Chapter 4 Revisited

          In, “The Impact of Legalized Abortion on Crime,” Steven Levitt and John Donohue further argue the same phenomenon that Levitt argues in chapter 4 of Freakonomics. Through statistics they present a causal argument that because the mothers who are having the abortions are unfit for parenting, for a variety of reasons, would have given birth to a child that had a high probability of becoming a criminal. The authors provide data and regressions in their article to show the grounding of the abortion theory.  They mention other factors that decrease crime rates but do not believe they would cause a sharp reduction in crime.  In Chapter 4 Levitt compares and analyze other theories of crime reduction to show that the abortion theory has the most significant impact.  

            Christopher Foote and Christopher Goetz argue the validity of Levitt’s hypothesis about increased rates of abortion as a potential cause for reduced crime rates.  They find errors in Levitt’s data as well as his regression that are skewing the relationship. Specifically, they find that using the method of cross-state rather than within-state comparisons of crime data caused a misrepresentation of the facts.  They also believe that a per-capita variable for crime rates instead of the total arrests variable that they used would have lessened the statistical significance on the relationship with abortion.

              These two articles are closely related to what we read in Freakonomics and bring up an interesting theory, regardless of its validity.  Even though Foote and Goetz argue the accuracy of Levitt’s findings, it still could have some power in telling the story of crime reduction.  

Freakonomics, Chapter 4

            In chapter 4, Levitt and Dubner seem to add more evidence to their underlying thesis that the conventional wisdom in society is not always true or accurate.  In this chapter they reveal that the reduction crime in New York city in the 1980’s was not due to economic growth or an improved police strategy, but instead it was the result of a rising abortion rate caused by the famous abortion legalization known as Roe Vs. Wade. 

            The authors succeed in making a convincing and interesting argument with their statistics in what they believe not to be a cause of crime reduction and what they believe to be a cause.  They are especially convincing with their statistic about the correlation between each states abortion rate and its crime rate. 

            My only question for the author is that although the increase in the abortion rate seems to be a reasonable and good argument, are there any other explanations or theories besides the main arguments that the authors refute in the chapter?  This correlation between abortion and crime seems to be more of theory than a causal relationship.  Stating that most of the children who were aborted would have grown up to commit homicides seems questionable.  

           Econometrics and the ability to find a correlation between different variables of interest is a useful tool to tell a story.  But one of the things I have learned in my Quantitative Methods class this semester is that Statistics and the construction of economic models is complex and far from a definite science.  Just because someone finds a correlation in a data set does not necessarily make their story the true one either.  

Elevator Pitch

             For my research paper I have sought to understand what exactly lengthens the duration of unemployment for recently laid off workers.  I believe this is an important question because unemployment in the U.S is an incredibly relevant and important topic in the economy today.  Any incite as to what increases unemployment could have large policy implications.  More specifically, I want to know whether unemployment insurance has a direct and significant impact on how long the recently unemployed stay unemployed. 

            I have created a linear regression model where the duration of unemployment is my dependent variable.  I used the amount of unemployment insurance claims, the amount of job losers as a percentage of the unemployed, and GDP as my independent variables. 

            I have found through my research that unemployment insurance has a strong positive relationship on the duration of unemployment.  Increasing the amount of unemployment insurance greatly increases the duration of unemployment.  This becomes even accurate when I control for the effect on job losers instead of the general unemployed population. Also, by controlling for GDP I am able to control for time trends and recessions that also affect the duration of unemployment.  My results show that incorporating the GDP variable increase the power of my model.   

Assignment #8

           It is easy and plausible to attribute high birth rates in developing countries to ignorance about sex or to a lack of supply of contraceptives.  Why would anyone who is already under financial stress want to take on the financial burden of being responsible for nine children? I certainly wouldn’t.   While it seems completely impractical to put oneself in this situation, the authors in chapter 5 show that many families in developing countries are fully aware about the risks of unsafe sex and make conscious decisions to produce large families.   The authors have come to find that making contraceptives free and available does little to incentivize the poor to use them.  They also find that education does little to keep young boys and girls from having unprotected sex. 

            What I found most interesting about this chapter is how much societal norms differ between cultures.  One of the reasons I answered in the negative to the question I asked myself above is that I do not live in those countries nor do I understand or share their norms and views on what is socially acceptable.  For me, having nine children might not be the best life decision and I’d imagine my family would feel the same way.  On the other hand, for Pak Sudarno, a father of nine living in Indonesia where social expectations make the children responsible for taking care of their elders, having nine children increases his probability of getting financial aid in the future. 

            Similar to the example I used above about Pak Sudarno, the authors argue that in China children are usually responsible for taking care of their elders.  But in 1972 when family planning was introduced, there was a sharp decline in births, which caused the parents to save more for their own future. 

            “Households that had their first child after 1972 have one less child on average than those who had that child before 1972, and their savings rates are approximately 10 percentage points higher”(120).  

            Despite the author’s argument that the Chinese savings rate increased in 1972 due to a fall in births caused by the introduction of family planning, I would argue that this may have an impact on the savings rate but there might be other economic factors causing the savings rate to go up as well.  Parents might have started saving more in order to invest more in their single child’s education and college career.   

          

  Y(Savings Rate)=B1(family planning)+B2(fertility rate)+B3(college education for child)

           

          My dummy variable in this regression model would be whether the parents made an investment in their child’s college education.  If the child went to college they would receive a 1, and if they did not they would receive a 0.  If this dummy variable proved to be statistically significant it could potentially show that the parents were saving to invest in their child rather than saving to take care of themselves in the future.

Assignment #7

The more I read about UI (unemployment insurance) the more I learn about how simple but unseen underlying incentives can cause a policy to work in opposition to its intention.  As a policy maker it would seem that helping people achieve financial stability by allocating money to them while they are suffering the financial stresses of unemployment is a humane and rational thing to do.  But what I have come to understand so far in my research is that government payments create dependency.  Simply giving people money destroys the incentive to make money on their own.  

  The title of the article, “Is Unemployment Insurance Addictive?,  is self explanatory as to the thesis of the argument.  The author uses research called “occurrence dependence” where he looks at whether using unemployment insurance in the past causes the unemployed to use the insurance more in the future, or simply put, is it addictive? Through using data and creating regression models he finds that there is a positive correlation between past and future claims of unemployment insurance. 

            Before reading this article I had not thought about unemployment insurance in terms of “occurrence dependence”.  In my thesis I focused on whether using unemployment insurance increases the duration of unemployment but not its affect of increasing future uses of UI. 

It also brought to light an interesting theory as to why this addiction might form.  The author said there is a stigma about unemployment insurance, but once an unemployed worker receives UI for the first time, the stigma goes away and they continue to use the insurance more frequently.  He also argues that when the unemployed use UI they learn more about the program and find the process of receiving UI to be very easy which gives them an incentive to use it more. 

The author’s regression line is different than mine because he is using the duration of UI as his dependent variable while I am looking at the duration of unemployment as my dependent variable.  He also made his regression an exponential one due to that fact that UI durations cannot be less than 0.  I am interested to see if this would work for my regression as well.   

Corak, Miles. 1993. Is unemployment insurance addictive? evidence from the benefit durations of repeat users. Industrial and Labor Relations Review 47 (1) (10): 62-72.

Assignment #5

Last year, as an assignment for one of my classes, I spent a substantial amount of time tutoring students at the local elementary school.  The county that this school is located in has one of the highest poverty rates in all of Pennsylvania.   While tutoring there, I had time to talk with the principal and interview her about the affects of poverty on the children at her school where she revealed a lot of interesting information to me. 

In Chapter 5 of Poor Economics, the authors examine what they believe to be the reasons behind the problems in education in developing countries.  They discover that these societies only allow certain students, usually of a higher economic background, to succeed in school for a number of reasons while the poorer students slip through the cracks and get deprived of their valuable educations.  

In a Washington Post article titled, Public educations biggest problems get worse, The author comments on the intellectual potential of the poor, “There is always a big hullabaloo when American students score average on international tests, but the fact is that American kids in very low-poverty schools score as high or higher than anybody else on the planet.”  The authors of Poor economics touch upon a very similar topic in their section about “wastes of talent.”  The story of “Rangaswami”, the young man with little formal education who performed well on an intellectual test and became a leader in one of India’s biggest IT giants shows that talent exists in poverty just as much as it does for the lucky ones who can afford to be properly educated.  But the problem that both these pieces of writing bring to light is that stories like Rangaswami’s are rare, and recognizing the intellectual ability of the poor in the real world does not happen very regularly.  In other words, just because the poor have the same talent or intellectual ability as their richer competitors does not translate into real world success for them.  Most children of poverty who have not had the opportunity for proper education never find opportunities like the one that Rangaswami found.

So if education is the stepping-stone out of poverty, what keeps poor children form succeeding academically? The book and the article offer different views as to what is the right answer to this question.  Even though the article speaks only about poverty and education in the US, I believe it is still worth comparing the two.  One of the main arguments of the book is that teaching hinders the academic achievement of the poor because it is designed for the elite rather than for the regular children who attend schools. It offers supportive and convincing statistics that back its argument.  On the other hand the article offers a different opinion with regards to teaching, “But we need to face facts: Problems in schools would remain even if every teacher were magnificent”.  This is where the article seems to diverge from its initial similarities with the text above.  It says that poor teaching, and unreasonable curriculums are major hindrances to the poor’s success with education.  But poverty itself causes problems that are external to education, and even if the affects of teachers were perfect or held constant, these external factors would still cause problems.  I think both the article and the book offer different but valid arguments in what really hurts the poor with education.  While the book offers a very interesting case about the importance of good teaching, it would have been interesting for them to address other factors such as home life besides the monetary aspects of the ability of parents to afford education. 

During my interview with the Principal of the local elementary school, she told me that one of the biggest problems with poverty and education is the lack of help at home by parents.  What a child learns at school is very important but it is just as important that the information learned at school is reinforced and planted into the child’s brain at home. This absence of help at home stems from a range of problems, such as unreal expectations by parents that teaching should be left to the school only, or the inability of parents to help their children because of a lack of education themselves due to poverty. 

So is it outside the responsibility of the teacher for a child’s success if it is impossible for a child to learn at home? According to the Principal it is not, she said  that you can try your hardest to improve the home life for a child but ultimately it is the schools responsibility for the academic success of the child, “45 percent of the kids are eligible for free and reduced lunch.  No one knows who these kids are.  But if we just said, ‘well their parents can’t help them so they’re just not going to do well.’... would we really want half of the kids in the school not to make it?  So we have to find ways to give them those tools so that they can believe they can make it.” 

So it seems that both good teaching accompanied with addressing external factors outside of the classroom that hinder poor children such as home life are both important in helping children reach their full potential, and the more of these factors the school can account for, the more improbable success stories like Rangaswami’s will come true. 

Article link

http://www.washingtonpost.com/blogs/answer-sheet/post/public-educations-biggest-problem-gets-worse/2011/09/13/gIQAWGz2RK_blog.html

Assignment #4

In Poor Economics and Freakonomics we learned that the conventional wisdom is not always the truth.  What seemed like logical and probable conclusions often are not.  Could this be the case with unemployment insurance?

Unemployment is one of the most relevant, studied and debated topics in the U.S economy today.  It is a major measurement of how our economy is preforming.  Unemployment rates have reached great heights in the last few years and have been the main focus of most policy makers and economists. Having said that, I thought now would be an interesting time more than ever to see what really helps the unemployed find employment and what hinders them. 

The benefits from unemployment insurance come in the form of payments made by the government to people who register themselves as unemployed.  These benefits act on a timeline, and at a certain point, their beneficiaries stop receiving them.  In the application process to receive benefits from unemployment insurance, recipients must state that they will seek work and provide proof of current employment

While unemployment insurance seems like a good way for the government to bring financial stability to the unemployed, could it also be creating disincentives for the unemployed to find work? Or put differently, could monetary handouts cause the unemployed to have an incentive to stay unemployed for a longer duration of time?  This is the question I am hoping to answer through my research. 

The policy implications might be that maybe the government should create other incentives besides monetary ones for the unemployed.  Maybe giving handouts isn’t the right way to motivate discouraged workers and that in fact it promotes unemployment. 

 Most of the data I have found so far on this topic are of a micro-economic sort.  One of the data sets shows the relationship between persons unemployed for 27 weeks as a percentage of total unemployment.  This data set should give me a good idea of the average duration of unemployment.  Another data set that should be informative about the duration of employment is on the average duration vs. the total U.S unemployment annual rate.  For data about unemployment benefits I will look at a set of data that shows the average duration of unemployment of persons collecting unemployment insurance benefits.  Most of this data dates back from 1970 - 1950 to 2012. 

What might make estimating the affects of unemployment insurance on the duration of unemployment difficult is the amount of other factors that might go into the job search.   I imagine it would be difficult to account for psychological factors in my regression model such as the presence of mental barriers like being discouraged.  Also during recessions, when the demand for labor becomes much smaller, it might be harder to see if unemployment insurance really has an effect.