The purpose of this thesis is to evaluate the relationship between levels of education and the levels of property crime. From examining previous literature and theory, one could conclude that a person’s level of education directly has an effect on their risk to commit a property crime. Theory and previous literature suggest that the following models and explanation of variables should be used in this research:
Pcrime= β0+ β1HGCBM –β2HGCBF – β3LWBP- β4weeksworked+ β5urbrur- β6hhincome+ β7hhunder18+ β8race+ β9sex+U
DestroyedProp= β0+ β1HGCBM –β2HGCBF – β3LWBP- β4weeksworked+ β5urbrur- β6hhincome+ β7hhunder18+ β8race+ β9sex+U
The first model is used to examine highest grades completed by biological mother and father of the respondent, whether …show more content…
These categories are for the different types of property crime. These different types of property crime are burglary, aggravated assault, larceny theft, motor vehicle theft, vandalism, stolen property, robbery, and arson. These are important in our analysis because this research looks at the type of property crime that is committed at the different levels of education. Our research will use data for the year 1997.
Through the review of several different pieces of literature, it has been determined that one of the main factors in the opportunity costs of committing a crime is wages; Therefore, household income is included in each of the mode. In this analysis household income is used to help determine the likelihood or probability that one would commit a crime and therefore increase the level of Pcrime. In this research the hypothesis is that a lower level of education yields a lower wage, which then increases the level of property …show more content…
The Classical Linear Model holds five assumptions that underlie this model. The first assumption is that the model is linear in the parameters. The second, is that the observations are randomly sampled. The third assumption of no perfect collinearity. The fourth assumption, is the mean of the error terms, given a specific value of the independent variable is zero. The fifth is the assumption of homoskedasticity. The data used in this analysis was retrieved from NLSY97, The National Longitudinal Survey. To clean the data, we followed the codebook provided by the data set NLSY97 to recode values that depicted missing values as missing. We recoded all skips as zero values. Any non-negative values that were also representative of missing data were also recoded as missing values. The final total of observations in the data set after recoding the missing values were 8,
Pcrime= β0+ β1HGCBM –β2HGCBF – β3LWBP- β4weeksworked+ β5urbrur- β6hhincome+ β7hhunder18+ β8race+ β9sex+U
DestroyedProp= β0+ β1HGCBM –β2HGCBF – β3LWBP- β4weeksworked+ β5urbrur- β6hhincome+ β7hhunder18+ β8race+ β9sex+U
The first model is used to examine highest grades completed by biological mother and father of the respondent, whether …show more content…
These categories are for the different types of property crime. These different types of property crime are burglary, aggravated assault, larceny theft, motor vehicle theft, vandalism, stolen property, robbery, and arson. These are important in our analysis because this research looks at the type of property crime that is committed at the different levels of education. Our research will use data for the year 1997.
Through the review of several different pieces of literature, it has been determined that one of the main factors in the opportunity costs of committing a crime is wages; Therefore, household income is included in each of the mode. In this analysis household income is used to help determine the likelihood or probability that one would commit a crime and therefore increase the level of Pcrime. In this research the hypothesis is that a lower level of education yields a lower wage, which then increases the level of property …show more content…
The Classical Linear Model holds five assumptions that underlie this model. The first assumption is that the model is linear in the parameters. The second, is that the observations are randomly sampled. The third assumption of no perfect collinearity. The fourth assumption, is the mean of the error terms, given a specific value of the independent variable is zero. The fifth is the assumption of homoskedasticity. The data used in this analysis was retrieved from NLSY97, The National Longitudinal Survey. To clean the data, we followed the codebook provided by the data set NLSY97 to recode values that depicted missing values as missing. We recoded all skips as zero values. Any non-negative values that were also representative of missing data were also recoded as missing values. The final total of observations in the data set after recoding the missing values were 8,