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book Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge cover

Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge

Edition 6ISBN: 130527010X
book Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge cover

Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge

Edition 6ISBN: 130527010X
Exercise 24

Use the data in BENEFITS.RAW to answer this question. It is a school-level data set at the K–5 level on average teacher salary and benefits. See Example 4.10 for background.

(i) Regress lavgsal on bs and report the results in the usual form. Can you reject H0 : ? bs = 0 against a two-sided alternative? Can you reject H0: ? bs = ?1 against H1 : ? bs > ?1? Report the p-values for both tests.

(ii) Define lbs = log(bs). Find the range of values for lbs and find its standard deviation. How do these compare to the range and standard deviation for bs?

(iii) Regress lavgsal on lbs. Does this fit better than the regression from part (i)?

(iv) Estimate the equation lavgsal = ? 0 + ? 1bs + ? 2 lenroll + ? 3lstaff + ?4lunch + u

and report the results in the usual form. What happens to the coefficient on bs? Is it now statistically different from zero?

(v) Interpret the coefficient on lstaff. Why do you think it is negative?

(vi) Add lunch2 to the equation from part (iv). Is it statistically significant? Compute the turning point (minimum value) in the quadratic, and show that it is within the range of the observed data on lunch. How many values of lunch are higher than the calculated turning point?

(vii) Based on the findings from part (vi), describe how teacher salaries relate to school poverty rates. In terms of teacher salary, and holding other factors fixed, is it better to teach at a school with lunch = 0 (no poverty), lunch = 50, or lunch = 100 (all kids eligible for the free lunch program)?

Reference: Example 4.10:

Let totcomp denote average total annual compensation for a teacher, including salary and all fringe benefits (pension, health insurance, and so on). Extending the standard wage equation, total compensation should be a function of productivity and perhaps other characteristics. As is standard, we use logarithmic form:

log(totcomp) = f ( productivity characteristics,other factors), where f (.) is some function (unspecified for now). Write

 Use the data in BENEFITS.RAW to answer this question. It is a school-level data set at the K–5 level on average teacher salary and benefits. See Example 4.10 for background. (i) Regress <i>lavgsal </i>on <i>bs </i>and report the results in the usual form. Can you reject H<sub>0</sub><sub> </sub>: <i>?</i><sub> bs</sub><i> </i>= 0 against a two-sided alternative? Can you reject H<sub>0</sub>: <i>?</i><sub> bs</sub><i> </i>= ?1 against H<sub>1</sub><sub> </sub>: <i>?</i><sub> bs</sub><i> </i>> ?1? Report the <i>p</i>-values for both tests. (ii) Define <i>lbs </i>= log(<i>bs</i>). Find the range of values for <i>lbs </i>and find its standard deviation. How do these compare to the range and standard deviation for <i>bs</i>? (iii) Regress <i>lavgsal </i>on <i>lbs</i>. Does this fit better than the regression from part (i)? (iv) Estimate the equation <i>lavgsal </i>= <i>?</i> <sub>0</sub> + <i>?</i><sub> 1</sub><i>bs </i>+ <i>?</i> <sub>2</sub> <i>lenroll </i>+ <i>?</i> <sub>3</sub><i>lstaff </i>+ <i>?</i><sub>4</sub><i>lunch </i>+ <i>u</i> and report the results in the usual form. What happens to the coefficient on <i>bs</i>? Is it now statistically different from zero? (v) Interpret the coefficient on <i>lstaff</i>. Why do you think it is negative? (vi) Add <i>lunch</i>2 to the equation from part (iv). Is it statistically significant? Compute the turning point (minimum value) in the quadratic, and show that it is within the range of the observed data on <i>lunch</i>. How many values of <i>lunch </i>are higher than the calculated turning point? (vii) Based on the findings from part (vi), describe how teacher salaries relate to school poverty rates. In terms of teacher salary, and holding other factors fixed, is it better to teach at a school with <i>lunch </i>= 0 (no poverty), <i>lunch </i>= 50, or <i>lunch </i>= 100 (all kids eligible for the free lunch program)? Reference: Example 4.10: Let <i>totcomp </i>denote average total annual compensation for a teacher, including salary and all fringe benefits (pension, health insurance, and so on). Extending the standard wage equation, total compensation should be a function of productivity and perhaps other characteristics. As is standard, we use logarithmic form: log(<i>totcomp</i>) = <i>f </i>( <i>productivity characteristics</i>,<i>other factors</i>), where <i>f </i>(.) is some function (unspecified for now). Write

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Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge
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