Final Assignment

Both Chapter 4 of Freakonomics and “The Impact of Legalized Abortion on Crime” article address the correlation between abortion and crime rates. According to the study, legalized abortion is the primary explanation for the large drops in crime rates during the 1990s. While I was a bit hesitant to accept this argument at first, Donohue and Levitt offered a good explanation on why this relationship may occur. First, an increase in abortion will decrease the number the young people being born, which means there are fewer individuals able to commit crimes. Therefore, crime rates would drop on the logic that there are simply less individuals to commit crimes. Second, individuals who receive abortions generally do so with good reasons. If an individual has an abortion, then we can assume that the child would not be as wanted and may be raised in poor conditions. If there are less young adults growing up in such conditions, then they are more unlikely to commit crimes and crime rates would drop.

Donohue and Levitt are able to support the relationship between legalized abortion and crime rates through a series of regressions. I was most interested in the finding that the states that legalized abortion before Roe v. Wade experienced greater drops in crime rates than all other states. It was also interesting to see that states with the most legalized abortions saw the largest drops in crime rates. Donohue and Levitt ultimately reach the conclusion that legalized abortion may account for as much as 50% of the overall crime reduction of the 1990s. While this is certainly a shocking statistic, it may give an indication that there are some issues in the regression analysis performed by Donohue and Levitt.

Foote and Goetz expressed their own concerns with the study and suggested that there were measurement and coding errors in the regression model used by Donohue and Levitt. Due to these errors and biases, the relationship between abortion and crime rates is actually not significant. However, Donahue and Levitt respond to this comment and offer a regression that corrects for the problems and finds a significant relationship, but not as strong as the initial regressions. However, even with these corrections, Foote and Goetz’s own regression analysis is unable to find significant evidence that legalizing abortion has caused a reduction in crime.

While the argument used by Donohue and Levitt makes logical sense, I am still a bit wary that abortion played such a large role in the drop in crime rates during the 1990s. While I am more accepting of a correlation between the two variables, I’m hesitant to conclude that legalizing abortion caused crime rates to drop, especially with so many errors found by Foote and Goetz. There is also the issue that abortion could be considered a crime in itself, which would completely alter the results and possibly change the direction of the relationship between abortion and crime.

Extra Credit: Finance Symposium

The first speaker, Bob Biersak from the Center for Responsive Politics, presented on the issues involved with financing campaigns during the 2012 election. The speaker suggested whether votes are truly equal if certain people are able to influence the campaign through generous monetary donations. Those who are able to donate a substantial amount of money to their party’s campaign may have more influence over the election. However, it seems that this was not the case in the 2012 since the winning campaign was less well funded.

He also brought up the fact that many donations were coming from individual people, such as Adelson Sheldon, who donated 9.2 million dollars alone. These people are able to use their own corporate resources in an attempt to sway voters toward their own choice in candidate. If certain individuals have more influence over the election than others, then there is a serious question of equality in regards to the say each citizen has in electing a candidate.

The second speaker, FEC Commissioner Ellen Weintraub, presented on similar issues, but focused more on what the money goes to and if it is actually being spent legally. About $7 billion was spent on the 2012 election, and while that is certainly a large sum of money, a lot of it actually didn’t help win elections. Perhaps this was because of the way the money was spent, such as on advertisements. Even with so many advertisements during the 2012 election, they didn’t actually translate into votes. This is not all that surprising because advertisements have a difficult time getting people’s attention, especially when they are repeated over and over again for months.

I was surprised to here that campaign funding is not considered to be corrupt as long as the money is donated independently. I personally feel that majority of campaign finance is corrupt, especially if it allows for certain individuals to have more influence over the campaign than others.  Even if the campaign that is better financed doesn’t win, there are still issues of corruptness and greed, which is why I believe something should be done to get money out of politics.

Assignment #10

In Chapter 7 of Poor Economics, the authors discuss lending to the poor and the issues surrounding it. I was immediately shocked to find out the interest rates some of the poor pay when borrowing money. For instance, in Chennai, India, a $5 loan will leave a debt of nearly $100 million if it goes unpaid for a year. That is an extremely startling statistic and it is well-placed by the authors to get the reader’s attention. It seems that the banks stay clear of the poor so the poor are left to borrow money from exploitative moneylenders. Survey data from India indicates that less than 7 percent of the rural poor have a loan from a bank, and less than 10 percent of the urban poor do. While these statistics help support the claim that banks do not lend to the poor, I wish the authors would have organized their eighteen-country data set to see where they acquired these numbers. Since poor individuals cannot obtain loans because they are poor, they have a difficult time bettering themselves and, therefore, remain poor.

Many poor individuals then borrow money from moneylenders, who threaten their clients if they do not repay the loan on time. Another option for the poor to borrow money is through microcredit, which is similar to traditional moneylending, but it is rather inflexible since borrowers have to repay a fixed amount every week. I do not have much knowledge on MFIs and microcredit, so I was rather shocked to find that there are between 150 and 200 million borrowers. I was also unaware of the consequences, both positive and negative, of microcredit in developing nations. For instance, the fact that 57 farmers committed suicide from the pressure of their loan is not something to be taken lightly.

There are also some indications that microfinance is working, such as the statistic that the fraction of families that started a new business over the fifteen-month period went up from 5 percent to just over 7 percent. This may show that microfinancing is helping, but the authors did not provide enough details to determine if that is really the case. Some other factor could have occurred during that 15-month period to increase the fraction of families starting a new business. Therefore, microcredit may be help to fight poverty because the program has managed to reach so many people, but there are certainly social consequences, such as the suicides. There is not enough evidence now, but perhaps these microcredit programs will create a trade-off between higher standards of living and social stability in the future.

Assignment #9

The article “Income Inequality and Economics Incentives” by Lonnie Stevans largely focuses on the basis and direction of the relationship between income inequality and economic growth. The author explores the equity-efficiency tradeoff, which predicts a positive relationship between inequality, capital formation, and real GDP growth. For the United States over the period 1970-2006, the author finds no empirical evidence that supports the equity versus efficiency hypothesis.  While I am looking mostly at inequality and overall GDP growth, Stevans looks at the inequality-capital-growth relationship with an emphasis on the divergence between savings and investment. Therefore, Stevans’ dependent variable is not real GDP growth, but rather US capital stock.

After looking at the model used in this article, I’m beginning to question the classical linear model that I am currently using in my paper. After looking at the regression outputs in the article, I think I may attempt to take the natural log of the Gini coefficient to see if that will be a better model. Even though I’m already measuring inequality with the Gini coefficient and the ratio of income share of the 90^th percentile relative to the income share of the 10^th percentile, I think I might explore more income share ratios. Stevans model finds that inequality had no influence on net investment, the capital stock, and consequently economic growth in three of the five inequality measures so I think it would be beneficial to looks at more ways of measuring inequality in my own model. I may also use more random independent variables in my model that were used in Stevans model, such as the price of capital, depreciation rate, and price level. Although my current main regression is showing a significant negative relationship between real GDP growth and income inequality, after looking at Stevans’ model I think I’m going to have make some changes and explore different function forms.

Stevans, L. K. (2012). Income inequality and economic incentives: Is there an equity-efficiency tradeoff? Research in Economics, 66(2), 149-160. Retrieved from:   http://www.sciencedirect.com/science/article/pii/S1090944311000500#

 

 

 

 

Assignment #8

Since I’m interested in the relationship between economic growth and income inequality, I ran a regression on GDP growth and the Gini coefficient, which is a popular measurement for national inequality. I want to see if economic growth will decrease if income inequality increases, which would be considered a negative relationship. While the Gini coefficient is my variable of interest, in my model I also included the unemployment rate, if a recession or war was taking place, political party of the president in office, interest rate, and consumer confidence index. After running the regression, it could be seen that there was a negative relationship between GDP growth and the Gini coefficient since the slope coefficient for the Gini was -21.29. The t statistic for the Gini was -2.36, which suggests that the negative relationship between GDP growth and the Gini is statistically significant. The F statistic tells us that all of the variables as a whole are significant, but individually some of the variables may not be significant because of their low t statistic. The R-squared value tells us that 58% of the variation can be explained in the regression. This suggests that there may be other variables not included in the model that are affecting GDP growth. Therefore, I may have to try different variables in my model, especially since some of the variables are not individually significant.  While I think this regression may give a good indication of the negative relationship between economic growth and income inequality, I do not believe income inequality is causing a decrease in economic growth, especially when there are so many other variables that affect GDP growth.

Assignment #7

In Chapter 4 of Poor Economics, the authors bring up several issues dealing with social and economic background and how it affects a child’s education in developing countries. It seems that the children of rich parents receive a better education than the children of the poor. This unequal opportunity may even be an issue in the United States, as explained in the article Education Preserves Class Inequalities.  

In the article, it was found that even though poor students have long trailed affluent peers in school performance, the performance gaps between the rich and poor are actually growing. For instance, thirty years ago, there was a 31 percentage point difference between the share of prosperous and poor Americans who earned bachelor’s degrees, but now the gap has risen to 45 points. Although the authors of Poor Economics do not provide such statistics, they have a similar proposition that rich children will get more education even if they are not particularly talented, and poor children may be deprived of an education. They will even take it one step further and suggest that these unequal opportunities have caused schools to be both unfair and wasteful. Although there are no clear statistics to support such a claim, we can see how having more equal opportunities is a much more efficient education system from Pratham’s summer school program. Because the teachers were willing to put effort into their job and help the children of poorer parents, the program saw rather dramatic results. By the end of the program, all the children who could not read before could at least recognize letters and those who could read letters previously were 26% more likely to be able to read a short story.

The results of the Pratham program just go to show that if poor children are given equal opportunities, they are able to succeed just like the children of the rich. Although Poor Economics included a number of similar statistics on the effects of certain programs, I’m unable to find any direct statistics that support the inequality between rich and poor students. While inequality is certainly there, it’s much easier to collect such data in the United States than in a developing nation. Both Poor Economics and the article I came across have similar claims suggesting education is not the great equalizer, but may in fact be having the opposite effect.  Even with these similar claims, I find the article a bit more convincing solely because it is easier to measure such inequality in a more developed nation. While I do believe there are certain unequal education opportunities in developing nations, it’s difficult to support a claim if there is no strong statistical evidence on the subject.

Assignment #6

Introduction

With the increasing income inequality in the United States, there appears to be much debate on the effects of such inequality. There have been recent arguments on the impact of income inequality on economic growth. Since a smaller group of individuals are beginning to hold majority of income and wealth in the nation, there are also debates on whether income should be distributed more equally. It is therefore my aim to analyze whether there is actually a negative relationship between income inequality and economic growth. I will be running several regressions that will include variables other than income inequality that effect GDP growth such as interest rates, party in office, war, recessions, unemployment, etc. Similar experiments have been performed on income inequality, but not using such variables and not on a national level. There is much data on the state and county levels and I am interested to see if my findings are able to support such previous experiments and theories. I would like to initially look at the previous data and literature and then provide my own analysis on the subject. After I’ve collected all my own data, I will then test my hypothesis to see if there is any significant evidence for my question on income inequality and economic growth. Then hopefully I will be able to draw my own conclusions and theories on the impact of income inequality on overall economic growth in the United States.

References

Atems, B. (2013). The spatial dynamics of growth and inequality: Evidence using U.S. county-level data. Economics Letters, 118(1), 19-22. Retrieved from http://www.sciencedirect.com/science/journal/01651765/

Berg, A., Ostry, J. (2011, September 11). Equality and efficiency. Finance & Development. 48(3). Retrieved from http://www.imf.org/external/pubs/ft/fandd/2011/09/Berg.htm

Rodriguez, C. B. (2000). An empirical test of the institutionalist view on income inequality: Economic growth within the United States. American Journal of Economics and Sociology, 59(2), 303-313. Retrieved from http://www.blackwellpublishing.com/journal.asp?ref=0002-9246&site=1

Plumer, B. (2011, October 6). IMF: Income inequality is bad for economic growth. The Washington Post. Retrieved from http://www.washingtonpost.com/blogs/wonkblog/post/imf-income-inequality-is-bad-for-growth/2011/10/06/gIQAjYADQL_blog.html

Shin, I. (2012). Income inequality and economic growth. Economic Modeling, 29(5), 2049-2057. Retrieved from http://www.elsevier.com/wps/find/journaldescription.cws_home/30411/description#description

Simon Kuznets. (1955). Economic growth and income inequality. The American Economic Review, 45(1), 1-28.

Stevans, L. K. (2012). Income inequality and economic incentives: Is there an equity-efficiency tradeoff? Research in Economics, 66(2), 149-160. Retrieved from: http://www.sciencedirect.com/science/journal/10909443

Assignment #5

In order to answer the question of why drug dealers still live with their moms, the right data has to found and it can only be done by found by the right person. Although there are many assumptions that drug dealers end up very wealthy from their transactions, it turns out that drug dealers and gangs are not much different from a typical business. Much like there is a hierarchy in a business, there is also a hierarchy in gangs which leads to different wages based on their role in the gang. Therefore, similar to a capitalist system, those few individuals at the top receive most of the profits while the others at the bottom of the pyramid are making minimum wage. It is for this reason why so many drug dealers are still living with their mothers. Unless they are the very top, they really aren’t making that much money and have no choice but to live with their moms. Similarly, those in a typical capitalist enterprise will not see a huge income unless they are a part of the few at the top who are able to direct and command those at the bottom.

Levitt and Dubner attempt to support the similarities between drug dealers and capitalists enterprises through the following statistics:

1. “J.T’s hourly wage was $66. His 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.” (pg 93).

• This information allows us to see the decrease in pay as you go further down the hierarchy.

2. “The top 120 men in the Black Disciples gang represented just 2.2 percent of the full fledged gang membership but took home more than half the money.” (pg 93)

• This statistic shows how only a few individuals at the top control majority of the wealth, which is very similar to structure in a typical capitalist firm.

3. “A 1-in-4 chance of being killed!” (pg 93)

• Even though the foot soldiers in the gang have a very high chance of being killed, they clearly aren’t being compensated for the risks they are taking to do their jobs.

4. “The neighborhood’s medium income was about $15,000 a year, well less than half the U.S. average.” (pg 95)

• This again supports that crack dealing won’t make you wealthy unless you make it to the top.

While I do believe these statistics help support the claim that gangs and capitalist enterprises have a similar structure, I found them to be a bit repetitious. It seems that after each statistic about the gang, the authors attempt to relate them to a typical business. Even though the statistics help support their argument, I would have liked to have seen more explanation on how the numbers show a relationship between gangs and businesses.

Assignment #4

For my research paper I am interested in analyzing the possible effects of income inequality on overall U.S. economic growth. I will be using Gini coefficients to measure income inequality and annual GDP percentage growth to measure overall economic growth.While I’m aware that there are a number of other factors that affect economic growth, I’ve recently started reading books and articles that suggest the rising income inequality over the past decade or so has been negatively effecting the economy. I’m interested in finding if these claims are accurate and can be supported through statistical evidence.

In all honesty I was not the least bit interested in this subject until a few weeks ago when I was introduced to it though another class. I’m currently reading a book called The Haves and the Have-Nots, which explores the growing income inequality in the U.S. and suggests that it is damaging our economic prospects. However, I’ve also read articles that suggest the opposite is true. I’m going to have to find more sources, especially those that including previous statistical analysis on the subject. Just looking at the data I have now it’s difficult to see if there is going to be a correlation or not, so hopefully I’ll be able to find some more previous research on the subject. However, looking at the Gini coefficients, there is a clear pattern that income inequality has been increasing since 1967 when the data was first collected. I want to find what out what this increase implies, specifically on economic growth, which does not show such a clear pattern.

Hopefully by the end of the project I will be able to state whether there is a relationship between the rising income inequality in the U.S. and overall economic growth. I’ve been reading many articles that debate on the subject and I want to reach a conclusion that hopefully I will be able to support with statistics through my findings after the completion of this project. I find this to be a very interesting topic and I’m looking forward to exploring the uneven distribution of income in the U.S. and its implications.

Assignment #3

I really enjoyed this article and found the comparison of decision-making between busy people and the poor to be extremely interesting. It puts into perspective why poor households seem to be spending more on expensive better-tasting food rather than cheaper food with higher calorie counts. According to the article, people who are very busy may tend to make bad decisions because they do not have enough time to think about the big picture. Similarly, the poor also make bad decisions because they are so stressed about just surviving the day that they are not thinking about about the long-run. Perhaps this is why the poor can’t seem to sensibly manage their budgets.

This article has influenced my views on poverty traps a bit since I now understand why poor households seem to be making bad decisions with their money. It is possible that households are so stressed that they can’t think about the effects of  their day-to-day decisions on long-run utility. They are blinded by the hardships of poverty so that they do not recognize how buying more expensive food is actually harming them. Therefore, as the poor continue to make more and more bad financial decisions, the more they are going to to be stressed by their poverty leading them to make even more bad decisions. Thus a poverty trap is created.

Although I am more accepting of the poverty trap now, I would have liked to see more statistics used in the Slate article to back up their claims. The Feud experiment did provide some information on the impact of time pressures on decision-making, but it was a bit abstract. In order for me to be completely convinced of their claims, I would have liked to see data that reflected decision-making maybe based on the amount of one’s daily obligations. I’m not exactly sure how such an experiment would be performed if even possible, but I think I need more than the Feud experiment to completely agree with their claims.

Finally, I think it would helpful if terms were defined more clearly. For instance, what exactly constitutes a bad decision? How do we determine if a decision is good or bad? It most likely comes down to what maximizes one’s utility, but it is still something interesting to think about especially in the field of behavioral economics.