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Tag Archives: Energy

Remembering Dale Jorgenson

The economics profession recently lost one of our leading scholars, Dale W. Jorgenson of Harvard University. His passing leaves a void in the landscape of economic theory, measurement, and data collection. Professor Jorgenson, in so many ways, was a major architect of this landscape, beginning with his early work developing a model of investment. He realized the importance of having theoretically sound measures and accurate data. Throughout his career, Professor Jorgenson’s research, as John Fernald wrote, applied “clear theory, data consistent with that theory, and sound econometrics” to stubborn economic problems and in doing so, built the theoretical, measurement, and data frameworks that underpin economic analysis worldwide.

He was the first to develop a theoretically sound method of measuring capital cost and the rental price of capital, which replaced previous ad hoc empirical work. This early work, which is just a single thread of Professor Jorgenson’s prolific research, is woven into countless aspects of modern-day economics, including the “Solow-Jorgenson-Griliches” growth accounting framework used to measure productivity. Professor Jorgenson made crucial contributions that changed how economists think about investment and how economists understand and measure productivity growth. Innumerable additional threads from Professor Jorgenson’s work permeate modern economics. In his remarkable professional life, Professor Jorgenson’s research furthered economic theory, strengthened economic measurement, and improved and broadened data collection.

Professor Jorgenson’s contributions were particularly important in the areas of investment modeling, growth accounting, national account development, and econometric modeling. His work with Zvi Griliches on the importance of using chain-linked Divisia indexes to measure output and input quantities eventually led to the adoption of these indexes in the U.S. national accounts. In the 1967 classic paper “The Explanation of Productivity Change,” Professors Jorgenson and Griliches developed a model that uses service prices to account for shifts to more productive forms of capital and similarly uses data on labor skills to account for shifts towards more productive forms of labor hours. This model of capital and the work on capturing the heterogeneous nature of labor input are now integral to productivity measurement not only here at BLS but worldwide.

The Organization for Economic Cooperation and Development and the United Nations System of National Accounts have embraced and recommended Professor Jorgenson’s growth accounting system, and statistical agencies throughout the world have adopted it. In the last 40 years, Professor Jorgenson has played a key role in how the Bureau of Economic Analysis produces the National Accounts. Through his 2006 book, “A New Architecture for the U.S. National Accounts,” Professor Jorgenson moved national accounting towards a fully integrated set of accounts, identified gaps and inconsistencies in the accounts, and incorporated nonmarket activities into accounts. Using his ideal Jorgenson System of National Accounts, Professor Jorgenson developed new national accounting features that statistical agencies in many countries have adopted. His work has contributed substantively to the National Income Accounts methods used throughout the world.

Professor Jorgenson also worked to develop an internationally consistent “KLEMS” growth accounting framework by initiating projects to generate industry-level data on outputs, inputs, and productivity in the European Union in 2000, India in 2009, and Asia in 2010. By 2010, the World KLEMS project was underway to further broaden this effort, with a Sixth World KLEMS conference to be held in October 2022. The influence of his research on growth accounting and national accounts extended to the G7 countries and China.

Throughout his career, Professor Jorgenson supported the U.S. statistical community by serving in many capacities: president of the Econometric Society (1987) and the American Economic Association (2000); member of the National Academy of Sciences since 1978, Founding Member of the Board on Science, Technology, and Economic Policy of the National Research Council in 1991 and Chair from 1998 to 2006; member of the Bureau of Economic Analysis Advisory Committee for two decades, serving as Chair from 2004 to 2011; and member of the U.S. Commerce Secretary’s Advisory Committee on Measuring Innovation in the 21st Century Economy from 2006 to 2008.

Professor Jorgenson received numerous awards for his research and service to the statistical system, including the Julius Shiskin Award in 2010, the Adam Smith Award in 2005, and the prestigious John Bates Clark medal in 1971. Professor Jorgenson was never awarded the Nobel Prize in economics, although many people, including myself, view this as an oversight.

As a guiding light on the frontier of economics, particularly in the fields of productivity measurement and national accounting, Professor Jorgenson showed the U.S. and international statistical communities the path forward. His contributions were immense, and many here in the United States and worldwide will grievously feel his loss.

Note: Dr. Lucy Eldridge, Associate Commissioner for the Office of Productivity and Technology, contributed significantly to this post.

How Timing and World Events Affect Price Statistics

Rising prices have certainly been in the news lately, and we have received a lot of questions about BLS price statistics. Some questions, however, are “evergreen.” Even in times of moderate price changes, BLS staff often hear that the Consumer Price Index (CPI) doesn’t reflect an individual’s experience. We address this concern and a wide range of other issues in our Questions and Answers about the CPI:

Q. Whose buying habits does the CPI reflect?

A. The CPI does not necessarily measure your own experience with price change. It is important to understand that BLS bases the market baskets and pricing procedures for the CPI-U and CPI-W populations on the experience of the relevant average household, not of any specific family or individual. For example, if you spend a larger-than-average share of your budget on medical expenses, and medical care costs are increasing more rapidly than the cost of other items in the CPI market basket, your personal rate of inflation may exceed the increase in the CPI. Conversely, if you heat your home with solar energy, and fuel prices are rising more rapidly than other items, you may experience less inflation than the general population does. A national average reflects millions of individual price experiences; it seldom mirrors a particular consumer’s experience.

Beyond the differences in individual spending habits, price statistics are affected by a variety of factors, including world events and the timing of price data collection. To explore these factors, we will look beyond the CPI to all BLS price indexes. We’ll focus on the price of oil and related items. Let’s start with a reminder of what is included in the BLS family of price indexes and look at how oil-related prices changed in March.

  • The Consumer Price Index measures the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.
    • The CPI for gasoline (all types) rose 18.3 percent in March and 48.0 percent over the last 12 months.
    • The CPI for energy rose 11.0 percent in March and 32.0 percent over the last 12 months.
  • The Producer Price Index (PPI) measures the average change over time in the selling prices domestic producers receive for their output.
    • The PPI for crude petroleum rose 7.2 percent in March and 62.2 percent over the last 12 months.
    • The PPI for petroleum refineries rose 17.0 percent in March and 62.1 percent over the last 12 months.
    • The PPI for fuels and lubricants retailing rose 22.7 percent in March and 40.0 percent over the last 12 months.
  • The Import and Export Price Indexes show changes in prices of nonmilitary goods and services traded between the United States and the rest of the world.
    • The Import Price Index for crude petroleum rose 15.6 percent in March and 62.0 percent over the last 12 months.
    • The Export Price Index for crude petroleum rose 19.1 percent in March. (This is a new measure, and we haven’t yet tracked it over 12 months.)

National or international events, whether started by Mother Nature or human action, affect the prices businesses and consumers pay for goods and services. We’ve seen this in the past with weather disruptions, such as hurricanes along the Gulf Coast that shut down oil drilling and refining. Current prices may be influenced by the war in Ukraine, the embargo on Russian oil, and other events around the world.

We can see the influence of these events in price changes throughout the production and distribution of oil-related goods and services. BLS estimates the changes in the prices that domestic producers receive through the PPI; this includes petroleum-related industries such as drillers and refiners and the margins on gasoline station sales. Gasoline retailers make money on the margins of their sales—the difference between how much they pay for the fuel they buy from wholesalers and the prices they receive from consumers. Margins for gas stations typically decline when oil prices increase. To learn more, see “As crude oil plunges, retail gasoline margins spike, then retreat.”

Some domestic producers import oil rather than purchase it domestically, and the Import Price Index reflects changes in prices they pay. Some domestic producers also export petroleum-related products, which is captured in Export Price Indexes. Ultimately, consumers purchase gasoline, home heating oil, and other petroleum-based products, and often producers pass price changes on to consumers. Thus, an increase in oil prices can result in higher costs at the pump, more expensive airline fares, and price increases for goods transported by trucks. The CPI reflects these higher prices consumers may face.

The price of oil and related products can change rapidly, adding to the challenges of collecting and publishing timely price statistics. Ideally, BLS would collect prices throughout the month for all goods and services in all price indexes. While that is a long-term goal, it is not simple to implement. Currently, BLS identifies the official “pricing date” for each index, as follows:

  • We collect prices for the CPI throughout the month, with each outlet (such as a gas station) assigned one of three pricing periods, which roughly correspond to the first 10 days, second 10 days, and third 10 days of the month. Once established, prices are updated each month during the same pricing period.
  • We collect prices for most items in the PPI as of the Tuesday of the week containing the thirteenth day of the month. This is the case for the petroleum-related items. (Some items in the PPI have prices collected throughout the month.)
  • We obtain import price data for petroleum from the U.S. Department of Energy. We obtain export price data for petroleum from secondary source market prices. These data represent a weighted average of imported and exported oil throughout the month.

Let’s look at the price of oil over the past few months and how the BLS pricing dates might affect the price indexes.

Daily price per barrel of West Texas Intermediate Crude, January to March 2022

Editor’s note: Data for this chart are available in the table below.

The chart shows the volatility of the oil prices, particularly in March. When the February CPI was released on March 10, West Texas Intermediate Crude Oil prices had already soared from $96 per barrel on the last day of February to over $123 two days before the CPI release. While consumers were feeling the pinch at the pump, this steep rise was not reflected in the February CPI data. Similarly, both the February and March PPI price dates (February 15 and March 15) missed the large run-up in oil prices in the first week of March. The Import Price Index, Export Price Index, and CPI did include the highest prices seen in early March, however.

BLS price indexes represent averages—average selections of goods and services, average weights, and typically average time periods. Over time, these indexes provide an accurate view of price change throughout the economy. But during periods of rapidly changing world events, and corresponding rapid changes in the price of individual commodities (and oil in particular), the index pricing periods may miss unusual highs and lows.

Daily price per barrel of West Texas Intermediate Crude, January to March 2022
DateDollars per barrel

Jan 3

$75.99

Jan 4

77.00

Jan 5

77.83

Jan 6

79.47

Jan 7

79.00

Jan 10

78.11

Jan 11

81.17

Jan 12

82.51

Jan 13

81.97

Jan 14

83.82

Jan 18

85.42

Jan 19

86.84

Jan 20

86.29

Jan 21

85.16

Jan 24

84.48

Jan 25

86.61

Jan 26

88.33

Jan 27

87.61

Jan 28

87.67

Jan 31

89.16

Feb 1

88.22

Feb 2

88.16

Feb 3

90.17

Feb 4

92.27

Feb 7

91.25

Feb 8

89.32

Feb 9

89.57

Feb 10

89.83

Feb 11

93.10

Feb 14

95.52

Feb 15

92.07

Feb 16

93.83

Feb 17

91.78

Feb 18

91.26

Feb 22

92.11

Feb 23

92.14

Feb 24

92.77

Feb 25

91.68

Feb 28

96.13

Mar 1

103.66

Mar 2

110.74

Mar 3

107.69

Mar 4

115.77

Mar 7

119.26

Mar 8

123.64

Mar 9

108.81

Mar 10

105.93

Mar 11

109.31

Mar 14

103.22

Mar 15

96.42

Mar 16

94.85

Mar 17

102.97

Mar 18

104.69

Mar 21

112.14

Mar 22

111.03

Mar 23

114.89

Mar 24

114.20

Mar 25

116.20

Mar 28

107.55

Mar 29

104.25

Mar 30

107.81

Mar 31

100.53

Expanding BLS Data on Total Factor Productivity

Our data on multifactor productivity are getting a makeover. You’ll get the same great data but with a new name, “total factor productivity.” Why change the name if it’s the same data? To reach you! More web searches seek total factor productivity than multifactor productivity. That’s probably because most other countries, including our major trading partners, call it total factor productivity. We want to make it easier to find us and stop having to answer how multifactor productivity differs from total factor productivity. They’re the same thing!

Besides the name change, we will expand our annual release of trends in total factor productivity for manufacturing to include not only manufacturing, but all the major industries in the private sector. With this addition, total factor productivity measures for all private major industries of the economy will be available in our news release and the BLS database.

Back to Basics

For those new to productivity data, let’s back up a bit. What is productivity and why should we care about it?

Productivity is a measure of economic performance, often touted as the engine of a nation’s economic growth. Productivity compares the output of goods and services with the inputs used to produce them. The difference in growth rates between these two amounts—the unexplained portion—equals productivity growth. Productivity tells us how good we are at using the inputs to create the output.

Productivity growth is important because, in the long run, it accounts for a third of the growth in a nation’s output. This growth supports increased wages, profits, public sector revenue, and global competitiveness. There are two types of productivity measures produced by BLS, labor productivity and total factor productivity. They are similar, as you can see in the chart below, but they have key differences.

Labor and total factor productivity, annualized percent change, private nonfarm business, 2010–19

Editor’s note: Data for this chart are available in the table below.

Much of the limelight goes to labor productivity, output per hour worked, which measures how many hours it takes to produce the goods and services in our economy. This measure is pretty simple to interpret and apply. If you used the same number of hours but produced more goods and services than last year, the economy became more efficient and labor productivity increased. Labor productivity increased because something besides worker hours (which we know stayed the same) contributed to the increased output. That something is productivity—the growth we didn’t account for in the calculation. With labor productivity, we only account for one input—hours worked. All the other inputs to production, like capital investment and materials, get lumped together into the unknown efficiency gains that can result from changes in technology or how work is organized.

This is where total factor productivity comes in. As the name suggests, total factor productivity measures more than just labor as an input to producing goods and services. It puts more “knowns” into the equation to help us pinpoint a more detailed story of why productivity is changing. For an industry, total factor productivity measures the output produced by a combined set of inputs: capital, labor, energy, materials, and purchased services. Total factor productivity tells us how much more output can be produced without increasing any of these inputs. The more efficiently an industry uses its inputs to create output, the faster total factor productivity will grow.

Total factor productivity gives us great insight into what drives economic growth. Is it the industries’ choice of capital investment? Better or more skilled labor? Or is it a change to the other factors of production, such as energy expenditures, materials consumed, or services purchased, or more efficient use of these inputs? The more detail with which we measure an industry, the more we can learn how these choices contribute to growth in this industry and ultimately our economy.

Let’s recap what we know:

Total factor productivity = output ÷ (combined inputs of capital, labor, energy, materials, and services)

And if we rearrange this equation and transform it to growth over time, we can see that increasing total factor productivity is a way to increase our nation’s output growth.

Output growth = total factor productivity growth + combined inputs growth

More is More

Previously, the annual release on Multifactor Productivity Trends in Manufacturing brought you information on the manufacturing sector and its 19 detailed industries. The manufacturing sector has often been a pioneer of technological development that drives productivity growth and is thus an important sector of the nation’s economy. You can see just how big of a role it has played in productivity growth in The Economics Daily.

And now we are providing a more complete picture. Not only will you get the first comprehensive look into what the COVID-19 pandemic in 2020 meant for labor, capital, and more, but we also will include all major industries and not just manufacturing. The chart below gives a taste of the expanded information that we will now include in the reimagined release with a new name. For example, we can see that in 2019, the information industry had strong output growth (third highest), stemming mostly from combined inputs growth and total factor productivity growth (those things that are harder to measure).

Percent change in total factor productivity, combined inputs, and output, by major private industry, 2019

Editor’s note: Data for this chart are available in the table below.

Connecting Total Factor Productivity to Labor Productivity

We can use total factor productivity and combined inputs for more than just an explanation of output growth. These measures give us is a way to break down the growth of labor productivity. We are the Bureau of Labor Statistics after all, so using our data to understand the growth in efficiency of the nation’s workforce is important.

We can express labor productivity growth as the sum of the growth of six components:

  • Total factor productivity
  • Contribution of capital intensity
  • Contribution of labor composition (shifts in the age, education, and gender composition of the workforce)
  • Contribution of energy
  • Contribution of materials
  • Contribution of purchased services

The contribution of each input is the ratio of the services provided by that input to hours worked. When we look at the contribution of each input, we can measure the effect of increasing the use of that input on an industry’s labor productivity.

The chart below shows sources of labor productivity in 2019 for each industry. The information industry had the second largest increase in labor productivity, rising 5.9 percent. That increase was driven by an increase in capital of 2.8 percent and total factor productivity growth of 1.5 percent. Knowing what drives productivity helps businesses make better decisions and pass those efficiencies on to workers and customers.

Sources of labor productivity change (in percentage points) by major private industry, 2019

Editor’s note: Data for this chart are available in the table below.

We will release new annual data for Total Factor Productivity in 2020 on November 18, 2021, at 10:00 a.m. Eastern Time. More detailed industry data are also available. For more information on productivity check, out our Productivity page. Want to help us improve our productivity data and publications? Please fill out our 10-minute survey by November 16, 2021.

Labor and total factor productivity, annualized percent change, private nonfarm business, 2010–19
YearLabor ProductivityTotal Factor Productivity

2010

3.4%2.7%

2011

-0.1-0.1

2012

0.80.7

2013

0.50.1

2014

0.80.6

2015

1.61.1

2016

0.4-0.4

2017

1.20.5

2018

1.50.9

2019

1.80.7
Percent change in total factor productivity, combined inputs, and output, by major private industry, 2019
IndustryOutputCombined inputsTotal Factor Productivity

Mining

6.7%2.0%4.6%

Management of companies

6.32.33.9

Information

5.74.21.5

Professional and technical services

3.72.21.5

Administrative and waste services

3.52.41.1

Real estate and rental and leasing

2.82.40.4

Accommodation and food services

2.82.10.7

Retail trade

2.30.51.7

Health care and social assistance

2.31.70.7

Transportation and warehousing

2.12.10.0

Arts, entertainment, and recreation

2.10.51.5

Finance and insurance

1.93.0-1.1

Agriculture, forestry, fishing, and hunting

0.50.8-0.3

Educational services

0.10.6-0.5

Construction

-0.70.7-1.4

Other services, except government

-0.8-2.21.3

Utilities

-1.1-4.63.6

Nondurable manufacturing

-1.60.3-1.9

Manufacturing

-1.70.3-2.0

Durable manufacturing

-1.80.1-1.9

Wholesale trade

-2.10.0-2.1
Sources of labor productivity change (in percentage points) by major private industry, 2019
IndustryServices intensityMaterials intensityEnergy intensityLabor compositionCapital intensityTotal Factor Productivity

Management of companies

1.9-0.10.00.50.03.9

Information

1.40.10.00.12.81.5

Mining

1.1-0.7-0.10.5-0.64.6

Arts, entertainment, and recreation

1.70.00.0-0.21.11.5

Administrative and waste services

1.80.30.00.30.31.1

Retail trade

1.70.0-0.10.00.51.7

Accommodation and food services

0.8-0.1-0.20.20.00.7

Finance and insurance

1.60.00.00.10.9-1.1

Professional and technical services

-0.1-0.20.0-0.10.31.5

Health care and social assistance

0.1-0.3-0.10.20.10.7

Real estate and rental and leasing

0.5-0.1-0.20.00.00.4

Utilities

-1.40.2-3.30.01.63.6

Other services, except government

-0.8-0.3-0.10.00.11.3

Agriculture, forestry, fishing, and hunting

0.00.4-0.30.1-0.3-0.3

Nondurable manufacturing

0.3-0.1-0.10.10.6-1.9

Manufacturing

0.00.1-0.20.00.5-2.0

Durable manufacturing

-0.20.1-0.10.00.4-1.9

Transportation and warehousing

-1.20.3-1.0-0.3-0.10.0

Wholesale trade

-1.00.0-0.10.10.7-2.1

Construction

-0.3-1.3-0.20.00.1-1.4

Educational services

-2.20.2-0.4-0.1-0.1-2.2