Project 1: The criminality narrative part 1
Miguel Greenberg
October 9, 2025
Overview
This project will allow you to access data on deportations in the United States from 2003 to 2024. In your analysis, you will be able to assess several claims that have been made regarding deportation. Are common narratives about deportation sustainable given the observed data? This is what social scientists do: we make, or attempt to make, evidence-based claims. The tasks I am asking you to do here are portable to any (or most any) data set you might encounter whether it is in international relations, comparative politics, economics, sociology, and so forth. The “POL 51” aspect of this assignment is to give you hands-on experience in interpreting plots, univariate statistics, and rudimentary hypothesis testing. In class, we will cover this extensively. I have assigned an article by Patler and Jones and it is posted on Canvas. It is expected you read this article in advance of writing up your responses.
Project 1 will roll out in two parts. This is the first and it is worth 400 points. The second part will be worth 400 points for a total of 800 points for full project. You will submit the HTML file you generate on Canvas by October 20. The second part of the project will be due by October 31.
Part 1: The Criminality Narrative
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?
Part 1 of 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 “NoneER” denoting that the deportee had no criminal convictions.
This chunk of code will access the data set.
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: Creation and interpretation of a line plot
The following is a shell of a line plot of the four levels of criminality (Levels 1-3 and None).
First add code to produce a publication-quality plot.
Second, provide a thorough substantive interpretation of the plot. This interpretation will require proper citation of research relevant to the criminality narrative.
If you were conveying the information from this plot to an audience interested in understanding deportation, what would you say? And perhaps, more importantly, what would you not say?
Mechanical answers, short answers, or answers that do not display a substantive understanding of the issue will receive low scores even if the mechanical interpretation is correct.
At minimum, I would expect a serious, substantive interpretation of this plot to be 2 to 3 paragraphs minimum.
The quality of the plot is worth 100 points and write-up/analysis is worth 100 points. This gives a total of 200 points.
#This is the chunk where you will edit this plot to produce something that looks useful
colors <- c("a. No criminal conviction" = "red",
"b. Level 1 conviction" = "gold",
"c. Level 2 conviction" = "green4",
"d. Level 3 conviction"="black")
figure1<-ggplot(reasons, aes(x = Year)) +
geom_line(aes(y = None, color="a. No criminal conviction"), size=1.1) +
geom_line(aes(y = Level1, color="b. Level 1 conviction"), size=1.1) +
geom_line(aes(y = Level2, color="c. Level 2 conviction"), size=1.1) +
geom_line(aes(y = Level3, color="d. Level 3 conviction"), size=1.1) +
scale_color_manual(values = colors) +
scale_x_continuous(breaks = seq(2003, 2024, 3)) +
scale_y_continuous(labels = label_comma()) +
labs(title="Vast majority of deportations between 2003 and 2024 were of\npeople with no criminal conviction",
y="Number of deportations", x="Year",
color="Level of conviction") +
theme_classic() +
theme(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, hjust = .7),
plot.caption=element_text(hjust=0),
legend.position=c(.9,.98),
legend.justification=c("right", "top"),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8),
plot.title = element_text(size=12))
figure1
Your substantive interpretation of the plot should be written below this line in the Rmd file.
The United States has a long history of deporting immigrants, which dates back to the end of the 18th century. Since then, a series of laws have been enacted that have granted the government increased power to deport immigrants. This power especially spiked in the mid-1990s when criminal law and immigration enforcement merged, a phenomenon referred to by Strump (2006) and other scholars as “crimmigration” (cited in Patler and Jones, 2025). Crimmigration was made possible largely through the passing of the 287g Agreements (part of the Illegal Immigration Reform and Immigrant Responsibility Act of 1996), which allowed immigration and law enforcement officials to coordinate with each other. This greatly jeopardized the legal status of many immigrants in the US.
These acts were especially taken advantage of following the attacks of 9/11, which led to the creation of the Department of Homeland Security and Immigration and Customs Enforcement (ICE) in 2003, which largely emphasized immigration enforcement as being essential to the national security of the US. The attacks also contributed to the large, pre-existent stigma that surrounds immigrants. As a result of this, deportations in the US grew exponentially. The belief that immigrants are highly associated with crime has since been instilled across the country and further emphasized by political figures such as Donald Trump (Patler and Jones 2025). If this narrative pertaining to immigrants and crime were in reality true, we would expect to see that a large number of immigrants deported from the United States had serious criminal convictions; however, the preceding data says otherwise.
The visual above takes data from the Department of Homeland Security and displays the number of deportations for 3 levels of criminal convictions (1 being high-level crimes, 2 being medium-level crimes, and 3 being low-level crimes) and for deportees with no criminal conviction from 2003 to 2024. One thing immediately clear in the graph is that the red line, which represents deportations of immigrants with no criminal conviction, is constantly above the other three lines throughout the graph (except for 2021, when the red line is level with the yellow line), illustrating that the vast majority of deportations within the given time frame were of immigrants with no standing criminal conviction. The black line, which represents deportees with level 3 convictions (low-level crimes), has the second most removals over the time frame. Between 2003 and 2024, there were 75,590 level 1 convictions (high-level), and between level 2 convictions, level 3 convictions, and deportees with no criminal conviction, there were 413,029 deportations (almost five and a half times more than level 1 convictions). Between only deportees with level 3 convictions and no convictions, there were 383,593 deportations which is just above 5 times the amount of deportations of immigrants with level 1 convictions. While level 1 convictions were present among immigrants, there were very little in proportion to immigrants with lesser convictions, especially immigrants with no convictions at all, and the extent of level 1 convictions among immigrants was very far from that associated with the criminality narrative.
The plot illustrates that the majority of immigrants deported between 2003 and 2024 were not criminals and that the amount of immigrants who had committed level 1 crimes was proportionately small compared to the immigrant population and especially compared to the way immigrants are represented in the false criminality narrative. Clearly, immigrants are not highly associated with crime; in fact it’s quite the opposite. Crime rates actually go down as more immigrants move to the US, and mass deportation of immigrants has not been found to decrease crime in the US (Patler and Jones 2025). Put plainly, the data shows that immigrants are not the problem.
Task 2: Understanding deportations and criminal offenses
For this task you will create three new variables from existing ones in the data set, create a plot, and provide a substantive interpretation of the plot. The write-up of this plot I would expect to be 1-2 well-written paragraphs. The quality of the plot is worth 100 points and the write-up is worth 100 points for 200 points total.
Task 2.1, 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. Enter the code to do this part of the task in this chunk.
#Insert code for 2.1 here
reasons <- reasons %>%
mutate(minor = None + Level3)
head(reasons)## # A tibble: 6 × 10
## Year President All None Level1 Level2 Level3 Undocumented ER_Non minor
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2024 Biden 102204 77494 9819 3846 11045 11200000 54870 88539
## 2 2023 Biden 145578 88697 23936 9044 23901 11000000 54933 112598
## 3 2022 Biden 73432 35109 18895 6433 12995 11000000 18874 48104
## 4 2021 Biden 56882 19495 19802 5788 11797 10500000 7607 31292
## 5 2020 Trump 180313 78725 29956 18247 53568 10350000 31520 132293
## 6 2019 Trump 269823 117858 37720 21462 92783 10200000 51387 210641Task 2.2, 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 new variable percent_minor.
#Insert the code for task 2.2 here
reasons <- reasons %>%
mutate(percent_minor = (minor/(minor + Level1 + Level2)) * 100)
reasons## # A tibble: 22 × 11
## Year President All None Level1 Level2 Level3 Undocumented ER_Non minor
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2024 Biden 102204 77494 9819 3846 11045 11200000 54870 88539
## 2 2023 Biden 145578 88697 23936 9044 23901 11000000 54933 112598
## 3 2022 Biden 73432 35109 18895 6433 12995 11000000 18874 48104
## 4 2021 Biden 56882 19495 19802 5788 11797 10500000 7607 31292
## 5 2020 Trump 180313 78725 29956 18247 53568 10350000 31520 132293
## 6 2019 Trump 269823 117858 37720 21462 92783 10200000 51387 210641
## 7 2018 Trump 257239 110178 40778 20132 85451 10500000 41665 195629
## 8 2017 Trump 224503 97850 43247 18625 64781 10500000 42504 162631
## 9 2016 Obama2 241258 102673 45149 20352 73084 10700000 46759 175757
## 10 2015 Obama2 236272 96266 46758 20314 72934 11000000 45743 169200
## # ℹ 12 more rows
## # ℹ 1 more variable: percent_minor <dbl>Task 2.3, in the chunk below, create a presentation-grade plot of the variable percent_minor and provide a thorough and substantive interpretation of the plot. Quality of the plot will be scored on a 100-point scale and quality of the write-up will be scored on a 100-point scale.
#Insert code for Task 2.3 here
figure2 <- ggplot(reasons, aes(x = Year, y = percent_minor,)) +
geom_col(fill = "cornflowerblue") +
geom_hline(yintercept = 50, linetype = "dashed") +
scale_x_continuous(breaks = seq(2003, 2024, 1)) +
scale_y_continuous(limits = c(0, 100),
breaks = seq(0, 100, 10),
labels = scales::label_percent(scale = 1)) +
labs(y = "Percentage of Deportees with Minor Convictions",
title = "The Percentage of Deportees with Minor Convictions Between 2003\nand 2024 was Continuously Higher than 50 Percent") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))
figure2
Your substantive interpretation of the plot should be written below this line in the Rmd file.
This plot takes data from the DHS and depicts a new variable: the percentage of deportees that had “minor” convictions from 2003 to 2024 (deportees with no criminal record and those with level 3 convictions). The pooling of deportees with no criminal record and those with level 3 convictions adds a powerful insight to the falsification of the criminality narrative. The narrative suggests that immigrants are highly associated with crime, and especially high-level crimes, as insinuated by President Trump at different times during his political career. For example, in October of 2020, Trump made November 1st the “National Day of Remembrance for Americans Killed by Legal Aliens” (Patler and Jones, 2025). Level 3 convictions however, can be as minor as breaking a traffic law or trespassing private property. Such crimes are very distinct from homicide or sexual assault, which are examples of crimes that have become associated with immigrants. And as mentioned in my interpretation of the previous plot, the number of minor convictions by immigrant deportees was five times the amount of the number of level 1 convictions recorded from 2003 to 2024 (75,590 to 383,593).
The plot makes clear the fact that the percentage of immigrant deportees with minor convictions was above 50% (the majority) every year between 2003 and 2024. In 17 years out of the 22 years represented in the plot, this percentage was above 70%, and it even rose above 80% in 2008 and 2024. Furthermore, the percentage dips down to 55% in 2021 (the lowest value within the given time frame), as seen in the plot. This can be largely attributed to the COVID 19 pandemic as during the pandemic, general US deportation numbers went down (Patler and Jones, 2025), and even with this substantial decrease in deportations of deportees with all levels of criminal convictions, deportees with minor convictions still remained the majority.
This plot vividly depicts the disparity between immigrants who had committed minor crimes and those who had committed major crimes, and furthermore, the disparity between the immigrant criminality narrative in the US and the reality of the situation. Not only is the narrative false, but it is essentially the opposite of what the data shows. Immigrants and especially undocumented immigrants have very little correlation to crime in the US, lesser so than US citizens even, and in fact, crime has been observed to decrease with the increase in immigration to US cities.