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Measuring Business Response to COVID-19 and Other Labor Market Developments

At BLS, we are always looking for new ways to produce timely, accurate, and relevant data. In the spring of 2020, it quickly became clear the COVID-19 pandemic was going to have a major impact on the labor market and working conditions in the United States and globally. New surveys require considerable time and resources, but we wanted to produce new, high-quality data about the impact of the pandemic on businesses and workers. We also knew we needed to produce timely data so they would be relevant and useful for policymakers, the business community, students, researchers, and the public.

In early 2020, our Quarterly Census of Employment and Wages program was piloting a platform that could add questions to the end of an existing BLS survey. In March 2020, staff quickly began creating survey questions to ask businesses about how their operations were changing because of the pandemic. Using this platform allowed BLS to leverage existing infrastructure to quickly create and field a new survey, the Business Response Survey to the Coronavirus Pandemic. It also allowed BLS to produce detailed data by state, industry, and business size.

BLS conducted surveys about the business response to the pandemic in 2020 and 2021. From the 2021 survey, 14.5 percent of establishments increased base wages because of the COVID-19 pandemic. Looking at the industry detail, we find that establishments in accommodation and food services, retail trade, health care and social assistance, and manufacturing increased base wages at a higher rate than the average for the nation overall.

Percent of establishments that increased base wages (straight-time wages or salary) because of the COVID-19 pandemic, by industry

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

From the 2021 survey, we also found that 34.5 percent of establishments increased telework for some or all their employees during the pandemic. Among the industries, we found that accommodation and food services, natural resources and mining, retail trade, and construction were the least likely to increase telework.

Percent of establishments that increased telework for some or all employees since the start of the COVID-19 pandemic, by industry

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

Among the establishments that increased telework, 60.2 percent expected to keep the increases permanent after the end of the pandemic. We found that the five states or areas with the highest expectation of continued telework were the District of Columbia (76.7 percent), Illinois (69.3 percent), North Carolina (68.5 percent), Arizona (68.1 percent) and Colorado (68.0 percent).

Percent of establishments with increased telework that expect the increase to continue when the pandemic is over, by state

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

The 2021 survey found that 28 percent of establishments offered some or all employees an incentive to get a COVID-19 vaccination. Incentives could have been financial, paid time off, or permitting employees to remain on the clock to get a COVID-19 vaccination. The five states or areas in which establishments were most likely to offer vaccination incentives were Puerto Rico (49.3 percent), California (39.2 percent), District of Columbia (37.9 percent), Washington (32.8 percent), and Maryland (31.3 percent).

Percent of establishments that offered any employees a financial incentive, paid time off, or permitted employees to remain on the clock to get a COVID-19 vaccination, by state

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

BLS produced additional estimates on telework, workplace flexibilities, changes in pay, COVID-19 workplace requirements, establishment space size, relocation, supplementing workforce, automation, drug and alcohol testing, and COVID-19 loans or grants. These estimates are available in the 2021 tables.

We’ve published new information about vaccine incentives, increasing pay, COVID-19 workplace safety measures, and the impact of the COVID-19 pandemic on businesses and employees by industry. Also, we recently published a new article using data on telework during the COVID-19 pandemic.

This year BLS is fielding another survey, asking businesses about telework, hiring, and vacancies. For telework, we are asking businesses about current telework practices, expectations about the future of telework, and telework practices before the COVID-19 pandemic. We also are asking about the ability to telework full time for new hires and vacancies. For hiring, we are asking about actions businesses have taken to attract more applicants and the length of time it takes to hire new employees with certain requirements such as professional licenses or advanced degrees. For vacancies, we are asking about how firms are advertising positions that require different education levels.

Have a question or idea for the Business Response Survey team? Email us at BRS_Inquiry@bls.gov.

Percent of establishments that increased base wages (straight-time wages or salary) because of the COVID-19 pandemic, by industry
IndustryPercent of establishments

Total private sector

14.5%

Accommodation and food services

34.3

Retail trade

20.1

Health care and social assistance

19.4

Manufacturing

17.9

Transportation and warehousing

13.8

Educational services

11.8

Arts, entertainment, and recreation

11.5

Construction

11.4

Other services, except public administration

11.3

Natural resources and mining

10.4

Wholesale trade

9.8

Professional and business services

9.7

Information

8.3

Financial activities

7.1

Utilities

4.7
Percent of establishments that increased telework for some or all employees since the start of the COVID-19 pandemic, by industry
IndustryPercent of establishments

Total private sector

34.5%

Educational services

62.7

Information

60.9

Professional and business services

53.1

Financial activities

52.2

Wholesale trade

43.9

Health care and social assistance

38.9

Utilities

38.7

Arts, entertainment, and recreation

33.4

Manufacturing

30.5

Other services, except public administration

25.3

Transportation and warehousing

23.4

Construction

16.8

Retail trade

14.9

Natural resources and mining

12.2

Accommodation and food services

3.6
Percent of establishments with increased telework that expect the increase to continue when the pandemic is over, by state
StatePercent of establishments

Alabama

Alaska

60.1

Arizona

68.1

Arkansas

51.9

California

67.4

Colorado

68.0

Connecticut

67.0

Delaware

60.8

District of Columbia

76.7

Florida

59.0

Georgia

56.6

Hawaii

65.0

Idaho

59.0

Illinois

69.3

Indiana

60.2

Iowa

53.6

Kansas

63.3

Kentucky

Louisiana

Maine

55.8

Maryland

66.4

Massachusetts

59.7

Michigan

56.4

Minnesota

67.2

Mississippi

Missouri

64.4

Montana

59.2

Nebraska

54.2

Nevada

55.0

New Hampshire

54.3

New Jersey

New Mexico

59.4

New York

52.1

North Carolina

68.5

North Dakota

56.6

Ohio

44.4

Oklahoma

57.3

Oregon

65.2

Pennsylvania

65.2

Rhode Island

64.8

South Carolina

64.7

South Dakota

56.2

Tennessee

56.7

Texas

54.9

Utah

59.4

Vermont

54.4

Virginia

64.7

Washington

63.5

West Virginia

36.4

Wisconsin

51.0

Wyoming

56.4

Puerto Rico

44.4
Percent of establishments that offered any employees a financial incentive, paid time off, or permitted employees to remain on the clock to get a COVID-19 vaccination, by state
StatePercent of establishments

Alabama

23.3

Alaska

22.6

Arizona

28.8

Arkansas

27.0

California

39.2

Colorado

25.7

Connecticut

29.8

Delaware

27.1

District of Columbia

37.9

Florida

25.2

Georgia

24.6

Hawaii

31.0

Idaho

21.0

Illinois

25.9

Indiana

20.0

Iowa

27.1

Kansas

23.2

Kentucky

21.8

Louisiana

23.1

Maine

25.2

Maryland

31.3

Massachusetts

30.4

Michigan

22.8

Minnesota

23.6

Mississippi

23.1

Missouri

25.7

Montana

19.4

Nebraska

22.1

Nevada

31.2

New Hampshire

26.7

New Jersey

27.6

New Mexico

27.0

New York

30.6

North Carolina

28.8

North Dakota

16.0

Ohio

23.2

Oklahoma

23.9

Oregon

26.5

Pennsylvania

27.1

Rhode Island

28.5

South Carolina

26.3

South Dakota

18.1

Tennessee

28.6

Texas

25.7

Utah

21.5

Vermont

27.1

Virginia

30.8

Washington

32.8

West Virginia

23.5

Wisconsin

21.0

Wyoming

20.6

Puerto Rico

49.3

BLS Now Publishing Monthly Data for Native Hawaiians and Other Pacific Islanders and People of Two or More Races

I am pleased to announce that BLS is now publishing monthly labor force estimates for Native Hawaiians and Other Pacific Islanders and people who are of Two or More Races. For several years we have published a small set of annual labor market estimates for these populations in our report on labor force characteristics by race and ethnicity. But we have not published monthly estimates of the unemployment rate, the employment–population ratio, the labor force participation rate, and other key metrics for Native Hawaiians and Other Pacific Islanders and people who are of Two or More Races. With the release of the Employment Situation report on September 2, 2022, we now have monthly data for both groups available back to January 2003.

The Native Hawaiian and Other Pacific Islander category is defined as people having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands. Pacific Islanders are diverse populations with different languages and cultures and include Polynesian, Micronesian, and Melanesian. Two or More Races is defined as people who identify as more than one race. Before 2003, people could identify only one race category as their main race in the Current Population Survey, the source of our data on unemployment and the labor force.

Unemployment

In February 2020, before the COVID-19 pandemic, the overall unemployment rate for the United States was 3.5 percent (seasonally adjusted). The rate was 2.7 percent (not seasonally adjusted) for Native Hawaiians and Other Pacific Islanders and 6.1 percent (not seasonally adjusted) for people who are of Two or More Races.

The jobless rate for Native Hawaiians and Other Pacific Islanders peaked at 14.6 percent in November 2020, about 9 months into the COVID-19 pandemic. This was double the seasonally adjusted rate of 6.7 percent for the total population. The unemployment rate for people who are of Two or More Races peaked at 19.3 percent in April 2020, early in the pandemic, compared with 14.7 percent for all groups.

The unemployment rate has declined for all groups since their peaks during the COVID-19 pandemic. By August 2022, the overall unemployment rate for the United States was 3.7 percent. The rate was 3.8 percent for Native Hawaiians and Other Pacific Islanders and 6.2 percent for people who are of Two or More Races.

Unemployment rates for Native Hawaiians and Other Pacific Islanders, Two or More Races, and the total population, January 2003 to August 2022

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

Employment

Before the COVID-19 pandemic, the employment–population ratio for the United States (the proportion of the population that is employed) was 61.2 percent (seasonally adjusted) in February 2020. The rate was 67.6 percent (not seasonally adjusted) for Native Hawaiians and Other Pacific Islanders and 63.7 percent (not seasonally adjusted) for people who are of Two or More Races.

The ratios for all groups declined sharply at the start of the COVID-19 pandemic and have not yet returned to their pre-pandemic levels. In August 2022, the employment–population ratio was 60.1 percent for the United States. The ratio was 65.7 percent for Native Hawaiians and Other Pacific Islanders and 61.3 percent for people who are of Two or More Races.

The employment–population ratio is generally higher for Native Hawaiians and Other Pacific Islanders than the U.S. average. The greater likelihood of employment among Native Hawaiians and Other Pacific Islanders reflects the fact that a larger share of this population is in the 25 to 54 age range than the overall population. People in this age range are more likely to be employed than people in younger and older age groups. By contrast, from 2003 to 2016, the employment–population ratio was generally lower for people who are of Two or More Races than the U.S. average. In recent years, the employment–population ratio for people who are of Two or More Races is little different than the national rate.

Employment–population ratios for Native Hawaiians and Other Pacific Islanders, Two or More Races, and the total population, January 2003 to August 2022

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

Monthly estimates give us timely measures to see how groups are faring in the labor market. However, one must exercise caution when analyzing and interpreting monthly data for small population groups. The measures for Native Hawaiians and Other Pacific Islanders and for people who are of Two or More Races tend to be volatile for two main reasons. First, the estimates are based on small sample sizes. We survey about 60,000 U.S. households every month. People who identify themselves as Native Hawaiians and Other Pacific Islanders make up about 0.5 percent of the total labor force. People who identify as having Two or More Races make up about 2.2 percent of the total labor force. Because of their small sample sizes, the month-to-month change in our key economic metrics must be pretty large to be statistically significant. On average, the unemployment rate for Native Hawaiians and Other Pacific Islanders must change by nearly 4 percentage points and the rate for people who are of Two or More Races must change by around 2 percentage points for the differences to be meaningful.

Second, these data are not seasonally adjusted. Seasonal adjustment is a statistical procedure used to remove the effects of seasonality from data so it is easier to see underlying trends. But not all data series can be seasonally adjusted; they must pass a battery of diagnostic tests to be fitted to a seasonal adjustment model. So far, we haven’t been able to do that for data for Native Hawaiians and Other Pacific Islanders and people who are of Two or More Races, but we’ll keep evaluating them as we get more data. Because these data aren’t seasonally adjusted, it can be challenging to compare one month to the following month.

We’re also publishing quarterly estimates for Native Hawaiians and Other Pacific Islanders and people who are of Two or More Races for the first time starting this month. Because these estimates are averages of three months of data, they are somewhat less volatile.

Unemployment rates for Native Hawaiians and Other Pacific Islanders, Two or More Races, and the total population, January 2003 to August 2022
MonthTotal (seasonally adjusted)Native Hawaiian or Other Pacific Islander (not seasonally adjusted)Two or more races (not seasonally adjusted)

Jan 2003

5.8%4.3%10.1%

Feb 2003

5.97.09.3

Mar 2003

5.96.58.8

Apr 2003

6.09.09.5

May 2003

6.17.79.3

Jun 2003

6.37.29.7

Jul 2003

6.26.89.0

Aug 2003

6.16.68.6

Sep 2003

6.18.69.4

Oct 2003

6.010.78.5

Nov 2003

5.810.39.1

Dec 2003

5.78.77.6

Jan 2004

5.77.48.8

Feb 2004

5.65.810.6

Mar 2004

5.84.28.6

Apr 2004

5.66.38.4

May 2004

5.63.78.8

Jun 2004

5.63.28.2

Jul 2004

5.55.28.0

Aug 2004

5.45.58.2

Sep 2004

5.47.89.2

Oct 2004

5.57.29.2

Nov 2004

5.46.57.5

Dec 2004

5.48.88.7

Jan 2005

5.33.39.0

Feb 2005

5.42.59.0

Mar 2005

5.24.29.2

Apr 2005

5.24.16.6

May 2005

5.15.87.7

Jun 2005

5.03.49.2

Jul 2005

5.05.48.1

Aug 2005

4.95.17.1

Sep 2005

5.05.67.6

Oct 2005

5.05.56.8

Nov 2005

5.04.38.7

Dec 2005

4.93.77.0

Jan 2006

4.77.67.0

Feb 2006

4.84.07.7

Mar 2006

4.75.47.8

Apr 2006

4.76.37.0

May 2006

4.67.56.0

Jun 2006

4.65.75.8

Jul 2006

4.74.46.6

Aug 2006

4.76.87.1

Sep 2006

4.54.96.6

Oct 2006

4.43.16.3

Nov 2006

4.55.16.1

Dec 2006

4.43.46.2

Jan 2007

4.64.07.8

Feb 2007

4.55.06.8

Mar 2007

4.47.38.5

Apr 2007

4.55.17.5

May 2007

4.45.56.4

Jun 2007

4.63.65.5

Jul 2007

4.76.06.9

Aug 2007

4.64.75.4

Sep 2007

4.76.47.2

Oct 2007

4.73.38.0

Nov 2007

4.74.67.4

Dec 2007

5.02.88.4

Jan 2008

5.04.48.6

Feb 2008

4.96.17.7

Mar 2008

5.14.29.6

Apr 2008

5.03.29.9

May 2008

5.44.19.7

Jun 2008

5.66.39.3

Jul 2008

5.86.18.6

Aug 2008

6.19.18.9

Sep 2008

6.19.410.5

Oct 2008

6.57.19.3

Nov 2008

6.87.311.0

Dec 2008

7.38.410.3

Jan 2009

7.87.412.7

Feb 2009

8.36.812.4

Mar 2009

8.713.414.7

Apr 2009

9.010.516.7

May 2009

9.48.914.7

Jun 2009

9.513.915.2

Jul 2009

9.514.611.4

Aug 2009

9.612.813.3

Sep 2009

9.814.413.5

Oct 2009

10.07.712.8

Nov 2009

9.910.112.5

Dec 2009

9.98.712.9

Jan 2010

9.810.914.4

Feb 2010

9.89.212.4

Mar 2010

9.911.314.4

Apr 2010

9.912.314.7

May 2010

9.610.614.9

Jun 2010

9.412.114.9

Jul 2010

9.412.914.9

Aug 2010

9.513.112.8

Sep 2010

9.510.512.9

Oct 2010

9.415.612.8

Nov 2010

9.815.112.2

Dec 2010

9.311.512.6

Jan 2011

9.111.913.1

Feb 2011

9.014.312.4

Mar 2011

9.013.014.7

Apr 2011

9.114.312.0

May 2011

9.09.512.1

Jun 2011

9.18.416.7

Jul 2011

9.06.715.3

Aug 2011

9.09.215.1

Sep 2011

9.09.213.8

Oct 2011

8.810.214.6

Nov 2011

8.69.711.2

Dec 2011

8.59.311.4

Jan 2012

8.39.313.6

Feb 2012

8.311.912.3

Mar 2012

8.210.412.2

Apr 2012

8.210.710.4

May 2012

8.214.711.2

Jun 2012

8.211.910.6

Jul 2012

8.211.512.8

Aug 2012

8.115.311.8

Sep 2012

7.811.810.8

Oct 2012

7.89.911.7

Nov 2012

7.79.713.6

Dec 2012

7.914.511.3

Jan 2013

8.09.512.1

Feb 2013

7.79.911.8

Mar 2013

7.511.811.6

Apr 2013

7.68.210.1

May 2013

7.58.510.2

Jun 2013

7.56.114.2

Jul 2013

7.310.011.3

Aug 2013

7.212.411.2

Sep 2013

7.210.39.7

Oct 2013

7.212.210.4

Nov 2013

6.912.19.5

Dec 2013

6.711.19.5

Jan 2014

6.69.611.2

Feb 2014

6.77.010.8

Mar 2014

6.74.79.9

Apr 2014

6.25.410.2

May 2014

6.34.19.6

Jun 2014

6.16.19.8

Jul 2014

6.27.311.8

Aug 2014

6.15.211.4

Sep 2014

5.95.710.8

Oct 2014

5.74.38.9

Nov 2014

5.87.09.2

Dec 2014

5.66.59.1

Jan 2015

5.77.911.0

Feb 2015

5.56.39.3

Mar 2015

5.42.510.1

Apr 2015

5.46.07.8

May 2015

5.65.69.6

Jun 2015

5.35.57.8

Jul 2015

5.24.18.8

Aug 2015

5.15.38.1

Sep 2015

5.09.18.0

Oct 2015

5.06.97.8

Nov 2015

5.14.56.9

Dec 2015

5.04.66.3

Jan 2016

4.82.08.6

Feb 2016

4.93.09.7

Mar 2016

5.04.29.4

Apr 2016

5.12.78.6

May 2016

4.83.36.9

Jun 2016

4.93.37.0

Jul 2016

4.85.88.0

Aug 2016

4.95.36.9

Sep 2016

5.07.87.3

Oct 2016

4.97.75.7

Nov 2016

4.73.85.7

Dec 2016

4.73.36.1

Jan 2017

4.72.77.4

Feb 2017

4.67.79.5

Mar 2017

4.42.86.6

Apr 2017

4.45.95.0

May 2017

4.43.47.7

Jun 2017

4.36.47.1

Jul 2017

4.311.66.8

Aug 2017

4.49.35.9

Sep 2017

4.37.36.2

Oct 2017

4.25.66.0

Nov 2017

4.25.66.0

Dec 2017

4.14.15.7

Jan 2018

4.04.75.6

Feb 2018

4.15.45.6

Mar 2018

4.09.06.4

Apr 2018

4.03.85.1

May 2018

3.84.56.0

Jun 2018

4.07.47.0

Jul 2018

3.82.96.9

Aug 2018

3.86.65.4

Sep 2018

3.75.65.3

Oct 2018

3.86.24.5

Nov 2018

3.84.44.6

Dec 2018

3.93.04.3

Jan 2019

4.02.35.5

Feb 2019

3.81.15.8

Mar 2019

3.84.16.3

Apr 2019

3.64.45.2

May 2019

3.62.74.9

Jun 2019

3.63.06.9

Jul 2019

3.72.16.1

Aug 2019

3.74.54.5

Sep 2019

3.54.74.9

Oct 2019

3.63.75.1

Nov 2019

3.63.04.9

Dec 2019

3.63.13.5

Jan 2020

3.52.66.3

Feb 2020

3.52.76.1

Mar 2020

4.43.07.3

Apr 2020

14.77.619.3

May 2020

13.210.517.7

Jun 2020

11.010.015.3

Jul 2020

10.211.814.6

Aug 2020

8.412.412.6

Sep 2020

7.914.511.2

Oct 2020

6.912.79.0

Nov 2020

6.714.69.3

Dec 2020

6.75.710.9

Jan 2021

6.410.010.1

Feb 2021

6.28.99.1

Mar 2021

6.07.19.0

Apr 2021

6.011.88.3

May 2021

5.85.68.1

Jun 2021

5.98.99.4

Jul 2021

5.47.29.3

Aug 2021

5.25.17.5

Sep 2021

4.75.98.0

Oct 2021

4.66.16.4

Nov 2021

4.22.77.7

Dec 2021

3.94.55.3

Jan 2022

4.05.26.4

Feb 2022

3.85.66.8

Mar 2022

3.62.96.2

Apr 2022

3.62.84.2

May 2022

3.64.14.4

Jun 2022

3.63.43.9

Jul 2022

3.54.55.6

Aug 2022

3.73.86.2
Employment–population ratios for Native Hawaiians and Other Pacific Islanders, Two or More Races, and the total population, January 2003 to August 2022
MonthTotal (seasonally adjusted)Native Hawaiian or Other Pacific Islander (not seasonally adjusted)Two or more races (not seasonally adjusted)

Jan 2003

62.5%66.5%59.8%

Feb 2003

62.563.059.4

Mar 2003

62.461.459.2

Apr 2003

62.459.859.8

May 2003

62.360.261.1

Jun 2003

62.362.162.8

Jul 2003

62.165.462.1

Aug 2003

62.162.862.0

Sep 2003

62.063.763.1

Oct 2003

62.164.963.7

Nov 2003

62.368.462.3

Dec 2003

62.265.761.7

Jan 2004

62.363.160.9

Feb 2004

62.365.858.1

Mar 2004

62.263.059.1

Apr 2004

62.363.460.4

May 2004

62.373.559.9

Jun 2004

62.469.961.9

Jul 2004

62.568.063.5

Aug 2004

62.465.463.1

Sep 2004

62.369.961.8

Oct 2004

62.369.062.4

Nov 2004

62.571.162.5

Dec 2004

62.468.760.8

Jan 2005

62.469.660.2

Feb 2005

62.469.560.5

Mar 2005

62.471.159.9

Apr 2005

62.771.261.6

May 2005

62.868.561.0

Jun 2005

62.769.462.1

Jul 2005

62.869.662.6

Aug 2005

62.967.763.0

Sep 2005

62.871.063.1

Oct 2005

62.867.462.9

Nov 2005

62.774.561.6

Dec 2005

62.872.260.5

Jan 2006

62.969.258.9

Feb 2006

63.070.459.1

Mar 2006

63.161.060.3

Apr 2006

63.063.460.3

May 2006

63.167.562.0

Jun 2006

63.170.664.9

Jul 2006

63.072.563.9

Aug 2006

63.170.162.1

Sep 2006

63.173.262.0

Oct 2006

63.373.961.8

Nov 2006

63.378.262.2

Dec 2006

63.475.160.4

Jan 2007

63.373.058.3

Feb 2007

63.369.461.0

Mar 2007

63.366.559.7

Apr 2007

63.066.759.5

May 2007

63.067.361.5

Jun 2007

63.066.063.1

Jul 2007

62.970.563.9

Aug 2007

62.768.963.5

Sep 2007

62.965.862.9

Oct 2007

62.773.061.9

Nov 2007

62.972.962.5

Dec 2007

62.773.060.9

Jan 2008

62.970.759.4

Feb 2008

62.864.159.2

Mar 2008

62.770.257.8

Apr 2008

62.771.659.1

May 2008

62.572.060.8

Jun 2008

62.465.560.3

Jul 2008

62.266.561.5

Aug 2008

62.065.861.4

Sep 2008

61.963.758.7

Oct 2008

61.767.558.6

Nov 2008

61.468.756.5

Dec 2008

61.068.758.6

Jan 2009

60.667.755.0

Feb 2009

60.365.756.2

Mar 2009

59.964.557.1

Apr 2009

59.863.855.4

May 2009

59.664.256.5

Jun 2009

59.460.256.9

Jul 2009

59.361.358.6

Aug 2009

59.159.557.5

Sep 2009

58.758.356.9

Oct 2009

58.560.657.0

Nov 2009

58.656.556.8

Dec 2009

58.359.956.1

Jan 2010

58.559.156.6

Feb 2010

58.563.857.2

Mar 2010

58.564.054.9

Apr 2010

58.761.255.1

May 2010

58.664.955.3

Jun 2010

58.563.057.2

Jul 2010

58.561.156.7

Aug 2010

58.659.356.6

Sep 2010

58.559.556.8

Oct 2010

58.354.256.9

Nov 2010

58.252.657.8

Dec 2010

58.358.456.6

Jan 2011

58.356.055.7

Feb 2011

58.457.957.0

Mar 2011

58.457.953.4

Apr 2011

58.460.354.3

May 2011

58.363.855.6

Jun 2011

58.265.255.0

Jul 2011

58.267.355.7

Aug 2011

58.364.154.5

Sep 2011

58.464.555.7

Oct 2011

58.460.554.9

Nov 2011

58.666.157.0

Dec 2011

58.663.255.3

Jan 2012

58.463.855.4

Feb 2012

58.566.856.9

Mar 2012

58.566.657.0

Apr 2012

58.464.060.1

May 2012

58.562.458.6

Jun 2012

58.664.458.7

Jul 2012

58.562.158.1

Aug 2012

58.459.658.5

Sep 2012

58.760.757.7

Oct 2012

58.863.558.4

Nov 2012

58.762.355.6

Dec 2012

58.760.055.3

Jan 2013

58.660.156.2

Feb 2013

58.660.956.0

Mar 2013

58.558.655.7

Apr 2013

58.665.555.1

May 2013

58.664.455.9

Jun 2013

58.664.155.6

Jul 2013

58.763.957.6

Aug 2013

58.763.359.3

Sep 2013

58.763.658.0

Oct 2013

58.363.056.2

Nov 2013

58.663.655.7

Dec 2013

58.764.155.2

Jan 2014

58.864.754.8

Feb 2014

58.762.155.7

Mar 2014

58.961.157.8

Apr 2014

58.960.057.4

May 2014

58.962.458.3

Jun 2014

59.062.459.7

Jul 2014

59.065.758.3

Aug 2014

59.064.256.8

Sep 2014

59.161.457.9

Oct 2014

59.366.958.1

Nov 2014

59.265.458.6

Dec 2014

59.366.857.8

Jan 2015

59.364.555.1

Feb 2015

59.263.056.9

Mar 2015

59.266.057.9

Apr 2015

59.364.259.0

May 2015

59.460.058.7

Jun 2015

59.461.459.6

Jul 2015

59.359.860.3

Aug 2015

59.458.660.3

Sep 2015

59.261.258.8

Oct 2015

59.364.760.5

Nov 2015

59.462.760.4

Dec 2015

59.669.260.7

Jan 2016

59.765.859.6

Feb 2016

59.866.257.4

Mar 2016

59.869.058.0

Apr 2016

59.763.858.7

May 2016

59.766.360.9

Jun 2016

59.766.361.7

Jul 2016

59.866.361.6

Aug 2016

59.862.260.7

Sep 2016

59.762.061.0

Oct 2016

59.766.760.5

Nov 2016

59.766.360.3

Dec 2016

59.767.861.6

Jan 2017

59.965.959.6

Feb 2017

60.058.761.0

Mar 2017

60.263.564.9

Apr 2017

60.258.764.1

May 2017

60.163.561.5

Jun 2017

60.164.363.2

Jul 2017

60.260.463.2

Aug 2017

60.161.761.9

Sep 2017

60.463.061.9

Oct 2017

60.165.463.2

Nov 2017

60.167.162.6

Dec 2017

60.163.861.8

Jan 2018

60.263.562.5

Feb 2018

60.463.762.2

Mar 2018

60.463.961.9

Apr 2018

60.464.762.6

May 2018

60.563.662.1

Jun 2018

60.461.763.9

Jul 2018

60.563.663.8

Aug 2018

60.363.963.0

Sep 2018

60.465.362.9

Oct 2018

60.564.663.6

Nov 2018

60.569.462.5

Dec 2018

60.672.064.2

Jan 2019

60.676.062.6

Feb 2019

60.871.162.9

Mar 2019

60.767.461.9

Apr 2019

60.668.462.4

May 2019

60.664.064.2

Jun 2019

60.764.164.4

Jul 2019

60.862.764.8

Aug 2019

60.860.465.2

Sep 2019

60.960.563.8

Oct 2019

60.966.963.3

Nov 2019

61.067.962.6

Dec 2019

61.068.564.5

Jan 2020

61.170.463.3

Feb 2020

61.267.663.7

Mar 2020

59.968.761.4

Apr 2020

51.365.650.4

May 2020

52.863.750.8

Jun 2020

54.763.253.7

Jul 2020

55.258.955.3

Aug 2020

56.555.954.4

Sep 2020

56.646.355.6

Oct 2020

57.450.757.4

Nov 2020

57.456.858.7

Dec 2020

57.464.659.3

Jan 2021

57.562.958.0

Feb 2021

57.662.558.7

Mar 2021

57.861.859.1

Apr 2021

57.960.159.9

May 2021

58.059.060.5

Jun 2021

58.060.161.5

Jul 2021

58.461.859.9

Aug 2021

58.566.559.0

Sep 2021

58.859.359.6

Oct 2021

58.961.459.6

Nov 2021

59.364.561.2

Dec 2021

59.566.763.0

Jan 2022

59.763.459.6

Feb 2022

59.962.260.2

Mar 2022

60.168.160.7

Apr 2022

60.064.363.4

May 2022

60.166.263.7

Jun 2022

59.961.864.5

Jul 2022

60.062.862.2

Aug 2022

60.165.761.3

Remembering Dale Jorgenson

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

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

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

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

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

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

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

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

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

Learning about the Producer Price Index Special Groupings

Inflation has been in the news—and on all of our minds—in recent months. Indeed, BLS price indexes have been prominent in nearly every inflation story. You may be familiar with the Consumer Price Index, which measures changes in the prices consumers pay for goods and services. You may be less familiar with the Producer Price Indexes (PPI), which measure price changes from the perspective of businesses.

The headline PPIs are the Final Demand-Intermediate Demand (FD-ID) Indexes. These indexes measure changes in the prices businesses receive for goods, services, and construction sold to end users (final demand) and to other businesses as inputs to production (intermediate demand). From July 2021 to July 2022, the Producer Price Index for Final Demand rose 9.8 percent.

BLS recently introduced two special grouping indexes that break government purchases into defense and nondefense purchases. I’ll say more below about these new groupings, but let’s first look at the variety of PPI data we publish.

PPI Basics

The PPI measures price change for more than 10,000 products and groups of products. We organize these items in many ways to show price change for categories of interest. We group the FD-ID indexes by the type of product sold. Within final demand, the three main indexes are final demand goods, final demand services, and final demand construction. These indexes measure price changes for products intended for personal consumption, capital investment, government, and export.

The main intermediate demand indexes are processed goods for intermediate demand, unprocessed goods for intermediate demand, and services for intermediate demand. These indexes measure price changes for business-to-business sales of fabricated goods, unfabricated goods, and services used as inputs to production.

In addition to the breakouts highlighted in the PPI news release, BLS also publishes special grouping indexes that further divide final demand and intermediate demand in useful ways.

The most popular special indexes are often called “core” indexes. These indexes remove historically volatile components, which may make it easier to understand the underlying rate of inflation. For example, you may have seen news stories that mention the special index for final demand less foods, energy, and trade services.

Indexes for Type of Buyer

A second set of special grouping final demand indexes organizes PPI data by type of buyer, based on Gross Domestic Product categories. These categories include personal consumption, private capital investment, government purchases, and exports. (The PPI measures price changes for domestic producers and thus excludes import prices.) Organizing final demand by type of buyer, rather than type of product, helps us identify when different categories of buyers experience differing rates of inflation.

The chart below shows the special grouping indexes for final demand by buyer type from January 2019 through July 2022. (For comparison purposes, these indexes are rebased to 100 in January 2019.) The chart highlights how different end-use buyers can experience different rates of inflation.

Producer Price Indexes for final demand by type of buyer, January 2019 to July 2022

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

At the beginning of the COVID-19 pandemic, prices for exports and government purchases initially dropped more than prices for personal consumption and capital investment. From January 2020 through April 2020, prices for exports decreased 3.0 percent and prices for government purchases fell 4.1 percent. At the same time, prices declined 1.6 percent for personal consumption and 0.7 percent for private capital investment. From April 2020 through July 2022, prices jumped 26.9 percent for exports and 26.8 percent for government purchases, compared with increases of 18.1 percent for personal consumption and 19.5 percent for private capital investment.

Indexes for Government Purchases of Goods and Services

Now let’s examine the new special grouping indexes for government defense and nondefense purchases. The most highly weighted items in the defense index include military aircraft; engineering services; jet fuel; search detection, navigation and guidance systems and equipment; and machinery and equipment wholesaling. The most highly weighted items in the nondefense index consist of new school building construction, portfolio management, commercial electric power, diesel fuel, and property and casualty insurance.

The chart below presents the new government purchases indexes from January 2019 through July 2022. (Again, for comparison purposes these indexes are rebased to 100 in January 2019.) Although the two indexes move similarly, there is one notable difference. Prices for defense purchases fell more sharply than prices for nondefense purchases at the start of the COVID-19 pandemic. Both indexes moved higher beginning in mid-2020, but prices for government defense purchases have increased more slowly than for nondefense purchases.

Producer Price Indexes for government purchases, defense and nondefense, January 2019 to July 2022

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

Indexes for Distributive Services

A third set of special grouping indexes are for distributive services. These indexes are particularly relevant now, given recent issues in U.S. and global supply chains. These indexes measure price changes for services associated with distributing goods sold to final demand and intermediate demand. Distributive services include transporting, storing, and reselling goods.

The chart below presents the indexes for final demand distributive services and intermediate demand distributive services from January 2019 through July 2022. The final demand distributive services index measures price changes for distributing goods sold to final demand (personal consumption, capital investment, government purchases, and exports). The intermediate demand distributive services index measures price changes for services associated with distributing goods sold to other businesses as inputs to production.

Producer Price Indexes for distributive services, January 2019 to July 2022

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

Both distributive services indexes rose sharply from 2019 through 2022. Notably, neither index showed the dip at the beginning of the COVID-19 pandemic that happened with many other BLS price indexes. The intermediate demand distributive services index rose nearly 30 percent since March 2020, compared with approximately 25 percent for final demand distributive services. The larger increase in prices for business-to-business distributive services potentially highlights supply chain issues early in the production pipeline.

These are just some of the special groupings available for the PPI. You can see the full list of special grouping final demand and intermediate demand indexes in table 1 of the PPI news release.

Producer Price Indexes for final demand by type of buyer
MonthFinal demandExportPersonal consumptionGovernment purchasesPrivate capital investment

Jan 2019

100.000100.000100.000100.000100.000

Feb 2019

100.343100.000100.345100.448100.086

Mar 2019

100.857100.442101.034101.166100.172

Apr 2019

101.542100.795101.724101.614100.516

May 2019

101.628100.618101.810101.794100.774

Jun 2019

101.542100.530101.897101.345100.430

Jul 2019

101.714100.618102.069101.794100.860

Aug 2019

101.799100.353102.241101.704100.688

Sep 2019

101.37199.735101.897101.345100.000

Oct 2019

101.79999.912102.414101.435100.430

Nov 2019

101.37199.823101.810101.256100.430

Dec 2019

101.457100.088101.810101.435100.602

Jan 2020

101.971100.707102.328101.883100.774

Feb 2020

101.457100.000102.069100.807100.172

Mar 2020

101.20099.558101.810100.000100.086

Apr 2020

100.00097.703100.69097.668100.086

May 2020

100.51497.968101.46697.84899.742

Jun 2020

100.85798.410101.63898.744100.172

Jul 2020

101.45798.763102.24199.641100.688

Aug 2020

101.54299.117102.328100.000100.516

Sep 2020

101.71499.735102.50099.910100.344

Oct 2020

102.399100.618103.190100.179101.032

Nov 2020

102.228101.148102.759100.538101.032

Dec 2020

102.314102.208102.586101.166100.946

Jan 2021

103.599104.417104.052102.332101.290

Feb 2021

104.456105.389104.828103.408101.978

Mar 2021

105.398106.979105.862104.843102.064

Apr 2021

106.512108.657106.897105.650103.181

May 2021

107.541110.777107.500106.996104.557

Jun 2021

108.483111.749108.534108.072104.901

Jul 2021

109.532112.973109.543108.996106.063

Aug 2021

110.330113.887110.360109.641106.740

Sep 2021

110.639113.716110.711110.048107.269

Oct 2021

111.480114.644111.262111.408109.044

Nov 2021

112.331116.096111.962112.271109.996

Dec 2021

112.548116.431112.008112.168111.042

Jan 2022

114.031117.875113.262114.346113.212

Feb 2022

115.322119.163114.484116.048114.586

Mar 2022

117.687122.023116.866119.236115.960

Apr 2022

118.414123.824117.125121.090117.153

May 2022

119.489124.885118.190123.078117.801

Jun 2022

120.793125.305119.741124.975118.516

Jul 2022

120.223124.008119.038123.879119.514
Producer Price Indexes for government purchases, defense and nondefense
MonthGovernment purchases, defenseGovernment purchases, nondefense

Jan 2019

100.000100.000

Feb 2019

100.672100.380

Mar 2019

101.344101.045

Apr 2019

101.631101.614

May 2019

101.919101.804

Jun 2019

101.248101.519

Jul 2019

101.536101.994

Aug 2019

101.344101.899

Sep 2019

100.960101.614

Oct 2019

101.536101.425

Nov 2019

101.440101.235

Dec 2019

101.631101.330

Jan 2020

102.303101.709

Feb 2020

100.576100.950

Mar 2020

99.424100.285

Apr 2020

96.16198.291

May 2020

96.06598.575

Jun 2020

97.60199.240

Jul 2020

98.273100.285

Aug 2020

98.369100.855

Sep 2020

98.081100.760

Oct 2020

98.464101.045

Nov 2020

98.752101.330

Dec 2020

99.808101.899

Jan 2021

100.864103.134

Feb 2021

101.823104.179

Mar 2021

102.975105.793

Apr 2021

103.935106.458

May 2021

105.278107.882

Jun 2021

105.950109.117

Jul 2021

106.656110.179

Aug 2021

107.261110.821

Sep 2021

107.808111.176

Oct 2021

108.995112.638

Nov 2021

109.702113.563

Dec 2021

109.312113.556

Jan 2022

111.486115.760

Feb 2022

113.509117.346

Mar 2022

117.406120.283

Apr 2022

119.475122.065

May 2022

121.279124.189

Jun 2022

122.261126.568

Jul 2022

121.121125.379
Producer Price Indexes for distributive services
MonthFinal demand distributive services Intermediate demand distributive services

Jan 2019

100.000100.000

Feb 2019

100.256100.000

Mar 2019

100.683100.654

Apr 2019

101.537101.552

May 2019

101.281101.307

Jun 2019

101.281101.307

Jul 2019

101.281101.471

Aug 2019

102.050102.451

Sep 2019

101.366103.023

Oct 2019

102.391102.533

Nov 2019

100.939102.288

Dec 2019

101.110102.941

Jan 2020

101.366103.350

Feb 2020

101.025103.105

Mar 2020

101.964103.268

Apr 2020

102.818103.023

May 2020

102.135103.186

Jun 2020

101.964103.676

Jul 2020

102.818103.758

Aug 2020

102.904105.147

Sep 2020

102.733106.454

Oct 2020

104.611107.598

Nov 2020

103.672107.353

Dec 2020

102.989109.069

Jan 2021

103.672110.376

Feb 2021

104.526111.111

Mar 2021

105.124112.337

Apr 2021

107.515114.951

May 2021

108.881117.075

Jun 2021

109.564119.690

Jul 2021

111.207120.672

Aug 2021

113.412122.481

Sep 2021

113.825122.740

Oct 2021

115.137123.395

Nov 2021

116.470123.114

Dec 2021

117.774124.770

Jan 2022

119.292126.529

Feb 2022

121.753127.859

Mar 2022

125.159132.128

Apr 2022

126.081133.998

May 2022

126.610134.707

Jun 2022

126.983133.757

Jul 2022

127.362133.879

Catching up on Recent BLS Activities

At BLS, we highly value feedback that can help us improve our economic statistics. Three groups regularly advise us on serving the needs of data users: the BLS Data Users Advisory Committee, the BLS Technical Advisory Committee, and the Federal Economic Statistics Advisory Committee.

I cannot overstate the value of these committees. They have given us truly wonderful ideas. If you want to join these meetings, they are open to the public. You can learn more about future meetings directly from the committee links provided above. I welcome and encourage you to attend.

As the Commissioner of BLS, my role at these meetings is to give an overview of all the new and exciting things happening at BLS. I want to share these updates directly with you, too.

Budgets for Fiscal Years 2022 and 2023

Let’s start with the budgets for fiscal years (FY) 2022 and 2023. For full information on the FY 2022 budget, please see the Department of Labor FY 2022 budget page, which has information on the budget for BLS and other agencies within the Department. You also can see the FY 2023 proposed budget, released on March 28, 2022.

In addition to funding our existing programs, the President’s FY 2023 proposed budget requests additional funds for several BLS initiatives.

We are requesting $14.5 million to continue developing a new National Longitudinal Survey of Youth cohort. We are developing plans for a new cohort called the National Longitudinal Survey of Youth 2026 (NLSY26). The NLSY26 will build upon our experience and analysis of two ongoing earlier cohorts:

  • NLSY79: A sample of 12,686 people who were born in the years 1957–64. The survey began in 1979, when sample members were ages 14–22. BLS has followed this cohort of late baby boomers for more than 40 years, recording their lives from their teens into their 50s and early 60s.
  • NLSY97: A sample of 8,984 people who were born in the years 1980–84. The survey began in 1997, when sample members were ages 12–17. BLS has followed this cohort for more than 20 years, and sample members are now in their mid-30s to early 40s.

As in previous National Longitudinal Surveys cohorts, BLS plans to ask NLSY26 cohort members a core set of questions on employment, training, education, income, assets, marital status, fertility, health, and occupational and geographic mobility. We also plan to administer cognitive and noncognitive assessments. We are considering other topics as we consult with stakeholders and subject matter experts in a range of fields.

The FY 2023 budget request for BLS also includes the following:

Expanding Our Data

Moving beyond the budget, one topic that’s getting a lot of attention lately is inflation. We’ve been measuring and reporting on inflation at BLS for over a century, and we are always looking for ways to improve our measurement. The National Academy of Sciences, Committee on National Statistics, recently completed a study that focuses on ways to improve the Consumer Price Index. The report provided 37 consensus recommendations on how BLS can adapt to the rapidly changing digital landscape to improve CPI methods. BLS staff are now reviewing the report and developing an action plan based on the committee’s recommendations. You can read my blog about the report and the full report itself.

BLS recently began publishing monthly and quarterly labor force measures for the American Indian and Alaska Native population on February 4, 2022. We have these data back to 2000. Previously, we published data for American Indians and Alaska Natives only annually. You can learn more about the new data in one of my February blog posts.

We now are evaluating whether we can begin publishing monthly and quarterly labor force data for the Native Hawaiian and Pacific Islander population and for detailed Asian groups. The populations of Native Hawaiians and Pacific Islanders and detailed Asian groups are relatively small, so we need to evaluate whether the Current Population Survey sample size is large enough to produce reliable monthly estimates for these groups. We currently publish annual data for Native Hawaiians and Pacific Islanders and detailed Asian groups in our report on Labor Force Characteristics by Race and Ethnicity.

Updates for Other Programs

I mentioned the National Longitudinal Surveys already, but the program is also doing other great work! In November 2021 we released data for the NLSY97 COVID-19 Supplement. We collected these data from February to May 2021. The survey asked questions about how the pandemic affected employment, health, and childcare. See our brief analysis of some of the COVID-19 data.

We’re also exploring how to measure the value of household production. BLS contracted with a vendor to consider how to use data from the American Time Use Survey on home production and impute the data to consumer units in the Consumer Expenditure Surveys. We expect to receive the recommendations by the end of the fiscal year.

Also in our Consumer Expenditure Surveys, we conducted an online survey test from November 2021 through January 2022 that will help us analyze alternative methods of collecting data. Response rates for most surveys have been declining for years. The COVID-19 pandemic also has made in-person interviewing less feasible. We are currently analyzing the results of the test to learn how we might reverse the trend of declining response rates and be ready for future events that might disrupt data collection.

Finally, we revamped the BLS Productivity program’s web space in April 2022. Information on labor productivity and total factor productivity is now available in a single cohesive and intuitive space. The new web space eliminates redundant material, improves consistency, and includes new material to fill information gaps. It truly enhances the customer experience!

I hope you find these updates useful and that they improve your experience with BLS data. We are always looking for opportunities to improve your experience with our gold standard economic statistics. Be on the lookout for more updates and improvements as we continuously adapt to meet your needs!