In my previous essay “Economics is not a Science”, I argued that mainstream economics fails to meet the basic criteria we would expect from a genuine science. The response to that piece has been interesting. Some readers pushed back, arguing that economics clearly is a science because it uses scientific methods. Others agreed with my critique but wanted a more precise framework for understanding when economic work crosses the threshold into genuine scientific inquiry.
Both responses point to something I left underexplored: What exactly would it take for economics to be a science? Not whether it uses scientific tools (it does), but whether it functions as an actual science in the way physics, chemistry, or biology do.
This essay attempts to answer that question. I’ve developed a framework for assessing scientific status that I believe clarifies the debate and, importantly, offers a path forward. The conclusion may surprise readers of my earlier piece: economics is not inherently incapable of being a science. The problem is that large portions of the discipline have chosen not to behave scientifically, even while claiming scientific authority.
The Distinction That Matters: Using Science vs. Being a Science
The core confusion in debates about economics’ scientific status comes from conflating two very different things. A discipline can use scientific methods without being a science. Engineers use physics, but engineering is not physics. Physicians use biology, but medicine is not biology. The use of scientific tools is necessary but not sufficient for scientific status.
What distinguishes a genuine science from a discipline that merely uses scientific methods? I propose six necessary attributes, drawing on the philosophy of science tradition from Karl Popper through Thomas Kuhn and Robert Merton.
The Six Necessary Attributes
1. Subject Matter Primacy. A science has a defined domain of investigation. For economics, this means the study of production, distribution, exchange, and consumption as the primary intellectual focus, not as a vehicle for some other project (political advocacy, moral philosophy, or ideological promotion).
Economics has always been capital-centric, concerned with how societies accumulate, allocate, and deploy resources for productive purposes. This focus distinguishes it from other social sciences. But scientific study of capital requires accurate understanding of the financial systems through which capital flows. Modern capital moves through digital networks, across global markets, with near-instantaneous settlement. This is a fundamentally different dynamic than capital formation 150 years ago, when physical commodity money constrained transactions and international flows took weeks to clear. A discipline claiming scientific authority over economic phenomena must have accurate theory of how these systems actually operate, from macro-level monetary policy down to micro-level payment clearing and credit creation.
Here mainstream economics reveals a significant gap. Standard macroeconomic models often exclude banks, debt, and money entirely. As Steve Keen observes, this is like an ornithologist studying bird flight while ignoring that birds have wings. The 2008 financial crisis exposed this gap catastrophically: models used by the Federal Reserve, the ECB, and central banks worldwide could not accommodate a major financial crisis because they did not include the financial sector in any meaningful way. A discipline that excludes the primary mechanisms through which its subject matter operates cannot claim scientific status over that domain.
2. Scarcity and Allocation. Economics specifically addresses how societies manage limited resources relative to competing wants. This is the Robbins definition from 1932, and it remains the analytical core that distinguishes economic analysis from other social sciences.
But scientific analysis of allocation requires understanding what is actually scarce. Orthodox economics treats government money as scarce, assuming governments must acquire money before spending it, either through taxation or borrowing. This framework, which I explored in detail in “Why Monetary Systems Matter”, gets the operational sequence exactly backward.
The documented operational sequence for sovereign currency issuers is: government spends first (creating money), then taxes (destroying money). Former Fed Chairman Beardsley Ruml stated explicitly in 1946 that “taxes for revenue are obsolete” for sovereign currency issuers. The Federal Reserve Bank of St. Louis confirms that the U.S. government, as the sole manufacturer of dollars, can never become insolvent. The Bank of England’s 2014 paper “Money Creation in the Modern Economy” confirmed that commercial banks create money when they make loans, not by lending out deposits.
These are operational facts, documented by central banks themselves. For sovereign currency issuers, money is not the scarce resource. The actual constraints are real: labor, materials, productive capacity, environmental limits. A scientific economics would distinguish real constraints from nominal constraints and build theory accordingly. Instead, mainstream economics treats nominal constraints as if they were real, producing systematic errors in policy analysis. When economists claim we “cannot afford” public investment, they are confusing nominal scarcity (government budget) with real scarcity (productive resources). This confusion is not scientific error but category mistake.
3. Quantification and Measurement. Science requires systematic engagement with measurable phenomena. This does not mean everything must be mathematical, but it does mean that claims must be capable of empirical assessment. The commitment to quantification enables testing, comparison, and cumulative progress.
Economics excels at quantification in some domains. National accounts, price indices, employment statistics, financial market data: these provide rich empirical material. The discipline has developed sophisticated econometric techniques for analyzing this data and testing hypotheses.
But quantification serves science only when measurements correspond to the phenomena being studied. Here monetary economics reveals problems. The Quantity Theory of Money (MV=PY), dating to at least the 16th century, assumes that increases in money supply lead proportionally to increases in prices when velocity is stable. This framework treats “money supply” as a well-defined quantity that can be measured and controlled.
In modern financial systems, this assumption fails. Money is created continuously through bank lending, destroyed through loan repayment, and flows globally through complex financial networks. The “money supply” measured by M1, M2, or other aggregates captures only a portion of credit creation and financial activity. Velocity, which the Quantity Theory assumes is stable, collapsed by 35% from its 2007 peak in the U.S., rendering the theory’s predictions meaningless.
The Fed expanded its balance sheet from $870 billion to $4.5 trillion between 2008 and 2014. Quantity Theory predicts substantial inflation. Actual inflation stayed below 2%. Japan’s central bank expanded its balance sheet to over 130% of GDP with continuous deficits for three decades. Quantity Theory predicts runaway inflation and currency collapse. Japan experienced deflation and a strong yen.
These are not minor discrepancies. They represent complete failure of the theory’s central predictions across multiple countries and time periods. A scientific discipline would abandon or fundamentally revise a framework that fails this consistently. Instead, mainstream economics continues teaching the Quantity Theory while explaining away contradictory evidence. This is the behavior pattern of ideology, not science.
4. Modeling and Abstraction. Sciences proceed through simplified representations that isolate key relationships. Economic models (whether verbal, mathematical, or computational) are legitimate scientific tools when they serve explanation and prediction rather than mere mathematical elegance.
The test is whether models include the mechanisms that actually matter for the phenomena being studied. Here mainstream macroeconomics fails conspicuously. Dynamic Stochastic General Equilibrium (DSGE) models dominate academic macroeconomics and central bank forecasting. These models are mathematically elegant, technically sophisticated, and built on assumptions that have been empirically falsified.
Most critically, standard DSGE models exclude banks, private debt dynamics, and realistic treatment of money creation. When you exclude the mechanisms through which financial crises actually unfold, you cannot predict financial crises. In 2003, Nobel laureate Robert Lucas declared that “the central problem of depression prevention has been solved.” In 2007, Fed Chairman Bernanke stated that subprime problems would be “contained.” The profession’s dominant models could not accommodate what was about to happen because they did not include the mechanisms causing it.
The heterodox economists who did predict the 2008 crisis (Steve Keen, Wynne Godley, Michael Hudson, Dean Baker) worked from models that included banks, debt dynamics, and financial fragility. Their models made better predictions because they included the mechanisms through which crises actually unfold. A scientific discipline would compare predictive accuracy across frameworks and adopt those that perform better. Instead, mainstream economics marginalized these approaches institutionally while preserving failed models.
The modeling problem extends to government finance. Standard models treat government as a large household that must acquire money before spending it. This produces predictions (crowding out, interest rate spikes from deficits) that consistently fail empirically. The 2020 deficit reached $3.1 trillion (14.9% of GDP). Standard models predicted rising interest rates and crowding out of private investment. Actual results: 10-year Treasury yields remained below 1.5%, corporate bond issuance reached records, private investment grew. When models consistently generate wrong predictions because they misrepresent how the system actually operates, continuing to use those models is not scientific behavior.
5. Falsifiability and Empirical Testing. This is the heart of the matter. Following Popper, scientific propositions must be formulated in ways that permit refutation. A theory that cannot be wrong is not scientific. More importantly, when theories are falsified, scientists must be willing to abandon or revise them.
Economics faces distinctive challenges in falsification. Controlled experiments are often impossible. Ceteris paribus clauses complicate testing. But these challenges do not excuse the discipline’s systematic failure to abandon falsified theories.
Consider the track record as I documented in “Why Economic Models Matter”:
The Quantity Theory predicted that quantitative easing would cause runaway inflation. It did not happen. The theory persists.
Crowding out theory predicted that large deficits would raise interest rates and reduce private investment. Japan and the U.S. demonstrated the opposite. The theory persists.
Mainstream models predicted that the 2008 crisis was virtually impossible. The crisis happened. The models were adjusted slightly rather than abandoned.
European austerity was imposed based on theories predicting that deficit reduction would restore confidence and growth. Greece’s GDP contracted 27%, unemployment hit 28%, similar devastation struck Spain and Portugal. The IMF eventually acknowledged their multiplier estimates were wrong. The underlying theory persists in textbooks and policy debates.
In genuine science, such repeated failures trigger fundamental reconsideration of basic assumptions. In economics, they are treated as anomalies while core frameworks remain untouched. This pattern reveals that mainstream economics is not practicing science but maintaining theoretical commitments through institutional power rather than evidence.
The 2021-2022 inflation episode provides a contemporary test. Using the monetary definition of inflation, policymakers initially diagnosed “too much money printing” and prescribed interest rate increases. But detailed analysis revealed different causes: Economic Policy Institute found corporate profits accounted for over 50% of price increases versus 11% historically. The Federal Reserve Bank of Kansas City documented supply chain bottlenecks as primary drivers: semiconductor shortages, port congestion, labor force disruptions, energy price spikes from the Ukraine invasion.
This was supply-side inflation requiring targeted responses, not monetary inflation requiring blanket demand destruction. The misdiagnosis risked creating both inflation (from unaddressed supply constraints) and recession (from interest rate increases) simultaneously. A scientific approach would distinguish types of inflation and calibrate responses accordingly. The monetary framework, which treats all inflation as monetary, cannot make these distinctions.
6. Emergent Order and Unintended Consequences. Economics characteristically investigates how individual decisions aggregate into systemic patterns that nobody intended. This distinguishes economic analysis from engineering (which assumes designed systems) and from purely descriptive social science.
This attribute represents economics at its best. Adam Smith’s invisible hand, Hayek’s spontaneous order, Minsky’s financial instability: these are genuine insights about how decentralized decisions produce systemic outcomes. The paradox of thrift (individually rational saving becomes collectively destructive), bank runs (individually rational withdrawal triggers collective collapse), speculative bubbles (individually rational investment aggregates into systemic fragility): these represent economic analysis contributing genuine understanding.
But emergent order analysis requires accurate understanding of the systems through which emergence occurs. When mainstream economics excludes banks, debt, and realistic monetary operations from its models, it cannot analyze the emergent dynamics that actually produce crises, cycles, and systemic instability. Minsky’s Financial Instability Hypothesis succeeds precisely because it models the actual mechanisms (hedge, speculative, and Ponzi financing) through which stability breeds instability. DSGE models fail because they exclude these mechanisms and assume markets clear automatically.
Modern financial systems create new forms of emergent dynamics. Digital trading systems, algorithmic decision-making, global capital flows with near-instantaneous settlement: these produce emergent patterns qualitatively different from pre-digital markets. The 2010 Flash Crash, when the Dow dropped nearly 1,000 points in minutes before recovering, illustrated emergent dynamics in modern financial systems that 19th-century theory cannot capture. A scientific economics would develop theory adequate to the systems it studies. Instead, mainstream economics continues applying frameworks developed when telegraph communication was cutting-edge technology to systems operating at millisecond speeds across global networks.
Applying the Framework: Where Economics Stands
When I apply this framework rigorously, I find that economics as a whole is not a science, but significant portions of economic work meet every criterion. The discipline contains genuine science alongside ideology, advocacy, and intellectual cargo cult behavior.
The Scientific Core
Certain domains of economics clearly function scientifically:
- Experimental economics runs controlled experiments on economic behavior, tests hypotheses, publishes results, and revises theories based on evidence. The replication crisis in this field (approximately 39% of studies fail to reproduce) is actually evidence of scientific practice: you cannot have a replication crisis if you never attempt replication. The crisis reveals underinvestment in scientific norms, but the norms themselves are present.
- Empirical industrial organization tests theories of firm behavior against data, using increasingly sophisticated identification strategies to establish causal relationships. When theories fail to match data, researchers revise them.
- Development economics has increasingly embraced randomized controlled trials, bringing genuine experimental methodology to questions about poverty, education, and health interventions. Esther Duflo and Abhijit Banerjee won the Nobel Prize for this work.
- Financial economics contains both scientific and ideological elements. Eugene Fama’s Efficient Market Hypothesis generated testable predictions that have been subjected to decades of empirical scrutiny. The fact that Joseph Stiglitz and Sanford Grossman demonstrated logical problems with the hypothesis, and that the 2008 crisis raised empirical challenges, represents scientific discourse as it should function.
The Pre-Scientific Mainstream
Much of mainstream macroeconomics operates in what Steve Keen aptly calls a “pre-scientific” mode. DSGE models that dominate academic macroeconomics are elegant mathematical constructions built on assumptions that have been empirically falsified.
The rational expectations hypothesis assumes that economic agents correctly anticipate future prices using the same model that economists use to analyze them. This assumption is not treated as a testable hypothesis but as an axiom. When real humans consistently violate rational expectations (as behavioral economics has documented for decades), DSGE modelers do not abandon the framework. They add epicycles.
The problem is not that these economists use mathematics. The problem is that they have immunized their core assumptions from empirical challenge, excluded the mechanisms (banks, debt, money creation) through which financial dynamics actually unfold, and continue applying frameworks developed for commodity-money systems to modern fiat currency economies with digital global capital flows.
The Heterodox Traditions
What about the heterodox schools I discussed in my earlier essay?
Post-Keynesian economics focuses on uncertainty, financial instability, and the role of money and debt. Hyman Minsky’s Financial Instability Hypothesis predicted that capitalist economies would endogenously generate financial crises through the evolution from stable to fragile financing structures. The 2008 crisis provided substantial corroboration. This is falsifiable theory that made risky predictions and was vindicated by events.
- Modern Monetary Theory challenges orthodox views about government deficits and monetary operations. Its core claims are testable: sovereign currency issuers face real resource constraints but not financial constraints; interest rates are a policy variable; inflation results from spending beyond productive capacity, not from deficits per se. Whether you find MMT persuasive or not, it is making scientific claims that permit empirical assessment and that correspond to documented monetary operations.
- Ecological economics treats the economy as a subsystem of the biosphere, subject to thermodynamic constraints. Herman Daly’s work generates testable propositions about the relationship between economic growth and environmental degradation.
- Feminist economics examines how gender biases shape economic theory and outcomes. Julie Nelson’s empirical work has refuted widely held assumptions about gender differences in economic behavior.
The heterodox traditions are not uniformly scientific. But the best heterodox economics meets scientific criteria at least as well as the best mainstream work, and often better, precisely because it includes the mechanisms (banks, debt, financial dynamics, real resource constraints) that mainstream models exclude.
Why “Schools of Thought” Are a Problem
The persistence of competing schools reveals something important about economics’ scientific status. The problem is not that competing paradigms exist. Physics has competing interpretations of quantum mechanics. Biology had debates about punctuated equilibrium versus gradualism. Disagreement at the frontier is normal in science.
The problem is how economics resolves these disagreements. In genuine sciences, competing theories are eventually adjudicated through evidence and experimentation. The disagreements move to the frontier as core questions get settled. We no longer debate whether atoms exist or whether evolution occurs.
Economics, after more than a century, has not resolved basic questions. Do free markets tend toward equilibrium or instability? Does monetary policy affect real output in the long run? Are financial crises inevitable features of capitalism? How do sovereign currency systems actually operate? These are not frontier questions. They are foundational questions, and economists still disagree fundamentally about them.
The reason these questions remain unresolved is that economics has not developed institutional mechanisms for allowing evidence to adjudicate between competing theories. Publication in top journals correlates with working within the dominant paradigm, not with empirical success. Failed predictions do not result in theory abandonment. Heterodox approaches with better predictive records are marginalized institutionally rather than refuted empirically.
This is the behavior pattern that prevents economics from being a science. Not the existence of disagreement, but the resolution of disagreement through institutional power rather than evidence.
What Would Scientific Economics Look Like?
If economics were to function as a genuine science, what would we observe?
Theories would specify their falsification conditions. Every economic model should be accompanied by a statement of what empirical findings would lead its proponents to abandon it. If no such findings can be specified, the model is not scientific.
- Failed predictions would have consequences. The economists who confidently predicted that the 2008 crisis was impossible should have faced professional consequences. Their models should have lost credibility.
- Replication would be routine. Currently, approximately 0.9% of papers in top economics journals attempt replication. This is embarrassingly low for a discipline claiming scientific status.
- Heterodox alternatives would be tested, not marginalized. If Minsky’s Financial Instability Hypothesis makes better predictions than DSGE models, that should matter for which approach receives resources and recognition.
- Models would include the mechanisms that matter. Banks, debt dynamics, money creation, financial fragility: these cannot be excluded from models claiming to explain financial crises or monetary policy.
- The positive/normative distinction would be maintained. Economic analysis would be clearly separated from policy advocacy.
- Schools of thought would gradually dissolve. As evidence accumulated, economists would converge on answers to basic questions. Remaining disagreements would concern genuinely uncertain frontier issues.
- Monetary operations would be taught accurately. Textbooks would describe how banks actually create money, how government spending actually works, and how central banks actually conduct policy. The gap between what practitioners at the Fed know and what students learn would close.
- Real constraints would replace nominal constraints. Policy debates would focus on whether we have the workers, materials, and productive capacity to accomplish goals, not on whether we can “afford” things in nominal terms.
None of this is impossible. Economics has the tools to be a science. The question is whether the discipline chooses to use them.
The Stakes
Why does this matter? Because economic ideas have enormous consequences for human welfare.
The belief that government deficits are inherently dangerous has constrained public investment in education, infrastructure, and healthcare. The belief that markets are inherently efficient has justified financial deregulation that contributed to devastating crises. The belief that unemployment is a voluntary choice has shaped policies that treated jobless workers as moral failures rather than victims of systemic forces.
As I documented in “Why Monetary Systems Matter”, the misunderstanding of monetary systems has particularly severe consequences. When politicians claim we “cannot afford” universal healthcare or infrastructure investment, they are applying hard money logic to a soft money system. The real question is not whether we can create the money but whether we have the real resources to accomplish the goal without causing inflation.
The “national debt narrative” that governments must operate like households is demonstrably false. Mainstream economists know this. Unlike households, governments that control their currency create money and cannot “run out.” Yet this framework dominates discourse because it serves those who prefer limited government while avoiding political debate about values. Framing spending as constrained by debt removes entire categories of social investment from democratic consideration.
These beliefs are not scientific findings. They are ideological commitments dressed up in mathematical formalism. And they have been imposed on democratic societies as if they were natural laws rather than contested claims.
A Path Forward
My earlier essay concluded that economics is not a science. This essay refines that conclusion: economics as currently practiced is largely not a science, but it contains scientific elements and has the potential to become genuinely scientific.
The path forward requires several things:
Institutional reform. Journal publication, hiring, and promotion decisions should weight empirical success and predictive accuracy more heavily than mathematical sophistication or conformity to dominant paradigms.
- Methodological pluralism within scientific constraints. Diverse methods (verbal, mathematical, statistical, computational) are legitimate when they serve scientific goals of explanation, prediction, and falsification.
- Organized skepticism. Following Robert Merton’s norms, economics needs more replication, more transparent data sharing, more pre-registration of hypotheses, and real consequences for failed predictions.
- Honest boundary-drawing. Economists should be clear about what they know, what they suspect, and what they are merely assuming. The discipline should stop presenting ideological commitments as scientific findings.
- Engagement with heterodox alternatives. Instead of marginalizing Post-Keynesian, ecological, feminist, and institutional approaches, mainstream economics should subject them to the same empirical tests it applies (or should apply) to its own theories. The best ideas should win, regardless of their provenance.
- Accurate monetary education. Economics curricula must teach how monetary systems actually operate, not how 16th-century theorists imagined they work. Students should learn the operational sequence of government spending and taxation, how banks create money through lending, and why the Quantity Theory fails empirically. The gap between what central bank practitioners know and what textbooks teach must close.
- Theory adequate to modern systems. Capital now flows through digital networks, across global markets, with near-instantaneous settlement. Economic theory developed for commodity-money systems with physical settlement cannot simply be assumed to apply to fiat currency systems with electronic capital flows. A scientific economics would develop theory adequate to the systems it studies.
- Reframe constraints honestly. Economic debates should distinguish real constraints (labor, materials, productive capacity, environmental limits) from nominal constraints (budget rules, debt limits, “affordability”). The question is never whether a sovereign currency issuer can create money but whether doing so would cause inflation by exceeding real productive capacity.
Conclusion
Is economics a science? The honest answer is: not yet, but it could be.
Economics meets some scientific criteria and violates others. It has the subject matter, the quantitative orientation, and the modeling tools of a science. What it lacks is consistent commitment to falsification, organized skepticism, accurate understanding of monetary operations, and the resolution of disputes through evidence rather than institutional power.
The discipline uses science without being a science. This distinction matters because it identifies what needs to change. The problem is not that economics studies human behavior, which is complex. The problem is not that economics uses mathematics, which is appropriate. The problem is that economics has developed institutional structures that insulate core theories from empirical challenge while teaching operational falsehoods about how monetary and financial systems work.
Economics has always been capital-centric, and rightly so. But capital formation and allocation in the era of digital global finance differs fundamentally from capital formation under commodity money with physical settlement. A scientific economics would develop theory adequate to the systems it studies, test predictions against outcomes, and revise frameworks that fail. Instead, mainstream economics continues applying 16th-century monetary theory and 19th-century equilibrium assumptions to 21st-century financial systems, preserving failed models through institutional power while marginalizing approaches with better predictive records.
Until economists start from operational reality rather than convenient assumptions, revise theories that fail empirically, and teach students how monetary systems actually work, the discipline cannot claim scientific status. Physics is a science because physicists behave scientifically. Economics will become a science when economists do the same.
Until then, we should be skeptical of economic claims presented with scientific authority. We should ask: What would falsify this theory? What predictions does it make? Has it been tested against alternatives? Does this model include banks, debt, and money, or does it assume these can be ignored? Does it distinguish real constraints from nominal constraints? If the answers are unsatisfactory, we are not dealing with science, however many equations are involved.
Economics matters too much for us to accept scientific pretension without scientific substance. The discipline has the tools to do better. The question is whether it will choose to use them.
This essay develops ideas from my earlier pieces “Economics is not a Science”, “Evolution of Economic Ideas: Insights into Major Schools of Thought”, “Why Monetary Systems Matter”, “Why Economic Models Matter”, and “Evolution of Economic Ideas: Insights into Major Schools of Thought”. The framework for assessing scientific status draws on Popper’s falsificationism, Kuhn’s paradigm theory, and Merton’s norms of science, as well as recent work on the replication crisis in economics and the operational approach to monetary systems developed by Post-Keynesian and MMT economists.