Midterm 1 Sample

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1629

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Economics

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Feb 20, 2024

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1 Economics 1629 Fall 2014 Midterm 1 NAME: ____________________________________ Instructions: You may use a calculator. You will also find a formula sheet to help you set up any necessary calculations on the last page. You may not use or share any other materials. We have provided space on the exam for your answers. Don’t feel compelled to use up all the space provided (if you are doing so, you are probably spending too much time on that question.) Show how you arrived at your answer, including all calculations.
2 PART I - Flu Shots and Sick Days Public health officials spend a great deal of time encouraging people to receive a vaccination for influenza (“the flu shot”) every year. In addition to health concerns about widespread outbreaks of the flu, other reasons to encourage widespread vaccination rates include minimizing the number of sick days people must take from work (and school) during flu season. Some employers, recognizing the productivity implications of employee illness, have begun sponsoring flu vaccinations in the workplace. To investigate a possible link between flu vaccination rates and number of sick days taken by employees, researchers obtained data on a random sample of 1,000 employers. The data include the total number of sick days taken by employees at each workplace in the January 2007-March 2007 period and a binary indicator (dummy variable) for whether the employer offered a flu shot on site (i.e. in its premises) between September and December 2006. Consider the following regression results: . regress sickdays flushot, robust; Linear regression Number of obs = 1,000 R-squared = 0.092 ------------------------------------------------------------------------------ | Robust sickdays | Coef. Std. Err. -------------+---------------------------------------------------------------- flushot | -42 20 _cons | 272 198 ------------------------------------------------------------------------------ where: sickdays= total number of employee sick days flushot= 1 if employer offered flu shots on site 0 if employer did not offer flu shots on site 1. (3 points) Write the sample regression function (i.e. estimated equation)
3 2. (2 points) Interpret the number “272” from the regression output above in language that a policymaker can understand. 3. (2 points) Interpret the estimated coefficient on the variable flushot from the regression output above in language that a policymaker can understand.
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4 4. Test the null hypothesis that the population coefficient on flushot is 0 at the 5% significance level. In doing so, specify: a) (1 point) Null hypothesis: b) (1 point) Alternative hypothesis: c) (1 point) Test Statistic: d) (2 points) What can you conclude from this test? Interpret your results in language specific to this context.
5 5. (4 points) An analyst argues that since the study used a random sample of employers, the results provide a good estimate of the causal effect of flu vaccination rates on the number of sick days. Do you agree with this analyst? Why or why not? 6. (4 points) Do you think assumption 1 (Zero Conditional Mean of the Error Term, E[u|X]=0) is satisfied in the regression equation being estimated above? Explain in a language specific to this context.
6 PART II – Poverty and Crime The Statistical Abstract of the United States (1993) has state-level data which we will use to analyze the relationship between poverty and crime. Variables crime: total crimes per 100,000 people pov: % of population living below the poverty level (units are 0 to 100) popdens: population per square mile urban: = 1 for urban states (popdens>=82 people/sq mile); 0 otherwise pov_urban = pov*urban low: = 1 if low population density (less than 34 people/sq mile); 0 otherwise medium: = 1 if medium population density (34-182 people/sq mile); 0 otherwise high: = 1 if high population density (over 182 people/sq mile); 0 otherwise Summary Statistics Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- crime | 50 4965.08 1228.668 2533 8351 pov | 50 14.11 4.263957 8 26.4 popdens | 50 169.888 236.9137 1.1 1062 Correlations | crime pov popdens -------------+--------------------------- crime | 1.0000 pov | 0.1451 1.0000 popdens | 0.0386 -0.2535 1.0000 Regression Results Dependent variable = Crime (total crimes per 100,000 people) Variable (1) (2) (3) (4) (5) (6) pov 41.82 (46.34) 47.71 (49.24) 42.30 (44.57) -20.63 (55.86) popdens 0.42 (0.50) urban 366.64 (347.11) 370.53 (346.90) -1313.73 (1101.13) pov_urban 119.35 (79.41) medium 149.29 (415.81) high 641.10 (478.22) _cons 4375.04 (622.54) 4220.92 (716.36) 4781.76 (246.47) 4183.03 (635.07) 4723.75 (330.95) 5073.78 (780.25) R2 0.021 0.027 0.023 0.044 0.040 0.087 N = 50; Robust standard errors in parentheses.
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7 1. (2 points) Using the regression results, interpret the relationship between crime and poverty (without controlling for anything else) in language that a person not well versed in statistics can understand. 2. When we control for population density (Regression 2), the coefficient on poverty changes. (2 points) Explain why this occurs. (2 points) Describe what we can learn from this change, using language specific to this context.
8 3. (2 points) In Regression 2, interpret the coefficient on population density in language a person not well versed in statistics can understand. 4. (3 points) Using Regression 2: compute the predicted crime rate for a state with 10% poverty and a population density of 100 people per square mile. 5. (3 points) Using Regression 2, compute the predicted difference in crime rate between a state with 10% poverty and 30% poverty, both having a population density of 100 people per square mile.
9 5. (1 point) What is the average crime rate in urban states (without controlling for anything else)? (Numeric answer) 6. (2 points) Interpret the coefficient on urban in Regression 4 in language a person not well versed in statistics can understand. The Urban variable divides states into two categories based on population density, high density (urban) and low density (non-urban). However, because it might be useful to look at more granular levels of population density, an analyst has defined three new dummy variables: Low: state below the 25 th percentile of population density Medium: state in the 25 th -75 th percentile range of population density High: state is above the 75 th percentile of population density 8. (3 points) Regression 5 looks at crime rates by levels of population density. Predict what would happen if we added the variable low as a control.
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10 9. (1 point) What is the average crime rate in states with population densities below the 25 th percentile? (Numeric answer) - (1 point) What is the difference in average crime rates between states above the 75 th percentile of population density and those in the 25-75 th percentile? (Numeric answer) 10. (4 points) What do the estimates from regression 6 tell us about the relationship between crime and poverty?
11 FORMULAS: 0 : : T C A T C H H μ μ μ μ = 1) ( ) T C T C Y Y t SE Y Y = 2) 2 2 ( ) ( ) ( ) C T Y Y T C T C SE Y Y n n σ σ = + 3) ( ) 1.96 ( ) T C T C Y Y SE Y Y ± 4) X Y X X Y Y X X i i i 1 0 2 1 ˆ ˆ ) ( ) )( ( ˆ β β β = = 5) 1 2 2 1 1 ( )( ) ( ) ( ) n i i i XY n n i i i i X X Y Y r X X Y Y = = = = 6) [ ] 2 2 2 2 ˆ 1 ) ( ˆ ) ( ) ˆ ( 1 = = X X u X X s SE i i i β β 7) ) ˆ ( 96 . 1 ˆ 1 1 β β SE ± 8) 1 1 ˆ ˆ ( ) a t SE β β =
12 Z table
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