Final Blog Post: Legalized Abortion on Crime

4 May

My blog post from April 27th, Where Have All the Criminals Gone, looked at the effects of Roe v. Wade on crime rates. Levitt and Dubner theorized that children who would have been born before the law was instated are now not being born. This in turn reduces the crime rate because children that would have been born into unwilling homes are not having to endure that tough childhood. I was skeptical of these results by Levitt and Dubner, as I thought that their theory that children that enter the world pre-Roe v. Wade are subject to a degree of un-wantedness is false. I argued that while there might be a short term feeling of un-wantedness, it would not affect their treatment of the baby in the long run. I believe that  more accurate indicators of whether or not a child would be a criminal would be their parents criminal history, and their parents combined income.

The scholarly article written by Donohue and Levitt, The Impact of Legalized Abortion on Crime, hypothesizes that the decreased crime rates in 1991 to 1997 can be attributed to the implementation of Roe V. Wade. Just like the Levitt and Dubner article from Freakonomics, they cite numerous different reasons for why a legalization of abortion would lead to a decrease in the crime rate. For one, they believe that abortion is a tool for potential mothers who are at a suboptimal time for child bearing to leave that situation (381). Additionally, they thought that women who would get abortions would be more likely to have children that would be prone to criminal activity (381). Their results found that around 50% of the decrease in crime rate can be attributed to abortion. They said the other half is due to increased imprisonment over the periods of 1991-1997. The prison population during this time rose about 50%. What I am skeptical about in these results is the prison population. Why is the prison population abortion statistics and abortion looked at independently? If the legalization of abortion is supposed to reduce the amount of children coming into the world that would commit crimes, than why does the jail population rise by 50%? This is something that Levitt and Donohue did not address.

The critique of Legalized Abortion on Crime finds numerous aspects of Levitt and Dubner’s study to be inaccurate. For one, Foote and Goetz argue that the “cross state results are not robust enough to control for omitted variables” (421). If the variables aren’t strong enough and there is omitted variable bias than it definitely could affect the results of the regression. Another problem with the study that Foote and Goetz found was when you correct the regression and use a per capita variable for crime the study garners much weaker results. Finally, there is a coding error when testing for the experiences of different age cohorts within the same state and year. All of these points are valid concerns when critiquing the original study, and when corrected for yield much weaker results.

It is difficult after the critique of Legalized Abortion on Crime to be able to reconcile the Freakonomics chapter with the other two papers. The Freakonomics chapter is essentially an extension of Legalized Abortion on Crime (Levitt is involved in both pieces). The critique applies to both pieces of literature, and even if Levitt were to correct for the proper regression his results would become smaller. This would somewhat dispose of his theory and therefore I do not think they can be reconciled.

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Chapter 4: Where have all the Criminals Gone?

27 Apr

Chapter 4: “Where Have all the Criminals Gone?” tries to prove that the legalization of abortion laws in the United States can explain the decrease in the crime rate. They cited many different reasons for a possible decrease in crime rate including an aging population, gun laws, the economy, and capital punishment as possible explanations for the decrease in crime rate. After looking at many possible explanations, they landed on abortion as the key determinant in the drop in crime rate. They argue that kids that would have entered the world before Roe v. Wade are now being aborted, and therefore crime rates are decreasing.

A strength of this chapter is the way that they structure their opinion. When disproving that the punishment of crimes had anything to do with criminals, Levitt and Dubner heavily tailor their writing structure towards their opinion. They first introduce a subject topic, yet they do not initially fill you in on their opinion. They then pull out a study that was done with extremely definitive wording. For example, they introduce the economist Isaac Ehrlich and say that his research found that “executing 1 criminal translates into 7 fewer homicides that the criminal might have committed.” Intentionally, they do not provide any evidence from Ehrlich as to why he may have found this ratio; they only compare his results to violent crimes in 2001. He shows that Ehrlich’s estimations do not come close to explaining the homicide change in that year. This year could have been an outlier for all we know, yet he strategically used it for dramatic effect. He then often finishes his statistics off with an unarming quote or observation. In the case of the death penalty he pulled a quote from Justice Harry Blackmun: “I shall no longer tinker with the machinery of death.” This quote conveniently agrees with their opinion and leaves the reader with a memory that will make them more likely to buy into the authors’ beliefs. The authors are showmen, and the structure of their arguments is down to a science.

 I am skeptical of their results because I believe that the premise of their argument is built on a stereotype that people that get abortions are more likely give birth to a criminal than people that do not get abortions. This stems from a degree of “un-wantedness,”  where children that were forced into the world in Roe v. Wade are more likely to be in unwilling households (139). My argument to this is that most parents will not hold a grudge on their children for their own mistakes. Whether they wanted the baby or not is irrelevant and does not affect how they treat the baby long term. Therefore I do not believe there is much of a behavioral change in households and therefore little long term effect on the crime rate.

Research Paper Update

13 Apr

After writing the first draft of my paper, I have found that there were particular areas of my paper that needed improvement, while other areas turned out to be strong. My data analysis was thorough and my analysis of different variables was pretty sharp; however, my literature review left much to be desired. While I had expanded on just about every facet of my paper to this point, my lit review remained more or less stagnant since the last time I wrote. I could use more insight into how other people approached my topic. One of the authors from the article I sighted actually commented on my blog post. He gave me some constructive criticism on how to interpret his paper. It was nice seeing that other people are taking notice of the research that I am doing, although my results will most likely be underwhelming to them. Now that I have started the auto correlation and heteroscedasticity chapters, I will able to include these tests into my paper. This will help me to more specifically diagnose what is wrong with my variables. While I have concluded that it is going to be impossible for me to prove a perfect relationship between steroids and homeruns, it is refreshing to know how to properly test for it.

Writing Assignment 8: Pak Sudarno’s Big Family

30 Mar

Chapter 5 of poor economics looks at countries that encourage planned parenthood and the effect it has on their population. The difference between voluntary versus forced planned parenthood was interesting to look at throughout the various studies.

The statistic that grabbed me was that in Colombia, women that had gotten contraception since their teenage years were seven percent more likely to work in the formal sector. I am skeptical of this statistic. My line of thinking is that the girls who are able to make the responsible decision to use contraception at an early age were more likely to work in the formal sector anyways. I think working in the formal sector is much more of a function of one’s level of education and what educational background their parents had.

In my regression model I would use employment levels in the formal sector as the dependent variable. On the right side of the equation I would use, parent’s level of income, level of education, parent’s level of education and academic performance as the independent variables. For my dummy variables I would use whether or not the observation used contraception growing up.

I would not expect to see a statistical difference in the dummy variables in the regression equation. You would have to check the coefficient and the t-statistic if you want to see a relationship between using contraception and not using contraception.  If there was a positive relationship you would see a positive coeffiecient with a t-statistic that would be greater than 1.945. If it the coefficient is minimal or the t-statistic is not large enough than there would not be a relationship.

Writing Assignment 8: Pak Sudarno’s Big Family

30 Mar

Chapter 5 of poor economics looks at countries that encourage planned parenthood and the effect it has on their population. The difference between voluntary versus forced planned parenthood was interesting to look at throughout the various studies.

The statistic that grabbed me was that in Colombia, women that had gotten contraception since their teenage years were seven percent more likely to work in the formal sector. I am skeptical of this statistic. My line of thinking is that the girls who are able to make the responsible decision to use contraception at an early age were more likely to work in the formal sector anyways. I think working in the formal sector is much more of a function of one’s level of education and what educational background their parents had.

In my regression model I would use employment levels in the formal sector as the dependent variable. On the right side of the equation I would use, parent’s level of income, level of education, parent’s level of education and academic performance as the independent variables. For my dummy variables I would use whether or not the observation used contraception growing up.

I would not expect to see a statistical difference in the dummy variables in the regression equation. You would have to check the coefficient and the t-statistic if you want to see a relationship between using contraception and not using contraception.  If there was a positive relationship you would see a positive coeffiecient with a t-statistic that would be greater than 1.945. If it the coefficient is minimal or the t-statistic is not large enough than there would not be a relationship.

Moneyball

19 Mar

The movie Moneyball is a great depiction of how econometrics and regression analysis can be used to predict player value. Billy Bean (Brad Pitt) hires special assistant Peter Brand (Jonah Hill) due to his seemingly progressive way of valuing baseball players. A recent economics graduate from Yale, Peter Brand created a formula for valuing player performance using independent variables like OPS, Slugging Percentage, and On-Base Percentage. Of these variables Brand believed that On Base Percentage most explains the sabermetric statistic Runs Created which is a statistic developed by statistician Bill James. The dependent variable, runs created, accounts for (Total Bases * (Hits + Walks))/(Plate Appearances).

The regression equation that Brand created went against the typical way of thinking that general managers and scouts in major league baseball held. Baseball people value such variables as appearance, off field concerns, fielding and age. These variables Brand deemed as insignificant to the runs created variable. Billy Beane was the target of early criticism as people could not explain some of his roster moves, which included trading All Star First Baseman Carlos Pena. Skeptics believed that the success that the A’s displayed was random and could not be explained. Could regression analysis have actually been that big of a role in the success of the A’s?

It would seem that regression analysis was a crucial component in helping Oakland overcome their measly 40 million dollar payroll. With little room for error, Billy Beane and Peter Brand had to take as much variability out of the scouting process as possible. The best way of doing this was by using statistics that could be explained. Variables like character issues are either insignificant or very difficult to compute and therefore should not be accounted for in the player evaluation process. I found it interesting how the team struggled early on in the season, and Brand noted that it was too small of a sample size to draw any conclusions from and that to judge a team’s true performance one needs to let the season play out. His statement held true as the team wound up performing better late in the season and fulfilling their potential. It also speaks to how the playoffs are so difficult to predict because

Billy Beane described their philosophy as being that of a card counter. The A’s had a competitive advantage in evaluating players that other clubs did not. They could pay players less money than they actually were worth in terms of runs created. That worked in the short term, however as people started to account for the same variables that Billy Beane accounted for, the A’s have found that their success is hard to sustain. When Billy tried to replace Jason Giambi and Johnny Damon, he already had underpaid All Stars in place like Miguel Tejada, Eric Chavez, Barry Zito and Mark Mulder. The role players that he added were positive additions and worked for one season, but to maintain success teams need to have adequate payrolls to keep franchise players in place.

This was my second time watching the movie and I picked up so much more after taking Quantitative Methods this semester. Bill James’ theories on baseball and statistics revolutionized the way we look at statistics and baseball. I am glad that Beane and Brand had the boldness to pursue his theories and that it worked out on a short term basis for the A’s.

 

 

Post # 7 The Law of Genius and Home Runs Refuted

9 Mar

The article by Dinardo and Winfree argues rather pessimistically that steroids effects on homeruns are almost impossible to measure. They argue that there are too many variables that can go into hitting homeruns, including quality of pitching, weather, distribution of talent across teams and the number of games played. They argued that to prove this hypothesis would take considerably more shoe leather than a simple statistical analysis.

The authors investigate bold claims by a researcher named DeVany, who claims that the law of homerun hitting is the same as the laws of human accomplishment. He assumes infinite variance of homeruns with a probability one. He claims that steroids have no effect on homerun output. The authors claim that the infinite variance is flawed and that the size distribution of homeruns cannot follow a power law distribution and a posited class distribution would misrepresent the data. While much of the analytics went over my head, the basic theory was that they could not prove the effects that steroids had in baseball. They did not refute the claim that there may be an effect on homeruns; however, they did make the point clear that it would be difficult to prove.

The author notes that “Inferring the existence of fundamental causal laws—that is, the law of genius—from the statistical distribution of some outcome is difficult, at best. The authors focused on looking at the distributions of these different causal laws, and they found that their distributions came out weird. For the power law, the distribution predicted that 11% of players hit negative homeruns. While I respect the overall message that finding the results from this data will be difficult, I felt as if the authors did not disprove that steroids could have an effect on performance. While I did not see any immediate issues with the assumptions of the classical linear regression model, I feel as if further analysis into the paper’s message is needed to grasp the full value of the article in its relation to my paper. 

http://web.ebscohost.com/ehost/pdfviewer/pdfviewer?sid=f50015ec-98fd-48e4-981c-8840d31396d3%40sessionmgr10&vid=4&hid=125