Project 3: The criminality narrative
B. Jones
June 2, 2025
Overview
A common trope in the immigration debate is that undocumented immigrants commit, at high rates, violent crimes. Therefore, the supposition is that migrants who are deported are migrants who have committed serious criminal infractions. This idea is prevalent in political rhetoric surrounding the issue of deportation. But is the claim consistent with the actual data? This is the basis of this short Project 3. This assignment is asking you to analyze real-world data on deportations in the United States between the years 2003 and 2024. The data you access records annual ICE removals (deportations) based on what ICE records as the “Most Serious Criminal Conviction” for someone who is deported. The following information is from TRAC (Transactional Records Access Clearinghouse) and describes what the classification levels mean:
“Seriousness Level of MSCC Conviction. ICE classifies National Crime Information Center (NCIC) offense codes into three seriousness levels. The most serious (Level 1) covers what ICE considers to be”aggravated felonies.” Level 2 offenses cover other felonies, while Level 3 offenses are misdemeanors, including petty and other minor violations of the law. TRAC uses ICE’s “business rules” to group recorded NCIC offense codes into these three seriousness levels.”
Essentially what this loosely means is that “Level 1” convictions are the most serious and “Level 3” convictions are generally minor legal infractions. In addition to Levels 1-3, there is a fourth category called “None” denoting that the deportee had no criminal convictions. Review the Patler and Jones article, especially the section on the criminality narrative. An HTML file of your results are due on Canvas by June 12 at 11:59 PM. No extensions will be granted and late submissions will not be graded. Rmd-only submissions will not be graded. This assignment is worth 300 points.
reasons="https://raw.githubusercontent.com/mightyjoemoon/POL51/main/ICE_reasonforremoval.csv"
reasons<-read_csv(url(reasons))
summary(reasons)## Year President All None
## Min. :2003 Length:22 Min. : 56882 Min. : 19495
## 1st Qu.:2008 Class :character 1st Qu.:178148 1st Qu.: 85446
## Median :2014 Mode :character Median :238765 Median :106426
## Mean :2014 Mean :248987 Mean :122287
## 3rd Qu.:2019 3rd Qu.:356423 3rd Qu.:165287
## Max. :2024 Max. :407821 Max. :253342
## Level1 Level2 Level3 Undocumented
## Min. : 9819 Min. : 3846 Min. : 11045 Min. :10100000
## 1st Qu.:38484 1st Qu.: 9056 1st Qu.: 34978 1st Qu.:10500000
## Median :46743 Median :17480 Median : 63186 Median :11050000
## Mean :46534 Mean :15601 Mean : 64541 Mean :11015455
## 3rd Qu.:57148 3rd Qu.:20342 3rd Qu.: 90950 3rd Qu.:11375000
## Max. :75590 Max. :29436 Max. :130251 Max. :12200000
## ER_Non
## Min. : 4018
## 1st Qu.:28563
## Median :41647
## Mean :38980
## 3rd Qu.:50230
## Max. :71686Task 1
The following is a line plot of the four levels of criminality (Levels 1-3 and None). First add proper labels to each axis and give a main title. Next, provide a thorough interpretation of the plot that is non-mechanical and substantive. If you were conveying the information from this plot to an audience interested in understanding deportation, what would you say? This task is worth 100 points.
ggplot(reasons, aes(x = Year)) +
geom_line(aes(y = None, color="None"), size=.6) +
geom_line(aes(y = Level1, color="Level 1"), size=.6, linetype=1) +
geom_line(aes(y = Level2, color="Level 2"), size=.6, linetype=1) +
geom_line(aes(y = Level3, color="Level 3"), size=.6, linetype=1) +
labs(title="",
y="", x="",
color="Severity of criminal record") +
theme_classic() +
theme(#panel.grid.major.y = element_line(colour = "grey", linetype = "dashed"),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9, angle=45, hjust=1),
axis.ticks = element_blank(),
plot.caption=element_text(hjust=0, size=10),
legend.position=c(.85,.98),
legend.justification=c("right", "top"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8),
plot.title = element_text(size=12)) 
Task 1 answer goes here
this graph shows us how US immigration has changed among the years. In the 2000s surprisingly people with no criminal record were deported the most. This shows that immigration is driven by citizenship status and not criminal history but the decline came in the 2010s for the non criminal immigrants and the reason for that is because the Obama administration switched their priority to immigrants that could be a threat to american society. However all levels dropped in 2020 which is obviously because of the 2020 pandemic but then it started rising a little bit which could be because things were going back to normal. Overall This graph shows us that it usually doesn’t matter if the immigrant is a law abiding citizen they will get deported no matter what which tell us that the graph shifts by priorites by administration and public opinion.
Task 2: Regression
For this task you will create three new variables from existing ones in the data set.
First, create a new variable called “minor” that sums all deportations associated with no criminal conviction (“None”) and Level 3 convictions. These are the deportations associated with minor or no criminal activity.
Second, compute the percentage of all deportations that are “minor” deportations (i.e. \(100 \times \frac{None + Level~3}{None + Level~1 + Level~2 + Level~3}\)). Call this variable “percent_minor.”
Third, center the variable using 2014 as the basis year (how to do this will be discussed in class). Name this variable “time.”
Fourth, estimate a linear regression model using the variable percent_minor as the dependent variable and the variable time as the independent variable. Provide an interpretation of the regression results including presenting the results visually using plot_model. What do we learn about the criminality narrative based on these results. This task is worth 100 points.
library(tidyverse)
library(sjPlot)
df <- read_csv("ICE_reasonforremoval.csv")
df <- df %>%
mutate(minor = None + Level3)
df <- df %>%
mutate(percent_minor = 100 * (minor / (None + Level1 + Level2 + Level3)))
df <- df %>%
mutate(time = Year - 2014)
model <- lm(percent_minor ~ time, data = df)
summary(model)##
## Call:
## lm(formula = percent_minor ~ time, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.0161 -1.9580 0.9281 3.0110 12.4647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 73.70904 1.41128 52.228 <0.0000000000000002 ***
## time 0.04559 0.22176 0.206 0.839
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.599 on 20 degrees of freedom
## Multiple R-squared: 0.002109, Adjusted R-squared: -0.04779
## F-statistic: 0.04227 on 1 and 20 DF, p-value: 0.8392plot_model(model, type = "pred", terms = "time") +
labs(
title = "Percent of Deportations That Are Minor vs. Time (Centered on 2014)",
x = "Years Since 2014",
y = "Percent Minor Deportations"
)
Task 2 answer goes here
As we see in the graph the line is nearly straight and the slope is close to 0 since there isnt much change in the graph and since there is not strong upward or downward movement that shows there is no trend. The shaded area is the confidence interval and as we see the shaded area is pretty big which shows there is no confidence which usually means that its statistically insiginficant. Overall this means that there is no trend in the percent of minor deportations among the years.
Task 3: Presidential differences
Are there differences in criminality levels of deportees by President? This is the question you will answer here. To do this, create a factor-level variable denoting each President. In the data set, there is a variable called “President” and records each president as: “Bush1”, “Bush2”, “Obama1”, “Obama2”, “Trump”, “Biden.” Estimate a regression model treating this factor-level variable as the indpendent variable and “minor” as the dependent variable. What do the results show? Provide an interpretation of the regression results, including a plot of the regression model. This task is worth 100 points.
library(tidyverse)
library(sjPlot)
df <- read_csv("ICE_reasonforremoval.csv")
df <- df %>%
mutate(
minor = None + Level3,
President = factor(President) # Step 2: Make President a factor
)
model_president <- lm(minor ~ President, data = df)
summary(model_president)##
## Call:
## lm(formula = minor ~ President, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -81672 -34615 -1716 23250 106254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 70133 24282 2.888 0.01070 *
## PresidentBush1 42788 42057 1.017 0.32411
## PresidentBush2 138545 34339 4.035 0.00096 ***
## PresidentObama1 232286 34339 6.764 0.00000455 ***
## PresidentObama2 144434 34339 4.206 0.00067 ***
## PresidentTrump 105165 34339 3.063 0.00744 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48560 on 16 degrees of freedom
## Multiple R-squared: 0.7672, Adjusted R-squared: 0.6945
## F-statistic: 10.55 on 5 and 16 DF, p-value: 0.0001288plot_model(model_president, type = "est") +
labs(
title = "Differences in Minor Deportations by President",
x = "President",
y = "Effect on Number of Minor Deportations"
)
Task 3 answer goes here
this plot shows the average minor deportartions under each presidental term. Bush1 is being used as the basline. The red dot represents how each term differs from bush1. The horizontal lines represents confidence intervals. Bush 2 and Obama 1 shows a solid increase compared to Bush1. Obama 2 continues that trend with having one of the highest levels. However even though trump was strong on deportations it seems as though trump actually had a decline in minor transportation. This could be because trump administraion focused on immigrants with actual serious criminal records.
works cited The Obama Record on Deportations: Deporter in Chief or Not? By Sarah Pierce Year: 2017 Container: Migration Policy Institute URL: https://www.migrationpolicy.org/article/obama-record-deportations-deporter-chief-or-not or