Aborting the Crime (Cont’d)

The article The Impact of Legalized Abortion on Crime essentially agrees with the statements presented in chapter 4 of Freakonomics stating the hypothesis that legalized abortion leads to lower crime rates. This article mentions the lagged phenomena, basically stating that since abortion has been completely legalized, we’re starting to see the sudden effects of reduced crime. In Freakonomics several potential causes are brought forth to explain the recent reductions in crime rates, concluding with abortion, but little evidence was provided in their argument.

The Impact of Legalized Abortion on Crime however, provides ample significant data and analysis that better explains the relationship to legalized abortion and crime reduction. However, Foote and Goetz comment on Donahue and Levitt criticizing their analysis, claiming that their misuse of variables created seemingly strong results, but when corrected provided weak results. They also claim that robust variables were not used, which would eliminate obvious biases in the original paper. While Foote and Goetz agree with Donahue and Levitt’s claim, they also say that other factors have a more significant factor than originally stated. Foote and Goetz bring forth a spatial element, claiming that where the crime is, is a huge determinate, and will utilize different variables. An example they use is crack cocaine. While crack cocaine may have significant impacts on crime in urban areas, middle/upper class suburban areas rarely see such drug use, therefore not making it a significant factor.

It would be interesting to utilize a program like ArcGIS to spatially analyze the data to find any underlying patterns. If this analysis was performed, then there may be changes that will allow for much more conclusive results.

Aborting the Crime

Chapter 4 of Freakonomics attempts to explain recent drops in crime rates with varying factors. Some of the variables explained are, increased police forces, increased punishment stipulations, decreasing prices of street drugs, and legalizing abortion.

A couple very strong arguments that I can make sense of that Levitt & Dubner make are that the decreasing in prices of street drugs lead to decreased crime in relation to that drug, and legalizing abortion leads to less crime. The figures and numbers provided for drug use and crime related to it make sense, and theoretically it is relatively easy to grasp. However, like noted in the chapter, this number is rather small. I compared this relationship to a regression with a very significant beta coefficient, but said beta coefficient was so small that it created an unmeasurable amount of change to the outcome variable.

Legalizing abortion also makes sense from a theoretical standpoint. Now I may be stereotyping, but it seems to me that most wealthier women have much better access to methods of contraception, not requiring them to undergo abortions, at least not as frequently as the poor. Poor women however, may not be able to afford contraception, and have to be rather reactionary once they are pregnant. By allowing abortions, there are far fewer newborns born into poverty because of this. Like we discussed several times in class, a child’s education can usually be attributed to the parent’s socioeconomic state. In this case, the poor would give birth to others who would become poor, and since crime can be mostly attributed to the poor, would give birth to more criminals.

Long Term Variations in Natural Gas Consumption

In a nutshell my research paper is analyzing several factors that may or may not explain the variations in the United State’s natural gas consumption. The variables that I chose to regress were natural gas consumption as a function of US natural gas exports, US coal consumption, and natural gas wellhead prices. I was anticipating to see coal consumption and price to have negative significant coefficients, and exports to have a positive coefficient.

Upon initial regression, all variables were statistically significant with coal consumption and exports being positive and prices being negative. I believe that coal consumption showed positive because of an overall increase in energy consumption. I originally thought that an increase in natural gas consumption would lead to a decrease in coal consumption, but I believe I was focused on the short term rather than trends over time.

There however was autocorrelation within my regression. After correcting for autocorrelation each individual coefficient no longer was significant at 95% confidence levels, but the R squared value drastically increased, along with my F statistic. This displayed strength in my overall regression even after correcting for errors.

Finance Symposium

It might be a little late considering the symposium was a few weeks ago, but I feel as if it is still appropriate to voice my opinion on the matter.

One particular statement, made by Dr. Hanushek struck me as being a bit abrasive. He claimed that the addition of a masters degree has no significant positive impact on a teacher’s ability to teach. No other supporting datat or staements were made, leading me to believe that his findings were a bit biased. I believe that the particular masters degree observations should be analyzed a bit further.

In particular, the addition of a dummy variable to asess whether the teacher’s masters degree is related to what their teaching. I feel like he would have obtained different results if this were the case, refuting what he was arguing during the symposium. This dummy variable would be able to tell us if there was any significant increase in a teacher’s ability given that their masters degree has to do with what they are currently teaching. This would weed out the teachers that have masters degrees with no association to what their teaching, thus potentially changing the initial findings.

Are large families poor families?

Chapter 5 of Poor Economics addresses key issues with relationships between developing countries and population growth. The standard perception is that poor countries tend to have higher population growth rates due to lack of education, necessity to rely on children for income, and other factors. A theory that is also proposed is the concept of quantity of quality, that is, the more children a family has, the less attention (schooling, money, food, etc.) each child will get.

A particular statistic that was presented in this chapter was that, “richer countries have lower population growth.” The statistic went on to state that, “a country like Ethiopia, where the total fertility rate is 6.12 children per woman, is fifty-one times poorer than the United States, where the total fertility rate is 2.05.” I would create a null hypothesis stating that a countries wealth has no significant impact on the country’s fertility rate.

If I were to create a regression to predict fertility rates I would utilize a model similar to this:

Y(fertility rate) = α – β1(GDP/capita) – β2(average education) + β3(# of agricultural workers/total population) + ε

GDP/capita has a negative coefficient considering the prior assumption, that as GDP increases, fertility decreases. I also believe that the more education a country offers (particularly to women) the lower the fertility rate becomes. Lastly, countries dependent on agriculture tend to require larger families to tend the farms. Therefore, the more agricultural workers a country has, the higher the fertility rate should be.

If I were to add a dummy variable I would insert a variable that defines if a country is developing or developed. This would ideally tell us (in general) if there is a significant influence on a country’s general socioeconomic standing (from a global perspective) and their fertility rates.

How Oil Prices Effect Natural Gas Useage

Although I have not particularly chosen oil consumption or prices to compare to natural gas consumption, I feel as if this commodity will have similar connections as will coal. This article outlines the complexities with attempting to correlate the natural gas market with the oil market. Some of the conclusions that this paper ended with were that currently, (within the past 20 years) natural gas prices have been volatile due to seasonal demands, but in the long term will be closely associate with oil prices.

Thankfully this authors of this paper have intensely researched trends in natural gas pricing, something that I don’t believe I would have been able to derive. They have also related the pricing to a substitute commodity, oil. This relationship I feel will be very similar to what I am looking to compare. There are some fundamental differences between oil and coal demand and pricing (with oil being mostly imported and coal actually be exported) but the information and methods the authors went about will be valuable information to have.

A concern of mine that I voiced in a prior post was further analyzed in the author’s paper. I was concerned with natural gas being a relatively new commodity, one that is rather volatile and has not yet established itself in the market. I believe that this will make trying to explain the pricing and consumption difficult. I believe this is why they concluded that future natural gas consumption trends will be more predictable than they have been in the recent past.

Original Paper:



Peter Brand from what I grasped from the movie, utilized the concept of causation vs regression. Many of the traditional scouts in the movie correlated looks, athletics, and salary with how well a player would perform, while Peter, having no prior baseball experience looked at the game from a purely statistical approach.

I believe Peter saw that there was no significant relationship between these variables, and how well a team could perform. Much similar to the ice cream truck/murder example, the causation for these players having these statistics was not related to the style of their play, or physical appearance, but rather related to something else. This problem, I believe can be directly associated with Billy’s failure in his professional baseball career. The individuals who scouted him valued his style of play and characteristics, but clearly did not recognize the underlying baseball oriented skills needed in the MLB.

Billy Beane, being a (relatively) younger scout among the others, recognized the essential flaw in his fellow scouts’ analysis and gave Peter’s concept a chance.

Potential Hiccups with Natural Gas Research

A few concerns have been raised after reading an issue in the New York Times titled, “Natural Gas News.” I initially was thinking a  regression analyzing natural gas consumption would be rather simple and easy to comprehend, but after hearing some things stated in class (in regards to multicollinearity and pricing) and reading some articles I’m starting to have second thoughts.

The New York Times article brings up the concept of pricing volatility and instability in regards to natural gas. Natural gas is a relatively new fuel (in comparison to coal) and has only just seemed to settle as a steadily priced commodity. In the early 1900’s natural gas was flared or vented out of oil fields and was seen as a nuisance. Only recently with the rising prices of oil, and concern with nuclear security has natural gas become appealing. This price instability might make it very difficult to find relationships between gas consumption, pricing, exports, and coal consumption.

I’ll have to further research and analyze my data and results to accept or refute these theories.

Teachers Who Don’t Go to Class

Chapter 4 of Poor Economics essentially highlights problems with education systems in the global south. Throughout the chapter many hypothesis and viable plans are presented, ranging from public/private incentives, required schooling, basic supply and demand structures, and long term investment. Although these are well thought out plans offering plausible options, the poverty in some of these countries is just too great to fund these methods.

Emily Donahue, from Kut News recently posted an article briefly analyzing poverty stricken Pakistan, and the lack of education plaguing the country. The statistics from the two readings are remarkably similar, with poor teacher attendance levels (up to 25% absent on a given day), to barely literate children, to even absolute negligence of the government sponsored school systems. I would love to see data and analysis comparing perhaps monetary incentives to students for attending and doing well in school, rather than incentives given to their parents. I believe that children and young adults respond very well to incentives, and if pushed to perform well, we would see good results.

I personally can connect and relate better with Donahue’s story. Donahue I believe properly utilizes global statistics to allow the reader to interpret and grasp the seriousness of the findings. She states, “Nearly half of Pakistan’s population is under age 20. Sixty percent are under 30. More than a third of Pakistan’s young people live in cities, and almost that many are uneducated.” These numbers, if true are remarkable. It shows the true potential of an educated younger generation. Don’t think that the Pakistani doesn’t see this either. The Pakistani government has implemented over 130,000 public primary schools, and tens of thousands of secondary schools. This however just leads us back to Banerjee and Duflo’s original question of why people in these countries aren’t being educated.

If I had an answer for that, I’m sure I wouldn’t be struggling in an entry level Econometrics course. I also would most likely not be worried about paying off student loans. The “cure” for developing countries to reach higher levels of education certanly is not going to be discovered over night. While some developed countries have advanced rather rapidly within the past 100 years, it’s going to take some of these more poverty stricken countries much longer to progress to our levels, either because of lack of resources, war, or lack of desire.

Natural Gas

The topic that peaked my personal interest has been the natural gas market. Natural gas consumption is currently as an all time high and yet demand for it is decreasing day by day. what has gotten me into further investigating the natural gas scene, has been all the hype, or should I say lack there of in the NYSE.

Natural gas in many ways, is seen as the new oil, and American has a whole heck of a lot of it. With the discovery and exploitation of the Marcellus Shale region natural gas prices also have never been so (comparatively) low. I plan to analyze the effects of natural gas pricing, in particular on natural gas power plant consumption (or electrical output). I’m planning to observe a negative correlation between the two variables, but there are also many other unseen variables that could effect the pricing or consumption structure.

First, policy could be a huge determinant of the future use of natural gas. Currently, the process of modern natural gas extraction (hydrofracking) is immune to the safe drinking water act (as of 2005), when the federal government passed an “Energy Act” allowing drilling companies to privatize chemicals they are using to fracture the shale.  I believe any sort of policy restriction or regulation would greatly effect the cost of natural gas and would change this rapidly growing industry.

I’m planning on using the U.S. Energy Information Administration for their supple archive of natural gas consumption, production, abstraction, pricing, and other general data. Acquiring and analyzing data all from the same source I feel will be beneficial to my study, with little conflict between comparing years of different variables. I may also look for relationships in coal consumption, another variable available through the EIA.

One main difficulty to my study will be pricing not related to simple supply and demand. From my recent observations natural gas has had a very strong public relationship, both good and bad. It seems as if the public’s mindset can really alter the value of natural gas regardless of how much is being produced, or how cheap it becomes. it will be interesting to see if natural gas follows coal’s path to being the US’ top energy reliance.