Consider the following hypothetical pooled cross-sectional data of daily COVID-19 cases per 1,000 people during the early two months of the pandemic, from March to April 2020 in Ohio and 5 other neighboring states. The variable April takes value 1 if the recorded cases are from April 2020, and takes value 0 if the recorded cases are from March 2020. The variable lockdown takes value 1 if the state put in place a Stay-at-home order beginning from March 2020, and takes value 0 otherwise. Using the difference-in-differences estimator, assess the impact of the lockdown policy on daily COVID-19 cases. Table 1. Daily COVID-19 Cases by State April Cases Lockdown State Indiana 0 2 0 Indiana 1 8 0 0 1 0 1 2 0 Kentucky Kentucky Michigan Michigan Ohio 0 3 0 1 8 0 0 2 1 Ohio 1 4 1 0 4 1 1 7 1 Pennsylvania Pennsylvania West Virginia West Virginia 0 1 1 1 2 1 Problem 1. Consider the following hypothetical pooled cross-sectional data of dailyCOVID-19 cases per 1,000 people during the early two months of the pandemic, fromMarch to April 2020 in Ohio and 5 other neighboring states. The variable April takesvalue 1 if the recorded cases are from April 2020, and takes value 0 if the recorded casesare from March 2020. The variable lockdown takes value 1 if the state put in place aStay-at-home order beginning from March 2020, and takes value 0 otherwise. Using thedifference-in-differences estimator, assess the impact of the lockdown policy on dailyCOVID-19 cases.Table 1. Daily COVID-19 Cases by StateStateAprilCasesLockdownIndiana2Indiana18Kentucky1Kentucky12Michigan3Michigan18Ohio21Ohio141Pennsylvania41Pennsylvania171West Virginia11West Virginia11

Given: Cases reported when there is no lockdown State March (cases per 1000) April (cases per…

Consider the following hypothetical pooled cross-sectional data of daily COVID-19 cases per 1,000 people during the early two months of the pandemic, from March to April 2020 in Ohio and 5 other neighboring states. The variable April takes value 1 if the recorded cases are from April 2020, and takes value 0 if the recorded cases are from March 2020. The variable lockdown takes value 1 if the state put in place a Stay-at-home order beginning from March 2020, and takes value 0 otherwise. Using the difference-in-differences estimator, assess the impact of the lockdown policy on daily COVID-19 cases. Table 1. Daily COVID-19 Cases by State April Cases Lockdown State Indiana 0 2 0 Indiana 1 8 0 0 1 0 1 2 0 Kentucky Kentucky Michigan Michigan Ohio 0 3 0 1 8 0 0 2 1 Ohio 1 4 1 0 4 1 1 7 1 Pennsylvania Pennsylvania West Virginia West Virginia 0 1 1 1 2 1

Consider the following hypothetical pooled cross-sectional data of daily COVID-19 cases per 1,000 people during the early two months of the pandemic, from March to April 2020 in Ohio and 5 other neighboring states. The variable April takes value 1 if the recorded cases are from April 2020, and takes value 0 if the recorded cases are from March 2020. The variable lockdown takes value 1 if the state put in place a Stay-at-home order beginning from March 2020, and takes value 0 otherwise. Using the difference-in-differences estimator, assess the impact of the lockdown policy on daily COVID-19 cases. Table 1. Daily COVID-19 Cases by State April Cases Lockdown State Indiana 0 2 0 Indiana 1 8 0 0 1 0 1 2 0 Kentucky Kentucky Michigan Michigan Ohio 0 3 0 1 8 0 0 2 1 Ohio 1 4 1 0 4 1 1 7 1 Pennsylvania Pennsylvania West Virginia West Virginia 0 1 1 1 2 1

Principles of Economics 2e

2nd Edition

ISBN:9781947172364

Author:Steven A. Greenlaw; David Shapiro

Publisher:Steven A. Greenlaw; David Shapiro

Chapter16: Information, Risk, And Insurance

Section: Chapter Questions

Problem 3SCQ: Why is it difficult to measure health outcomes?

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