Chapter 4 of Freakonomics

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I found chapter 4 of Freakonomics to be very interesting.  Abortion is such a controversial issue and clearly always has been, it’s interesting to see how it could be affecting our society in terms of crime rates.  I like how the chapter started with the issue of abortion in Romania, it was remarkable to read about how these people lived for so long and about the power trip Ceausescu was on.  It’s amazing to think that the children born after the abortion ban did worse in every way measurable than children born beforehand.  But I think it needs to be taken into account that these children not only had parents that did not actually want them, or perhaps weren’t ready for them, but they also were born into a Communist society in which the leader was particularly cruel.  I think that that could definitely increase the possibility of crime rates as opposed to “unexpected” children born into a peaceful and fair society.

The chapter switches to American crime rates and entertains a variety of explanations for the unexpected drop in crime in the 1990s.  These explanations include: innovative police strategies, increased reliance on prisons, changes in crack and other drug markets, aging of the population, gun-control laws, strong economy, and an increased number of police.  A lot of statistics were given in order to explain the significance of these possible reasons for a drop in crime.  I thought the statistics presented regarding death row were very surprising: “Even among prisoners on death row, the annual execution rate is only 2 percent—compared with the 7 percent annual chance of dying faced by a member of the Black Gangster Disciple Nation crack gang (123).”  I think that’s insane! And really sad actually.

Once the information about abortion regarding possible drops in crime rate was presented, I thought it makes a lot of sense.  I was certainly very convinced that there is a causal relationship between legalized abortion and the drop in crime rates once I was done reading the chapter, and further convinced once I read the article.  Even after reading the article regarding the errors made in Donohue and Levitt’s regression, I still think the legalization of abortion had some sort of impact on the crime rate.  Although, if Steven D. Levitt contributed both to writing the book and the article, I guess that doesn’t really add to the number of people who have discovered this finding.  I was actually surprised that once the errors were accounted for a significant relationship wasn’t found.  Theoretically, it makes sense that criminals are more likely to grow up in a harsh or unstable environment, which seems like a plausible inference to make about the homes of children who grew up with parents who were literally forced to have them.  Even if it is the case of parents really wanting children someday, that’s not the only factor that makes good parents or successful citizens.  The parents may not be financially able to support a child and having that child could drive them into poverty.

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Poor Economics Chapter 7

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Chapter 7 of Poor Economics was about lending money to the poor, I found it very interesting.  The authors caught my attention at the beginning of the chapter when they presented the statistic that the interest payment in Chennai, India is 4.69% per day.  After a year, this absurd interest rate results in a borrower owning 1,843,459,409 rupees, which is $93.5 million USD (pg 158).  This really makes you wonder about how anyone who finds themselves in poverty in an economy like this can rebuild their life.  It also makes you wonder who would actually agree to take out a loan with that high of an interest rate.  But then again, if someone is in a desperate situation, they might do just about anything for a temporary solution.

They point out that the main reason for high interest rates are the expenses that lenders incur by keeping track of the borrowers.  They present a statistic that in Udaipur, India, two-thirds of the poor had a loan, but only 6.4% of those people had a loan from a formal source (pg 159). This is because it is less expensive to borrow from someone who knows the borrower well, because the lender won’t have to charge such a high interest rate to cover the expenses to keep track of/acquire information on the borrower.  This also explains why people borrow from dangerous lenders who have the ability to seriously hurt the borrower if the loan is not paid back.  These lenders do not feel the need to spend much money on getting to know their borrower, because they know that people would pay them back, considering the consequences.

Another reason for high interest rates that is presented in the chapter is that high interest rates reflect a higher chance of the borrower defaulting.  The authors present the statistic that in Pakistan, the median rate of default across moneylenders is 2% and the average interest rate is 78% (pg 162).  They also discuss the option of a borrower providing a down payment for the loan.  This leads into the concept that people tend to only loan money to the rich, because the rich can afford a larger down payment and, therefore, a larger loan.  I thought an interesting fact the authors presented was about the Indian branch of Citibank using local hooligans to threaten their borrowers who did not repay their vehicle loans.  It was shocking to read something like that about a bank that many people use in the United States, I feel like people don’t commonly associate well-established banks with sketchy activity like that.

Another shocking statistic the authors present is that the Andhra Pradesh government blamed SKS for the suicide of 57 farmers who were put under too much pressure by the officers in charge of their loans.  I think such a high number of suicides really illustrates how serious these situations get.  I like the concept that the MFIs have started of creating groups whose members can support one another in times of difficulty.  I think it is a much more productive way of helping poor people build up savings and hopefully they can continue improving upon the strategy.  I would be curious to know what kinds of initiatives they are taking to help out people who are in need of emergency funds though.

Drug Abuse and the Economy

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The article “Alcohol, drug abuse cost Alaska’s economy $1.2 billion in 2010” was published on October 28, 2012 and discusses the significant negative effects drug use has on the economy, specifically in Alaska.  The article is based on a report done by the McDowell Group and it provides the full report at the bottom of the article, which is great additional reading for my paper.  The article mentions the impacts of drug use on the economy in various forms.  For example, productivity losses reached $673.2 million and criminal justice and protective services reached $217.7.  Productivity is measured in a few different ways: lost production due to mortality, diminished productivity, incarceration, and alcohol/drug treatment.  They also measure economic impact in terms of health care and public assistance and social service programs.  The absurdly high numbers are causing the state to question drug education programs they have implemented in the past and the effectiveness of substance abuse recovery programs.  One statistic that ties in with my paper is that 42.8% of the estimated 16,951 Alaskan adults who have a problem with substance abuse are low-income. 

The article does not necessarily provide suggestions for other variables that I could include in my regression.  But the paper does provide alternative ways of how drug use can be measured to affect the economy in ways other than people’s incomes, some ways that I hadn’t thought of as possibilities before- specifically the criminal justice and protective services statistics.  I’m not considering changing my dependent variable at this point but it’s interesting literature to add to my research on the subject.  The article also reinforces the thought I had on the implications that this information would have on the government reconsidering their approach on substance abuse education. 

Here is a link to the article: http://juneauempire.com/state/2012-10-28/alcohol-drug-abuse-cost-alaskas-economy-12-b-2010

Literature Review

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Thomas C. Buchmueller and Smuel H. Zuvekas studied the relationship between drug use and income and employment in order to determine how drug use affects labor market outcomes.  In order to improve past studies on the issue, they distinguished between problematic and non-problematic drug use with specific age groups of men, 18-29-year olds and 30-45-year olds, which proved to make a difference.  Their results indicate that, for younger men, income is positively related to non-problematic drug use and negatively related to problematic drug use with no relationship between drug use and employment.  For the older age group, problematic drug use is negatively related to both market outcomes tested but not for non-problematic drug use.

In their literature review, the authors point out two common weaknesses of past studies done on this topic.  The first is that the data set commonly used, the National Longitudinal Survey of Youth, does not include drug use intensity in the survey—it is important to define what makes someone a drug user.  The second weakness, also regarding the NLSY data, is that adverse effects of drug use on labor market outcomes may not show up immediately.  They use the National Institute of Mental Health’s Epidemiologic Catchment Area (ECA) Survey, which assesses the prevalence of psychiatric disorders including substance abuse in the general population.  One thing they did that I didn’t think to do was to create categories of drug use levels.  For example, nonusers and experimental users are in one group if they have used drugs five times or less in their lives.

The authors set up a regression for income using the log of annual income.  They also use indicator variables to identify non-problematic and problematic drug users.  They use the grouped data model, “which is similar to an ordered probit model but the categorical cut-points are directly observed.”  Although, they point out that it is more difficult to interpret the coefficient estimates in this model.  To make things easier, they used other researcher’s data and used the midpoints of the income intervals to use in an OLS regression.  They also point out that there is potential endogeneity of drug use which could lead to two sources of bias.  One could be a direct effect of income on drug consumption and another is unobserved variables.  They suggest looking at other studies to help figure out the direction of such a bias, since endogeneity is difficult to rule out.  For employment, they use a reduced form probit model where the dependent variable takes on 1 if employed and 0 if unemployed.  They state that OLS has understated the negative employment effect of problematic drinking, which could have the same implication for problematic drug use.  For their control variables they used: age, ethnicity, marital status, education, ECA site, survey year, and dummy variables for industry and occupation. 

            They mention that the variables marital status and educational attainment could be influenced by drug use themselves.  Past researchers have tried to prevent this from throwing off their results by estimating models with and without controls for the two variables.  For the employment regressions, dropping the controls for these two variables increases the negative effect of problematic use.  Indicator variables for other types of mental illnesses, such as alcoholism, have been included in past regressions in order to confirm that the drug variables aren’t picking up the negative effects of these illnesses.

Buchmueller, Thomas C., and Samuel H. Zuvekas. “Drug Use, Drug Abuse, And Labour Market Outcomes.” The Economics of Health Behaviours. Volume 2. 468-484. n.p.: Elgar Reference Collection. International Library of Critical Writings in Economics, vol. 223. Cheltenham, U.K. and Northampton, Mass.: Elgar, 2008. EconLit. Web. 24 Oct. 2012.

http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1099-1050(199805)7:3%3C229::AID-HEC315%3E3.0.CO;2-R/pdf

Outline

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Introduction

Illegal drug use is a significant issue in American society today.  Not only is it known for negatively affecting people’s health, but it could potentially affect the economy.  One way of determining this is to study the relationship between illegal drug use and income levels.  Economists may find this relationship interesting because it could affect the productivity of the labor market.  If a strong negative relationship between drug use and income is established, then it is clear that the labor market is not functioning at its’ full potential.  If this is the case, the government may be willing to invest more money into substance abuse education in order to improve the labor market and, in the long-run, economic growth.  Drugs are also very common in today’s society, to the point where most people know at least one person who has done or is currently involved in using drugs.  Therefore, it is of interest to the public to know how using drugs affects one’s lifestyle, even economically.  Variations of this study have been done in the past and some of the findings are surprising.  Andrew M. Gill and Robert J. Michaels did a study using data from the National Longitudinal Survey of Youth and found that if an individual’s reasons for doing drugs in the first place is taken into account, then drug users receive higher wages than non-drug users.  Thomas C. Buchmueller and Samuel H. Zuvekas did a study on the relationship between illicit drug use and labor market success.  They focused on the effect of the intensity of use on the income of an individual; finding that accounting for the different levels of intensity actually makes a difference.  In the next section, I will go into further depth about Gill and Michaels and Buchmueller and Zuvekas’s approach to this problem; as well as describe other economists’ methods of investigating this topic.  In the third section, I will discuss the model I will be using to analyze this question from a different angle.  In the fourth section I will discuss the data collected to test my hypothesis, and in the fifth section I will use that data with my theory as evidence for my question.  Finally, I will present my conclusion on the question of how drug use affects income and suggestions on further improvements of to answer this question.

Literature Review

  • Thomas Buchmueller and Samuel H. Zuvekas utilize data from the National Institute of Mental Health’s Epidemologic Catchment Area Survey that was collected between 1980 and 1984 in five (they utilize four) different locations of the United States.  They focus their study on 30-45 year old men and put an emphasis on the importance of accounting for specific reasons for using drugs.  For young workers, they find that there is a positive relationship between using drugs and income.  But for their sample, they find that common drug use, measured by dependence, results in lower income.
  • Andrew M. Gill and Robert J. Michaels use microdata from the National Longitudinal Survey of Youth, specifically from the 1980 and 1984 waves, to answer the question of whether drug use lowers wages.  They focus on the age group of 18-27 in the year 1984.  They emphasize the importance of accounting for unobservable factors that affect the decision to use drugs and wages.  It was found that once these variables are factored into the model, drug users actually earn a higher wage than non-users.
  • Pinka Chatterji and Jeffrey DeSimone focus their study on high school alcohol use and its effect on the young adult labor market outcomes.  They study the relationship between 10th grade binge drinking in 1990 and labor market outcomes using the National Educational Longitudinal Survey, putting a specification on gender.  There is no relation between the two variables for females while, for males, there is a positive effect on wage.
  • Jeff DeSimone does another study on the relationship between employment and the use of marijuana and cocaine, specifically for males.  He utilizes the National Longitudinal Survey of Youth data for years 1984-1988.  He includes variables not used in previous studies such as the regional cocaine price and a state marijuana decriminalization indicator.  His results show that drug use, for both drugs, reduces employment.

Data

My data come from the National Survey on Drug Use and Health (NSDUH) series of 2010, which measures the frequency of drug use in the United States and potential associated factors.  The sample size of the survey in total is 68,487 people of the age of twelve or older.  I will be focusing on the age group of 26-34 year olds.  This is a great survey to use as they include 3,112 variables, many of which are repetitive in an attempt to gain truthful answers from the respondents.  It is also very up to date, as it was only collected two years ago.  The data include an individual’s history of using drugs such as marijuana, cocaine, and heroine and their present situation with the specified drugs.  NSDUH  takes note of common individual characteristics that may influence income such as age, gender, race, ethnicity, marital status, education level.  The survey also includes information on past substance abuse treatment, reasons for going/not going, and criminal records due to a variety of factors, including drug use.

Modeling

The dependent variable of my model is income level.  The independent variables that I will utilize from this survey are: age, gender, race, ethnicity, marital status, education level, job status.  I am testing how the use of different drugs (marijuana, cocaine, and heroin) affect income levels.       

Evidence

Here I will describe and interpret my findings.

Conclusions

Here I will conclude my findings and discuss how my techniques affected my findings and how I could better improve the accuracy of my findings.

 References

Buchmueller, Thomas C., and Samuel H. Zuvekas. “Drug Use, Drug Abuse, And Labour Market Outcomes.” The Economics of Health Behaviours. Volume 2. 468-484. n.p.: Elgar Reference Collection. International Library of Critical Writings in Economics, vol. 223. Cheltenham, U.K. and Northampton, Mass.: Elgar, 2008. EconLit. Web. 2 Oct. 2012.

Chatterji, Pinka, and Jeffrey DeSimone. “High School Alcohol Use And Young Adult Labor Market Outcomes.” (2006): EconLit. Web. 2 Oct. 2012.

DeSimone, Jeff. “Illegal Drug Use And Employment.” Journal Of Labor Economics 20.4 (2002): 952-977. EconLit. Web. 4 Oct. 2012.

Gill, Andrew M., and Robert J. Michaels. “Does Drug Use Lower Wages?.” Industrial And Labor Relations Review 45.3 (1992): 419-434. EconLit. Web. 2 Oct. 2012.

United States Department of Health and Human Services. Substance Abuse and Mental Health Services Administration. Center for Behavioral Health Statistics and Quality. National Survey on Drug Use and Health, 2010. ICPSR32722-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2011-12-05. doi:10.3886/ICPSR32722.v1

 

Poor Economics Chapter 4

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Chapter 4 of Poor Economics addresses the issues with education policy mainly in India and Pakistan.  The authors Abhijit V. Banerjee and Esther Duflo discuss the tendency of children in poverty-stricken areas of the world to skip school by choice; when many people are under the impression that schooling is just not available to them.  A large problem with the education system in these places is the lack of belief placed in the children who should be attending school-many teachers are unwilling to invest time or energy in children who come from a lower caste and, sadly, a lot of parents feel similarly.  Policy makers seem to be under the impression that if they can get the students into the school with a decent teacher then everything will take care of itself.  NGO’s like Pratham are trying to improve the educational system. The authors provided statistic results of Pratham’s intelligence testing, for example: 35 percent of children in the 7 to 14 age group could not read a simple paragraph.  They provide a lot of shocking statistics about the intelligence level of children and their attendance level in school.  They point out that private schools don’t do any better at educating low-caste children, as their main goal is to “prepare the best-performing child.”  They end the chapter with suggestions on how to improve the education system such as: focusing on teaching children basic skills, believing in all children to succeed, and possibly making it more acceptable for children to be in multiple grade levels at once depending on their needs.

Dawn.com wrote an article on September 11, 2012 titled “Cram schools boom widens India’s class divide.”  The article discusses “cram schools,” institutions that prepare mostly middle class students for competitive entrance exams into technical and medical colleges, such as Bansal Classes.  Similar to Poor Economics, they speak to students on campus about their daily lives (which consist of studying and only studying) and the cost of the school.  They refer to the main issue that Poor Economics addresses: “Such cram schools compound the inequalities of an education system plagued by absentee teachers and high drop-out rates, which have left a quarter of Indians illiterate and lacking the skills to match the country’s growing economic needs.”  Contrary to what was stated in Poor Economics, many students are their because of parental pressure.  The article shares statistics that are almost, in my opinion, more startling– maybe that’s because I can relate to these people better because we are closer in age.  They mention that 50 kids committed suicide last year because they failed the exams, therefore wasting their family’s money on schooling.  That makes what follows even worse: “In 2012, more than 500,000 students took the IIT entrance exam and less than 10,000 cleared it, making admission statistically harder than getting into America’s Ivy League colleges.”  You have to feel for these kids, as they are facing issues similar to us here- passing exams- but on a level ten times worse than us, with family pressure and severe money issues.  They also discuss the controversy with cram schools- they provide false hope and many of the teachers mock the students.  Both of these pieces of writing discuss the corrupt and dysfunctional system of education in India- with Poor Economics focusing on early childhood education and the dawn.com article focusing on preparation for higher-level education.  I think that both pieces are convincing, but I was more affected by the news article as it was just more relatable- being a college student and with these people going through so much just to be able to do the same.

http://dawn.com/2012/09/11/cram-schools-boom-widens-indias-class-divide/

**I tried to comment but it wouldn’t let me– I think they may have closed it for comments.

Freakonomics: Why do drug dealers still live with their moms?

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Levitt and Dubner begin this chapter with stating that it is ok to ask “freakish” questions because the answers could potentially “overturn conventional wisdom.”  The term “conventional wisdom” means that we tend to accept statistics given to us as true and don’t challenge them because it could put people in awkward situations.  They discuss a few examples of absurd statistics told to the public and stress that in order to gain an accurate statistic, it is important to find the right data; which ultimately lies with the right person.  In the case of this chapter, the right people are drug dealers.

Page 57, directly after defining conventional wisdom: “In the early 1980s, an advocate for the homeless named Mitch Snyder took to saying that there were about 3 million homeless Americans…More than 1 of every 100 people were homeless?”  I think this is an important statistic to start with because it enforces their point that not all statistics told to the public are necessarily true, some may even be absurd, and this specific example leaves an impression.  They continue to discuss this statistic in a slightly sarcastic way, pointing out the absurdities of what Snyder said and what the implications would be if it were actually true.

Page 58: “Women’s rights advocates, for instance, have hyped the incidence of sexual assault, claiming that one in three American women will in their lifetime be a victim of rape or attempted rape.  (The actual figure is more like one in eight…).”  This adds to the point that many widely accepted statistics are false; it left an impression on me because I have been told that statistic many times and I never actually thought to question it much.  After stating this statistic, they discuss that it is important to know the “expert’s” incentive in telling the statistic, in this case is it a political advisor or a women’s health advocate?

Page 65, they discuss the wages of each type of member in a drug-dealing gang.  They state that the leader of the network earns $8,500/month, while his foot-soldiers earned about $3.30/hour, and estimate that there are about 5,300 men working under 120 bosses.  The answer to the initial question is then presented: “The top 120 men in the Black Disciples gang represented just 2.2 percent of the full-fledged gang membership but took home well more than half the money.”  These statistics are presented after a thorough discussion of where this data was gathered from, how it was collected, and answers a very interesting question.  If I had only read the statistics with no information given, I may not have found it very believable (after reading the intro); but after reading all of the information, it makes sense.  I think this only adds to the point the authors are making.

Page 66, the fate a foot soldier faces by working in J.T.’s gang for four years:

Number of arrests- 5.9

Number of nonfatal wounds or injuries- 2.4

Chance of being killed- 1 in 4

I think these statistics are important to telling the story because they are also statistics that seem unbelievable- possibly because of the privileged society that I’ve lived/ grew up in.  But given the description of what exactly foot soldiers jobs consist of along with all of the other information provided in this chapter, it adds up.