Assignment #8

In chapter 5 of Poor Economics, the authors discuss family size in the under developed countries that they have studied.  They ask the question whether families want to have large families or if there are out side sources that cause family size to be much larger in these countries.  They also look at the effects of having large families in these areas.  They discuss fertility decisions among the poor in these countries, contraception in those areas, family dynamics, and the economics of large families and how children can be of financial use to the parents. 

The chapter talks about areas with family-planning clinics versus areas without them and how it affected fertility rates in those areas.  The book states that though the areas with clinics did have lower fertility rates, it was not due to the clinics themselves.  The clinics were most abundant where people wanted them to be.  In other words, people demanded these clinics and therefore they were built in those areas. However, the clinics had no direct effect on fertility.  Despite what the book concludes, my hypothesis about fertility rates and family-planning clinics would be: the more family-planning clinics, the lower the fertility rates.  I would test this by collecting data on the number of family-planning clinics in different areas and the related fertility rates in those areas. 

My guess is that there would be other factors that effect fertility rates outside of whether or not family-planning clinics exist.  They may be population, family income, marital status of the mother, and perhaps whether there was access to good education (people may not start families in areas where they cannot send their children to school).  My model would look something like this:

Fertility Rate = B#family-planning clinics + Bpopulation + BMedian family income + B#of schools + E

The dummy variable in this equation could be the marital status of the mother.  If she was married she would be a 1 and if she were not married she would be a 0.  If I believe that being married causes a woman to have more children I would expect the coefficient of my dummy variable to be a positive number.  I wouldn't expect it to be very high numerically because I don't think that being married increases fertility rates by a huge number, but I would expect it to be positive.  If it were positive it would tell me the married women, on average, have more babies than do unmarried women.  To tell whether or not this variable is significant I would look at the t-statistic that I get from running a t-test.  After seeing that value I could determine whether or not this dummy variable of being married or not were significant. 

Assignment #7

The article that I read was one from the New York Daily News from September of this past year.  It discusses how poverty rates in New York City rose according to the 2010 census.  They state that the economy is to blame for the rise in poverty rates and that there is concern of how poverty is spread throughout the city.  There is a lot of disparity in poverty among different demographics of people.  The article also states that every borough except for Manhattan experienced an increase in poverty rates which is also concerning to government officials in the City. 

The article made me realize that something that I may need to keep in mind are years in which there are recessions and how it may affect the poverty rate.  Obviously, if the economy is not doing well then poverty rates are more likely to go up in those years.  Therefore, I could possibly create a dummy variable for years that are and are not recession years.  This way I could hopefully control for the economy and get a better measure of how population is effecting poverty rates. 

Another thing the article talks about is poverty among different racial groups.  This is something that I could maybe think about as well.  I am not exactly sure how I would take that into account or whether it would skew my data at all if I don't consider it, but it would definitely be something interesting to explore.  I don't think that any of these new variables would violate any of the assumptions of the linear model however, it is possible that there may be correlation among variables that I choose to use.  There is likely correlation between recession years and the poverty rates which implies co-linearity among my explanatory variables.  However, I think including information about the economy, specifically the recession years as dummy variables will help to give me a stronger regression.

Poverty Among Urban Youth

I found an article that was posted a few days ago on the recent report by UNICEF on "The State of the World's Children 2012".  The article discusses how millions of children around the world are growing up in urban poverty.  Despite having greater access to modern services and facilities than their rural counterparts, children in urban areas still lack access to clean water, electricity, and education.  Also, due to overcrowding in cities and the often unsanitary environments they reside in, children tend to suffer from deadly diseases.  So, as cities continue to grow, people in those areas, particularly children, continue to suffer because they are often not able to afford the aid or medicine that they need. The article also points out that people often say that those in urban areas are better off than those in rural areas.  However, the authors say that the relative wealth of those living in cities offsets those living in poverty making it look like urban areas are less poor when, in reality, that is not always the case.

Although this article is summarizing a report of urban areas in other, poorer parts of the world, it still brings up factors that I will need to think about in my research.  The article stresses that children who are poverty stricken are often held back later in life because they grew up in an environment where they did not have the opportunities to receive a good education, eat nourishing foods, and receive good health care. All of these factors lead to children not being as healthy and productive as they can possibly be.  This is related to what we read in Poor Economics where the authors found that malnourished workers could not be as productive as possible and therefore were not able to get good jobs and therefore lift themselves out of poverty.  The report states that because the children growing up in these parts of the world suffer from under nutrition and lack of education they are not able to bring themselves out of poverty later in life. 

These factors are things that I think are also true for the urban poor in America as well.  If children in urban America are unable to afford a sufficient amount of food or afford to go to college and receive a good education it is unlikely that they will be able to receive a good job and bring themselves out of poverty.  So these are some of the factors that I will need to try and account for in doing my research.  I will probably need to find some data on schooling systems in poor areas of American cities and whether or not the people who live there receive some sort of financial aid for food and other necessities.  These will be very important in whether or not people will remain poor or not and also whether or not they will be able to move out of these areas. 

http://www.newsroomamerica.com/story/220341/millions_of_children_in_cities_face_poverty_and_exclusion:_report_.html