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Topic Archives: Survey Respondents

A New Tool at BLS: Video Data Collection Interview

The pandemic that has gripped the world for over a year has resulted in many challenges for BLS, notably in collecting key economic data from employers and households. It has also brought about innovation, as we were forced to find new ways to do things. We’ve gotten rid of many paper forms; we’ve learned to “sign” documents electronically; and we’ve done our best to remember to unmute in video meetings. Data collection is now almost entirely paperless. It involves more email and web-based interactions, and the latest BLS innovation—the video data collection interview.

Pretend with me that you are peeking in on a recent video interview. BLS Philadelphia Region Field Economist Joseph Wright is the star of this video show. His mission today is to interview a representative from a business for the Producer Price Index (PPI). The PPI measures the average change over time in the selling prices received by domestic producers for their output. To do that, Joe and his colleagues talk to domestic producers (businesses) to identify products and track selling prices.

Earlier, Joe contacted this business and got permission for video collection. As the scene opens, both Joe and the official at this company (we call these individuals “respondents”) are working from home. Joe has done video collection several times before, and that experience is evident. After he shows his credentials to verify he is legit, he shares his screen to point to information about BLS confidentiality protections and to highlight some PPI data.

View of the webpage on Confidentiality Pledge and Laws at https://www.bls.gov/bls/confidentiality.htm

Thinking about this process, it seems like sharing a few highlights on a screen is much easier than shuffling a bunch of papers while meeting with the respondent in person. Advantage—video.

Next, Joe starts the actual data collection process. He begins with some questions and examples designed to verify the firm’s industry classification. Then the conversation pivots to information about the products produced, where Joe and the respondent clarify things like new formulations, quantities in metric tons, and shipping lingo such as free-on-board. Fortunately, Joe and the respondent speak the same language. In fact, one of the hallmarks of BLS data collection is familiarity with detailed industries, occupations, work processes, and more. We are talking to experts, so it’s best to know our stuff. And Joe clearly does.

The goal of an initial PPI interview like this one is to select a sample of products sold by the business. We also want to get a detailed description of the products so we can follow the correct selling price. Finally, we want to set up the process for the business to easily report updated selling prices each month. From information provided by the respondent, Joe did some quick math and identified a random selection of products to follow, based on sales volume. He confirmed product descriptions, which will be provided back to the respondent when it’s time to update the selling prices. Clearly Joe is a pro, as he made quick work of the entire process.

This respondent’s data will eventually be part of the monthly PPI release, which provides considerable detail on changes in selling prices for a wide range of industries and products. Here’s a look at 12-month changes in the PPI over the past decade. More charts and more details are available on the BLS website.

Chart on Producer Price Index for final demand, 12-month percent changes

Data collection is a tough job. This particular respondent was comfortable with the video process and willing to provide information. It helps that the respondent said more than once that “we use these [BLS data] in our contracts” and that he was “glad to be part of this [since we] use a lot of these indexes.” While many respondents are indeed cooperative, and familiar with BLS data, others are not. Fortunately, BLS field economists are equipped with a marketing toolbox, which includes training in how to work with small and large companies; factsheets and related material that highlight how businesses can use BLS data, for example, in contract escalation; and details on BLS procedures to protect the confidentiality of respondent data. The video data collection interview is the latest tool.

BLS confidentiality procedures deserve extra emphasis. While our goal is to give respondents various data collection options, to make the process as convenient as possible, we never introduce a new collection option without a thorough confidentiality vetting. In the case of video collection, that vetting led to the development of strict standards and detailed procedures. These efforts are designed to ensure respondents of the value of their participation, and the care with which BLS handles their data. Enough said.

At BLS, we see the value in building relationships with respondents, and thus in-person data collection will continue to be part of our toolbox. But we also want to limit respondent burden and be good stewards of the taxpayer’s money. As Joe’s example demonstrates, the video data collection interview is an effective option to limit burden and expense while obtaining quality information to support key economic indicators. Even in a post-pandemic world, BLS video data collection is here to stay.

Looking Back on the 2020 U.S. Labor Market and Economy

I know many of us are glad to see 2020 in the rearview mirror and have higher hopes for 2021. The COVID-19 pandemic has caused so much suffering and hardship for people in the United States and around the world. During these challenging times, it remains important to have good, reliable, timely data. Good data are essential for the public health response to the pandemic and for tracking its economic and social effects, as well as the progress toward recovery. Let’s reflect back on some of the historic measures we saw in 2020.

Throughout the pandemic, the BLS staff and our colleagues across the statistical community have remained on the job to meet the growing needs for high-quality data. We are thankful we have been able to keep working; millions of other people haven’t been so fortunate. In part this is due to the way our work life at BLS changed in 2020. Nearly the entire staff has teleworked full time since March. That means we have needed to figure out new ways to collaborate with each other to continue producing essential data about the economy. That change in work life also meant that many staff members faced the challenges of new care arrangements for young children, schooling—often online—for older children, and keeping all their loved ones safe and healthy.

When the pandemic began in March 2020, many consumers began avoiding stores, restaurants, and other public gatherings to reduce the risk of catching or spreading the virus that causes COVID-19. Many businesses and other organizations reduced their operations or closed completely. At the recommendation of public health authorities, many governors and other public leaders issued stay-at-home orders. The economic impact of COVID-19 was breathtaking in its speed and severity.

National employment data. The nation experienced steady employment growth in recent years; BLS recorded average monthly increases in nonfarm employment between about 170,000 and 200,000 from 2016 to 2019. January and February 2020 brought continued job gains before the bottom dropped out in March (down 1.7 million jobs) and especially in April (down 20.7 million). These were the two largest declines in history, dating to 1939. These declines were then followed by the 4 largest increases in history: 2.8 million, 4.8 million, 1.7 million, and 1.5 million. You have to go back to 1983 to find the next highest increase, 1,118,000. Employment in December 2020 was nearly 10 million lower than in February.

Nonfarm payroll employment, January 1970–December 2020

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

National unemployment data. The year started with some record-low unemployment rates. The 3.5-percent unemployment rate in both January and February 2020 tied for the lowest rate since December 1969 (also 3.5 percent). The unemployment rates for several demographic groups were at or near their record lows. For example, the unemployment rate for African Americans in February 2020, at 6.0 percent, was close to the all-time low of 5.2 percent in August 2019.

Then came the pandemic in March 2020. The unemployment rate that month rose 0.9 percentage point to 4.4 percent. In April, the unemployment rate increased by 10.4 percentage points to 14.8 percent, the highest rate and largest one-month increase in history (dating to January 1948). Nearly all demographic groups experienced record-high unemployment rates in April; for example, the rate for Hispanics was a record 18.9 percent, after a record low of 4.0 percent in September 2019. And for the first time since data became available for both groups in 1973, the unemployment rate for Hispanics in April 2020 exceeded the rate for African Americans.

Unemployment rates for selected groups, February, April, and December 2020

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

State unemployment data. We see a similar pattern when looking at state unemployment rates, with record-setting lows early in 2020 followed by record-setting highs. In February, state unemployment rates ranged from a low of 2.2 percent in North Dakota to a high of 5.8 percent in Alaska, with 12 states at their historic lows that month. By April, rates had increased in all states, with 40 states and the District of Columbia setting new highs in that month, and another 7 states cresting in subsequent months. (The state data began in 1976.) State unemployment rates in April ranged from 8.3 percent in Connecticut to 30.1 percent in Nevada. Check out our animated map showing the rapid transformation of state unemployment rates.

Consumer price data. Beyond the job market, the pandemic had a big effect on other aspects of everyday life, including consumers’ buying habits. Toilet paper and wipes were disappearing from store shelves, while fewer people were commuting or traveling. Those trends were reflected in rapid changes in consumer prices.

One-month changes in the Consumer Price Index are typically 0.1 or 0.2 percent; the 0.8 percent decrease in April 2020, was the largest monthly decline since December 2008. The overall change included some large movements in both directions. For example, the price of gasoline declined 20.6 percent in April, the largest one-month decline since November 2008. In contrast, prices for food at home rose by 2.6 percent, the largest monthly increase since February 1974. Looking below the surface even further, several items experienced record one-month price changes, with some records going back over 50 years.

Percent change in consumer prices for selected items in April 2020, seasonally adjusted

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

Labor Productivity data. The BLS quarterly measure of labor productivity in the nonfarm business sector compares output to hours worked. If output rises more than hours worked, productivity increases. The pandemic saw large declines in both output and hours starting in mid-March. There was a small decline in labor productivity in the first quarter of 2020, down 0.3 percent, as output declined (-6.4 percent) slightly more than hours worked (-6.1 percent). While we had not experienced declining productivity in nearly 3 years, small increases or decreases in the quarterly change are common. The second quarter saw labor productivity soar by 10.6 percent, the largest increase since 1971, when productivity increased 12.3 percent in the first quarter. The second quarter 2020 increase reflected a greater decline in hours worked (-42.9 percent) than in output (-36.8 percent).

Since its beginnings in 1884, BLS has built consistent data to allow comparisons across the decades. Maintaining this history allows data users to quickly learn “when was the last time.” We also have collected and published new data specifically about the COVID-19 pandemic. Still to come, BLS will release more 2020 data in the coming year. Those new results will add to the unique story of the extraordinary 2020 economy.

Nonfarm payroll employment, January 1970–December 2020
MonthEmployment levelOver-the-month change

Jan 1970

71,176,000-65,000

Feb 1970

71,305,000129,000

Mar 1970

71,451,000146,000

Apr 1970

71,348,000-103,000

May 1970

71,124,000-224,000

Jun 1970

71,029,000-95,000

Jul 1970

71,053,00024,000

Aug 1970

70,937,000-116,000

Sep 1970

70,944,0007,000

Oct 1970

70,521,000-423,000

Nov 1970

70,409,000-112,000

Dec 1970

70,792,000383,000

Jan 1971

70,865,00073,000

Feb 1971

70,807,000-58,000

Mar 1971

70,860,00053,000

Apr 1971

71,036,000176,000

May 1971

71,247,000211,000

Jun 1971

71,254,0007,000

Jul 1971

71,315,00061,000

Aug 1971

71,373,00058,000

Sep 1971

71,614,000241,000

Oct 1971

71,642,00028,000

Nov 1971

71,847,000205,000

Dec 1971

72,109,000262,000

Jan 1972

72,441,000332,000

Feb 1972

72,648,000207,000

Mar 1972

72,944,000296,000

Apr 1972

73,162,000218,000

May 1972

73,469,000307,000

Jun 1972

73,758,000289,000

Jul 1972

73,709,000-49,000

Aug 1972

74,141,000432,000

Sep 1972

74,264,000123,000

Oct 1972

74,674,000410,000

Nov 1972

74,973,000299,000

Dec 1972

75,268,000295,000

Jan 1973

75,617,000349,000

Feb 1973

76,014,000397,000

Mar 1973

76,284,000270,000

Apr 1973

76,455,000171,000

May 1973

76,648,000193,000

Jun 1973

76,887,000239,000

Jul 1973

76,913,00026,000

Aug 1973

77,168,000255,000

Sep 1973

77,276,000108,000

Oct 1973

77,607,000331,000

Nov 1973

77,920,000313,000

Dec 1973

78,031,000111,000

Jan 1974

78,100,00069,000

Feb 1974

78,254,000154,000

Mar 1974

78,296,00042,000

Apr 1974

78,382,00086,000

May 1974

78,549,000167,000

Jun 1974

78,604,00055,000

Jul 1974

78,636,00032,000

Aug 1974

78,619,000-17,000

Sep 1974

78,610,000-9,000

Oct 1974

78,630,00020,000

Nov 1974

78,265,000-365,000

Dec 1974

77,652,000-613,000

Jan 1975

77,293,000-359,000

Feb 1975

76,918,000-375,000

Mar 1975

76,648,000-270,000

Apr 1975

76,460,000-188,000

May 1975

76,624,000164,000

Jun 1975

76,521,000-103,000

Jul 1975

76,770,000249,000

Aug 1975

77,153,000383,000

Sep 1975

77,228,00075,000

Oct 1975

77,540,000312,000

Nov 1975

77,685,000145,000

Dec 1975

78,017,000332,000

Jan 1976

78,503,000486,000

Feb 1976

78,816,000313,000

Mar 1976

79,048,000232,000

Apr 1976

79,292,000244,000

May 1976

79,312,00020,000

Jun 1976

79,376,00064,000

Jul 1976

79,547,000171,000

Aug 1976

79,704,000157,000

Sep 1976

79,892,000188,000

Oct 1976

79,911,00019,000

Nov 1976

80,240,000329,000

Dec 1976

80,448,000208,000

Jan 1977

80,690,000242,000

Feb 1977

80,988,000298,000

Mar 1977

81,391,000403,000

Apr 1977

81,728,000337,000

May 1977

82,088,000360,000

Jun 1977

82,488,000400,000

Jul 1977

82,834,000346,000

Aug 1977

83,075,000241,000

Sep 1977

83,532,000457,000

Oct 1977

83,800,000268,000

Nov 1977

84,173,000373,000

Dec 1977

84,410,000237,000

Jan 1978

84,594,000184,000

Feb 1978

84,948,000354,000

Mar 1978

85,460,000512,000

Apr 1978

86,162,000702,000

May 1978

86,509,000347,000

Jun 1978

86,950,000441,000

Jul 1978

87,204,000254,000

Aug 1978

87,483,000279,000

Sep 1978

87,621,000138,000

Oct 1978

87,956,000335,000

Nov 1978

88,391,000435,000

Dec 1978

88,671,000280,000

Jan 1979

88,808,000137,000

Feb 1979

89,055,000247,000

Mar 1979

89,479,000424,000

Apr 1979

89,417,000-62,000

May 1979

89,789,000372,000

Jun 1979

90,108,000319,000

Jul 1979

90,217,000109,000

Aug 1979

90,300,00083,000

Sep 1979

90,327,00027,000

Oct 1979

90,481,000154,000

Nov 1979

90,573,00092,000

Dec 1979

90,672,00099,000

Jan 1980

90,800,000128,000

Feb 1980

90,883,00083,000

Mar 1980

90,994,000111,000

Apr 1980

90,849,000-145,000

May 1980

90,420,000-429,000

Jun 1980

90,101,000-319,000

Jul 1980

89,840,000-261,000

Aug 1980

90,099,000259,000

Sep 1980

90,213,000114,000

Oct 1980

90,490,000277,000

Nov 1980

90,747,000257,000

Dec 1980

90,943,000196,000

Jan 1981

91,033,00090,000

Feb 1981

91,105,00072,000

Mar 1981

91,210,000105,000

Apr 1981

91,283,00073,000

May 1981

91,296,00013,000

Jun 1981

91,490,000194,000

Jul 1981

91,601,000111,000

Aug 1981

91,565,000-36,000

Sep 1981

91,477,000-88,000

Oct 1981

91,380,000-97,000

Nov 1981

91,171,000-209,000

Dec 1981

90,895,000-276,000

Jan 1982

90,565,000-330,000

Feb 1982

90,563,000-2,000

Mar 1982

90,434,000-129,000

Apr 1982

90,150,000-284,000

May 1982

90,107,000-43,000

Jun 1982

89,865,000-242,000

Jul 1982

89,521,000-344,000

Aug 1982

89,363,000-158,000

Sep 1982

89,183,000-180,000

Oct 1982

88,907,000-276,000

Nov 1982

88,786,000-121,000

Dec 1982

88,771,000-15,000

Jan 1983

88,990,000219,000

Feb 1983

88,917,000-73,000

Mar 1983

89,090,000173,000

Apr 1983

89,364,000274,000

May 1983

89,644,000280,000

Jun 1983

90,021,000377,000

Jul 1983

90,437,000416,000

Aug 1983

90,129,000-308,000

Sep 1983

91,247,0001,118,000

Oct 1983

91,520,000273,000

Nov 1983

91,875,000355,000

Dec 1983

92,230,000355,000

Jan 1984

92,673,000443,000

Feb 1984

93,157,000484,000

Mar 1984

93,429,000272,000

Apr 1984

93,792,000363,000

May 1984

94,098,000306,000

Jun 1984

94,479,000381,000

Jul 1984

94,789,000310,000

Aug 1984

95,032,000243,000

Sep 1984

95,344,000312,000

Oct 1984

95,629,000285,000

Nov 1984

95,982,000353,000

Dec 1984

96,107,000125,000

Jan 1985

96,372,000265,000

Feb 1985

96,503,000131,000

Mar 1985

96,842,000339,000

Apr 1985

97,038,000196,000

May 1985

97,312,000274,000

Jun 1985

97,459,000147,000

Jul 1985

97,648,000189,000

Aug 1985

97,840,000192,000

Sep 1985

98,045,000205,000

Oct 1985

98,233,000188,000

Nov 1985

98,443,000210,000

Dec 1985

98,609,000166,000

Jan 1986

98,732,000123,000

Feb 1986

98,847,000115,000

Mar 1986

98,934,00087,000

Apr 1986

99,121,000187,000

May 1986

99,248,000127,000

Jun 1986

99,155,000-93,000

Jul 1986

99,473,000318,000

Aug 1986

99,588,000115,000

Sep 1986

99,934,000346,000

Oct 1986

100,121,000187,000

Nov 1986

100,308,000187,000

Dec 1986

100,509,000201,000

Jan 1987

100,678,000169,000

Feb 1987

100,919,000241,000

Mar 1987

101,164,000245,000

Apr 1987

101,499,000335,000

May 1987

101,728,000229,000

Jun 1987

101,900,000172,000

Jul 1987

102,247,000347,000

Aug 1987

102,420,000173,000

Sep 1987

102,647,000227,000

Oct 1987

103,138,000491,000

Nov 1987

103,372,000234,000

Dec 1987

103,661,000289,000

Jan 1988

103,753,00092,000

Feb 1988

104,214,000461,000

Mar 1988

104,489,000275,000

Apr 1988

104,732,000243,000

May 1988

104,962,000230,000

Jun 1988

105,326,000364,000

Jul 1988

105,550,000224,000

Aug 1988

105,674,000124,000

Sep 1988

106,013,000339,000

Oct 1988

106,276,000263,000

Nov 1988

106,617,000341,000

Dec 1988

106,898,000281,000

Jan 1989

107,161,000263,000

Feb 1989

107,427,000266,000

Mar 1989

107,621,000194,000

Apr 1989

107,791,000170,000

May 1989

107,913,000122,000

Jun 1989

108,027,000114,000

Jul 1989

108,069,00042,000

Aug 1989

108,120,00051,000

Sep 1989

108,369,000249,000

Oct 1989

108,476,000107,000

Nov 1989

108,752,000276,000

Dec 1989

108,836,00084,000

Jan 1990

109,199,000363,000

Feb 1990

109,435,000236,000

Mar 1990

109,644,000209,000

Apr 1990

109,686,00042,000

May 1990

109,839,000153,000

Jun 1990

109,856,00017,000

Jul 1990

109,824,000-32,000

Aug 1990

109,616,000-208,000

Sep 1990

109,518,000-98,000

Oct 1990

109,367,000-151,000

Nov 1990

109,214,000-153,000

Dec 1990

109,166,000-48,000

Jan 1991

109,055,000-111,000

Feb 1991

108,734,000-321,000

Mar 1991

108,574,000-160,000

Apr 1991

108,364,000-210,000

May 1991

108,249,000-115,000

Jun 1991

108,334,00085,000

Jul 1991

108,292,000-42,000

Aug 1991

108,310,00018,000

Sep 1991

108,336,00026,000

Oct 1991

108,357,00021,000

Nov 1991

108,296,000-61,000

Dec 1991

108,328,00032,000

Jan 1992

108,369,00041,000

Feb 1992

108,311,000-58,000

Mar 1992

108,365,00054,000

Apr 1992

108,519,000154,000

May 1992

108,649,000130,000

Jun 1992

108,715,00066,000

Jul 1992

108,793,00078,000

Aug 1992

108,925,000132,000

Sep 1992

108,959,00034,000

Oct 1992

109,139,000180,000

Nov 1992

109,272,000133,000

Dec 1992

109,495,000223,000

Jan 1993

109,794,000299,000

Feb 1993

110,044,000250,000

Mar 1993

109,994,000-50,000

Apr 1993

110,296,000302,000

May 1993

110,568,000272,000

Jun 1993

110,749,000181,000

Jul 1993

111,055,000306,000

Aug 1993

111,206,000151,000

Sep 1993

111,448,000242,000

Oct 1993

111,733,000285,000

Nov 1993

111,984,000251,000

Dec 1993

112,314,000330,000

Jan 1994

112,595,000281,000

Feb 1994

112,781,000186,000

Mar 1994

113,242,000461,000

Apr 1994

113,586,000344,000

May 1994

113,921,000335,000

Jun 1994

114,238,000317,000

Jul 1994

114,610,000372,000

Aug 1994

114,896,000286,000

Sep 1994

115,247,000351,000

Oct 1994

115,458,000211,000

Nov 1994

115,869,000411,000

Dec 1994

116,165,000296,000

Jan 1995

116,501,000336,000

Feb 1995

116,697,000196,000

Mar 1995

116,907,000210,000

Apr 1995

117,069,000162,000

May 1995

117,049,000-20,000

Jun 1995

117,286,000237,000

Jul 1995

117,380,00094,000

Aug 1995

117,634,000254,000

Sep 1995

117,875,000241,000

Oct 1995

118,031,000156,000

Nov 1995

118,175,000144,000

Dec 1995

118,320,000145,000

Jan 1996

118,316,000-4,000

Feb 1996

118,739,000423,000

Mar 1996

118,993,000254,000

Apr 1996

119,158,000165,000

May 1996

119,486,000328,000

Jun 1996

119,769,000283,000

Jul 1996

120,015,000246,000

Aug 1996

120,199,000184,000

Sep 1996

120,410,000211,000

Oct 1996

120,665,000255,000

Nov 1996

120,961,000296,000

Dec 1996

121,143,000182,000

Jan 1997

121,363,000220,000

Feb 1997

121,675,000312,000

Mar 1997

121,990,000315,000

Apr 1997

122,286,000296,000

May 1997

122,546,000260,000

Jun 1997

122,814,000268,000

Jul 1997

123,111,000297,000

Aug 1997

123,093,000-18,000

Sep 1997

123,585,000492,000

Oct 1997

123,929,000344,000

Nov 1997

124,235,000306,000

Dec 1997

124,549,000314,000

Jan 1998

124,812,000263,000

Feb 1998

125,016,000204,000

Mar 1998

125,164,000148,000

Apr 1998

125,442,000278,000

May 1998

125,844,000402,000

Jun 1998

126,076,000232,000

Jul 1998

126,205,000129,000

Aug 1998

126,544,000339,000

Sep 1998

126,752,000208,000

Oct 1998

126,954,000202,000

Nov 1998

127,231,000277,000

Dec 1998

127,596,000365,000

Jan 1999

127,702,000106,000

Feb 1999

128,120,000418,000

Mar 1999

128,227,000107,000

Apr 1999

128,597,000370,000

May 1999

128,808,000211,000

Jun 1999

129,089,000281,000

Jul 1999

129,414,000325,000

Aug 1999

129,569,000155,000

Sep 1999

129,772,000203,000

Oct 1999

130,177,000405,000

Nov 1999

130,466,000289,000

Dec 1999

130,772,000306,000

Jan 2000

131,005,000233,000

Feb 2000

131,124,000119,000

Mar 2000

131,596,000472,000

Apr 2000

131,888,000292,000

May 2000

132,105,000217,000

Jun 2000

132,061,000-44,000

Jul 2000

132,236,000175,000

Aug 2000

132,230,000-6,000

Sep 2000

132,353,000123,000

Oct 2000

132,351,000-2,000

Nov 2000

132,556,000205,000

Dec 2000

132,709,000153,000

Jan 2001

132,698,000-11,000

Feb 2001

132,789,00091,000

Mar 2001

132,747,000-42,000

Apr 2001

132,463,000-284,000

May 2001

132,410,000-53,000

Jun 2001

132,299,000-111,000

Jul 2001

132,177,000-122,000

Aug 2001

132,028,000-149,000

Sep 2001

131,771,000-257,000

Oct 2001

131,454,000-317,000

Nov 2001

131,142,000-312,000

Dec 2001

130,982,000-160,000

Jan 2002

130,852,000-130,000

Feb 2002

130,736,000-116,000

Mar 2002

130,717,000-19,000

Apr 2002

130,623,000-94,000

May 2002

130,634,00011,000

Jun 2002

130,684,00050,000

Jul 2002

130,590,000-94,000

Aug 2002

130,587,000-3,000

Sep 2002

130,501,000-86,000

Oct 2002

130,628,000127,000

Nov 2002

130,615,000-13,000

Dec 2002

130,472,000-143,000

Jan 2003

130,580,000108,000

Feb 2003

130,444,000-136,000

Mar 2003

130,232,000-212,000

Apr 2003

130,177,000-55,000

May 2003

130,196,00019,000

Jun 2003

130,194,000-2,000

Jul 2003

130,191,000-3,000

Aug 2003

130,149,000-42,000

Sep 2003

130,254,000105,000

Oct 2003

130,454,000200,000

Nov 2003

130,474,00020,000

Dec 2003

130,588,000114,000

Jan 2004

130,769,000181,000

Feb 2004

130,825,00056,000

Mar 2004

131,142,000317,000

Apr 2004

131,411,000269,000

May 2004

131,694,000283,000

Jun 2004

131,793,00099,000

Jul 2004

131,848,00055,000

Aug 2004

131,937,00089,000

Sep 2004

132,093,000156,000

Oct 2004

132,447,000354,000

Nov 2004

132,503,00056,000

Dec 2004

132,624,000121,000

Jan 2005

132,774,000150,000

Feb 2005

133,032,000258,000

Mar 2005

133,156,000124,000

Apr 2005

133,518,000362,000

May 2005

133,690,000172,000

Jun 2005

133,942,000252,000

Jul 2005

134,296,000354,000

Aug 2005

134,498,000202,000

Sep 2005

134,566,00068,000

Oct 2005

134,655,00089,000

Nov 2005

134,993,000338,000

Dec 2005

135,149,000156,000

Jan 2006

135,429,000280,000

Feb 2006

135,737,000308,000

Mar 2006

136,047,000310,000

Apr 2006

136,205,000158,000

May 2006

136,244,00039,000

Jun 2006

136,325,00081,000

Jul 2006

136,520,000195,000

Aug 2006

136,694,000174,000

Sep 2006

136,843,000149,000

Oct 2006

136,852,0009,000

Nov 2006

137,063,000211,000

Dec 2006

137,249,000186,000

Jan 2007

137,477,000228,000

Feb 2007

137,558,00081,000

Mar 2007

137,793,000235,000

Apr 2007

137,842,00049,000

May 2007

137,993,000151,000

Jun 2007

138,069,00076,000

Jul 2007

138,038,000-31,000

Aug 2007

138,015,000-23,000

Sep 2007

138,095,00080,000

Oct 2007

138,174,00079,000

Nov 2007

138,284,000110,000

Dec 2007

138,392,000108,000

Jan 2008

138,403,00011,000

Feb 2008

138,324,000-79,000

Mar 2008

138,275,000-49,000

Apr 2008

138,035,000-240,000

May 2008

137,858,000-177,000

Jun 2008

137,687,000-171,000

Jul 2008

137,491,000-196,000

Aug 2008

137,213,000-278,000

Sep 2008

136,753,000-460,000

Oct 2008

136,272,000-481,000

Nov 2008

135,545,000-727,000

Dec 2008

134,839,000-706,000

Jan 2009

134,055,000-784,000

Feb 2009

133,312,000-743,000

Mar 2009

132,512,000-800,000

Apr 2009

131,817,000-695,000

May 2009

131,475,000-342,000

Jun 2009

131,008,000-467,000

Jul 2009

130,668,000-340,000

Aug 2009

130,485,000-183,000

Sep 2009

130,244,000-241,000

Oct 2009

130,045,000-199,000

Nov 2009

130,057,00012,000

Dec 2009

129,788,000-269,000

Jan 2010

129,790,0002,000

Feb 2010

129,698,000-92,000

Mar 2010

129,879,000181,000

Apr 2010

130,110,000231,000

May 2010

130,650,000540,000

Jun 2010

130,511,000-139,000

Jul 2010

130,427,000-84,000

Aug 2010

130,422,000-5,000

Sep 2010

130,357,000-65,000

Oct 2010

130,625,000268,000

Nov 2010

130,750,000125,000

Dec 2010

130,822,00072,000

Jan 2011

130,841,00019,000

Feb 2011

131,053,000212,000

Mar 2011

131,288,000235,000

Apr 2011

131,602,000314,000

May 2011

131,703,000101,000

Jun 2011

131,939,000236,000

Jul 2011

131,999,00060,000

Aug 2011

132,125,000126,000

Sep 2011

132,358,000233,000

Oct 2011

132,562,000204,000

Nov 2011

132,694,000132,000

Dec 2011

132,896,000202,000

Jan 2012

133,250,000354,000

Feb 2012

133,512,000262,000

Mar 2012

133,752,000240,000

Apr 2012

133,834,00082,000

May 2012

133,934,000100,000

Jun 2012

134,007,00073,000

Jul 2012

134,159,000152,000

Aug 2012

134,331,000172,000

Sep 2012

134,518,000187,000

Oct 2012

134,677,000159,000

Nov 2012

134,833,000156,000

Dec 2012

135,072,000239,000

Jan 2013

135,263,000191,000

Feb 2013

135,541,000278,000

Mar 2013

135,680,000139,000

Apr 2013

135,871,000191,000

May 2013

136,093,000222,000

Jun 2013

136,274,000181,000

Jul 2013

136,386,000112,000

Aug 2013

136,628,000242,000

Sep 2013

136,815,000187,000

Oct 2013

137,040,000225,000

Nov 2013

137,304,000264,000

Dec 2013

137,373,00069,000

Jan 2014

137,548,000175,000

Feb 2014

137,714,000166,000

Mar 2014

137,968,000254,000

Apr 2014

138,293,000325,000

May 2014

138,511,000218,000

Jun 2014

138,837,000326,000

Jul 2014

139,069,000232,000

Aug 2014

139,257,000188,000

Sep 2014

139,566,000309,000

Oct 2014

139,818,000252,000

Nov 2014

140,109,000291,000

Dec 2014

140,377,000268,000

Jan 2015

140,568,000191,000

Feb 2015

140,839,000271,000

Mar 2015

140,910,00071,000

Apr 2015

141,194,000284,000

May 2015

141,525,000331,000

Jun 2015

141,699,000174,000

Jul 2015

142,001,000302,000

Aug 2015

142,126,000125,000

Sep 2015

142,281,000155,000

Oct 2015

142,587,000306,000

Nov 2015

142,824,000237,000

Dec 2015

143,097,000273,000

Jan 2016

143,205,000108,000

Feb 2016

143,417,000212,000

Mar 2016

143,654,000237,000

Apr 2016

143,851,000197,000

May 2016

143,892,00041,000

Jun 2016

144,150,000258,000

Jul 2016

144,521,000371,000

Aug 2016

144,664,000143,000

Sep 2016

144,953,000289,000

Oct 2016

145,071,000118,000

Nov 2016

145,201,000130,000

Dec 2016

145,415,000214,000

Jan 2017

145,612,000197,000

Feb 2017

145,795,000183,000

Mar 2017

145,934,000139,000

Apr 2017

146,154,000220,000

May 2017

146,295,000141,000

Jun 2017

146,506,000211,000

Jul 2017

146,734,000228,000

Aug 2017

146,924,000190,000

Sep 2017

146,966,00042,000

Oct 2017

147,215,000249,000

Nov 2017

147,411,000196,000

Dec 2017

147,590,000179,000

Jan 2018

147,671,00081,000

Feb 2018

148,049,000378,000

Mar 2018

148,244,000195,000

Apr 2018

148,397,000153,000

May 2018

148,667,000270,000

Jun 2018

148,881,000214,000

Jul 2018

149,030,000149,000

Aug 2018

149,259,000229,000

Sep 2018

149,364,000105,000

Oct 2018

149,576,000212,000

Nov 2018

149,668,00092,000

Dec 2018

149,908,000240,000

Jan 2019

150,145,000237,000

Feb 2019

150,095,000-50,000

Mar 2019

150,263,000168,000

Apr 2019

150,482,000219,000

May 2019

150,545,00063,000

Jun 2019

150,720,000175,000

Jul 2019

150,913,000193,000

Aug 2019

151,108,000195,000

Sep 2019

151,329,000221,000

Oct 2019

151,524,000195,000

Nov 2019

151,758,000234,000

Dec 2019

151,919,000161,000

Jan 2020

152,234,000315,000

Feb 2020

152,523,000289,000

Mar 2020

150,840,000-1,683,000

Apr 2020

130,161,000-20,679,000

May 2020

132,994,0002,833,000

Jun 2020

137,840,0004,846,000

Jul 2020

139,566,0001,726,000

Aug 2020

141,149,0001,583,000

Sep 2020

141,865,000716,000

Oct 2020

142,545,000680,000

Nov 2020

142,809,000264,000

Dec 2020

142,582,000-227,000
Unemployment rates for selected groups, February, April, and December 2020
Race and Hispanic or Latino ethnicityFebruary 2020April 2020December 2020

Total, 16 years and older

3.514.86.7

White

3.014.16.0

Black or African American

6.016.79.9

Asian

2.414.55.9

Hispanic or Latino

4.418.99.3
Percent change in consumer prices for selected items in April 2020, seasonally adjusted
Expenditure categoryPercent change

Car and truck rental (1998)

-16.6

Airline fares (1989)

-15.2

Hotel and motel lodging (1967)

-8.1

Women’s footwear (1978)

-5.2

Full service meals and snacks (1998)

-0.3

Carbonated drinks (1978)

4.5

Household paper products (1997)

4.5

Cookies (1978)

5.1

Chicken (2004)

5.8

Innovations at BLS during the COVID-19 Pandemic

Our work at the Bureau of Labor Statistics is driven by the idea that good measurement leads to better decisions. Good measures of economic and social conditions help public policymakers and private businesses and households assess opportunities and areas for improvement. Measuring these conditions consistently over time helps people who use our data evaluate the impact of public and private decisions.

We also believe we must be completely transparent about the design of our surveys and programs and the methods we use to conduct them. It isn’t enough to publish statistics and expect people simply to trust their quality. We gain this trust by documenting the design and procedures for all our programs in our Handbook of Methods. Our website also explains our policies for ensuring data quality and protecting the confidentiality and privacy of the people and businesses who participate in our surveys and programs. Further, BLS works with the wider U.S. statistical community to ensure and enhance the quality of statistical information.

Good measures are essential in “normal” times, but the global COVID-19 pandemic has made these last few months anything but normal. I am so proud of the work of the career professionals at BLS and our fellow statistical agencies for continuing to produce vital economic statistics. Our entire BLS staff moved to full-time telework in mid-March and didn’t miss a beat. We continue to publish measures of labor market activity, working conditions, price changes, and productivity like BLS has done since its founding in 1884. See our dashboard of key economic indicators in the time of COVID-19.

Publishing these measures hasn’t been easy. The pandemic has raised new questions about how businesses, households, and consumers have changed their behavior. BLS also has had to innovate to find new ways of doing things during the pandemic.

Today I want to tell you about the new data we have been collecting to learn more about the effects of the pandemic. I also want to tell you about some of the ways the BLS staff has innovated to keep producing data that are accurate, objective, relevant, timely, and accessible.

New Data

How businesses have responded to the pandemic

We have collected new data on how U.S. businesses changed their operations and employment from the onset of the pandemic through September 2020. This information, combined with data collected in other BLS surveys, will aid in understanding how businesses responded during the pandemic. Other statistics we have collected and published during the pandemic show changes in employment, job openings and terminations, wages, employer-provided benefits, prices, and more. These new data provide more insights by asking employers directly what they experienced as a result of the pandemic and how they reacted. Data for the Business Response Survey to the Coronavirus Pandemic will be released in early December 2020.

Changes in telework, loss of jobs, and job search

The Current Population Survey is the large monthly survey of U.S. households from which we measure the unemployment rate and other important labor market indicators. We added questions to the survey to help gauge the effects of the pandemic on the labor market. These questions were added in May 2020 and will remain in the survey until further notice. One question asks whether people teleworked or worked from home because of the pandemic.

Percent of employed people who teleworked at some point in the previous 4 weeks because of the COVID-19 pandemic, May through October 2020

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

Other questions ask whether people were unable to work because their employers closed or lost business because of the pandemic; whether they were paid for that missed work; and whether the pandemic prevented them from searching for jobs.

Number of people not in the labor force who did not look for work because of the COVID-19  pandemic, May through October 2020

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

Changes in sick leave plans

We added several questions to the National Compensation Survey to understand the effects of the pandemic on sick leave plans. The questions asked whether private industry establishments changed their leave policies and whether employees used sick leave between March 1 and May 31, 2020.

Receiving and using stimulus payments during the pandemic

BLS is one of several federal agencies that developed questions for the rapid response Household Pulse Survey. The survey is a collaboration among the U.S. Census Bureau, BLS, the U.S. Department of Housing and Urban Development, the National Center for Education Statistics, the National Center for Health Statistics, and the U.S. Department of Agriculture’s Economic Research Service. BLS contributed questions on the receipt and use of Economic Impact Payments and on sources of income used to meet spending needs during the pandemic.

Our staff will continue to publish research on how the pandemic has affected the labor market and markets for goods and services. Check back regularly as we add to this library of research.

Innovations in Data Collection and Training

The COVID-19 pandemic has caused profound changes in the daily lives of Americans. BLS is no exception. As I mentioned earlier, all BLS staff moved to full-time telework in March. The pandemic hasn’t prevented us from continuing to publish high-quality data, but we have had to change some of our data-collection methods and estimation procedures. We will continue to explain those changes so you can understand how they affect the quality of our measures.

Our survey respondents are the heart of everything we do at BLS. Without their generous and voluntary cooperation, we would not be able to publish high-quality data for public and private decision making. Respondents have businesses and households to run, and a pandemic is a challenging time to ask for their help. The data-collection staffs at BLS, the U.S. Census Bureau, and our state partners form great relationships with survey respondents. We must continue to protect the health of data collectors while also training them in a rapidly changing environment. Let me highlight a few of the innovative changes we have made during the pandemic that focus on our relationships with respondents and how we train data collectors.

Using videoconferencing technology for data collection

Several of our surveys have started using videoconferencing tools to speak with respondents and collect data from them. Some of the surveys that now use this technology include the National Compensation Survey, the Occupational Requirements Survey, and the Producer Price Index. Many of our surveys previously relied on interviewers visiting businesses or households to collect data. We suspended all in-person data collection in March to protect the health of data collectors and respondents, so we had to find other ways to collect data. Many of our surveys also use telephone and internet to collect data, but those modes aren’t always ideal for every kind of data. We often need to develop personal relationships with respondents to gain their trust and cooperation and ensure high-quality data. Videoconferencing helps us accomplish what we often can’t do with phones or web survey forms.

The Occupational Requirements Survey is one that has begun using videoconferencing in data collection. The survey provides information about the physical demands; environmental conditions; education, training, and experience; and cognitive and mental requirements for jobs in the U.S. economy. Collecting data for this survey often requires visual aids, hand gestures, and other nonverbal information to understand job characteristics. It often helps to watch jobs as they are performed at a worksite, but that’s not an option during the pandemic. Videoconferencing is the next best alternative.

Many of our data collectors and respondents have mentioned how helpful videoconferencing is for developing a rapport and for sharing screens and other visual information. Videoconferencing also helps us reduce travel and lodging costs, so we likely will continue to rely on videoconferencing at least partly even after the pandemic.

Using videoconferencing technology for training and mentoring

Many of our surveys are complex and require considerable ongoing training for data collectors. For example, before the pandemic, our Consumer Price Index Commodities and Services (C&S) survey involved in-person training at our Washington, DC, headquarters. There were two classroom training courses: a 2-week introductory course and a 1-week advanced course. Each course was followed by on-the-job training held in our regional offices. Even before the pandemic, we were developing videoconference training. The pandemic caused us to accelerate these plans. We now provide C&S survey training through video collaboration tools. We also integrate on-the-job training throughout the classes.

Several other surveys have adopted a similar training approach as the Consumer Price Index. Our data-collection staffs also increasingly use videoconferencing for mentoring and to share ideas about how to make the data-collection experience better for data collectors and respondents.

A final note

Before I conclude, I want to share some sad news about one of the people who played an indispensable leadership role in developing the new survey questions and innovative data-collection and training methods. Jennifer Edgar, our Associate Commissioner for Survey Methods Research, died November 8 in a tragic fall in her home. She leaves behind her husband and two young children, her parents, and her sister. Moreover, she leaves hundreds of BLS colleagues and many more throughout the statistical community and beyond, who will grieve the loss of an exceptionally gifted friend and professional whose great promise was cut suddenly and tragically short. Jennifer was using her considerable energies to move BLS forward. Her passing is a huge blow to her family, loved ones, and the entire statistical community. We are working on ways to ensure Jennifer’s memory and passion is forever present at BLS.

Percent of employed people who teleworked at some point in the previous 4 weeks because of the COVID-19 pandemic
MonthPercent

May 2020

35.4%

Jun 2020

31.3

Jul 2020

26.4

Aug 2020

24.3

Sep 2020

22.7

Oct 2020

21.2
Number of people not in the labor force who did not look for work because of the COVID-19 pandemic
MonthNumber not in the labor force

May 2020

9,740,000

Jun 2020

7,043,000

Jul 2020

6,454,000

Aug 2020

5,200,000

Sep 2020

4,499,000

Oct 2020

3,563,000

How Much Does a Cup of Coffee Cost? It’s Complicated

We have a guest blogger for this edition of Commissioner’s Corner. Rob Cage is the Assistant Commissioner for Consumer Prices and Price Indexes at the U.S. Bureau of Labor Statistics.

The pandemic has changed my morning routine. Before the outbreak of the COVID-19 pandemic and full-time telework at BLS, two things motivated me each morning.

Person holding mobile phone and ordering coffee on an app.

First, I was always on a mission to minimize my commute to work. I would do things each night so I wouldn’t waste time in the morning. Things like shaving, setting out clothes, and preparing the next day’s lunch. I timed my alarm to go off to allow just enough time to shower, suit up, grab that sandwich, and catch my commuter train as it rolled into the station.

The second thing I needed to start each work day was a strong, fresh, hot cup of joe—actually more like two or three cups. Not one of those fancy drinks with mocha, caramel, steamed milk, or anything like that. Ordinary drip-brewed, filtered coffee. Medium to dark roast and like Betty MacDonald, my coffee had to be “…so strong it snarled as it lurched out of the pot.” Then I add some cream (and by cream, I mean half-and-half), but no sugar. But by far the most important element of the drink: temperature. I like coffee precisely at a certain temperature. If it’s too hot, you taste nothing but a scalded tongue. If it’s too cold, you’re met with an overwhelming sense of disappointment. In that ideal temperate zone, you are jolted alive with a satisfying sip of silky cocoa and nutty fragranced bliss.

Through trial and error, I eventually unearthed a way to satisfy both of these morning habits efficiently: getting my coffee along the commute. Brewing the coffee at home took too much time, and I’d drink most of it on the train, arriving at my desk empty handed. Getting my first cup after I arrived was also uneconomical since I’d have to backtrack to get it. The simple solution? Find a place to get the coffee along the way, and preferably as close to my office as possible. This way, the temperature of the drink was in that sweet spot as I turned on my computer.

With four different coffee shops located along my route in Washington’s Union Station, one would think I could easily achieve this. But no, I’m foiled by impatience. According to a 2014 Journal of Consumer Behavior study, the time before ordering has the greatest influence on how customers perceive waiting times and service quality. A customer who has to wait 10 minutes in line before ordering will feel more dissatisfied than a customer waiting 10 minutes after ordering, even if the total wait time is the same. I couldn’t agree more. Queues at Union Station during the morning rush were just too long and unpredictable to meet my needs. I didn’t have the patience to wait behind customers pondering through a long order recital: Quad Grande nonfat extra hot caramel macchiato upside down, please. I needed my expeditiously stated, two-word order quickly. Luckily, the employee cafeteria in my building—conveniently located just off the lobby—had self-serve coffee. No competing commuters. No preorder queue. No postorder queue. Only a payment queue. I had found my routine: a 55-minute total commute, landing at my desk with strong, hot coffee in hand.

Then one day, I bumped into a coworker on the train. As we walked through Union Station and approached the maze of coffee shops with the insufferable queues, she stopped in front of one; took two steps to the left, scanned the drinks on top of a cart, found one with her name on it, picked it up, and met me back in stride. Amazed, I asked her how she pulled off this sensational stunt. She had placed her order on the coffee shop’s mobile app, of course. That was her routine. Curious but unconvinced, I asked her if she was concerned the coffee would be too cold by the time she picked it up. Through trial and error, she had figured out that if she placed her order on the app as the train rolled out of the L’Enfant Plaza stop, her drink would typically be hot and ready as she passed the cart. Could this be coffee-ordering nirvana? Guaranteed no-wait service, with guaranteed handoff at perfect temperature? Surely this improvement in the quality of the purchasing experience would cost more, which was my next question. And the astonishing answer: the coffee was the same price! I immediately downloaded the app, copied her process, and shaved three minutes off my morning routine. An equilibrium commute down to 52 minutes, about a 5-percent improvement!

Which brings me to how this tortured story relates to the business of BLS and specifically the measurement of the cost of living and consumer inflation. If the cost of my preferred cup of coffee was identical ($2.45 before sales tax) whether I stood in line to get it or not, then surely I would be better off by ordering on the app. Doing that resulted in a 5-percent time savings on my commute—an attribute of purchasing coffee that was critically important to me. In other words, the app-ordered coffee represented a higher-quality product, even though the price was the same. Using the federal minimum wage rate of $7.25\hour (or 12 cents a minute), an estimate of the time savings is 3 minutes x $0.12 = $0.36. One could say $0.36 is a reasonable estimate of the difference in quality. So what is the correct measure of price change between these two choices?

ApproachWalk-up purchaseApp purchasePrice changeNote

Ignore purchase time

$2.45 $2.45 0%No change in price

Add purchase time

$2.81 $2.45 -13%Deflation

Assume purchase time is built into market price, and adjust prices to reflect zero purchase time

$2.09 $2.45 17%Inflation

This is the million dollar question in consumer price index measurement, and the answer depends on how a unique consumer good—in this case a prepared cup of coffee—is defined. In the price index literature, the buzzword is homogeneity. To measure inflation accurately, goods that are homogenous must be identified and grouped together for proper treatment. This is at the core of getting the CPI right. Homogenous is defined as “of the same kind, alike; consisting of parts all of the same kind.” In CPI jargon, the component “parts” of a unique item in the sample are called “attributes.” So what are the attributes that define a cup of coffee? We could consider a list of attributes that most baristas might say are important, like size, bean variety, country of origin, blend, roast, freshness, or caffeine content; and a couple you already know that are important to me: temperature and queue time.

How many of these attributes do we explicitly control for in the CPI as obvious, overt, and separate variables used in scientifically selecting a sample of coffee drinks from quick service establishments, for use in calculating the index each month? You might be surprised by the answer: none! How, then, do we capture constant-quality price change for prepared coffee drinks accurately in the CPI?

We implicitly account for all of these characteristics one way or another. The CPI uses the matched-model approach to index measurement. We select a sample of 100,000+ unique, well-specified, strictly homogenous goods and services for the sample. Then we compare the price of each unique sampled item to the price of the exact same item in subsequent months. The key, of course, is defining and selecting the unique items. Generally speaking, sample selection has two major components: selection of the establishments (for example, a coffee shop) and then selection of a unique item (for example, 16-ounce dark roast drip coffee) at the selected establishments. Limited budget requires BLS to take a sample rather than a census of all goods and services consumers purchase. Thus, we group unique products into broadly homogeneous categories so the selected products can accurately reflect price change for unsampled items in those categories. We bundle prepared coffee from quick service establishments into the elementary category “limited service meals and snacks.” Comparatively, this is one of the more broadly defined components in the CPI basket. With a variety of different food and beverage items eligible for the sample, there are simply too many attributes to consider as separate selection steps to create the sample of unique items. Instead, we base the selection largely on the descriptions of different items listed on the menu. This is how we would distinguish an ordinary brewed coffee drink from other coffee drinks, such as a latte and cappuccino.

Any attribute expressly identified in the description of the menu item becomes a characteristic defining the unique item. For example, “12-ounce Cup of Organic Single Origin Light Roast Coffee” and “12-ounce Cup of Organic Classic Blend Medium Roast Coffee” may be two different menu items at a coffee shop. By rule, they are treated as distinct, unique, separate products for CPI sample selection. Then each month, CPI data collectors meticulously capture the price of the exact same product. If any of the characteristics change, that would trigger a quality review. Suppose medium roast was no longer available. A decision would have to be made to substitute the most comparable item to the originally selected item. Then a commodity analyst in the national office would have to decide if the new item was comparable to the old item. For example, is there a difference in quality between the light roast and the medium roast? Obviously, consumer taste and preferences are idiosyncratic, and the difference in quality of light roast and medium roast is a function of individual preference. But to the average consumer, perhaps not. In fact, prices tend not to vary by roast type. So in this situation, the analyst might judge medium and light to be comparable, and the price of the light will be matched to the previous price of the medium and used in the index. However, if a single-origin coffee was selected, a different outcome might result, especially if the price of the single-origin coffee was considerably different from a previously selected blend coffee, with all other characteristics being the same. Then a decision would need to be made as to how much of the difference was a quality difference (single origin versus blend), and how much was pure price change.

But what about the other factors that are not expressly identified in the description of the menu item, like temperature, freshness, and queue time? These are ostensibly identified, and held constant month after month, by the selection of the establishment. The outlet itself is associated with many attributes of product quality which are not observed. Over time, customers come to expect a certain level of service or product quality within each specific store, or at specific locations of chain stores. So, by controlling for the outlet, we are effectively able to hold constant these unobservable attributes.

Now that I am teleworking, my morning habits are out of equilibrium. My commute time is drastically shorter, reduced to the time it takes me to walk from my bedroom to the guest room, which has been hastily converted into a home office. My problem is the coffee. I haven’t figured out the roast, or the precise coffee-to-water ratio for the perfect strength; I don’t like spending time grinding whole bean, so I substitute ground coffee instead. My barista tells me that’s a quality decrease.

I’d say I am better off commute-timing wise but worse off coffee wise. A push. All in all, I can’t wait to return to on-premises work, mostly for that reliable cup of java.

A Closer Look at Recent Employment Trends

BLS has closely tracked the upheaval in the U.S. job market in recent months, most notably through the monthly “payroll jobs” data. These data, from the Current Employment Statistics survey, provide detail on the change in employment in each industry. We count jobs by asking thousands of employers every month the number of employees on their payroll for the pay period that includes the 12th of the month. For August, we reported that employers added 1.4 million jobs. Today I want to scratch beneath that surface and examine recent employment trends in several industries.

But before I go on, let me take a moment to thank all those businesses that respond voluntarily to our request for information every month. With so much going on, responding to a BLS survey may not be your highest priority. Yet, you continue to come through every month, and for that we extend our sincere thanks.

Using February 2020 as our starting point, let’s look at the job losses that occurred through April. From the nearly 152 million jobs recorded in February, we lost just over 22 million by the end of April. That’s a drop of 14.5 percent in total nonfarm employment. But that decline varied across industries. The leisure and hospitality industry, including restaurants, hotels, and amusements, saw the largest percentage decline, down 49.3 percent from February. Other industries saw percentage declines similar to the overall total, such as retail trade (decline of 15.2 percent) and construction (decline of 14.2 percent). And some industries experienced small declines, such as financial activities (decline of 3.2 percent). These differences stem from many factors, including stay-at-home orders, the need for workers in essential industries, the ability for some work to be done remotely, and on and on.

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

Following large losses through April, many industries gained jobs over the next four months. By August, about 10.6 million jobs were added to employer payrolls. One way to look at these figures is to consider what share of the March/April job loss was “recovered” by the May/June/July/August job gain. Overall, 47.9 percent of the decline was recovered. The retail trade industry restored the greatest percentage of job losses, 72.5 percent, followed by other services (including barbers and salons, 61.2 percent) and construction (60.8 percent). Education and health services recovered 47.6 percent of lost jobs, nearly equal to the overall percentage of jobs recovered, as did manufacturing (47.2 percent). Utilities, mining and logging, and the information industry had fewer jobs in August than in April.

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

While the percentages let you compare industries, digging a little deeper uncovers other interesting stories. For example, three sectors, professional and business services; manufacturing; and transportation and warehousing, each lost between 10 and 11 percent of jobs from February to April 2020. But those losses amounted to vastly different numbers of jobs: 2.3 million in professional and business services; 1.4 million in manufacturing; and 570,000 in transportation and warehousing.

Some detailed industries provide interesting contrasts. Within health care from February to April, hospital employment showed a slight decline while offices of physicians lost about 11 percent of jobs. In contrast, offices of dentists declined by 56 percent, losing more than half a million jobs. As of August, employment had rebounded in most health care industries, with the notable exception of nursing and residential care facilities, which has declined each month since February.

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

Americans were encouraged to stay at home and only venture out for essential items, which is reflected in employment in various retail industries. For example, food and beverage stores showed little employment change from February to August. In contrast, clothing store employment declined by 62 percent through April, and only half of that loss had been recovered by August. Jobs in electronics and appliance stores declined through May and in August stood at about 90 percent of their February total.

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

A reminder that Current Employment Statistics data are updated as new information becomes available. Thus, the July and August data shown here are preliminary and will be revised. Employment data by industry are also available for states and localities.

When looking for trends or comparing industries of different sizes, the comparisons shown here can be helpful. The detailed data are available for you to compare other industries, too. Get the data through the BLS data query system.

Percent decline in payroll employment from February through April 2020, by major industry
IndustryPercent decline

Leisure and hospitality

-49.3

Other services

-23.1

Retail trade

-15.2

Total nonfarm

-14.5

Construction

-14.2

Education and health services

-11.3

Professional and business services

-10.7

Manufacturing

-10.6

Transportation and warehousing

-10.0

Information

-9.8

Mining and logging

-8.5

Wholesale trade

-6.7

Government

-4.3

Financial activities

-3.2

Utilities

-0.7
Percent of payroll employment decline from February to April 2020 that was recovered by August 2020, by major industry
IndustryPercent recovered

Retail trade

72.5

Other services

61.2

Construction

60.8

Leisure and hospitality

50.2

Total nonfarm

47.9

Education and health services

47.6

Manufacturing

47.2

Professional and business services

35.8

Transportation and warehousing

33.2

Financial activities

31.5

Wholesale trade

17.4

Government

14.2

Information

-9.5

Mining and logging

-59.0

Utilities

-86.8
Percent of February 2020 employment level in months after February, selected health care industries
IndustryAprilMayJuneJulyAugust

Offices of physicians

89.291.594.195.296.2

Offices of dentists

43.869.289.093.996.1

Hospitals

97.797.097.197.697.8

Nursing and residential care facilities

96.494.994.393.793.2
Percent of February 2020 employment level in months after February, selected retail industries
IndustryAprilMayJuneJulyAugust

Electronics and appliance stores

89.874.780.286.290.5

Building material and garden supply stores

97.3101.8104.3105.1106.1

Food and beverage stores

98.6100.4101.7101.0101.2

Clothing and clothing accessories stores

38.244.562.470.371.1

Department stores

75.279.490.094.597.5

General merchandise stores, including warehouse stores

104.6106.2109.0105.8110.1