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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.
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.
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.
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.
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.
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
Month
Employment level
Over-the-month change
Jan 1970
71,176,000
-65,000
Feb 1970
71,305,000
129,000
Mar 1970
71,451,000
146,000
Apr 1970
71,348,000
-103,000
May 1970
71,124,000
-224,000
Jun 1970
71,029,000
-95,000
Jul 1970
71,053,000
24,000
Aug 1970
70,937,000
-116,000
Sep 1970
70,944,000
7,000
Oct 1970
70,521,000
-423,000
Nov 1970
70,409,000
-112,000
Dec 1970
70,792,000
383,000
Jan 1971
70,865,000
73,000
Feb 1971
70,807,000
-58,000
Mar 1971
70,860,000
53,000
Apr 1971
71,036,000
176,000
May 1971
71,247,000
211,000
Jun 1971
71,254,000
7,000
Jul 1971
71,315,000
61,000
Aug 1971
71,373,000
58,000
Sep 1971
71,614,000
241,000
Oct 1971
71,642,000
28,000
Nov 1971
71,847,000
205,000
Dec 1971
72,109,000
262,000
Jan 1972
72,441,000
332,000
Feb 1972
72,648,000
207,000
Mar 1972
72,944,000
296,000
Apr 1972
73,162,000
218,000
May 1972
73,469,000
307,000
Jun 1972
73,758,000
289,000
Jul 1972
73,709,000
-49,000
Aug 1972
74,141,000
432,000
Sep 1972
74,264,000
123,000
Oct 1972
74,674,000
410,000
Nov 1972
74,973,000
299,000
Dec 1972
75,268,000
295,000
Jan 1973
75,617,000
349,000
Feb 1973
76,014,000
397,000
Mar 1973
76,284,000
270,000
Apr 1973
76,455,000
171,000
May 1973
76,648,000
193,000
Jun 1973
76,887,000
239,000
Jul 1973
76,913,000
26,000
Aug 1973
77,168,000
255,000
Sep 1973
77,276,000
108,000
Oct 1973
77,607,000
331,000
Nov 1973
77,920,000
313,000
Dec 1973
78,031,000
111,000
Jan 1974
78,100,000
69,000
Feb 1974
78,254,000
154,000
Mar 1974
78,296,000
42,000
Apr 1974
78,382,000
86,000
May 1974
78,549,000
167,000
Jun 1974
78,604,000
55,000
Jul 1974
78,636,000
32,000
Aug 1974
78,619,000
-17,000
Sep 1974
78,610,000
-9,000
Oct 1974
78,630,000
20,000
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
76,624,000
164,000
Jun 1975
76,521,000
-103,000
Jul 1975
76,770,000
249,000
Aug 1975
77,153,000
383,000
Sep 1975
77,228,000
75,000
Oct 1975
77,540,000
312,000
Nov 1975
77,685,000
145,000
Dec 1975
78,017,000
332,000
Jan 1976
78,503,000
486,000
Feb 1976
78,816,000
313,000
Mar 1976
79,048,000
232,000
Apr 1976
79,292,000
244,000
May 1976
79,312,000
20,000
Jun 1976
79,376,000
64,000
Jul 1976
79,547,000
171,000
Aug 1976
79,704,000
157,000
Sep 1976
79,892,000
188,000
Oct 1976
79,911,000
19,000
Nov 1976
80,240,000
329,000
Dec 1976
80,448,000
208,000
Jan 1977
80,690,000
242,000
Feb 1977
80,988,000
298,000
Mar 1977
81,391,000
403,000
Apr 1977
81,728,000
337,000
May 1977
82,088,000
360,000
Jun 1977
82,488,000
400,000
Jul 1977
82,834,000
346,000
Aug 1977
83,075,000
241,000
Sep 1977
83,532,000
457,000
Oct 1977
83,800,000
268,000
Nov 1977
84,173,000
373,000
Dec 1977
84,410,000
237,000
Jan 1978
84,594,000
184,000
Feb 1978
84,948,000
354,000
Mar 1978
85,460,000
512,000
Apr 1978
86,162,000
702,000
May 1978
86,509,000
347,000
Jun 1978
86,950,000
441,000
Jul 1978
87,204,000
254,000
Aug 1978
87,483,000
279,000
Sep 1978
87,621,000
138,000
Oct 1978
87,956,000
335,000
Nov 1978
88,391,000
435,000
Dec 1978
88,671,000
280,000
Jan 1979
88,808,000
137,000
Feb 1979
89,055,000
247,000
Mar 1979
89,479,000
424,000
Apr 1979
89,417,000
-62,000
May 1979
89,789,000
372,000
Jun 1979
90,108,000
319,000
Jul 1979
90,217,000
109,000
Aug 1979
90,300,000
83,000
Sep 1979
90,327,000
27,000
Oct 1979
90,481,000
154,000
Nov 1979
90,573,000
92,000
Dec 1979
90,672,000
99,000
Jan 1980
90,800,000
128,000
Feb 1980
90,883,000
83,000
Mar 1980
90,994,000
111,000
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
90,099,000
259,000
Sep 1980
90,213,000
114,000
Oct 1980
90,490,000
277,000
Nov 1980
90,747,000
257,000
Dec 1980
90,943,000
196,000
Jan 1981
91,033,000
90,000
Feb 1981
91,105,000
72,000
Mar 1981
91,210,000
105,000
Apr 1981
91,283,000
73,000
May 1981
91,296,000
13,000
Jun 1981
91,490,000
194,000
Jul 1981
91,601,000
111,000
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
88,990,000
219,000
Feb 1983
88,917,000
-73,000
Mar 1983
89,090,000
173,000
Apr 1983
89,364,000
274,000
May 1983
89,644,000
280,000
Jun 1983
90,021,000
377,000
Jul 1983
90,437,000
416,000
Aug 1983
90,129,000
-308,000
Sep 1983
91,247,000
1,118,000
Oct 1983
91,520,000
273,000
Nov 1983
91,875,000
355,000
Dec 1983
92,230,000
355,000
Jan 1984
92,673,000
443,000
Feb 1984
93,157,000
484,000
Mar 1984
93,429,000
272,000
Apr 1984
93,792,000
363,000
May 1984
94,098,000
306,000
Jun 1984
94,479,000
381,000
Jul 1984
94,789,000
310,000
Aug 1984
95,032,000
243,000
Sep 1984
95,344,000
312,000
Oct 1984
95,629,000
285,000
Nov 1984
95,982,000
353,000
Dec 1984
96,107,000
125,000
Jan 1985
96,372,000
265,000
Feb 1985
96,503,000
131,000
Mar 1985
96,842,000
339,000
Apr 1985
97,038,000
196,000
May 1985
97,312,000
274,000
Jun 1985
97,459,000
147,000
Jul 1985
97,648,000
189,000
Aug 1985
97,840,000
192,000
Sep 1985
98,045,000
205,000
Oct 1985
98,233,000
188,000
Nov 1985
98,443,000
210,000
Dec 1985
98,609,000
166,000
Jan 1986
98,732,000
123,000
Feb 1986
98,847,000
115,000
Mar 1986
98,934,000
87,000
Apr 1986
99,121,000
187,000
May 1986
99,248,000
127,000
Jun 1986
99,155,000
-93,000
Jul 1986
99,473,000
318,000
Aug 1986
99,588,000
115,000
Sep 1986
99,934,000
346,000
Oct 1986
100,121,000
187,000
Nov 1986
100,308,000
187,000
Dec 1986
100,509,000
201,000
Jan 1987
100,678,000
169,000
Feb 1987
100,919,000
241,000
Mar 1987
101,164,000
245,000
Apr 1987
101,499,000
335,000
May 1987
101,728,000
229,000
Jun 1987
101,900,000
172,000
Jul 1987
102,247,000
347,000
Aug 1987
102,420,000
173,000
Sep 1987
102,647,000
227,000
Oct 1987
103,138,000
491,000
Nov 1987
103,372,000
234,000
Dec 1987
103,661,000
289,000
Jan 1988
103,753,000
92,000
Feb 1988
104,214,000
461,000
Mar 1988
104,489,000
275,000
Apr 1988
104,732,000
243,000
May 1988
104,962,000
230,000
Jun 1988
105,326,000
364,000
Jul 1988
105,550,000
224,000
Aug 1988
105,674,000
124,000
Sep 1988
106,013,000
339,000
Oct 1988
106,276,000
263,000
Nov 1988
106,617,000
341,000
Dec 1988
106,898,000
281,000
Jan 1989
107,161,000
263,000
Feb 1989
107,427,000
266,000
Mar 1989
107,621,000
194,000
Apr 1989
107,791,000
170,000
May 1989
107,913,000
122,000
Jun 1989
108,027,000
114,000
Jul 1989
108,069,000
42,000
Aug 1989
108,120,000
51,000
Sep 1989
108,369,000
249,000
Oct 1989
108,476,000
107,000
Nov 1989
108,752,000
276,000
Dec 1989
108,836,000
84,000
Jan 1990
109,199,000
363,000
Feb 1990
109,435,000
236,000
Mar 1990
109,644,000
209,000
Apr 1990
109,686,000
42,000
May 1990
109,839,000
153,000
Jun 1990
109,856,000
17,000
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
108,334,000
85,000
Jul 1991
108,292,000
-42,000
Aug 1991
108,310,000
18,000
Sep 1991
108,336,000
26,000
Oct 1991
108,357,000
21,000
Nov 1991
108,296,000
-61,000
Dec 1991
108,328,000
32,000
Jan 1992
108,369,000
41,000
Feb 1992
108,311,000
-58,000
Mar 1992
108,365,000
54,000
Apr 1992
108,519,000
154,000
May 1992
108,649,000
130,000
Jun 1992
108,715,000
66,000
Jul 1992
108,793,000
78,000
Aug 1992
108,925,000
132,000
Sep 1992
108,959,000
34,000
Oct 1992
109,139,000
180,000
Nov 1992
109,272,000
133,000
Dec 1992
109,495,000
223,000
Jan 1993
109,794,000
299,000
Feb 1993
110,044,000
250,000
Mar 1993
109,994,000
-50,000
Apr 1993
110,296,000
302,000
May 1993
110,568,000
272,000
Jun 1993
110,749,000
181,000
Jul 1993
111,055,000
306,000
Aug 1993
111,206,000
151,000
Sep 1993
111,448,000
242,000
Oct 1993
111,733,000
285,000
Nov 1993
111,984,000
251,000
Dec 1993
112,314,000
330,000
Jan 1994
112,595,000
281,000
Feb 1994
112,781,000
186,000
Mar 1994
113,242,000
461,000
Apr 1994
113,586,000
344,000
May 1994
113,921,000
335,000
Jun 1994
114,238,000
317,000
Jul 1994
114,610,000
372,000
Aug 1994
114,896,000
286,000
Sep 1994
115,247,000
351,000
Oct 1994
115,458,000
211,000
Nov 1994
115,869,000
411,000
Dec 1994
116,165,000
296,000
Jan 1995
116,501,000
336,000
Feb 1995
116,697,000
196,000
Mar 1995
116,907,000
210,000
Apr 1995
117,069,000
162,000
May 1995
117,049,000
-20,000
Jun 1995
117,286,000
237,000
Jul 1995
117,380,000
94,000
Aug 1995
117,634,000
254,000
Sep 1995
117,875,000
241,000
Oct 1995
118,031,000
156,000
Nov 1995
118,175,000
144,000
Dec 1995
118,320,000
145,000
Jan 1996
118,316,000
-4,000
Feb 1996
118,739,000
423,000
Mar 1996
118,993,000
254,000
Apr 1996
119,158,000
165,000
May 1996
119,486,000
328,000
Jun 1996
119,769,000
283,000
Jul 1996
120,015,000
246,000
Aug 1996
120,199,000
184,000
Sep 1996
120,410,000
211,000
Oct 1996
120,665,000
255,000
Nov 1996
120,961,000
296,000
Dec 1996
121,143,000
182,000
Jan 1997
121,363,000
220,000
Feb 1997
121,675,000
312,000
Mar 1997
121,990,000
315,000
Apr 1997
122,286,000
296,000
May 1997
122,546,000
260,000
Jun 1997
122,814,000
268,000
Jul 1997
123,111,000
297,000
Aug 1997
123,093,000
-18,000
Sep 1997
123,585,000
492,000
Oct 1997
123,929,000
344,000
Nov 1997
124,235,000
306,000
Dec 1997
124,549,000
314,000
Jan 1998
124,812,000
263,000
Feb 1998
125,016,000
204,000
Mar 1998
125,164,000
148,000
Apr 1998
125,442,000
278,000
May 1998
125,844,000
402,000
Jun 1998
126,076,000
232,000
Jul 1998
126,205,000
129,000
Aug 1998
126,544,000
339,000
Sep 1998
126,752,000
208,000
Oct 1998
126,954,000
202,000
Nov 1998
127,231,000
277,000
Dec 1998
127,596,000
365,000
Jan 1999
127,702,000
106,000
Feb 1999
128,120,000
418,000
Mar 1999
128,227,000
107,000
Apr 1999
128,597,000
370,000
May 1999
128,808,000
211,000
Jun 1999
129,089,000
281,000
Jul 1999
129,414,000
325,000
Aug 1999
129,569,000
155,000
Sep 1999
129,772,000
203,000
Oct 1999
130,177,000
405,000
Nov 1999
130,466,000
289,000
Dec 1999
130,772,000
306,000
Jan 2000
131,005,000
233,000
Feb 2000
131,124,000
119,000
Mar 2000
131,596,000
472,000
Apr 2000
131,888,000
292,000
May 2000
132,105,000
217,000
Jun 2000
132,061,000
-44,000
Jul 2000
132,236,000
175,000
Aug 2000
132,230,000
-6,000
Sep 2000
132,353,000
123,000
Oct 2000
132,351,000
-2,000
Nov 2000
132,556,000
205,000
Dec 2000
132,709,000
153,000
Jan 2001
132,698,000
-11,000
Feb 2001
132,789,000
91,000
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
130,634,000
11,000
Jun 2002
130,684,000
50,000
Jul 2002
130,590,000
-94,000
Aug 2002
130,587,000
-3,000
Sep 2002
130,501,000
-86,000
Oct 2002
130,628,000
127,000
Nov 2002
130,615,000
-13,000
Dec 2002
130,472,000
-143,000
Jan 2003
130,580,000
108,000
Feb 2003
130,444,000
-136,000
Mar 2003
130,232,000
-212,000
Apr 2003
130,177,000
-55,000
May 2003
130,196,000
19,000
Jun 2003
130,194,000
-2,000
Jul 2003
130,191,000
-3,000
Aug 2003
130,149,000
-42,000
Sep 2003
130,254,000
105,000
Oct 2003
130,454,000
200,000
Nov 2003
130,474,000
20,000
Dec 2003
130,588,000
114,000
Jan 2004
130,769,000
181,000
Feb 2004
130,825,000
56,000
Mar 2004
131,142,000
317,000
Apr 2004
131,411,000
269,000
May 2004
131,694,000
283,000
Jun 2004
131,793,000
99,000
Jul 2004
131,848,000
55,000
Aug 2004
131,937,000
89,000
Sep 2004
132,093,000
156,000
Oct 2004
132,447,000
354,000
Nov 2004
132,503,000
56,000
Dec 2004
132,624,000
121,000
Jan 2005
132,774,000
150,000
Feb 2005
133,032,000
258,000
Mar 2005
133,156,000
124,000
Apr 2005
133,518,000
362,000
May 2005
133,690,000
172,000
Jun 2005
133,942,000
252,000
Jul 2005
134,296,000
354,000
Aug 2005
134,498,000
202,000
Sep 2005
134,566,000
68,000
Oct 2005
134,655,000
89,000
Nov 2005
134,993,000
338,000
Dec 2005
135,149,000
156,000
Jan 2006
135,429,000
280,000
Feb 2006
135,737,000
308,000
Mar 2006
136,047,000
310,000
Apr 2006
136,205,000
158,000
May 2006
136,244,000
39,000
Jun 2006
136,325,000
81,000
Jul 2006
136,520,000
195,000
Aug 2006
136,694,000
174,000
Sep 2006
136,843,000
149,000
Oct 2006
136,852,000
9,000
Nov 2006
137,063,000
211,000
Dec 2006
137,249,000
186,000
Jan 2007
137,477,000
228,000
Feb 2007
137,558,000
81,000
Mar 2007
137,793,000
235,000
Apr 2007
137,842,000
49,000
May 2007
137,993,000
151,000
Jun 2007
138,069,000
76,000
Jul 2007
138,038,000
-31,000
Aug 2007
138,015,000
-23,000
Sep 2007
138,095,000
80,000
Oct 2007
138,174,000
79,000
Nov 2007
138,284,000
110,000
Dec 2007
138,392,000
108,000
Jan 2008
138,403,000
11,000
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
130,057,000
12,000
Dec 2009
129,788,000
-269,000
Jan 2010
129,790,000
2,000
Feb 2010
129,698,000
-92,000
Mar 2010
129,879,000
181,000
Apr 2010
130,110,000
231,000
May 2010
130,650,000
540,000
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
130,625,000
268,000
Nov 2010
130,750,000
125,000
Dec 2010
130,822,000
72,000
Jan 2011
130,841,000
19,000
Feb 2011
131,053,000
212,000
Mar 2011
131,288,000
235,000
Apr 2011
131,602,000
314,000
May 2011
131,703,000
101,000
Jun 2011
131,939,000
236,000
Jul 2011
131,999,000
60,000
Aug 2011
132,125,000
126,000
Sep 2011
132,358,000
233,000
Oct 2011
132,562,000
204,000
Nov 2011
132,694,000
132,000
Dec 2011
132,896,000
202,000
Jan 2012
133,250,000
354,000
Feb 2012
133,512,000
262,000
Mar 2012
133,752,000
240,000
Apr 2012
133,834,000
82,000
May 2012
133,934,000
100,000
Jun 2012
134,007,000
73,000
Jul 2012
134,159,000
152,000
Aug 2012
134,331,000
172,000
Sep 2012
134,518,000
187,000
Oct 2012
134,677,000
159,000
Nov 2012
134,833,000
156,000
Dec 2012
135,072,000
239,000
Jan 2013
135,263,000
191,000
Feb 2013
135,541,000
278,000
Mar 2013
135,680,000
139,000
Apr 2013
135,871,000
191,000
May 2013
136,093,000
222,000
Jun 2013
136,274,000
181,000
Jul 2013
136,386,000
112,000
Aug 2013
136,628,000
242,000
Sep 2013
136,815,000
187,000
Oct 2013
137,040,000
225,000
Nov 2013
137,304,000
264,000
Dec 2013
137,373,000
69,000
Jan 2014
137,548,000
175,000
Feb 2014
137,714,000
166,000
Mar 2014
137,968,000
254,000
Apr 2014
138,293,000
325,000
May 2014
138,511,000
218,000
Jun 2014
138,837,000
326,000
Jul 2014
139,069,000
232,000
Aug 2014
139,257,000
188,000
Sep 2014
139,566,000
309,000
Oct 2014
139,818,000
252,000
Nov 2014
140,109,000
291,000
Dec 2014
140,377,000
268,000
Jan 2015
140,568,000
191,000
Feb 2015
140,839,000
271,000
Mar 2015
140,910,000
71,000
Apr 2015
141,194,000
284,000
May 2015
141,525,000
331,000
Jun 2015
141,699,000
174,000
Jul 2015
142,001,000
302,000
Aug 2015
142,126,000
125,000
Sep 2015
142,281,000
155,000
Oct 2015
142,587,000
306,000
Nov 2015
142,824,000
237,000
Dec 2015
143,097,000
273,000
Jan 2016
143,205,000
108,000
Feb 2016
143,417,000
212,000
Mar 2016
143,654,000
237,000
Apr 2016
143,851,000
197,000
May 2016
143,892,000
41,000
Jun 2016
144,150,000
258,000
Jul 2016
144,521,000
371,000
Aug 2016
144,664,000
143,000
Sep 2016
144,953,000
289,000
Oct 2016
145,071,000
118,000
Nov 2016
145,201,000
130,000
Dec 2016
145,415,000
214,000
Jan 2017
145,612,000
197,000
Feb 2017
145,795,000
183,000
Mar 2017
145,934,000
139,000
Apr 2017
146,154,000
220,000
May 2017
146,295,000
141,000
Jun 2017
146,506,000
211,000
Jul 2017
146,734,000
228,000
Aug 2017
146,924,000
190,000
Sep 2017
146,966,000
42,000
Oct 2017
147,215,000
249,000
Nov 2017
147,411,000
196,000
Dec 2017
147,590,000
179,000
Jan 2018
147,671,000
81,000
Feb 2018
148,049,000
378,000
Mar 2018
148,244,000
195,000
Apr 2018
148,397,000
153,000
May 2018
148,667,000
270,000
Jun 2018
148,881,000
214,000
Jul 2018
149,030,000
149,000
Aug 2018
149,259,000
229,000
Sep 2018
149,364,000
105,000
Oct 2018
149,576,000
212,000
Nov 2018
149,668,000
92,000
Dec 2018
149,908,000
240,000
Jan 2019
150,145,000
237,000
Feb 2019
150,095,000
-50,000
Mar 2019
150,263,000
168,000
Apr 2019
150,482,000
219,000
May 2019
150,545,000
63,000
Jun 2019
150,720,000
175,000
Jul 2019
150,913,000
193,000
Aug 2019
151,108,000
195,000
Sep 2019
151,329,000
221,000
Oct 2019
151,524,000
195,000
Nov 2019
151,758,000
234,000
Dec 2019
151,919,000
161,000
Jan 2020
152,234,000
315,000
Feb 2020
152,523,000
289,000
Mar 2020
150,840,000
-1,683,000
Apr 2020
130,161,000
-20,679,000
May 2020
132,994,000
2,833,000
Jun 2020
137,840,000
4,846,000
Jul 2020
139,566,000
1,726,000
Aug 2020
141,149,000
1,583,000
Sep 2020
141,865,000
716,000
Oct 2020
142,545,000
680,000
Nov 2020
142,809,000
264,000
Dec 2020
142,582,000
-227,000
Unemployment rates for selected groups, February, April, and December 2020
Race and Hispanic or Latino ethnicity
February 2020
April 2020
December 2020
Total, 16 years and older
3.5
14.8
6.7
White
3.0
14.1
6.0
Black or African American
6.0
16.7
9.9
Asian
2.4
14.5
5.9
Hispanic or Latino
4.4
18.9
9.3
Percent change in consumer prices for selected items in April 2020, seasonally adjusted
We have a guest blogger for this edition of Commissioner’s Corner. Savi Swick is a Supervisory Research Analyst in the U.S. Department of Labor’s Office of Disability Employment Policy.
During National Disability Employment Awareness Month each October, there is much to celebrate and reflect upon. It’s a time to reaffirm our commitment to increasing access and opportunity for workers with disabilities and honor the value and talent they add to America’s workforce and economy. And this year’s celebration is doubly essential, marking both 75 years of this observance and helping bring to a close the yearlong commemoration of the 30th anniversary of the Americans with Disabilities Act.
For researchers like me, it’s also an opportunity to celebrate the importance of data. As a research analyst in the U.S. Department of Labor’s Office of Disability Employment Policy, my thoughts turn to how credible, consistent data are key to delivering on the promise of disability inclusion inherent in the Americans with Disabilities Act, today and into the future.
That’s why we partnered with BLS in 2008 to add six disability-related questions to the monthly Current Population Survey, the official source for estimates on U.S. labor force participation, employment, and unemployment. As a result, monthly data on the employment status of people with disabilities were released for the first time in January 2009—and have been every month since.
In addition, we collaborated with BLS and the U.S. Department of Labor’s Chief Evaluation Office to gather additional data through supplements to the Current Population Survey in May 2012 and July 2019. Through these supplements, we gleaned critical information on barriers to employment, prior work experience, career and financial assistance, requested changes to the workplace, and other related topics from respondents with disabilities.
Today, these data provide reliable, accurate information to a range of stakeholders on a topic of critical importance to America’s families and communities. Most significantly, they help facilitate evidence-based policymaking at the national, state, and local levels.
What exactly is evidence-based policymaking? It’s the simple notion that public policy should be informed by established, objective evidence. While that may seem obvious in principle, the reality is that, absent such evidence, policymakers often make decisions based on assumptions derived from anecdotal evidence, which can be subjective. This can lead to inefficient use of public resources and poor outcomes.
That’s because—and this is what often fascinates data geeks like me—things are not always as they seem. Often, data reveal that what we assume to be true, in fact, may not be true, or at least not the whole truth. This is especially the case for complex, multifaceted issues, such as the employment of people with disabilities.
Increasing access and opportunity requires us to first understand what the barriers to access are and where the opportunities exist. It also requires us to anticipate changes and identify intersections. For example, data from May 2012 and July 2019 supplements pinpointed a lack of transportation as an ongoing barrier to work for many people with disabilities. As a result, the Office of Disability Employment Policy, in partnership with the U.S. Department of Transportation and the U.S. Access Board, is engaging disability advocates and private industry to promote more accessible transportation options—especially inclusive autonomous vehicles that can help people with disabilities get to work.
Of course, the employment landscape has shifted this year due to the COVID-19 pandemic. The changes it has brought to our workforce and economy compel us to consider what questions we need to ask now to ensure we can meet the needs of workers with disabilities in the years ahead. Already, BLS data are helping us detect trends, especially in the context of different occupations.
In any climate—whether the historically robust economy before the pandemic or one recovering in the wake of unprecedented challenges—quality data helps us serve America’s 15 million working-age people with disabilities better. They also help us deliver on the spirit of the bipartisan 2018 Foundations for Evidence-Based Policymaking Act. Going forward, with continued support from BLS, the Chief Evaluation Office, and Department of Labor’s Chief Data Officer, we will continue to develop and implement data-driven policies and programs that meet the needs of America’s workers with disabilities, every month of every year.
BLS recently began publishing a new set of measures on employment changes in states and metropolitan areas. For decades we have published monthly estimates of employment, hours, and earnings for each state and metro area. Our new measure summarizes how widespread employment increases or decreases are across all states or metro areas. We call this measure a diffusion index.
What’s a diffusion index? Let me explain how we create the measure.
Let’s say we’re creating a diffusion index for the 50 states and the District of Columbia. We start by assigning each state and D.C. a value depending on whether its employment decreased, stayed the same, or increased over the period we’re looking at.
The assigned value is 0 if employment decreased.
The assigned value is 50 if employment stayed the same.
The assigned value is 100 if employment increased.
The diffusion index is the average of those 51 values. To create a diffusion index for metro areas, we assign values of 0, 50, or 100 for each of 388 metro areas and then average those values. We calculate diffusion indexes for employment changes over 1 month, 3 months, 6 months, and 12 months.
Now that we understand the simple arithmetic for calculating diffusion indexes, what do they mean? An index greater than 50 means more states or metro areas had increasing employment over the period. An index below 50 means more states or metro areas had decreasing employment. At the extremes, an index of 0 means employment fell in all states or metro areas; an index of 100 means employment rose in all of them. A diffusion index of 50 doesn’t necessarily mean 50 percent of the states or areas had increasing employment and the other 50 percent had decreasing employment. It just means the same number of states or areas had increases and decreases, with any of the other states or areas having no change.
The chart below shows 3-month diffusion indexes for all states and metro areas. You can see how all states and nearly all metro areas had job losses during the worst of the 2007–09 recession. We see it again more recently with the downturn associated with the COVID-19 pandemic.
Editor’s note: Data for this chart are available in the table below.
Diffusion indexes aren’t a new analytical tool. We publish other diffusion indexes using national employment data that summarize how employment change is dispersed across industries. The Federal Reserve Bank of Philadelphia publishes diffusion indexes using a variety of data. The new BLS diffusion indexes summarize how employment is changing across geographic areas to give us another perspective of the labor market.
Soon after I became Commissioner, the top-notch BLS staff shared with me their vision to expand the Job Openings and Labor Turnover Survey (JOLTS). The JOLTS program publishes data each month on the number and rate of job openings, hires, and separations (broken out by quits, layoffs and discharges, and other separations). These data are available at the national level and for the four large geographic regions—Northeast, Midwest, South, and West.
That left a major data gap on labor demand, hires, and separations for states and metropolitan areas. BLS provides data on labor supply for states and metro areas each month from the Local Area Unemployment Statistics program. We also provide data on employment change in states and metro areas each month from the Current Employment Statistics survey. Employment change is the net effect of hires and separations, but it doesn’t show the underlying flow of job creation and destruction. Having better, timelier state and metro JOLTS data would provide a quicker signal about whether labor demand is accelerating or weakening in local economies.
About 2 months after the staff briefed me, the JOLTS program published experimental state estimates for the first time on May 24, 2019. We have been updating those estimates on a quarterly basis since then. We use a statistical model to help us produce the most current state estimates. We then improve those estimates during an annual benchmark process by taking advantage of data available from the Quarterly Census of Employment and Wages. The JOLTS program is well on its way to moving these state estimates into its official, monthly data stream. Look for that to happen in the second half of 2021!
Expand the sample to support direct sample-based estimates for each state.
Accelerate the review and publication of the estimates.
Add questions to provide more information about job openings, hires, and separations.
If funded, this proposal would allow BLS to improve the data quality available from the current JOLTS state estimates. It also would let us add very broad industry detail for each state and more industry detail at the national level.
The proposed larger sample size may also let us produce model-assisted JOLTS estimates for many metro areas. To demonstrate this potential, the JOLTS team produced a one-time set of research estimates for the 18 largest metropolitan statistical areas, those with 1.5 million or more employees. These research estimates show the potential for data that would be available regularly with a larger JOLTS sample. I encourage you to explore this exciting new research series and let us know what you think.
Editor’s note: Data for this chart are available in the table below.
This is just one example of the excellent work I see at BLS every day. The BLS staff are consummate professionals who continue to do outstanding work even in the most trying of times. The entire BLS staff has been teleworking now for several months due to COVID-19, and every program continues to produce high quality data on schedule! Even in these extraordinary circumstances, BLS professionals continue to innovate and find ways to improve quality and develop new gold standard data products to help the policymakers, businesses, and the public make better-informed decisions.
Number of unemployed per job opening in the United States and four large metropolitan areas
United States Department of Labor
