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American International Journal of Business Management (AIJBM)
ISSN- 2379-106X, www.aijbm.com Volume 4, Issue 04 (April 2021), PP 05-13
*Corresponding Author: Sonal Pandey 1
www.aijbm.com 5 | Page
Covid-19 and Recession or Shecession?
Sonal Pandey, Ph.D.
Middlesex College New Jersey, North America International Education (NAIE)
Abstract:The present research paperhighlights the women employment and the tremendous decline of womenโ€™s
employment rate during recent years due to pandemic attack on our county United states of America. we all know
that women as a warrior fought very well to achieve their potential from past several years, and history tells us
about their victory and struggle to success over gender inequalityin work environment. Working women pick up a
considerable measure of new skill and abilities associated with driving an organization as well as with personal
improvement. my research work is to overview the impact of pandemic on women employment in 2020 by 2 sample
t-test (took 2019 and 2020 monthly women's employment as my sample data). I also tried to quantify the
relationship between two variables that is covid-19 cases from starting of outbreak till December2020 and the
change in employment opportunity during that period through linear regression analysis. Research also
highlighted the struggle of women with different racesand situation of job availability in most women friendly
industries where women are the leading to provide services. I also tried to discuss and strategizethe plan to
enhance the current women employment opportunitiesfor the productive growth from the Recession and She-
cession.
Keywords: Women Employment, Covid-19 , Regression Analysis , Hypothesis testing
I. Introduction
Since 1990, the employment rate of women1
in the United States has stayed steady. In 1990, the female employment
rate was 54.3 percent, and in 2020, the employment (Kashen Julie, October 30, 2020,) (Sargsyan Anahit, 2020)rate
was at 51.5 percent. However, it reached a peak in 2000 at 57.5 percent.
Women in the workforce have historically not been treated the same way as men. There are many inequalities in all
aspects of work, from salaries to promotion, although worldwide, the number of women joining the workforce has
been increasing. Women were originally relegated to being stay-at-home wives and mothers who were supposed to
take care of the household while men worked. For a long time, women were not able to attend university, which
barred them from gaining an education and a professional job. However, as society developed, women have been
granted equal access to university. Despite this, the unemployment rate of women in the United States has fluctuated
significantly since 1990. In 2017, Minnesota was the state with the highest percentage of women participating in
thecivilian labor force. Today, the wage gap is still a problem for women, but has improved from years past. The
wage gap is where women are paid less for doing the same job as men, despite having the same level of education
and experience. One of the industries that saw the most disparity in pay between men and women in the United
States was the finance and insurance industry.
According to Bureau of labor statistics one group in particular hits specifically extremely hard and that is working
moms. In one month alone that is in September 2020 865,000 women left the labor force, that is about four time the
number of men. Because most employment is most concentrated in industries most effected by the virous retail and
hospitality. That number is even worse for single mom a study from the national bureau of economic research
estimates that 15 million single mothers will be negatively impacted by the pandemic. (source National Bureau of
Economic Research)
As womenโ€™s job are more concentrated on retail and hospitality, we can dive in deep to analyze more
closely for the impact. So, Bureau of labor statistics reported unemployment in February 2021, 13.5% in hospitality
industry which is more than double if we compared with last year in fab 2020 as 5.7%. Now the in-retail industry
reported unemployment rate in February 2021 is 6.7% which is also up if we look at 2020 fab that is 4.5%. So,each
industry has higher unemployment rate than has national average 6.3%. Although unemployment rate in 2019 and
2020 was 3.6 % and 8.3% among women.
Covid-19 and Recession or She cession ?
*Corresponding Author: Sonal Pandey 1
www.aijbm.com 6 | Page
Table:1 U.S. female workforce: unemployment rate 2019-2020
Unemployment rate of women in the United States from 1990 to 2020
'2019 3.6 in %
'2020 8.3 in %
MY ANALYSYS /FINDING
Table:2
year Employment Level - Women,
Thousands of Persons, Monthly,
Seasonally Adjusted
Cumulative Covid-19 cases
1/1/2020 74762 0
2/1/2020 74865 0.19
3/1/2020 73234 0.15219
4/1/2020 61478 0.751273
5/1/2020 63530 0.1501876
6/1/2020 66444 0.2172212
7/1/2020 67520 0.368546
8/1/2020 69063 0.5431046
9/1/2020 68880 0.6662003
10/1/2020 70131 0.8065615
11/1/2020 70542 0.11413788
12/1/2020 70350 0.17314834
SUMMARY
OUTPUT
Table:3
Regression Statistics
Multiple R 0.44
R Square 0.19
Adjusted R Square 0.11
Standard Error 3866.34
Observations 12.00
ANOVA
Table:4
df SS MS F
Significance
F
Regression 1.00
36109936.7
0
36109936.
70 2.42 0.15
Residual 10.00
149485535.
55
14948553.
55
Total 11.00
185595472.
25
Covid-19 and Recession or She cession ?
*Corresponding Author: Sonal Pandey 1
www.aijbm.com 7 | Page
Coefficien
ts
Standard
Error t Stat
P-
value Lower 95%
Upper
95%
Intercept 71493.35 1833.11 39.00 0.00 67408.92 75577.78
X Variable 1 -6562.79 4222.55 -1.55 0.15 -15971.22 2845.64
RESIDUAL
OUTPUT
Table:5
Observation
Predicted
Y Residuals
Standard
Residuals
1.00 71493.35 3268.65 0.89
2.00 70246.42 4618.58 1.25
3.00 70494.56 2739.44 0.74
4.00 66562.90 -5084.90 -1.38
5.00 70507.70 -6977.70 -1.89
6.00 70067.77 -3623.77 -0.98
7.00 69074.66 -1554.66 -0.42
8.00 67929.07 1133.93 0.31
9.00 67121.22 1758.78 0.48
10.00 66200.06 3930.94 1.07
11.00 70744.29 -202.29 -0.05
12.00 70357.01 -7.01 0.00
PROBABILITY
OUTPUT
Table:6
Percentile Y
4.17 61478.00
12.50 63530.00
20.83 66444.00
29.17 67520.00
37.50 68880.00
45.83 69063.00
54.17 70131.00
62.50 70350.00
70.83 70542.00
79.17 73234.00
87.50 74762.00
95.83 74865.00
Covid-19 and Recession or She cession ?
*Corresponding Author: Sonal Pandey 1
www.aijbm.com 8 | Page
Figure:1
Figure:2
Figure:3
X coordinates represents -Change in cumulative number of positive cases in United States.
Y coordinate represents-Change in womenโ€™semployment in United States.
A= -6562.79B=71493.35R^2=.19
R=-0.44
Here is some important piece of information โ€“ R is the correlation co-efficient; it tells us how close to perfect or
positive/negative correlation in this case and it is -.44
R squared is co-efficient of determination and that is 0 .19 and if we need to interpret the meaning of that we say
that 19 % of the variability in the y variable which is our womenโ€™s employment can be accounted by the variability
in the x variable (cumulative number of COVID-19 cases) so concisely we can say 19 % of variability in the
Womenโ€™s employment can be accounted by the variability in cumulative number of cases. The remaining 81% is
coming from unexplained factors that are outside of the scope of problem, natural factors, are outside of the scope of
concern here.
Covid-19 and Recession or She cession ?
*Corresponding Author: Sonal Pandey 1
www.aijbm.com 9 | Page
Sometimes these results are come up if some months give drastic dependence of change than gradually coming up
so it is hard to blame only one thing as a cause overall.
Here equation of the regression line is โ€“ Y = -6562.79 X + 71493.35
Y = B+ AX Here, B is 71493.35 which is the value of Y when X is 0. But in this case the Cumulative number of
positive cases can never be practically zero during that period. A is -6562.79 which is the number of changes in Y
for each one unit change in X. So, by this regression equation we can conclude that increase in Cumulative number
of positive cases decrease the womenโ€™s jobacross the nation by6562.79 degree.
2020 employment level- Seems She cession
Here is my analysis to see how bad the situation was during 2020 and onwards.
Tabel:7
Frequency: Monthly
observation date
EmploymentLevel -
Women,
ThousandsofPersons,
Monthly,
SeasonallyAdjusted
Frequency: Monthly
observation date
EmploymentLevel -
Women,
ThousandsofPersons,
Monthly,
SeasonallyAdjusted
1/1/2019 73628 1/1/2020 74762
2/1/2019 73757 2/1/2020 74865
3/1/2019 73689 3/1/2020 73234
4/1/2019 73695 4/1/2020 61478
5/1/2019 73635 5/1/2020 63530
6/1/2019 73726 6/1/2020 66444
7/1/2019 73945 7/1/2020 67520
8/1/2019 74290 8/1/2020 69063
9/1/2019 74526 9/1/2020 68880
10/1/2019 74688 10/1/2020 70131
11/1/2019 74582 11/1/2020 70542
12/1/2019 74740 12/1/2020 70350
Economic Research Division, Federal Reserve Bank of St. Louis
T-Test: Two-Sample Assuming Equal Variances and .05 significance level
State: wish to test the following hypothesis at the ฮฑ=.05
level
H0: ยต1=ยต2
H1: ยต1โ‰ ยต2
Where ยต1 is the true mean number of employments in 2019
ยต2 is the true mean number of employments in 2020.
Table:8
Variable 1 Variable 2
Mean 74075.08 69233.25
Variance 204663.54 16872315.66
Observations 12.00 12.00
Pooled Variance 8538489.60
Hypothesized Mean Difference 0.00
df 22.00
t Stat 4.06
P(T<=t) one-tail 0.00
t Critical one-tail 1.72
P(T<=t) two-tail 0.00
t Critical two-tail 2.07
Covid-19 and Recession or She cession ?
*Corresponding Author: Sonal Pandey 1
www.aijbm.com 10 | Page
Conclude: With a P value zero, which is less than any reasonableฮฑ value, I reject H0. There is overwhelming
evidence to support the claim that there is difference in the true mean number of employments in 2019 and
employment in 2020.
Womenโ€™s unemployment rate 2019-2020
Table:9
Industry and class of worker Women
2019 2020
Total, 16 years and over 3.6 8.3
Nonagricultural private wage and salary
workers (2)
3.4 8.7
Mining, quarrying, and oil and gas
extraction
2 7.4
Construction 3.8 7.5
Manufacturing 3.5 7.5
Durable goods 3 7
Nonmetallic mineral products 2.2 5.1
Primary metals and fabricated
metal products
3.3 5.9
Machinery manufacturing 3 4.1
Computers and electronic
products
2.2 2.7
Electrical equipment and
appliances
1.1 6.4
Transportation equipment 3.6 10.3
Wood products 2 4.6
Furniture and related products 4.6 6.2
Miscellaneous manufacturing 3 8.7
Nondurable goods 4.1 8.1
Food manufacturing 5.7 8.7
Beverage and tobacco products 2.5 8.1
Textiles, apparel, and leather 4.8 11.3
Paper manufacturing and
printing
2.6 9.1
Petroleum and coal products โ€“ โ€“
Chemicals 2.1 5
Plastics and rubber products 5 6.7
Wholesale and retail trade 4.5 9.4
Wholesale trade 3.3 5.8
Retail trade 4.7 9.9
Transportation and utilities 3.9 11.3
Covid-19 and Recession or She cession ?
*Corresponding Author: Sonal Pandey 1
www.aijbm.com 11 | Page
Transportation and warehousing 4.2 12.6
Utilities 2.1 2.4
Information)
3.5 8.5
Publishing, except Internet 4.3 8.4
Motion pictures and sound
recording industries
5.1 20.8
Broadcasting (except internet) 2.6 2.1
Telecommunications 3.1 5.2
Libraries, archives, and other
information services
โ€“ 11
Financial activities 2 4
Finance and insurance 1.9 3
Finance 1.9 2.7
Insurance 1.9 3.6
Real estate and rental and leasing 2.3 7
Real estate 2.4 6.7
Rental and leasing services 1.6 10.8
Professional and business services 3.8 7.5
Professional and technical services 2.7 5.2
Management, administrative, and
waste services(
6.1 12.3
Administrative and support
services
6.3 12.6
Waste management and
remediation services
1.4 8
Education and health services 2.5 5.9
Educational services 3.4 8
Health care and social assistance 2.3 5.5
Hospitals 1.3 2.5
Health services, except hospitals 2.4 5.7
Social assistance 3.9 11.2
Leisure and hospitality 5.2 19.2
Arts, entertainment, and recreation 5.2 23.6
Accommodation and food services 5.1 18.3
Accommodation 4.9 24.7
Covid-19 and Recession or She cession ?
*Corresponding Author: Sonal Pandey 1
www.aijbm.com 12 | Page
So after looking the unemployment data above given by US bureau of labor statistics everyone can see that how true
is the name of this research paper. There are more than 65 industries listed in this table and not a single industry is
there who is not showing the increased women unemployment rate. Compared to 2019 and 2020 data
Unemployment different race
From the table below about the unemployment rate in 2020, we can see that most effected among all women are
Hispanic women than black and Asian. I found the reason behind that is mostly retail, hospitality industry served by
Hispanic and black ethnic women or man.
Table:10
Unemployment rate by sex, race and Hispanic ethnicity, 2020 annual averages
Race and Hispanic ethnicity Women Men
Total 8.3 7.8
White 7.6 7
Black 10.9 12.1
Asian 9.6 7.8
Hispanic 11.4 9.7
Notes: Refers to people 16 years of age and older. Data for the individual race groups do not include people of
two or more races. Hispanics can be of any race.
Source: 2020 annual averages, Current Population Survey, U.S. Bureau of Labor Statistics
Suggestions to Improve Womenโ€™s conditions after Pandemic-
๏‚ท Now it is time to pay back to all women for their effort to achieve a place in each and every industry as an
active participant, so government should make proper arrangement to help them after covid โ€“19 unemployment
impact.
๏‚ท Inequality should be watched in each sector.
๏‚ท Proper management for working moms with flexible working hours.
๏‚ท Aid and grants should be provided more for women centered industries like โ€“ service, hospitality, retail,
education etc.
๏‚ท To stop gender discrimination and help both men and women combine jobs with family care
responsibilities.
๏‚ท No more wage discrimination
Food services and drinking
places
5.2 17.3
Other services 3.4 11.5
Other services, except private
households
3.1 11.1
Repair and maintenance 3.9 8.5
Personal and laundry services 3.2 16.9
Membership associations and
organizations
2.8 4.9
Private households 4.7 13.2
Agricultural and related private wage
and salary workers(2)
10.5 11.5
Government workers 2.4 5.5
Covid-19 and Recession or She cession ?
*Corresponding Author: Sonal Pandey 1
www.aijbm.com 13 | Page
References
[1]. Kashen Julie, G. S. ( October 30, 2020,). How COVID-19 Sent Womenโ€™s Workforce Progress Backward.
center for American Progress.
[2]. Oscar, C. (2019, February). Forecasting the unemployment rate using the degree of agreement in consumer
unemployment expectations. Journal for Labour Market Research.
[3]. Oscar, C. (2021, February). A new metric of consensus for Likert-type scale questionnaires: An application
to consumer expectations. Journal of Banking and Financial Technology.
[4]. Sargsyan Anahit, M. K. (2020, 22 Sep). The impact of COVID-19 on gender inequality in the labor market
and gender-role attitudes. Taylor and Francis online .
[5]. (March 2021). unemployment-rate-of-women-in-us/. Bls.gov.
[6]. women Unemployment Rate. (2021, march). Bureau of Labor Statistics.
[7]. YoungWeiwei, A. E. (2020, july). Investigating the impact of auto loans on unemployment: the US
experience. Applied Economics.

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B440513

  • 1. American International Journal of Business Management (AIJBM) ISSN- 2379-106X, www.aijbm.com Volume 4, Issue 04 (April 2021), PP 05-13 *Corresponding Author: Sonal Pandey 1 www.aijbm.com 5 | Page Covid-19 and Recession or Shecession? Sonal Pandey, Ph.D. Middlesex College New Jersey, North America International Education (NAIE) Abstract:The present research paperhighlights the women employment and the tremendous decline of womenโ€™s employment rate during recent years due to pandemic attack on our county United states of America. we all know that women as a warrior fought very well to achieve their potential from past several years, and history tells us about their victory and struggle to success over gender inequalityin work environment. Working women pick up a considerable measure of new skill and abilities associated with driving an organization as well as with personal improvement. my research work is to overview the impact of pandemic on women employment in 2020 by 2 sample t-test (took 2019 and 2020 monthly women's employment as my sample data). I also tried to quantify the relationship between two variables that is covid-19 cases from starting of outbreak till December2020 and the change in employment opportunity during that period through linear regression analysis. Research also highlighted the struggle of women with different racesand situation of job availability in most women friendly industries where women are the leading to provide services. I also tried to discuss and strategizethe plan to enhance the current women employment opportunitiesfor the productive growth from the Recession and She- cession. Keywords: Women Employment, Covid-19 , Regression Analysis , Hypothesis testing I. Introduction Since 1990, the employment rate of women1 in the United States has stayed steady. In 1990, the female employment rate was 54.3 percent, and in 2020, the employment (Kashen Julie, October 30, 2020,) (Sargsyan Anahit, 2020)rate was at 51.5 percent. However, it reached a peak in 2000 at 57.5 percent. Women in the workforce have historically not been treated the same way as men. There are many inequalities in all aspects of work, from salaries to promotion, although worldwide, the number of women joining the workforce has been increasing. Women were originally relegated to being stay-at-home wives and mothers who were supposed to take care of the household while men worked. For a long time, women were not able to attend university, which barred them from gaining an education and a professional job. However, as society developed, women have been granted equal access to university. Despite this, the unemployment rate of women in the United States has fluctuated significantly since 1990. In 2017, Minnesota was the state with the highest percentage of women participating in thecivilian labor force. Today, the wage gap is still a problem for women, but has improved from years past. The wage gap is where women are paid less for doing the same job as men, despite having the same level of education and experience. One of the industries that saw the most disparity in pay between men and women in the United States was the finance and insurance industry. According to Bureau of labor statistics one group in particular hits specifically extremely hard and that is working moms. In one month alone that is in September 2020 865,000 women left the labor force, that is about four time the number of men. Because most employment is most concentrated in industries most effected by the virous retail and hospitality. That number is even worse for single mom a study from the national bureau of economic research estimates that 15 million single mothers will be negatively impacted by the pandemic. (source National Bureau of Economic Research) As womenโ€™s job are more concentrated on retail and hospitality, we can dive in deep to analyze more closely for the impact. So, Bureau of labor statistics reported unemployment in February 2021, 13.5% in hospitality industry which is more than double if we compared with last year in fab 2020 as 5.7%. Now the in-retail industry reported unemployment rate in February 2021 is 6.7% which is also up if we look at 2020 fab that is 4.5%. So,each industry has higher unemployment rate than has national average 6.3%. Although unemployment rate in 2019 and 2020 was 3.6 % and 8.3% among women.
  • 2. Covid-19 and Recession or She cession ? *Corresponding Author: Sonal Pandey 1 www.aijbm.com 6 | Page Table:1 U.S. female workforce: unemployment rate 2019-2020 Unemployment rate of women in the United States from 1990 to 2020 '2019 3.6 in % '2020 8.3 in % MY ANALYSYS /FINDING Table:2 year Employment Level - Women, Thousands of Persons, Monthly, Seasonally Adjusted Cumulative Covid-19 cases 1/1/2020 74762 0 2/1/2020 74865 0.19 3/1/2020 73234 0.15219 4/1/2020 61478 0.751273 5/1/2020 63530 0.1501876 6/1/2020 66444 0.2172212 7/1/2020 67520 0.368546 8/1/2020 69063 0.5431046 9/1/2020 68880 0.6662003 10/1/2020 70131 0.8065615 11/1/2020 70542 0.11413788 12/1/2020 70350 0.17314834 SUMMARY OUTPUT Table:3 Regression Statistics Multiple R 0.44 R Square 0.19 Adjusted R Square 0.11 Standard Error 3866.34 Observations 12.00 ANOVA Table:4 df SS MS F Significance F Regression 1.00 36109936.7 0 36109936. 70 2.42 0.15 Residual 10.00 149485535. 55 14948553. 55 Total 11.00 185595472. 25
  • 3. Covid-19 and Recession or She cession ? *Corresponding Author: Sonal Pandey 1 www.aijbm.com 7 | Page Coefficien ts Standard Error t Stat P- value Lower 95% Upper 95% Intercept 71493.35 1833.11 39.00 0.00 67408.92 75577.78 X Variable 1 -6562.79 4222.55 -1.55 0.15 -15971.22 2845.64 RESIDUAL OUTPUT Table:5 Observation Predicted Y Residuals Standard Residuals 1.00 71493.35 3268.65 0.89 2.00 70246.42 4618.58 1.25 3.00 70494.56 2739.44 0.74 4.00 66562.90 -5084.90 -1.38 5.00 70507.70 -6977.70 -1.89 6.00 70067.77 -3623.77 -0.98 7.00 69074.66 -1554.66 -0.42 8.00 67929.07 1133.93 0.31 9.00 67121.22 1758.78 0.48 10.00 66200.06 3930.94 1.07 11.00 70744.29 -202.29 -0.05 12.00 70357.01 -7.01 0.00 PROBABILITY OUTPUT Table:6 Percentile Y 4.17 61478.00 12.50 63530.00 20.83 66444.00 29.17 67520.00 37.50 68880.00 45.83 69063.00 54.17 70131.00 62.50 70350.00 70.83 70542.00 79.17 73234.00 87.50 74762.00 95.83 74865.00
  • 4. Covid-19 and Recession or She cession ? *Corresponding Author: Sonal Pandey 1 www.aijbm.com 8 | Page Figure:1 Figure:2 Figure:3 X coordinates represents -Change in cumulative number of positive cases in United States. Y coordinate represents-Change in womenโ€™semployment in United States. A= -6562.79B=71493.35R^2=.19 R=-0.44 Here is some important piece of information โ€“ R is the correlation co-efficient; it tells us how close to perfect or positive/negative correlation in this case and it is -.44 R squared is co-efficient of determination and that is 0 .19 and if we need to interpret the meaning of that we say that 19 % of the variability in the y variable which is our womenโ€™s employment can be accounted by the variability in the x variable (cumulative number of COVID-19 cases) so concisely we can say 19 % of variability in the Womenโ€™s employment can be accounted by the variability in cumulative number of cases. The remaining 81% is coming from unexplained factors that are outside of the scope of problem, natural factors, are outside of the scope of concern here.
  • 5. Covid-19 and Recession or She cession ? *Corresponding Author: Sonal Pandey 1 www.aijbm.com 9 | Page Sometimes these results are come up if some months give drastic dependence of change than gradually coming up so it is hard to blame only one thing as a cause overall. Here equation of the regression line is โ€“ Y = -6562.79 X + 71493.35 Y = B+ AX Here, B is 71493.35 which is the value of Y when X is 0. But in this case the Cumulative number of positive cases can never be practically zero during that period. A is -6562.79 which is the number of changes in Y for each one unit change in X. So, by this regression equation we can conclude that increase in Cumulative number of positive cases decrease the womenโ€™s jobacross the nation by6562.79 degree. 2020 employment level- Seems She cession Here is my analysis to see how bad the situation was during 2020 and onwards. Tabel:7 Frequency: Monthly observation date EmploymentLevel - Women, ThousandsofPersons, Monthly, SeasonallyAdjusted Frequency: Monthly observation date EmploymentLevel - Women, ThousandsofPersons, Monthly, SeasonallyAdjusted 1/1/2019 73628 1/1/2020 74762 2/1/2019 73757 2/1/2020 74865 3/1/2019 73689 3/1/2020 73234 4/1/2019 73695 4/1/2020 61478 5/1/2019 73635 5/1/2020 63530 6/1/2019 73726 6/1/2020 66444 7/1/2019 73945 7/1/2020 67520 8/1/2019 74290 8/1/2020 69063 9/1/2019 74526 9/1/2020 68880 10/1/2019 74688 10/1/2020 70131 11/1/2019 74582 11/1/2020 70542 12/1/2019 74740 12/1/2020 70350 Economic Research Division, Federal Reserve Bank of St. Louis T-Test: Two-Sample Assuming Equal Variances and .05 significance level State: wish to test the following hypothesis at the ฮฑ=.05 level H0: ยต1=ยต2 H1: ยต1โ‰ ยต2 Where ยต1 is the true mean number of employments in 2019 ยต2 is the true mean number of employments in 2020. Table:8 Variable 1 Variable 2 Mean 74075.08 69233.25 Variance 204663.54 16872315.66 Observations 12.00 12.00 Pooled Variance 8538489.60 Hypothesized Mean Difference 0.00 df 22.00 t Stat 4.06 P(T<=t) one-tail 0.00 t Critical one-tail 1.72 P(T<=t) two-tail 0.00 t Critical two-tail 2.07
  • 6. Covid-19 and Recession or She cession ? *Corresponding Author: Sonal Pandey 1 www.aijbm.com 10 | Page Conclude: With a P value zero, which is less than any reasonableฮฑ value, I reject H0. There is overwhelming evidence to support the claim that there is difference in the true mean number of employments in 2019 and employment in 2020. Womenโ€™s unemployment rate 2019-2020 Table:9 Industry and class of worker Women 2019 2020 Total, 16 years and over 3.6 8.3 Nonagricultural private wage and salary workers (2) 3.4 8.7 Mining, quarrying, and oil and gas extraction 2 7.4 Construction 3.8 7.5 Manufacturing 3.5 7.5 Durable goods 3 7 Nonmetallic mineral products 2.2 5.1 Primary metals and fabricated metal products 3.3 5.9 Machinery manufacturing 3 4.1 Computers and electronic products 2.2 2.7 Electrical equipment and appliances 1.1 6.4 Transportation equipment 3.6 10.3 Wood products 2 4.6 Furniture and related products 4.6 6.2 Miscellaneous manufacturing 3 8.7 Nondurable goods 4.1 8.1 Food manufacturing 5.7 8.7 Beverage and tobacco products 2.5 8.1 Textiles, apparel, and leather 4.8 11.3 Paper manufacturing and printing 2.6 9.1 Petroleum and coal products โ€“ โ€“ Chemicals 2.1 5 Plastics and rubber products 5 6.7 Wholesale and retail trade 4.5 9.4 Wholesale trade 3.3 5.8 Retail trade 4.7 9.9 Transportation and utilities 3.9 11.3
  • 7. Covid-19 and Recession or She cession ? *Corresponding Author: Sonal Pandey 1 www.aijbm.com 11 | Page Transportation and warehousing 4.2 12.6 Utilities 2.1 2.4 Information) 3.5 8.5 Publishing, except Internet 4.3 8.4 Motion pictures and sound recording industries 5.1 20.8 Broadcasting (except internet) 2.6 2.1 Telecommunications 3.1 5.2 Libraries, archives, and other information services โ€“ 11 Financial activities 2 4 Finance and insurance 1.9 3 Finance 1.9 2.7 Insurance 1.9 3.6 Real estate and rental and leasing 2.3 7 Real estate 2.4 6.7 Rental and leasing services 1.6 10.8 Professional and business services 3.8 7.5 Professional and technical services 2.7 5.2 Management, administrative, and waste services( 6.1 12.3 Administrative and support services 6.3 12.6 Waste management and remediation services 1.4 8 Education and health services 2.5 5.9 Educational services 3.4 8 Health care and social assistance 2.3 5.5 Hospitals 1.3 2.5 Health services, except hospitals 2.4 5.7 Social assistance 3.9 11.2 Leisure and hospitality 5.2 19.2 Arts, entertainment, and recreation 5.2 23.6 Accommodation and food services 5.1 18.3 Accommodation 4.9 24.7
  • 8. Covid-19 and Recession or She cession ? *Corresponding Author: Sonal Pandey 1 www.aijbm.com 12 | Page So after looking the unemployment data above given by US bureau of labor statistics everyone can see that how true is the name of this research paper. There are more than 65 industries listed in this table and not a single industry is there who is not showing the increased women unemployment rate. Compared to 2019 and 2020 data Unemployment different race From the table below about the unemployment rate in 2020, we can see that most effected among all women are Hispanic women than black and Asian. I found the reason behind that is mostly retail, hospitality industry served by Hispanic and black ethnic women or man. Table:10 Unemployment rate by sex, race and Hispanic ethnicity, 2020 annual averages Race and Hispanic ethnicity Women Men Total 8.3 7.8 White 7.6 7 Black 10.9 12.1 Asian 9.6 7.8 Hispanic 11.4 9.7 Notes: Refers to people 16 years of age and older. Data for the individual race groups do not include people of two or more races. Hispanics can be of any race. Source: 2020 annual averages, Current Population Survey, U.S. Bureau of Labor Statistics Suggestions to Improve Womenโ€™s conditions after Pandemic- ๏‚ท Now it is time to pay back to all women for their effort to achieve a place in each and every industry as an active participant, so government should make proper arrangement to help them after covid โ€“19 unemployment impact. ๏‚ท Inequality should be watched in each sector. ๏‚ท Proper management for working moms with flexible working hours. ๏‚ท Aid and grants should be provided more for women centered industries like โ€“ service, hospitality, retail, education etc. ๏‚ท To stop gender discrimination and help both men and women combine jobs with family care responsibilities. ๏‚ท No more wage discrimination Food services and drinking places 5.2 17.3 Other services 3.4 11.5 Other services, except private households 3.1 11.1 Repair and maintenance 3.9 8.5 Personal and laundry services 3.2 16.9 Membership associations and organizations 2.8 4.9 Private households 4.7 13.2 Agricultural and related private wage and salary workers(2) 10.5 11.5 Government workers 2.4 5.5
  • 9. Covid-19 and Recession or She cession ? *Corresponding Author: Sonal Pandey 1 www.aijbm.com 13 | Page References [1]. Kashen Julie, G. S. ( October 30, 2020,). How COVID-19 Sent Womenโ€™s Workforce Progress Backward. center for American Progress. [2]. Oscar, C. (2019, February). Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations. Journal for Labour Market Research. [3]. Oscar, C. (2021, February). A new metric of consensus for Likert-type scale questionnaires: An application to consumer expectations. Journal of Banking and Financial Technology. [4]. Sargsyan Anahit, M. K. (2020, 22 Sep). The impact of COVID-19 on gender inequality in the labor market and gender-role attitudes. Taylor and Francis online . [5]. (March 2021). unemployment-rate-of-women-in-us/. Bls.gov. [6]. women Unemployment Rate. (2021, march). Bureau of Labor Statistics. [7]. YoungWeiwei, A. E. (2020, july). Investigating the impact of auto loans on unemployment: the US experience. Applied Economics.