Gender and Economic Development Problem Set

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ynat22

Economics

Econ485

SUNY at Binghamton

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Go through the questions in attachment. (4 short questions)

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2. Suppose I wanted to know whether a surprise quiz before the midterm improves your midterm score. Since I’m teaching two sections of this class, I decide to test this by setting a surprise quiz for the first section and not for the second section, and then comparing the average scores of the two sections on the midterm. What assumption(s) would need to hold in order for me to estimate the true effect of a surprise quiz on midterm score in this manner? Explain.

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Problem Set 1 ECON 485G Due in class on 7th September 1. There are two routes to get from the student dorms to class - a long route, which is scenic, and a short route, which is not scenic. You want to study whether the route that the students choose to take is independent of the weather (in the context of the table, this will mean whether X and Y are independent), and you generate the accompanying table of probabilities. Rainy (Y = 0) Sunny (Y = 1) Total Long (X = 0) 0.1 0.3 0.4 Short (X = 1) 0.2 0.4 0.6 Total 0.3 0.7 1 (a) Calculate E(X) and E(Y ). (b) Calculate E(X | Y = 0). How is this different from E(X)? (c) Are the route picked and the weather independent of each other? Why or why not? Use the numbers from the table to arrive at the answer. 2. Suppose I wanted to know whether a surprise quiz before the midterm improves your midterm score. Since I’m teaching two sections of this class, I decide to test this by setting a surprise quiz for the first section and not for the second section, and then comparing the average scores of the two sections on the midterm. What assumption(s) would need to hold in order for me to estimate the true effect of a surprise quiz on midterm score in this manner? Explain. 1 3. Suppose the average number of hours per week that Sophomores at Binghamton University spend studying is 12, that Juniors spend studying is 15, and that Seniors spend studying is 20. How would you incorporate this information into a conditional expectation function? I am looking for something that looks like E(hours|X1 , X2 , X3 , X4 ), where the X 0 s would be different school years, and as soon as I input a school year, the conditional expectation function would tell me how many hours students in that school year spend studying on average. How many such X 0 s would you need? What would they be? (Hint: Think of how we dealt with qualitative variables in regressions.) 4. Suppose that we are interested in understanding whether eating a balanced diet can reduce obesity. Let bal be a dummy variable denoting whether a person consumes a balanced diet, and weight be a variable denoting the person’s weight in pounds. (a) Suppose you ran a regression of bal on weight, and you estimated the regression ÷ = 150 − 5bal. Comment on the following: if a person who used line, as weight to eat unhealthy now starts eating a balanced diet, they can expect to lose 5 pounds. (b) Suppose you have data on their gender (gender), their education level (educ), weight of their parents (mweight and f weight), family income (inc), and a dummy variable denoting whether they go to the gym (gym). Will the frequency with which they eat out be a confounder, once you control for all these variables? (c) Interpret the coefficients in the following regression: ◊ = β̂0 + β̂1 bal + β̂2 educ + β̂3 inc weight (d) Interpret the coefficients β̂1 and β̂5 in the following regression: ◊ = β̂0 + β̂1 bal + β̂2 educ + β̂3 inc + β̂4 gym + β̂5 bal.gym weight (e) Is there any difference in the interpretation of β̂2 in the two specifications above? (f) Suppose the gender variable is coded the following way in your data: gender = 1 if a person is female, and gender = 2 if a person is male. How would you modify this variable, if you wanted to control for gender in your regression? Interpret the β̂ coefficient(s) for the variable(s) you included in your regression to control for gender. (g) Suppose you estimate that β̂2 = 3. Can you come up with an example of a standard error for the variable educ such that it is significant at a 10% level of significance, but not at a 5% level of significance? Can you come up with an 2 example of a standard error such that it is now significant at 5% but not at 10% level of significance? 3
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