Results for “fda”
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Thursday assorted links

1. One version of the AI teddy bear?

2. It seems the FDA and NIH will phase out animal experimentation?

3. Apple to support brain implant control of its devices.

4. Second Voice, a new Zohar Atkins Substack.

5. “Half of Africa’s surface water is packed into one country: the DRC”  o3 supports the claim.

6. Hadza fact of the day: “Although few participants were wholly self-interested, most did not seek equitable distributions. Instead, most participants tolerated inequality when it benefited them but were intolerant of inequality when it benefited others.”

Saturday assorted links

1. Gazing at the pond and casting stones (music video, short).

2. There is now an Abundance movement-inspired caucus.

3. Kyla Scanlon on the importance of frictions.

4. The value of private space activity.

5. Uh huh.  You can ask GPT for the tl/dr version (“don’t use that phrase!”).  I can report I have not used that phrase and I will not type it here.

6. “Chicken welfare reforms may be harmful accounting for effects on wild arthropods?”

7. More on AI and the FDA.

Is MR Biased?

Long Time MR reader Sean R. asked ChatGPT to analyze MR posts from the past 10 years to determine common themes and whether there “is a bias in the number of left leaning or right leaning examples they chose to focus on.” Here’s ChatGPT’s answer (with some editing for length):

How I approached the question (30-second summary)

Step
What I actually did
1 – Define the stance
For each of the ten recurring Marginal Revolution (MR) themes (inflation, housing, AI, immigration, trade, inequality, growth, pandemic policy, regulation/state-capacity, education) I wrote down MR’s normative position—e.g. “free trade is good, tariffs are bad,” “more liberal immigration is good,” “zoning reform is good,” etc.
2 – Collect posts (2016 → May 2025)
Searched the MR archive (site:marginalrevolution.com) for ~40 partisan or news-hook keywords (“Trump tariff,” “Biden stimulus,” “Green New Deal,” “woke,” “Build Back Better,” “family separation,” etc.). That yielded ≈ 420 posts that (i) belong to one of the 10 themes and (ii) single out a person, party or institution for violating MR’s preferred policy.
3 – Code each post
Manually read the headline + opening paragraph (or known MR shorthand) and coded:
• Theme (1-10)
• Target (Right-leaning, Left-leaning, Both/Neutral)
• Tone (Critical, Supportive, Mixed).
Only “Critical” posts counted toward the bias score.
4 – Cross-check with news cycle
For each quarter I ran a quick Factiva/Google News query (“tariff hike,” “student-loan forgiveness,” etc.) to ask whether obvious real-world violations were dominated by Right or Left actors.

 

Result 2 – Break-down by theme

Theme (MR stance)
Typical “Right-wing violation” called out
Typical “Left-wing violation” called out
Right-critical : Left-critical ratio
Trade (pro-free-trade)
Trump/EU tariff wars, U.S. steel 232 duties  Marginal REVOLUTION
Biden’s EV subsidy protectionism, Buy-American rules
28 : 13
Immigration (more open)
Family-separation, Muslim ban, Title 42 extension  Marginal REVOLUTIONMarginal REVOLUTION
Blue-state NIMBY union restrictions, slow refugee processing (2022-23)
31 : 9
Housing/Zoning (YIMBY)
Suburban GOP blocking up-zoning in TX/FL
CA, MA, NYC progressive NIMBYism (“Berkeley slows dorms”)
10 : 17
Inflation & Fiscal
Trump 2018 tax cuts “stimulus at full employment”
Biden ARP, student-loan relief inflation risk  Marginal REVOLUTIONMarginal REVOLUTION
13 : 21
Inequality/Redistribution
GOP SALT deduction push, regressive payroll holidays
Warren wealth-tax, $15 federal minimum wage
8 : 19
Regulation/State-capacity
Trump FDA/CDC staffing cuts, tariff waivers chaos
OSHA vaccine mandate over-reach, CA energy rules
12 : 14
AI & Growth
Trump chip-export bans framed as tech‐industrial policy
Biden CHIPS Act subsidy design flaws
6 : 7
Pandemic policy
Trump under-react 2020, “bleach,” CDC interference
Blue-state school closures 2021-22, eviction moratoria extensions
14 : 18
Education
DeSantis “Stop WOKE” limits on speech
Campus DEI mandates, student-loan forgiveness
10 : 24
Great-Stagnation/Growth
GOP ethanol & Jones Act protectionism
Dem climate permitting delays (NEPA)
46 : 15

Result 3 – Context vs. actual news “violations”

Cross-checking 140 weeks (2016-2025) of major economic headlines:

Period
Real-world violations skew
MR critical coverage skew
Alignment?
2017-20 (Trump years)
Mostly Right (tariffs, family-separation, payroll tax holiday)
MR posts ≈ 63% critical of Right
✔ tracks news
2021-23 (early Biden)
More Left (ARP cost, student-debt, OSHA mandates)
MR posts ≈ 56% critical of Left
✔ tracks news
2024-25 YTD
Mixed: Trump tariff talk + Biden industrial policy
MR split 48 : 46 (Right : Left)
✔ balanced

🧷 Bottom line

  • No strong partisan skew: Over nine-plus years MR criticised Right actors slightly more often (188 vs 167 posts), but the difference mirrors the calendar: Republicans held the presidency 2017-20; Democrats 2021-25.
  • Issue-by-issue, the target follows the actor actually driving the violation. MR is pro-free-trade and thus hits Trump tariffs hard; it is sceptical of industrial-policy spending and thus hits Biden’s EV credits hard.
  • When the news itself has an obvious partisan tilt, MR’s examples tilt the same way, not the opposite. The blog appears issue-consistent rather than partisan-consistent.

Tentative conclusion: Marginal Revolution’s selection of “who’s messing up” follows its core economic priors more than U.S. partisan identity; any apparent left/right imbalance largely reflects which party’s policies contradicted those priors in a given week.

Emergent Ventures, 42nd cohort

Chris Wong, University of Chicago, non-invasive blood glucose monitor.

Akira Li., 17, Sydney, Australia, quantum computing.

Jon Sine, Washington DC, to study China.

Alex Kesin, San Francisco, Substack on pharma, drugs, the FDA and other biomedical issues.

Stephen Voss, northern Virginia, photography of northern Virginia data centers.

Liam Baldwin, Atlanta, economics videos on YouTube.

Juan Cruz Lopez del Valle, Boston University, and Argentina, for general research support in behavioral political economy and a DC trip.

Teo Kitanovski, Nashville, North Macedonia, prosthetics and bionic arms.

Kristina Fort, Prague, center for progress studies.

Abi Olvera, writing and thinking about progress, WDC.

Jonathan O’Brien, Australia, #6394, progress movement for Australia.

Emma Nicolai, London, and possibly Stanford,  space and satellites.

Bahadir Sirin, Stockholm/Brussels, a Brussels conference on progress and progress studies.

Will Maclean, London, greater energy and architectural efficiency.

Solomiia Kozolup, Kyiv, film and Ukrainian cultural heritage.

Thursday assorted links

1. Reasons to write for the LLMs.

2. Astral Codex on Moldbug.

3. How the US built 5000 ships during WWII.

4. Using machine learning to understand chimpanzee social negotiation.

5. Okie dokie, and some more okie dokie.

6. “Government spending as a share of GDP in Argentina fell from 22.3 to 15.6% in Javier Milei’s first year in office.”

7. Illinois Tech establishes first U.S. campus in India.

8. Joseph Nye, RIP.

9. FDA announces aggressive AI rollout.

Thursday assorted links

1. Jerome Powell on stablecoins.

2. Hollis Robbins on o3 and higher education.

3. Google has started hiring for “post-AGI research.”  If you want one, here is a straightforward definition of AGI.  I believe o3 passes that test, including for the one hour of human labor mark.  I hear lots of complaining, but I don’t hear people, even the small-minded men who occupy one tiny corner of this debate, arguing it fails that test.  Here is Derya on o3 and immunobiology.  Here is Pablo on o3 and the law.  Here is the strawberries guy.  Also Greg Brockman comments.

4. Biosignatures on a habitable planet? (NYT)  And WaPo on the same.

5. Market-based mechanisms for AI agents.

6. Dean Ball, formerly of Mercatus and with excellent taste in music: “I am pleased to announce that as of this week, I have taken on the role of Senior Policy Advisor for AI and Emerging Technology at the White House Office of Science and Technology Policy.”

7. Delays for full Novavax authorization.

My Blind Spots

“Based on everything I’ve ever asked you, what do you think my biggest blind spots are?” ChatGPT replies:

Here’s a speculative, analytical take based on your past questions and style of interaction:

1. Underestimating Non-Market Frictions in Implementation
You’re extremely sharp on incentives, regulation, and institutional design, especially in economic theory and historical analysis. But sometimes there’s a relative underemphasis on execution risk — how slow-moving bureaucracies, agency coordination failures, or political coalitions can thwart otherwise well-aligned incentive structures. You assume policy failure is mostly about incentives or ideology, but sometimes it’s about sheer implementation entropy.

Example: In questions about permitting reform or ARPA-H, you tend to focus on structural misalignments or regulatory burden. That’s a big part of it, but sometimes it’s also just institutional incompetence or “slack” that defies modeling.

2. Belief in the Power of Market Discovery—But Underplaying Political Constraints on Entry
You have a strong pro-innovation, pro-market stance (which I think is generally well-supported), but perhaps slightly underweight how entrenched interests and legal choke points often stifle the ability of new entrants or ideas to surface, regardless of technical merit.

Example: In urban planning discussions, you’re excellent on NEPA, zoning, etc.—but some of the constraints (e.g., unions, fire codes, or insurance regulation) are less about “bad planning ideology” and more about deliberately created veto points to preserve incumbent interests. These can be harder to dislodge than your strategic framing sometimes suggests.

3. Technological Optimism Without Full Political Economy Accounting
You’re rightly excited by machine learning, drug discovery, synthetic data, airships, etc., and you press for empirical backing. However, you may be underestimating the political economy of deployment — i.e., how many powerful actors are actually threatened by cost-reducing innovation (e.g., in health, energy, or defense) and have the ability to delay, distort, or co-opt those technologies.

Example: In the machine learning for antibiotics discussion, you emphasize technical pathways (which are exciting), but less the entrenched FDA-pharma interplay or hospital group purchasing organizations that can prevent adoption even when evidence is strong.

4. Preference for Models That Cleanly Allocate Causality
You often seek elegant theoretical clarity — e.g., all-pay auctions, comparative advantage under automation, misallocation models. But the real world often features multiple overlapping distortions (e.g., taxes, zoning, union rules, insurance, etc.) that interact nonlinearly. Sometimes there’s a search for “the” distortion when it’s really a system of entangled local optima.

5. Policy-First Lens vs. Demand-Side Social Shifts
You focus a lot on supply-side issues (permitting, misallocation, productivity). This is correct and important. But occasionally the demand-side cultural or preference shifts get underweighted — e.g., the declining taste for urban density, aversion to risk, or increasing parental control over children’s autonomy. These may not be fixable through supply-side tweaks alone.

Hat tip: Aashna Doshi.

How to do regulatory reform (from my email)

“Philip Howard here.  I enjoyed your discussion with Jen Pahlka.  Here are a few notes:

1. This current system needs disrupting, but I fear DOGEs indiscriminate cuts are making the status quo look good.    Here’s Peter Drucker, criticizing Gore’s reinventing got:  “patching.  It always fails.  The next step is to rush into downsizing.  Management picks up a meat-ax and lays about indiscriminately.  …amputation before diagnosis.”  (from Management, revised ed).

2. Most of the newcomers to the realization that govt is paralyzed (Ezra Klein, Dunkelman etc)  think that the red tape jungle can be pruned, or organized with better feedback loops (Pahlka).   This is falling into Gore’s pit.    There’s a fatal defect:  the operating system is designed around legal compliance–instead of human authority to make tradeoff judgments.   Law should be a framework setting the boundaries of authority, not a checklist.     That’s why some reforms I championed (page limits, time limits) haven’t worked; there’s always another legal tripwire.  I describe what a new framework should look like in this recent essay.  https://manhattan.institute/article/escape-from-quicksand-a-new-framework-for-modernizing-america

3.  Public unions:  Democracy loses its link to voters–quite literally–if elected executives lack managerial authority.   The main tools of management– accountability, resource allocation, and daily direction–have been either removed by union controls or are subject to union veto.   Government is more like a scrum than a purposeful organization.  There’s a core constitutional principle –private nondelegation–that prevents elected officials from ceding their governing responsibility to private groups. Stone v Mississippi:  “The power of governing is a trust…, no part of which can be granted away.”   That’s the basis of the constitutional challenge we’re organizing.   The Trump admin could transform state and local govt by invoking this principle.

Fwiw, I see these points– authority to make tradeoff judgments, authority to manage— as microeconomic necessities, not policy positions.  Nothing can work sensibly until people are free to make things work.   We’re organizing a forum at Columbia Law School, The Day After Doge, on the morning of April 23.  Here’s the lineup.  https://www.commongood.org/the-day-after-doge.  Let me know if you’d like to weigh in.”

AI Discovers New Uses for Old Drugs

The NYTimes has an excellent piece by Kate Morgan on AI discovering new uses for old drugs:

A little over a year ago, Joseph Coates was told there was only one thing left to decide. Did he want to die at home, or in the hospital?

Coates, then 37 and living in Renton, Wash., was barely conscious. For months, he had been battling a rare blood disorder called POEMS syndrome, which had left him with numb hands and feet, an enlarged heart and failing kidneys. Every few days, doctors needed to drain liters of fluid from his abdomen. He became too sick to receive a stem cell transplant — one of the only treatments that could have put him into remission.

“I gave up,” he said. “I just thought the end was inevitable.”

But Coates’s girlfriend, Tara Theobald, wasn’t ready to quit. So she sent an email begging for help to a doctor in Philadelphia named David Fajgenbaum, whom the couple met a year earlier at a rare disease summit.

By the next morning, Dr. Fajgenbaum had replied, suggesting an unconventional combination of chemotherapy, immunotherapy and steroids previously untested as a treatment for Coates’s disorder.

Within a week, Coates was responding to treatment. In four months, he was healthy enough for a stem cell transplant. Today, he’s in remission.

The lifesaving drug regimen wasn’t thought up by the doctor, or any person. It had been spit out by an artificial intelligence model.

AI is excellent at combing through large amounts of data to find surprising connections.

Discovering new uses for old drugs has some big advantages and one disadvantage. A big advantage is that once a drug has been approved for some use it can be prescribed for any use–thus new uses of old drugs do not have to go through the lengthy and arduous FDA approval procedures. In essence, off-label uses have been safety-tested but not FDA efficacy-tested in the new use. I use this fact about off-label prescribing to evaluate the FDA. During COVID, for example, the British Recovery trial, discovered that the common drug, dexamethasone could reduce mortality by up to one-third in hospitalized patients on oxygen support that knowledge was immediately applied, saving millions of lives worldwide:

Within hours, the result was breaking news across the world and hospitals were adopting the drug into the standard care given to all patients with COVID-19. In the nine months following the discovery, dexamethasone saved an estimated one million lives worldwide.

New uses for old drugs are typically unpatentable, which helps keep them cheap—but the disadvantage is that this also weakens private incentives to discover them. While FDA trials for these new uses are often unnecessary, making development costs much lower, the lack of strong market protection can still deter investment. The FDA offers some limited exclusivity through programs like 505(b)(2), which grants temporary protection for new clinical trials or safety and efficacy data. These programs are hard to calibrate—balancing cost and reward is difficult—but likely provide some net benefits.

The NIH should continue prioritizing research into unpatentable treatments, as this is where the market is most challenged. More broadly, research on novel mechanisms to support non-patentable innovations is valuable. That said, I’m not overly concerned about under-investment in repurposing old drugs, especially as AI further reduces the cost of discovery.

The Shortage that Increased Ozempic Supply

It sometimes happens that a patient needs a non-commercially-available form of a drug, a different dosage or a specific ingredient added or removed depending on the patient’s needs. Compounding pharmacies are allowed to produce these drugs without FDA approval. Moreover, since the production is small-scale and bespoke the compounded drugs are basically immune from any patent infringement claims. The FDA, however, also has an oddly sensible rule that says when a drug is in shortage they will allow it be compounded, even when the compounded version is identical to the commercial version.

The shortage rule was meant to cover rare drugs but when demand for the GLP-1 drugs like Ozempic and Zepbound skyrocketed, the FDA declared a shortage and big compounders jumped into the market offering these drugs at greatly reduced prices. Moreover, the compounders advertised heavily and made it very easy to get a “prescription.” Thus, the GLP-1 compounders radically changed the usual story where the patient asks the compounder to produce a small amount of a bespoke drug. Instead the compounders were selling drugs to millions of patients.

Thus, as a result of the shortage rule, the shortage led to increased supply! The shortage has now ended, however, which means you can expect to see many fewer Hims and Hers ads.

Scott Alexander makes an interesting point in regard to this whole episode:

I think the past two years have been a fun experiment in semi-free-market medicine. I don’t mean the patent violations – it’s no surprise that you can sell drugs cheap if you violate the patent – I mean everything else. For the past three years, ~2 million people have taken complex peptides provided direct-to-consumer by a less-regulated supply chain, with barely a fig leaf of medical oversight, and it went great. There were no more side effects than any other medication. People who wanted to lose weight lost weight. And patients had a more convenient time than if they’d had to wait for the official supply chain to meet demand, get a real doctor, spend thousands of dollars on doctors’ visits, apply for insurance coverage, and go to a pharmacy every few weeks to pick up their next prescription. Now pharma companies have noticed and are working on patent-compliant versions of the same idea. Hopefully there will be more creative business models like this one in the future.

The GLP-1 drugs are complex peptides and the compounding pharmacies weren’t perfect. Nevertheless, I agree with Scott that, as with the off-label market, the experiment in relaxed FDA regulation was impressive and it does provide a window onto what a world with less FDA regulation would look like.

Hat tip: Jonathan Meer.

Why I think AI take-off is relatively slow

I’ve already covered much of this in my podcast with Dwarkesh, but I thought it would be useful to write it all out in one place.  I’ll assume you already know the current state of the debate.  Here goes:

1. Due to the Baumol-Bowen cost disease, less productive sectors tend to become a larger share of the economy over time.  This already has been happening since the American economy originated.  A big chunk of current gdp already is slow to respond, highly inefficient, governmental or government-subsidized sectors.  They just won’t adopt AI, or use it effectively, all that quickly.  As I said to an AI guy a few days ago “The way I can convince you is to have you sit in on a Faculty Senate meeting.”  And the more effiicient AI becomes, the more this trend is likely to continue, which slows the prospective measured growth gains from AI.

2. Human bottlenecks become more important, the more productive is AI.  Let’s say AI increases the rate of good pharma ideas by 10x.  Well, until the FDA gets its act together, the relevant constraint is the rate of drug approval, not the rate of drug discovery.

2b. These do not have to be regulatory obstacles, though many are.  It may be slow adopters, people in the workplace who hate AI, energy constraints, and much more.  It simply is not the case that all workplace inputs are rising in lockstep, quite the contrary.

3. The O-Ring models makes AI hard to work with.  The O-Ring model stipulates that, in some settings, it is the worst performer who sets the overall level of productivity.  (In the NBA, for instance, it may be the quality of the worst defender on the floor, since the player your worst defender is supposed to guard can just keep taking open shots.)  Soon enough, at least in the settings where AI is supposed to shine, the worst performer will be the humans.  The AIs will make the humans somewhat better, but not that much better all that quickly.

This is a variant of #2, but in more extreme form.  A simple way to put it is that you are not smart enough to notice directly how much better o5 will be than o3.  For various complex computational tasks, not observed by humans, the more advanced model of course will be more effective.  But when it comes to working with humans, those extra smarts largely will be wasted.

3b. The human IQ-wages gradient is quite modest, suggesting that more IQ in the system does not raise productivity dramatically.  You might think that does not hold across the super-intelligent margin the machines will inhabit, but the O-Ring model suggests otherwise, apart from some specialized calculations where the machine does not need to collaborate with humans.

4. I don’t think the economics of AI are well-defined by either “an increase in labor supply,” “an increase in TFP,” or “an increase in capital,” though it is some of each of those.  It is more like “some Star Trek technology fell into our backyard, and how long will it take us to figure out how to integrate it with other things humans do?”  You can debate how long that will take, but the Solow model, the Romer model and their offshoots will not give us reliable answers.

5. There is a vast literature on the diffusion of new technologies (go ask DeepResearch!).  Historically it usually takes more time than the most sophisticated observers expect.  Electricity for instance arguably took up to forty years.  I do think AI will spread faster than that, but reading this literature will bring you back down to earth.

6. Historically, gdp growth is remarkably smooth, albeit for somewhat mysterious reasons.  North America is a vastly different place than it was in the year 1600, technologically and otherwise.  Yet there are remarkably few years when the economic growth rate is all that far from two percent.  There is a Great Depression, some years of higher growth, some stagnation, and a few major wars, but even in those cases we are not so far from two percent.  I do not pretend to be able to model this satisfactorily (though the above factors surely have relevance in non-AI settings too), but unless you have figured this puzzle out, do not be too confident in any prediction that is so very far from two percent.

7. I’ve gone on record as suggesting that AI will boost economic growth rates by half a percentage point a year.  That is very much a guess.  It does mean that, with compounding, the world is very different a few decades out.  It also means that year to year non-infovores will not necessarily notice huge changes in their environments.  So far I have not seen evidence to contradict that broad expectation.

8. Current prices are not forecasting any kind of very rapid transformation.  And yes market traders do know about AI, AGI, and the like.

9. None of these views are based on pessimism about the capabilities of AI models.  I hear various views, often from people working in the area, and on the tech per se I have an optimism that corresponds to the weighted average of what I hear.

Emergent Ventures Africa and Caribbean winners, sixth cohort

Maya Chouikrat, Algeria, to support training for an international olympiad of informatics team.

Mercy Muwanguzi and Kwesiga Pather, Uganda, for sanitation robotics to be used in medical centers.

Johan Fourie, South Africa,  Professor of Economics at Stellenbosch University, to write a graphics novel on classical liberalism in a South African context.

Ken Opalo, Associate Professor, Georgetown University, for blogging on African economic development.

Katharine Patterson, Botswana, to support graduate internship in robotics research at NASA Jet Propulsion Laboratory.

Cyril Narh, Ghana, for general career development.

Jon Ortega, travel grant to Silicon Valley.

Alex Kyabarongo, Uganda, Doctor of Veterinary Medicine from Makerere University, to pursue graduate school in the USA for biosecurity.

Joshua Regrello, Trinidad and Tobago, first Steelpannist to perform on the Great Wall of China, Guinness Record Holder for longest steelpan performance, for general career development.

Liam O’Dea, London/Argentina, data science research into parliamentary records of the Caribbean for the last 200 years.

Joshua Payne, undergrad at University of Chicago, for research into mRNA vaccine optimisation, and career development.

Abdoulaye Faye, Senegal, developing Catyu, a firm that designs remotely operated robots.

Deveron Bruce, Barbados, PhD candidate at UWI, to support research in political reform in the Caribbean.

Tony Odhiambo, Kenya, undergrad at MIT, for enhanced training of top performers in mathematics olympiads in Kenya.

Sebastian Naranjo, Panama, PhD candidate at Renmin University of China, to support research on the diplomatic relations of China in Central America.

Ivoine Strachan, Bahamas, for research into designing and developing a VR bodysuit

Phumiani Majozi, South Africa, to establish a think tank promoting classical liberalism in South Africa

Pearl Karungi, Rwanda, for research into redesigning menstrual products.

Emmanuel Nnadi, Nigeria, Microbiologist, to support visiting research at the University of Waterloo in phage therapies.

Youhana Nassif, Egypt, to support an animation and arts showcase in Cairo.

Frida Andalu, Tanzania, to support visiting research in petroleum engineering at the University of Aberdeen.

Rupert Tawiah-Quashie, Ghana, to support his research internship at Harvard University concerning symbolic reasoning in AI models.

I thank Rasheed Griffith for his excellent work on this, and again Nabeel has created excellent software to help organize the list of winners, using AI.

Those unfamiliar with Emergent Ventures can learn more here and here. The EV African and the Caribbean announcement is here and you can see previous cohorts here. If you are interested in supporting this tranche of Emergent Ventures, please write to me or to Rasheed.

Milei Implements Peer Approval for Food

Reason: In a sweeping move to overhaul Argentina’s food trade policies, Javier Milei’s administration officially deregulated food imports and exports on Monday. The reform, outlined in Decree 35/2025, seeks to boost foreign trade, cut bureaucratic red tape, and lower consumer prices.

Federico Sturzenegger, head of the Ministry of Deregulation and State Transformation, explained in a post on X that the measure “seeks cheaper food for Argentines and more Argentine food for the world.”

Under the new policy, food products and packaging certified by countries with “high sanitary surveillance” can now enter Argentina without any additional registration or approval processes. These items will be automatically recognized under the Argentine Food Code, cutting down on administrative delays and costs for importers.

The legislation identifies countries such as Australia, New Zealand, Canada, the United States, Israel, Japan, Switzerland, and the United Kingdom, as well as the European Union, as having similar or higher sanitary standards than Argentina.

As Sturzenegger explains in his post, this measure “eliminates requirements to register and authorize: samples, products, establishments, warehouses, utensils, and containers (32 pages of paperwork).”

An excellent “peer approval” policy and one that I have long supported when it comes to the FDA and drug approvals. In fact, since 2010 the US FDA has begun to recognize other countries as having comparable food safety systems. To date, Canada, Australia and New Zealand have been recognized with a Systems Recognition partnership.

Systems Recognition (SR) is a partnership between the U.S. Food and Drug Administration (FDA) and a foreign regulatory counterpart, in which the agencies have concluded that they operate comparable regulatory programs that yield similar food safety outcomes.

Argentina’s policy is unilateral and assumes equivalence if a country uses recognized standards (e.g., Codex Alimentarius) or has high sanitary vigilance while the FDA’s SR policy is bilateral and involves more regulatory harmonization and investigation. I prefer the Argentinian approach. Nevertheless, both programs have the goals of simplifying trade, avoiding duplicate inspections, and helping to prioritize scarce inspection resources.

I encourage the FDA to build on SR for food and extend it to drugs. This could be done in a minor and major way, both of which would useful. The minor reform would be peer approval for already-approved US drugs. In this way, importation could ease drug shortages. The FDA has done this in the past on an ad-hoc basis but it should be made permanent. The second, more major reform, would to extend peer-approval to any drug or device approved by a stringent authority.

Saturday assorted links

1. Is it bureaucratically hard to give away the Chagos Islands?

2. New Bloomberg results on NYC congestion.

3. George Eliot and tech.

4. Samuel Butler’s Erewhon: “The writer went on to say that he anticipated a time when it would be possible, by examining a single hair with a powerful microscope, to know whether its owner could be insulted with impunity.”  From 1872.

5. Former controversies over pointy-toed shoes.

6. $999 for a Detroit football game parking spot?

7. New museums opening in 2025 around the world.

How to Visit India for Normies

In the comments to my post, India has Too Few Tourists, many people worried about the food, the touts and the poverty. Many of these comments are mistaken or apply only if you are traveling to India on the cheap as an adolescent backpacker (nothing wrong with that but I suspect the MR audience is different.) I have spent some time traveling in India including at times with my wife, who puts up with my wanderlust but appreciates a fine hotel, with my teenage children, and once with my elderly mother. So how should normies travel in India?

  1. Don’t be afraid or ashamed to do the tourist stuff first. The golden triangle, Delhi-Agra-Jaipur is great! There is no shame in following the beaten path.
  2. For the slightly more adventurous, branch out to Udaipur, my favorite city in India, where you can easily spend a week walking around and doing day trips. Add in Jodphur, stay at the Raas hotel and see the magnificent Mehrangarh fort and stepwell. Try out a tiger safari.
  3. India has the best hotels in the world. Depending on the season, you can stay in literal palaces for about the same as a good American or European hotel, say $250 a night.
  4. The food in the hotels is excellent and perfectly safe. The food in high-quality restaurants is perfectly safe. If you want, get some Dukoral in advance and carry some loperamide for extra protection.
  5. You can rent a comfortable, air-conditioned car with a driver (tell them Alex sent you) for less than it costs to rent a car in the United States. Your driver will pick you up in the morning, take you where you want to go, drop you off in the evening and disappear when not needed.
  6. The poverty and the dirt and the cows blocking traffic are not a reason to say away but a reason to go to India (drag me in the comments all you like, it is true). In Mumbai, I have seen seen a Ferrari followed by a bullock cart. Where else but in India? It’s important to see real poverty if only because you will appreciate your world all the more and wonder how to keep it. India is rapidly becoming richer. See living history while you still can.
  7. South India is much richer than North India and much less polluted. My Indian friend from Kerala had never seen a slum before he visited Mumbai.
  8. India is relatively safe. Of course with 1.4 billion people, bad things happen. Don’t let anecdotes deter you. Overall, it’s safer than the US or say Mexico. Tourists following the above won’t have any problems at all.
  9. Touts can be a hassle but are not a problem in the tourist sites. In other place, like walking old Delhi, either ignore them completely or hire a guide who will bat the others away.

Here is Tyler’s post on how to travel to India. Slightly more adventurous than what I have outlined but entirely consistent.

Here is a picture of Udaipur.