ECO WORK

Introduce

This paper will research the economic projects, which examine the effect of median income in crime. Therefore, this paper will look into the literature review, the model part and the empirical results. Another part of the report shall analysis 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 project assumes that the economy can affect the crime rate. In other words, the paper will test to see if the economic situation can affect the crime rate for citizens. Therefore, there are several variables that will be evaluated in the model to determine the position of the above hypothesis. The variables include, economic (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:

2.

Variable table

the table from below use fixed effect model ,T-test, and F-test to analysis these data:

Dependent Variable: PCRIMES
Method: Panel Least Squares
Date: 12/07/17 Time: 16:04
Sample: 2009 2015
Periods included: 7
Cross-sections included: 50
Total panel (balanced) observations: 350
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	3908.495	883.4404	4.424175	0
MEDIANINCOME	-0.020649	0.005334	-3.871289	0.0001
URATE	-92.39022	52.65586	-1.754605	0.0802
REALGDP	-0.016513	0.004613	-3.58006	0.0004
PCTWHITE	-2377.273	417.3629	-5.695936	0
PCTBLACK	-827.7186	403.0144	-2.053819	0.0408
PCTASIAN	-1198.019	499.8169	-2.396916	0.0171
AGE4549	-59.39759	5590.068	-0.010626	0.9915
AGE4044	11598.41	6401.069	1.81195	0.0709
AGE3539	17178.51	6338.114	2.71035	0.0071
AGE3034	-12249.36	6016.789	-2.035864	0.0425
AGE2529	39343.19	6015.756	6.540025	0
AGE2024	1925.904	5585.258	0.344819	0.7304
AGE1519	-16464.81	6757.85	-2.436398	0.0154
R-squared	0.478598	Mean dependent var	2744.686
Adjusted R-squared	0.458425	S.D. dependent var	597.4182
S.E. of regression	439.6506	Akaike info criterion	15.04902
Sum squared resid	64946340	Schwarz criterion	15.20333
Log likelihood	-2619.578	Hannan-Quinn criter.	15.11044
F-statistic	23.72437	Durbin-Watson stat	0.252747
Prob(F-statistic)	0

Dependent Variable: VCRIMES
Method: Panel Least Squares
Date: 12/07/17 Time: 16:06
Sample: 2009 2015
Periods included: 7
Cross-sections included: 50
Total panel (balanced) observations: 350
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	505.4943	41.99932	12.03577	0
MEDIANINCOME	-0.002844	0.000785	-3.623373	0.0003
R-squared	0.036355	Mean dependent var	355.3351
Adjusted R-squared	0.033586	S.D. dependent var	129.7991
S.E. of regression	127.6008	Akaike info criterion	12.54139
Sum squared resid	5666124	Schwarz criterion	12.56343
Log likelihood	-2192.743	Hannan-Quinn criter.	12.55016
F-statistic	13.12883	Durbin-Watson stat	0.046511
Prob(F-statistic)	0.000334

Dependent Variable: VCRIMES
Method: Panel Least Squares
Date: 12/07/17 Time: 16:07
Sample: 2009 2015
Periods included: 7
Cross-sections included: 50
Total panel (balanced) observations: 350
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	505.8775	42.05145	12.02996	0
MEDIANINCOME	-0.002836	0.000786	-3.608988	0.0004
URATE	-7.577961	15.09274	-0.502093	0.6159
R-squared	0.037055	Mean dependent var	355.3351
Adjusted R-squared	0.031504	S.D. dependent var	129.7991
S.E. of regression	127.7381	Akaike info criterion	12.54638
Sum squared resid	5662011	Schwarz criterion	12.57944
Log likelihood	-2192.616	Hannan-Quinn criter.	12.55954
F-statistic	6.676356	Durbin-Watson stat	0.048
Prob(F-statistic)	0.001429

Dependent Variable: VCRIMES
Method: Panel Least Squares
Date: 12/07/17 Time: 16:09
Sample: 2009 2015
Periods included: 7
Cross-sections included: 50
Total panel (balanced) observations: 350
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	987.3317	97.82204	10.09314	0
MEDIANINCOME	-0.004095	0.00115	-3.562279	0.0004
URATE	-11.01661	12.60487	-0.873997	0.3827
REALGDP	0.001648	0.00102	1.615129	0.1072
PCTWHITE	-611.917	94.18756	-6.496792	0
PCTBLACK	88.99652	93.38981	0.952958	0.3413
PCTASIAN	-385.9051	111.0086	-3.476354	0.0006
R-squared	0.339154	Mean dependent var	355.3351
Adjusted R-squared	0.327594	S.D. dependent var	129.7991
S.E. of regression	106.4357	Akaike info criterion	12.19276
Sum squared resid	3885699	Schwarz criterion	12.26992
Log likelihood	-2126.733	Hannan-Quinn criter.	12.22347
F-statistic	29.33863	Durbin-Watson stat	0.087416
Prob(F-statistic)	0

Dependent Variable: VCRIMES
Method: Panel Least Squares
Date: 12/07/17 Time: 16:08
Sample: 2009 2015
Periods included: 7
Cross-sections included: 50
Total panel (balanced) observations: 350
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	338.7859	187.6215	1.805689	0.0719
MEDIANINCOME	-0.003808	0.00085	-4.481187	0
URATE	-5.900753	14.07034	-0.419375	0.6752
AGE4549	-2341.323	1479.477	-1.582534	0.1145
AGE4044	3988.203	1648.414	2.419418	0.0161
AGE3539	3755.535	1674.546	2.242719	0.0256
AGE3034	-4205.107	1613.9	-2.605555	0.0096
AGE2529	8462.972	1523.81	5.553825	0
AGE2024	-1523.518	1428.622	-1.066425	0.287
AGE1519	-4540.6	1673.039	-2.713983	0.007
R-squared	0.19166	Mean dependent var	355.3351
Adjusted R-squared	0.170262	S.D. dependent var	129.7991
S.E. of regression	118.234	Akaike info criterion	12.41136
Sum squared resid	4752951	Schwarz criterion	12.52159
Log likelihood	-2161.989	Hannan-Quinn criter.	12.45524
F-statistic	8.957205	Durbin-Watson stat	0.128865
Prob(F-statistic)	0

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