Impact of China's Retaliatory Tariffs on Democratic Vote Share Between 2016 and 2018

Bhabishya Neupane Author

04/05/2021 Added

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This research takes a look into the impacts of China's retaliatory tariffs on Democratic Vote Share between 2016 and 2018. Link to the presentation: https://nbhabish-bbrpresentation.netlify.app/#1

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[00:00:02.520]Hi there. I'm Bhabishya Neupane with
[00:00:04.620]the Bureau of business research at the University of Nebraska-Lincoln,
[00:00:08.520]I'm a senior majoring in math and economics.
[00:00:11.640]And the topic of my research for Nebraska Research Day is impact of China's
[00:00:16.200]retaliatory tariffs on democratic vote share between 2016 and
[00:00:21.330]2018. Motivation.
[00:00:26.180]So the motivation behind this project was two years back,
[00:00:30.350]I was reading an article on the US and China trade dispute.
[00:00:34.190]The article talked about how China had imposed tariffs on the US's agricultural
[00:00:37.970]products, especially soybean.
[00:00:40.760]And as soon as I was done with the article,
[00:00:43.130]I realized that China's retaliation might have had some impact
[00:00:46.610]in the midterm election of 2018,
[00:00:49.100]given how the majority of soybean producing States supported president
[00:00:53.930]Trump in the presidential election of 2016.
[00:00:57.350]So this got me quite interested in knowing if there was any impact and the
[00:01:01.070]midterm election of 2018
[00:01:05.870]Research question. The research question for this project is
[00:01:09.590]following China's retaliatory tariffs, before the midterm election,
[00:01:13.580]were soybean producing counties,
[00:01:15.230]more likely to see shifts in the vote share toward democratic candidates.
[00:01:19.940][Inaudible].
[00:01:23.810]Method. To run our analysis
[00:01:26.810]We decided to use ordinary least square regression.
[00:01:30.170]We have Change in democratic vote share from 2016 to 2018
[00:01:35.000]as our dependent variable. And for our independent variables,
[00:01:39.800]we have percent change in soybean production from 2018 to
[00:01:44.030]2019,
[00:01:46.100]percent change in corn production from 2018 to 2019 median household
[00:01:50.990]income in 2018 unemployment rate in 2018
[00:01:56.180]acres of soybean planted in 2018 and Change in
[00:02:00.620]Republican vote share from 2008 to 2010,
[00:02:05.930]Data Sources. Collected data are all County level data
[00:02:11.120]Corn and soybean data came from the United States department of agriculture
[00:02:17.030]Election data for house of representatives came from each state's,
[00:02:20.810]secretary of state website,
[00:02:24.020]the median household income and unemployment rate data came from American
[00:02:28.040]community survey and Bureau of labor statistics.
[00:02:31.160][pause]
[00:02:37.310]Model. So this is what our model looks like on the first model.
[00:02:42.110]We haven't controlled for historical voting patterns.
[00:02:44.660]Whereas on the second model, we have controlled for historical voting patterns.
[00:02:49.370]I'll stay on the slide for a few seconds.
[00:02:52.160][pause]
[00:03:02.760]Results. Looking at our results.
[00:03:05.100]We observe a statistically significant negative coefficient for percent change in
[00:03:10.020]soybean production from 2018 to 2019, in both of our models,
[00:03:15.750]this implies that change in soybean production from 2018 to 2019,
[00:03:20.010]had an effect on the change in democratic vote share. However,
[00:03:24.030]when controlling for historical voting patterns,
[00:03:26.370]this effect was slightly reduced,
[00:03:33.330]predicting change in democratic votes share from 2016 to 2018.
[00:03:39.060]So most of the counties that produce soybean at high levels,
[00:03:42.930]saw a decrease in the soybean production in 2019 after the tariffs were imposed.
[00:03:49.110]So to put things in perspective
[00:03:52.260]We just observed a statistically significant negative coefficient for percent change in
[00:03:57.030]soybean production from 2018 to 2019 in the previous slide.
[00:04:02.700]Now,
[00:04:03.570]if we were to take a negative percent change in soybean production value
[00:04:08.490]from the table on the left and plug it into the equation below,
[00:04:13.320]we will observe a positive value since negative beta coefficient is mulitplied
[00:04:17.550]with a negative value and like mentioned on the slide.
[00:04:21.570]This means if there was a decrease in soybean production from 2018 to 2019,
[00:04:26.400]then we would observe a positive association with change in democratic votes share
[00:04:30.750]from 2016 to 2018.
[00:04:33.750][pause]
[00:04:38.520]Limitations. Talking about the limitations,
[00:04:41.820]we had a couple of limitations in our research,
[00:04:45.060]missing production and data for many counties in 2018 and 2019 limited,
[00:04:50.040]the number of observations in our analysis,
[00:04:53.190]We also did a follow-up analysis,
[00:04:54.810]and it showed that there were no systematic differences between counties
[00:04:58.110]with and without missing data. Also,
[00:05:01.260]the difference in voter turnout from Presidential to midterm elections were
[00:05:05.400]not accounted for in the model. And finally,
[00:05:09.990]the model only contents data from 4s out of 50 States
[00:05:17.700]Conclusion. So to conclude my presentation,
[00:05:21.180]like we observed Changes in soybean production from 2018 to
[00:05:25.680]2019,
[00:05:26.400]had an effect on the Change in democratic vote share from 2016 to
[00:05:31.260]2018. A decrease in soybean production from 2018 to
[00:05:35.580]2019 is associated with an increase in democratic vote share. There,
[00:05:40.740]this effect is slightly reduced when historical
[00:05:42.870]voting patterns are controlled for. Historical voting patterns.
[00:05:47.220]the controlled variable, is accounted for by the Change in Republican vote share from 2008
[00:05:52.170]to 2010 in the model. Likewise, when controlling for historical voting patterns,
[00:05:57.740]the median household income was able to explain some of the variance in the
[00:06:01.430]dependent variable. Finally,
[00:06:05.650]the model is able to explain very little about the variance in Change in
[00:06:09.790]democratic vote share from 2016 to 2018,
[00:06:14.560]since the r-square is very.
[00:06:15.880]Low.
[00:06:18.640]Acknowledgements, I want to take this time to,
[00:06:21.340]thanks Dr. Herian and Dr. Jarrett for helping me with this
[00:06:25.870]project. And Thank you so much for everyone who is watching this,
[00:06:31.180]have a good one.

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Impact of China's Retaliatory Tariffs on Democratic Vote Share Between 2016 and 2018

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