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Staying afloat: Income and poverty determinants for Southern Maine

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  1. Introduction and motivation.
  2. Brief literature review.
  3. Conceptual or Theoretical framework.
  4. Data discussion and descriptive analysis.
  5. Econometric models, estimation methods, and specification testing.
  6. Econometric results.
  7. Policy implications, recommendations, and conclusions.

In this study I will be looking at data from the Southern Maine subset within the 2000 United States Census. This sample includes a 5% random sampling of the residents of Androscoggin, Cumberland, Kennedy, Lincoln, Sagadahoc, and York counties. The data that I will use comes from Integrated Public Use Microdata Series database for the United States (IPUMS-USA) compiled by the Minnesota Population Center. For the purposes of this report, I will consider every individual within the 5% sample as the sole representative of a distinct family, and, accordingly, limit my data set to those between the ages of 18 and 65. The focus of the study is to find first what the determinants are of total family income, and, next, if the same factors are able to explain whether a family lives below the poverty threshold. I will attempt to locate any explanatory factors such as education, race, gender, age, marital status, hours worked, etc. that may possibly account for the disparities in family income and status in regard to the poverty line.

[...] Despite any real life implications my data may have for reducing poverty in Southern Maine, no single program, subsidy, or referendum will vastly change the economic outlook. In the results for my economic variables, each level up until achievement of a professional degree reduced the probability of leading an impoverished family substantially more. Therefore, it is natural to assume that any policy promoting education will be instrumental in reducing poverty. To that end, schools need to increase efforts in higher education promotion. [...]

[...] Southern Maine has a limited minority population due to its isolation, and I expect that most minorities who do come do so with a distinct, well-paying profession in mind for the head of the family. Lastly, Female having a positive but statistically insignificant influence suggests that the labor market is relatively gender-blind. Most of the studies suggesting otherwise are outdated, so this contrary result is relatively consistent with my personal expectations. For my second model, I created the dummy dependant variable Pov2K. [...]

[...] I will begin my reporting with this model predicting total family income. Next I will move to a limited dependant variable model predicting whether the same variables that have explanatory power on total family income have similar power in determining whether a family is above or below the poverty threshold. I will only focus on factors that I see as determinants of both income and poverty status, but I do feel the need to control for several factors that may only explain one or the other. [...]

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