Yes, Econ 101 is underrated.

It’s especially good at prediction.

Matthew Yglesias

recently penned a piece titled “Econ 101 is underrated,” ($) extolling the virtues of basic economics as a toolkit for policy analysis. As a card-carrying member of the Econ 101 fan club, I couldn’t agree more with Yglesias’s central point that the humble concepts from an introductory econ class form “a flexible and powerful paradigm that sheds much-needed light on important questions, and plays a crucial role in structuring and disciplining policy analysis.”

Today, I want to take the discussion in a complementary direction. Rather than focusing on policymaking, which I’ve been doing too much lately, I want to make a different point: Econ 101 is damn good at helping to understand the world. The positive economics of supply and demand—and just the simple act of asking which side of the market shifted—explains a remarkable amount of what happens around us. Even if you dislike the policy conclusions that Econ 101 might imply, its descriptive power is undeniable.

Everything comes down to a simple idea: if costs rise, prices tend to rise. If demand surges, prices tend to rise. These simple insights, drawn from the first pages of any econ textbook, go a long way toward explaining phenomena as varied as soaring egg prices, housing shortages, tariffs on imports, and New York City’s new congestion charge. This may seems trivial but an incredible amount of people don’t make the same prediction and many actively deny it when it happens.

Econ 101 won’t give us every answer, of course. As Yglesias notes, “life is, of course, more complicated” than any basic model. But time and again, the basic supply-and-demand framework proves to be a reliable lens for predicting and interpreting market outcomes. Let’s walk through how this works in practice.

Supply, Demand, and Predicting the World

A famous quip says you could teach a parrot to be an economist by just teaching it to squawk “supply and demand.” The joke is that economists seemingly explain everything with those two words. My colleague Irving Fisher once lamented this “glib phrase” that supposedly “fully explained” prices and wages. And indeed, just shouting “supply and demand!” by itself doesn’t tell us much; it’s what we do with the concept that matters.

Here at Economic Forces, we wear supply-and-demand on our sleeves. Indeed, it forms the central premise of the book I’m writing. But it’s not meant as a magic catchphrase or a tautology. Instead, we use it as a starting point, an invitation to ask more questions. The key question Econ 101 encourages us to ask about any market puzzle is wonderfully simple: What shifted? Which curve moved? Supply or demand? Answering that unlocks a chain of predictions. As I’ve argued, this framework forces us to specify what we’re comparing, identify what is different, and trace out the implications. It disciplines us to think in terms of cause and effect. Once we identify a shock—say a bad harvest, a new tax, or a surge in consumer preferences—Econ 101 directs us to which side of the market that shock hits. From there, the theory makes a clear prediction about prices and quantities.

Crucially, this kind of positive economic analysis is value-neutral. It doesn’t tell us if an outcome is good or bad or what policy should be adopted; it just predicts what will happen. You might not like the outcome (I certainly don’t like when the price of eggs doubles) but if the theory says that outcome will occur given the conditions, you dismiss it at your peril.

Let’s apply this to a few real-world cases, almost all of which we have talked about before. In each story, notice how identifying the shock and its impact on supply or demand provides a powerful guide to understanding the result. Econ 101 won’t give us the entire richness of each story (there are always institutional details), but it will get us about 80% of the way there, and sometimes that’s all we need to grasp the big picture.

Eggs: A Price Surge With a Simple Explanation

In late 2022 and early 2023, Americans got shell-shocked by the price of eggs. Grocery shelves were emptying and average egg prices more than doubled compared to the prior year. The public outcry was palpable. Some pointed fingers at Big Egg, accusing producers of price gouging or colluding to pad profits. Even a commissioner of the FTC weighed in, suggesting an investigation into possible market manipulation after seeing empty shelves and skyrocketing prices.

It’s an understandable reaction: when a staple food’s price explodes, it feels like something must be amiss or unfair. The conspiracies start flying!

Enter Econ 101. Instead of leaping to conspiracy theories, we grab our supply-and-demand toolkit and ask: What changed in the egg market? On the supply side, the answer was straightforward and grisly. In total, more than 136 million birds were lost in the U.S. over the year—a staggering supply shock to egg production. Fewer hens, fewer eggs—the supply curve for eggs shifted leftward (a decrease in supply). What does Econ 101 predict from a supply decrease? Higher prices. Or as I phrased it in our newsletter on egg prices: with a sudden supply disruption, especially in a market like eggs, “even small supply disruptions can generate large price changes.” Why large? Because both the supply and demand for eggs are highly inelastic in the short run. You can’t ramp up egg production overnight; raising new chickens takes months, so the immediate supply is basically fixed (vertical). And consumers don’t have great substitutes for eggs. If you’re baking or want your breakfast omelet, there’s only so much swapping you can do. Jayson Lusk suggests the price elasticity of demand for eggs is around -0.15, meaning a 1% price increase only cuts quantity demanded by 0.15%.

In plain English, people pretty much buy eggs even if they get more expensive. Combine an inelastic demand with a big supply shock, and Econ 101 will tell you to brace for a huge jump in price. I’ve walked through this logic before, in detail, concluding that the egg price spike was “exactly what we expect to see in competitive markets with highly inelastic short-run supply.” Yes, egg producers’ profits rose in the short run, but that’s a natural result of a supply shock, not prima facie proof of foul play.

Housing: Shortage or Wall Street Greed?

Housing affordability (or lack thereof) is a complex issue with many facets, but at its heart lies a straightforward Econ 101 story: when growing demand meets constrained supply, prices will rise.

Over the past decade, many U.S. cities have seen rapid growth in demand for housing. Young adults striking out on their own, job seekers flocking to dynamic metros, families wanting more space. There are plenty of reasons housing demand has risen.

Americans (and many would-be foreign buyers) are richer today than a generation ago, and the expected returns to owning in the hottest job markets have been eye-popping. Higher incomes push the entire demand curve for living space outward. That is a crucial aspect of the story. But it only drives up prices over the long run if the quantity of housing doesn’t respond.

Unfortunately, for home buyers, the supply of housing in many of these places has not expanded commensurately. Why? In large part because of policy choices: restrictive zoning, lengthy permitting processes, NIMBY opposition. The list of barriers is long.

Econ 101 tells us that when demand increases (shifts right) and supply is inelastic or capped, the result is exactly what we observe: higher equilibrium prices (home prices and rents) and a “housing shortage.” Some commentators prefer to blame alternate villains for high housing costs—popular scapegoats include greedy developers, speculators, or even “Wall Street” landlords. But while those make for catchy headlines, they often miss the bigger picture. We can go into more depth but often we will just be doing what an Urban Institute report succinctly put it,

The key point is that home prices and rents are primarily determined by the supply and demand for housing units and by changes to that supply and demand. This seemingly obvious point has enormous implications for how we deal with high home prices and rents.

In other words, look to your Econ 101 supply-and-demand basics: too many buyers chasing too few homes. The same report notes that this “seemingly obvious point has enormous implications,” because it “cuts through much of the finger pointing” in housing debates. When supply is tight, anything that boosts demand (like easier mortgages, lower interest rates, or investors snapping up properties) can indeed push prices up—but those demand factors are interacting with a fundamental supply shortfall. Factors like low interest rates hit all housing markets but not all housing markets see crazy prices.

Focusing on supply and demand can also suggest that some things are probably just red herrings and political scapegoats. I’ve previously tackled the notion that institutional investors were uniquely to blame for rising home prices. The analysis showed that what matters for prices is the total demand for housing relative to supply—whether the demand comes from regular families or big investors is largely beside the point. Every occupied home is one household demanding housing; institutional buyers often just convert owner-occupied demand into rental demand, which doesn’t change the underlying shortage. The upshot was that without enough new construction, home prices would be high with or without Wall Street’s involvement. Again, Econ 101’s prediction holds: binding supply constraints + strong demand = rising prices. High housing costs are exactly what our basic model would predict given the circumstances. None of this is to say policy can’t improve the situation—of course, reforming zoning or promoting building would help enormously (because they’d shift the supply curve out, alleviating the shortage).

Indeed, as Yglesias noted, an era of “anti-market policies in crucial areas like land use” has real consequences. Econ 101 predicts those consequences. You can dislike the unaffordability, you can crusade for social housing or rail against landlords, but you can’t ignore the basic supply-and-demand imbalance at the root of the issue.

Congestion Pricing: A Victory Lap for Econ 101

Let’s talk about congestion pricing. This is a textbook case (literally, the NYC example will be in textbooks soon) of Econ 101 logic being applied in real time, with predictive success. For ages, economists have complained that roads in busy cities are under-priced (usually free) despite being a scarce resource. The result, unsurprisingly, is excess demand in some sense: too many cars want to use limited road space, leading to the familiar congestion we all despise. How can you reduce traffic (whether you want to or not)? The straightforward Econ 101 idea is to put a price on the scarce resource. Charge a toll that makes drivers take into account the cost of the congestion they impose on others. By forcing drivers to “pay” for the road, you effectively shift the demand curve left (fewer people will want to drive at any given time). Alternatively, you can think in terms of the time plus monetary price and the congestion price causes a movement along the demand curve.

So, what happens when a city actually implements such a charge? Econ 101 predicts: fewer cars where the charge applies, less congestion, and a more efficient allocation (those who value the trip most will still go, those on the margin will opt out or switch to transit). It’s one thing to predict this in theory—but the proof is in the pudding. London, Singapore, and Stockholm have done it successfully in the past. Now New York is taking a turn. As I wrote before, the early evidence from Manhattan’s rollout is encouraging for the theory. Initial traffic data showed exactly the pattern Econ 101 would anticipate: at the tolled entry points into Manhattan, traffic volumes dropped significantly, whereas within the city (where a lot of traffic is taxis and delivery vehicles that are less price-sensitive) congestion didn’t change as much. Why? Because drivers with alternatives (commuters who could take the subway, for example) responded to the price by driving less—the reduction in driving was greatest among the more elastic users. Meanwhile, those with inelastic demand (say, high-income drivers or commercial vehicles) continued to drive and pay the fee.

The result is exactly what the model predicts: the marginal trips were pruned away, freeing up road space for those who value it more, and overall congestion eased. My initial reaction to this outcome, as I wrote, was “of course, pure joy. Another W for supply and demand.” In other words, chalk one up for Econ 101—the policy worked as advertised by basic economic theory. To be sure, whether congestion pricing is popular or politically fair is a separate question (hence the second half of that article debating if it’s a “good idea” overall). But in terms of predicting results, the Econ 101 approach nailed it. Demand curves slope down—make something more expensive, and people do less of it. It doesn’t get much more fundamental than that.

Even in this example, Econ 101 provides more nuance than critics sometimes credit it for. The framework not only predicted an overall drop in traffic, but also suggests who will stop driving (those with more elastic demand) versus who will keep driving (inelastic demand). That’s an important insight for policy design: it tells us where the burden will fall and what the side effects might be. All from a simple model of behavior and a demand curve. Pretty powerful for a so-called “simplistic” theory, no?

Love, Sex, and Supply & Demand

The predictive power of supply and demand isn’t confined to commodities like eggs or services like road space; it can even shed light on complex social phenomena, such as marriage patterns. Economists often analyze relationships through the lens of a “marriage market,” where individuals with certain characteristics effectively “supply” partnership and “demand” companionship. While this might sound unromantic, the framework helps predict how changes in the availability of partners—specifically, shifts in sex ratios—can dramatically alter societal outcomes.  

Consider what happens when the relative supply of men and women becomes imbalanced. If, for example, a society experiences a significant shortage of men relative to women (perhaps due to war casualties or sex-specific emigration), women face increased competition for male partners. Econ 101 would predict that the “scarcer good” (in this case, men) gains bargaining power. Conversely, if there’s a shortage of women, men must compete more intensely for female partners.

Empirical work often validates these S&D predictions. For example, Josh Angrist (2002) used historical variation in U.S. immigrant flows—which often had skewed sex ratios—as a natural experiment to study effects on the children of immigrants. Because second-generation marriages were often endogamous (within the same ethnic group), immigrant sex ratios impacted the relevant marriage market. Angrist found that higher sex ratios (more men per woman) significantly increased the likelihood of marriage for second-generation women and decreased their labor force participation, consistent with improved marriage prospects reducing the need for independent income. Interestingly, higher sex ratios also seemed to slightly increase marriage rates for second-generation men and were associated with higher male earnings, potentially reflecting Becker’s idea that men invest more in their attractiveness as mates when the market is competitive. These findings were broadly consistent with higher sex ratios increasing female bargaining power in the marriage market.

More recently, Shang-Jin Wei and Xiaobo Zhang (2011) proposed a “competitive saving motive” to explain China's high and rising household savings rate, linking it directly to the country’s increasingly skewed sex ratio (many more young men than women). Their S&D logic is that as the sex ratio rises, families with sons compete fiercely to improve their sons’ marriage prospects by accumulating wealth. Using both household-level and cross-provincial data, they found strong evidence supporting this: savings rates for households with sons were significantly higher in regions with higher sex ratios. Furthermore, they estimated that this competitive saving motive, driven by the marriage market imbalance, could account for about half (or even more based on IV estimates) of the dramatic increase in China’s household savings rate between 1990 and 2007. They also found evidence suggesting this competition bids up housing values.

These examples demonstrate that the fundamental logic of Econ 101—that relative scarcity affects value and behavior, and that markets (even unconventional ones) tend to respond to shifts in supply and demand—provides powerful insights. While human relationships are vastly more complex than a simple market transaction, understanding the underlying “supply” of and “demand” for partners, as influenced by demographic factors like sex ratios, helps explain a surprising array of social and economic patterns observed across different cultures and time periods. The framework doesn’t capture the full emotional content of marriage, of course, but it does offer a valuable, and often empirically supported, perspective on how broad societal forces shape individual choices and outcomes.

But isn’t there market power?

An objection I hear after any Econ 101 explainer is: “Sure, Brian, but your neat supply-and-demand diagrams assume perfect competition. Real-world firms have market power.”
Fair point. Yet almost all of the directional predictions survive even under monopoly (or oligopoly) because they hinge on comparative statics, not on the competitive-markup assumption.

A monopolist sets output where marginal revenue equals marginal cost. Raise the cost curve—say feed costs for egg producers spike—and the profit-maximizing quantity falls. Slide up the demand curve and the new price is higher. Same arrows as the competitive chalkboard: supply shock → P↑, Q↓.

Shift the demand curve outward—think consumers have a lot more money from stimulus checks—and both the revenue curve and the monopoly’s optimal quantity move right. The price rises along with sales. Again, P↑, Q↑ just like the competitive model. The monopoly mark-up changes the size of those moves, not the sign. Josh has written on this before.

So when avian flu wipes out hens, or the U.S. slaps a tariff on washers, or New York sticks a fee on driving into Manhattan, you don’t need atom-sized competitors for “price goes up” to be the right first guess. Monopoly mostly stretches the magnitudes; it rarely reverses the arrows.

(For the econ nerds keeping score: the formal proof traces back to Cournot, and Weyl-Fabinger 2013 is a nice modern treatment of pass-through under imperfect competition.)

Now, this isn’t to say the predictions are always identical in direction. Market power can introduce scenarios where standard Econ 101 competitive predictions get more nuanced, or even flip. For instance, while Econ 101 typically teaches that price floors like minimum wages reduce employment, this isn’t necessarily true under monopsony (a market with a single dominant employer). An appropriately set minimum wage in a monopsonistic labor market can actually increase employment, pushing it closer to the competitive outcome. So, context matters. However, the broader Econ 101 insight that price controls—like price ceilings—generally create shortages remains a remarkably robust starting point for analysis.

The takeaway is liberating: before we litigate structure, we can still lean on Econ 101 comparative statics to get the sign right. Market power matters but the first-order “which curve moved?” logic travels surprisingly far.

Oh, and Econ 101 does cover market power! Another W for Econ 101.

None of this is to say Econ 101 is the be-all and end-all. I took a lot more courses because there was more to learn! (Actually, I never took 101, so maybe it’s not that important…) It’s not a flawless oracle, and it won’t always capture every complication—no economist would claim otherwise. Yglesias rightly points out that “Econ 101 thinking isn’t perfect or all there is,” but emphasizes it remains an “underrated framework with a lot of explanatory power.”

The examples above bear that out. The basic supply-and-demand approach predicted real-world outcomes with impressive accuracy. That is descriptive power that we ignore at our own risk. Econ 101 is not about telling you what to think, but how to think—how to break down a question, identify the forces at play, and foresee potential outcomes. It’s a positive, analytical tool. You might not like the story it tells. Sometimes Econ 101 implies trade-offs or outcomes that are politically inconvenient.

Before we argue about what to do, whom to subsidize, what to regulate, and whom to blame, we should use our intro-econ toolkit to understand why things are happening. In my experience, nine times out of ten, identifying which side of the market shifted (and how responsive people are) will get you most of the way toward that understanding. Supply down? Price up. Demand up? Price up. It’s not the whole story, but it’s the first and most important chapter. Basic economics doesn’t give us license to be glib or to avoid digging into details—rather, it gives us a disciplined way to organize those details. It’s a way to unravel puzzles about the world, not the final answer in itself. But unraveling puzzles is a pretty great tool to have!