3/23/2017

-Need a more complete interpretation of you empirical result, in which you discuss magnitude and economic as well as statistical significance. -Given  Table 4, you need to run same models with fixed effects and report those.

 

Does  the economic have an affect on the crime

Introduction

The paper will research the economic project, which looks into the effect of the median income in the determination of crime. therefore, the paper will look into the literature review, the model section and empirical results. The other part of the report shall look into the data to be used in the sample study. Finally, the paper will consider the empirical results, which include, regression results for at least one given model. Consequently, the paper will provide interpretation and discussion of the results obtained from the regression models.

The hypothesis of the project involves that an economy is able to affect the crime rate. In other words, the paper will test to see if the economic conditions are able to affect the crime rates for citizen. As a result, there are several variables that will be evaluated in the model to determine the stand in the above hypothesis. The variables include, economics(median income), unemployment rate, race, and age. The types of crimes that are violent crimes and property crimes.

Literature Review

there is the evaluation of the past studies that have been done and they are apparently similar or related to the current study. According to Goulas and Zervoyianni, (Zervoyianni, 2013) they argue that where is increased crime rates there’s an asymmetrical impact on the development in the economy. As seen, the report looks at the other angle of crime affecting economics and hence provides insights on the counterargument that can be developed in the research.

The other paper that we can refer to the argument by Randi and Lance (Lochner, 2012), that propose that the education levels are far more impactful on the crime than ever considered. They argue that the initial policies to eradicate crime focus on the punishment and retribution instead of embarking on education policies that can be more efficient in the determination of lower crime levels. They conclude that there is very close correlation of the crime and the education levels of a particular set of population statistics.

The other research paper that is relevant in the study is the discussion by Benjamin, Manish and Nair  (Benjamin Powell, 2010). This is a similar paper to the analysis we had in the first paper. The counterargument that the crime is the variable that relates to the economy. They are of opinion that certain crimes such as corruption can both increase or decrease economic growth. However, the comparison between the different nations was difficult, owing to the fact that it is difficult to ascertain what is crime in one nation and what is crime in another nation. This was the biggest challenge in the research.

The Regression Model.

The use of the model is to enable, generalization of the crime in terms of the variables that are considered to affect it. It allows the calculation of any given crime rate provided the coefficients affecting the impact of the variables in the given model. The model is described below:

 

In order to justify the variables, we only need to look into the effect the said variables could have on the crime rate. As we have seen, from the past results, in the studies covered by other statisticians, there is a lot significant involvement in the chosen variables in determination of the crime rates.       ( Need to discuss economic theory behind some of your  perdictions for signs of

The coefficient estimates.)

Variable

Dependent Variable:	Crime
Key Independent Variables:	Economic- (real GDP& median income)
Other Control Variables:	U= Unemployment rate R=race: black, Asian, white A=Age %age 15-29 %age 30-54 %age 55-69

Model Prediction table:

Model
Predictions		¶y/ ¶x	¶2y/ ¶x2	¶2y/ ¶x¶z
Dependent Variable	Crime		n.a.	n.a.
Key Independent Variables:	Economic – (real GDP)	b1>0	n.a.	n.a.
Other control Variables			n.a.	n.a.
	Education background	b>0	n.a.	n.a.
	Income	b>0	n.a.	n.a.
	Unemployment	b>0	n.a.	n.a.
	Race	b>0	n.a.	n.a.
	Age	b>0	n.a.	n.a.

 

Table 2

	Mean	Std-Dev	Min	25th%	median	75th%	max
Violent Crimes	355.504	129.8843	99.3	255.55	329.85	245.55	705.2
Property Crimes	2746.686	599.4182	1406.6	2226.43	2682	3255.22	4015.3
Real GDP	46925.79	8955.848	31169	40137.25	45474	51910.50	73464
Median Income (dollars)	52804.3	8703.047	36796	46655	50723.5	58297.25	74551
Social welfare
Unemployment Rate	0.103474	0.453161	0.029	0.07	0.079	0.09	8.55
%White	0.802063	0.115777	0.269	0.719	0.815	0.88825	0.972
%Black	0.111174	0.095412	0.006	0.035	0.083	0.161	0.38
%Asian	0.04538	0.076554	0.006	0.017	0.027	0.047	0.573
Age 15-29	0.070126	0.003803	0.057	0.203	0.07	0.213	0.082
Age 30-54	0.064129	0.004696	0.054	0.328	0.064	0.349	0.079
Age 55-69	0.071577	0.00562	0.052	0.154	0.072	0.172	0.088

2-3 decimal places only.

Need to discuss some key summary

statistics from Table 2.

In this section, the paper will research the description of data. [From these regression results, the interesting points is that the real GDP and income have a negative coefficient on the model. This is meant to reveal that the increase in these variables are meant to reduce the crime rates particularly the property crime rates. The meaning that the median income and real GDP are （but not statistically significant ）determinant of property crime. The comparison of the different data from the two models, (one for violent and another for the property) the property crime is important than violent crime. The economy is a not a very strong determinant of violent crime as compared to the property crime.

Table 3. Empirical Results

Dependent Variable: Property Crime	Coefficient	t Stat	P-value
Real GDP	-0.008966	-0.08	0.933
Median Income (dollars)	-42.5936	-0.83805	0.165
Unemployment Rate	674.3145	1.16944	0.243055
%White	541.4287	0.808612	0.41931
%Black	-249.709	-0.02876	0.977076
%Asian	-13911.7	-1.87635	0.061474
Age 15-29	-107.56	-1.19	0.241
Age 30-54	151.98	-1.23	0.226
Age 55-69	-258.23	-2.04	0.047
Number of Observations	350
R2	0.7539

Report coefficient estimates with standard error below in parantheses.

To compare the results, we can undertake comparison with the violent crime as the dependent variable and the same input independent variables. The output is as follows:

 

Dependent Variable: Violent Crime	Coefficient	t Stat	P-value
Real GDP	-0.0006	-0.66904	0.503931
Median Income (dollars)	-2.49009	-0.22415	0.822776
Unemployment Rate	674.904	-1.9987	0.046446
%White	-113.21	-7.78393
%Black	-735.21	-3.87626	0.000128
%Asian	-50.31	-3.1318	0.00189
Age 15-29	13766.16	4.827639	0.00109
Age 30-54	-229.2	-5.51522	0.001253
Age 55-69	-99.85	-3.17054	0.001662
Number of Observations	350
R2	0.499185

 

In terms of the hypothesis, it can be well noted that there is a negative correlation between the crime and the economics. It is apparent that the increase in the GDP and the median income results to decline in the rates of crime of property. It also shows that the property crime is more controlled by the economy as compared to the violent.

Table 4:                *Need to interpret magnitude of coefficient estimates, and discuss economic

And statistical significance.

Hausman Test	Chi^2	P value	conclusion
Ho= bre=bfe	45.38	0	reject
F-Test	F stat	P value	conclusion
Ho:all FE coef are zero	41.74	0	reject

Need to discuss the fact that Table 4 implies your models should include fixed effects.

Table 5:

Dependent Variable: Property Crime	Alternative model 2	Alternative model 3
Median Income (dollars)	-.0000175	-.8792437
Real GDP	1.75e-06	1.98e-06
Unemployment Rate	-.0249207	-.0247894
%White	.6818503	.3872131
%Black	-1.846826	-2.035659
%Asian	2.50183	2.58868
Age 15-39	4.246971	4.097063
Age 40-69	-7.093295	-6.959051
Number of Observations	350	350
R2	0.4695	0.4639

Need to explain these models and interpret the results as compared to your earlier results

Also need to report standard errors.

*At least some of your models need to be run with fixed effects.

 

 

References

Benjamin Powell, G. M. (2010). Corruption, Crime and Economic Growth . Benson Print.

Lochner, R. H. (2012). The Impact of Education on Crime: International Evidence . Dice Report.

Zervoyianni, E. G. (2013). Economic growth and crime: does uncertainty matter? Retrieved from Econ Papers: http://econpapers.repec.org/article/tafapeclt/v_3a20_3ay_3a2013_3ai_3a5_3ap_3a420-427.htm