Regression analysis. (economic)
47290v3o0
presentation.pptxWhat Influence the Unemployment Rate in the U.S?
Presented by: Zhijing Zhang, Ruohan Zhou, Yangshu Zhang, Wei Zhou
Backgrounds
Labor Market
Backgrounds
Bureau of Labor Statistics (BLS)
Unemployment Rate
Unemployment Rate = U/LF
Labor force(LF) = Employed(E) + Unemployed(U)
Employment Rate = E/P
Unemployment Rate in Oct. 2015
Pennsylvania: 5.1% ( 27 out of 51)
Lowest: North Dakota:2.8%
Highest: West virginia: 6.9%
Our Model
Female (percentage)
Education level (bachelor)
Age (from 25-34)
Native
population growth
UnemployementR = β0 + β1Female% + β2Bachelor%+ β 3%PopulationGro+ β4 Age2534+ β5Native%+ u
Regression analysis
Female showed positive impact on the unemployment rate.
It explains that when female increases the unemployment rate is increased too.
female always focus family works and part time jobs.
Regression analysis
Bachelor showed negative relation with the unemployment which means that when rate of bachelor increases, unemployment decreases as well.
Highly education is like a ticket access to the good company with higher salary.
Regression analysis
Population also showed positive impact on the unemployment rate. It explains that when population grows unemployment rate is increased too.
Being a limited market, a saturation state is developed which raises the unemployment rate.
Explanatory-Native
The coefficient on Native increases by 1 percent, the Unemployment rate is predicted to increase by 0.103%.
Negative effect, Native-born have more advantages over foreign-born in finding jobs.
Insignificant variable in the model.
Explanatory-Age(25-34)
A one percent increase in Age(25-34) is predicted to decrease Unemployment rate by 0.048%
Positive relationship
Also is insignificant variable
UnemploymentR~e=unemployment rates for states annual average
Female=the percentage of female population
Bachelor=percentage of bachelor degree education
PopulationGro~e=population growth rates
Native=percentage of native population
Age2534=percentage of population age 25 to 34
Regression analysis results:
UnemploymentR~e=-50.488+1.206Female-0.212Bachelor+0.154PopulationGro~e-0.103Native+0.048Age2534
N=51 R2=0.347, Adj R2=0.275
Based on the their p-values, each of the explanatory variables is significantly different from zero at the 0.10 level, so we reject the null hypothesis that each variable by itself has no effect on the rates of unemployment except the variables percentage of native population and percentage of population age 25 to 34.
In addition to being individually statistically significant, the explanatory variables are jointly significant at 0.01 level, 0.05 level, 0.10 level based on the F statistic.
Therefore, the result of regression analysis showed the explanatory variables have certain influences on unemployment rates.
Works Cited
"Unemployment Rates for States Annual Average Rankings." Unemployment Rates for States Annual Average Rankings. N.p., n.d. Web. 01 Dec. 2015.
"U.S. Population Growth Rate State Rank." U.S. Population Growth Rate State Rank. N.p., n.d. Web. 01 Dec. 2015.
"U.S. Female Population Percentage State Rank." Based on US Census 2010 Data. N.p., n.d. Web. 01 Dec. 2015.
"U.S. Bachelor Degree Education Percentage State Rank." Based on ACS 2006-2010 Data*. N.p., n.d. Web. 01 Dec. 2015.
"U.S. Native Population Percentage State Rank." Based on US Census 2010 Data. N.p., n.d. Web. 01 Dec. 2015.
"U.S. Percentage of Population Age 25 to 34 State Rank." Based on US Census 2010 Data. N.p., n.d. Web. 01 Dec. 2015.
