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Work Disruption, Worker Health, and Productivity Mariesa Herrmann Columbia University Jonah Rockoff Columbia Business School and NBER Evidence from Teaching.

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Presentation on theme: "Work Disruption, Worker Health, and Productivity Mariesa Herrmann Columbia University Jonah Rockoff Columbia Business School and NBER Evidence from Teaching."— Presentation transcript:

1 Work Disruption, Worker Health, and Productivity Mariesa Herrmann Columbia University Jonah Rockoff Columbia Business School and NBER Evidence from Teaching October, 2009

2 1 Motivation and Background Work disruptions, including worker absence, have potentially important effects on labor productivity Work disruption and health are tightly linked –Two-thirds of lost work time due to illness or injury Health can also affect labor productivity directly –Dubbed presenteeism by soc. psych/med literature Considerable lit in economics on causes and consequences of absence; role of incentives –Many of these studies focus on teachers –Absences have significant negative impacts on students (much larger estimates in developing world than U.S.)

3 2 Overview of Our Study Examine work disruption and teacher productivity –Measured by student achievement Use detailed information on teachers employment histories (including extended leaves) and absences –Exact timing and reason for each event Separate health from disruption using exam timing –If health is correlated over time, then a negative health shock can occur prior to a health-related disruption –Health disruptions occurring post-exam can thus be causally related to teacher productivity pre-exam Panel data allow for identification within teachers and student background controls

4 3 Data Sources New York City public schools, to –Largest district in U.S., ~80,000 teachers, ~1m students Teacher data –Timing and reason for work disruptions (e.g., maternity leave, sick leave, retirement, termination, etc.) –Date and reason for each day of absence –Background: education, experience, demographics… Student data –Demographics, program participation (free lunch, ELL, Spec Ed), absences, and suspensions –Test scores in math and English, grades 3-8 –Links to math and English teachers (same in 3-5, ~6) Administrative data –School calendarscoding of disruptive effects of leaves/departures –Exam datesMath ~March-May, English ~January-April

5 4 NYC Public Schools Leave/Absence Policy Extended leave policies governed by FMLA (e.g., maternity leave) and details of teachers union contract (e.g., sabbaticals) Teachers earn 10 sick days per year –Use is capped at 10 per year, but medically certified sick days do not count towards the cap –Unused days accumulate; can be used later or cashed in at retirement/resignation 1/400 th of annual salary per unused day

6 5 Extended Disruption Measures Initially code 11 extended disruption types –Maternity, Child Care, Medical, Sick Family Member, Personal, Sabbatical, Resignation or Retirement, Involuntary Termination, Certification Termination, Death, and Other Use 4 categories in regression analysis due to small frequencies for many types –Maternity, Medical, R-R-T, and Other

7 6 Daily Absence Measures Code daily absences into 3 categories –Self-treated sickness / personal –Medically certified sickness –Other (e.g., jury duty, funeral, religious holiday) Self-treated days may be due to illness, but many are likely due to other causes

8 7 Who Experiences Teaching Disruptions?

9 8 Estimation Methodology Baseline specification: Y it = L it + A it + g X it + W it + S it + gt + it –Y it : achievement score of student i in year t –L it : indicator for extended disruption for i s teacher –A it : number of daily absences for i s teacher –X it : student characteristics –W it : teacher characteristics (teacher-school-grade FE) –S it : school characteristics – gt : grade-year fixed effect Standard errors clustered at school level – Tend to be larger than at classroom or teacher level

10 9 Potential Source of Bias In years when disruptions/absences occur, students are worse than usual for a teacher –Teachers take a leave, depart, or show up less often when their students are more difficult –Principals expect disruption, assign students –Students expect disruption, behave worse Address using two strategies: –Timing of exam –Placebo test with teacher in other subject

11 10 Separating Disruption and Health Effects Allow effects to vary by timing (pre/post exam) and cause (health/non-health) Alternate specification: Y it = D it + P it + D it H it + P it H it + Z it + it –D it : Indicates pre-exam disruption –P it : Indicates post-exam disruption –H it : Indicates disruption is health-related –Z it : Baseline controls (including teacher FE) If both disruption and health reduce productivity, should find 0, 2, 3 < 0, 1 = 0 – If health-related post-exam disruptions are unrelated to pre-exam health, then should find 3 = 0

12 11 Baseline Specifications

13 12 Disruption/Health: Extended Disruptions

14 13 Disruption/Health: Daily Absences

15 14 Placebo test using other-subject teacher –Limited to students in grades 6-8 More flexible post-exam timing –Exams moved up in later years Student absences as a (spurious?) pathway Suspensions to address reactive behavior Heterogeneity across student ability Heterogeneity by teacher experience Duration / day of week of daily absences Checks and Extensions

16 15 Findings and Conclusions Work disruptions in teaching have large negative effects on educational production –Akin to move from 50 th 30 th pctile of teacher quality Limited evidence on the effect of worker health on productivity beyond its impact via disruption –Negative effect of health related absences but not extended leaves –Supports giving some weight to presenteeism in design of optimal compensation for absences due to illness Estimates imply a role for educational policy in dampening impact of work disruption in teaching –Substitute-ability; work standardization (e.g., uniform curriculum); resource allocation in cases of predictable disruption (e.g., maternity) Finally, raises concern about identifying impact of absences in data without information on timing and cause, even in specifications with teacher FE


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