Microcredit in India

              In chapter 7 of Poor Economics, the authors use statistics to introduce and back-up their main points in the chapter. For example, a couple of paragraphs into the chapter, the authors bring statistics on the owed amount of rupees of renting a cart for one day in Chennai, India. This style of writing a main idea in one-to-two sentences, and then spending six sentences on statistics to back up the idea is seen throughout the chapter.

              This chapter in Poor Economics focuses on microcredit that is found in the South Asian area of the world: more specifically Bangladesh and India. More specifically, the chapter focuses on the problems that microcredit faces in these areas. I found it surprising that even though microcredit is offered to poverty stricken families, most still choose moneylenders whose interest rates are ridiculously high. On that note, the interest rates of moneylenders came as some surprise as well. I knew that they were high, but I never realized they went up to 70-80% in most cases. That was one good example of statistics that the authors used throughout the chapter. I also like the fact that the authors brought in more social and psychological reasoning, instead of just pure economic statistics, to back up their claims on why poor families choosing moneylenders over MPIs. It made the chapter more rounded and less biased. 

Assignment #9

For my research paper, the article “Economy-Wide Effects of Reducing Illegal Immigrants in U.S. Employment” by Peter Dixon, Martin Johnson, and Maureen Rimmer focuses on analyzing the effects of illegal immigration in the U.S. economy by looking at tighter border security, taxes on employers, and vigorous prosecution of employers. The article does this by using the USAGE model. This article brings up new possible independent variables that could be useful in my model. The article doesn’t necessarily bring up possible issues regarding assumptions in a linear regression model, but it does show that parameters determining the elasticity of demand for employers of illegal labor needed to be changed. The article however also explains the negative effects that are caused by the government trying to put in place programs that limit the employment of illegal immigrants; costs in general. The article also explains the problems with using such a broad model as they did; USAGE. They explain the advantages of it being broad: reveals certain aspects and effects that would normally be ignored. The disadvantages being the amount of work needed to go through every specification needed in the data. 


website: http://ehis.ebscohost.com/eds/detail?vid=2&sid=f65c2054-0ef7-4f71-884a-1e3b563f7862%40sessionmgr4&hid=3&bdata=JnNpdGU9ZWRzLWxpdmU%3d#db=bsh&AN=57220003

Actual Regression to Assignment #8

reg avgannualunemprate receipts approvals avgannualunemprateforhispanics

      Source |       SS       df       MS              Number of obs =      33
————-+——————————           F(  3,    29) =   79.10
       Model |  78.8768928     3  26.2922976           Prob > F      =  0.0000
    Residual |  9.63947576    29  .332395716           R-squared     =  0.8911
————-+——————————           Adj R-squared =  0.8798
       Total |  88.5163686    32  2.76613652           Root MSE      =  .57654

avgannualu~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
    receipts |   .0068164   .0091416     0.75   0.462    -.0118804    .0255132
   approvals |  -.0232225   .0235329    -0.99   0.332    -.0713526    .0249077
avgannualu~s |   .6757607    .044796    15.09   0.000     .5841425    .7673789
       _cons |   .3049342   .4381871     0.70   0.492     -.591259    1.201127
Ok, this better work this time.

Regression for Project

My topic is looking at President Obama’s new immigration policy reform of 2013. The policy’s main focus centers on granting citizenship to illegal immigrants in the United States. I am looking at the U.S. average annual unemployment rate and regressing that with immigrant receipts, approvals, and the average annual unemployment rate for Hispanics.



Looking at this regression, there are several good and bad things going on. Receipts have a positive relationship with the average annual unemployment rate meaning that the more immigrants there are, the higher the unemployment rate rises. However, it cannot reject the null hypothesis meaning that it is not very significant. Approvals has a negative relationship meaning that approvals of immigrants might increase employment; this makes sense if the U.S. are granting citizenship to higher educated immigrants who are more likely to create businesses and thus jobs. Again however, it cannot reject the null hypothesis meaning it is not very significant to the unemployment rate. The average annual unemployment rate for Hispanics has the highest significance, easily rejecting the null hypothesis. This has a positive relationship with the average annual unemployment rate because there is a direct correlation between the two. I did do other regressions where instead of using the average annual unemployment rate as my dependent variable, I used Receipts (and also Approvals). This led to my independent variables (avg. ann. Unemp. Rate and avg. ann. Unemp. Rate for Hispanics) being so insignificant that neither can reject the null hypothesis. These regressions also caused extremely low F-stats (lower than 1.0).

Laotian Girl Dreaming Big

In chapter four of Poor Economics, the author talks about several occurrences happening in third world countries that have worked towards equality of education for girls and boys, and better quality and longer education in general for all children. Some of these occurrences include Conditional Cash Transfers, Top-Down education, and the installation of private schools where public schools were failing most. Because of all these new institutions and programs being put in place, girls have achieved better quality of education, as well as being able to complete secondary schooling. Girls also have better job prospects and income levels later on in life.

            In the blog article “In Laos, Life Skills Training Helps One Little Girl Dream Big” by Shauna Carey, she explains the positive effect that the Room to Read Girl’s Education Program has had on a young Laotian girl named Pamoxong whose dream is to become her village’s medical physician. Pamoxong’s village called Oudomxay is one of the few villages in Laos that does not speak the national language; they speak Hmong instead. Pamoxong has become one of the few people in the village able to translate Lao, the national language, into Hmong. Because of this, the Room to Read Program has been able to further promote its educational program throughout the village with Pamoxong’s help.

            This article by Carey brings up what was spoken in Poor Economics; how girls are finally moving towards equality with boys. It shows a large step in helping poor families’ children gain higher income jobs later on in life. However, this article does show how slow going this process is. While the article does glamorize Pamoxong’s fortune by finding the program, it only subtly shows how much more girls in these parts of the world need help.

Paper Intro/Bibliography

Since the 1980’s, illegal immigration has been on the rise in the United States of America. Currently, it is estimated at around eleven million illegal immigrants are living in the U.S.[1] President Obama, at the start of his first term of presidency, claimed he was going to work on immigration policy reform, but as of yet, done nothing. Just after the 2013 New Year, Obama finally brought his comprehensive immigration policy reform to the for-front. This new immigration policy reform is focusing on granting illegal immigrants citizenship in the United States. With the increase in citizenship given to illegal immigrants, foreigners will have a higher labor force participation rate which will lead to an increase in the level of unemployment in the country.

[1] Parker, Ashley. “House G.O.P. Open to Residency for Illegal Immigrants.” New York Times, February 5, 2013, Politics section. U.S. edition. 


Anderson, Stuart. “America’s Incoherent Immigration System.” CATO Journal 32, no. 1 (Winter2012 2012): 71-84. Academic Search Premier, EBSCOhost (accessed February 27, 2013).

Dixon, Peter B., Martin Johnson, and Maureen T. Rimmer. “Economy-Wide Effects of Reducing Illegal Immigrants in U.S. Employment.” Contemporary Economic Policy 29, no. 1 (January 2011): 14-30. Business Source Elite, EBSCOhost (accessed February 27, 2013).

Jason T. Castillo, et al. “Fear vs. Facts: Examining the Economic Impact of Undocumented Immigrants in the U.S.” Journal Of Sociology & Social Welfare 39, no. 4 (December 2012): 111-135. Academic Search Premier, EBSCOhost (accessed February 27, 2013).

LeMay, Michael C. Illegal Immigration: A Reference Handbook/Michael C. LeMay. n.p.: Santa Barbara, Calif.: ABC-CLIO, c2007., 2007 .MUSCAT, EBSCOhost(accessed February 27, 2013).

LeMay, Michael C. U.S. Immigration: A Reference Handbook / Michael C. LeMay. n.p.: Santa Barbara, Calif. : ABC-CLIO, c2004., 2004. MUSCAT, EBSCOhost (accessed February 27, 2013).

Mac Donald, Heather, Victor Davis Hanson, and Steven Malanga. The Immigration Solution: A Better Plan than Today’s/Heather Mac Donald, Victor Davis Hanson, Seven Malanga. n.p.: Chicago : Ivan R. Dee, 2007., 2007. MUSCAT, EBSCOhost(accessed February 27, 2013).

Orrenius, Pia M., and Madeline Zavodny. “The Economic Consequences of Amnesty for Unauthorized Immigrants.” CATO Journal 32, no. 1 (Winter2012 2012): 85-106. Academic Search Premier, EBSCOhost (accessed February 27, 2013).

Why Drug Dealers Live with Their Mothers

The main argument of the chapter is to prove that the pre-conception of drug dealers making millions of dollars is wrong. Levitt and Dubner go on to say that it is really only the upper tier (aka the Board of Directors and the Leaders) that get most of the money. All the people under the leaders get very little. It is the hope though, that keeps people from leaving the business; the hope that they would someday get high enough in the dealing chain to start racking in the money. The only problem is that there are so many people competing for those positions that very few ever achieve them.


Some statistics that Levitt and Dubner mention throughout the chapter are:

1.)    J.T.’s Monthly Salary (pg. 99)

It is shown that J.T.’s net monthly profit is $8,500, tax free and not including bribes and other payments not put in the books. This statistic is important in the chapter because it shows that the Leader’s profit is always much larger than anybody else’s under him. The chapter goes on to say that the officers right under the Leader get around $700 a month (pg. 100). This just shows the differences in money between the upper and lower tier members of the gangs.

2.)    Chances of Being Killed (pg. 101)

After looking through J.T.’s finance books, Levitt and Dubner come up with a 1-in-4 chance of a drug dealer getting killed. This is incredibly important because it means that the people under J.T. wanted to be compensated from the risk via money. With business slow already because of the gang war going on, it made things difficult.

3.)    Top 120 Men in Black Disciples Gang (pg. 100)

It is said in the chapter that only the top 120 men in the Black Disciples Gang took home more than half the profits. Those 120 men represented only 2.2% of the gang. This statistic shows how large each gang in Chicago, or even in other cities, used to be; especially around the time that Crack Cocaine became popular in the 1980’s. It also shows how large the competition was to get high in the dealer chain.  

4.)    Crack Dealing Salary (pg. 102)

The average salary for a crack dealer in 1990’s Chicago was $3.30. This helps prove the chapter’s thesis that most drug dealers live with their mothers because they really don’t earn much money at all. The book even says that most crack dealers need another legitimate job in order to support their families.

Project Idea

My project for this semester revolves around the new Immigration Policy reform that President Obama is trying to pass. Currently, the bill is expected to be introduced to the House of Representatives in either March or April. The policy reform’s main objective is to allow illegal immigrants living in the United States a chance toward citizenship without repercussions of being in the country illegally. What is kind of surprising about this, especially in present day politics, is that both the Democrats and Republicans are working to pass this bill, instead of just one party for it and one opposing it.

In the paper, I will be looking to see if the increase in immigrants that is caused by the bill being passed will bring an increase in unemployment in the country. Personally, I’m actually pretty curious on how this will affect employment because I am entering the job market in a year. I’ve gotten data from the U.S. Citizenship and Immigration Services (data on immigrant citizenship) and the Bureau of Labor Statistics (labor force statistics from CPA). One thing I’m going to need to do is combine the two data sets into something I can run regression on. I’m also going to need to look up past immigration reforms to see possible effects this immigration reform might cause. A question I have right now is whether there is a better data set out there I could use? 

Stress and Time Problems – Slate Article

After reading the Slate article by Mullainathan and Sharfir, my concept on poverty traps hasn’t really changed. The slate article only brings up another poverty trap that a poor person can fall into. In the real world, there are so many scenarios of different poverty traps because each person’s life is different. People have different jobs, cultures, incomes, lifestyles, etc… To say there is only one type of poverty trap seems very short-sighted to me. This brings me to my statistical problem with the slate article. The lab experiment, Family Feud, that Mullainathan and Sharfir run with Anuj Shah only involves undergraduates from Princeton. I think it would be more credible if they included graduates, employed and unemployed workers, and retirees. It would be interesting to see how each person would react in the experiment, since each come from a different “lifestyle” you could say.

While adding these people to the experiment would definitely broaden and add credibility (in my eyes at least) to the lab experiment, it would still not be perfect as it’s basically impossible in this experiment to include every type of person in the world.