Chapter 2: Thinking Like an Economist

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Hey there curious minds, welcome back to the deep dive where we unpack complex ideas and

hopefully find those aha moments you've been searching for.

Today we're doing something a bit different.

We're not just learning economic terms.

We're diving deep into the very methodology of economics itself.

It's about learning to think like an economist.

Every field gives you a unique lens to see the world, doesn't it?

Mathematicians have axioms, psychologists cognitive dissonance.

Well, economists have this powerful toolkit to supply, demand, elasticity,

comparative advantage, all that.

But before we get into those specific tools, we really need to understand the fundamental framework, the way an economist approaches a problem.

Exactly.

Our deep dive today is all about this core economic methodology.

We're drawing heavily from chapter two of Mankey's principles of microeconomics.

We'll be exploring what it really means to adopt this particular mindset.

Our mission is to go beyond just definitions and get into the surprising implications, the strengths, and maybe even some limitations of thinking this way.

Okay, sounds good.

Let's unpack this journey into the economist's mind then.

We'll start with economics as a science.

That sounds interesting.

Right.

Then we'll look at two foundational models, you know, the building blocks.

Okay.

Then differentiate micro from macro perspectives.

Big picture, small picture.

Got it.

Then the role of economists as policy advisors.

That gets complicated.

And finally, tackle that classic question.

Why do these experts sometimes disagree?

Right.

Okay.

So first up,

economists try to be scientists.

Now that might sound a bit odd to some people.

You don't usually picture economists with test tubes, do you?

No, not usually.

So what does scientific really mean here?

What does that rigor look like when you can't exactly, you know, run controlled experiments on whole countries?

Well, what's fascinating is that the core of science isn't really the equipment.

It's the approach.

It's about the dispassionate development and testing of theories about how the world works.

Albert Einstein actually said science is like a refinement of everyday thinking.

And that's what economists do.

They observe something happening.

Maybe prices rising fast.

Okay.

Like inflation.

Exactly.

They formulate a theory.

Perhaps it's because the government is printing too much money.

And then they test that theory.

They look at data, maybe from many countries over time, try to see if there's a link between money growth and price increases.

It's systematic.

That comparison to Newton seeing the apple fall.

It actually makes sense, doesn't it?

How observation sparks theory.

It does.

But unlike Newton, economists face this big hurdle.

They can't just, you know, tweak a nation's monetary policy slightly and see what happens like dropping an apple again and again in a lab.

So how do they test their ideas?

That's a major challenge.

You absolutely can't just manipulate a national economy for an experiment.

So economists often have to rely on what we call natural experiments.

These are historical events or real world situations that happen to mimic controlled conditions, at least somewhat.

Like studying how a sudden oil supply disruption in the Middle East affects global prices or how it impacts living standards in different countries.

It's not perfect.

The real world is messy.

But these events provide invaluable data.

Okay.

So they use history and real world events as their sort of lab.

And to even start analyzing this complexity, they use assumptions, right?

Like the physicist assuming no air friction for a falling marble.

Precisely.

Assumptions are essential for simplifying complex reality.

The physicist knows air friction exists, but for the marble problem, it might be small enough to ignore to focus on gravity.

But for us, the key isn't just that they use assumptions, but which ones they choose.

That choice must matter a lot for the model can tell us and what it can't.

Absolutely.

Which assumptions are the right ones?

That's a crucial question.

And the answer is, it depends entirely on the question you're asking.

A physicist uses different assumptions for a falling marble versus, say, a falling beach ball where air resistance is huge.

Right.

Similarly, an economist studying the short -run effects of a policy might assume prices are fixed or sticky because they often adjust slowly.

Okay.

But for long run effects, assuming prices are flexible and adjust fully is probably more appropriate.

The skill lies in simplifying just enough to isolate the key mechanism without making the model useless for the real world.

So these carefully chosen assumptions become the foundation for economic models, like diagrams or equations.

Exactly.

And these models are like, think of a plastic model of the human body in a biology class.

It shows the major organs, but leaves out every single muscle fiber or tiny blood vessel.

Right.

It simplifies to clarify.

Precisely.

Economic models omit many details to help us see what's truly important.

They simplify reality to improve our understanding, but we always need to remember what's been left out.

Okay.

That makes sense.

So let's look at the first specific model you mentioned, the circular flow diagram.

You said it's fundamental for seeing how things fit together.

Yes.

The circular flow diagram.

It's a visual model, really simple but powerful, showing how dollars flow through markets between the main actors,

households and firms.

It boils the economy down to just two types of decision makers.

You've got firms, they produce goods and services using inputs like labor, land, capital, you know, the factors of production.

Okay.

Firms make stuff.

And then you have households.

Households own those factors of production.

They own the labor, the land, the capital, and they consume the goods and services the firms produce.

Right.

So producers and consumers basically, and they interact in markets.

Exactly.

Two main types of markets.

First, the markets for goods and services.

Here, households are the buyers and firms are the sellers, the grocery stores, car dealerships.

Second, the markets for the factors of production.

Here, the rules reverse.

Households are the sellers.

They sell their labor time, rent out their land, lend their capital.

And firms are the buyers.

They hire the labor, rent the land, borrow the capital to produce things.

Okay.

So goods market and factor market.

And the diagram shows flows between these.

Yes.

Two loops.

Think of an inner loop and an outer loop.

The inner loop is the flow of real things, inputs and outputs.

Households sell their labor, land, capital factors to firms.

Firms use these factors to produce goods and services, which are then sold back to the households.

That's the inner loops, the flow of stuff.

And the outer loop, money.

Exactly.

The outer loop shows the corresponding flow of dollars.

Households spend money to buy goods and services.

That spending becomes revenue for the firms.

Okay.

Firms then use that revenue to pay for the factors of production wages, for labor, rent for land, profit interests for capital.

And that payment becomes income for the households.

And the cycle continues?

Precisely.

It's a continuous loop.

So if I take a dollar out of my wallet and buy, say, a coffee.

Right.

That dollar goes to the coffee shop, the firm, that's their revenue.

Yep.

They then use part of that dollar to pay their barista or their rent.

Right.

And that dollar then becomes income for the barista's household or the landlord's household.

And it's back in someone's wallet, ready to be spent again.

You got it.

It's a simple picture, but it beautifully illustrates those fundamental connections and flows in the economy.

Okay, cool.

Let's move to the second model, then.

The production possibilities frontier, the PPF.

This one sounds a bit more abstract, maybe.

It shows limits on production.

It can seem abstract, but it illustrates some really core economic ideas.

Scarcity, efficiency, trade -offs, opportunity cost, even economic growth.

The production possibilities frontier, or PPF, is basically a graph.

It shows the various combinations of output, let's say just two goods to keep it simple, like cars and computers that an economy can possibly produce.

And this is key.

Given its available factors of production, labor, capital, et cetera, and the available production technology, it shows the maximum possible output.

Okay, so picture a graph.

Maybe quantity of cars on the vertical axis, quantity of computers on the horizontal.

Perfect.

The frontier itself is usually shown as a curve connecting the maximum possibilities.

Like if the economy puts all its resources into making cars, it might produce,

say, a thousand cars, but zero computers.

That's one point on the graph up on the vertical axis.

Right.

Or if all resources go to computers, maybe it can make 3 ,000 computers, but zero cars.

That's another point.

Out on the horizontal axis.

Those are the end points of the curve.

And in between?

In between,

the economy divides its resources.

So it might produce, say, 600 cars and 2 ,200 computers.

That combination would be a point right on the curve, the frontier line itself.

Okay.

So points on the curve are possible combinations if you use all your resources.

What about points inside the curve, like 300 cars and a thousand computers?

That's possible, too.

Any point on the frontier or inside it is feasible, achievable.

But if you're inside, it means you're not using all your resources effectively.

Ah, so that relates to efficiency.

Exactly.

An outcome is efficient if the economy is getting everything it can from its scarce resources.

Those are the points on the PPF curve.

At any point on the frontier, you literally cannot produce more of one good, say, cars without producing less of the other computers.

You're maxed out.

And if you're inside the curve?

That's inefficient.

Maybe there's widespread unemployment or factories are sitting idle.

You could produce more of both goods by putting those idle resources to work and moving out towards the frontier.

Okay.

So on the line is efficient, inside is inefficient.

What about points outside the curve, like 1 ,500 cars and 3 ,500 computers?

Impossible.

Given the current resources and technology, the economy simply cannot produce that combination.

That point lies beyond the frontier.

It highlights scarcity resources are limited.

Got it.

And you mentioned trade -offs and opportunity costs.

Yes, the PPF shows this very clearly.

Because resources are scarce, producing more of one good means producing less of another.

That's the trade -off.

To move along the curve, say, to produce 100 more cars, you might have to give up producing 200 computers.

Ah, so the 200 computers you give up is the cost of getting the 100 extra cars.

Precisely.

That's the opportunity cost.

What you must give up to some item.

In this case, the opportunity cost of 100 cars is 200 computers.

Or you could say the opportunity cost of one car is two computers.

And the slope of the PPF measures this.

Exactly.

The slope of the PPF at any point tells you the opportunity cost of one good in terms of the other.

Rise over run, remember.

Changing cars divided by changing computers.

Now here's something interesting.

Mankiw says the PPF is usually bowed outward, not a straight line.

Why is that?

Does it mean the opportunity cost changes?

Yes, absolutely.

A bowed out shape means the opportunity cost is not constant.

It changes as you shift resources from one industry to the other.

How so?

Think about it.

Some resources are better suited for producing cars, others for computers.

When the economy is using most resources for computers, maybe making only a few cars, the resources being used for those few cars are probably the ones least suited for computers, maybe specialized autoworkers or car factories.

Right.

If you decide to make a few more cars, you shift some of the resources most suited for car production away from computers.

Since they weren't great at making computers anyway, you don't lose much computer output.

So the opportunity cost of a car in terms of computers given up is low.

The PPF is relatively flat here.

Okay, low cost initially.

But now imagine the economy is already producing lots of cars and few computers.

To produce even more cars, you now have to shift resources that were actually very good at making computers,

maybe skilled programmers or specialized chip factories.

Ah, so now giving up those resources means losing a lot of computer production for only a small gain in cars.

Exactly.

The opportunity cost of producing an additional car becomes very high.

You give up many computers and that's why the PPF becomes steeper as you produce more cars.

That increasing opportunity cost creates the bowed out shape.

That makes intuitive sense.

Resources aren't perfectly interchangeable.

And finally, economic growth.

How does the PPF show that?

Simple.

Economic growth means the economy can produce more of everything.

Maybe there's a technological advance, say, in the computer industry.

Now for any given number of cars produced, the economy can make more computers than before.

So the whole curve shifts.

The entire production possibilities frontier shifts outward.

Society's production possibilities expand.

It can now reach points that were previously impossible, potentially producing more cars and more computers.

That's growth.

Okay, those models are powerful visual tools.

Now let's shift gears slightly.

You mentioned economics studies things at different levels, micro and macro.

Right, like biology have molecular biology and ecology.

Economics has these two main branches.

So microeconomics is?

Microeconomics where individual households and firms make decisions and how they interact in specific markets.

Examples.

A microeconomist might study, say, the effects of rent control on housing availability in New York City or the impact of foreign competition on the U .S.

auto industry or how deciding to get more education affects a worker's future wages.

It's focused on individual agents and markets.

Okay, individual trees, specific markets.

And macroeconomics.

Macroeconomics things like inflation, the overall increase in prices, or unemployment, the percentage of the labor force out of work, or economic growth, the rate at which national income is increasing.

Macroeconomists study the effects of government borrowing, changes in the money supply, international trade policies on a national or global scale.

So micro is the pieces, macro is the whole puzzle, but they must be related, right?

The big picture depends on what all the individuals are doing.

Absolutely.

They are distinct, but deeply intertwined.

Changes in the overall economy macro phenomena arise from the decisions of millions of individual households and firms micro foundations.

For example, if you want to analyze the macroeconomic effect of a federal income tax cut, you have to consider how that tax cut affects the decisions of millions of households, but how much to spend versus save.

That's a microeconomic question feeding into a macroeconomic analysis.

You can't fully understand one without the other.

Okay, that makes sense.

Now this next part sounds really interesting.

The economist wearing two hats, scientist and policy advisor.

Yes, this distinction is crucial.

When economists are trying to explain how the world works, they're acting as scientists.

Think observing, theorizing, testing.

Okay.

But often, they're also asked to recommend policies to improve economic outcomes.

That's when they put on the language.

There is.

We distinguish between positive statements and normative statements.

This is a really key distinction.

Okay.

Positive statements.

Positive statements are descriptive.

They make a claim about how the world is.

They attempt to describe reality.

Right.

For example, minimum wage laws cause unemployment.

Now you might agree or disagree, but crucially, it's a statement about how the world works.

In principle, you could test it by looking at data on minimum wage changes and unemployment rates.

It's about facts or alleged facts.

Okay.

Descriptive, testable, in theory,

and normative.

Normative statements are prescriptive.

They make a claim about how the world ought to be.

They involve value judgments.

The government should raise the minimum wage.

See the difference.

This isn't just describing.

It's recommending a course of action.

You can't just prove or disprove this statement with data alone.

Or not.

Because it values.

Is reducing unemployment more important than ensuring a living wage?

Is efficiency more important than equity?

Data can inform the decision.

Your positive analysis of whether minimum wage does cause unemployment will surely influence your normative view.

But ultimately, deciding what should be done requires blending facts with values, ethics, political philosophy.

So positive is what is, normative is what should be, and economists advise governments using both.

Yes.

Economists are found all over government, treasury, labor, justice, the Federal Reserve.

The president has the Council of Economic Advisers.

Their job often involves analyzing the likely consequences of different policies, positive analysis, and then sometimes recommending which policy seems best based on certain goals,

normative judgment.

Which brings us to President Truman's joke about wanting a one -armed economist.

Huh.

Yes.

Because they always say, on the one hand, on the other hand.

Which reflects trade -offs, right?

Exactly.

It reflects that most policy decisions involve trade -offs, a core economic principle.

They're usually benefits and costs.

Good economic advice highlights both sides.

As Keane said, the ideas of economists, both right and wrong, are more powerful than commonly understood.

They shape the world.

But does the advice always get taken?

I mean, economists might recommend something based on efficiency, but politicians might ignore it.

Oh, absolutely.

That happens all the time.

Yeah.

In economics textbooks, we sometimes simplify things and imagine a benevolent dictator or a policymaker who just figures out the best policy and implements it.

But the real world isn't like that.

Not at all.

In reality, the policy process is messy.

A president or prime minister gets advice not just from economists, but from communications advisors worried about public perception, legislative affairs staff worried about getting votes, political advisors worried about the next election.

So economic advice is just one input among many.

It's exactly.

Political feasibility, timing, competing priorities, public opinion, powerful interest groups, all these factors weigh heavily on the final decision.

Economic advice is crucial, often essential, but it's rarely the only factor and sometimes not even the deciding one.

Okay, which leads nicely into the final big question.

Why do economists seem to disagree so often?

We hear the jokes like Shaw's line about laying them end to end.

Right.

They would not reach a conclusion.

It's common perception.

There are basically two main reasons why economists might offer conflicting advice.

Okay.

Reason one.

First, differences in scientific judgments.

Even acting purely as scientists, economists might disagree about the validity of alternative positive theories.

They might have different ideas about how the world actually works.

Oh, so.

They might disagree on the quantitative importance of certain relationships.

For example, consider changing the tax system from taxing income to taxing consumption spending.

Some economists might believe, based on their models and data analysis, that this would cause people to save much more, boosting investment and growth.

Others, looking at different models or interpreting the data differently, might conclude that saving wouldn't respond much at all.

These are different positive views about how households behave.

And these different scientific views lead to different policy advice.

Naturally.

If you think a consumption tax will massively boost saving, you might strongly advocate for it.

A normative conclusion.

If you think it won't do much for saving, but might be unfair to poorer people who consume most of their income, you might oppose it.

The scientific disagreement fuels the policy disagreement.

Okay.

Differences in scientific judgment.

What's the second reason?

The second reason is differences in values.

This goes straight back to the positive versus normative distinction.

Ah, the should part again.

Exactly.

Sometimes economists agree on the positive consequences of a policy.

They might agree, say, that a certain tax policy will make the economic pie bigger, but also distribute it less equally.

Okay.

They agree on the what is.

But they might disagree on whether that policy should be implemented, because they have different values regarding the trade -off between efficiency, a bigger pie, and equity, how the pie is sliced.

Manke uses that example of taxing Jack and Jill for a time.

Well, right.

If high income Jill pays a lower percentage than middle income Jack, is that fair?

Right.

There's no single scientific answer to what is fair.

Economists, like all citizens, can have different philosophical views on fairness and the role of government in redistribution.

These differing values can lead to differing normative conclusions about policy, even if they agree on the positive analysis.

Scientific disagreements and value differences.

But is the disagreement really as widespread as people think?

You hear about consensus sometimes, too.

That's a crucial point.

It's easy to overstate the disagreement.

On many fundamental economic propositions, there's actually a remarkable degree of consensus among economists.

Really?

Surveys consistently show strong agreement on many issues.

For example, something like 93 % of economists surveyed typically agree that policies like rent control reduce the quantity and quality of housing available.

Wow, 93 % is high.

Or that tariffs and import quotas usually reduce general economic welfare again, typically over 90 % agreement.

Even on minimum wage, while there's more debate than on rent control, a large majority, maybe around 79%, generally agree it increases unemployment among young and unskilled workers.

So if there's so much agreement on things like rent control or tariffs being bad for the economy overall,

why do those policies still exist?

That's the million -dollar question, isn't it?

It could be those complexities of the political process we talked about, maybe the beneficiaries of the policy are concentrated in lobby effectively while the costs are spread thinly across many people.

Or maybe the public isn't convinced.

Or perhaps economists haven't yet done a good enough job of persuading the public and policymakers that the consensus view is correct and that the policies are, on balance, undesirable.

Communication matters.

Okay, one last thing.

The chapter appendix talks about graphs.

We've been describing them, but it warns about pitfalls in using them, especially with real -world data,

cause and effect.

Yes, this is incredibly important when moving from theory to data.

Graphs are powerful, but they can also mislead if you're not careful about inferring causality.

Two big traps are omitted variables and reverse causality.

Omitted variables?

What's that?

An omitted variable is something left out of your analysis that actually explains the relationship you're seeing between the variables you are looking at.

The lighter example.

Exactly.

Mancu uses the example.

You might plot data and find that the number of cigarette lighters in a household correlates positively with the risk of cancer in that household.

So lighters cause cancer?

Of course not.

The omitted variable is smoking.

People who smoke are more likely to have lighters and are more likely to smoking.

If you ignore smoking, you'd draw a totally wrong conclusion.

Right.

Always look for hidden factors.

And reverse causality.

Reverse causality is when you assume A causes B, but actually B causes A, or at least the causation runs in the opposite direction from what you initially thought.

Like the police and crime example.

Yes.

You might plot data showing that cities with larger police forces also have higher crime rates.

Does this mean police cause crime?

Seems unlikely.

Highly unlikely.

It's far more plausible that cities experiencing high crime rates respond by hiring more police.

The high crime rate, B, is causing the increase in police, A, not the other way around.

Or the minivan example.

Minivans don't cause babies.

Exactly.

People buy minivans in anticipation of having more children, or because they already have them.

The family size influences the minivan purchase, not the other way around.

The point is correlation doesn't apply causation.

When you see a relationship in data, especially real -world data, you always have to ask, is there an omitted variable?

Could the causality run the other way?

Thinking critically about these issues is vital.

Wow.

Okay.

That was quite a journey through The Economist's mindset and toolkit.

We covered the scientific method in economics, the crucial role of assumptions, and those core models, the circular flow and the production possibilities frontier.

Right.

And we dug into micro versus macro, the important difference between positive statements about what is and normative statements about what should be.

We also touched on the tricky role of economists as policy advisors, navigating that complex world where their advice meets politics.

And we explored why economists sometimes disagree differences in scientific judgment and differences in values, while also highlighting that there's actually a lot of consensus on core issues.

And finally, that crucial reminder about graphs and data, watch out for omitted variables and reverse causality.

Definitely.

These aren't just abstract ideas for exams.

They are genuinely useful tools for you, the listener, to analyze the world, whether you're reading the news, thinking about a policy debate, or even making personal financial decisions.

Understanding this framework helps you cut through some of the noise, evaluate arguments more critically, and see the economic forces at play with, hopefully, a bit more clarity.

So as you reflect on this, what really stands out, maybe it's hell models, by simplifying, actually help clarify complex realities.

Or perhaps that constant interplay between objective scientific analysis and the subjective values that inevitably shape policy choices.

It's a fascinating tension.

It really is.

Keep exploring, keep questioning, and keep thinking like an economist.