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<!DOCTYPE html>
<html>
<head>
<title>When is the State of the Union delivered?</title>
<meta charset="utf-8">
<meta name="author" content="Gina Reynolds" />
<meta name="date" content="2019-06-08" />
<link rel="stylesheet" href="xaringan-themer.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# When is the State of the Union delivered?
## ⚔ <br/>with xaringan and xaringanthemer
### Gina Reynolds
### 2019-06-08
---
```
## [1] "xaringan-themer.css"
```
---
# Abstract
- State of the Union addresses are a classic corpus for students of text analysis.
- In this paper you'll see time-in-year by year plots and a model of the timing.
---
- Motivation for addressing this question actually comes from Kenneth Benoit who conducted a quick [analysis](https://twitter.com/kenbenoit/status/1088304778088566785) on this question.
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Far from unprecedented for a <a href="https://twitter.com/hashtag/stateoftheunion?src=hash&amp;ref_src=twsrc%5Etfw">#stateoftheunion</a> <a href="https://twitter.com/hashtag/SOTU?src=hash&amp;ref_src=twsrc%5Etfw">#SOTU</a> address to be held in Feb or to be delivered written. And if written, likely to use more sophisticated language! Details in my forthcoming <a href="https://twitter.com/AJPS_Editor?ref_src=twsrc%5Etfw">@AJPS_Editor</a> paper with <a href="https://twitter.com/kmmunger?ref_src=twsrc%5Etfw">@kmmunger</a> <a href="https://twitter.com/arthur_spirling?ref_src=twsrc%5Etfw">@arthur_spirling</a>. <a href="https://t.co/NKhqkjLoM8">https://t.co/NKhqkjLoM8</a> <a href="https://t.co/7Rb7XLT8FW">pic.twitter.com/7Rb7XLT8FW</a></p>&mdash; Kenneth Benoit (@kenbenoit) <a href="https://twitter.com/kenbenoit/status/1088304778088566785?ref_src=twsrc%5Etfw">January 24, 2019</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
---
# Introduction
The 2019 government shutdown had many people wondering, when will US State of the Union address be delivered by the President. Would it be pushed later than what is considered "normal."
---
# Literature Review
The texts of the State of the Union addresses have been studied by scholars including Benoit, Munger and Spirling; they offer an interesting analysis of State of the Union addresses by US presidents, noting that SOTU that are not delivered orally tend to use more complex language. There are several R text analysis packages . These entries are included in the literature/bibliography.bib document that is referenced in the YMAL.
[SR16; BN16; BMS18]
> In this section you might write quota a large selection which you will want to indent.
---
# Theory and Hypotheses
This analysis was exploratory. It is well known that modern SOTU addresses are delivered early in the year. After plotting the data, what seemed to matter was year of delivery, and if the SOTU followed an election year. We tried to follow the advice in the e-book [The Fundamentals of Data Visualization](https://serialmentor.com/dataviz).
Data visualization categories from *"The Fundamentals of Data Visualization."*
Category | Description
-----|------
Bad | Misrepresents data or confuse
Ugly | Not pleasing
Good | Not bad and not ugly
---
# Data
Last 8 presidents:
<table>
<thead>
<tr>
<th style="text-align:left;"> President </th>
<th style="text-align:right;"> Number of Addresses </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> Donald J. Trump </td>
<td style="text-align:right;"> 3 </td>
</tr>
<tr>
<td style="text-align:left;"> Barack Obama </td>
<td style="text-align:right;"> 8 </td>
</tr>
<tr>
<td style="text-align:left;"> George W. Bush </td>
<td style="text-align:right;"> 8 </td>
</tr>
<tr>
<td style="text-align:left;"> William J. Clinton </td>
<td style="text-align:right;"> 8 </td>
</tr>
<tr>
<td style="text-align:left;"> George Bush </td>
<td style="text-align:right;"> 4 </td>
</tr>
<tr>
<td style="text-align:left;"> Ronald Reagan </td>
<td style="text-align:right;"> 8 </td>
</tr>
<tr>
<td style="text-align:left;"> Jimmy Carter </td>
<td style="text-align:right;"> 7 </td>
</tr>
<tr>
<td style="text-align:left;"> Gerald R. Ford </td>
<td style="text-align:right;"> 3 </td>
</tr>
</tbody>
</table>
---
class: center
# Analysis
---
## Visualization of relationships
<div class="figure" style="text-align: center">
<img src="figures/sotu_timing_delivery.png" alt="Timing of state of the union by year. SOTUs that follow election years are colored with blue." width="\textwidth" />
<p class="caption">Timing of state of the union by year. SOTUs that follow election years are colored with blue.</p>
</div>
<!-- In figure \@ref(fig:year) we use LOESS smoothing to summarize the relationship between year and time in year for the categories "following election year" and "not following". In the linear modelling exercise below, we remove the 1973 cases, given that they are so unusual --- in light of the Nixon impeachment, there were multiple SOTU's delivered that year. -->
---
<!-- If compiling to html: -->
<!-- We load the models that we've estimated and save out,then prepare the table using stargazer. -->
<table style="text-align:center"><caption><strong>Models of time elapsed in year before State of the Union Address</strong></caption>
<tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4">Days elapsed since January 1st</td></tr>
<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
<tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">year</td><td>0.401<sup>***</sup></td><td></td><td>0.408<sup>***</sup></td><td>0.320<sup>***</sup></td></tr>
<tr><td style="text-align:left"></td><td>(0.038)</td><td></td><td>(0.035)</td><td>(0.036)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
<tr><td style="text-align:left">post election</td><td></td><td>7.700<sup>**</sup></td><td>8.654<sup>***</sup></td><td>-653.628<sup>***</sup></td></tr>
<tr><td style="text-align:left"></td><td></td><td>(2.991)</td><td>(1.894)</td><td>(139.861)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
<tr><td style="text-align:left">year*post election</td><td></td><td></td><td></td><td>0.335<sup>***</sup></td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.071)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
<tr><td style="text-align:left">Constant</td><td>-773.950<sup>***</sup></td><td>17.300<sup>***</sup></td><td>-789.534<sup>***</sup></td><td>-615.478<sup>***</sup></td></tr>
<tr><td style="text-align:left"></td><td>(75.412)</td><td>(1.511)</td><td>(68.468)</td><td>(71.731)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
<tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Notes:</em></td><td colspan="4" style="text-align:right"><sup>***</sup>Significant at the 1 percent level.</td></tr>
<tr><td style="text-align:left"></td><td colspan="4" style="text-align:right"><sup>**</sup>Significant at the 5 percent level.</td></tr>
<tr><td style="text-align:left"></td><td colspan="4" style="text-align:right"><sup>*</sup>Significant at the 10 percent level.</td></tr>
</table>
R^2 is 0.704.
---
The full model formula with the interaction is:
$$ DaysSinceJan1 = $$
$$\beta_0 + \beta_1YEAR + B_2FollowingElection + B_3Year*FollowingElection + \epsilon $$
---
## Visualization
<div class="figure" style="text-align: center">
<img src="figures/sotu_timing_model.png" alt="Timing of state of the union by year. SOTUs that follow election years are colored with blue." width="\textwidth" />
<p class="caption">Timing of state of the union by year. SOTUs that follow election years are colored with blue.</p>
</div>
---
# Conclusion
- State of the Union addresses, in general are being delivered later and later
--
- SOTU addresses are delivered later if they follow an election year
--
- The 2019 State of the Union timing is not a unusual departure from the trend
---
# References
Benoit, K, K. Munger and A. Spirling (2018). "Measuring and
Explaining Political Sophistication through Textual Complexity".
In: _Available at SSRN 3062061_.
Benoit, K. and P. Nulty (2016). "quanteda: Quantitative analysis
of textual data". In: _R package version 0.9_ 8.
Silge, J. and D. Robinson (2016). "tidytext: Text mining and
analysis using tidy data principles in r". In: _The Journal of
Open Source Software_ 1.3, p. 37.
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