LOGO Analysis of Unemployment Qi Li Trung Le David Petit Brian Weinberg Dwaraka Polakam Doug Skipper-Dotta Team #4

Table of Contents Concepts of Unemployment 1 Descriptive Data Analysis 2 Statistical Analysis 3 Conclusions 4 Questions? 5

Group #4 Concepts of Unemployment EmployedUnemployedNot Looking Population Labor Force Labor Force: People willing to work at market equilibrium wage, both employed and unemployed Unemployment Rate: Number of Unemployed/Labor Force Keynesian View: Unemployment consists of excess labor supply in market economy Classical View: The unemployed consist of those searching for jobs

Team #4 Descriptive Statistics  Data from prior studies

Variables  Unemployment Rate  No Degree/Degree  Men/Women  White/Minority  Other Rates  Crime Rate  Suicide Rate  Welfare Budget  Annual Income Per Capita

Team #4 Descriptive Statistics  Histograms  Crime Rate  Annual Income  Suicide Rate  Welfare Budget  Unemp Rate

Team #4 Descriptive Statistics  Histograms No Degree Women White Minor Unemp Rate Men Degree

Exploratory Data Analysis Team #4 Test for Equality of Means Between Series Date: 11/25/10 Time: 21:04 Sample: 1 10 Included observations: 10 MethoddfValueProbability t-test Satterthwaite-Welch t-test* Anova F-test(1, 18) Welch F-test*(1, ) *Test allows for unequal cell variances Analysis of Variance Source of VariationdfSum of Sq.Mean Sq. Between Within Total Category Statistics VariableCountMeanStd. Dev.Std Mean Err WOMEN_UNEMP MEN_UNEMP All  Unemployment rates between Men and Women have no significant difference  High f-test probability  A labor market that does not discriminate on the basis of sex

Team #4 Exploratory Data Analysis  Unemployment Rate is Regressed against male unemployment rate and female unemployment rate  The regression is Significant as seen by the F-stat  The variables are both equally significant in the unemployment rate as seen by their the t-stat  Therefore male and female unemployment rates are very close. Dependent Variable: UNEMP_RATE Method: Least Squares Date: 11/25/10 Time: 21:20 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C MEN_UNEMP WOMEN_UNEMP R-squared Mean dependent var5.538 Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)0

Team #4 Exploratory Data Analysis  Without a constant, the regression variables have even greater significance Dependent Variable: UNEMP_RATE Method: Least Squares Date: 11/25/10 Time: 21:15 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. MEN_UNEMP WOMEN_UNEMP R-squared Mean dependent var5.538 Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat

Team #4 Exploratory Data Analysis  Unemployment rates between those with a degree and those without differ significantly Test for Equality of Means Between Series Date: 11/25/10 Time: 21:06 Sample: 1 10 Included observations: 10 MethoddfValueProb t-test Satterthwaite-Welch t-test* Anova F-test(1, 18) Welch F-test*(1, ) *Test allows for unequal cell variances Analysis of Variance Source of VariationdfSum of Sq.Mean Sq. Between Within Total Category Statistics VariableCountMeanStd. Dev. Std. Err. Of Mean DEGREE_UNEMP NO_DEGREE_UNEMP All

Team #4 Exploratory Data Analysis  There is no significant relationship (as seen by the t-stats) between having a degree and being unemployed or having no degree and being unemployed  Intuitively this seems very wrong and can be accounted for by the constant.  In the next slide the constant will be removed Dependent Variable: UNEMP_RATE Method: Least Squares Date: 11/25/10 Time: 21:18 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C DEGREE_UNEMP NO_DEGREE_UNEMP R-squared Mean dependent var5.538 Adjusted R-squared S.D. dependent var1.461 S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion-0.57 Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat0.437 Prob(F-statistic)0

Team #4 Exploratory Data Analysis  With the Constant removed both variables become significant  Small coefficients imply a very small effect on the unemployment rate Dependent Variable: UNEMP_RATE Method: Least Squares Date: 11/25/10 Time: 21:19 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. DEGREE_UNEMP NO_DEGREE_UNEMP R-squared Mean dependent var5.538 Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat

Team #4 Exploratory Data Analysis  Annual Income is not significant when regressed with a constant  Low t-stat and R 2 Dependent Variable: AN_INC_PER_CAP Method: Least Squares Date: 11/25/10 Time: 21:24 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C UNEMP_RATE R-squared Mean dependent var34687 Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid1.29E+08 Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

Team #4 Exploratory Data Analysis  This regresses the Unemployment rate vs the Crime rate  We found that the unemployment rate is not a significant factor in the crime rate as seen by the low f-stat and the low t-stat Dependent Variable: CRIMERATE Method: Least Squares Date: 11/25/10 Time: 21:26 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C UNEMP_RATE R-squared Mean dependent var Adjusted R-squared S.D. dependent var11.8 S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

Team #4 Exploratory Data Analysis  This regression has the Unemployment Rate vs Suicide Rate  We found that there is a slight relationship between the two  The f-stat is low, but the R 2 indicates that there is some relationship between the variables Dependent Variable: SUICIDE_RATE Method: Least Squares Date: 11/25/10 Time: 21:31 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C UNEMP_RATE R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

Team #4 Exploratory Data Analysis  Welfare regressed against unemployment shows a significant relationship between the two  Intuitively, as the number of unemployed people grows, the greater demand for welfare Dependent Variable: WELFARE_BUDGET Method: Least Squares Date: 11/25/10 Time: 21:34 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C UNEMP_RATE R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid5.07E+11 Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

Team #4 Exploratory Data Analysis  Here the Unemployment Rate is regressed against multiple variables  All variables are significantly contribute to the Unemployment Rate  Annual Inc per cap coefficient is negative, suggesting a higher income implies a lower unemployment rate  Surprisingly, as crime rate increases unemployment decreases Dependent Variable: UNEMP_RATE Method: Least Squares Date: 11/28/10 Time: 12:20 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. AN_INC_PER_CAP CRIMERATE SUICIDE_RATE WELFARE_BUDGET5.61E E C R-squared Mean dependent var5.538 Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic)

Team #4 Statistical Analysis Income Welfare Suicide Constant Crime Unemployment What does it effect? – +

Team #4 Statistical Analysis UnemploymentUnemployment Significant Regressions Education Sex Ethnicity

Team #4 Conclusion  Recap:  Regressing unemployment rate with these a few durations has no meanings.  Unemployment rates between Men and Women have no significant difference  We can compare different sample means:  Unemployment rates between Men and Women have no significant difference:  Unemployment rates between Degree and No Degree have significant difference:  Regress unemployment rate with men and women unemp (with c and without c):  Regress unemployment rate with degree and no degree unemp (with c and without c):  Regress annual income with unemployment rate (not significant, no relationship):  Regress crime rate with unemployment rate (not significant, no relationship):  Regress suicide rate with unemployment rate (not significant, some relationship):  Regress welfare budget with unemployment rate (significant, strong relationship):  Regressing unemployment rate with these four variables has no meanings.  Regress Unemployment with Annual Income, Crime rate, Suicide rate, Welfare budget(Significant)

Team #4 Conclusions  I have no money and cannot get any work  Father, can ’ t I have a piece of bread  I say father, could you get some specie claws?  I ’ m so hungry  My dear, cannot you continue to get some food for the children I don ’ t care for myself  I say Sam, I wonder where we are to get our Costs  **Warrant Distraint for rent**

Team #4 Future Investigations  Next time, I top down approach how does state and county unemployment break down.

Team #4 Future Investigations  Or a bottom up approach that considers the dynamic between US unemployment and international unemployment.

LOGO Team #4

Technical Appendix CountryRates:InterestGrowthInflationJoblessExchange Current Account United States 0.25%2.00%1.20%9.60% Year JanFebMarAprMayJunJulAugSepOctNovDec

Team #4 Works Cited   mployment  e:Panic1873.jpg  ipedia/commons/c/ce/Chomage- oecd-t png