Tag Archives: Jesús Fernández-Villaverde

Immigration and Merit As Fiscal Propositions

10 Wednesday Dec 2025

Posted by in Fiscal Impact, Immigration

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Immigration into the U.S. can be a powerful force for economic growth. This takes on special importance given low fertility rates in the U.S. and the effective insolvency of our entitlement systems (with dim prospects for reform). But whether a given flow of immigrants will mitigate the negative growth and fiscal effects of unfavorable demographic trends is a conclusion requiring some qualification.

This certainly isn’t a case of “the more, the merrier”. Sharp tradeoffs bear on whether and how immigration can be a part of the solution to our demographic and fiscal woes.

Fiscal Contribution

University of Pennsylvania economist Jesús Fernández-Villaverde asserts that a high volume of immigrants will not solve our fiscal challenges. His reasoning is straightforward: immigrants are concentrated in the lower part of the income distribution, and therefore relatively few provide a surplus contribution to the nation’s fiscal balance. In fact, our large fiscal imbalance is driven by the country’s generous welfare state. With near-open borders, it serves as a magnet for low-income migrants. Thus, a broadly lenient immigration policy will not solve fiscal issues caused by low birth rates. However, Fernandez-Villaverde offers no direct empirical evidence except to say that data from some European countries support his claim.

Daniel Di Martino of the Manhattan Institute recently published a detailed analysis of the fiscal effects of immigration, including the fiscal contributions of both immigrants and two subsequent generations of offspring. He provided an excellent summary in a later tweet:

“… when it comes to immigration, the main question isn’t how many immigrants but which immigrants.”

In particular, highly-educated immigrants engender a surplus fiscal contribution. All else equal, so do immigrants less than 40 years of age. Low-skill immigrants are likely to produce a fiscal deficit, however. Legal immigrants tend to have a positive fiscal effect, while illegal immigrants tend to add to deficits.

This 2017 report from the National Academies of Sciences found similarly mixed results on the fiscal impact of immigrants. Education and age were again important determinants.

Country of Origin

Garret Jones argues that place of origin is a vital indicator of fiscal contribution. Here is a chart he posted at the link (from The Economist):

The chart pertains to immigration into Denmark, so like Jesús Fernández-Villaverde, we’re relying on European data. However, I suspect this generalizes to most other western countries. Of course, the plots above represent averages; individuals from any of the categories shown in the chart might differ substantially. Nevertheless, the average non-western immigrant into Denmark makes a weak or negative fiscal contribution relative to immigrants of western origin. The contrast is especially sharp for immigrants from the category that includes the Middle East, North Africa, Pakistan, and Turkey.

There are a variety of explanations for these disparate results. Westerners emigrating to Denmark probably have a strong advantage in terms of common languages and communication. Average skill levels are probably higher for westerners as well. Cultural differences almost surely make assimilation into society and the workplace more difficult for non-westerners.

A strict ban or quota on immigration from certain countries is probably unwise, however. Given our growth and fiscal objectives, we should seek to attract talented individuals from all over, and humanitarian imperatives suggest acceptance of legitimate refugees from political, religious, racial, or ethnic persecution. That might well mean a greater annual number of legal immigrants into the U.S. But if the question is whether it’s fiscally sound to encourage broad inflows of non-western immigrants, the answer is mostly no.

Vetting

It should go without saying that all potential immigrants must be vetted, and the intensity of the process could be made a function of an individual’s place of origin. Military-age males from hostile countries should receive particular scrutiny so that we can mitigate risks like those described here.

It’s reasonable to demand that those entering the country meet some subset of possible qualifications, some of which might override other criteria. For example, highly productive workers make wonderful immigrants, contribute to economic growth, make a greater fiscal contribution, and are more likely to assimilate successfully. Those are key rationales for a merit-based immigration system. But an overriding consideration might apply to individuals or families fleeing their homeland due to persecution, who have legitimate claims to refugee status regardless of economic potential. It’s also reasonable to extend favorable treatment to individuals having close family members already in the U.S., barring any red flags.

A related concern is birthright citizenship, which is a constitutional right. As long as immigrants clear reasonable hurdles for legal entry, birthright citizenship should stand going forward. The Supreme Court is likely to rule against the Trump Administration’s challenge to birthright citizenship, and it should, though the vast number of illegals who entered the U.S. under Biden certainly creates a birthright burden for U.S. taxpayers. It also sometimes complicates efforts to deport individuals who never should have been allowed to enter.

Merit

Rigid immigration quotas don’t make economic sense. It’s desirable to allow flexibility as labor market conditions evolve. Those capable of work might be ranked by education or skill, and in turn assigned priority based on the strength of domestic opportunities in their areas of experience or expertise. This can accommodate unskilled workers when they are in heavy demand. But merit and labor-market pressures aside, please don’t adopt preferences like the last two sentences shown here (from the White House’s latest national security strategy document).

Legal immigration should not be handled as a residual. Employers will often find that an immigrant is more qualified for a certain job. They should be free to hire that individual assuming the immigrant is vetted. As Robby Soave notes at the tweet linked above, the White House position is economically equivalent to hiring on the basis of DEI preferences.

Needless to say, almost any formula or decision tree can be manipulated unless it is spelled out in detail by law. However, that too might subvert economic and fiscal objectives by imparting too much rigidity to the system.

Crimes and Misdemeanors

Notwithstanding protestations from many economists I admire, who make endless assertions that illegal immigrants have lower crime rates than the domestic population, those arguments are beside the point. There seems little justification for allowing anyone having a record of serious crime to enter the country. It is hard to imagine many circumstances under which exceptions should be considered. Yet we have managed to allow large numbers of proven criminals to enter the U.S. (similar numbers reported here). It goes without saying that we cannot properly vet potential immigrants unless they go through the proper legal process for entering the country. For example, this is what happened in Europe as countries allowed unchecked inflows of migrants (and continue to do so).

Illegal immigrants are obviously in violation of immigration laws, which cannot simply be rewarded. Rather than the traditional fines or jail time for improper entry, so-called “remigration” is an increasingly popular solution. Voluntary deportation is one possibility; should the immigrant refuse, there must be a greater price to pay for the violation of law. Involuntary deportation is more controversial but might be warranted if the alternative is state dependency. Other possibilities include private sponsorship with a price tag high enough to pay what would otherwise become an obligation imposed on taxpayers. Factors that could weigh in favor of an illegal immigrant would be employability, a commitment to learn the English language, and a course of study toward meeting the requirements for citizenship.

Summary

An open borders policy is idealized by some libertarians, but it has severe drawbacks. Among those are potential compromises in national security and a blind eye to the ingress of dangerous criminals. Furthermore, many potential immigrants contribute to fiscal deficits due to their reliance on the welfare state and the generous entitlements available to many U.S. residents. A well-designed immigration system would screen for merit across a number of dimensions, with responsiveness to labor market conditions.

The Impotence of AI for the Socialist Calculation Debate

05 Monday Jun 2023

Posted by in Artificial Intelligence, Central Planning, Markets

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Recent advances in artificial intelligence (AI) are giving hope to advocates of central economic planning. Perhaps, they think, the so-called “knowledge problem” (KP) can be overcome, making society’s reliance on decentralized market forces “unnecessary”. The KP is the barrier faced by planners in collecting and using information to direct resources to their most valued uses. KP is at the heart of the so-called “socialist calculation debate”, but it applies also to the failures of right-wing industrial policies and protectionism.

Apart from raw political motives, run-of-the-mill government incompetence, and poor incentives, the KP is an insurmountable obstacle to successful state planning, as emphasized by Friedrich Hayek and many others. In contrast, market forces are capable of spontaneously harnessing all sources of information on preferences, incentives, resources, as well as existing and emergent technologies in allocating resources efficiently. In addition, the positive sum nature of mutually beneficial exchange makes the market by far the greatest force for voluntary social cooperation known to mankind.

Nevertheless, the hope kindled by AI is that planners would be on an equal footing with markets and allow them to intervene in ways that would be “optimal” for society. This technocratic dream has been astir for years along with advances in computer technology and machine learning. I guess it’s nice that at least a few students of central planning understood the dilemma all along, but as explained below, their hopes for AI are terribly misplaced. AI will never allow planners to allocate resources in ways that exceed or even approximate the efficiency of the market mechanism’s “invisible hand”.

Michael Munger recently described the basic misunderstanding about the information or “data” that markets use to solve the KP. Markets do not rely on a given set of prices, quantities, and production relationships. They do not take any of those as givens with respect to the evolution of transactions, consumption, production, investment, or search activity. Instead, markets generate this data based on unobservable and co-evolving factors such as the shape of preferences across goods, services, and time; perceptions of risk and its cost; the full breadth of technologies; shifting resource availabilities; expectations; locations; perceived transaction costs; and entrepreneurial energy. Most of these factors are “tacit knowledge” that no central database will ever contain.

At each moment, dispersed forces are applied by individual actions in the marketplace. The market essentially solves for the optimal set of transactions subject to all of those factors. These continuously derived solutions are embodied in data on prices, quantities, and production relationships. Opportunity costs and incentives are both an outcome of market processes as well as driving forces, so that they shape the transactional footprint. And then those trades are complete. Attempts to impose the same set of data upon new transactions in some repeated fashion, freezing the observable components of incentives and other requirements, would prevent the market from responding to changing conditions.

Thus, the KP facing planners isn’t really about “calculating” anything. Rather, it’s the impossibility of matching or replicating the market’s capacity to generate these data and solutions. There will never be an AI with sufficient power to match the efficiency of the market mechanism because it’s not a matter of mere “calculation”. The necessary inputs are never fully unobservable and, in any case, are unknown until transactions actually take place such that prices and quantities can be recorded.

In my 2020 post “Central Planning With AI Will Still Suck”, I reviewed a paper by Jesús Fernández-Villaverde (JFV), who was skeptical of AI’s powers to achieve better outcomes via planning than under market forces. His critique of the “planner position” anticipated the distinction highlighted by Munger between “market data” and the market’s continuous generation of transactions and their observable footprints.

JFV emphasized three reasons for the ultimate failure of AI-enabled planning: impossible data requirements; the endogeneity of expectations and behavior; and the knowledge problem. Again, the discovery and collection of “data” is a major obstacle to effective planning. If that were the only difficulty, then planners would have a mere “calculation” problem. This shouldn’t be conflated with the broader KP. That is, observable “data” is a narrow category relative the arrays of unobservables and the simultaneous generation of inputs and outcomes that takes place in markets. And these solutions are found by market processes subject to an array of largely unobservable constraints.

An interesting obstacle to AI planning cited by JFV is the endogeneity of expectations. It too can be considered part of the KP. From my 2020 post:

“Policy Change Often Makes the Past Irrelevant: Planning algorithms are subject to the so-called Lucas Critique, a well known principle in macroeconomics named after Nobel Prize winner Robert Lucas. The idea is that policy decisions based on observed behavior will change expectations, prompting responses that differ from the earlier observations under the former policy regime. … If [machine learning] is used to “plan” certain outcomes desired by some authority, based on past relationships and transactions, the Lucas Critique implies that things are unlikely to go as planned.”

Again, note that central planning and attempts at “calculation” are not solely in the province of socialist governance. They are also required by protectionist or industrial policies supported at times by either end of the political spectrum. Don Boudreaux offers this wisdom on the point:

“People on the political right typically assume that support for socialist interventions comes uniquely from people on the political left, but this assumption is mistaken. While conservative interventionists don’t call themselves “socialists,” many of their proposed interventions – for example, industrial policy – are indeed socialist interventions. These interventions are socialist because, in their attempts to improve the overall performance of the economy, proponents of these interventions advocate that market-directed allocations of resources be replaced with allocations carried out by government diktat.”

The hope that non-market planning can be made highly efficient via AI is a fantasy. In addition to substituting the arbitrary preferences of planners and politicians for those of private agents, the multiplicity of forces bearing on individual decisions will always be inaccessible to AIs. Many of these factors are deeply embedded within individual minds, and often in varying ways. That is why the knowledge problem emphasized by Hayek is much deeper than any sort of “calculation problem” fit for exploitation via computer power.

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Note: The image at the top of this post is attributed by Bing to the CATO Institute-sponsored website Libertarianism.org and an article that appeared there in 2013, though that piece, by Jason Kuznicki, no longer seems to feature that image.

Central Planning With AI Will Still Suck

23 Sunday Feb 2020

Posted by in Artificial Intelligence, Central Planning, Free markets

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Artificial intelligence (AI) or machine learning (ML) will never make central economic planning a successful reality. Jesús Fernández-Villaverde of the University of Pennsylvania has written a strong disavowal of AI’s promise in central planning, and on the general difficulty of using ML to design social and economic policies. His paper, “Simple Rules for a Complex World with Artificial Intelligence“, was linked last week by Tyler Cowen at Marginal Revolution. Note that the author isn’t saying “digital socialism” won’t be attempted. Judging by the attention it’s getting, and given the widespread acceptance of the scientism of central planning, there is no question that future efforts to collectivize will involve “data science” to one degree or another. But Fernández-Villaverde, who is otherwise an expert and proponent of ML in certain applications, is simply saying it won’t work as a curative for the failings of central economic planning — that the “simple rules” of the market will aways produce superior social outcomes.

The connection between central planning and socialism should be obvious. Central planning implies control over the use of resources, and therefore ownership by a central authority, whether or not certain rents are paid as a buy-off to the erstwhile owners of those resources. By “digital socialism”, Fernández-Villaverde means the use of ML to perform the complex tasks of central planning. The hope among its cheerleaders is that adaptive algorithms can discern the optimal allocation of resources within some “big data” representation of resource availability and demands, and that this is possible on an ongoing, dynamic basis.

Fernández-Villaverde makes the case against this fantasy on three fronts or barriers to the use of AI in policy applications: data requirements; the endogeneity of expectations and behavior; and the knowledge problem.

The Data Problem: ML requires large data sets to do anything. And impossibly large data sets are required for ML to perform the task of planning economic activity, even for a small portion of the economy. Today, those data sets do not exist except in certain lines of business. Can they exist more generally, capturing the details of all economic transactions? Can the data remain current? Only at great expense, and ML must be trained to recognize whether data should be discarded as it becomes stale over time due to shifting demographics, tastes, technologies, and other changes in the social and physical environment. 

Policy Change Often Makes the Past Irrelevant: Planning algorithms are subject to the so-called Lucas Critique, a well known principle in macroeconomics named after Nobel Prize winner Robert Lucas. The idea is that policy decisions based on observed behavior will change expectations, prompting responses that differ from the earlier observations under the former policy regime. A classic case involves the historical tradeoff between inflation and unemployment. Can this tradeoff be exploited by policy? That is, can unemployment be reduced by a policy that increases the rate of inflation (by printing money at a faster rate)? In this case, the Lucas Critique is that once agents expect a higher rate of inflation, they are unlikely to confuse higher prices with a more profitable business environment, so higher employment will not be sustained. If ML is used to “plan” certain outcomes desired by some authority, based on past relationships and transactions, the Lucas Critique implies that things are unlikely to go as planned.  

The Knowledge Problem: Not only are impossibly large data sets required for economic planning with ML, as noted above. To achieve the success of markets in satisfying unlimited wants given scarce resources, the required information is impossible to collect or even to know. This is what Friedrich Hayek called the “knowledge problem”. Just imagine the difficulty of arranging a data feed on the shifting preferences of many different individuals across a huge number of products,  services and they way preference orderings will change across the range of possible prices. The data must have immediacy, not simply a historical record. Add to this the required information on shifting supplies and opportunity costs of resources needed to produce those things. And the detailed technological relationships between production inputs and outputs, including time requirements, and the dynamics of investment in future productive capacity. And don’t forget to consider the variety of risks agents face, their degree of risk aversion, and the ways in which risks can be mitigated or hedged. Many of these things are simply unknowable to a central authority. The information is hopelessly dispersed. The task of collecting even the knowable pieces is massive beyond comprehension.

The market system, however, is able to process all of this information in real time, the knowable and the unknowable, in ways that balance preferences with the true scarcity of resources. No one actor or authority need know it all. It is the invisible hand. Among many other things, it ensures the deployment of ML only where it makes economic sense. Here is Fernández-Villaverde:

“The only reliable method we have found to aggregate those preferences, abilities, and efforts is the market because it aligns, through the price system, incentives with information revelation. The method is not perfect, and the outcomes that come from it are often unsatisfactory. Nevertheless, like democracy, all the other alternatives, including ‘digital socialism,’ are worse.”

Later, he says:

“… markets work when we implement simple rules, such as first possession, voluntary exchange, and pacta sunt servanda. This result is not a surprise. We did not come up with these simple rules thanks to an enlightened legislator (or nowadays, a blue-ribbon committee of academics ‘with a plan’). … The simple rules were the product of an evolutionary process. Roman law, the Common law, and Lex mercatoria were bodies of norms that appeared over centuries thanks to the decisions of thousands and thousands of agents.” 

These simple rules represent good private governance. Beyond reputational enforcement, the rules require only trust in the system of property rights and a private or public judicial authority. Successfully replacing private arrangements in favor of a central plan, however intricately calculated via ML, will remain a pipe dream. At best, it would suspend many economic relationships in amber, foregoing the rational adjustments private agents would make as conditions change. And ultimately, the relationships and activities that planning would sanction would be shaped by political whim. It’s a monstrous thing to contemplate — both fruitless and authoritarian.