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Applied Economics And Policy- Statistics

EC116: Applied Economics And Policy- Statistics Assignment Help

QUESTION 1 [15 Marks] Table 1.1 In Mastering Metrics (MM) Compares The Health And Demographic Characteristics Of Insured And Uninsured Couples In The NHIS.

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Panel A Compares The Health Across Husbands (And Across Wives) In This Sample With And Without Health Insurance.

(A) [1 Mark] Calculate The T-Statistic For The Null Hypothesis That There Is No Difference Between The Health Of Husbands With And Without Health Insurance In This Sample. Is The Difference Significantly Different From Zero?

(B) [1 Mark] Calculate The T-Statistic For The Null Hypothesis That There Is No Difference Between The Health Of Wives With And Without Health Insurance In This Sample. Is The Difference Significantly Different From Zero? Panel B Of Table 1.1 Shows That Husbands With And Without Health Insurance Differ Along Many Demographic Dimensions. The Same Is True For Wives. It Is Possible That The Difference In Health Between The “Some HI” And “No HI” Groups May Be Smaller If We Compare Across Groups That Are More Homogeneous. To Investigate This, Go To Http://Masteringmetrics.Com/Resources/ And Download The Stata Data And .Do File Used To Produce MM Table 1.1. Execute The Stata Code In NHIS2009_hicompare.Do Through Line 35 To Make Sure That You Use The Same Selection Criteria That Were Used To Produce Table 1.1.

(C) [1 Mark] How Is The Variable Health Coded In STATA? To Answer The Question, Use The Command “Tabulate Health, Su (Health)”. Is The Difference Between The Health Of Husbands With Some And No HI (Variable Hi) Significantly Different From Zero If You Restrict To Men Who:

(D) [2 Marks] Are Employed?

(E) [2 Marks] Are Employed And Have At Least 12 Years Of Education?

(F) [2 Marks] Are Employed, Have At Least 12 Years Of Education, And Earn Income Of At Least $80,000? [Hint: Use The If Modifier And The Variables Fml, Empl, Educ And Inc] Is The Difference Between The Health Of Wives With Some And No HI Significantly Different From Zero If You Restrict To Women Who:

(G) [2 Marks] Are Employed? (H) [2 Marks] Are Employed And Have At Least 12 Years Of Education?

(I) [2 Marks] Are Employed, Have At Least 12 Years Of Education, And Earn Income Of At Least $80,000? [Hint: Use The If Modifier And The Variables Fml, Empl, Educ And Inc]

QUESTION 2 [40 Marks] The RAND Health Insurance Experiment (HIE).

(A) [2 Marks] What Causal Questions Was The RAND HIE Designed To Answer?

(B) [2 Marks] Download The Stata Data Associated With

Tables 1.3 And 1.4 In MM From The MM Resources Page. The “Person_years.Dta” Dataset Contains Information On The RAND HIE Sample, Including Demographic Characteristics And Treatment Assigned. The “Annual_spend.Dta” Dataset Contains Information On Annual Hospital Expenditures. To Link These Together, Merge “Person_years.Dta” With “Annual_spend.Dta” Using The Variables Person And Year. Keep Only Those Person/Year Observations That Appear In Both Datasets. [Hint: Use The Command “Merge 1:1”].

(C) [2 Marks] Generate A Variable For Total Hospital Spending (Name It Totspen), Equal To The Sum Of Dollars Spent On Inpatient Care (Inpdol) And Outpatient Care (Outsum).

(D) [10 Marks] Calculate The Difference In Average Hospital Spending Between People Who Report Being In Excellent Health (Exc_health) Versus Those Who Report Being In Bad Health (Bad_health). Is This Difference Statistically Significant At The 5% Level? [Hint: Generate A Dummy Variable (Call It Excellent_bad) That Has Value Equal To 1 When Health Is Excellent, To 0 When Health Is Bad, And Has A Missing Value When Both Excellent Health And1 Bad Health Are Equal To 0].

(E) [4 Marks] As Described In MM Chapter 1, The RAND HIE Had Many Small Treatment Groups – In Fact, The Variable Plan In Your Dataset Shows That There Were 24 Different Groups. Define A New Variable “Plantype” That Divides These Into 4 Larger Categories As Follows. Plan Type 1 (“Free”) Is Plan 24; Plan Type 2 (“Individual Deductible”) Is Plans 1 And 5; Plan Type 3 (“Cost-Sharing”) Is Plans 9-23, Inclusive; And Plan Type 4 (“Catastrophic”) Is Plans 2-4 And Plans 6-8, Both Inclusive.

(F) [10 Marks] What Is The Average Hospital Spending In Each Group? Is The Difference In Hospital Spending Between Plan Types 1 And 4 Significant At The 5% Level? [Hint: Generate A Dummy Variable (Call It Plan1_4) That Has Values Equal To 1 When Plan Type Is 1, To 0 When Plan Type Is 4, And Has A Missing Value When Plan Type Is Either 3 Or2 4]

(G) [7 Marks] Clear Your Stata Session And Read In “Rand_initial_sample_2.Dta”. The Four Plan Types Have Already Been Defined In This Dataset (Plantype_1, Plantype_2, Plantype_3 And Plantype_4), Which Also Contains The Variable Ghindx, A General Health Index. Is The Difference In The Average Health Between Plan Type 1 And Plan Type 4 Significant At The 5% Level? [Hint: Generate A Dummy Variable (Call It Plan1_4) That Has Values Equal To 1 When Plan Type Is 1, To 0 When Plan Type Is 4].

(H) [3 Marks] How Do Your Results From Parts (E) – (G) Relate To The HIE Findings Discussed In MM Chapter? 1 Use The Operator “&” 2 Use The Operator “|”

QUESTION 3 [45 Marks] Reload The NHIS Data You Used In Question 1. Again, Execute The Stata Code In NHIS2009_hicompare.Do Through Line 35 To Make Sure That You Use The Same Selection Criteria That Were Used To Produce

MM Table 1.1. (A) [5 Marks] Use The Sum Command To Calculate Average Health Separately For Husbands With And Without Health Insurance. What Is The Difference In Average Health By Insurance Status? Is This Difference Statistically Significant At The 5% Level? What Is The 95% Confidence Interval For The Difference?

(B) [7 Marks] Use The NHIS Data To Construct A Variable (Call It Uni) Such That A Regression Of Health On This Variable Reproduces The Difference Calculated In Question (3a), Above. [Hint: Uni Is A Dummy Variable That Is Equal To 1 When The Variable Hi Is Equal To 0, And Is Equal To 0 When The Variable Hi Is Equal To 1). Compare The Difference, T-Statistic, And Confidence Interval For Your Regression Estime Of Differences In Health With Those You Computed In (3a). In Question 1, We Showed That Some Of The Difference In Average Health Between Those With And Without Health Insurance In The NHIS Can Be Attributed To The Fact That The Insured Differ From The Uninsured Along Many Relevant Dimensions. We Can Also Show This Using Regressions. Starting With Your Regression From Point (3b) Above, Sequentially Add Controls For Age (Age), Years Of Education (Yedu), And Income (Inc).

(C) [7 Marks] Does Any Set Of Controls Eliminate The Difference In Health Between Insured And Uninsured? Explain How The Results Change As You Add Controls And What Changes In The Estimates As You Add More Controls Might Mean.

(D) [5 Marks] Comment The Relationship Between Health And The Other Variables In The Regression: Age, Yedu, Inc. Reload The RAND HIE Dataset “Rand_initial_sample_2.Dta” Used In Question 2.

(E) [6 Marks] Define A Dummy Variable Called Anydum, Which Is Equal To 1 For Individuals With Plan Types 1-3 (“Any Insurance”) And Equal To 0 For Individuals With Plan Type 4 (Only “Catastrophic” Insurance). Regress The General Health Index Ghindx On A Dummy For Any Insurance (Ghindx, Is A General Health Index Similar To That In The NHIS, But Scaled Differently: The Higher The Index The Healthier The Person).

(F) [3 Marks] Interpret Your Estimates Of This Model.

(G) [4 Marks] Sequentially Add Controls For Age (Age), Education (Educper), And Income (Income1). Do These Controls Have Much Of An Effect On Your Estimates?

(H) [8 Marks] Why Is The Effect Of Adding These Demographic Controls So Different From What You Saw In Question 3b? (Hint: Think About The Differences Between The NHIS And The RAND HIE Data.)

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