Chapter 4: Where have all the criminals gone?

In Chapter 4 of Freakonomics, Levitt and Dubner examine the drop in crime rates in the 1990s and the various factors that may have caused it. Some of the crime-drop explanations include innovative policing strategies, increased number of police, tougher gun-control laws, a strong economy, among others. However, Levitt and Dubner conclude that legalizing abortion through Roe v. Wade was the main reason why crime rates dropped: “…Roe v. Wade was indeed the event that tipped the crime scale” (141).

Levitt and Dubner provide much evidence as to why abortion is the main factor in the decline of crime rates; however, I have a problem with the fact that the authors assert that abortion was THE reason for the drop in crime. They spend much of the chapter examining all of these factors other than abortion rates , but the authors conclude that legalizing abortion was the sole event that caused the decline. In my mind, that brings up another point of contest; it seems as if Levitt and Dubner have confused the concepts of correlation and causation. The previously mentioned quote that states that the legalization of abortion was the “event that tipped the crime scale” certainly implies a causal relationship. But, in the sentence immediately following on page 141, the authors discuss correlation: “There are even more correlations, positive and negative, that shore up the abortion-crime link.” The authors give readers reason to believe that abortion contributed to the decline in the crime rate, but I am still skeptical about their assertion that it was the single most important contributor.

An interesting portion of the chapter is right at the beginning when the authors provide an example from Romania. In 1966, Nicolae Ceausescu—the Communist dictator of Romania at the time—made abortion illegal. Researchers found that this contributed to crime. “Compared to Romanian children born just a year earlier, the cohort of children born after the abortion ban would do worse in every measurable way: they would test lower in school, they would have less success in the labor market, and they would also prove much more likely to become criminals” (116).

The structure of this chapter was a definite strength. The authors began and ended the chapter by discussing abortion as a main factor, and examined other factors in between. I thought this certainly strengthened their argument.


Brief summary of paper

Is there a correlation between temperature and electricity prices in Philadelphia, PA? Through the use of day-ahead pricing data from PJM—a regional transmission organization that supplies electricity to Philadelphia—and temperature data from the Franklin Institute in Philadelphia, I will examine the potential relationship between temperature and electricity prices.

The relationship between supply and demand is vital to determining electricity prices. Excess consumption of electricity can lead to black-outs; excess production of electricity can be wasteful and costly. There is currently no way to harness large amounts of electricity for long periods of time. This means that supply and demand must line up closely at all times, or some party is going to experience the negative impact.

I found a definite correlation between temperature and electricity prices. Because temperature impacts demand, prices are affected. Mild temperatures exhibit the lowest electricity prices, while at both hot and cold temperatures, prices are high. This is a U-shaped relationship.


In 2002, the Oakland Athletics set the American League record for consecutive wins with 20 in a row. The team finished the season with 103 wins and won the American League West division, and they did so after losing three of their biggest stars. How did the A’s accomplish such a feat? Some would call it luck; some would call it a fluke. When compared to other MLB teams, the 2002 A’s bankroll was tiny. But the A’s succeeded that year because they assembled a team of low-cost achievers. The players who they acquired throughout the season were not superstars; rather, these players were guys who could get on base and could score runs. Without runners on base, it’s impossible to win baseball games.

In Moneyball, A’s general manager Billy Beane and assistant GM Peter Brand employed statistical analysis to discover which players provided the best production for the lowest cost. Traditionally, players were judged based on statistics like batting average, runs batted in, and home runs. However, using statistical analysis, Brand displays that on-base percentage and slugging percentage are better indicators of offensive production. Beane takes Brand’s advice and acquires new players—many who appear to have little value to other teams.

Beane was supposed to be a superstar himself; he was drafted out of high school in the first round for his ability to run, to throw, and to hit for good batting average. However, his professional career did not pan out. I found it funny when Beane asked Brand to use his statistical analysis to predict what round of the draft Beane should have been drafted. His results displayed that Beane should have been drafted in a much later round. After hearing this, Beane realizes that Brand’s method could be really useful. He experienced failure first-hand, and he was one of the expensive players who 99% of scouts loved. Perhaps the cheap players with modest statistics are the best ones to have on your team.

Mild winter has decreased electricity consumption

This article from the Los Angeles Times discusses the decline in electricity consumption this winter in the US due to the mild temperatures. “Americans spent $163 billion on electricity in the fourth quarter, down from $175 billion in the third quarter…” This article coincides with my research topic because it shows a relationship between temperature and demand for electricity. Because it has been warmer this winter, people have been less inclined to heat their homes. The electricity portion of Americans’ energy bills has decreased.

Rising gas prices in the US have threatened to “choke off the economic recovery”; however, with less spending going towards electricity, Americans can afford to spend more on gasoline. According to the article, gas accounted for 61% of total household energy consumption in the fourth quarter. In my research, perhaps I should examine the fluctuations in electricity as a proportion of total household energy consumption. More research about rising gas prices and its impact on electricity consumption could be interesting and helpful to my paper. I could look further into finding data about this potential relationship and use it to strengthen my paper.

Here is the link to the article:,0,32216.story

Topic, Thesis Statement, Data

Topic: Is there a relationship between temperature and electricity prices in Philadelphia, PA?

Thesis Statement: There is a relationship between temperature and electricity prices. When temperatures are very low, prices will be higher because people will be more inclined to use heating in their homes. When temperatures are very high, prices will be higher because people are more likely to turn on their air conditioners.

Data Sets: Temperature data from a specific time period will be from the Franklin Institute in Philadelphia: This site provides historical weather data for Philadelphia. Electricity pricing data will be from PJM, which is a Regional Transmission Organization that serves Philadelphia and many other areas:

I chose this topic because the energy industry interests me. Also, I have always had an interest in meteorology. The opportunity to search for a relationship between energy and weather (temperature, specifically) is exciting. I took an energy economics class sophomore year, and I look forward to doing further research on the topic throughout this semester.

This topic is important because it is not only a study on electricity prices and temperature, but it is also a study on human behavior. Will more people demand electricity at times when temperatures are extreme (either very high or very low)? I anticipate that they will. However, those who wish to save money on electricity have the opportunity to do so. By changing daily routines and perhaps doing laundry or using other appliances at nighttime (or on days when the weather is moderate), people may save some money. This idea relates to demand response electricity pricing, in which prices vary during peak and off-peak demand periods. This topic is practical because smart grid technology is growing very rapidly today.

Other weather aspects could have effects on electricity pricing as well. For example, if it is snowing, people might believe that it is colder outside than it actually is. This compels them to turn on their heaters, increasing demand for electricity, and driving up prices. Similarly, I think people would be more inclined to use their air conditioning on a sunny summer day as opposed to a cloudy summer day. As with any other economic research, human behavior and decision making is likely to play a major role.

Why do drug dealers still live with their moms?

In chapter 3 of Freakonomics, authors Steven D. Levitt and Stephen J. Dubner discuss people’s motivation for selling crack cocaine, as well as the parallels between the unequal distribution of income in the crack cocaine industry and corporate America. That is, the people at the top make huge salaries, while all others make very little. The “bosses” of the drug-dealing gangs are not the ones living with their mothers; rather, the hundreds of people inferior to them within the gang are living with their mothers. The issue is that the number of people at the top of the pyramid is tiny compared to the total number of gang members. Young people join gangs and work as “foot soldiers” with hopes of one day obtaining the glamorous job as one of the gang’s primary leaders. However, the chance of foot soldiers making it to the top is very slim. The authors go so far as to compare the gang’s organizational structure to that of McDonald’s. Throughout the chapter, the authors employ statistics to depict the distribution of income within the gang.

“At $8500 per month, J.T.’s annual salary was about $100,000—tax-free, of course, and not including the various off-the-books money he pocketed” (99). This statistic is important to the chapter’s argument because it shows that J.T.—a high-up gang member just beneath the bosses—was making a six-figure salary, which is extremely desirable. The authors introduce the statistic by describing it as “the single line item in the gang’s budget that made J.T. the happiest” (99). This is notable because it displays the satisfaction that one experiences as a superior gang member. Immediately after mentioning J.T.’s salary, the authors point out that he makes much more as a gang leader than he did at “his short-lived office job” (99). The rewards of getting to the top of the gang are massive.

“Each of those top 20 bosses stood to earn about $500,000 a year” (99). This statistic demonstrates just how much those at the top of the gang make. Prior to introducing the bosses’ salary, the authors describe the gang’s board of directors’ lifestyle as “extremely large”. The italicization of the word “extremely” emphasizes the great amount of money made by the board of directors. The authors waste no time in pointing out the riskiness of being a gang leader: “A third of them, however, were typically imprisoned at any time, a significant downside of an up position in an illicit industry” (99).

“His (J.T.’s) three officers, meanwhile, each took home $700 a month, which works out to about $7 an hour. And the foot soldiers earned just $3.30 an hour, less than the minimum wage” (100). Before this statistic, the authors point out that J.T.’s hourly wage was $66—clearly more than the gang members beneath him. After presenting the low wages of the low officers and foot soldiers, the authors return to the original question: “…If drug dealers make so much money, why are they still living with their mothers?…except for the top cats, they don’t make much money” (100). The authors then continue by paralleling the income inequality within the gang with the inequality in “standard capitalist enterprise”.

“If you were a member of J.T.’s gang for all four years, here is the typical fate you would have faced during that period: Number of times arrested, 5.9. Number of nonfatal wounds or injuries, 2.4. Chance of being killed, 1 in 4” (101). This group of statistics is the most shocking to me; it displays just how risky—and ultimately foolish—dealing crack cocaine can be. People are willing to risk a 25% chance of dying simply for the dream of someday becoming a boss in an illicit industry. The motives make sense, but the chance of being killed is much higher than the chance of making it to the top.

I thought the authors made interesting points in their mentioning the pretty country girl who moves to Hollywood and the high school football star who lifts weights every day. Becoming a star actor or NFL player are extremely desirable and rewarding professions, but it is difficult to achieve such high positions. Many people have dreams of making it “big”, but there is such little room at the top.

Chapter Two: A Billion Hungry People?

In chapter two of Poor Economics, Banerjee and Duflo discuss the relationship between nutrition and economic success. The authors assert that poor people are often hungry and that hungry people are often poor. What is the direction of the casual chain here? I think it can go both ways. Banerjee and Duflo provide examples displaying that malnutrition and hunger are reasons for poverty; without enough calories, humans lack the strength and energy to perform many jobs. Another main point of the chapter is the examination of the allocation of income to food and other goods. Upon researching this topic, the authors discover that people in poverty struggle to properly allocate their incomes to satisfy their hunger. Improper allocation of the household budget seems to be the biggest issue, not the “hunger-based poverty trap” which the authors discuss.

In order to escape the hunger-based poverty trap, it would make sense that people in poverty spend as much as they can on food. This is not how they spend their money, however. The authors provide examples and data supporting this conclusion: “In our eighteen-country data set on the lives of the poor, food represents from 45 to 77 percent of consumption among the rural extremely poor, and 52 to 74 percent among their urban counterparts” (Banerjee & Duflo, 22). Shouldn’t these percentages be higher if they actually wish to escape the nutrition-based poverty trap? One would think so. Instead of spending a greater portion of their household budget on food, they spend it on other items. The authors provide an example from Udaipur, India: “…the typical poor household could spend up to 30 percent more on food than it actually does if it completely cut expenditures on alcohol, tobacco, and festivals. The poor seem to have many choices, and they don’t elect to spend as much as they can on food” (Banerjee & Duflo, 23).

When poor people experience an increase in income, they do not purchase more food in order to eat more calories. Rather, they spend this on “better-tasting, more expensive calories”. This idea is directly related to the economic concept of normal vs. luxury goods; an increase in income leads to an increase in consumption of luxury goods. An example of this concept is provided through Robert Jensen and Nolan Miller’s study discussed on page 24. The subsidy placed on rice and noodles (staples of a Chinese diet) should have led people to buy more of these two goods. Jensen and Miller discovered that the exact opposite occurred: “Households that received subsidies for rice or wheat consumed less of those two items and ate more shrimp and meat, even though their staples now cost less” (Banerjee & Duflo, 24). Another example of improper budget allocation among the poor is found on page 36. Oucha Mbarbk, a man from Morocco who struggles with poverty and hunger, has a television, a parabolic antenna, and a DVD player in his house. When asked why he spent money on these items instead of food, Mbarbk replied, “Oh, but television is more important than food!” (Banerjee & Duflo, 36). Through this and several other examples, the authors assert that “things that make life less boring are a priority for the poor”.

The original statistic involving the proportion of income spent on food in eighteen countries could be flawed because it captures data from only eighteen countries. There are close to 200 countries in the world. Although the data is drawn from only eighteen of 190-plus countries, I believe that it makes sense and is realistic. In most cases, the poor are not trapped in poverty due to hunger; they are trapped in poverty due to irresponsible budget allocation.