Taking Inventory of Conventional GDP Accounting

Taking Inventory of Conventional GDP Accounting
Profile photo of Robert P. Murphy

From a Misesian perspective, there are numerous problems with conventional GDP accounting. The most fundamental 1.5problem is that it focuses on spending as the measure of production. So long as we’re dealing with genuine market transactions, this is somewhat defensible–if a producer sells $100 in output, that means a customer must be buying $100 in output–but it totally breaks down when the government spends money. This leads to the absurdity of saying government military expenditures during World War II were an engine of economic growth.

Yet there is a particular problem with the way conventional analysts assess the impact of inventories on a headline report of GDP growth. I tackled this issue back in 2010 when analysts were dismissing the apparently healthy GDP growth numbers as due to an “inventory bounce.” Fast forward to the present, and we find analysts explaining that in the first quarter of 2014, real GDP growth in the US was so miserable in part because inventories “subtracted 60 basis points” from the figure. A commenter at my blog and I hashed out these issues and came to a pretty good understanding, which both illustrates where the conventional analysts are coming from when using inventories to explain GDP growth, but which also shows just how nonsensical this talk can become.

The best way to zoom in on the issues is to first realize the following:

(1) GDP = Final Spending on goods and services + Spending on Inventory.

But notice that spending on inventory involves looking at the change in inventory (measured in market prices) over the course of the period. For example, if inventory starts out at $1 trillion, and businesses on net spend $100 billion adding to inventories, then inventory ends up at $1.1 trillion. So we can rewrite (1) as:

(2) GDP = Final Spending on goods and services + Change in Value of Inventory.

But nobody in the media ever cares about the absolute level of GDP. Instead, what we always want to hear is, “What was the growth in GDP last quarter (or year, etc.)?” This means we write:

(3) Change in GDP = Change in Final Spending on goods and services + Change in Change in (sic) Value of Inventory.

That’s not a typo in the last term; the conventional GDP accounting involves the second derivative of the market value of total inventories. Let’s walk through a simple example to see how this works, and why it can lead to serious trouble when analysts try to interpret the official numbers:

Inventory Example 1


First, let’s make sure we understand the cells in the table. In 2014, inventories didn’t change, and so the only way people could consume $2 trillion in final purchases is if that output were actually produced during 2014. Hence GDP is also $2 trillion.

Things are different in 2015. People still bought $2 trillion worth of total stuff. However, only half of that was newly produced in 2015, because the other $1 trillion was taken from the inventory stockpile. That’s why GDP fell in half, down to $1 trillion.

In 2016, people once again spent a total of $2 trillion on final purchases. Since inventories didn’t change during the year, obviously these purchases were consummated through entirely new production during the period. Hence, GDP rose back up to $2 trillion for the year, a 100-percent increase over the previous year’s level of output.

Now in this context, a typical analyst might say when the numbers were announced in 2016: “Sure, the BEA and the press are running around celebrating the ostensible doubling of real output in 2016. But if you dig into the numbers, you see that fully 100 percent of the growth is attributed to the $1 trillion acceleration in private inventory investment. If we net out this massive inventory bounce, we see that growth during the year was actually zero. We should be prepared for a double dip in 2017, after this one-time blip of statistical GDP growth.”

Now I’ve course I’ve chosen unrealistic numbers to make a point. Inventories didn’t even exist in 2016, either at the start or the end. And yet the conventional approach would force an analyst to attribute all of the GDP growth in 2016 to “inventory adjustment,” even though there was no inventory adjustment. Also, to the extent that considerations of inventory could in any way influence our forecast of future GDP growth, the fact that there is $0 in inventory right now can only make us expect more production in the future. Yet to repeat, the way analysts look at these figures, they might say, “Uh oh, GDP growth surged in 2016, but it wasn’t because businesses actually sold more stuff to their customers, instead it was just due to a relative piling up in inventory.” (If you think I’m attacking a straw man, go read my 2010 article.)

Using our equation (3) from above, we can see exactly what happened in this hypothetical example: During 2015, the change in inventories was negative $1 trillion (going from $1 trillion down to $0). In 2016, the change was $0 (starting at $0 and ending at $0). So, the change in the change was a positive $1 trillion, because ($0 minus negative $1 trillion) = +$1 trillion. Since the increase in GDP was also $1 trillion (going from $1 trillion to $2 trillion from 2015 to 2016), that is how we conclude that fully 100% of the increase in GDP is attributable to the “inventory bounce.” But to repeat, there was no bounce in inventory.

Now let’s work with a more plausible example, showing an economy recovering from a major recession:

Inventory Example 2

For this second example, I stuck to nice round numbers, but they are more realistic. As the unemployment rate falls, production increases and consumers begin spending with more confidence. GDP grows at a whopping 20% in 2015, and a still very respectable 10% in 2016.

Now if the BEA analysts want to break down the contribution of inventories, they would report in 2015 that 3 percentage points of total GDP growth was caused by inventories, while GDP grew 17% because of an increase in final purchases. In 2016, the ten total percentage points of GDP growth would be split with about 8.3% growth due to that size of an increase in final purchases, with the roughly 1.7 remaining percentage points of  GDP growth due to inventories. (Remember, we focus on the “change in the change” of inventories of $200 billion in 2016, not simply the change in inventories of $500 billion. Since inventory spending was responsible for $300 billion of the level of GDP in 2015, if we want to explain the growth of GDP from 2015 to 2016, it can only be due to the amount by which spending on inventories exceeded $300 billion in 2016–i.e., $200 billion.)

So what should our hypothetical analyst think, now that he has decomposed the total GDP growth figures into their final purchase and inventory components? I submit that we could tell any story we want. What matters is not the ex post figures, but what the business owners planned ex ante.

Before I explain what I mean, first ask yourself: Why do businesses carry inventory in the first place? One major reason is that they want to have a buffer in between final demand and production. The owners don’t want to lose potential sales while the factories are churning out additional units. So that desire leads businesses to accumulate inventories. On the other hand, they don’t want to carry too large of an inventory, because that ties up capital which could be earning interest. The actual desired level of inventories are an interplay of these (and possibly other) factors.

What these observations mean is that if a business owner expects sales to pick up in the future, he might intentionally bulk up his inventory. For example, the inventories carried by Halloween costume shops are certainly much higher on October 15 than on November 15. This is why we can’t derive much information from looking at changes in inventory per se.

Turning back to our numbers in the second table, we see that inventories grew from $100 billion to $400 billion in 2015. Should that inventory accumulation have been a good or bad omen for 2016? Well, it’s natural to expect businesses to start building up inventories again, coming off the heels of the awful recession. To know whether the inventory pile-up of $300 billion is bullish or bearish, we would need to know how much the business owners planned on accumulating. If they had planned on adding $500 billion, but their sales surged $200 billion more than they had expected, then things would be bullish. In contrast, if they had planned on only adding $100 billion, but they ended up adding $300 billion because final sales were $200 billion lower than they had planned, then the inventory accumulation would be bearish.

In closing, we can see the absurdities and confusion into which the conventional GDP accounting plunges the typical analyst. For example, he could conceivably be led to proclaim that “inventory adjustments” had had an impact on GDP growth, even though inventories hadn’t changed. In any event, looking at the ex post movements in inventory have little bearing on what we should expect in the future. The real benchmark is to compare actual inventory changes with planned inventory changes–and these are subjective items in the minds of the owners.

  • citizen101

    I agree GDP being the end-all and be-all of economic measures is just a bunch of keynesian clap trap, but it does still provide some useful information if its interpreted corectly… still its no GNP 😛

Profile photo of Robert P. Murphy

Robert P. Murphy is the Senior Economist at the Institute for Energy Research, and a Senior Fellow with the Fraser Institute. He holds a PhD in economics from New York University. Murphy is the author of Choice: Cooperation, Enterprise, and Human Action (Independent Institute, 2015) as well as numerous other books and hundreds of articles.

More in Blog


Bank of Canada Raises Interest Rates… Again

Caleb McMillanSeptember 6, 2017

Free the Arctic!

Patrick BarronAugust 29, 2017

Preposterous Bubble Predictions and the Madness of Crowds

Doug FrenchAugust 21, 2017

The Bond Bubble

Caleb McMillanAugust 16, 2017

The Reason for Statist Immigration

Caleb McMillanAugust 15, 2017

Is Bitcoin a Bubble?

Caleb McMillanAugust 14, 2017

Why Obamacare Repeal Failed

Taylor LewisAugust 2, 2017
Screen Shot 2017-07-25 at 12.27.53 PM

GDP, GPS, & Growth Without Well-Being

Caleb McMillanJuly 25, 2017

The Real EU Aim in Brexit Talks and Why It Will Fail

Patrick BarronJuly 19, 2017