Abortion and crime rates

The common theme between these two papers and the chapter from Freakonomics is that they both focus on the relationship between the legality of abortion relative to crime rates and how they decreased. In the first paper, by Donohue and Levitt, they focus on why crime rates dropped in America in the 1990s and how abortion influenced if not caused the dramatic decrease. Both Freakonomics and the first paper focus on this area and look at the time when abortion was made legal, in 1973 as a result from Roe v. Wade. Basically what they find is fairly straight forward; legal abortion caused a drastic decrease, as much as 50%,  in crime rates; however, not immediately after it became legal. Since people do not start committing crimes when they are babies or young children, there was a period of time that passed before observing these dramatic drops in the crime rates. Thus, in addition to studying how abortion affects crime rates, they also discovered that crime rates are highest amongst teenagers, from about fifteen to twenty years of age. This is because there was about an seventeen year lag from when abortion became legal and when crime rates started dropping. They also provide convincing arguments for why this is the case, and evidence with regressions. In addition, Levitt and Donohue account for several other possible causes of the sudden reason for the reduction of crime. However, they find that they did not have much of an impact upon crime rates relative to what they had been doing before 1990. This is primarily due to the fact that most of the regulations, laws, and other contributing factors to crime reduction had been in place before and after the drop, thus being fairly unimportant.

Although Donohue and Levitt do present a good argument for abortion and crime rates, the critique by Foote and Goetz offers some interesting points that they say Donohue and Levitt seemed to miss. The primary thing that they talk about is the way in which Donohue and Levitt ran their regression model and how they incorporated the data. They say that in order to look at something like crime rates, one cannot collect data from different states, cross-state data, and in this case different cities and then compare them. They suggest that collect data from each state individually, in-state data, and then analyze crime rates in cities amongst themselves. Then you can compare city to city rather than making larger, less accurate assumptions. In addition to this critique of their work, they also look at the variables in their regressions and find that by changing some of them, it makes the regression no longer significant. They reason that in all the regressions that Donohue and Levitt run, they use the amount of arrests per capita to prove their point, however, in one regression, they stray from this logic and simply use the total amount of arrests, which would completely change the output from the data. Thus, Foote and Goetz run the regression using per capita assets and find that the regression becomes insignificant, consequently nullifying the conclusions made by Donohue and Levitt.

Although after reading the first paper and comparing it to Freakonomics and then reading the second paper and finding out that it was mostly based off false conclusions was kind of annoying, however nonetheless interesting. Despite the fact that Donohue and Levitt’s point is technically based on a poor regression, they still do provide a very valid point; that the legalization of abortion do in fact decrease crime rates due to the basic principle of reducing the amount of people that could commit crimes several years down the road.

Cyclical Trends in Macroeconomic Predictors and related fatalities

In my paper, I am focusing on finding out whether or not there is any correlation between the quarterly and annual Gross domestic product and construction related injuries and fatalities, and additionally how strong that correlation might be. The reason I am focusing on this is to see if you are able to predict levels of construction safety spending and insurance policy costs based on the cyclical trends in the gross domestic product. I will carry this out by looking at the GDP and running regressions with construction incidence rates based on the size of the firms. I include the size of the firm to see whether there are higher incidence rates amongst larger firms, relative to the smaller ones. This is important because it relates back to my hypothesis because I predicted that as the economy grows, so do construction companies and once they begin to spread them selves too thin over too many projects, more injuries begin to occur. I found that there is a correlation between GDP fluctuations construction related fatalities and injuries, therefore, it is possible to predict, with some accuracy, the relative amount of incidents that will occur based on cyclical trends in the economy

Post #7

The Article that I have read, related to my topic was an article titled “Construction Accidents and Injuries Both Declined in 2011, While Construction Permits Increased 7.7 Percent” at http://www.nyc.gov/portal/site/nycgov/menuitem. The main point of the article is that due to the increased insurance costs and more serious construction precautions, construction related accidents have decreased in the last two years.

Mayor Bloomberg is quoted in the article, for saying that he “announced an 18 percent decrease in construction-related accidents in New York City for 201.” This raised an interesting idea for what I was focusing on. Originally, I was looking at simply the impact of economic cycles on construction fatalities, but I didn’t think to include the policies that the mayor would have initiated related to construction safety.

Fatal Construction Accident

A recent article that I found pertaining to construction fatalities in New York City was an article about a crane collapse trial. http://newyork.construction.com/yb/ny/article.aspx?story_id=169745949. The main focus of the article is that James Lomma, the manager of a construction company has been accused of manslaughter for intentionally dodging regulations on a crane repair. 

The article, although short provides a very interesting and important detail to the research that I am doing because I am focusing on why the economy effects fatalities in New York construction workers. I am analyzing the deaths and when they occur relative to economic cycles to see if there is a correlation between construction companies and deaths. The idea being that the companies try to take on more jobs by buying cheaper unskilled labor and cutting corners in areas such as safety in economic boom periods, thus putting their workers at a higher risk. 

This article made me consider several alternate sources of information that I could use in my paper. The primary thing that I thought of is to look outside New York City into other areas around the country for construction related accidents and see how common they are. Additionally I found that I could approach my paper from a broader angle, looking at how people have addressed the issues in other areas and then further relate it to New York City.

Poor Education

The whole point of this chapter was basically to show how there is a correlation between poverty and lack of education. They show how through several different programs that were implemented in India, Pakistan, Kenya, and several other countries, that if there was some type of economic stimulus, education would increase amongst children. Additionally they show that public schools, when they are in poor condition, push the local people to demand private schools. This in turn further promotes education for the children because they show a dramatic increase in the level of education from private school kids relative to public school kids. However, all in all, the education system in India and other countries alike is horrendous.

 

In a blog post online, located at http://www.africaw.com/forum/f2/education-is-the-most-neglected-sector-in-india-t2633/, there is a similar message described like the one in the chapter. Mainly, education is absolutely horrible in India. The author makes it clear that the education system is in shambles and nothing has really been done to re-direct it. One point that he brings up is that a major problem that has arisen from this detachment of education is that diplomas and degrees do not carry any weight in the job market which further deters individuals from pursuing education.

 

The blog and the chapter are very similar in the message that they address; however, the chapter in poor economics is obviously much more convincing. Banerjee and Duflo provide a plethora of accurate and comparative statistics to prove their several points about child education and its relation to the family situation and their economic standing. The primary statistics that they focus on are percentages of students being able to read a first grade level passage relative to the grade they may currently be in to show how poor the education system is. The primary difference between the two readings was that the blog addressed the issue of adult education as a separate issue, as opposed to the chapter which focused primarily on the lack of child education.

Economic Cycles and Construction Fatalities

The topic that I am focusing on is the correlation between the amount of construction dollars spent per annum and the construction fatalities during economic boom and bust periods in New York City. As economic cycles take place, construction spending tends to follow in a similar manor, increasing as the economy prosper and declining as the economy too, declines. However, construction related fatalities tend to follow the same trend as well. Using data from the Bureau of Labor Statistics (http://www.bls.gov/iif/oshsum.htm#10Summary%20News%20Release ) and from the New York Building Congress (http://www.buildingcongress.com/research/costs/01.html ), I will analyze the correlation between construction hard costs and construction fatalities in New York City.

This topic is important because it will show if there is a strong correlation or not between increased costs and fatalities, and it will additionally explain why there might be increased fatalities during times of economic prosperity. The primary reason for choosing my topic is because I am interested in insurance and my uncle’s company works with construction insurance.

The primary difficulty in this estimation will be finding the relevant data amongst all the other variables that could affect construction costs and also fatalities. Also because the Bureau of Labor Statistics does not just have construction fatalities for New York City alone per annum, it will be difficult to separate the data and account for any outside factors.

Who is joining the US Military and why?

The topic that I am focusing on for my paper is the United States military and the impact of various cultural and geographical elements upon individuals and their decision to join the military. The United States has long had a tradition of military power, but who exactly makes up the United States military. Is it an individual’s socio economic position, geographical location, religious association, or a combination of all three which dictate whether an individual joins the United States military? This question is the main idea that I am investigating, as stated in my thesis and I will be using data sets from the United States Census and several additional research projects conducted by military officers. This research data can be found at this link: http://www.civicyouth.org/PopUps/WorkingPapers/WP32Adamshik.pdf.

The reason that I decided to research this topic is because my Uncle and Grandfather were both in the military, as well as several other family members and I was interested if their reason for joining might have been for any specific reason, or just a sense of duty or patriotism. I feel that it is an important topic because national defense and the military are crucial to any country’s survival and strength. Additionally, I feel that it is important for the military to see who they are recruiting and for what purposes.

Since the question focuses on the United States military, which affects almost every citizen, there are several policy implications that will have to do with the data. Some examples of this are military spending to provide students with an education, or people on welfare being more likely to join the military. Additionally, in the south, gun laws tend to be more lackadaisical than in other parts of the country and may also impact a persons willingness to join the military.

Although the United States military is a powerful, well organized government body, there are several difficulties in obtaining data and making estimation difficult. The primary problem that I have already encountered is obtaining data sets that haven’t been shut down or controlled by the government. I have run into the problem already that a majority of the data providing information about members in the military and much of their religious, socio-economic, and geographical data is undisclosed. This makes it primarily difficult to find a large amount of observations for data and the accuracy of the data. Additionally, the United States military is an enormous institution, drawing from members of every social class and geographical location. Therefore, it will be important to pay very close attention to the detail of the data, because it may be difficult to find the specific observations that will answer my question.

Why Drug Dealers Still With Their Moms

In Chapter 3 of Freakonomics by Levitt and Dubner, an interesting idea is put to question, which is why drug dealers live with their moms. The reason that this question is interesting is because of the stereotype that drug dealers make a lot of money, otherwise, why would you be a drug dealer. Levitt and Dubner explain the answer to this question by analyzing the field work of a University of Chicago student, named Sudhir Venkatesh. They follow the work he did in Chicago on the crack market there and found some shocking and interesting results.

They first address the large problem of crack dealing that is going on in Chicago’s many housing projects. After looking at Venkatesh’s studies, as well as the notebooks obtained from a member of the gang, Levitt and Dubner make several interesting conclusions about how the specific gang, the Black Disciples, actually worked. They break down the chain of command of the gang, relating it almost directly to McDonald’s chain of command, with a board of directors, and so on. They go on to break down the financial structure of the gang to answer the question presented in the chapter and find out why drug dealers aren’t as rich as they are made out to be.

Using a series of accurate statistics, recorded in the gang’s ledgers, Levitt and Dubner can accurately address the question and find an answer. First, they explain the growth boom that occurs and where that money goes.  On page 97, they show the monetary expansion quadrupling “from $18,500 a month to an astonishing $68,400 a month”. They use this statistic to show how much money is made per month; however, they couple it with another statistic on page 99, showing that the gang section’s leader, J.T. would “make $8,500 per month (roughly $66 per hour) in contrast to his foot soldiers earning only $3.30 an hour”. This statistic shows the dramatic gap between members of the gang and it helps show how most drug dealers don’t actually make much money at all. Additionally another statistic that Levitt and Dubner use to further strenghten this point, found on page 100, is “that the top 120 men in the Black Disciples gang represented just 2.2% of the full-fledged gang membership but took home well more than half the money.” This shows just how much the few men were making and how roughly 98% of the drug dealers in just that gang were making nearly nothing.

Finally, Levitt and Dubner use another statistic to express another interesting point. On page 101, they show that if you were a foot soldier in the gang, (making $3.30 and hour) you had  “a 1-in-4 chance of being killed.” They relate that statistic to the odds of a timber cutter, which is considered to be the most dangerous job in America, where they experience a “1-in-200 chance of being killed.” It is obvious that it is not worth it at all to be a foot soldier, which is why, after a year or two and realizing how little they get paid, the men drop out.These, along with several other reasons, show exactly why drug dealers still live with their moms. It is because the vast majority of them make such drastically little pay compared to the few that actually run the show.

The Health Trap. Review of Poor Economics Chapter 3

     Just as the poverty trap was discussed in Chapter 2, Banerjee and Duflo explain a similar trap in Chapter 3. They introduce another theory known as the Health Trap which focuses on the correlation between poverty and health quality mainly in poor, developing countries. It is similar in several ways because it maintains the same S-curve that is used to explain a persons position in the poverty trap.  Additionally however, there are a variety of very affordable effective measures, known as “low-hanging fruits” that can be taken to reverse the problem, yet people do not seem to take advantage of them. 

     One major area that Banerjee and Duflo talk about in detail comes as shocking is the under utilization of technologies by poor people. They introduce this problem by explaining the most obvious and simply forms of technology, such as breast feeding and continue their explanation on the poor usage of water piping. They combine the usage, or lack there of, of these technologies and relate them to the cost. Breast feeding for example is completely free, however, for some reason over 60% of infants are breast fed for the recommended amount of time, and for only 190 rupees per month, a family can get clean water piped into their home. 

     Banerjee and Duflo do a good job in this section of reinforcing the main thesis of the health trap with the statistics of under utilized, cheap technology. They provide simple technological improvements such as bug nets, Chlorin, and several other technologies. In addition, they go into specific detail about the addition of mosquito nets to reduce malaria. They explain how people would take bug nets for free more often than pay for them, but even as demand decreased there was an interesting problem, specifically that having mosquito nets led to a dramatic 15% increase in income. Why this wouldn’t be incentive enough alone to buy a a simple mosquito net for roughly $0.75 USD PPP. 

     Although the statistics in this section are quite useful to backing up the main argument in the chapter, one statistic that I feel they could have included is families, or individuals income when assessing the demand of mosquito nets. This statistic would strengthen the argument by showing how a persons income directly affected what they would buy. This chapter is much like the previous, in the sense of how it addresses a trap that involves impoverished people, however, it supplies a much wider series of data which helps support the main argument of a health trap. 

Review of Poor Economics Chapter 2

In the second chapter of Poor Economics, Banerjee and Duflo analyze a series of interesting problems taking place in certain countries such as India and others. The problems that they address are focused on the relation between poverty and malnutrition. Throughout the chapter, they expand upon a theory, which they refer to as a hunger-based poverty trap. This idea suggests that poverty and hunger go hand-in-hand, specifically that a lack of food in a family results directly in poverty.  This creates a cycle, in which if a person is able to eat enough calories to live, they can begin working harder, making more money, buying more food, and so on and so forth. This increases the gap between the rich and the poor, with the rich getting richer and the poor getting poorer.

This basic idea about the hunger-based poverty trap is what many people, who are suffering from malnutrition, believe is the problem. However, as the chapter progresses, it becomes quite obvious that this idea of the hunger-based poverty trap is actually based on a series of incorrect assumptions which dramatically affect the relationship between poverty and hunger. Banerjee and Duflo use several specific statistical methods to prove the cause of a hunger-based poverty trap. Initially they use an S-shaped curve to graphically display what this poverty trap might look like. This curve includes several of those assumptions which actually weaken the idea of a hunger-based poverty trap. This is primarily because it assumes people consume as much as they can afford, when in actuality its opposite. People suffering from malnourishment and poverty generally spend a proportion of their income on other goods, as well as more expensive food goods with the same caloric intake. Additionally, the FAO (Food and Agricultural Organization)  released a statistic that showed there was enough food in the world to feed everyone.

Although the evidence seems like it would make sense, along with Pak Sohlin’s views, the data that Banerjee and Duflo present does in fact show the existence of the hunger-based poverty trap, however it also shows that it does not include everyone. I do not believe that the trap effects all people who are impoverished or malnourished. I feel that it is an understandable assumption, however, it needs much more specific statistics in order to prove that people act rationally and fall into this trap. Based off the reading, I feel that people react irrationally to the market when they buy more expensive food products and do not feed their children iodine pills. Although it is a terrible problem, I think that there are many ways in which people in Asia and countries such as India can act rationally to reverse their problem.