Project 1: The criminality narrative part 1
Kelly Zamudio
October 20, 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 :11015454
## 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" = "darkblue",
"b. Level 1 conviction" = "blue",
"c. Level 2 conviction" = "coral3",
"d. Level 3 conviction"="orange3")
figure1<-ggplot(reasons, aes(x = Year)) +
geom_line(aes(y = None, color="a. No criminal conviction"), size=1) +
geom_line(aes(y = Level1, color="b. Level 1 conviction"), size=1) +
geom_line(aes(y = Level2, color="c. Level 2 conviction"), size=1) +
geom_line(aes(y = Level3, color="d. Level 3 conviction"), size=1) +
scale_color_manual(values = colors) +
scale_y_continuous(labels = label_comma()) +
labs(title="Most deportations of non-criminal or level-3 immigrants",
y="Number of Deportations ", x="Year",
color="") +
theme_bw()+
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=.2, size=10),
legend.position=c(.85,.98),
legend.justification=c("right", "top"),
legend.title = element_text(size = 6),
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.
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$minor<- reasons$None+reasons$Level3
reasons$minor## [1] 88539 112598 48104 31292 132293 210641 195629 162631 175757 169200
## [11] 238791 274523 306239 294292 289610 319536 314932 236852 155922 127006
## [21] 120172 105670Task 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$percent_minor <- 100 * (reasons$minor)/(reasons$None + reasons$Level1 + reasons$Level2 + reasons$Level3 )
reasons$percent_minor## [1] 86.62968 77.34548 65.50823 55.01213 73.29415 78.06636 76.25702 72.44046
## [9] 72.85023 71.61238 74.79375 74.43501 75.09152 75.23456 75.44933 79.58536
## [17] 81.64973 78.91384 73.57414 69.30259 67.73039 66.32105Task 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
ggplot(reasons,aes(x=Year))+geom_line(aes(y=percent_minor), color="blue") +theme_classic()+ labs (title= "Proportion of Non-Criminal Deportations from 2003 - 2024 ", y= "People", x= "Years") + scale_x_continuous(breaks= seq(2003,2024,2)) +
scale_y_continuous (limit=c(0,100), breaks=seq(0,100,10)) +
theme(plot.caption = element_text(hjust = 0)) +
geom_vline(xintercept = c(2020, 2022), color="gray", linetype=2)
Your substantive interpretation of the plot should be written below this line in the Rmd file.
As stated in the article by The California Aggie, A surge in deportation efforts impacts communities. “The vilification of undocumented immigrants in the U.S. has grown into vast, punitive… systems with devastating fiscal, emotional and social costs.” (Authored by Graciela Tie, Interviewee Kelly Zamudio “Student one”)
The Immigration Reform and Control Act of 1986, passed during the presidency of Ronald Reagan, was a comprehensive immigration reform. It sought to provide a pathway to citizenship for undocumented migrants who entered the US before January 1982 and had not left the country, and were able to apply for legal assistance. However, this provision also imposed per-country limits on migration, which led to the creation of Crimmigration. The term “Crimigration” has been used to describe the relationship between the criminal justice system and immigration. Before 1965, there were no restrictions on the Western Hemisphere; there was no cap on the number of individuals who could come to the United States. As stated in the lecture, The Politics of Immigration, Part 1, “ Immigration caps are woefully too small to handle demand, which means that even if there were a line, the waiting time in that line would be massive”. (Jones) However, I do want to preface that this amnesty form did not incentivize individuals to migrate into the United States, but a 10-20-year citizenship process doesn’t seem feasible or sustainable, as demand, labor, and potential family ties do not disappear, leading many to “illegally” migrate.
Following the Immigration Reform and Control Act of 1986 was the implementation of the Anti-Drug Abuse Acts of 1986 (ADAA I) and 1988 (ADAA II). As stated by the piece, “The US Deportation System: History, Impacts, and New Empirical Research,” by Caitlin Patler and Bradford Jones, the ADAA I was a foundational point for establishing a relationship between local law enforcement and immigration officials. (Patler & Jones) If an immigrant was arrested and convicted of a drug offense, an immigration official could take the individual into custody. Although the ADAA II further expanded the criminalization of immigrants, as it inserted different classifications of punishable offenses for minor crimes such as theft, battery, and tax fraud. As stated in the lecture, The Politics of Immigration, Part 1, “the notion of ‘aggravated felonies,’ a special class of offenses defined only in US immigration law that carry harsh immigration consequences.” (Jones)
In 1996, the Illegal Immigration Reform and Immigrant Responsibility Act (IIRAIRA) was enacted to further strengthen immigration laws. The IIRAIRA, increased the types of criminal offenses for which an immigrant could be deported. Within this provision, individuals who committed a minor offense would face criminal repercussions that citizens would not. One of the most notable examples is enforcement programs such as the 287(g) Agreements between local law enforcement and federal immigration enforcement, in which they agree to detain someone until ICE can come to pick them up, as stated in the presentation, “The attorney general may enter into a written agreement with a State, or any political subdivision of a State, pursuant to which an officer or employee of the State or subdivision, who is determined by the Attorney General to be qualified to perform a function of an immigration officer” (Jones) With the rise of enforcement, workplace raids became a common form of enforcing the provisions. Additionally, the IIRAIRA implemented a new category of removal, called the expedited removal process, in which noncitizens are deported without due process.
Although these programs made immigrants, documented and undocumented, vulnerable to the legal system, the 9/11 attacks shifted immigration policy. The USA Patriot Act of 2001 and the National Security Entry-Exit Registration System (NSEERS) required male immigrants over the age of 16 from certain countries in the Middle East and North Africa to register with the U.S. government. Thousands of these individuals were subsequently detained, and many of them were deported for reasons having nothing to do with terrorism. Following these acts, in 2003, the Bush Administration established the Department of Homeland Security (DHS), within this department, three offices were created as well: Immigration and Customs Enforcement (ICE), U.S Customs and Border Protection (CBP), and the U.S. Citizenship and Immigration Services (CIS). Following the establishment of these departments, the Bush and Obama administrations continued to utilize 287g agreements for immigrant removals. The Obama administration even created the Secure Communities Act during his second presidential term. Secure Families was similar to the 287g program; however, there were three different levels of arrests (most severe to least severe).
Using the policy context as a framework, we can begin to observe that the removal of immigrant individuals began to increase during the 1990’s as pictured in Figure 1 of the Patler and Jones paper. When utilizing the data from the Department of Homeland Security, there is a visible increase ranging from the 1990s to the 2000s. However, there was a significant increase in deportations after 2001. We see that this trend continues until the year 2020, given the 2020 COVID-19 pandemic, in between the two gray lines in Figure 2.3, which had an almost stagnant movement of both DHS employees and individual immigration patterns. Using the data points provided and the line plot shows a revelation that the Obama Administration had the most deportations than any other administration: Bush (1st & 2nd term), 1st Trump (1st term), and Biden. When incorporating the policy frameworks, the data also shows that these enforcement provisions made it effortless to remove immigrant individuals.