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Topic Archives: Geographic Information

Improving Key Labor Market Estimates during the Pandemic and Beyond

If things were good enough yesterday, why would we change them today? Good enough is OK for folding laundry, cleaning the junk drawer, and raking leaves, but not for official statistics from BLS. We do our best to provide a timely look at the labor market and economy, but we often learn more after we publish those initial data. As a result, we sometimes revise our statistics. That’s mostly a good thing, but there is a fine line between the frequency of revisions and introducing noise and possibly confusion.

I recently wrote about the importance of maintaining and sometimes changing official historical records, using baseball as an example. Today I want to highlight two of our statistical programs: the Job Openings and Labor Turnover Survey (JOLTS) and the Local Area Unemployment Statistics (LAUS) data. We publish monthly statistics from these programs and revise them the following month as more information comes in. In addition to the monthly revisions, we incorporate more information once a year.

The COVID-19 pandemic continues to have a huge impact on our lives. Check out our summary of how the pandemic affected the labor market and economy in 2020. The magnitude of the labor market changes stress tested the JOLTS and LAUS programs. Based on what we observed in real time, and what we know now, we realized we needed to respond to this unusual economic environment. We change our estimating techniques infrequently, but even the best techniques need adjustments to respond to such significant shocks. These adjustments reflect our commitment to continuous improvement.

Changes in Job Openings and Labor Turnover Estimates

The economic conditions caused by the pandemic led us to make two changes to JOLTS procedures. First, we changed the way we handled unusual reports, which we call outliers. In normal times, these outliers may be businesses with unusually large numbers of job separations. This process mutes the outlier impact on the estimates because those outliers are unlikely to represent other businesses. At the start of the pandemic, however, very large increases in separations were followed by very large increases in hires in many businesses. During this period, we adjusted the JOLTS outlier-detection techniques to accept as normal those extreme changes. Under these circumstances, these “outlier” reports did in fact represent many other businesses.

Second, JOLTS uses data from the much larger Current Employment Statistics (CES) sample to adjust estimates of hires and separations to stay in sync with the monthly employment changes. This procedure assumes that, over the long term, the difference between JOLTS hires and separations is close to the CES employment change. This assumption, however, was not appropriate in late March 2020 as people, businesses, and governments tried to contain the spread of COVID-19. The two surveys have different reference periods. The CES reference period is the pay period that includes the 12th of the month, whereas JOLTS estimates of hires and separations cover the entire month. Hires and separations during the latter half of March 2020 were not included in the CES employment change for March but were included in the JOLTS estimates for the month. To accurately capture the timing of this unprecedented event, we stopped aligning the JOLTS estimates of hires and separations with the CES employment change from March through November 2020.

More changes to JOLTS estimates came with the publication of the January 2021 news release. As we do every year, we revised the past 5 years of historical JOLTS data using updated CES employment estimates. We also updated the seasonal adjustment factors and applied them over the past 5 years. In addition, because we stopped using the alignment procedure for most of 2020, the difference between CES and JOLTS estimates had become quite large by December. To preserve the true economic differences between CES and JOLTS but reduce the divergence by the end of 2020, we adjusted estimates of hires and separations for the months in which the alignment procedure was turned off. These adjustments ensure that we report the highest quality data as quickly as we can, while improving accuracy as we learn more information.

Changes in State Labor Force and Unemployment Estimates

We also made real-time changes during the pandemic to the models we use to produce state labor force and unemployment estimates. The primary inputs to the models are from the Current Population Survey (CPS), the source of the monthly national unemployment rate and other labor market measures. Because the CPS sample is not large enough to support state estimates on a monthly basis, we also use CES employment data and counts of continued claims for unemployment insurance to help inform the models. All of these model inputs experienced extreme movements, especially in the early part of the pandemic.

Starting with March 2020, we introduced two monthly adjustments we usually perform only once a year. These adjustments involved closer review and adjustment of outliers from all model inputs and level shifts. We discussed these changes in notes that appeared in the State Employment and Unemployment news releases for March 2020, April 2020, and May 2020.

These changes in 2020 provided a short-term solution for the state models. For the longer term, we respecified the relationships of the model inputs to provide more flexibility when unusual disruptions occur in the labor market. We explain these changes in our “Questions and Answers.”

We implemented the new estimation procedures for model-based areas in early 2021. They were reflected in the estimates published in the Regional and State Unemployment – 2020 Annual Averages news release. We replaced all previously published state data using the new procedures to ensure historically comparable estimates. The recent data revisions also reflect the best available inputs for model estimation. If you are interested in the details, you can read all about them at the LAUS Estimation Methodology page.

The speed with which the JOLTS and LAUS staff researched and implemented these improvements reflects the high quality of the BLS staff and their commitment to producing gold standard data. They make me proud to lead this great agency.

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

The Challenges of Seasonal Adjustment during the COVID-19 Pandemic

In a previous edition of Commissioner’s Corner, we described seasonal adjustment, the process BLS and many others use to smooth out increases and decreases in data series that occur around the same time each year. Seasonal adjustment allows us to focus on the underlying trends in the data. Seasonal adjustment works well when seasonal patterns are pretty consistent from year to year. But what about when there are large shocks to the economy, such as natural disasters and the massive effects of the COVID-19 pandemic and resulting business closures and stay-at-home orders? Today we’ll look at how BLS addressed this issue.

First, a little background on seasonal adjustment. Here’s an example similar to one we have used before, looking at employment in the construction industry. Construction employment varies throughout the year, mostly because of weather. As the chart shows in the “not seasonally adjusted” line, construction adds jobs in the spring and throughout the summer before it starts to lose jobs when the weather turns colder. The large seasonal fluctuations make it hard to see the overall employment trend in the industry. That makes it harder to study other factors that affect the trend, like changes in consumer demand or interest rates. After seasonal adjustment, the construction industry grew by 1.2 million jobs from the beginning of 2015 to the end of 2019.

Construction employment, 2015–19

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

BLS seasonally adjusts data in several of its monthly and quarterly news releases.

Two Approaches to Seasonal Adjustment

BLS uses one of two approaches to seasonally adjust data in these releases—projected factors or concurrent seasonal adjustment. When we project seasonal adjustment factors, we only use historical data in the models. That means we calculate factors in advance, so they are not influenced by the most recent trends. Concurrent seasonal adjustment uses all the data available, including the most recent month or quarter. As a result, the factors are influenced by recent changes.

Regardless of whether the factors are projected or concurrent, the seasonal adjustment models can be additive or multiplicative. We’ll explain more about that below. The COVID-19 pandemic affected the seasonal adjustment process in different ways depending on how the seasonal factors are calculated.

Approach #1

The Consumer Price Index, Producer Price Indexes, and Employment Cost Index use the projected-factor approach and calculate seasonal factors once a year. BLS staff estimated the 2020 seasonal factors at the beginning of 2020 and have used them throughout the year. When new factors for 2021 and revised historical factors are calculated, BLS will examine the effects of the pandemic on the seasonal adjustment models.

Approach #2

We use a concurrent process to calculate the seasonal factors each month for nonfarm employment estimates for the nation, states, and metro areas, unemployment and labor force estimates for the nation, states, and metro areas, and job openings and labor turnover estimates. Each quarter, BLS also uses a similar concurrent process to calculate seasonal factors for productivity measures and business employment dynamics. This helps create the best seasonal factors when seasonality may shift over time. For example, think of schools letting out for summer a little earlier than they usually do each year, or the changing nature of delivery services because of online shopping. Using the most recent data to calculate seasonal factors helps pick up these changes to seasonality faster than the forecasted method. The risk of using the concurrent process is that it may attribute some of the movement in the estimates to a changing seasonal pattern when it really resulted from a nonseasonal event. BLS also annually examines and revises the historical seasonal factors even if the factors were originally calculated using concurrent adjustment. As the saying goes, hindsight is 20/20.

Before the COVID-19 pandemic, the concurrent seasonal adjustment models required limited real-time intervention. Examples of potential reasons for intervention include major events like hurricanes. The COVID-19 pandemic is unusual in its severity and duration, so significant intervention was needed.

BLS intervened in several ways to create the highest quality, real-time seasonal factors. The tool we use most often is called outlier detection. We consider outliers not to represent a normal or typical seasonal movement. When we label an observation as an outlier, we don’t use it to inform the seasonal adjustment model. Since economic activity is still being heavily influenced by COVID-19 and efforts to contain it, BLS has detected more outliers. When this happens, concurrent models behave more like projected-factor models because the most recent data are not used to create seasonal factors.

The Local Area Unemployment Statistics program uses another type of intervention, a technique call a level shift. It is used when there is a sudden change in the level of a data series. In this case, level shifts were used over a series of months.

Additive versus Multiplicative Models

As noted earlier, all BLS programs review their seasonal adjustment models each year. One of the steps during this process is to select a model—either additive or multiplicative. We use an additive model when seasonal movements are stable over time regardless of the level of the series. A multiplicative model is better to use when seasonal movements become larger as the series itself increases—that is, the seasonality is proportional to the level of the series. That means a sudden large change in the level of a series, such as the large increase in the number of unemployed people in April 2020, will be accompanied by a proportionally large seasonal effect. BLS did not want this to occur. When there are large shifts in a measure, multiplicative seasonal adjustment factors can result in adjusting too much or too little. In these cases, additive seasonal adjustment factors usually reflect seasonal movements more accurately and have smaller revisions.

Because of the unusual data patterns beginning in March 2020, both the Current Population Survey, which we use to measure unemployment and the labor force, and the Job Openings and Labor Turnover Survey switched from multiplicative to additive seasonal models for most series and did not wait until the typical yearend model review.

BLS does not produce the weekly data on unemployment insurance. We do, however, compute the seasonal adjustment factors used by the Department of Labor’s Employment and Training Administration for their Unemployment Insurance Weekly Claims data. As we recommended, the Employment and Training Administration recently switched from using multiplicative to additive seasonal adjustments.

Our quarterly Labor Productivity and Costs news release uses input data from the Bureau of Economic Analysis, the U.S. Census Bureau, and several BLS programs. Most of the input data are already seasonally adjusted by the source agencies or programs. The productivity program only seasonally adjusts monthly Current Population Survey data on employment and hours worked for about ten percent of workers, mostly the self-employed, who are not included in the monthly data from the Current Employment Statistics survey on nonfarm employment and hours. The productivity program detected outliers in some of the data beginning at the start of the COVID-19 pandemic in March 2020 and accounted for them in the estimates.

Science and Art

Seasonal adjustment of economic data is a scientific process that involves complex math. But seasonal adjustment also involves some art in addition to science. The art comes in when we use our judgment about outliers in the data or when we decide whether an additive or multiplicative model more closely reflects seasonal variation in economic measures. The art also comes in when we recognize how complicated the world is. During 2020 we have experienced not just a global pandemic but also massive wildfires in several western states, a historic number of hurricanes that made landfall, and other notable events that affect economic activity. Did our seasonal adjustment models properly account for all of these events? I can say we have tried our best with the information we have available. As we gather more data for 2020 and future years, we will continue to examine how we can improve our models to help us distinguish longer-term trends from the seasonal variation in economic activity.

Acknowledgment: Many BLS staff members helped make the technical details in this blog easier to understand, and they all have my gratitude. Three who were especially helpful were Richard Tiller, Thomas Evans, and Brian Monsell.

Construction employment, 2015–19
MonthSeasonally adjustedNot seasonally adjusted

Jan 2015

6,320,0005,953,000

Feb 2015

6,361,0005,962,000

Mar 2015

6,334,0006,051,000

Apr 2015

6,392,0006,300,000

May 2015

6,427,0006,491,000

Jun 2015

6,441,0006,633,000

Jul 2015

6,472,0006,718,000

Aug 2015

6,490,0006,754,000

Sep 2015

6,508,0006,704,000

Oct 2015

6,547,0006,740,000

Nov 2015

6,598,0006,685,000

Dec 2015

6,630,0006,542,000

Jan 2016

6,620,0006,252,000

Feb 2016

6,650,0006,256,000

Mar 2016

6,680,0006,402,000

Apr 2016

6,701,0006,614,000

May 2016

6,691,0006,758,000

Jun 2016

6,702,0006,913,000

Jul 2016

6,736,0006,989,000

Aug 2016

6,737,0006,997,000

Sep 2016

6,768,0006,971,000

Oct 2016

6,798,0006,981,000

Nov 2016

6,819,0006,903,000

Dec 2016

6,821,0006,700,000

Jan 2017

6,847,0006,459,000

Feb 2017

6,889,0006,527,000

Mar 2017

6,909,0006,634,000

Apr 2017

6,916,0006,820,000

May 2017

6,928,0006,998,000

Jun 2017

6,955,0007,169,000

Jul 2017

6,960,0007,212,000

Aug 2017

6,990,0007,248,000

Sep 2017

7,004,0007,201,000

Oct 2017

7,027,0007,208,000

Nov 2017

7,066,0007,147,000

Dec 2017

7,093,0007,004,000

Jan 2018

7,114,0006,729,000

Feb 2018

7,200,0006,840,000

Mar 2018

7,205,0006,933,000

Apr 2018

7,223,0007,129,000

May 2018

7,266,0007,336,000

Jun 2018

7,282,0007,497,000

Jul 2018

7,304,0007,554,000

Aug 2018

7,335,0007,586,000

Sep 2018

7,355,0007,535,000

Oct 2018

7,378,0007,557,000

Nov 2018

7,376,0007,454,000

Dec 2018

7,402,0007,311,000

Jan 2019

7,452,0007,069,000

Feb 2019

7,423,0007,062,000

Mar 2019

7,443,0007,170,000

Apr 2019

7,469,0007,377,000

May 2019

7,478,0007,540,000

Jun 2019

7,497,0007,699,000

Jul 2019

7,504,0007,753,000

Aug 2019

7,508,0007,760,000

Sep 2019

7,524,0007,700,000

Oct 2019

7,541,0007,720,000

Nov 2019

7,539,0007,609,000

Dec 2019

7,555,0007,447,000

Celebrating World Statistics Day 2020

At the Bureau of Labor Statistics, we always enjoy a good celebration. We just finished recognizing Hispanic Heritage Month. We are currently learning how best to protect our online lives during National Cybersecurity Awareness Month. We even track the number of paid holidays available to workers through the National Compensation Survey. Today I want to focus on a celebration that happens once every 5 years — World Statistics Day. While there may not be parades, special meals, or department store sales to honor this day, we at BLS and our colleagues worldwide take time out on October 20, 2020, to recognize the importance of providing accurate, timely, and objective statistics that form the cornerstone of good decisions.

United Nations logo for World Statistics Day 2020

World Statistics Day, organized under the guidance of the United Nations Statistical Commission, was first celebrated in October 2010. This year, the third such event, focuses on “connecting the world with data we can trust.” At BLS, the trustworthy nature of our data and processes has been a hallmark of our work since our founding in 1884. Our first Commissioner, Carroll Wright, described our work then as “conducting judicious investigations and the fearless publication of results.” That credo guides us to this day. As the only noncareer employee in the agency, I am surrounded by a dedicated staff of data experts  whose singular mission is to produce the highest-quality data, without regard to policy or politics. BLS and other statistical agencies throughout the federal government strictly follow Statistical Policy Directives that ensure we produce data that meet precise technical standards and make them available equally to all. For nearly 100 years, we have regularly updated our Handbook of Methods to provide details on data concepts, collection and processing methods, and limitations. Transparency remains a hallmark of our work.

The United States has a decentralized statistical system, with numerous agencies large and small spread throughout the federal government. Despite this decentralization, the agencies work together to improve statistical methods and follow centralized statistical guidance. This partnership was recently strengthened by the Foundations for Evidence-Based Policymaking Act of 2018, which reinforced how the statistical agencies protect the confidentiality of businesses and households that provide data. The Act also designated heads of statistical agencies, like myself, as Statistical Officials for their respective Departments. In my case, my BLS colleagues and I advise other Department of Labor agencies on statistical concepts and processes, while continuing to stay clear of policy discussions and decisions.

World Statistics Day is a global event, so this is a good time to share some examples where BLS participates in statistical activities around the world:

  • We have regular contact with colleagues at statistical organizations around the world. Just recently, I participated in a very long-distance video conference on improvements to the Consumer Price Index. For me, it was 6:00 a.m., and I made sure I had a mug of coffee handy; for my colleagues in Australia, it was 6:00 p.m., and I’m certain their mug had coffee as well.
  • We have a well-established training program for international visitors, focusing on our processes and methods. We hold training sessions at BLS headquarters (or at least we did before the pandemic), we send experts to other countries, and we are exploring virtual training. We are eager to share our expertise and long history.
  • We participate in international panels and study groups, such as those organized by the United Nations, the Organization for Economic Cooperation and Development, and others, with topics ranging from measuring the gig economy to use of social media.
  • We provide BLS data to international databases, highlighting employment, price, productivity and related information to compare with other countries.

And that’s just a taste of how BLS fits into the World of Statistics. As Commissioner, I’ve had the honor to represent the United States in conferences and meetings across the globe. The BLS staff and I also hold regular conversations with statistical officials worldwide. In a recent conversation with colleagues in the United Kingdom, we were eager to learn about each other’s changes in the ways we provide data and analyses to our customers. These interactions expand everyone’s knowledge and keep the worldwide statistical system moving forward.

To celebrate World Statistics Day, I asked some BLS cheerleaders if they would join me in a video message about the importance of quality statistical data. Here’s what they had to say:

In closing, let’s all raise a toast to World Statistics Day, the availability of high-quality and impartial data, and the dedicated staff worldwide who provide new information and analysis every day.

Happy World Statistics Day!

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