In the article, The Impact of Legalized Abortion On Crime by John J. Donohue III and Steven D. Levitt, the authors discuss the relationship between the amount of criminal activity in the United States and the legalization of abortion. The article discusses the impact that the legalization of abotion in the United States Supreme Court case, Roe v. Wade, on January 22, 1973 had on the crime rate between 1991 and 1997. The authors combine the research concerning how legalized aborion affects birth rates across different groups and the crime rates across groups to attempt to predict the impact of legalized abortion on crime. They also analyze the factors of race, motherhood, unmarried motherhood, and unwantedness in the scheme of their research as well. They come up with a variety of findings, such as the resulting 5.4 percent reduction in the total number of homicides that occurred following the legalization of abortion (Levine et al. ). The authors go on to consider differential abortion rates by race to predcit that the impact of abortion legaliztion on homicde rises from 5.4 percent to 8.4 percent when this factor is taken into account. Taking into account the reduction in fertility of teenagers and unwed women is also estimated to raise the impact of abortion on homicide from 8.9 percent to 12.5 percent. When adjusting for unwantedness, they predict that this would predictably increase the effects to 18.5 percent. As a result, the authors predict that crime should fall by 18.5 percent in areas where abortion is legalized. The article also considers sources of variation in the national time series of crime and abortion, differential crime patterns across early legalizers and other states, and the impact of state abortion rates on state crime rates. Through statistical and regressional analysis the authors are able to make further predictions about their findings. The time series data showed that the the crime rate should continue to fall as long as the effective abortion rate increases. The analysis of states that legalized abortion earlier than others also discovered a growing gap in the effective abortion rate between the states that legalized abortion early and those that did not. However, the authors decide that the three year difference in legalization is to large a reduction in the crime rate to be attributed to the date of legalization. The third area of research reveals positively increasing effects of factors on the crime rate, although these effects are not that large.
The two articles and the chapter in Freakonomicsshare similar opinions in regards to their views of the effects of legalized abortion on the crime rate in the United States. Freakonomics doesn’t go into as much detail in regards to regressional analysis as Donohue and Levitt go into in The Impact of Legalized Abortion on Crime, but still does a good job of providing evidence to suport its predictions. Both pieces pursue findings in the same area of research and find essentially the same results. The Impact of Legalized Abortion on Crime is a little harder to follow and comprehend than Freakonomics, but overall both are very thorough in their research and analysis. Through reading both pieces, I can say that I was able to develop a deeeper and more comprehensive understanding in regards to the relationship between the legalization of abortion and the crime rate in the United States.
In Chapter 4 of Freakonomics, the authors discuss the potential causes for the decline in the crime rate beginning in the early 1990s. They present these causes and describe the significance of them in how great an effect that they have on the crime rate. I thought this chapter was very interesting, but think that the the authors could have gone into greater depth in explaining how their determined causes are relevant in their significance. While they explain some of the reasons in great detail, others they merely touch on briefly. The chapter also kind of flip flops on it opinion of the degree of of influence the variables have on the crime rate.
I think the strong point of this chapter is that they analyze the relevance of certain variables in great detail. They go spent a great deal of time discussing the effects of the unemployment, the increase in the number of police officers, and the increase in capital punishment among other variables. The authors provide a lot of evidence to support their claims through the use of a great deal of statistical data, which they analyze in depth. They study the significance and the amount of correlation that exists for each hypothesis they make, and as a result are able to narrow their beliefs down to the most likely causes for the decline in the crime rate. These reasons can be due to improvements, like the increase in police, and also by detterants like the fall in conviction rates for drug offense charges.
One question that I would have for the authors would be pertaining to whether or not one would see the same or at least similar results in other countries throughout the globe? It would be interesting to see if these conclusions are just an observation of American behavioral changes or if has to deal with human nature as a whole. I would also be curious as to whether the authors believe their are other factors that can influence the crime rate that do not pertain to the economy and government action. Almost all the factors discussed are in reference of these areas, so it would be interesting to see if there are other more unique variables that can later the crime rates.
The only thing that I am suspicious about in regards to this chapter is whether or not all the data is entirely correct, and not possibly biased or exaggerated to an extent. Some of the authors’ thoughts seem somewhat opinionated, and therefore compromise the validity of their observations to an extent. While this bias is not that great of a deterrant to the chapter, it is something that I feel should be considered.
In my term paper I hypothesize about the relationship between drunk driving fatalities and gasoline prices. Using a regression model that takes into account the effects of gasoline prices, seat belt usage rate, unemployment rate, public transportation use, average alcohol consumption, and vehicle miles travelled (VMT) on total drunken driving deaths, I looked to discovery if there is any correlation between the variables. Over the course of my research I saw that certain variables possessed a stronger relationship than others and that other variables didn’t really have much of an effect on total drunken driving fatalities. The data that I uncovered was truely fascinating in my opinion, and ended up being a source of some potential leads in achieving the goal of my paper. My hope was to try to uncover ways that drunken driving could be reduced and save the lives of thousands of people each year through the relevance of the independent variables in my regression model. This would likely need to be done through the implementation of new taxes, policies, and regulations by the government, which I believe could reduce the amount of driving fatalities without increasing the financial strain on American families.
In Chapter 5 of Poor Economics discusses the issue of population control and contraception. It discusses the issues faced by impoverished countries within families with many children. The result of them having more additional children ends up with them having less of their income to spend on each child in terms of food, healthcare, education, and various other beneficial investments in human capital.
A statistic that I found interesting was on p. 100 concerning women in Columbia. It states that “women who had access to family planning as teenagers through this program had more schooling and were 7 percent more likely to work in the formal sector than those who did not.” I think this statistic is appealing to analyze because a lot of causality can be found from it with proper research. A hpothesis worth testing in regards to this statistic is if the closer the proximity of a family planning program center to a teenage women’s place of residence will receive more education and be more likely to receive employment than those who do not. This hypothesis can be tested by analyzing the percentage of teenage Columbian women who go on to more education and employment in a town relative to the closes family planning program in the area. Through testing and researching, it would be possible to see if there is statistical significance to this hypothesis.
This can be done in the form of a regression in terms of the total amount of education received by an individual as the dependent variable and variables such as proximity to a family planning program, total family income, and amont of sexual education received being independent variables in the regression model. This could show if there is statistical significance in regards to the independent variables relative to the dependent variable as well as the other variables on the model. One could use a dummy variable in this model as well. The dummy variable could be whether or not a faimly is in possession of a motor vehicle, which can be expressed a V, in which V=1 if the family owns a motor vehicle and V=0 if the family does not own a motor vehicle. Availability of a car could effect the data because it would allow the woman to have greater access to family planning as she can travel further with a motor vehicle than she could have otherwise without one. I would expect the dummy variable to reveal that access to a motor vehicle would result in an increase in access of family planning, which would then cause an increase in the amount of educationreceived by a woman as well as the likelihood of working in a formal sector than the original regression model did.
Although I enjoyed the movie Moneyball, I must say that it was merely a Hollywood version that did not divert much attention to the numbers and statistical analysis, but rather only really mentioned them in little detail. I read the book many years ago before I really had much knowledge of the material discussed, but still recall that the book spent a lot more time on the numbers aspect then the movie did. Billy Beane, the Oakland Athletics’ GM, with the help of assistant GM Peter Brand, a recent Yale graduate, the two are able to implement statistical analysis to figure out how to assemble a talented team on an affordable payroll. The two place a great deal of emphasis on signing players that score and get base frequently for below market value. Using this strategy, they are able to acquire players whose value would likely go unnoticed by the majority of the other MLB teams. They also look for young pitchers that show great potential and are able to be signed for cheap salaries. This strategy led to the Athletics advancing to the postseason during the 2002 season, much to the disbelief of most baseball experts. The film touched on the main themes of the book, but did not soend a great deal of time on the specifics that made up a large portion of the book.
There was certainly a great deal of time that was poured into the valuing of players by Billy Beane and other members of the Athletics organization. I’m sure they placed a great deal of emphasis calculating the significance of certain statistics relative to runs scored, runs allowed, and ultimately games won. Peter Brand spends large amounts of time creating algorithms to predict performance of players and compare their performance to the rest of the league. The vagueness of the film limits the depth that I can actually go into explainign how he went about this, but my understanding reveals that the use of mathematical and statistical analysis helped turn a once downtrodden franchise (after the loss of their three most popular players to free agency) into one of the great success stories of the 2002 season. I have no doubt that the Oakland Athletics front office did a multitude of regressions in relation to certain statistics to allow them to identify the players that they wanted to field on their team. In the end, the risks they took paid off for them, and opened an era of baseball in which the use of statistical analysis is now widely used throughout Major League Baseball.
Over break I read an article on Foxsports.com that describes the degree to which statistical analysis and regreesion analysis is used in baseball, and it is truely fascinating. One statistic they analyzed is the speed that the ball comes off players’ bats using a slowed-down, multi-frame camera. This show true dedication, and proves just how far this once foreign style of baseball analysis has come in just about a decade. Here’s the article for those interested in checking it out: http://msn.foxsports.com/mlb/story/Seamheads-gather-to-analyze-baseball-analytics-68473419
Here is the link for the paper that I will be discussing in my blog post: https://ceprofs.civil.tamu.edu/dlord/Papers/Ye_et_al._NBIGARCH_Model.pdf
Here is the citation for the article as well:
Ye, Fan, Tanya P. Garcia, Mohsen Pourahmadi, and Dominique Lord. “Extension of a Negative Binomial GARCH Model: Analyzing the Effects of Gasoline Price and VMT on DUI Fatal Crashes in Texas.” (2011). 15 Nov. 2011. Web. 28 Feb. 2012. https://ceprofs.civil.tamu.edu/dlord/Papers/Ye_et_al._NBIGARCH_Model.pdf.
This paper is a compilation of the data by two professors and two research assisstants at Texas A&M University. In their research, they explore the relationships between gasoline prices, DUI fatal crashes, and vehicle miles travelled (VMT) in the state of Texas. This study adds a variable that I had considered slightly before, in terms of the amount of travel done by individuals. This is a variable that could prove to play a role in determining the correlation between gasoline prices and DUI fatal crashes. I already have considered many other variables that could influence the number of fatal DUI crashes, including public transportation use, seatbelt use, unemployment rate, andtotal automobile crash fatalities. This new variable could prove valuable as well because I would expect that the longer that a person spends driving in an automobile is likely to result in an increased probaility of being involved in an automobile accident, possibly involving alcohol.
The authors also manipulate their two regression models, so that they are more valid to their research by taking into account “overdsipersion and serial corelation issues in the temporal crash data.” The second model they use to analyze the relationship between VMT and gasoline prices. By adjusting their regression models, they are able to make more precise estimations of the different parameters in their models. Their research reveals some adjustments to my own model that I might need to take into account as well in order to improve the significance of their relationship to the number of fatal DUI crashes in my own research.
Their findings uncover no real significance between the effect of gasoline prices on fatal automobile accidents, which as a result would include fatal DUI collisions. They instead find greater interest in the positive relationship between vehicle miles travelled (VMT) and fatal DUI crashes. They contribute the lack of public transportation in Texas cities as a significant reason for this along with the fact that they discovered that the behavior of drivers in Texas. Thus, it becomes clear that gasoline prices have no effect on the likelihood that drivers will drive more cautiouslessly (ex. speed less and drive less aggressively) or consume alcohol before getting behind the wheel. This is congruent with some of what I came across in my own research, which is a good sign. This article is only representative of Texas over a short period of time though, so I must do further research and not make assumptions that coincide with the discoveries of these researchers. Overall. this paper does provide me with some good evidence that can assist me in the writing aof my own paper.
In an article on Insureme.com (http://www.insureme.com/auto-insurance/higher-gas-prices-mean-fewer-drunken-driving-crashes), the author, Justin Stoltzfus, discusses the research done in one of my sources as well as his opinion concerning the article. He finds it great that despite the fact that gas prices sit around $4 per gallon, at least drunken driving has seemingly decreased due to this. Obviously, this article being from an insurance website reveals just how important this article is from an insurance stand point in term of insuring people. Stoltzfus describes how the increase in gasolone prices resuts inAmericans having less money to spend on alcohol, and thus as a result decrease the likelihood of them being involved in a drunken driving accident.
The article also describes just how expensive it is to get convicted for a DUI. It describes how it puts a great strain on the budgets of people convicted along with significantly increasing their auto insurance rates by putting them in a higher risk category. I find this article to be interesting since it is from an insurance stand point, and not that if academic’s point of view like most of my sources. Insurance is costly enough as is, so this provided incentive to not get behind the wheel while intoxicated is encouraging. I think that it is extremely interesting to see how various areas view this issue, and look to research this further.