Business Optimism Amid the Covid19 Pandemic

Ningyu Wang Author

04/02/2021 Added

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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 is Ningyu.
[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
[00:01:48.330]located.
[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
[00:02:03.090]stops,
[00:02:03.900]which included the January and the February will be considered as before the
[00:02:07.830]pandemic,
[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
[00:04:35.710]industry.
[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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Business Optimism Amid the Covid19 Pandemic

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