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I hope Major League Baseball’s recent decision to recognize the records of the Negro Leagues excited you as much as it did me. This move is long overdue. It’s about time that the home runs of Josh Gibson stand beside those of Babe Ruth.
This is a very big change for baseball, and some long-term fans may worry about all of the records that will change. Continuity sometimes has to give way to better statistics, however, when new information becomes available or old but overlooked information emerges. We know about changes like this at BLS. We have been around for 137 years, and we have some statistics that go back almost that far. Just as important, we don’t make changes lightly. Any update to our programs must be balanced against the need for historical comparability. Even so, we change when new or overlooked data must be recognized. Just see what we are doing today with contingent or gig economy workers; we’re finding ways to incorporate these workers that have been around for a long time into our official statistics.
But, back to baseball… Such concern for historical continuity is also a hallmark of baseball statistics, but even baseball has changed. Abner Doubleday might not recognize the designated hitter or a runner starting on second base in extra innings. The same is true for baseball statistics. Hits, runs, and the like are largely reported the same today as 100 years ago, but just look at the explosion of other statistics. Would Doubleday have the foggiest idea what WHIP means? (It measures the number of walks plus hits a pitcher allows per inningpitched.)
Beyond my keen interest in statistics, the updating of MLB data is important to me because I’m a big baseball fan. And that means all baseball. As both a numbers person and a baseball fan, it’s nice to see the statistics and records of these approximately 3,400 Major League-caliber ballplayers from the Negro Leagues counted within the official historical record.
Satchel Paige of the Kansas City Monarchs (photo from the collection of the National Baseball Hall of Fame and Museum)
One of the game’s best players ever was Satchel Paige of the Kansas City Monarchs, who I had the pleasure of meeting at one of my first jobs as an archivist at the Black Archives of Mid-America. He was donating his baseball memorabilia to the museum at the time. Documenting cultural history at the Archives provided me with a first-hand experience learning about the African American community—its history, heritage, and incomparable stories. That work also heightened my love for baseball, and I count meeting Satchel Paige among my baseball highlights.
As we remember the legacy of Dr. Martin Luther King and observe African American history, it’s important to step back, to reflect, and to pay tribute to the generations of African Americans who, like Satchel Paige, struggled with adversity and became catalysts for change.
And we continue to see change. By recognizing the records of the Negro Leagues, Major League Baseball provides an example of how to embrace and celebrate our diversity. I look forward to studying these expanded baseball records and learning more about the great players of the Negro Leagues.
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
BLS recently participated in the North American Transportation Statistics Interchange, better known as the NATS Interchange. (Not to be confused with the local baseball team, as the Washington Nationals are known. I look forward to the day when I’m back in the stands yelling “N-A-T-S, Nats, Nats, Nats — whoooo!” after each run scores. But I digress.)
Like many recent conferences, the NATS Interchange was held virtually and focused on the pandemic—how statistical agencies in the United States, Mexico, and Canada continued operations, produced new data, and are planning for the future. Our friends at the Bureau of Transportation Statistics, part of the U.S. Department of Transportation, led the U.S. effort and invited several other U.S. statistical agencies to share information. BLS was asked to participate in a short session on the transportation-related information we produce that may be useful in measuring the economic recovery. This turned into a great opportunity to focus on the BLS Industry at a Glance feature on our website, and to look further into what BLS has available related to transportation.
We classify workplaces by industry based on their principal product or activity. Industries are categorized using the North American Industry Classification System, or NAICS. BLS releases considerable data by NAICS classification, including employment, wages, workplace safety, and more. The BLS Industry at a Glance webpages bring these different statistics together for over 100 industries. Want to know everything BLS produces for the transportation and warehousing industry classification (NAICS codes 48–49)? It’s all there at Industry at a Glance. Want to dig deeper and look just at the air transportation industry (NAICS code 481)? We’ve got that, too. Of course, we may have less information available as you ask for more detailed classifications, but if we’ve got it, it’ll be there.
Let’s look at a couple of examples, starting with employment. In April 2020, BLS reported a loss of more than 20 million jobs in one month, based on data from the Current Employment Statistics program. The job losses were widespread, including a loss of 570,000 jobs in the transportation and warehousing industry from February to April. That’s a decline of 10 percent from the January 2020, level of 5.7 million workers in this industry. Through December, the sector had recovered about 84 percent of that job loss and still had a net loss of 90,000 jobs since January.
But looking at the overall sector hides some of the details. The job losses in early 2020 occurred in all components of transportation and warehousing except couriers and messengers. This industry recorded an increase of 210,000 employees from January to December 2020, likely due to the surge in online shopping and associated shipping and delivery. While initially losing jobs, employment in warehousing and storage was up 79,000 in December from the March level. All other sectors continue to have net losses. Of particular note is employment in air transportation, which showed inconsistent recovery for several months before recording new jobs losses in October.
Editor’s note: Data for this chart are available in the table below.
16.1 percent of wage and salary workers in the transportation and warehousing industry were members of a union in 2019, and 17.6 percent were represented by a union.
The occupation with the most workers in this industry is heavy and tractor-trailer truck drivers, with nearly 1.1 million workers in 2019. The next largest occupation was school bus drivers, with about 284,000 workers.
948 workers in this industry suffered a fatal work injury in 2019, up from 909 fatalities in 2018.
In preparing for the NATS interchange, BLS took a broader look at the world of transportation statistics. Turns out, if you look beyond the industry classification, you find even more information. For example, BLS programs on prices and spending look at what consumers spend on transportation, and the change in transportation prices over time. From the BLS Consumer Expenditure Surveys, we know the average “consumer unit” (our fancy name for households) spent an average of $10,742 on transportation in 2019, including vehicle purchases and maintenance and public transportation.
The pandemic revealed major disruptions in certain transportation activity, and those disruptions were evident in the BLS Consumer Price Index. The CPI as a whole declined by 0.8 percent in April, the largest one-month decline in more than a decade. Many of the declines were the result of stay-at-home orders and related shutdowns, as prices for gasoline, airfares, and other transportation-related items declined sharply. Of note was a sharp decline in the price of gasoline—down over 20 percent in April.
Editor’s note: Data for this chart are available in the table below.
To stretch the transportation concept just a little further, the BLS Census of Fatal Occupational Injuries records the “event or exposure” that results in each fatal work injury. Of the 5,333 fatal work injuries in 2019, nearly 40 percent were the result of a transportation incident. Such incidents may occur to workers in the transportation industry, such as truck drivers, but also to many other workers, including farmers, protective service officers, landscapers, and construction laborers. Transportation incidents are most often on a roadway but can also involve aircraft, rail, and water vehicles.
The NATS interchange asked BLS to consider what data might be helpful in tracking the recovery. Many of the transportation statistics discussed here, such as employment, consumer expenditures, and price changes, will likely provide a clue about returning to activity levels reached before the pandemic.
This exercise provided an opportunity to dig a little deeper into the transportation and warehousing industry and to expand the definition to explore related information. The BLS Industry at a Glance webpages offer that same opportunity to explore the current economic landscape of over 100 industries.
Share of January 2020 employment in selected transportation industries through December 2020
Industry
January
April
December
Transportation and warehousing
100.0%
90.0%
98.4%
Air transportation
100.0
85.1
76.9
Warehousing and storage
100.0
93.4
107.9
Couriers and messengers
100.0
100.2
124.5
Percent change in consumer prices for transportation-related items, April and May 2020
BLS publishes employment data for every industry under the sun. If you are looking for employment in shoe stores, we have it. What about bowling alleys or laundromats? We have those too.
But what is an industry? BLS classifies industry employment according to the North American Industry Classification System (NAICS). Each industry has its own NAICS code number.
NAICS uses a production-oriented framework to group establishments into industries based on the activity in which they are primarily engaged. In other words, establishments that do similar things are classified together. The first two digits of a NAICS code correspond to an economic sector, such as construction or manufacturing. Each subsequent digit provides progressively more detail.
Let’s take the oil and gas industry as an example. If we want to know how many people are employed in that industry, we would look at four 6-digit NAICS codes within sector 21 (mining, quarrying, and oil and gas extraction). Specifically, we’re interested in the NAICS codes in the table below. Since the first two digits all start with 21, we can say they all belong to sector 21.
Oil and gas industry
NAICS code
Title
211120
Crude petroleum extraction
211130
Natural gas extraction
213111
Drilling oil and gas wells
213112
Support activities for oil and gas operations
So now we should be able to get total employment in the oil and gas industry, right? Well, let’s take a look.
The 2019 average annual U.S. employment in these industries combined was about 472,000. But wait! You might think that figure is too low. While it captures people who work in extraction, well drilling, and support for oil and gas operations, what about people who work in industries related to the oil and gas industry? You have now stumbled upon one thing NAICS is not designed to do directly: capture an entire industry’s supply and output chain. But what if you are interested in employment across that industry’s supply and output chain?
Let’s continue with the oil and gas example. If you think about all of the activities in the oil and gas industry, they run the gamut from construction to transportation to retail. For example, workers build oil drilling platforms, refine the oil into gasoline and other products after extraction, operate and maintain the pipelines that carry the oil and gas products closer to the end user, and run the gas stations. With that in mind, you can group industries to capture more of the oil and gas industry’s input and output chain. Such a grouping might look like this:
Oil and gas supply and output chain
NAICS code
Industry sector
Title
486110
Transportation and warehousing
Pipeline transportation of crude oil
486210
Transportation and warehousing
Pipeline transportation of natural gas
486910
Transportation and warehousing
Pipeline transportation of refined petroleum products
486990
Transportation and warehousing
All other pipeline transportation
221210
Utilities
Natural gas distribution
324110
Manufacturing
Petroleum refineries
237120
Construction
Oil and gas pipeline and related structures construction
454310
Retail trade
Fuel dealers
424710
Wholesale trade
Petroleum bulk stations and terminals
424720
Wholesale trade
Petroleum and petroleum products merchant wholesalers (except bulk stations and terminals)
447110
Retail trade
Gasoline stations with convenience stores
447190
Retail trade
Other gasoline stations
541360
Professional and technical services
Geophysical surveying and mapping services
By adding total employment for the oil and gas industry group and the oil and gas supply and output chain group, you get a 2019 annual average total employment for the oil and gas and related industries of just over 2 million.
Oil and gas, 2019 average employment
Group
Employment
Oil and gas industry
471,772
Oil and gas supply and output chain
1,535,198
Total
2,006,970
As we have seen in this example, you can use BLS data to build measures of employment in sectors like those related to the oil and gas industry. If you experiment on your own, you will realize there is no official guide for creating these groupings of industry sectors. It may even be difficult to identify all the sectors or subsectors you should include.
BLS employment data by industry are very powerful, and you can use them to paint a picture of employment across an entire supply chain. When using these data, be mindful of which NAICS industry sectors are included in the definition of, say, the oil and gas supply and output chain. As we have seen in this example, two perspectives about what makes up that industry can result in a difference of more than 1.5 million workers.
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.
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.
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.
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.
United States Department of Labor
