LEGALIZATION OF ABORTION

5

LEGALIZATION OF ABORTION

Legalization of Abortion

Fernando Fuentes

SYG2323.004

David Manning

04/16/2020

-See comments below.

-Secondary data analysis section is too limited and you need to use the sources you have listed on the reference page to make your points in this section. You have several journals listed so you need more secondary data analysis in this section. See feedback in this section.

-Citations: I see what your doing here its not Levitt the correct citations is Donohue, and Levitt (2019) so you can’t have one authors name without the other and you need the year too.

-Theory: I think rational choice theory was Siegels version of Free Will or classical theory by Adler, Mueller and Laufer (2018). You need to look at our lecture outlines in canvas and just use our textbook to define Free Will and then in one paragraph apply it to your topic without secondary data analysis. Your journal secondary data analysis belongs in your secondary data analysis section.

-Findings and Abstract should match up much better.

Abstract

The legalization of abortion has been a public health and social issues for many decades. This paper discusses issues surrounding abortion and why it should be legalized (do you mean should remain legalized, because it has been since the mid 70s in the USA?). The paper utilizes two theories: Free will theory, which argues that individuals should be allowed to make decisions or choose between different available courses of action without impediment. Supreme Court affirmed this through its 1973 decision on women's right to decide whether to have a child or not. Routine Activities theory describes how offenders commit crimes partially based on their normal ordinary activities and decisions. In this paper, data was not collected, but secondary data was explored. The paper concludes that safe and legal abortions have not only promoted the quality of life for women but also reduced the crime rates over the last two decades. Are there examples of it decreasing the crime rates? If so, they should also be here.

Introduction

The debate surrounding the legalization of abortion has been actively discussed since the 1973 Roe vs. Wade case. The Supreme Court, at the time, legalized abortion but set specific conditions for the practice. Since then, legal and safe abortions have been conducted across America by certified medical practitioners. However, this might soon change as anti-abortion movements continue to call for its illegalization. The decision to terminate the pregnancy seems to be associated with women's health as one of the fundamental rights. The Supreme Court ruling in 1973 reaffirms the right of women to decide on whether to have a child or not (Kimuyu, 2017). In such a case, the free will theory is applicable in legalizing abortion. Routine Activities theory describes how offenders commit crimes partially based on their normal ordinary activities and decisions. Women should be allowed to choose between different courses of action unobstructed regarding the termination of unwanted pregnancies. Therefore, free will can be applied to justify the legalization of abortions, not only in America but across the world.

Secondary Data Analysis

Safe and legal abortions have promoted the quality of life for women in the last two decades. Over the years, much research has been conducted about the legalization of abortions. This research paper is based on previous studies conducted by various authors over the last few years. Levitt's perspective about abortion does not account for the many social forces that influence abortion decisions in society (Chernomas & Hudson, 2013). Crime rates unexpectedly started declining in America from about 1991 as a result of the legalization of abortion in the country. This realization thus suggests that this article was right in the analysis; the legalization of abortion translates to a decline in crime rates (Donohue, & Levitt, 2019). Donohue and Levitt (2019) research was conducted under two assumptions; aborted babies were unwanted and that the unwanted babies were likely to become criminals in the future.

Religiosity influences the negative attitudes and perceptions of abortions in society. Americans who are more religious and believe in God are the greatest opposition to abortions. The holy books used as a guide for many religious people condemn the taking of innocent life. Religious people believe that preservation of life is mandatory under all circumstances (Unnever, Bartkowski, & Cullen, 2010). Upholding life ethics among religious people includes rejecting abortion. The study by Unnever, Bartkowski and Cullen (2010) offers a partial assessment of religious ideas for a consistent life ethic by evaluating the relationship between God and the religious believer, which brings opposition to abortion. However, this is against the free will approach to justify abortion without impeding the decision-maker.

Free Will Theory

While secondary data is easier to find and utilize, primary data yields more accurate results in research as long as other factors are well-controlled. The illegalization of safe abortions may adversely affect the female gender. The free will theory allows individuals and, in this case, women the right to make decisions about whether to have a baby or not. The argument is linked to the concept of moral responsibilities, praise, sin, as well as personal judgments that apply to the freely chosen actions. Women and young teenage girls are the people who have to deal with the availability or absence of abortion services. As such, they are the people who should mainly be involved as research participants. Donohue and Levitt (2019) state that medical practitioners, as well as law enforcement officers, should also be asked to give their opinion about the medical implication of either legalization or abolition of abortions.

Routine Activities theory

The Routine Activities theory that evolves from the rational choice theory states that criminal activity is committed when a target is available, a motivated offender exists, and there is no guardian to monitor or control the offender (Adler, Mueller, & Laufer, 2018). With this in mind, it is easy to understand the source of crime in society. The unwanted babies are often abandoned or raised in the streets are motivated to commit a crime to fend for their needs. Also, the lack of a responsible guardian to control unwanted children makes it easier for them to commit crimes (Donohue & Levitt, 2019). This theory, therefore, supports that the legalization of abortions can reduce crime in society.

Methodology (Primary Data Collection Not Conducted But Explored)

The research methodology will combine the use of surveys as well as statistical correlation analysis to establish the relationship between abortions and crime. Survey research is the systematic collection of respondents’ answers to questionnaires or interviews (Adler, Mueller, & Laufer, 2018). In this case, surveys can be conducted, and questionnaires would be used to collect primary data whenever necessary. Conducting surveys that involve the use of open-ended questionnaires creates an opportunity for the researcher to gain reliable information from the affected parties (Chernomas, & Hudson, 2013). Some of the questions that should be included in the survey questionnaires include;

1. Should safe and legal abortions be allowed in the world?

2. Can the legalization of abortions increase or decrease the budget allocation for the criminal justice department?

3. Does the legalization of abortions reduce crime rates in America?

4. What is the public opinion about the issues around abortion legalization?

5. What is the current legal situation?

Discussion & Conclusions

Based on the secondary data utilized for this activity, the legalization of abortion does not only promote the quality of life for women, but it also reduces the crime rates. Donohue and Levitt (2019) say illegal abortion cases also make up part of the criminal records in the country. Abolition of abortion will not reduce the number of abortions but will instead mean that women are at risk of developing life-threating complications while undergoing illegal and unsafe procedures. Although religion influences negative attitudes about abortion, the same religion can be used to spread awareness and support the legalization of safe abortions nationwide (Donohue, & Levitt, 2019).

In conclusion, safe and legal abortions have not only promoted the quality of life for women but also reduced the crime rates over the last two decades. Fewer children have been found either dead or abandoned since abortion was legalized (Chernomas, & Hudson, 2013). The debate should not be politicized but preferably conducted in a manner that leads to the best decision that promotes social welfare.

References

Adler, F., Laufer, W., & Mueller, G. (2018). Criminology 9th edition. New York, NY: McGraw-Hill.

Chernomas, R., & Hudson, I. (2013). Steven Levitt on Abortion and Crime: Old Economics in New Bottles. American Journal of Economics and Sociology, 72(3), 675-700. Retrieved from https://www.jstor.org/stable/23526056

Donohue, J. J., & Levitt, S. D. (2019). The Impact of Legalized Abortion on Crime over the Last Two Decades (No. w25863). National Bureau of Economic Research. Retrieved from https://bfi.uchicago.edu/wp-content/uploads/BFI_WP_201975.pdf

Kimuyu, P. (2017). Should Abortion Be Legalized?. GRIN Verlag. Not complete.

Unnever, J. D., Bartkowski, J. P., & Cullen, F. T. (2010). God imagery and opposition to abortion and capital punishment: A partial test of religious support for a consistent life ethic. Sociology of Religion, 71(3), 307-322. Retrieved from https://www.jstor.org/stable/40961207

--- title: "STAT 341/641 Midterm Two Project" author: "Your Name Here" date: "Enter the Date Here" output: html_document --- --- * * * For the second midterm you will fill in missing pieces of the code in the following blocks. Suppose we observe $N$ data points, $\left\{\mathbf{x}_i\right\}_{i=1}^N$, in three dimensions so that $\mathbf{x}_i=(x_{i1},x_{i2},x_{i3})$. Recall that the isolation forest separates points by repeating the steps in the following algorithm. 1. Randomly choose one of the variables in the dataset, say $x_{1}$; 2. Randomly choose a number $c$ in the interval $(\min_i(x_{i1}),\max_i(x_{i1})$. 3. Divide the data into groups depending on whether $x_{i1} >c$ or $x_{i1}\leq c$. *The midterm project is worth 16 points and due on April 2nd. You may consult other students in the class to finish the midterm, but you must submit your own project. You may not ask for help from me or the teaching assistants.* **Code Block #1:** This code block divides the data into groups. The results are recorded in an $N\times N$ matrix where entry $(i,j)$ indicates whether observation $i$ and $j$ belong to the same cluster. In this block, I have replaced parts of the code with a question mark. Make the necessary changes to the code. ```{r} ## you will need to change the path to load the data out_dat <- read.csv("~/Dropbox/Teaching/341/data/cluster_outlier_set.csv") getClusters <- function(mydata, J){ N <- nrow(mydata) K <- ncol(mydata) cluster_matrix <- matrix(1,N,N) for (j in c(1:J)){ ## 1. sample a dimension (1 point) #mydim <- sample(1:?,1) ## 2. sample a number c greater than the minimum and less than the maximum of that dimension (1 point) #c <- runif(1,min(mydata[,?]),max(mydata[,?])) ## 3. compute a matrix that determines whether the each pair of points satifies the condition (1 point) #tmp <- (mydata[,mydim] < c) %*% t(mydata[,mydim] < c) ? (mydata[,mydim] >= c) %*% t(mydata[,mydim] >= c) ## 4. Update the cluster matrix (1 point) #cluster_matrix <- ? * tmp } return(cluster_matrix) } set.seed(341) mycluster_matrix <- getClusters(out_dat, J = 4) ``` **Code Block #2:** This code block divides the data into groups. The results are recorded in an $N\times N$ matrix where entry $(i,j)$ indicates whether observation $i$ and $j$ belong to the same cluster. In this block, I have replaced parts of the code with an asterik. Make the necessary changes to the code. ```{r} ## 5. determine the number of groups (1 point) #unique_row <- mycluster_matrix[which(!duplicated(mycluster_matrix)),] #number_groups <- nrow(?) print(number_groups) ## 6. get the group assignments (1 point) #mygroups <- apply(unique_row,1,function(x){which(x==1)}) #cluster_assignments <- numeric(nrow(out_dat)) #for (ii in c(1:number_groups)){ # cluster_assignments[mygroups[[ii]]] <- ? #} ## 7. assign a colour to each group (1 point) #mycols <- rainbow(n = ?, alpha = .75) #group_cols <- mycols[cluster_assignments] ## 8. Make a plot the data with the points coloured by cluster number (1 point) #plot(out_dat[,c(1,2)],typ="p",col=?,pch = 20) #plot(out_dat[,c(1,3)],typ="p",col=?,pch = 20) #plot(out_dat[,c(2,3)],typ="p",col=?,pch = 20) ``` **Code Block #3:** Now, let's repeat this $R=2,500$ times and average the results of the cluster matrices. Entry $ij$ in the resulting matrix is the probability that two points belong to the same cluster, $q_{ij}$. We will set the number of clusters, $G$, to be equal to the number of clusters in the single run. Then we will sample from the set of cluster assignments and plot the results. In the following code block, I have written a function that computes the logged probability of a cluster $$p(C_1,\ldots,C_{G}) = \prod_{i=1}^N \prod_{j<i} q_{ij}^{\mathbf{x}_i\text{ and }\mathbf{x}_j \text{ are in the same cluster}}* (1-q_{ij})^{\mathbf{x}_i\text{ and }\mathbf{x}_j \text{ are not in the same cluster}}$$ You only have to run this. You needn't worry about how I derived this. ```{r} set.seed(641) R <- 2500 avg_cluster_matrix <- matrix(0,nrow(out_dat),nrow(out_dat)) for (r in c(1:R)){ ## 9. call the function (1 point) #obj <- getClusters(?,J = 4) ## 10. average the results (1 point) #avg_cluster_matrix <- obj/R ? avg_cluster_matrix } ## print some entries avg_cluster_matrix[ 1:10,1:10] ## 11. set the number of clusters equal to that for the single run (1 point) #ngroups <- ? ## Sampling from the set of all clusters by changing one point at a time ## You don't need to understand this part!!! S <- 250000 set.seed(341) mycluster <- cluster_assignments s <- 0 while(s < S){ cluster_proposal <- mycluster ind <- sample(c(1:nrow(avg_cluster_matrix)),1) newc <- sample(c(1:ngroups),1) newvec <- as.numeric(newc == cluster_proposal[-ind]) oldvec <- as.numeric(cluster_proposal[ind] == cluster_proposal[-ind]) p1 <- sum(log(newvec*avg_cluster_matrix[ind,-ind] + (1-newvec)*(1-avg_cluster_matrix[ind,-ind]))) p2 <- sum(log(oldvec*avg_cluster_matrix[ind,-ind] + (1-oldvec)*(1-avg_cluster_matrix[ind,-ind]))) if(p1 >p2){ mycluster[ind] <- newc } s <- s + 1 } ## 12. plot the results (1 point - you can use the other code block to do this) ``` **Code Block #4:** Challenge: Now compare the results of the single run to the 2,500 runs. For the two results, compute the sum of squared residuals given by $$\text{SSR} = \sum_{g = 1}^G \sum_{i: \mathbf{x}_i \in C_g}d(\mathbf{x}_i,\bar{\mathbf{c}}_g)^2$$ where $G$ is the number of groups, $C_g$ is the set of points in cluster $g$, and $d(\mathbf{x}_i,\bar{\mathbf{c}}_g)$ is the Euclidean distance between $\mathbf{x}_i$ and the mean of its cluster $\bar{\mathbf{c}}_g$. ```{r} ## 13. Compute the SSR for the two set of results (2 points) ## 14. In the space below, answer the following question. What happends to the SSR for a single run of our clustering method as the number of cuts (J) grows large? Why? (2 points) ```