The regression results here show exactly what I was hoping for. I am looking to find the relationship between war time years and the unemployment rate, specifically focusing on why the recent war may have a different effect. With significant values of t for both independent variables, the two variables have opposite effects on unemployment shown by their coefficients. The war variable now has almost a full 1% larger of an effect on the unemployment rate than it did when the current war years were included (as shown in a previous regression). This is clearly because the most recent war has somehow had a completely different effect than would be expected. The iraq coefficient of 2.36 shows that, since 2003, the average unemployment rate has been 2.36% higher than in years without war. Going further with my regressions, I hope to find other independent variables that can help me explain this effect.
The fourth chapter of Poor Economics mainly deals with the issue of education in developing and poorer countries. An article I found to relate to this chapter is from my home paper, The Washington Post, called Education Panel: To close achievement gap, urgent state, federal action needed. The author’s argument in this article is that the poorest schools in America need to get better and we can’t allow kids to continue to learn at poverty-level schools. She supports her arguments with the statistic: “More than 40 percent of U.S. children attend high-poverty schools and 22 percent of children are living below the poverty line, the government said.” Although the article is different from the book, they both talk about options of schooling for poorer people and areas. The book talks about private schools that have popped up in South Asia and Latin America that cost as low as $1.50 per month. I would like to see more data on how teachers can be afforded at that level. The article from The Washington Post is very opinionated and needs more data and solution to sell me on the argument. The chapter from our book relates more to education in other parts of the world where I am less familiar with the economic situations.
- What is the relationship between war and job growth in the United States?
- With the current state of our economy and our continued high unemployment rate, it is important to see what factors play a role in affecting jobs in our country. For the past decade we have been in a war in the Middle East and the relationship between that and our employment should be evaluated.
- A look at this connection should interest the rest of the world in seeing how international affairs like war can directly affect economic issues domestically.
- By examining data from time periods in which the U.S. was engaged in other wars dating back to 1929, the consistency of the connection between war and employment can be checked. Perhaps other economic decisions and factors have counteracted the projected affects of a war. The unemployment rate in our country has sparked many questions.
- In my first section I will introduce literature from other authors that have reviewed the same or similar topics. By looking at their thoughts and using their ideas, I can move to actual data to test the validity of their arguments. That data will be presented in the following section after a thorough literature discussion. I will then continue to look at data connecting war and employment, as well as other economic factors playing a role in the unemployment rate. My hypothesis will be tested and the results will be explained in the conclusion to my paper.
Goldin, Claudia, and Claudia Olivetti. “Shocking Labor Supply: A Reassessment of the Role of World War II on U.S. Women’s Labor Supply.” NBER. N.p., Jan. 2013. Web. Mar. 2013.
Hollister, Matissa. “Employment Stability in the U.S. Labor Market: Rhetoric versus Reality.” Annual Review of Sociology 37 (2011): 305-24. Web.
Harvey, Philip. “Learning From The New Deal.” Review Of Black Political Economy 39.1 (2012): 87-105. EconLit. Web. 2 Mar. 2013.
Nicholson, J. L. “Employment And National Income During The War.” Bulletin Of The Institute Of Economics And Statistics (Oxford University) 7.(1945): 230-244. EconLit. Web. 3 Mar. 2013.
Datar, B. N., and I. G. Patel. “Employment During The Second World War.” Indian Economic Review 3.1 (1956): 13-27. EconLit. Web. 3 Mar. 2013.
Sautter, Udo. Three Cheers For The Unemployed: Government And Unemployment Before The New Deal. n.p.: Cambridge; New York and Melbourne:, 1991. EconLit. Web. 3 Mar. 2013.
Mutari, Ellen. “Women’s Employment Patterns During The U.S. Inter-War Period: A Comparison Of Two States.” Feminist Economics 2.2 (1996): 107-127. EconLit. Web. 3 Mar. 2013.
The author really begins his argument a few pages into the chapter posing the thought of why most crack dealers still live in the projects, and most even with their moms. He immediately turns to the data to answer this question, and that’s what this chapter is about: Finding and using data to show why crack dealers live at home, supplying the business structure and outcomes of the black market trade.
1. The first statistic comes on page 99 and states J.T.’s salary: $8,500 monthly plus other “off-the-books” money.
2. On page 100, adding to the first statistic, “the top 120 men in the Black Disciples represented just 2.2% of the full-fledged gang membership but took home well more than half the money.”
Both of the above two statistics show the rare opportunity for a poor black man to become relatively wealthy through selling crack.
3. Page 107. Not a direct stat to the drug trade, but “by 1941 some sixty four million pairs of nylon stockings had been sold.” The author compares the newly invented stocking to crack cocaine and the immense appeal to the customer. If there were statistics for how much crack was sold I’m sure it would have been astounding.
4. To show a statistic that goes against what the chapter thesis is, on page 112: “within a five-year period, the homicide rate among young urban blacks quadrupled.” This statistic, to me, would help make a point as to why drug dealers wouldn’t stay near home.
Although the statistics are somewhat hard to believe, the numbers go along nicely with the story told of J.T. and his gang. I didn’t see the statistics detracting from the story at all, but the mix of positive and negative statistics gave me mixed views.
My project for the semester will focus on the unemployment rate and other data surrounding the job market in relation to our war in the middle east. While I will provide and study other sources both domestically and internationally that affect our jobs, my focus will be comparing the low employment rate we currently have during war times versus the relatively higher rates during past wars in our country’s history. My motivation for this topic came from my growing interest in our country’s jobs market as well as the numerous unseen positive and negative effects of the war as well as what we are doing here at home that may be holding us back. My hope is to use the data provided in jobs reports over the past decade (mainly) and also from time dating back to the first world war. Going forward, I would like to collect and find more useful data and possibly other articles and papers written on similar topics.
In a previous comment I made on someone else’s blog about hunger based poverty traps, I stated my view on how I don’t understand how or why someone stuck in poverty would use their money more wisely. Well, being somewhat ignorant, my comment on that blog was based on little real life knowledge or examples of different types of poverty traps. After reading the Slate article, my concept of poverty traps changed to be more open minded and realistic. If I were presented the opportunity to trade one present second for two future seconds, I would immediately take the one second without thinking much about my future.
I would have liked to see a little more information to make up the experiments from the article, though. Relating back to the example of one present second to two future seconds, questions should be asked about when the two future seconds will be taken, who the contestant was, if more than one contestant answered the question, etc. As I said in my second blog post, information and statistics can be presented in a number of different ways depending on what you would like to portray.
I would like to see this test run a little bit differently to see the outcome. In hopes to better resemble a poverty trap, the two options should have more impact behind them. For example, maybe offer one present minute (or hour) and two in the future to take. This will most likely cause the contestant to think more about his/her decision. Although the article definitely swayed my opinion and concept of poverty traps, I think more tests and information can be provided to better explain the true situation.
The authors of Poor Economics have so far presented their arguments and points in a fair and understandable manner. What makes their arguments clear, in part, is the easy to read graphs and data that they present thereafter. Along with that are facts and simple numbers backing up the points being made. The writing style is almost too consistent throughout the beginning of the book, though, whereas one point after another is being made with the data following in a way that brings the reader a somewhat repetitive feel. As the topics vary I’m sure this will change.
On page 26, the authors give a statistic to demonstrate a decrease in “the percentage of people who consider that they do not have enough food.” The numbers given are 17 percent in 1983 and 2 percent in 2004. Being someone who enjoys reading and examining statistics, whether that be sports statistics or economic statistics, I know enough to realize that statistics can be presented in a number of different ways depending on what the author wants to portray. In this case, the author is making an argument for the decrease in hungry people and the percentages given comply with that statement. To really understand the given statistic, though, we would need more background information including sample size, sampled persons, and location of sample(s). The authors goes on to give their beliefs on why the percentage dropped as dramatically as it did, but another explanation might just lie in what is missing from the data.