In \"From Political to Material Inequality:...\" Jeremy Levine and Carl Gershens

Question

In "From Political to Material Inequality:..." Jeremy Levine and Carl Gershenson perform a regression to see whether neighborhood characteristics affect the response from the city of Boston to service requests. The dependent variable (time to respond to requests) was evaluated for a number of neighborhood characteristics (e.g. racial and economic makeup). The results are shown below.

a) Which variables were statistically significant in the model?

b) Based on the regression results above, what can we say about the city of Boston's response to service requests?

Table IV. Ordinary Least Squares Regression of Average Time-to-Close in Minutes for 22 Endoge- nous Service Requests, March 1, 2010-April 30, 2011 (I) Time-to-Close % Black % Hispanic % Asian % Foreign Born % Owner-Occupied Housing % on Public Assistance Unemployment Rate % College Students % Higher Degree % Tenure 1 Yr + -5,662.0 (2,564.4) 5,142.2 (5,297.7) 69.19 (6,491.6) -8,539.0 (6,840.5) 2,514.9 (5,323.0) -1,5071.7 (13,865.7) 1,753.2 (10,204.1) 7,820.0 (5,338.7) 307.1 (4,528.9) 12,363.9 (7,170.8) -15,655.6 (11,271.0) City Employees Population Median Income Median Age Constant -0.144 0.007 -43.05 (137.9) (6,788.8) (0.289) (0.067) 10,505.4 156 R-sq 0.1498 Notes: Income measured in $10,000 'p

Explanation / Answer

Answer to part a)

Median income is the only variable that is statistically significant in the model as it has a P value of 0.007 , which is less than the significance level 0.05

.

Answer to part b)

Based on the regression results above we can interpret the R square value. R square value is called coefficient of determination. It tells us about the percent of variation explained in the dependent variable by the model . We got R square = 0.1498. This means 14.98% of the variation is explained by the model . This percent is too less. From this we get to know that the model does not help us predict the dependent variable correctly.

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