Business Optimism Amid the Covid19 Pandemic
Find the variables that will impact business optimism and how the business optimism changed after the pandemic.
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- [00:00:01.020]Hello everyone. My name
- [00:00:03.750]I'm a senior major in marketing,
minor in business analytics.
- [00:00:08.190]The research topic that I'm going to
present today is Business Optimism
- [00:00:12.570]Amid the COVID-19 Pandemic.
- [00:00:15.000]The COVID-19 pandemic caused a
huge impact on businesses.
- [00:00:19.290]Business optimism is a very important
factor for economic recovery
- [00:00:24.210]after the COVID-19 pandemic.
- [00:00:26.760]My motivation for this topic is
to learn about what variables
- [00:00:30.690]will impact business optimism.
- [00:00:33.300]This poster's objective is to
determine if businesses in counties
- [00:00:38.310]with a larger population will be
less optimistic after the pandemic.
- [00:00:44.070]The data I use for this is the poster is
from the Nebraska Economic Conditional
- [00:00:48.870]Survey from 2020. This survey
is sent out from
- [00:00:52.940]Bureau of Business Research
- [00:00:54.450]at UNL each month to 500
randomly selected Nebraska businesses.
- [00:01:00.000]Due to the outbreak of pandemic,
- [00:01:02.220]the survey has suspended
from March to June.
- [00:01:05.640]Therefore there will be a gap between
the data I use for this research.
- [00:01:10.170]The County population data I used in
this research is from the United States
- [00:01:14.910]census in 2019.
- [00:01:18.170]I build a logistic regression model
to estimate whether businesses expect the
- [00:01:22.650]dollar sales value to increase
in the next six months.
- [00:01:27.000]The logistic regression equation
has shown in a poster. For the dependent
- [00:01:31.680]variable in the model,
one represents business
- [00:01:34.920]expect the dollar sales value to increase,
- [00:01:38.160]and zero represents business expect
the dollars sales value to decrease or
- [00:01:42.510]stay at the same.
- [00:01:44.550]The independent variable will be the
County population that businesses are
- [00:01:49.770]The control variables inculde
employee size, month,
- [00:01:53.940]and the industry. Due to the
suspension of the survey,
- [00:01:58.230]the month will be separated into
two parts. Month before the survey
- [00:02:03.900]which included the January and the
February will be considered as before the
- [00:02:09.090]month after June will be
considered as post-pandemic month.
- [00:02:13.770]The industry has been classifying
into four categories.
- [00:02:17.610]The retail and hospitality
industry includes industries
- [00:02:21.570]such as accommodation,
- [00:02:23.400]and retail trade. Healthcare
and a social assistance industry.
- [00:02:28.230]The good producing industry,
- [00:02:29.670]which includes the industries such as
agriculture and the manufacturing.
- [00:02:34.740]Other industries include the industries
such as finance and insurance,
- [00:02:39.720]information, and real estate.
- [00:02:43.680]I used 10% of the
significance level in my results.
- [00:02:48.090]The retail and hospitality,
- [00:02:50.070]and month before pandemic have been
eliminated in the model as the base,
- [00:02:55.500]the results interaction variables are
done by multiplying the variables with
- [00:03:00.430]the post-pandemic month variable,
representing the difference of variables
- [00:03:05.020]between before the pandemic
and after the pandemic.
- [00:03:08.710]The population variable turns out
to be not statistically significant.
- [00:03:13.660]There are two statistically
significant variables in a result.
- [00:03:17.920]the interaction of month
and a good producing industry
and that the interaction of
- [00:03:22.540]month and the employee size.
- [00:03:25.090]the coefficient of these two variables
represents the changes from month
- [00:03:30.010]before and after the pandemic. By
adding these coefficients with
- [00:03:34.990]the coefficient of the
variables without interaction,
- [00:03:38.830]we will know how the industry
performs after the pandemic.
- [00:03:43.360]After adding them up,
- [00:03:44.860]the coefficient for interact month
with employee size will be negative,
- [00:03:49.600]and the coefficient for
interact month with good
- [00:03:52.390]producing industry is still positive.
- [00:03:55.510]The marginal effect represents how
the probability of business expect the
- [00:04:00.400]dollar sales value to increase or
decrease with a one unit change of
- [00:04:05.050]variables. Therefore,
- [00:04:07.780]my conclusion for this poster is the
county's population does not have a
- [00:04:12.260]significant impact on business optimistic
- [00:04:15.370]levels at all. Before the
pandemic, variables in the model
- [00:04:19.840]do not infulence optimism levels.
- [00:04:23.410]The influence of business size on
optimism falls after the onset of
- [00:04:28.360]pandemic while good producing firms'
- [00:04:31.060]optimism rises compared to
the retail and hospitality
- [00:04:37.780]The limitation of my poster
is the relatively low response
rates of the survey.
- [00:04:43.510]It's usually about 20%,
- [00:04:46.120]which might cost response bias
in the data, and might impact,
- [00:04:50.110]my conclusion. So these are what
I have for the presentation today.
- [00:04:55.420]Finally, I want to thank the
Bureau of Business Research and Dr.
- [00:04:58.990]Thompson for his help and
guidance. Thank you for watching.
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