cameron, G. (Eds.). (2020). Strangers to neighbours: Refugee sponsorship in context (Vol. 3). McGill-Queen’s Press.

• Cameron, G. (2021). Send them here: Religion, politics, and refugee resettlement in North America (Vol. 5). McGill-Queen’s Press.

• Kaida, L., Hou, F., & Stick, M. (2020). The long-term economic integration of resettled refugees in Canada: A comparison of privately sponsored refugees and government-assisted refugees. Journal of Ethnic and Migration Studies, 46(9), 1687-1708.

• Labman, S. (2016). Private sponsorship: Complementary or conflicting interests?. Refuge, 32, 67.

• Manks, M., Monsef, M., & Wagner, D. (2022). Sponsorship in the Context of Complementary Pathways. Knowledge Briefs.

• Sustainable Practices of Integration (SPRING) podcast about Canada’s sponsorship.

• Private Refugee Sponsorship in Canada - 2021 Market Study, Environics Institute.

• Orth, T. (2023). Most Americans support “Welcome Corps,” Biden’s new refugee sponsorship program. YouGov.

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In Part I, I argued that AI can already do social science research better than most professors. In Part II, I engaged with over a thousand responses, conceding where critics were right, while standing by my main claim: the academic status quo was already broken, and AI is just forcing the reckoning.1 In this Part III, written collaboratively with AI and my peers over the last month, I move from diagnosis to what academics can and can't actually do about it.

The rather unlikely proximate cause of this third installment on AI was visiting the 2026 International Studies Association (ISA) Annual Convention in Columbus, Ohio—a preeminent multidisciplinary conference of the world’s leading international studies professionals. Or so I was told. What I actually witnessed were presentations so rough they would barely get a C in any of my classes: arguments with no thesis or coherence, grammar errors any spell-checker would catch, presenters reading off their slides as if encountering their own bad arguments for the first time. All without any AI involved, as far as I could tell, judging by the presence of typos and inconsistencies. These were not just grad students, but people with PhDs, tenure, and research budgets.

If AI slop is the crisis everyone warns about, I’d like to know what to call what I saw at ISA or most other big social science conferences, for that matter.2 The contrast was impossible to ignore: I was sitting through these presentations at precisely the moment I was receiving death threats and calls to fire me online for suggesting AI can do research better than most professors. That juxtaposition crystallized the argument for this piece.

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21. Most “slop” has always been and still is human slop.

My first thesis was the most provocative thing I’ve said, and I’ve adjusted it only slightly since then: agentic AI can already do most social science research tasks better than most professors globally. I still stand by it. In my recent interview with the Chronicle, they put it more bluntly: “AI Is a Better Researcher Than You.” If you still don’t believe that’s true, let’s talk in a few years.

But the flip side is just as important. If AI can produce better research output than professors, that’s also an indictment of the output those professors were and are still producing without AI.

“Slop” was Merriam-Webster’s 2025 Word of the Year, defined as low-quality digital content produced by AI. But the ISA conference was a reminder that the vast majority of slop has always been human slop. The academic journal system and big conferences in much of the humanities and social sciences were slop factories long before anyone had a ChatGPT subscription. Yes, I really mean that most research is slop.3

Some of it is also what the philosopher Harry Frankfurt would call “bullshit“: work that is indifferent to whether its claims are true, especially on politically charged topics like immigration, where researchers start with the left-wing conclusion and work backward. But slop is broader than bullshit. It also includes work that makes no claim at all, work that is supposed to have craft value, and simply fails. The researcher who finds a dataset before having a question, then dredges for significant results worth publishing, is producing slop. These researchers existed long before AI. They were just slower.

22. Academics were hallucinating and cheating before AI.

The concerns about hallucination and cheating that people raise about AI in academia describe problems that predate it by decades, if not centuries. A massive new project published in Nature this month (which I was a small part of) tested hundreds of social science papers: only about half of statistically significant claims replicated, with median effect sizes shrinking dramatically. This study confirms what the 2015 Open Science Collaboration study found in psychology alone, where roughly two-thirds of findings failed to replicate, and extends it across fields.

We didn’t call these “hallucinations” before AI. We called them “science.” If you think about it, hallucination and inspiration are actually not as different as they sound. Both involve generating combinations that go beyond the input. We call it hallucination when the result is wrong and a breakthrough when it’s right.

Meanwhile, academics routinely cite papers they haven’t read beyond the abstract.4 At least AI hallucination rates are tracked and improving. Human hallucination rates in academia are not tracked at all. We just call them “contributions to the literature.” And if you’re a peer reviewer, you don’t even have to hallucinate on your own: you just write “please cite me” and move on.

Some research is genuinely excellent. But before we worry about AI-assisted cheating, let’s reckon with the human kind for a moment. Diederik Stapel, Marc Hauser, Francesca Gino, Dan Ariely: the list of high-profile fraud cases keeps growing, and these are just the people who were caught. Aside from outright fabrication, p-hacking, HARKing (hypothesizing after results are known), and selective reporting were for years so common that they barely registered as misconduct. We’ve made real progress in understanding these practices, but they remain widespread enough to shape what gets published. Beyond all this, senior professors have always put their names on papers written primarily by graduate students, and entire books have been assembled by teams of research assistants working under a famous scholar’s name. None of this was considered cheating until AI made the process cheaper and more apparent.

23. The “stochastic parrot” metaphor describes humans better than AI.

One of the most influential slogans in the AI debate5 has always functioned as a thought-terminating cliche. As Cate Hall observed, it is a potent coinage: fun to say, conceptually efficient, and it has permanently colonized many people’s minds despite not being true of today’s models. A genuine linguistic work of art. It is also empirically false: every major frontier model since GPT-4 has been trained on non-textual input, and the original argument’s own logic requires text-only training to work.

Now consider something completely different yet still very familiar: the “In This House We Believe” yard signs. Science Is Real. Love Is Love. No Human Is Illegal, etc. You can believe every line and still have no coherent policy position on any of these issues. What does “no human is illegal” imply for enforcement policy: open borders, amnesty, something else? The sign doesn’t say, because saying would require confronting contradictions. It is a loyalty oath, not an argument.

But the deeper point is that this is exactly what AI gets criticized for: producing shallow, feel-good statements that communicate belonging rather than meaning. Turns out humans have been doing that with or without AI for as long as we’ve had lawns to put signs on.

The yard sign is not an outlier. It is how most people seem to engage with most issues: adopt the position of your group, repeat it, move on. The substance of what you argue matters far more than how you frame it, but framing is easier, which is why it dominates public discourse, from Twitter threads to conference presentations.

I saw this firsthand at ISA, where tenured peers presented work that amounted to the academic equivalent of a yard sign, complete with a few misidentified regressions to give it the veneer of science. The stochastic parrot critique was meant to diminish AI. It ended up being a better description of human intellectual life than anyone intended.

24. Yes, consenting adults can use AI for writing. Policing it doesn’t work.

In Part II, I noted that AI detectors are often not very useful and create more problems than they solve. But the deeper problem is the impulse behind them: the belief that AI involvement is inherently contaminating, regardless of what it produces. Quinn Que makes a fascinating case that the obsession with AI writing detectors is akin to enforcing a “one-drop rule,” the principle from 19th-century American racial classification: any trace of AI involvement contaminates the entire work, regardless of quality or the author’s intent.

I was initially skeptical of the analogy, but it’s quite right. In the view of the anti-AI activist, any word you have not written yourself is a moral pollution. Although using AI for writing is not technically “illegal,” there is basically a one-drop rule governing whether you are a legitimate writer or a fraud, a good person or a bad one. Hence, the condemnations and death threats to people like me who disclose their AI usage for “outsourcing their thinking to the machine.”

As I argued in Part I, much of the opposition to AI is status protection dressed up as principle. takes this further, arguing that the chatbot moral panic may have a source beyond the status project. This source is something closer to superstition (“chatbots are demonic”): the sense that AI-produced text is spiritually tainted, that there is something wrong or even evil about a machine that can write, regardless of what it writes.6

Even , who recently honorably disclosed her AI use and sparked among journalists the same conversation academics had been having on Bluesky a few months earlier, felt compelled to defend herself by insisting that “AI didn’t touch copy.” I admire her for speaking up. But why should the copy question even be an issue? If the work is good and the process is disclosed, the rest is aesthetic preference dressed up as ethics. Where does impurity start? Google search? Auto-correct? Spell check? Transcriptions?

And even setting all that aside: barring some Dune-style scenario of an AI catastrophe followed by humanity coordinating to ban the technology, widespread AI-assisted non-fiction writing is almost inevitable in equilibrium, given the existing incentives. The flip side worth mentioning is that even if you do manage to write well entirely on your own without “AI pollution,” no one is going to be rewarding you for that purity very soon, either.

25. Not using the latest AI tools in your research and writing is malpractice.

recently argued that large language models are underused in journalism. His point applies to academics, too: the purpose of research is the useful output, not the human-mediated process. The rigor should be in the thinking and the verification, not in whether a human or a machine typed the sentences or pressed enter when running the regressions in R. As Hollis Robbins put it well, professors should probably be testing AI models before breakfast (basically be like ).

The most mundane use of AI is catching errors. Consider the New York Times headline on April 3, 2026: “A North American Treaty Organization Without America?” The correct name is, of course, the North Atlantic Treaty Organization. The Times ran a correction the next day. Some speculated the error was caused by AI. We’ll never know, and frankly, it doesn’t matter. Any sufficiently smart LLM workflow would have caught the mistake in seconds. A simple automatic routine for all headlines—”Does this contain any errors? Verify against two separate sources”—would have saved the Times a news cycle of embarrassment.

But fact-checking is the floor, not the ceiling. The more interesting use is doing things that weren’t possible before. My now co-author, , recently had Codex build her an interactive website to help her actually understand a political science paper she was reviewing, then did the task herself the way the study’s participants had. AI doesn’t just compress the time it takes to produce an output. It lowers the cost of the kind of active engagement most researchers skip: building the thing yourself, stress-testing an argument, rerunning an analysis the way participants did. That’s what deeper understanding actually requires.

The same applies to academia. Half the ISA presentations I attended in Columbus could have been meaningfully improved by a cursory loop through ChatGPT: checking grammar, tightening arguments, catching logical gaps. These tools are free or nearly free. Choosing not to use them is a choice to deliver worse work than you’re capable of, especially if your calling is to inform the public. But journalists and academics alike should not use AI just to catch typos in headlines. They should use it to build interactive visualizations, stress-test arguments, and genuinely understand the complicated things they are writing about.

But what about writing itself? You may have seen a common response to anyone using AI in their writing: “Why should I read the article and not the prompt that caused it?” Sounds reasonable, right? Well, no, if you think about it a bit.

Why eat the meal instead of reading the grocery list? Why look at a chart instead of the ggplot script? Why read the book instead of the author’s notes? The prompt gives you no new knowledge. The output does. That’s the whole point. As I argued in Part II, different people with different expertise, data, and context (as manifested in their claude.md files) produce entirely different outputs from the same prompt. It’s a skill issue.

I get it: it can feel strange to spend more time reading something than the author spent producing it. But we’ve been here before. A chart that took seconds to generate in R can take minutes to read carefully. Nobody demands or fantasizes about seeing the code only instead of the chart. They evaluate the chart and what it says—the new knowledge not available before.

26. LLMs may indeed produce new knowledge.

As recently documented by , AI chatbots consistently pulled users toward expert consensus, the opposite of what social media does. The tools don’t just help you write better. Used actively, they help you think more carefully.7 This way, LLMs may even reverse the rise of populism. But fact-checking is not the same thing as producing new knowledge.

Before we ask whether LLMs can produce genuinely new ideas (there are good examples of that), we should ask how many humans do. Most academics spend their careers recombining existing ideas in minor variations, applying the same methods to slightly different datasets, producing incremental work that nobody outside their subdiscipline will ever read. Original thinking is extraordinarily difficult and rare in any generation. I don’t mean it as an insult to any of my colleagues (or myself). But the bar that LLMs need to clear is lower than we like to admit.8

My friend Robert Kubinec, a brilliant political methodologist and published fiction author9 at the University of South Carolina, seems to be pretty skeptical about much of the AI hype. He argues that “LLMs never create knowledge, [which] only exists in human brains. They can only compare one set of knowledge to another.” I respect Bob, so it’s OK if we disagree. My response: The philosophical question about self-awareness is real, but it’s orthogonal to the practical one. Whether or not the model “understands” anything, the output either contains new information useful to humans or it doesn’t.10

The most suggestive recent example is Claude Mythos, Anthropic’s new frontier model. In a few weeks of testing, it flagged thousands of previously unknown security vulnerabilities across major operating systems and browsers, including one that had gone undetected for 27 years. Whatever you want to call that, “compare one set of knowledge to another” does not quite cover it.

The best conceptual theories in social science are already recombinations of existing ideas. Anthony Downs built his rational choice theory by transplanting microeconomic utility maximization into voting and literally called his book An Economic Theory of Democracy. Baumgartner and Jones borrowed punctuated equilibrium from evolutionary biology and applied it to policymaking. Social network analysis imported graph theory wholesale from mathematics. Axelrod fused the prisoner’s dilemma with evolutionary fitness selection. Alexander Wendt went further and applied quantum theory to international relations in Quantum Mind and Social Science—a move many found (rightly) ridiculous, but which Cambridge University Press still published. Social science rarely invents from scratch. It translates across domains, and the translation is the theoretical contribution.

That is structurally identical to what LLMs do: recombine patterns and concepts across contexts. Sometimes the result is nonsense. Sometimes it is productive. The same is true of human recombination. Wendt’s quantum IR has been criticized as a mere metaphor masquerading as physics, but as mentioned above, Cambridge University Press published it. If that counts as knowledge production, it is hard to see why LLM-generated recombinations wouldn’t.

To his credit, Bob conceded the practical point even while holding the philosophical one: “the question is how to use this capability to advance knowledge.” Exactly. As I said in Part I, stop debating whether LLMs “truly understand” while the people with the most at stake are already using the tools to improve their work.

27. For critics, the mental model of an AI user is stuck in 2023, which is ages ago.

Remember how we got into this mess. Students got enthusiastic about using AI before their professors, using the very first public, free models like ChatGPT 3.5. The result is that many professors and intellectuals still picture the typical AI user as an undergraduate trying to cheat on an essay or their assistant failing by submitting AI slop. That may still describe a lot of casual AI use around the world. It does not describe how top researchers in most fields are working with these tools, or how anyone trying to use AI responsibly and mindfully actually operates.

put his finger on this the best: we underrate other people’s rationality, assuming they apply new tools much more mindlessly than they really do. There is a vast space between asking ChatGPT to write a whole paper and writing down every single word yourself. When you write “yourself,” you are already using shortcuts. You are not doing deep research on every reference you make. You Google a statistic and trust the headline number. You skim an abstract and cite the paper. If you have resources, you outsource verification to a research assistant or a fact checker. AI lets you do much of this in a more systematic, automated, and cheaper way.

Like many, rightfully believes that writing is thinking and that outsourcing the full writing process to AI leaves your mind empty. But, while much writing is thinking, thinking is not only writing. Making art is thinking. Talking is thinking, too. As Dina Pisareva argues, prompting is also thinking, if you have a good Socratic partner on the other side of the exchange.

Thompson also acknowledges that all writing has always involved reaching outside the writer’s mind for ideas, facts, editing, and fact-checking. The line between legitimate assistance and illegitimate outsourcing has always been blurry, and AI didn’t create that blur. It just made it impossible to ignore. Thompson’s own conclusion: “We should be honest and open about the blur rather than declare everybody with an open Claude window a part of the slopclass.”

Where does that leave disclosure? Many good folks and top academic journals suggest complete AI-use disclosure as a solution. But disclosure is not sustainable in a game-theoretic sense: disclosers bear reputational costs while secret users free-ride, so the equilibrium pushes toward non-disclosure. People like Megan McArdle and I are all living proof of this problem. And the better framing, as Ryan Briggs puts it, is that AI complements expertise: automated RAs checking your work, formalizing arguments as you go, gathering data on demand. It’s a multiplier that lets capable people think better and faster.

My sense is that the only case where disclosure is genuinely owed is when the audience has a reasonable expectation of fully human-produced work, and that expectation is part of what they’re paying for. The best analogy is a live concert in the narrow case of personal or creative writing: if you show up expecting a live performance and the artist is lip-syncing, that’s a legitimate grievance. More precisely, disclosure is owed when non-disclosure would mislead the audience about what they are getting. A journalist publishing under a byline is promising accountability and originality, not keystroke provenance. A memoirist is promising both. The test is what the author is putting their name on, not whether a tool was involved.

But science and journalism are not live shows. They’re about discovering and sharing new knowledge. Nobody discloses that they used spell-check, research assistants, or a Google search. The norms for creative writing and art will be different, rightly so, because audiences there are partly paying for the human experience of making the thing. Humanities will probably land somewhere in between through some lengthy and contentious process, with many friendships being broken. But for research and journalism, “disclose everything” doesn’t get us where we need to go. What readers should care about is accuracy and whether the author takes full responsibility for what’s on the page. Provenance is a much weaker signal than either of them.

28. Nobody knows anything, myself included. That’s OK—we’ll figure it out together.

I initially shied away from talking to journalists about AI because I’m not an expert on it in any meaningful sense. But I’m increasingly realizing that nobody is an expert on whatever this all is, not really, and waiting for one to appear is how you end up with no norms at all. I still shy away from giving any advice beyond installing Claude Code (or your agentic tool of choice) and talking to it to solve a problem you have at hand.

As usual, Arthur Spirling was blunt on this point: the AI-academic accounts that constantly offer “advice” to colleagues and PhD students, as if they were in the labs developing the models, are tedious. They’re spectators, like the rest of us.11 Even the people in the labs have no idea how good the models will get beyond a few months.

So we’re in an awkward position: somebody has to start establishing the norms of the new workflow environment, and the people doing it will inevitably be people who don’t fully understand what they’re building norms for. That’s weird and uncomfortable. It’s also how most other institutional transitions have probably worked, though at a much slower pace.

We went through a version of this that seems like ages ago, when professors had to accept that policing students’ AI use fully and meticulously was neither productive nor possible. The same is now true for researchers. You can audit the parts that actually matter, like the replication code, and LLMs already make it easier. Provenance is the one thing you can’t audit, and it’s the thing we shouldn’t be spending energy on anyway. You have to follow the incentives and build systems that ensure the tools are used well, not pretend they aren’t being used at all.

On a related note regarding teaching, I’ve been on leave, so I’ve shied away from advice here too. Given the many questions I’ve received, I’m more pessimistic about the effects of AI on teaching than on research. For instance, while I use AI openly and extensively in my research and writing, I plan to ban all electronic devices in my substantive classes and bring back in-person written and oral exams.

These are not contradictory positions. The classroom is precisely where students need to build the cognitive skills that make AI collaboration productive later. You cannot meaningfully direct an AI tool if you never learned to think without one. The skill-atrophy concern from Part II is most valid here: students need to internalize the fundamentals before they outsource them. Research is where you deploy the best tools at your disposal to produce a worthy outcome. Teaching is where you make sure the next generation can actually use those tools well.12

29. The best work happens when humans and AI collaborate.

I still see many folks pledging not to use AI in their writing. This is about as sensible as pledging not to accept help from research assistants or co-authors. So, I pledge the opposite: I will use the latest LLMs, and for that matter any other available tool or human co-author, to best improve my research or the way I communicate it. That way, if my name is on it, you can be sure it reflects my own best judgment.

Many academics, especially in the humanities, still seem to believe that it matters how much time you spend on doing your work. But the folk labor theory of value is wrong in economics, and it is wrong here as well. If you’re a weirdo who wants to spend years running all your robustness checks manually with matrices inverted by hand instead of running a few R commands or manually translating or transcribing original manuscripts, that’s on you. I didn’t care much if those international studies professors at ISA spent 10 or 100 hours on their human slop presentations. The work is either good or it isn’t.

That said, there are some norms worth establishing. When you write “I believe” or “I feel,” that first person should genuinely be you. The pronoun “I” carries an implicit promise of a human voice behind it. A factual claim, like the correct name of NATO, doesn’t care who typed it. But personal conviction does. Think of a live concert: the audience pays for a real performance, not a lip-sync. When you use “I,” the reader is entitled to expect that you mean it. That doesn’t require literally typing (or dictating) every word yourself. You can direct the model, work from your notes, and read it over carefully, but it does require that the conviction is yours.

This piece, for instance, was openly and proudly written using an iterative back-and-forth conversation with Claude Code, based on my original ideas, conversations with peers, and useful suggestions by human academic and non-academic colleagues.13 Part I experimented with AI capabilities with no human editing. Part II reflected with 100% human voice. Part III, which you’re reading now, combines my voice with AI capabilities, completing the circle. I’ll let you decide which one was best.

So, where do humans retain an edge? As Yiqing Xu argues, probably in open-ended environments where training data doesn’t yet exist and in tasks that require direct human interaction. This may change soon. But we chose this career because we find meaning in figuring things out, not just because we’re still better at it. That’s why I don’t see any contradiction in outsourcing most grunt work to AI (or RAs, if you hold traditional values) and focusing on what gives us the most meaning—coming up with important questions about the world and answering them with the best tools available.

30. The real risk in science is human slop at AI speed. We can still prevent it.

AI amplifies whatever you bring to it. Bring genuine curiosity and hard questions, and AI will help you produce something worth reading. Bring nothing, and you’ll produce nothing faster.

But there’s reason for optimism. The same Nature findings that revealed how broken reproducibility is also showed that journals with mandatory data and code sharing had meaningfully higher reproducibility rates. AI can already accelerate this: nothing but inertia precludes journal editors from instituting automated reproducibility checks and mandatory code verification as a condition of submission. These systems can be set up as quasi-deterministic, with very little room for error, and authors can always challenge an automatic desk rejection if they believe the in-house agentic workflow failed to determine how to reproduce their code.

More ambitiously, AI lowers the cost of doing the kind of large-scale, data-intensive work that used to require being Raj Chetty with a team of 30 research assistants and access to every administrative dataset in the country, or being Daron Acemoglu… with pencil and paper. A junior scholar with Codex can now attempt projects that would have been logistically impossible five years ago. The bar for what gets published should rise, because the bar for what can be produced already has.

The standard hasn’t changed: if you put your name on something, you stand by it. Judge the quality of the output, not the process. The conversation about AI slop is a distraction from the harder question, one we should have been asking long before the chatbots arrived: why have we been tolerating so much human slop in the first place?

***

My goal when I posted Part I was simple: to bring the conversation that was already happening behind closed doors and in DMs into the open. Still, I keep hearing the same thing from my colleagues: “Alex, we know you use AI, we do it too, but can’t you just be quiet about it?”

I understand the fear that AI will be used irresponsibly, producing more slop than it prevents. But if you think about it, “be quiet about it” is just advocating for universal hypocrisy as a professional norm. Part of what I wanted was for the uncertainty everyone is experiencing about their workflows and their futures to reach a more stable equilibrium. That requires honest conversation, not silence.

And it is already happening. Emily Oster recently shared Isaiah Andrews’ advice on AI for MIT PhD students in economics, calling it something that should be circulated to all PhD cohorts. As noted, the most important thing about it was not any particular advice per se but having high-profile faculty send a clear signal: this is something you need to take seriously. Even , one of the most thoughtful people in journalism, recently admitted the AI people have been right a lot.

The change is real. Academics are already half-awake, and they are not going back. Aspiring grad students and junior scholars should tread carefully but embrace learning about and using AI tools fully. The scholars, intellectuals, and writers who refuse to engage will not be rewarded for their purity. They will simply be outperformed by colleagues who bring the same intellectual rigor, the same curiosity, and better tools.

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Here’s where I think the argument turns:

Many of my academic colleagues felt differently, however. To them, “I just oppose illegal immigration” was a socially acceptable way to express opposition to foreigners in general—xenophobia dressed in procedural language. I have been guilty of dismissing popular attitudes myself; when giving lectures about, say, the H-1B visa backlog or refugee processing times, I have found myself exasperated by audience members who stand up to ask why I haven’t gone out of my way to condemn illegal immigration.

Over the years, many scholars and advocates thus came to see the distinction as illegitimate. For some, the American immigration system is already so unfair and restrictive—fewer than 1 percent of people who want to immigrate can do so legally—that saying “Just follow the rules” can feel cruel. Others go further: Unauthorized border crossing is a victimless regulatory violation, they argue, and any law restricting people’s free movement is unjust.

I now think this dismissal was a mistake. The norm was imperfect and often insincere—but it was holding something together. Now that it’s gone, I’ll try to be a little less annoyed the next time someone in my audience stands up to ask why I haven’t condemned illegal immigration. Turns out that was the good version of the conversation after all. Read the full piece here (archive link).

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Some personal news first. I finally bought a Japanese toilet. A TOTO Nexus WASHLET+ S7A, to be specific. If you read my piece on Japan last year, you know I came back radicalized about how the Japanese make familiar things work better. The toilet is one of them. Heated seat, genuinely life-improving in ways it’s not possible to describe in polite company. It’s also arguably a progress and a policy design issue just like the shower heads: the technology has existed for decades, it’s demonstrably beneficial, and most Americans would love it if they tried it. The main barrier is outdated plumbing and electric codes, and the fact that nobody in power has bothered to update regulations that would make installation easier and cheaper. Sound familiar?

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Despite spending too much time on social media and receiving a fair share of death threats for my hot AI takes (I had to deactivate my Bluesky account), March was, by a wide margin, the most productive month in my entire academic career. Somehow, thanks to delegating grunt work to Claude Code, I’ve now actually been able to write more and better artisanal, hand-crafted human prose than ever before. Here is everything I published this month:

• On Popular by Design, I wrote “Academics Need to Wake Up on AI” and Part II (with Part III coming soon), “The Gay Marriage Playbook Won’t Work for Immigration”, “Public Engagement Is Good for Your Research”, and “What Is Populism Actually Good For?” (with Yaoyao Dai).

• I published guest posts on ’s Substack: “Spread the Word: Legal Immigration Is Incredibly Difficult” (with Michelangelo Landgrave). Marc Helbling and I wrote “How We Miscategorize the Categorizers” for Futures of Difference. and I co-authored “Why America Is So Much Better Than Europe at Immigration” at . Check those out if you haven’t yet.

• If you’re into the audio format, I also appeared on three podcasts (one still forthcoming): Opinion Science with Andy Luttrell and Money & Macro Talks, with more to come soon.

• And lest anyone wonder whether all this public writing comes at the expense of “real” research: I also published two peer-reviewed pre-registration reports: one with Yaoyao Dai, “What is Populism Good for?” in Research & Politics, on the mobilization effects of populism, and another (still in progress) on “Preventing Backlash by Shifting Issue Priorities: Immigration and Depopulation in Japan” in the Journal of Experimental Political Science (with Akira Igarashi, Rieko Kage, and Seiki Tanaka).

Before we get to the links, two quick questions for you all.

Drop your answers in the poll or the comments. The ideology question was inspired by interesting conversations I’ve been having with readers who assume very different things about where I sit politically. The branching-out question is genuine: some of my most popular posts this month were not about immigration at all, and I want to know whether that reflects what you actually want more of.

Here are the March links (linking does not imply endorsement):

• The Supreme Court will hear Trump v. Barbara this spring, challenging the executive order that would strip birthright citizenship from children whose parents lack permanent legal status. My colleague Amy Hsin at Notre Dame’s Keough School co-authored an amicus brief laying out the social science evidence against it. Among the numbers: Phillip Connor, Matt Hall, and Francesc Ortega estimate that birthright citizenship beneficiaries will contribute $7.7 trillion to the U.S. economy between 1975 and 2074. Revoking it could deny citizenship to 4.8 million U.S.-born children by 2045.

  • My other Notre Dame colleague Ashley Sanchez at The Conversation has a useful explainer on the Trump administration’s new interpretation of refugee detention rules. Legal refugees who haven’t yet received green cards now face prolonged detention under a reinterpreted DHS statute. Another case of the administration using administrative tools to restrict legal pathways without passing new legislation.

• (follow him!) at the Niskanen Center has a great piece in City Journal on assimilation. He looks at areas of the country with the best assimilation outcomes a century ago (measured by second-generation out-marriage) and asks what that tells us about the challenges we face today.

• My friend Hannah Postel (who should absolutely start a Substack) at Duke Sanford has new work in the Journal of Economic Perspectives on how U.S. immigration law shaped a century of Asian American immigration. Exclusion laws kept the Asian population below 1% of the U.S. total for nearly a hundred years. After the 1965 act abolished national-origin quotas, the Asia-born population grew by roughly 2,700%. The characteristics often attributed to Asian American "culture" like high educational achievement trace back to which entry pathways policy made available. A powerful case that policy design determines not just how many people come, but who comes.

• has a thoughtful piece on identity politics and the case for making identities broader in liberal democracies. Connects to the immigration debate in ways he doesn’t fully spell out but that readers of this newsletter will recognize: narrow identities are what make immigration politically toxic, and policies that encourage broader identification can defuse that.

• The Friedrich-Ebert-Stiftung published a brief on progressive immigration policy in Germany and the U.S., authored by Hannah Tyler, Stephanie von Meien, and Cristobal Ramon. I disagree with some of their points, but the general framing is close to what I’ve been arguing: humane and flexible policies that serve the national interest are good or at least better than the status quo. Restoring public confidence in the immigration system should be among the top priorities.

• tested whether AI can detect known errors in published papers. GPT (5.4) scored 5.9 out of 10 on errors in Moretti (2021), an AER paper with a huge number of identifiable problems. Human reviewers missed all of them the first time around. If journals adopted systematic AI checks, even an imperfect system would catch mistakes that currently sail through review.

• at WYSR has a piece comparing nanotech hype in the 2000s to AI hype today. The comparison is instructive. Nanotech was supposed to change everything, attracted massive federal funding, and produced genuine scientific advances that quietly found their way into useful products. But the transformative revolution didn’t arrive on the timeline the boosters promised.

• on the credential as a democratic institution. An argument about why expertise and credentials still matter in a populist moment. Connects to a question I keep coming back to: if populism is partly a rebellion against credentialed gatekeepers, how do you defend the value of expertise without sounding like you’re defending your own guild?

• has a new paper forthcoming in International Migration Review testing whether high skills insulate immigrants from ethnic bias. In a pre-registered experiment with British respondents, a Black South African doctor was rated less welcome than an identical White one (but not in the case of fast food workers). It's a well-designed study, but, with all due respect to Alexandre and his work, I worry this is also a common case of missing the forest for the trees.

  • The detected racial penalty is about 0.3 points on a 10-point scale, with race explaining less than 1% of variance. The skill premium, by contrast, is at least 7 times larger (2.3 points), explaining >20% of variance. If anything, this paper shows how remarkably little ethnicity moves the needle once you hold other factors constant. As I keep beating this drum, skilled immigration is popular regardless of ethnicity, and we never lose sight of that.

• at Field Notes on Progress has the best headline of the month: “The AI Revolution No One’s Talking About.”1 It’s about artificial insemination transforming dairy and beef farming. Really fascinating read, especially if you haven’t thought about the politics of cattle and dairy before.

• Published today: Madeleine Sumption's What Is Immigration Policy For? (Bristol University Press). I simply can’t recommend the book enough. Sumption directs the Migration Observatory at Oxford and knows more about what actually happens when immigration policies meet reality than almost anyone alive. The book explains why so many governments struggle to design immigration policies that people trust, and why many find current arrangements unsatisfying. The essential guide I wish every policymaker, advocate, and journalist would read before entering the immigration debate. Exactly in line with the spirit of Popular by Design, the book won't tell you what to think, but it will change how you think.

As before, if you want me to write more about one of these or other related topics, let me know!

Why does yelling about “corrupt elites” seem to work in politics? From Donald Trump to Viktor Orbán to Marine Le Pen, politicians who rail against the establishment and claim to speak for “the real people” keep winning elections. The populist playbook, us versus them, the pure people against the rotten elite, appears to be one of the most effective strategies in modern democratic politics. But what if it isn’t?

I’ve spent the better part of a decade studying populist rhetoric with my co-author Yaoyao Dai, now at the University of Pittsburgh. We just published our third and latest paper on the topic, and I thought this was a good moment to reflect on what our research program has found. The short version: populism’s power is real, but much more limited than most people assume. And the reasons why it works are not what you’d expect.

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What we mean by populism

Before getting into the findings, a quick definition. Political scientists generally follow Cas Mudde’s influential framework, which defines populism not as a full political program but as a simple worldview (or what Mudde calls a “thin ideology”). This worldview is based on three pillars: people-centrism (politics should reflect the will of “the people”), anti-pluralism (there is one authentic popular will, not many competing interests), and moralized anti-elitism (elites are not merely wrong but evil). This is what scholars call “thin” populism because it doesn’t tell you much about actual policy. A left-wing populist like Hugo Chávez and a right-wing populist like Trump share the same rhetorical structure, the people versus the elite, but disagree on virtually everything else.

This distinction between populism and its “host ideology” (the actual policy positions a politician holds) turns out to be crucial. Because when you peel them apart, something surprising emerges.1

When politicians gamble on populism

Our first paper, “When Do Politicians Use Populist Rhetoric?“ published in Political Communication in 2022, asked a deceptively simple question: if populist rhetoric is so effective, why don’t all politicians use it all the time?

To answer this, we built the most comprehensive corpus of U.S. presidential campaign speeches at the time: 4,314 speeches from 1952 to 2016. We used a novel text analysis method combining active learning and word embeddings to measure how much populist rhetoric each candidate employed across the campaign trail. I (Alex) should say, thanks to the prowess of Yaoyao, we did all that fancy text analysis stuff before it was cool and before LLMs were even around.

The pattern was striking. Candidates who were trailing in the polls consistently used more populist rhetoric, regardless of whether they were Republicans or Democrats, incumbents or challengers. Populism, we argued, is a gamble: a high-risk, high-variance strategy that trailing candidates adopt because conventional campaigning isn’t working. If you’re already behind, why not shake things up?

Think of it like a football team that’s losing in the fourth quarter. You start throwing long passes not because they have a higher expected value, but because safe plays guarantee you lose. Barry Goldwater, George McGovern, and Donald Trump (in 2016, when most polls had him behind) all fit this pattern. They reached for populist rhetoric when they had little to lose.2

The (in)effectiveness of populist rhetoric

But does the gamble actually pay off? Our second paper, published in Political Science Research and Methods in 2024, tested this directly with a survey experiment.

We presented U.S. respondents with pairs of realistic campaign messages from hypothetical primary candidates. The messages varied on two dimensions: populist features (people-centric language, anti-elite attacks, anti-pluralist framing) and substantive policy positions (on immigration and other issues). This design let us isolate the effect of populist rhetoric from the underlying policy content, something that is nearly impossible to do when observing real elections, where populism and policy positions come bundled together.

The result was unambiguous: none of the populist features had an independent effect on candidate choice. Not people-centrism, not anti-elitism, not anti-pluralism. Not individually, and not in combination. What did matter, enormously, were policy positions that aligned with voters’ own preferences. Voters chose candidates based on what they promised to do, not on how dramatically they framed the conflict between the people and the elite.3

This finding is consistent with other experimental work. When researchers across multiple countries carefully separate populist style from policy substance, the style itself contributes very little to voter decisions.

So: if populist rhetoric doesn’t actually persuade voters, why does it seem to work? Why do populist candidates keep winning?

What populism is actually good for

This puzzle motivated our newest paper, our first ever registered report (where scholars publicly specify their hypotheses before running their experiment), now published at Research & Politics. We hypothesized that populism’s real contribution might not be persuasion but mobilization: getting people who already agree with you to actually show up and vote.

Previous studies, including our own, used what’s called a “forced choice” conjoint experimental design: respondents had to pick one candidate or the other. But in real elections, people can also stay home. To capture this, we ran a large-scale, preregistered survey experiment that added an “abstain” option, a seemingly small change that turns out to matter a lot.

What did we find? First, the basic persuasion result replicated: policy positions still dwarfed populist rhetoric in driving vote choice. Having a policy-congruent candidate increased the probability of voting by a massive 27 percentage points. Populist rhetoric, by contrast, had no meaningful persuasion effect.

But here is the twist: populist rhetoric did have a small but statistically significant mobilization effect. Having at least one populist candidate in a race was associated with a ~1.5 percentage point decrease in abstention. The effect was concentrated among voters who already held populist attitudes and encountered a candidate whose policy positions they liked. In other words, populist rhetoric didn’t convert skeptics; it energized true believers to get off the couch.

Meanwhile, non-populist voters did not appear to punish their preferred candidates for using populist rhetoric. This asymmetry is key: populism is a low-cost mobilization tool. It fires up your base without alienating persuadable voters.

Are hypothetical but cleanly identified ~1.5 percentage points a lot? In most elections, no. But in a close race (and modern elections in the U.S. and Europe are often decided by razor-thin margins) even a small mobilization advantage can be decisive. This may help explain the apparent paradox: populist rhetoric doesn’t change many minds, but it doesn’t need to. It just needs to get a few more supporters to the polls.

The media amplification question

There’s one more possibility worth considering: the role of the media. Populist rhetoric is, almost by design, dramatic and newsworthy. When a candidate calls the entire political establishment corrupt and claims to be the voice of the forgotten people, that generates coverage, and coverage generates name recognition, which generates votes.

The most vivid illustration is Trump’s 2016 campaign, which received an estimated $2 billion in free media during the primaries alone, far more than any rival. Much of that coverage was driven by his populist style: the outrageous claims, the attacks on the “swamp,” the rallies designed for television. Journalists couldn’t look away. And there is some evidence in the growing media populism literature that this pattern generalizes beyond Trump, with populist candidates across countries receiving disproportionate media attention relative to their actual electoral standing. That’s what our Notre Dame colleague Marc Jacob recently found too, but in the case of negative politics and political insults more generally—it captures and generates attention.

If populist rhetoric’s main benefit is generating outsized media attention, which then translates into awareness and mobilization, then the mechanism isn’t really about what populism says to voters. It’s about what populism says to journalists. This is consistent with our finding that populism mobilizes rather than persuades. But the media-amplification hypothesis still needs direct testing, and ambitious PhD students should certainly take this on (unless we or our Claude Code get to it first).

What does this all mean?

So what is populism actually good for? Based on our and other recent research, we’d summarize it this way:

1. Populist rhetoric is a gamble, adopted primarily by candidates who are already losing. It’s a variance-increasing strategy, not a winning formula.

2. It doesn’t persuade. Voters care about policy positions, not populist framing. The “host ideology,” what you actually promise to do, matters far more than how dramatically you frame the people-versus-elite conflict.

3. It may slightly mobilize, particularly among voters who already hold populist attitudes and agree with the candidate on substance. This is a modest but potentially consequential effect in close elections.

4. Media amplification may be a key mechanism, turning populist drama into disproportionate coverage. But we need more direct evidence.

The biggest takeaway, both for those who fear populism and those who are tempted by it, is that substance matters more than style. Politicians who deliver tangible results, or credibly promise to, will outperform those who simply shout louder about corrupt elites. This is consistent with what I (Alex) argue in my recent book, In Our Interest, in the context of immigration: policies that are demonstrably beneficial do more to win and maintain public support than any amount of rhetorical framing.

Populism is not nothing. But it’s not the all-powerful electoral weapon it’s often made out to be. Don’t confuse volume for effectiveness. The people who keep winning elections on populist platforms are winning mostly because of what they promise and do, not because of how they talk about it.

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