If you're like pretty much everyone I know, you've
got this constant feeling in your gut that everything
just costs more. You know, the gas for your car,
the groceries, your rent. It just feels like
it's creeping up and sometimes jumping up from
last year. That's inflation. It's that slow erosion
of what your dollar can actually buy. Exactly.
And for decades, the entire economic world, from
policymakers to your pension fund, has relied
on this one. Massive, unbelievably complex number
to track that feeling. The Consumer Price Index,
the CPI. The big one. Okay, let's unpack this.
We always hear the CPI reported as just a single
number, right? Inflation is up 4 % or 7%, whatever
the headline is. But it is anything but simple.
Not at all. It's the single most closely watched
economic statistic on the planet, really, because
it has so much leverage. It's the government's
best shot at taking a huge weighted snapshot
of what we all spend our money on. The so -called
market basket of goods and services. And that
basket is supposed to represent what you, the
listener, are buying every month. We've got a
fantastic stack of sources today that really
get into the weeds of how this colossal number
is built, why it's always so controversial and
what the tiny choices in its calculation mean
for your wallet. It really is the metric that
tries to, I guess, arbitrate economic reality
for a whole country. And its main function is,
on the surface, pretty straightforward. Just
tracking price changes. Right. Tracking price
changes over time. If the CPI goes up by, say,
5 % over a year, it means that basket of typical
household stuff now costs 5 % more. But the stakes
are just immense. Oh, absolutely. Because those
changes, that percentage, it's not just a number
on a screen. It's hardwired into the economy.
It's used to index or adjust the real value of
wages. It determines Social Security payment.
It determines pensions. It regulates prices for
utilities. And critically, it lets us deflate
all these huge monetary numbers so we can actually
see if the economy is growing in real inflation
adjusted terms. And this is the big but we're
going to keep coming back to. Our sources are
crystal clear on this. While it's indispensable,
the CPI is, and I'm quoting here, not a perfect
measure of inflation or the cost of living. It's
an approximation. It has to be. It's a necessary
compromise, really, between what economists would
consider a perfect theoretical ideal and the
practical reality of needing a reliable number
fast every single month. And it's in those compromises,
those little judgment calls, that you find all
the intense economic and, frankly, political
fights that are always swirling around this number.
That's where all the debate lives. And so today,
we're going to trace the history of this market
basket idea. Then we're going to dive right into
the mechanics of how statistical agencies like
the BLS actually handle this mountain of data.
The weighting, the collection, all of it. Then
we're getting into the deep water. The single
biggest, most heated controversy in the entire
index, which is what, over 40 % of the whole
thing? Housing. How in the world do you accurately
measure the cost of owning a home? The billion
-dollar question. And finally, we'll look at
all the different flavors of the CPI they use
here in the U .S., that whole alphabet soup of
acronyms, and what they mean for policy, especially
for Social Security. The cost of living adjustments,
or COALAs. Right. So let's start at the beginning.
The anatomy of the basket itself. Where did this
idea even come from? Well, the idea of trying
to quantify this feeling of rising prices, it's
not new at all. It actually has some surprisingly
simple historical roots. We're not talking about
something from the 1970s then. No, not even close.
The concept of a price index was first proposed
by an English economist, Joseph Lowe, way back
in 1822. Wow. Okay, so what did his version look
like? I'm picturing something a lot simpler than
what we have today. Oh, much simpler. It was
beautiful in its simplicity, really. Lowe's whole
approach was just an elementary comparison. He
said, let's compare the total price of a fixed
basket of goods and services in one period. Call
it period zero. The starting point. With the
total price of the exact same fixed basket in
the next period, period one. That's it. So that
basic idea, the fixed basket, that's still the
DNA of what we do now, even with all the supercomputers
and, you know, modern economic theory. It is.
That fixed basket approach remains the elementary
basis of our current systems. We establish a
list of what people buy and we just track its
price over time. It sounds so manageable when
you put it like that, but then you try to apply
that to, what, 330 million people buying millions
of different things? And it scales out of control
very, very quickly. So let's ground this with
actual math, but for just one item to make sure
we get the mechanism. Good idea. So if we look
at just one specific thing, let's say a gallon
of milk, the single item price index is calculated
by taking its current updated cost. What it costs
today. Right. And you divide that by... what
it costs in the base period, and then you multiply
that whole thing by 100. Okay. So if the base
year is, say, 1982, and milk cost $1 then, and
today it costs $4, the math is 4 divided by 1
times 100. The index for milk is 400. Exactly
right. And that multiplication by 100, that's
just a convention. It's a way to rescale the
entire time series so that the base period value
is always 100. It just creates a nice, clean
starting point for comparison. It's a consistent
benchmark. Yep. And this index is then reported
monthly or quarterly, comparing prices now with
those in that established price reference month.
OK, now let's talk scale, because this is where
the simple theory just smacks right into the
wall of reality. That simple formula for milk
has to be done, what, thousands and thousands
of times? Tens of thousands. Our sources get
into the sheer volume here. In the United States,
the Bureau of Labor Statistics collects and averages
prices for approximately 85 ,000 specific items
sourced from 22 ,000 different stores and 35
,000 rental units. I mean, just think about that.
That means there are actual people, statisticians,
whose job it is to track the price of a specific
brand of laundry detergent in a specific type
of store in a specific city month after month
after month. It's a logistical and mathematical
Everest. It really is. And all of that data then
has to be compiled into one single cohesive number.
The weighted average? The weighted average, exactly.
A weighted average of all those thousands of
item area indices. And the weight that's assigned
to each item just reflects how much we as consumers
spend on it. Right. So the index for gasoline
matters a whole lot more than the index for,
I don't know, chewing gum. Precisely. Because
we spend a heck of a lot more on fuel than we
do on gum. So before we get into how those weights
are decided, which sounds like its own can of
worms, we should talk about the exclusion zone.
What gets deliberately left out of the basket?
Because sometimes what's missing tells you a
lot about what the index is trying to do. That's
a really critical point. The CPI is designed
first and foremost to measure current consumption.
OK, so things we use up. Right. So anything related
to saving or investing or capital formation,
it's always excluded. So my stock portfolio,
my 401k contributions. None of that is in there.
Nope. Not factored in at all. And there's a geographic
limit, too. Yes. The CPI is a domestic measure.
So consumer spending that happens abroad is usually
excluded. The goal is to focus on the price changes
that residents experience inside the country's
borders. Now, the third exclusion, this one is,
I think, the most politically interesting one.
Yeah. Our source gives this example where taxes
made up a staggering 43 percent of the final
cost of a good. But those taxes are completely
left out of the CPI calculation. Why? Well, the
CPI is generally designed to measure the price
paid for a good or service before most consumption
taxes are applied. Things like income tax are
definitely out. The idea is to track the price
of the commodity itself, not government policy.
But the implication there is huge, isn't it?
It is. If a government decides to shift how it
taxes, people say, it lowers income taxes, which
are excluded from the CPI, but raises sales taxes,
which might be included but often aren't. Then
the CPI might not actually show the real increase
in what you're paying at the checkout. Exactly.
It might only capture the change in the product's
price, completely missing the change in tax policy
that's making it more expensive for you. Wait
a minute. So if a government has an incentive
to keep that headline CPI number low, maybe to
avoid paying out higher Social Security adjustments,
they could, in theory, shift the tax burden in
a way that the CPI doesn't fully capture. It
creates a potential statistical or political
incentive. Absolutely. Because the CPI is the
benchmark for so much, how you define what's
in and what's out can have massive budgetary
consequences. It's a very fine line to walk.
And then the last exclusion is sometimes entire
groups of people. That's right, especially for
more specialized indices. Yeah. The goal is always
to measure the experience of the average consumer.
So the outliers get trimmed. Sometimes. In the
UK's old retail price index, the RPI, they used
to specifically exclude the very highest income
households and certain low income pensioner households.
The argument was that their spending patterns
were just too different from the median experience
and would skew the average. It just goes to show
that even defining who the representative consumer
is, that's a statistical and sometimes political
choice in itself. So even after all this incredible
data collection, all the exclusions, all the
waiting, it's still just an approximation. It
is. You mentioned that economists have this sort
of holy grail, the true cost of living index.
They do. They call it the constant utility index.
Constant utility. OK. In theory, this perfect
index would measure exactly how much more money
you'd need to maintain the exact same standard
of living, the same level of satisfaction or
utility. even as prices change around you. So
if steak prices double, the perfect index would
track how much you'd need to spend on, I don't
know, chicken or pork to be just as happy as
you were before. That's the idea. It accounts
for substitution. That sounds incredibly useful.
So why don't we just use that? The problem is
speed. A perfect constant utility index can only
really be calculated retrospectively. It requires
these huge, complex sets of consumer behavior
data that take months or even years to... properly
analyze. But the CPI has to come out now. It
has to be released monthly, within weeks of the
data being collected. And that need for speed
forces a pragmatic approximation. And that approximation
usually takes the form of a fixed weight index,
what's technically known as a Lesper's index.
Let's break that term down, Lesper's index, because
that's really the technical name for this fixed
basket problem, isn't it? It is. The easiest
way to think about a true LeSpares index is to
imagine you're stuck with the exact shopping
list you used in the base year, let's say 2010.
Even if over the next 10 years you completely
stopped buying CDs and started buying streaming
subscriptions, or you stopped buying beef because
it got way too expensive. The index assumes you
are still buying the original 2010 quantity of
CDs and beef. Which means it's inherently overestimating
inflation. Exactly. Because it doesn't see us
doing the smart thing and switching to cheaper
alternatives. It suffers from what's called substitution
bias, precisely because it can't track those
rapid changes in our behavior in real time. It's
essentially using yesterday's economy to calculate
today's prices. So we've laid the foundation,
but now we get to the really tricky part. The
devil is in the details. And for the CPI, the
biggest detail is the structure of the weights.
This is the recipe, right? It's the proportion
of each ingredient in the basket. If you get
the weights wrong, the final headline number
is just wrong. That's absolutely correct. The
weights are just the proportions assigned to
all the different spending categories. They have
to sum up to 100%, of course. And they are what
define the statistical picture of that typical
consumer. Let's use the U .S. expenditure weights
from our sources to really anchor this. elephant
in the room. It makes up 41 .4 % of the US CPI.
A massive chunk. I mean, nearly half of all measured
inflation is being driven by what happens with
housing costs. And that huge percentage right
there, it instantly tells you why the housing
controversy we're about to discuss is so high
stakes. Right. But OK, moving down the list,
what are the next biggest pieces? After housing,
you've got food and beverages at 17 .4%, and
then transport is right on its heels at 17 .0%.
So those three, housing, food, and transport,
that's the vast majority of the index. It is.
After that, you see medical care, which is a
huge point of policy debate, at 6 .9%. Then apparel
at 6 .0%, entertainment at 4 .4%, and then a
catch -all other category at 6 .9%. So if some
crisis makes gas prices soar by, say, 50 % overnight,
That's going to have a much, much bigger impact
on the final CPI number than if, I don't know,
movie ticket prices did the same. Of course.
The weights are the leverage points in the whole
equation. Yeah. And they're constructed through
this layered system, what statisticians call
the hierarchy of indices. You can't just jump
from those 85 ,000 individual items to the final
number. It has to be built up in stages. Right.
It's an aggregation process. So let's clarify
that hierarchy. What's at the very bottom, the
most granular level? At the very bottom, you
have what are called elementary aggregates. price
of a very specific type of good in a very specific
place. The example from the source is something
like the price of men's short sleeve cotton shirts
sold in San Francisco department stores. That's
specific. That's specific. It has to be. Because
price movements are so localized and so dependent
on the type of store, a shirt at a discount store
in one city costs something very different than
at a fancy boutique in another. So you take all
those elementary aggregates and you average them
together to form a low -level index, like outer
garments, and then those combine into a higher
-level subindex, like clothing, and eventually
you get to the big categories we just talked
about, like apparel. Precisely. And it's a standardized
process, even in the European Union. Their harmonized
index requires every member country to compute
about 80 of these prescribed sub indices just
to make sure everyone is building the number
in a comparable way. OK, but here's a huge practical
problem that jumps out at me. The unweighted
challenge. If you're the statistician tracking
the price of those shirts in San Francisco, do
you actually know the sales volume of brand A
versus brand B or how many were sold on sale
versus at full price? Often, no. That's the problem.
At that lowest, most granular level of the elementary
aggregate, the detailed sales data you'd need
for perfect weighting. It's just not available
to the statistical office. So what do they do?
They know the average price of the 10 different
shirts they're tracking, but they don't know
the market share of each of those 10 shirts.
So they often have to compute the price using
an unweighted arithmetic or geometric mean. They
just average them. They just average the prices
together, basically assuming that each shirt
they're tracking contributes equally to what
people are buying, which, of course, we know
is almost never true. That feels like a bit of
a weak link in the chain, you know, relying on
a simple average right at the foundation. It
has been for a long time, but the data landscape
is changing very, very quickly. Which brings
us to barcode scanner data. The game changer.
Our sources really highlight this. This technology
is slowly making it possible to have real weights,
even at that most detailed level. So instead
of a person with a clipboard writing down a price.
Exactly. Instead of that, scanner data gives
the statistical office the actual fails volumes
for specific products. Brand X, SQ number, Y
plus where it was bought, and the exact price
paid, including any discounts. So scanner data
is finally getting the index a little closer
to that constant utility ideal by cutting down
on that substitution bias at the ground level.
It is, and it helps with the whole data collection
process itself. It addresses a major operational
problem, which is what they call respondent burden.
Making it easier for the people who participate
in the surveys. Right. One source mentioned Statistics
Iceland found that when people in their surveys
are given detailed cash register receipts from
scanner purchases, they don't have to write down
every single item they bought in a diary. They
can just keep the receipt. They just record the
total and keep the receipt. It dramatically reduces
the burden on them. It increases the accuracy
of what was bought. And crucially, it gives you
that specific point of purchase data. So you
know if it was bought at a supermarket versus
a corner store. Which helps you estimate those
outlet -type weights we were just talking about.
It's a huge leap forward. Okay, so we've got
the weights and we've got the data sources, these
periodical household expenditure surveys and
the big national accounts. But these two sources
don't always line up perfectly, do they? No,
they don't. And managing that divergence is a
huge part of the job. For instance, a household
survey usually excludes what foreign visitors
spend. But the national accounts, they track
total consumption in the whole economy, including
tourists. So you have to adjust for that. You
have to adjust. Or, on the flip side, the national
accounts include something called imputed rent
for people who own their homes, an estimate of
what the rental value would be. A household survey
might only look at what people actually paid
out of pocket. So you have to harmonize those
different definitions. What's really fascinating
here and something I think most people miss is
that this isn't just about counting how many
dollars were spent on beef nationally. No, it's
so much more complex. You have to account for
regional differences and store type differences
all at the same time. It's exponential complexity.
I mean, the ideal would be to estimate the spending
for every single detailed category, for every
single type of outlet in every single region
of the country. Which is impossible. It's impossible.
So you have to make educated estimates. You might
lack detailed regional data. So you assume that,
say, the northern region, which has 24 % of the
population, also consumes 24 % of all sliced
bread. A rough proxy. A rough but necessary proxy.
Then you apply the national outlet data, say
70 % of sliced bread is sold in supermarkets,
to get an estimate for sliced bread sold in supermarkets
in the northern region. They use every scrap
of data they have because a rough estimate is
better than ignoring that spending completely.
And even with all this incredibly meticulous
work, the whole system still suffers from what
you called the cost of infrequency. The reweighting
problem. Right. This is the LaSperre's index
problem in the real world. It is. Ideally. The
weights in the CPI would reflect what we're spending
our money on right now this year. But in reality,
they're historical. They're always looking in
the rearview mirror. They are. They reflect spending
patterns from the last big survey, which might
be a year or two old. Rewaiting is incredibly
expensive and labor intensive for statistical
offices. So they just can't do it constantly.
And the result of that is that the index is always
chasing a moving target. It creates this huge
time lag before new types of products ever get
into the basket. A significant delay. The sources
give two perfect examples. Internet service subscriptions
and digital cameras. OK, what happened there?
When digital cameras first exploded in popularity,
they initially had to be lumped into the same
elementary aggregate as film cameras. But there's
a completely different technology. Completely
different. But the basket structure hadn't been
updated to give them their own category yet.
So if a brand new category just appears on the
market like VR headsets or some new digital service,
the official CPI basically pretends it doesn't
exist until the next big re -weighting cycle.
Which means the official inflation rate for that
whole period is fundamentally incomplete. The
older the weights get, the more the official
number diverges from our actual lived reality.
Okay. So we've established that housing is the
single biggest piece of the pie. 41 .4 % in the
U .S. The 800 -pound gorilla. And now we are
wading into the philosophical deep end. Because
measuring the cost of owner -occupied housing
might be the single most divisive, controversial
element in all of statistics. It's a genuine
dilemma. Because a house, it's two things at
once. It's this massive, long -term, durable
asset, an investment. Right. And it's also something
that provides a continuous consumption service.
Yeah. Shelter. The whole problem is how do you
surgically separate the consumption cost from
the investment component? So let's break down
the three competing ways to do this. Approach
one, the economist's view, which is all based
on opportunity cost. The core idea here is that
a homeowner... is consuming the service that
the house provides. And that service is basically
identical to what a renter gets. Okay, so the
cost isn't what you pay on the mortgage. No.
The true cost in this view is the opportunity
cost, is what the owner gives up by living in
the house themselves instead of, say, renting
it out to someone else. And the most famous version
of this is the rental equivalent approach, which
is what the U .S. largely uses. Right. Under
this approach, the cost of owning your home is
the rent you could get if you leased it out.
They're trying to price the service of shelter.
But the practical problem seems huge. It is.
How do you estimate the rental value of a house
that isn't for rent? If you live in a unique
five -bedroom house and there are no other five
-bedroom houses for rent anywhere near you, the
statisticians have to find comparable properties.
Maybe two three -bedroom apartments? Maybe. And
then they have to statistically adjust for the
differences. And that whole process is just full
of error and subjectivity. So what's the other
side of that opportunity cost coin, the alternative
cost approach? This one defines the cost differently.
It looks at the capital expense. It asks, what
is the interest the owner would have earned if
they sold the house, invested that money for
a year, and then bought the house back after
accounting for depreciation? It's trying to capture
the cost of having all that capital tied up in
a house instead of in the stock market. Exactly.
But there's a huge problem. Wait, let me guess.
If house prices are going up really fast, the
capital gain you get from owning it for that
year could be bigger than the interest and the
depreciation combined. You got it. And that could
make the cost of housing in the CPI negative.
Which would drag the whole inflation number down
even while home prices are soaring. Right. It
creates extreme volatility. If prices rise fast,
the cost goes negative. If they suddenly fall,
the cost spikes way up. And statisticians absolutely
hate that kind of volatility in a headline number.
And you brought this up earlier. There's an intellectual
inconsistency here, isn't there? If you use this
logic for a house, why not for everything else
that lasts a long time? You should if you're
being logically consistent. Your refrigerator
provides a cooling service for 15 years. An appendectomy
provides the service of good health. for the
rest of your life. My couch, my car. All durable
consumer goods and services should, by this logic,
be measured by their rental equivalent or their
opportunity cost. The fact that economists generally
only apply this incredibly complex logic to housing.
And not to my sofa. It shows that it's really
about the sheer size of the housing category,
not about pure theoretical consistency. Okay,
that brings us to the second way, approach two,
the spending approach or the debt profile method.
This side just says, forget all the theory. Let's
just track what homeowners actually spend. This
feels the most intuitive to the average person,
right? It focuses on the actual outlays, repairs,
maintenance, property taxes, and the big one,
mortgage interest payments. Because that's the
bill you see every month. Right. But the complexity
of actually implementing this is kind of horrifying.
Because you can't just track current spending.
You have to compare it to the spending in the
base period. You have to create a counterfactual.
You do. If you're calculating the CPI today,
you have to compare the interest someone is paying
now to the interest that would have been paid
on a similar house that was bought and mortgaged
way back in the original reference period, like
2005 or something. So you're tracking the historical
debt profile of a hypothetical house bought 15
years ago and then adjusting it for today's conditions.
It's a staggering... Statistical exercise. And
it leads directly to the great policy paradox.
Which is, if the central bank raises interest
rates to stop inflation, what does this method
do to the CPI? It makes inflation look worse.
That rise in interest rates, which is designed
to cool the economy, shows up in the debt profile
method as a higher cost of homeownership. So
it pushes the measured inflation rate up. It's
a broken feedback loop. The cure for inflation
looks like more inflation. Precisely. It makes
the index look unstable and counterproductive
to the very policy it's supposed to be guiding.
And is this method applied consistently to all
our debt, car loans, credit cards? No, which
is another big consistency failure. If you're
going to cover mortgage interest, logic says
you should cover the interest on all consumer
credit instead of just the full price of the
things bought on credit. But most countries that
use this debt profile method don't do that. They
treat mortgages as a special case. Okay, finally,
let's get to the third way, approach three, the
transaction prices approach. This seems like
the simple, direct method. Just treat a house
like you'd treat a washing machine. This is the
most straightforward interpretation. You just
use the agreed -upon transaction prices for houses,
you ignore all the financing and borrowing stuff,
and you only include the sale of new dwellings.
Why only new ones? You exclude secondhand sales
because that's just an asset transfer between
two consumers. It's not new consumption for the
economy as a whole. This sounds so much cleaner.
But you said it creates a fight between investment
and consumption. This is where the CPI clashes
with broader economic measurement. In the big
system of national accounts, the purchase of
a new house is almost always treated as an investment,
as capital formation, not as consumption. I see.
So if the CPI, which is supposed to be a consumption
index, suddenly starts including new houses as
consumption, it creates a serious break with
all the other major government statistics. And
economists really, really want the definition
of consumption to be consistent everywhere. And
then there's one last almost philosophical problem
with the land itself. Right. The argument is
that land is this irreproducible permanent asset.
You can't consume land. Therefore, the price
used in the CPI should exclude the value of the
land underneath the house. So the statisticians
would have to somehow figure out what the empty
lot would be worth and subtract that from the
sale price of the house? Yes, which is incredibly
difficult, if not impossible. to do reliably,
especially in a dense city where the house's
value is completely tied up with the land's value.
So to sum up the housing confusion, it's this
three way battle. And the reasons it's never
been solved are feasibility, volatility and just
plain old logical disagreement. That's it exactly.
Statisticians struggle with the feasibility of
getting good data. They worry about the volatility
if they include interest rates or capital gains.
And the theorists can't agree on which approach
is the most logically consistent. It remains
the most fundamentally challenging piece of the
entire index. So these fights over data and housing,
they're happening all over the world. But here
in the United States, the debate gets this specific
political urgency because the Bureau of Labor
Statistics doesn't compute just one CPI. No,
they compute several. each one aimed at a different
population or using a different method. It's
the alphabet soup of U .S. CPIs. It is. You have
the CPI -U for all urban consumers. That's the
main headline number you always hear about. Then
there's the CPI -W for urban wage earners and
clerical workers. There's a rarely used CPI -E
for the elderly. And then there's these statistically
refined CCPIU, which is the chain CPI for all
urban consumers. And the really crucial thing
to understand here is that all of these different
indices, they're using the exact same raw price
data. That's right. They all start with those
8 ,018 separate item area indices we talked about.
The only thing that's different between a CPIU
and a CKIW is the weights that are applied to
that data. Exactly. The weights are adjusted
to reflect the spending patterns of their target
population. So the standard ones, the CPI -U
and CPI -W, they use weights that are held constant
for 24 months. Right. They're only updated in
January of even -numbered years, which means
for up to two full years, the index is assuming
that you're buying the exact same proportional
basket of stuff, no matter what happens to prices.
And that fixed weight structure is exactly why
we have the substitution effect problem we talked
about earlier. of beef skyrockets. The normal
CPIU just assumes I'm still buying the same amount
of beef, even though I've obviously switched
to cheaper chicken. That is the classic substitution
bias. And that is exactly what the CCPIU, the
chained CPI, was designed to fix. How does it
do it? The chained CPI updates its weights every
month. Oh, wow. It immediately reflects changes
in our spending patterns. If millions of us substitute
chicken for beef, That shift shows up in the
very next month's CCPIU calculation. So it's
a much more dynamic, much more accurate measure
of what's actually happening. Statistically,
yes. It's a superior measure of inflation because
it tracks consumer behavior in almost real time.
And because we consumers always tend to buy the
cheaper thing when prices change, the chain CPI
has to, by its very nature, run a little lower
than the fixed weight CPIU, right? That's right.
Because the fixed weight one is artificially
inflated by pretending we're still stuck buying
the expensive items. The chain CPI accounts for
our adaptation. OK, now let's get to the biggest
political pressure point in this whole system,
the COLA controversy. The cost of living adjustment
for, what, 70 million Social Security recipients
is based on the annual increase in the CPIW.
The Index for Urban Wage Earners and Clerical
Workers. Yes. And this is just a classic case
of a statistical mismatch with huge social consequences.
It is. You're using an index designed for the
working population to adjust the incomes of the
retired population. And Social Security recipients
are primarily the elderly. And the elderly spend
a substantially larger portion of their income
on health care. Some estimates say between 20
and 40 percent. Then younger working people do.
And we all know that for decades, inflation in
health care has just massively outpaced inflation
in the rest of the economy. So the CPIW, the
index being used for the CLA, severely underweights
the one category where seniors are spending most
of their money and facing the highest price increases.
Which means the CLA they get may not be adequately
compensating them. for the real increase in their
personal cost of living. Does the BLS know this
is a problem? Oh, they do. They recognize this,
and they actually track the CPIE and index specifically
for the elderly. But it's not used for policy.
It is not used. And the reason is purely about
the budget. Our sources lay out the brutal economics
of it. The Social Security Trust Fund currently
has a projected solvency of about 40 years. If
we were to switch to using the CPIE, which is
more accurate for seniors and would provide higher
annual COLA payments. It would drain the fund
faster. It's estimated to shorten that solvency
forecast by about five years. Wow. So a seemingly
neutral statistical choice, which indexed to
use it, has consequences that ripple through
the entire federal budget. It literally changes
the retirement age and tax burdens for future
generations. The policy implications are just
staggering. And this all came to a head and became
a huge political flashpoint around 2010 during
budget negotiations. And the debate was all about
that chain CPI, the CCPIU. Right. And the proponents,
people like former White House chief of staff
Erskine Bowles, they argued really passionately
that we should switch to the chain CPI for COLA
adjustments. This argument was twofold. It's
more accurate and it saves a ton of money. They
argue the CCPIU is a more accurate measure of
true inflation because it captures substitution.
And switching to it was estimated to save the
government over $290 billion over a decade. And
that's because, as we said, the chain's CPI always
runs a little bit lower than the standard CPI.
Right. Historically, about a quarter to a third
of a percentage point lower every year. So by
adopting the more accurate index. The government
would save hundreds of billions of dollars by
automatically giving out slightly smaller annual
increases to federal pensions and Social Security.
That's the dynamic. Exactly. It was framed as
a marriage of fiscal responsibility and statistical
accuracy. But the critics, people like former
Labor Secretary Robert Reich and groups representing
retired federal employees, they pushed back hard.
Very hard. What was their argument? Their argument
was that. The CCPIU, while maybe it's better
at capturing the substitution effect for things
like groceries, still completely fails the elderly
demographic because of that huge weight of health
care. They said it doesn't solve the core problem.
Exactly. They argued it still doesn't adequately
address the fact that seniors spend 20 to 40
percent of their income on these high inflation
health care costs. They saw it as a backdoor
way to cut benefits for the most vulnerable people.
And in the end? In the end? While it was seriously
considered during major deficit talks in 2013,
the political blowback was just too intense and
is not adopted. So we've got the CPIU, the W,
the E, the CCPRU. With all this variation and
controversy, where does the most powerful economic
player of all, the Federal Reserve, look for
its main signal on inflation? They use yet another
index, the Personal Consumption Expenditures
Price Index, or PCEPI. So why do they prefer
that one? The Fed favors the PCEPI because it
believes it addresses some of the CPI's biggest
structural flaws. It's a broader measure that
comes from the national accounts, not just household
surveys. And crucially, it updates its weights
more dynamically than the standard CPI -U. So
it's better at handling that substitution effect.
Much better. It's also seen as having a more
comprehensive scope across the whole economy.
And the statistical difference between the two,
it might seem small, right? We're talking about
maybe half a percentage point difference in the
historical average. It seems small. but over
the span of a generation, that difference is
enormous. How so? Historically, inflation, as
measured by the PCEPI, has averaged about 3 .3
% a year. But CPI measured inflation has averaged
3 .8%. So that 0 .5 percentage point difference,
it just compounds year after year after year.
It does. Imagine calculating a worker's real
wage growth over 30 years using the 3 .8 % CPI
benchmark versus the 3 .3 % PCEPI benchmark.
The real value... of your wages, of your savings,
of your debt. It all shifts massively. And the
PCAPI showing lower inflation, that's a key reason
why the Fed uses it for its 2 % long -term inflation
target. It is. OK, so let's bring this complex
statistical world crashing right into the real
world with some recent headlines. Our sources
track the US CPI data using 1982 as the base
year, where the index is 100. By December of
2021, that CPI reading hit 7%. And that 7 % figure,
that was the highest level in over 40 years.
It was the headline shockwave that really marked
the beginning of a major shift in monetary policy.
So even though the Fed prefers the PCEPI for
its long -term thinking. The CPI is the metric
the public sees and feels. And it was that really
dramatic CPI signal that prompted the Federal
Reserve under Jerome Powell to begin aggressive
quantitative tightening and start signaling the
rate hikes that were expected to kick off in
March of 2022. So the CPI. For all of its flaws,
the housing problems, the substitution bias,
all of it, it's still the immediate vital signal
that forces the central bank to act. It's the
number that makes people feel the heat, even
if the Fed is looking at its sibling index for
the long term plan. Hashtag tag you tag outro.
So if we try and connect this entire deep dive
to the bigger picture, I think what we've really
uncovered is that the Consumer Price Index, it's
a statistic that's built on a whole series of
really difficult philosophical and, frankly,
political choices. We've really seen two main
battlegrounds that define the whole number. The
first one is that fundamental philosophical problem
of how to measure owner -occupied housing. Right.
Do you choose the subjective but maybe theoretically
better opportunity cost method? Or do you choose
the debt payments method that's unstable and
creates that policy paradox? Or do you just choose
a transaction price approach that messes up all
your other economic statistics? Each path gives
you a fundamentally different inflation number.
And the second battleground is that political
and social conflict over what's fair versus what's
statistically rigorous. Right. You have the policy
choice of which index is the fairest one to use
for Coale. Is it the CPI -W, which is designed
for workers, not retirees? Is it the CPI -E?
which is demographically perfect for seniors
but creates huge fiscal problems down the road.
Or is it the chain CPI, which is statistically
more robust but ends up resulting in lower benefit
increases for people? The statistician is in
this impossible position of being tasked with
both scientific accuracy and managing political
consequences. The CPI is never just about the
math. It's a series of policy compromises disguised
as a single number. So what does this all mean
for you, the listener? Well, it means that the
headline inflation figure you hear, whether it's
2 % or 7%, it's never a perfect reflection of
your personal experience. Right. If you're an
elderly person who relies heavily on prescription
drugs and hospital visits, your personal inflation
rate is almost certainly much higher than the
CPI -W suggests. But if you're a young, adaptable
consumer who's really quick to substitute from
one product to another when prices change? Your
personal inflation rate might actually track
much closer to that lower. chained CPI figure.
Understanding the weights helps you figure out
where you sit on that spectrum. And it really
encourages you to see the power in these small
statistical choices. That difference between
the historical average inflation of the CPI at
3 .8 % and the PCEPI at 3 .3%, that has massive
cumulative implications for the real value of
your savings, for the future of entitlement programs,
and for the purchasing power of your wages over
your entire lifetime. That half a percent can
absolutely be the difference between a comfortable
retirement and a really strained one. And the
whole process is always one of catch -up. The
statisticians are always looking at the past
to try and calibrate the present. Which brings
us to our final provocative thought for you to
chew on. If the CPI basket is always being updated
periodically every two years, every decade to
reflect our current habits, what happens during
periods of genuinely rapid, massive behavioral
change? Think about the sudden explosion of digital
services, the shift from buying CDs and DVDs
to streaming, or the huge reliance on virtual
everything during the pandemic. If the index
is always - lagging behind measuring the prices
of yesterday's consumption mix? Is it inherently
incapable of capturing the true speed of modern
inflation? Is the one number we rely on for our
most crucial decisions always by its very nature
a step behind the curve? We'll leave that with
you.