Example Project 2: Understanding deportation through the lens of quantitative analysis
student
May 20, 2025
Overview
I have compiled data of deportations by fiscal year from 1948 to 2022. Your job is to use some of the skills we are learning in this class to better understand these data. As such, I will be asking you to engage in a number of tasks requiring the use of \(t\)-tests and simple regression. Your grade will be based on analysis and presentation of the data. This assignment is worth 600 points. It will be due May 30 by 11:59 PM. You need to submit an HTML document or a document that includes code and viewable output.
Reading in the deportation data
This chunk reads in the data on deportations from 1948 to 2022.
urlfile="https://raw.githubusercontent.com/mightyjoemoon/POL51/main/ICE_removals_1948.csv"
remove.1<-read_csv(url(urlfile))
summary(remove.1)## Year Apprehensions President Party
## Min. :1948 Min. : 45336 Length:75 Min. :0.0000
## 1st Qu.:1966 1st Qu.: 444232 Class :character 1st Qu.:0.0000
## Median :1985 Median : 889212 Mode :character Median :0.0000
## Mean :1985 Mean : 852071 Mean :0.4667
## 3rd Qu.:2004 3rd Qu.:1194182 3rd Qu.:1.0000
## Max. :2022 Max. :2584220 Max. :1.0000
##
## PCGdp Decade Deportations VR
## Min. : 1833 Min. :1940 Min. : 5989 Min. : 52383
## 1st Qu.: 4231 1st Qu.:1960 1st Qu.: 17362 1st Qu.: 174562
## Median :18237 Median :1980 Median : 29277 Median : 673169
## Mean :24128 Mean :1978 Mean :109287 Mean : 648029
## 3rd Qu.:40607 3rd Qu.:2000 3rd Qu.:188746 3rd Qu.:1017324
## Max. :77247 Max. :2010 Max. :432334 Max. :1675876
## NA's :2
## Administrative EnforcementReturns Criminal Noncriminal
## Min. : 15072 Min. : 49664 Min. : 61117 Min. : 24666
## 1st Qu.: 44947 1st Qu.: 81191 1st Qu.:114680 1st Qu.:161440
## Median : 60150 Median : 86800 Median :135509 Median :190058
## Mean : 70965 Mean :159377 Mean :139193 Mean :168409
## 3rd Qu.: 85478 3rd Qu.:171374 3rd Qu.:176722 3rd Qu.:215554
## Max. :180266 Max. :523153 Max. :200039 Max. :233846
## NA's :61 NA's :61 NA's :63 NA's :63
## Title 42 Foreign Born Naturalized Noncitizen
## Min. : 206770 Min. : 9619300 Min. :14967828 Min. :20722014
## 1st Qu.: 638922 1st Qu.: 9738100 1st Qu.:17003818 1st Qu.:21671389
## Median :1071074 Median :19767300 Median :19639724 Median :21965584
## Mean : 793937 Mean :23434849 Mean :19752182 Mean :21939190
## 3rd Qu.:1087520 3rd Qu.:36154329 3rd Qu.:22459486 3rd Qu.:22364709
## Max. :1103966 Max. :46182177 Max. :24509131 Max. :22593269
## NA's :72 NA's :7 NA's :57 NA's :57
## Unauthorized population US Population App_lagged
## Min. : 3500000 Min. :146631302 Min. : 45336
## 1st Qu.:10237500 1st Qu.:197636197 1st Qu.: 382740
## Median :10850000 Median :237923795 Median : 885587
## Mean :10168182 Mean :241806480 Mean : 820197
## 3rd Qu.:11375000 3rd Qu.:291456616 3rd Qu.:1183164
## Max. :12200000 Max. :333287557 Max. :1865379
## NA's :53Task 1: Interpret barplot of deportations
Below is code to produce a barplot of deportations over the time frame. I want you to provide a professional-grade interpretation of the plot you are seeing. This task is worth 100 points.
df_melted <- aggregate(data = remove.1, Deportations ~ Year, mean)
names(df_melted) <- c("Year", "mean_Deportations")
ggplot(df_melted, aes(x = Year, y = mean_Deportations, width=1)) +
geom_bar(stat = "identity") +
scale_x_continuous(n.breaks = 10) +
labs(title="Figure 1: Deportations by year (FY 1948-2022)",
y="Number of deportations", x="Fiscal year",
color="") +
theme_bw() +
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),
#axis.title.y=element_blank(),
#axis.title.x=element_blank(),
#legend.title=element_blank(),
#legend.position=c(.01, .77),
#legend.justification=c("left", "bottom"),
#legend.title = element_text(size = 5),
#legend.text = element_text(size = 5),
#legend.margin=margin(0,0,0,0),
#legend.box.margin=margin(-1,-1,-1,-1),
plot.title = element_text(size=12)) 
Task 1 answer here
The plot above illustrates the number of deportations in relation to the years 1948-2022 and appears to emphasize an evolution and change in U.S immigrant policies.
From the years 1945 to around 1980 there seems to be a slow stable increase in the number of immigrants deported, but from 1980 there appears to be a gradual increase which continues up till 2010 with a sudden spike in the number of deportations around this time. The trend of a gradual increase over time is likely linked to changes in U.S immigration policies that increase, evolve, and are further amplified by prior policies. There are many possible factors to explain the rise of deportations around the1980s, a plausible explanation was 1986’s “Anti-Drug Abuse Act” (ADAA I), which granted immigrant officers the authority to access immigrant records involving drug associated arrest or convictions (Jones and Palter 6) These records would be key in making immigrants easily locatable and deported. This legislation would come to evolve in the 1988 second version of the Anti-Drug Abuse Act (ADAA II). It would come to broaden access to criminal records to not only include drug related cases but also simple offenses. This adjustment would create for a broader immigrant demographic to be easily deported no matter how simple their crime may have been. Such simple offenses include not showing up to court or a speeding ticket. Another immigrant policy which could explain the increase in the plot leading is the “Illegal immigrant Reform and Immigrant Responsibility Act” proposed in 1996 with the inclusion of section 287 (Jones and Palter 7) This provision allowed local officers to act as immigrant officials and permitted for quick deportations without the need for a court hearing. This would not only increase the number of officers in search of immigrants but also increase the number deportations occurring at a rapid rate. These policies with its evolution were one of the first to incorporate criminal laws to immigrant laws, opening the door for officers to be able to easily locate immigrants through personal information leading to their eventual deportation.
Following 2001, there was a drastic increase in the number of deportations. A plausible explanation for such a spike around this time could be from the strict policies that came about after 9/11 promoting the government to rely on policies of the past. The years following 2001, Immigration and Customs Enforcement (ICE) was developed to solely focus on enforcing immigration policies, magnifying the amount of manpower that went into deportations. Another strict policy that came about was 2005’s “Operation Streamline” which accelerated the number of immigrants being deported in large scale numbers with up to 80 people able to be deported at one time (Jones and Patler 8) Although 9/11 wasn’t specifically a sole cause for the spike seen after, with the creation of more policies and the reliance of policies developed prior, made it simpler for mass deportations to occur Immigration policies would continue with its rise till 2020.
The big decline in the year 2020, is highly likely due COVID-19 pandemic. With the pandemic at its peak, the priority at the time was to decrease the spread of COVID thus, social distancing decreased the number of apprehensions and deportations.
Overall, the increases in the number of deportations seen in the plot are seemingly connected to the policies created overtime. There is not just one sole policy linked to the increase in deportation but the development of recent policies expanding upon prior policies have possibly made deportations easier and at a larger scale. Regarding the drastic decrease in deportations in 2020, they are likely linked to the lack of ability to continue apprehensions due to risks posed during COVID-19.
Work Cited (MLA): Patler, Caitlin, and Bradford Jones. The US Deportation System: History, Impact, and New Empirical Research. pp. 1–46. Accessed 30 May 2025.
Task 2: T-test by Party
Create a factor-level variable for Party of the President labeled “Republican” for Republicans and “Democrat” for Democrats. Following this, compute a two-group difference-in-means test assessing the following research question: Are the number of Deportations under a Democratic Presidency significantly different from Deportations under a Republican Presidency? In a paragraph, report results from the analysis using substantive language that could be understandable to a lay-person. This task is worth 100 points.
#Insert code to do this task in this chunk
remove.1$PartyLevel <- factor(remove.1$Party,
levels=c(0,1),
labels=c("Republican", "Democrat"))
t.test(remove.1$Deportations~remove.1$PartyLevel, var=TRUE)##
## Two Sample t-test
##
## data: remove.1$Deportations by remove.1$PartyLevel
## t = -0.99223, df = 73, p-value = 0.3244
## alternative hypothesis: true difference in means between group Republican and group Democrat is not equal to 0
## 95 percent confidence interval:
## -93396.17 31310.31
## sample estimates:
## mean in group Republican mean in group Democrat
## 94800.32 125843.26Task 2 answer goes here
Based on the two-sample t-tests conducted to measure if the number of deportations has anything to do with the presidential political party, a comparison in Democratic Presidency in contrast to a Republican Presidency, seems to indicate that there is no significant difference in the number of deportations in relation to a political party. In comparing the means of both groups, we can see there is some difference. With Republicans’ mean at 94,800.33 and Democrats’ mean at 125,843.26, there is a difference of 31,042.93. At a glance, it would be easy to assume that there is a significant difference here since 31,000 is a moderate difference, however the t-test gives a more accurate depiction if this difference is significant. Here the null hypothesis (H0) assumes that there is no effect on deportations regarding political party presidency. In contrast, the alternative hypothesis (H1) proposes that there is a true significant difference in connection to deportations and the political party. Looking into the t-value of -0.99, which measures the difference between the two groups while considering variability, we can see that there’s no difference. The t-value isn’t a large value to suggest that there is a significant difference with deportations and the presidency. Thus, we cannot reject the null hypothesis. The p-value of 0.3244, gives us the probability of extreme result assuming that the null hypothesis is true. The p-value 0.3244 in comparison to 0.05 (significance level), is greater than 0.05 suggesting that there is no statistical significance. If there was to be a significant difference the p-value would have been less than 0.05. Furthermore, the 95% confidence interval further emphasizes the lack of significant difference between the two groups’ effect on the number of deportations. The 95% confidence interval shows the range in which we are 95% confident that the true mean difference lies within that range, for this case the range is -93,396 to 31,310. This range suggests that we are 95% confident that there is no difference in deportations for 0 is included in the range meaning we cannot reject the null hypothesis. Thus, based on the t-test we get a clear idea that there is in fact no difference, meaning that the number of deportations taken place has no significant change when either the Republican or Democratic party takes the presidency.
Task 3: Regression with a dummy variable
Estimate a bivariate regression model of the form: \(\hat{Deportations}=\beta_0 + \beta_1*Party~of~President\) and report the results from the regression model by summarizing the regression object. Based on the table of results, what would be the predicted number of deportations for Republicans and for Democrats. What does \(\beta_0\) and \(\beta_1\) tell us? Based on the model, is there evidence to reject the null hypothsis that \(\overline{D}_{Dem}=\overline{D}_{Rep}\)? This task is worth 100 points. Before doing this, you should read “The US Deportation System: History, Impacts, and New Empirical Research” by Caitlin Patler and Bradford Jones.
#Insert code to do this task in this chunk
regPARTY <- lm(Deportations~PartyLevel, data=remove.1)
summary(regPARTY)##
## Call:
## lm(formula = Deportations ~ PartyLevel, data = remove.1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -118080 -87243 -71318 82297 306491
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 94800 21372 4.436 0.0000319 ***
## PartyLevelDemocrat 31043 31286 0.992 0.324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 135200 on 73 degrees of freedom
## Multiple R-squared: 0.01331, Adjusted R-squared: -0.0002093
## F-statistic: 0.9845 on 1 and 73 DF, p-value: 0.3244plot_model(regPARTY, type = "pred", terms = c("PartyLevel"),ci.lvl = .95,
title= "Use of deportations by Republican and Democratic Presidents \nshows no difference, 1948-2022", axis.title=c("Party", "Number of removals"),
colors=c("skyblue3")) +
geom_line() +
theme_classic()
Task 3 answer goes here
Based on the table of the results showing the number of deportations under different political party presidents, we can easily make out the predicted number of deportations for both the Republican and Democratic party. Taking into consideration that the political party is a categorical variable, the Democratic and Republican party are assigned dummy values of 0 and 1. Here the Democratic party assignment of the value 1, is represented in the table as “PartyLevelDemocrat” and the Republican Party assignment of 0 depicted in the intercept.
Regression can be used as a mean function model, estimating the average of each party and identifying the difference between both parties. By looking at the estimated intercept, we can see that the estimated number of deportations under a Republican party presidency would be 94,800. Using the equation of β0 (94,800) + β1 (31,043) x dummy variable of 0 we get 94,800 as the estimated number of deportations. Similarly, the predicted number of deportations under a Democratic presidency is distinguished by adding up 94,800 + 31,043 multiplied by its dummy variable of 1, indicating an estimated 125,843 number of deportations. Below the estimate, the p-value of about 32% tells us that since it’s above the significance level of about 5% there is a probability that there is no correlation between the political party presidency and the number of deportations but instead of other factors at place causing differences in the number of deportations. Nonetheless, the difference between both parties of 31,043 indicates that under a Democratic presidency there are more people deported in contrast to a Republican party but there is no evidence the number of deportations is correlated with political party presidency.
The model based on the table indicates that there is no concrete evidence to reject the null hypothesis of there being a significant difference of deportations when the presidency is under a certain political party. In the model both party averages are displayed, with the Republican party average of 94,800 and Democratic party average at 125,843. The difference between the averages suggests that although there is some difference between the two parties, in reality the difference is not a significant amount. This is emphasized by an overlap in the number of removals between the two parties. Both parties overlap around 80,000 to 130,000 removals, suggesting that the difference is not drastic in comparison between the two parties so much so that both parties’ data share more similarities in the number of deportations than differences. Additionally, the p-value of the plot, seen on the table, with it being greater than the 5% significance level, we fail to reject the null hypothesis of there being no significant difference of the number deportations in correlation to the presidential political party.
Thus, looking at both the predicted number of deportations and the model of that table, we can see that although Democratic presidents remove more immigrants in comparison to Republican presidents, they are more alike in the amount of removal. Overall suggesting that the number of removals isn’t significant enough to say that deportations are affected by which political party takes the U.S. presidency.
Task 4: Plot regression object
Using \(\textrm{plot_model}\) (from the \(\textrm{sjPlot}\)), provide a professional-grade plot of the regression model along with an interpretation of the plot. Which hypothesis is the plot most consistent with? This task is worth 100 points.
#Insert code to do this task in this chunk
plot_model(regPARTY, type= "pred", terms= ("PartyLevel"), ci.lvl = .95, title = "Deportations in Relation to Political Presidency, 1948-2022", axis.title =c("Party of the President","Number of Deportations"), colors=c ("skyblue2"))+
geom_line() +
theme_classic()
Task 4 answer goes here
The regression model suggests that there is no correlation between presidential political party and its effect on the number of deportations. The plot displays the data difference with the Republican average of 94,800 and Democrat average of 125,843 depicted through the single plotted points on each party’s side. The height difference between the plots reveals that a Democratic presidency deports more immigrants than a Republican presidency. It would be easy to assume that this supports the alternative hypothesis of a significant difference inferring that there is a correlation between the political party presidency and the number of deportations. However, when examining the spread of data there is an overlap in the party’s data. More than half of the Democrat sample data overlapped with the Republican sample, indicating that there is an uncertainty. This uncertainty means that we cannot confidently say that this difference is in correlation with the political party but instead from the spread of the data from the average. Therefore, the plot appears to be most consistent with the null hypothesis, stating no significance in the number of deportations and the effect political parties may have on the number.
Task 5: Regression by decade
In the Patler and Jones article I asked you to read, they point out that several policies were enacted that made deportations easier to carry out. Among one of the most important policy was the Illegal Immigration Reform and Immigrant Responsibility Act, 1996. One prediction might be that after changes in the 1990 (like the IIRIA), we should observe and increase in deportations starting in the 1990s. To assess this claim, do the following:
Create a well-labled factor-level variable denoting each decade starting with the 1950s (1951-1960) going up to the 2010s (2011-2020) and then estimate a regression model treating the dependent variable (i.e the number of deportations) as a function of the decade-factor level variable. Following this plot the regression model using $. Provide a thorough interpretation of the regression model with a focus on the claims made in the paragraph above. Are the results consistent with the basic claim made? This task is worth 100 points.
#Insert code to do this task in this chunk
remove.1$period<- factor (remove.1$Decade,
levels=c(1950, 1960, 1970, 1980, 1990, 2000, 2010),
labels=c("1951-60", "1961-70", "1971-80", "1981-90", "1991-00", "2001-10", "2011-20"))
mode12 <- lm(Deportations~period, data=remove.1)
summary(remove.1$period)## 1951-60 1961-70 1971-80 1981-90 1991-00 2001-10 2011-20 NA's
## 10 10 10 10 10 10 10 5model2 <- lm(Deportations~period, data=remove.1)
summary(model2) ##
## Call:
## lm(formula = Deportations ~ period, data = remove.1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -112306 -8002 -742 7322 104976
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15047 14387 1.046 0.299619
## period1961-70 -4927 20347 -0.242 0.809460
## period1971-80 8974 20347 0.441 0.660666
## period1981-90 8236 20347 0.405 0.687015
## period1991-00 79603 20347 3.912 0.000227 ***
## period2001-10 262426 20347 12.898 < 0.0000000000000002 ***
## period2011-20 334623 20347 16.446 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 45500 on 63 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.9016, Adjusted R-squared: 0.8923
## F-statistic: 96.25 on 6 and 63 DF, p-value: < 0.00000000000000022plot_model(mode12, type= "pred", terms= ("period"), ci.lvl = .95, title = "Number of Deportations throughout the Decades", axis.title =c("Decade","Number of Deportations"), colors = c("black")) +
geom_line() +
theme_classic()
Task 5 answer goes here
Analyzing the regression table and plot by decade on the number of deportations, the results seem to be consistent with the prediction that after changes in 1990, we will observe an increase in deportations starting in 1990. The table, based on the decades in correlation with the number of deportations taken place, displays the seven decades depicting a significant increase from the years prior 1990 to after 1990. Based on the intercept estimate, we can infer that the beginning decade 1951-60 has an estimated 15,047 deportations. With the intercept as the base value, all following estimates indicate the difference from it, allowing us to observe that the beginning decade from 1961-70 to 1981-90 has a steady increase up to 8,236 more deportations than 15,047. There is a decrease in 1961-70 where there was a decline of 4,927 deportations. This slow trend is further supported by looking at the p-value of the decades prior to 1990. The p-values from the decade before show values above the significance level of 0.05, such as the decade of 1981-90 p-value of around 0.69, suggesting that there was no significant change in those decades affecting the number of deportations. Thus, this analysis suggests that from the years prior to 1990 there is a slow increase in the number indicating that these decades seem to not see much change when it comes to deportations.
In contrast, the decade after 1990 produced an opposite effect. With estimated values reflecting how each decade’s average number of deportations differs from the baseline (intercept), we can see how much more deportations went on after 1990. Immediately starting 1990 there was a substantial increase in the number of deportations which followed the decades after. From 1991-2000, there was an increase of 79,603, this increase trend would continue with the decade 2001-2010 showing an increase of 262,426 deportations. The last decade in the table 2011-20 would be the greatest increase yet with 334,623 more deportations than the baseline recorded in the intercept. Such a significant increase is emphasized in the p-values of the decades after 1990, indicating a significant difference based on their p-values falling below 5%. The p-values go as far as being less than 0.002 highlighting a substantial significance difference in the years after 1990 in comparison to the years prior. The plot supports the findings from the table, visually showing us change over time. One of the most notable things from the plot is the slight decrease in the number of deportations seen in the decade 1961-70. However, is a slight increase starting around 1951-60 to 1981-90 with the number of deportations staying below 100,000. This suggests that although there is a small increase in deportations, this isn’t a significant increase possibly due to consistent laws in place or other factors at play. However, following this slow increase, in 1990-2000 there is an exponential growth that continues till 2011-20. After 1991-2000 we see that the number of deportations go up to around 100,000 then jumping to around 300,000 deportations that took place in 2001-2010. This visual depiction makes it clear that there is a considerable increase in the number of deportations after 1981-90 that started in 1991-2000.
Thus, in analyzing the model in the number of deportations in relation to the decade there is sufficient evidence from the average of each decade, difference between the decade, and the p-value to suggest that the data is consistent with the prediction that we should see an increase in deportation after 1990 possibly due to enactment of several policies regarding immigration laws.
Task 6: Pre-post 1996
Create a dummy variable (or binary variable) coded 1 if the year is 1996 or later and 0 otherwise. Estimate a regression model treating deportations as a function of this dummy variable. Plot the regression model and provide a thorough substantive interpretation of the regression results. To start, what would be the null and alternative hypotheses for \(\beta_1\) given the research question? Suggested ways to interpret this would be to report the predicted number of deportations in the later period compared to the earlier period as well as the discussing the coefficient showing the difference. You should tie your interpretation back to the regression estimates. This task is worth 100 points.
remove.1$post1996 <- ifelse(remove.1$Year >= 1996, 1, 0)
remove.1$post1996_factor <- factor(remove.1$post1996,
levels = c(0, 1),
labels = c( "1948-1995", "1996-2022"))
model4 <- lm(Deportations~post1996_factor, data=remove.1)
summary(model4) ##
## Call:
## lm(formula = Deportations ~ post1996_factor, data = remove.1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -196855 -12510 -2139 14015 165799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20835 9385 2.22 0.0295 *
## post1996_factor1996-2022 245700 15642 15.71 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 65020 on 73 degrees of freedom
## Multiple R-squared: 0.7717, Adjusted R-squared: 0.7686
## F-statistic: 246.7 on 1 and 73 DF, p-value: < 0.00000000000000022plot_model(model4, type= "pred", terms= ("post1996_factor"), ci.lvl = .95, title = "Number of deportations prior 1995 in comparison to post 1996", axis.title =c("Time period; 1948-1995 compared to 1996-2022","Number of Deportations"), colors = c("black")) +
geom_line() +
theme_classic()
Task 6 answer goes here
The model in comparing the null hypothesis to the alternative hypothesis indicate that events during 1996 are likely correlated with the increase in the number of deportations. The null hypothesis (β0) claims that there is no statistically significant difference between the number of deportations occurring in the period prior to 1996 in contrast to the year 1996 to 2022. Meanwhile, the alternative hypothesis (β1) proposes that there is a statistical difference between the two periods. The table provides us with the baseline value, giving us the estimated average of 20,835 deportations that took place during the period 1948-1995. In the table we can also see the estimated average number of deportations during and after 1996 had an increase of 245,700 deportations in comparison to the period prior to 1996. When adding both these values up we get the estimated average of 266,535 deportations that went on during 1996 and after. Given the difference of 245,700 deportations, there’s a clear substantial difference between both estimated averages, strongly suggesting a significant difference between the periods. Once examining the p-value, we can also see another indication of a significant difference between the two periods. The p-value is less than 0.0000000000000002 falling under the significance level of 0.05, implying that the difference in the data isn’t statistically significant. Moreover, we can say that the p-value gives us sufficient evidence to reject the null hypothesis. To further strengthen this conclusion, the T-value in the table is displayed as 15.71. The greater the t-value, the stronger the estimates are, confirming that the data is not by chance but instead, it’s more accurate to what is going on in the real world.
Furthermore, the plot helps us visually see the significance of the difference between the data. In the plot, we see that the period from 1948 to 1995 estimated an average of 20,835 in comparison to the big difference to the period 1996-2022, as depicted through the two points in each period. The difference in height of the estimated average points between both parties highlights how significant the difference of 266,535 is visually. The visual aid of the plot suggests that there are certain event(s) that possibly occurred during 1996 and could have contributed to the substantial increase of 245,700 deportations.
Due to the data demonstrating substantial evidence to reject the null hypothesis, a possible event that could have influenced this difference in deportations is with the1996 Act of Illegal immigrant Reform and Immigrant Responsibility Act. This act brought upon much substantial legislation, section 287 in which expanded the number of deportations by granting local law enforcement members to act as immigration officers. Section 287 would go on to be used in the years following 1997 and would inspire new legislation such as the development of U.S. Immigration and Customs enforcement. However, there are other possible events that could have impacted the number of deportations and section 287 alone could have had a small effect on the deportations. What we do know though is that, based on the model, there is a significant difference between the periods of 1948-1995 versus 1996-2022, indicating there is something or multiple things that went on from 1996 to 2022 that heavily impacted the big increase in deportations.