Notes from class that I wanted to capture

Rogoff and dollar dominance (theme to understand)
Daron Acemoglu and what his work says about Macro
Find the researchers who say that Japan had their rise becuase their manufacturing was abolished and had to innovate, ask her about that
how to undersatnd the nuance and reliance of different kinds of labor where in ana eocnomy and how that transitions and infuences how we think about it. Chinca as an example, what will they do once alll rural move in and change the available labor supply? Pull in the chinese plan and public annoucnements for robots
Ask: Capital gain, I bought an asset at a particular price.. but as it appreciates, nothing switched hands, so I don’t count the sell of that, the appreciation itself should correlate with activity that the firm did to appreciate.
In the value added slide, how does it account for the public subsidy of industries that are unprofitable, industrial policy
PCE removes food and energy b/c those are commodities that are more volatile
AI productivity

Deployment
New job

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GDP

1. What GDP leaves out

Non-market activity: Household labor, caregiving, and volunteer work aren’t counted.
Underground economy: Informal or illegal transactions aren’t captured (cash work, black markets).
Environmental costs: Pollution, resource depletion, and negative externalities don’t reduce GDP — even disasters can raise GDP (rebuilding boosts spending).
Distribution of income: GDP measures total output, not who benefits. GDP can rise while inequality worsens.
Quality of life factors: Health, leisure, safety, and subjective well-being aren’t in GDP.

2. Measurement challenges

Intangibles & digital economy: R&D, software, free digital goods (Google, Wikipedia) are hard to value.
Globalization effects: Multinational profit-shifting and transfer pricing distort GDP (Ireland’s GDP spike in 2015 is a famous example).
Cross-country comparability: Exchange rates vs PPP (purchasing power parity) can tell different stories.

3. Alternative or complementary metrics

GNI (Gross National Income): Adjusts for income from abroad.
GDP per capita: Closer to living standards.
Green GDP: Adjusts for environmental depletion.
Human Development Index (HDI): Adds health and education.
Median income / consumption: Distribution-sensitive measures.

📌 So the gap:

This slide captures GDP’s definition and why it’s the standard macro measure, but it doesn’t capture GDP’s blind spots — it doesn’t measure welfare perfectly, it misses distribution, environment, informality, and quality-of-life dimensions.

Capital gain, I bought an asset at a particular price.. but as it appreciates, nothing switched hands, so I don’t count the sell of that, the appreciation itself should correlate with activity that the firm did to appreciate.

Helpful to look at why you would use CPI and PCE and what they fix and don’t

While that slide gives a great foundational definition of GDP, it leaves out some critical details that are essential for a complete picture, especially for business and policy decisions.

The two biggest things this slide doesn't cover are the difference between Nominal and Real GDP and the significant limitations of GDP as a measure of a country's well-being.

The Elephant in the Room: Nominal vs. Real GDP

This is the most crucial omission. The slide doesn't distinguish between GDP growth that comes from producing more stuff versus growth that just comes from rising prices.

Nominal GDP measures a country's output using current market prices. The problem is that nominal GDP can go up simply because of inflation, even if the economy isn't producing anything more than it did the previous year.
Real GDP measures a country's output adjusted for inflation. It's calculated using the prices of a fixed base year. This is the number economists and leaders focus on because it reflects the actual change in the volume of goods and services produced.

Without this distinction, you can't accurately compare economic output over time. It's the difference between seeing your company's revenue grow and knowing if your actual sales volume has increased.

What GDP Fails to Measure 🤔

The slide states that GDP reflects "overall prosperity," but it's a very blunt instrument that misses key aspects of economic and social health:

Income Distribution: GDP per capita is an average. It tells you nothing about inequality. A country could have a high GDP per capita driven by a few billionaires while the majority of the population is poor.
Non-Market Transactions: A huge amount of valuable work isn't counted in GDP because no money changes hands. This includes things like childcare at home, volunteer work, and home maintenance.
Negative Externalities: GDP often counts the "bads" as "goods." For example, a factory polluting a river increases GDP through its production, and the cleanup effort also increases GDP. The initial environmental damage is never subtracted.
Quality of Life: GDP doesn't account for leisure time, stress levels, or overall happiness. A country where everyone works 70-hour weeks might have a higher GDP than one where people work 35-hour weeks, but it's not clear that its citizens are better off. This is a point Professor Eberly hints at later in the deck when comparing US and French GDP per hour1.
The Underground Economy: It misses illegal activities and unreported cash transactions, which can be a significant part of the economy in some countries.

Sale of a home doesn’t count

Seat installed into a car doesn’t count

If it leaves the country it’s a final good (tricky trade examples)

Services: Could be final or intermediate. (consultant hired to advise on production, so B2B usually mean intermediate)

Higher tax rates often correlate with more cash transactions.

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Real vs. Nominal Interest Rates

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Silicon Valley bank + Interest Rates

The Inverse Seesaw: Interest Rates and Bond Prices

The relationship between interest rates and bond prices is inverse. Think of it like a seesaw: when one goes up, the other must come down.

When interest rates rise, new bonds are issued with more attractive, higher payments. This makes older, existing bonds with their lower fixed payments less appealing. To compete, the market price of these older bonds must fall.
When interest rates fall, the opposite is true. Older bonds with their higher fixed payments become more valuable compared to new bonds with lower yields. As a result, the market price of older bonds rises.

Simple Analogy: Imagine you own a "$1,000 bond" that pays 2% interest per year. Now, the central bank raises rates, and new $1,000 bonds are being sold that pay 4%. If you want to sell your 2% bond, no one will pay you the full $1,000 for it. Why would they, when they can get a new one that pays 4%? You'd have to sell your bond at a discount—a lower price—to make its overall return attractive to a new buyer.

A Vignette: The Silicon Valley Bank Story

Imagine SVB as a specialized banker for a single, booming industry: tech. During the tech frenzy, startups and venture capital firms were flush with cash and deposited billions into SVB. The bank needed to put this money to work. Seeing interest rates at historic lows, they made what they believed was an ultra-safe bet: they bought billions of dollars in long-term U.S. Treasury bonds.

This is where we see the first kind of risk: credit risk. This is the risk that the borrower won't pay you back. For U.S. Treasury bonds, credit risk is practically zero. SVB correctly assumed the U.S. government would not default.

However, they catastrophically misjudged a second, more abstract danger: interest rate risk. This is the risk that the value of your investment will fall because of changes in prevailing interest rates. In 2022, to combat soaring inflation, the Federal Reserve began hiking interest rates at an incredible pace111.

Suddenly, the low-yield bonds SVB held were worth far less than their face value. This wasn't a problem as long as SVB could hold them to maturity. But then, a downturn hit the tech sector. SVB's concentrated base of customers started burning through cash and withdrawing their deposits to make payroll. To meet these withdrawals, SVB was forced to sell its devalued bonds, realizing a massive loss. News of this loss spread instantly, causing other clients to panic and pull their money, triggering a fatal bank run2.

The Risks Hiding in Plain Sight

SVB's collapse was caused by a fatal combination of risks that fed off each other:

Interest Rate Risk: This was the primary failure. The bank held a massive portfolio of long-term bonds that were highly sensitive to interest rate changes. They failed to use common financial tools (like derivatives) to hedge, or protect against, the risk of rising rates.
Concentration Risk: SVB's client base was almost entirely made up of tech startups and VC firms. This lack of diversity meant that when the tech industry stumbled, nearly all of their customers faced financial pressure at the same time, leading to simultaneous, large-scale withdrawals.
Liquidity Risk: This is the risk of not having enough cash or easily sellable assets to meet short-term obligations. SVB's assets were locked up in long-term bonds that couldn't be sold without a huge loss. Their liabilities were massive, uninsured deposits that could be withdrawn at a moment's notice. This mismatch was a classic recipe for a liquidity crisis.

What They Should Have Thought About

SVB's management should have adopted a more holistic view of risk, understanding that "safe" assets can become dangerous under the wrong conditions. Specifically, they should have:

Managed Duration Mismatch: They should have better aligned the duration of their assets (bonds) with the nature of their liabilities (deposits). Using short-term deposits to buy long-term bonds is a fundamental banking gamble that requires careful management.
Actively Hedged Risk: They should have implemented a robust hedging strategy to protect their bond portfolio from rising interest rates. This is a standard practice known as "asset-liability management" that they seemingly ignored.
Stress-Tested Their Concentration: They should have rigorously modeled what would happen if their key industry (tech) faced a downturn. A proper stress test would have shown that their concentrated and highly-connected depositor base was prone to a mass, correlated withdrawal—a bank run.

Great question — this is where finance theory meets real-world crisis. Let’s take it in two steps:

1. The relationship between interest rates and bond prices

This is a cornerstone of macro-finance:

Bonds pay fixed coupons. If you buy a 10-year Treasury at 2%, you’ll keep receiving 2% on the face value until maturity.
When market interest rates rise, new bonds are issued with higher yields. Suddenly, your old 2% bond is less attractive compared to a new 5% bond.
To make your old bond competitive, its price falls in secondary markets.
Conversely, if interest rates fall, old bonds with higher coupons become more attractive, and their prices rise.

🔑 Rule of thumb: Interest rates up → bond prices down (and vice versa). The longer the bond’s maturity, the more sensitive its price is to rate changes (this is called duration risk).

2. The Silicon Valley Bank (SVB) vignette

SVB (collapsed March 2023) is a case study in misunderstanding different types of risk.

What happened:

SVB parked a massive share of deposits (short-term liabilities) into long-dated Treasury and mortgage-backed securities (long-term assets).
When interest rates rose sharply in 2022–2023, the market value of these bonds plunged.
If SVB could have held the bonds to maturity, no problem — they’d get full value back.
But depositors (startups, VCs) demanded their cash back quickly (a liquidity shock).
To meet withdrawals, SVB had to sell those bonds at a loss → realized huge losses → panic → bank run → collapse.

3. What risks were present?

Interest Rate Risk (duration risk):

They held long-term bonds whose value was very sensitive to Fed rate hikes.
They failed to hedge this with derivatives or asset-liability management.

Liquidity Risk:

Their deposits were highly concentrated in venture-backed startups.
When confidence cracked, withdrawals spiked, and they didn’t have cash on hand.

Concentration Risk:

Customer base wasn’t diversified — mostly tech/startups with correlated behavior.

Reputational/Confidence Risk:

Banking is fragile; once word spread, even solvent banks can fail if withdrawals accelerate (classic Diamond–Dybvig model).

4. What they should have better thought of

Hedging: They should have used interest rate swaps or shorter-duration assets to protect against rate increases.
Liquidity management: Keep more high-quality liquid assets (short-term Treasuries, cash-like instruments).
Deposit diversification: Fewer concentrated depositors, more retail/insured deposits.
Stress testing: Consider extreme but plausible scenarios (Fed hikes by 500bps in 18 months) instead of assuming the low-rate environment would persist.

📌 Takeaway lesson:

SVB wasn’t a credit-risk failure (the Treasuries were safe), it was a duration + liquidity failure. They confused “safe assets” with “safe balance sheet.” A bond that’s guaranteed to pay at maturity can still sink a bank if it has to be sold early at a loss to meet withdrawals.

👉 Do you want me to make you a 2×2 framework of banking risks (Credit, Liquidity, Duration, Concentration) with SVB mapped into it, so you can use it as a quick reference in discussion?

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Class 1 - Measurement & Growth

GDP