Federal Reserve Economic Data

The U.S. military campaign against Iran and its spillover effects in the Middle East have pushed oil prices sharply higher, renewing interest in a longstanding question: What do movements in oil prices mean for the broader cost of living? Data in FRED offer a long historical perspective on how oil prices have co-moved with food prices and consumer inflation, which can help inform that question.

Oil and food prices

Our first FRED graph, above, tracks the Global Food Price Index alongside Brent crude oil prices from 1998 to the present. The two series exhibit a notable degree of co-movement across the full sample. Both surged during the global commodity boom of the mid-2000s, peaked sharply around 2008, collapsed during the global financial crisis, recovered through the early 2010s, and spiked again in 2022.

This co-movement is consistent with the fact that oil is an input at multiple stages of agricultural production and distribution, including farm equipment, fertilizer production, transportation, and refrigeration. To the extent that energy costs are passed through the supply chain, one would expect food prices to respond to oil price movements. At the same time, the relationship is likely not unidirectional. Broad macroeconomic conditions (recessions, recoveries, geopolitical shocks) can drive both series simultaneously, making it difficult to attribute movements in one series solely to movements in the other.

Oil and consumer price inflation

Our second FRED graph, above, takes a longer view, plotting annual percent changes in Brent crude oil prices against global consumer price inflation from the late 1980s to the present. Some co-movement is apparent across multiple episodes: The sharp dislocations of 2008–2009, the oil price decline of 2014–2016, the COVID-related collapse of 2020, and the post-pandemic surge of 2021–2022 all have visible counterparts in world inflation.

Oil price changes may feed into consumer prices through energy and production costs, but the relationship can also run in the other direction: Broad shifts in global demand affect both output and energy consumption, putting simultaneous pressure on oil prices and inflation. Geopolitical disruptions add a third channel, where a common shock moves both series at once with no clear direction of causality.

What the data show

Taken together, these two graphs suggest that large and sustained oil price movements have historically coincided with changes in both food prices and broader consumer inflation. The 2022 episode is a clear example: Brent crude surged above $120 per barrel following Russia’s invasion of Ukraine, the Global Food Price Index reached its highest level in the sample, and world inflation rose sharply. While these historical patterns do not imply a precise causal relationship, they suggest that developments in oil markets are often an important signal for broader price pressures.

How these graphs were created: For the first graph, search FRED for “Global price of Food Index” and select the IMF series. Click “Edit Graph” and then “Add Line,” and then search for “Crude Oil Prices: Brent – Europe.” Place the second series on the right axis under the “Format” tab. Set the start date to 1998-01-01. For the second graph, search FRED for and select “Crude Oil Prices: Brent – Europe” and set units to “Percent Change from Year Ago.” Click “Add Line” and search for “Inflation, consumer prices for the world” (the World Bank series), placing it on the right axis. Set the start date to 1988-01-01.

Suggested by Fernando Leibovici.

Recent speeches by Federal Reserve Bank officials and members of the Board of Governors suggest that productivity is on the minds of policymakers. One common theme is that artificial intelligence could increase productivity.

First, let’s define productivity.

Economists use two alternative definitions of productivity—labor productivity and total factor productivity (TFP)—that are somewhat correlated but not necessarily equivalent.

Our FRED graph above shows both of these measures, annually, for the period 1988-2024. Labor productivity is calculated as real GDP divided by hours worked. This can vary with changes in technology and in the amount of capital per worker. TFP, on the other hand, is computed by effectively holding capital per worker fixed and more directly represents the effect of technology on productivity. One thing to note, though, is that changes in labor productivity encompass changes in TFP.

How could AI impact productivity?

Artificial intelligence is a technology that can improve efficiency and increase productivity capacity. So, it has the potential to increase both labor productivity and TFP.

Some economists believe that a portion of this productivity boost might have already been realized but that most of the gains are likely yet to come, as AI adoption widens. AI’s effect on these two measures of productivity could also vary. For example, construction of data centers—now and in the future—increase capital. This could lead to an increase in labor productivity, but it has an uncertain effect on TFP, depending on whether output rises more than the capital stock. Read more on this topic in the St. Louis Fed On the Economy Blog.

How this graph was created: Search FRED for and select “Private Nonfarm Business Sector: Total Factor Productivity.” Click on the “Edit Graph” button and select the “Add Line” tab to search for and add “Private Nonfarm Business Sector: Labor Productivity.”

Suggested by Brooke Hathhorn and Michael T. Owyang.

“The central element of the Penn World Table has always been real GDP per capita, a measure of relative living standards across countries at different points in time.”

—Penn World Table authors Feenstar, Inklaar, and Timmer

FRED recently added data from the 11.0 version of the Penn World Table (PWT), produced by the University of Groningen and the University of California–Davis. This academic data collection provides information about real GDP per capita, as noted above, but also other historical economic conditions around the world. It complements other sources of international data in FRED, such as the International Monetary Fund (IMF), the World Bank, and the Organization for Economic Co-operation and Development (OECD).

How so?

The PWT offers national income accounts–type data converted to international prices and adjusted for differences in purchasing power. Also, the many available PWTs are not data releases like those put out by the IMF, World Bank, or the OECD. The authors call their product “versions” and number them because the methodologies change from issue to issue. It may be more helpful to think of them as “vintages.” Even when there is a correspondence between concepts across versions, there are changes to the methodologies that impact the historical values of the series.

Here’s an example:

Our FRED graph above shows the reported exchange rate between the Sudanese currency and the US dollar between 1950 and 2023: The solid blue line plots data from PWT 7.1 (available for 1950-2010), and the dashed green line plots data from PWT 11.0 (available for 1970-2023). Note that the labels for the data series are different. But, during the period when the two overlap (1970-2010), the majority of values are exactly the same. For a handful of years (1989-1992 and 2010), there are substantial differences in the values, as shown in our FRED graph below.

The PWT authors invite caution when using data from different versions. FRED makes it easy for data users to keep track of PWT versions, and data vintages in general, by suggesting a detailed data citation that includes the date when the data were accessed.

How this graph was created: Search FRED for and select “Exchange Rate to U.S. Dollar for Sudan.” Click the “Edit Graph” button and select the “Add Line” tab to search for “Exchange Rate (market+estimated) for Sudan.” Don’t forget to click “Add data series.”

Suggested by Diego Mendez-Carbajo.