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.:178149   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.:38485   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.:20343   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.   :71686

Task 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" = "maroon", 
            "b. Level 1 conviction" = "purple", 
            "c. Level 2 conviction" = "navy",
            "d. Level 3 conviction"="pink")


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 are of People with \nMinor or No Criminal Convictions",
     y="Number of Deportations", x="Year",
     color="") +
  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.

The United States has a unique and complex immigration system, in which deportations have increased in recent years due to various political policies that have increasingly intertwined the immigration system with the criminal justice system. Between 1892 and 1995, there was an average of 17,000 deportations annually, however between 2001 and 2022, there has been almost 6.5 million removals(Patler and Golash-Boza 2017). In addition, 80% of all deportations have occurred since the mid 1990s.

Although the U.S. has had a long history of forced removals before this time, the rapid increase can be attributed in large part to “crimmigration”(Stumpf 2006), which is defined by Patler and Jones as an integration of some of the most punitive facets of immigration and criminal laws. Since the 1980s, various presidents have implemented policies that accelerated crimmigration. During the Reagan administration, the Anti-Drug Abuse Acts of 1986 and 1988 allowed for information sharing and requests to detain between local law enforcement and immigration officials for immigrants arrested or convicted of drug offences (referred to as 287(g) agreements). ADAAII also created the category of aggravated felonies, which only exist in immigration law. Crimes that fall in to this category do not actually need to be aggravated or felonies, and have expanded to include many minor crimes such as theft, or failing to appear in court (American Immigration Council 2021). The Clinton administration limited previously existing protections from removal, limited due process protections for noncitizens, and expanded types of deportable offenses. With crimmigration further entrenched into the legal system, and more legal pathways to citizenship restricted, deportations continued to increase.

After September 11, 2001, the Homeland Security Act created Immigration and Customs Enforcement(ICE) to enforce immigration law and consolidated all immigration matters under the Department of Homeland Security, including immigration courts. The Bush and Obama administrations used this consolidation to enforce mass deportations, expedited removals, and further cooperation between local law enforcement and ICE. In 2008, the Secure Communities Program from the Obama administration allowed data-sharing between federal, state, and local law enforcement agencies, and was implemented with widespread use. The effects of these policies can be seen in the graph, with the maroon line beginning to increase during the Bush administration, and peaking around 2008 or 2009, where 250,000 people with no criminal convictions were deported.

Those with a level 3 conviction, the lowest level of offense(designated by the pink line), follow those with no convictions as the second group with the most deportations. The acceleration of crimmigation has made it easier to deport noncitizens who commit no or minor crimes. The purple line represents those with Level 1 convictions, the most serious offenses. Although this sees a slight peak after 2010, it stays below 75,000, showing that the priority of deportations is not on those who commit the most dangerous crimes.

During the second Obama administration, Secure Communications was phased out, evidenced by the steady decrease in deportations until 2015, as seen in the graph. Under the Trump administration, 287(g) agreements and Secure Communications were reestablished, along with a number of executive orders with the goal of mass deportations. The Biden administration continued many of these policies. From 2015 to 2019, the graph shows an increase in deportations, then a stark decrease, likely due to the Covid-19 pandemic. Around 2021, deportations begin to increase again.

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

Task 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

table(reasons$percent_minor)
## < table of extent 0 >
reasons$percent_minor <- 100 * (reasons$minor) / (reasons$All)

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
ggplot(reasons, aes(x = Year)) +
  geom_line(aes(y = percent_minor), color = "darkgreen") + theme_minimal() +
labs(title="Deportations of those with Minor Convictions Make Up \nOver 50% of Deportations",
     y="Percentages of Deportations", 
     x="Year") +
  scale_x_continuous(breaks = seq(2003, 2024, 3)) +
  scale_y_continuous(limits = c(0,100), breaks = seq(0, 100, 10))

summary(reasons$percent_minor)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   55.01   71.82   74.61   73.68   77.02   86.63

Your substantive interpretation of the plot should be written below this line in the Rmd file.

From 2003 to 2020, over 60% of deportations were of individuals with no or minor convictions. The green line on the graph shows an increase to over 80% around 2007 and 2008, showing the effects of Bush and Obama era policies. When large-scale deportations and communication between law enforcement agencies were prioritized, deportations of those with minor convictions increased. During the first Trump administration, this number increased again to 78%. Almost 1,300 deportation actions were taken by the administration, including efforts to limiting entry for immigrants and refugees and limiting rights of those living in the U.S.(American immigration Council 2017) As stated by Patler and Jones, mass deportation is enabled by policies limiting access to legal pathways.

There was a decrease during Covid-19, although, during the Biden administration, percentages quickly rose again to 85%. Along with continuing many Trump era policies, asylum claims were greatly restricted, fundamentally changing how claims could be made, and creating an extensive backlog in the system.

Throughout both Democratic and Republican presidencies, there have been deliberate efforts to increase deportations as well as limit lawful avenues for entry and citizenship. The line graph shows how the policies implemented by Presidents have affected the percentage of deportees with minor criminal convictions.