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Use CRIMEA for this exercise.(i) Add the logs of each wage variable in the data set and estimate the model by first differencing. How does including these variables affect the coefficients on the criminal justice variables in Example 13.9 ?(ii) Do the wage variables in (i) all have the expected sign? Are they jointly significant? Explain.

(i) See video (ii) Not all have expected signs

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Chapter 13

Pooling Cross Sections Across Time: Simple Panel Data Methods

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are born. This is a result when we add the changes. Of the nine log wage variables to equation 13.33 there are 540 observations. Our square is 0.445 and the adjusted our square is 0.42 four. The coefficients on the criminal justice Variables change very modestly, and the statistical significance of each variable is basically unaffected. So we see that for the wage variable. Uh huh. Not all of them have expected signs. For example, the coefficient on the wage for transportation, utilities and communication is positive and marginally significant. Higher manufacturing wages lead to lower crime, as we might expect it. But why? The estimated coefficient is by far the largest in magnitude. It is not statistically different from zero. Well, the F test for the joint significance of the wage variables with nine and 529 degrees of freedom is 1.25 and the P value of 0.26 So we are unable. Thio reject the null hypotheses and in other words, the wage variables are jointly insignificant

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