Reevaluating the Relationship of Abortion and Crime

The article by Donahue and Levitt basically is the foundation of the information Levitt and Dubner present in Freakonomics. Essentially, abortion was the major factor that lead to decreased crime rates in the 1990’s because unwanted children, who are more likely to have a troubled youth, were being born ultimately decreasing the number of likely future criminals. This article negates the issue of regional promiscuity untouched. Looking at the state that legalized abortion first, they are all traditionally liberal states. There is a possibility that these states had higher pregnancy rates originally, so abortion would have a greater effect on crime. Also, New York and California being two of the pioneers in abortion have the two largest cities in the US. Urban areas are more subject to crime, therefore it is possible to expect that these areas would experience higher changes in the crime rate.

Foote and Goetz challenge Donahue and Levitt on the basis of how normalized their test results were. By expanding the scope of the regression across years and more evenly distributing the crime by capita, they show that the relationships Donahue and Levitt previously found were overestimated. By comparing age groups within in states per year, the regression results overstated the correlation between abortion and crime rates. Also, the samples Donahue and Levitt used were not normally distributed per capita. Finally, the original study over used cross-state comparisons which would skew the results for the regional promiscuity, urbanization, and traditional political views discussed above.

These differences can be reconciled by the persistent problem in econometrics, imperfect information. Without entirely comprehensive data no study will be perfect, nor with the experimenters be able to detect where the shortfalls in their research lie. It is easy to become blinded by your data and place too much faith in what is available to you because of convenience. What corrects this issue is the relationship displayed between these two articles. By reevaluating previous research we are able to detect where researchers have gone wrong in the past and correct their mistakes to increase the accuracy of our understanding of the world.

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Brief Research Update

My research is on the effects of the Texas Top 10% law which aimed at increasing college admissions and enrollment for poorer students. Understanding the effects of such a law can provide insight into ways eliminating the educational poverty trap that exists in the US. As a Texas public high school graduate, this law played a significant role in deciding where to go to school for many of my peers, so I was curious to see its actual effectiveness.

Thus far I have found that the law did achieve its desired result. Admission of lower economic students increase once the law was put into effect; however I am skeptical about my results. My F-stat was 8875, which seems too high for me to be fully confident in the results. As of know I do not know what to attribute this absurdly high confidence level to, but I have a feeling that my large number of observations (about 360,000) is pulling up the confidence level. More research needs to be done…

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Overpopulation and HIV Infection Rates Across Genders

The world’s available resources continue diminish making population a common concern. Population problems seem to be highest in developing countries. Banerjee and Duflo explore the possibility of a lack of accessibility of contraceptives and education about such, but conclude that poor people tend to have larger families because it increases the parents’ chances of being provided for in old age. This is not the defining factor on which poor people base their decision, education and social influences also play large roles in the amount of children people have, but ultimately poor people are striving for sons and doing so that increases their chances of having successful, and eventually supportive, children.

An interesting statistic that stood out in the chapter is that “women from the ages of fifteen to nineteen are five times more likely to be infected [with HIV] than young men in the same cohort” (111). Initially one might infer that women are more sexually active than men which could be justified by the social generalization that men are the pursuers and women the choosers in romantic relationships; however, I would think that this statistic represents a higher level of sexual activity in men than in women. This thought would lead me to hypothesize that men are more sexually than women, thus HIV infections are higher among women.

To test this, one would need to conduct a survey, possibly paired with a simple medical test, that looked at whether or not someone is infected with HIV, their gender, their number of sexual partners, how long they have been sexually active, and their sexual orientation. Upon regressing the results, the model would look something like:

Yhat = A + B1HIV + B2Gender + B3Partners + B4TimeSexActive + B5Orientation + u

(where the numbers following the B’s are subscripts and A is constant).

HIV, Gender, and Orientation would all need to be converted to dummy variables where HIV infection, male and, heterosexual are equal to 1 and their counter parts equal to 0. These dummies would eliminate all subjects who were not infected with HIV, who are irrelevant to this study, and by making gender and sexual orientation dummy variables the model can identify who is male and female heterosexuals, male and female homosexuals. Doing so would display what my hypothesis predicted because we would expect higher infection rates among homosexual males than females.

The significance of these dummies could be tested by running a VIF test, which will tell us the accuracy of the dummies even if they are disguised by a strong F statistic.

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Disguising Affirmative Action

This week, I read an article titles “Is the “Top 10″ Plan Unfair?” The article discusses the enrollment results of the Texas Top 10% law which has essentially flooded public universities with student who would not have been able to attend prior to the law. In terms of my research, this sheds light on college performance and education quality.

The article talks about how high schools are relatively segregated. Given the large amount of land in Texas, high schools are spread out and so are communities. For example, my high school, was in Kingwood, TX, a predominantly white uppermiddle to middle class suburb of Houston. As a result, the majority of students who graduate in the 10% of their class are from that demographic. Effectively, enrollment at state universities, such as UT Austin, have increased their diversity significantly. Assuming that high schools in poorer communities are worse, what has this done to overall college performance and the quality of the education?

Students from low-income areas who graduated in the top 10% of their classes most likely went to weaker high schools than a student from a school similar to mine. The difference in high school quality may be attributed to a number of factors, but it all boils down to one thing, the students’ college GPA’s. Two effects are possible here, 1) the average GPA at state universities has declined, or 2) the average has stayed the same but the rigor of the curriculum has been adjusted to maintain the appearance of consistency. This will make my research difficult because if the second case is the truth (which I feel that most schools would pick between the two), then how do I measure curriculum difficulty between schools or between years at any given school?

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Insufficient Education and Returns

Education, or the lack thereof, has long been thought as a contributing factor to poverty. Banerjee and Duflo discuss how education limits one’s potential earnings, but explore the topic past the mere presence or availability of schooling. One of the authors’ main points is that the existence of schools and the demand for education do not fulfill the role education needs to play.

The overarching themes are quality, incentives, and confidence. Children who receive low quality educations do not accumulate the benefits they should and arguably could be better off learning a skilled labor instead of being in school. The number of children who attend school has increased significantly over the past decades, but that has done little for the economic well-being of many third world countries. This rules out the possibility that poor people do not value education like wealthier people. The problem is that the schools available to a lot of poor people, for a lack of a better term, suck. Understandably this leaves those poor students either bored, or turned off by academics because they feel they are not smart enough.

What makes the quality of these schools so terrible? The incentive structure for teachers is not structured in a way that is conducive for maximizing child performance. In fact, the teachers make an effort to weed out students who do not perform well in the existing learning environment. It is no great mystery that everyone learns in different ways at different speeds. Unfortunately, teachers in poor countries pressure students out of school, leaving them without the education and demoralized.

As a result, many poor people have come to generalize poor academic performance with the areas they live in. This may be the most inhibiting factor because children are surrounded by adults that do not encourage them to be persistent or the teachers to work harder to discover the methods necessary for teaching each child. Essentially, the poor are creating their own poverty trap by way of conventional wisdom which is passed along generations in a vicious cycle.

In this New York Times article, the author looks at the returns on education in relation to determining expenditures. The most difficult aspect is calculating the exact returns to education, an almost impossible task. The returns on education is subject to so many variables, the most important being the student’s effort and the quality of the curriculum. Education opportunities are everywhere, but students and curriculum all vary so greatly that it makes it difficult to generalize economic returns.

This article is written in relation to the United States, where a high percentage of the population knows the importance of education and the government imposes education on all citizens because of its importance; however, if the US is uncertain about the exact returns and the necessary spending on education, how far behind are poor countries?

I think both points are equally convincing and actually support each other. Unfortunately the statistics or numbers I would like to see on this topic are virtually unobtainable. The assumptions necessary to actually derive some sense of the actual returns on education would completely skew the result almost to a point of uselessness. Determining an average return could be helpful, but at the same time it could lead to students going to school who would be better just working. Now that only applies to higher level education, but at the primary level there should be a set standard of capabilities that students have to reach regardless of the time it takes. Another way to look at it would be to set a benchmark of returns on education students are supposed to receive and educate them to that point. Such a benchmark would vary across countries, but progress is progress.

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Texas Higher Education Opportunity Project

For my research project I have chosen the Texas Higher Education Opportunity Project. THEOP is a longitudinal study that analyzed the effect of a Texas law passed in 1997 that guaranteed admission to the state’s public university to high school students who were in the top 10% of their high school graduating class. The study consists of a compilation of data of college applicants to nine Texas universities (seven public and two private) in their sophomore and senior year to analyze admission trends before and after the law was passed.

The top 10% law was passed as an effort to increase higher education enrollment for students of low economic status. I intend to examine the data collected by THEOP to see if the 10% actually achieved its goal; I believe that the law did in fact increase enrollment of poorer students; however, this led to a decrease in enrollment for students in the middle and upper classes due to a lack of available acceptances. Consequently, the quality of students being accepted by the public universities in Texas declined because of a difference in the quality of public high schools.

The quality of the students is a key factor in the quality of a school. The top 10% effectively excluded qualified students from being admitted to the public universities because it did not adjust for the quality of high schools. As the graduate of a Texas public high school, I have seen the implications of this law firsthand, and believe that it excluded large number of fully qualified students from going to the university of their choice. The motivation behind the law was correct; however, giving opportunity to under privileged people loses its luster when it’s countered by taking away opportunity from others.

Differences in the quality of education exist, but actually carrying out a comparison that ranks public schools against each other is difficult. This presents a weak point in my thesis because study did not take into account the quality of the high schools of the subjects. Also, the data can be affected by the personal preferences of the students. Just because a student was accepted, rejected, or qualified, there is no way of know which school each student preferred and it is likely that none of the schools in this study were anything more than a back-up school.

Finally, cyclical changes of the economy can affect college enrollment. During the time that this study was conducted the economy was flourishing which could easily contribute to a person’s choice to go to college or go directly into the work force. If a family is making more money each year, they have an incentive to send their children to college because it is more affordable and the opportunity cost of not going to college can significantly change one’s long-term success.

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The Ignorance Within Conventional Wisdom

In answering why drug dealers live with their moms, Dubner and Levitt reveal the astonishing similarities between modern gang structure and corporate America. The authors start by outlining the research done by Sudhir Venkatesh on a Chicago street gang, the Black Disciples, that was primarily in the business of distributing crack-cocaine. Venkatesh’s original purpose was to gather data from the project neighborhoods in Chicago, but quickly had to change his approach when he had a run-in with crack-dealing who showed him that his questions were not going to elicit the information he was after.

Soon thereafter, Venkatesh paired up with the gang’s leader, J.T.,  who found interest in his research. Although, J.T. was not actually the absolute leader of the Black Disciples; in actuality, he was a manager of sorts and would report to a board of directors who collected a percentage of revenues from each branch. J.T. was college educated businessman and even held a legitimate job prior to becoming a gangster. One might wonder why a college graduate with a steady job would turn to gang life? Simply, J.T. saw more potential in the gang than he did at his job and an examination of his earnings would suggest he made the right decision.

After spending six years of intimate interaction with the gang, the Black Disciples were indicted and one of the members, Booty, was being accused by most the gang members of causing such. Booty knew that he was going to be killed soon and wanted to ease his guilty conscience for the harm his work did to the black community. Most of the gang members believe that selling crack does not hurt the black community; in fact, they argue that it helps because it keeps money within the black community. As a result, Booty gave four years of the gang’s financial records to Venkatesh which J.T. demanded be kept.

Such data had never been put into the hands of academics before, so upon showing the records to Levitt, the two decided to write a paper about the actions of the gang. The financial records revealed a lot about the gang’s operations. First, it gave an indication as to how much J.T. was really making as well as the rest of the gang members. J.T. paid out about $9,500 a month to his officers and foot soldiers (about 53 people), and took home about $8,500 himself (78). The foot soldiers are the people actually doing the drug dealing, but they only made $3.30 an hour and the officers only made about $7.00. The foot soldiers were making significantly less money than minimum wage, so why would they perform such low-paying, risky work (78)?

This is where Levitt and Dubner make their point. Many of the foot soldiers saw the gang as an opportunity for advancement. While many of them maintained regular jobs to make ends meet, they dreamed of working their way up the ladder to be like J.T. The sad truth however is that the likelihood of such mobility is so low. In effect, a lot of the drug dealers are quite poor and need to live with their families because they do not have the means to support themselves.

A major similarity between the Black Disciples and corporate America is the distribution of wealth. Of all full-fledged gang members, 2.2% of them account for more than half of the money (79). This makes the high-level positions extremely attractive from a third-party perspective, but what the aspiring youths of the poor neighborhoods do not realize is the risk involved in making it into the 2.2%. The U.S. Bureau of Labor Statistics rates timber cutting as the most dangerous job with a death rate of 1 out of 200 (80). Drug dealing on the other hand carries a 1 in 4 chance of being killed (79).

The attractiveness of reaching the top of the drug game continually feeds the flame despite the apparent danger involved and extremely low wages. The problem only perpetuates itself because the high-level gangsters become the role models in these poor communities, so the youth aspire to be like them without realizing the virtual impossibility of their endeavor. A parallel to this would be a high school athlete who works hard toward becoming a professional athlete; he make work extremely hard, but the chances of advancing to the professional level are usually slim to none.

What is it that all of these statistics show? Succeeding in the drug game is exceedingly hard. Most crack dealers earn next to nothing, which requires them to hold other jobs consequently leaving less time for them to sell, diminishing their chances of advancement and making good money, thus forcing them to live with their mothers. Also, their chances of dying are so high they will most likely be killed before they are able to make significant strides in the hierarchy of the gang.

The low wage rates observed by Levitt and Dubner highlight the idea of why the crack dealers are living with their mothers. Their comparison of death rate between crack dealing and the government’s stated most dangerous job displays the low probability that many of the dealers live long enough to achieve their goal and the frequent change in employees of the gang. In essence, it is job security for people like J.T. They pay their employees low wages to keep them working, but put them in dangerous situations that will most likely prevent them from contending for a management position.

From the information presented in the text, we can see another way in which crack-cocaine has hurt the black community. Apart from how addictive it is, crack has caused countless deaths and perpetual poverty by way of false conventional wisdom that selling drugs is  a lucrative business for anyone who decides to do it. The development of said conventional wisdom came from a long process by which cocaine became an icon of affluence and glamour and the resulting demand by the general public for an affordable substitute.

Questions like why do most drug dealers live with their moms challenge conventional wisdom, and can potentially yield answers that defeat conventional wisdom. In doing so, we can create the opportunity to improve the lives of many and prevent people from making harmful decisions under false pretenses.

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