Recover Overnight? Work Interruption and Worker Productivity

This paper investigates the effect of work interruption on workers’ subsequent productivity. We employ a data set of individual productivity and machine conditions, in which each worker faces the chance, on a daily basis, that her machine will break down randomly. Our analysis finds that compared to a workday with smooth production, experiencing a machine breakdown is associated with a 3.3 percentage point decline in the worker’s productivity the following day. We discuss possible explanations for the observed effect, including negative emotions, increased cautiousness in operating the machine and proficiency loss. Our findings shed light on the importance of understanding and managing interruptions in the workplace, and contribute to a growing literature on the determinants of productivity at the micro level.

[1]  Steven D. Levitt,et al.  FIELD EXPERIMENTS IN ECONOMICS : THE PAST , THE PRESENT , AND THE FUTURE , 2008 .

[2]  G. Loewenstein.  Emotions in Economic Theory and Economic Behavior , 2000 .

[3]  Donald F. Roy.  “Banana Time” Job Satisfaction and Informal Interaction , 1959 .

[4]  John A. List,et al.  One swallow doesn’t make a summer: new evidence on anchoring effects , 2014 .

[5]  Axel Ockenfels,et al.  Bonus Payments and Reference Point Violations , 2015, Manag. Sci..

[6]  O. Bandiera,et al.  Social Connections and Incentives in the Workplace: Evidence from Personnel Data , 2009, SSRN Electronic Journal.

[7]  Colin Camerer,et al.  Aging and decision making: a comparison between neurologically healthy elderly and young individuals , 2005 .

[8]  C. Syverson.  What Determines Productivity? , 2010 .

[9]  David Card,et al.  Family Violence and Football: The Effect of Unexpected Emotional Cues on Violent Behavior , 2009, The quarterly journal of economics.

[10]  Guido Imbens,et al.  Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs , 2014 .

[11]  J. Graff Zivin,et al.  The Impact of Pollution on Worker Productivity , 2011, The American economic review.

[12]  C. I. Jones,et al.  Why Do Some Countries Produce so Much More Output Per Worker than Others? , 1998 .

[13]  A. Falk,et al.  Individual Risk Attitudes: Measurement, Determinants and Behavioral Consequences , 2009 .

[14]  D. Sgroi,et al.  Happiness and Productivity , 2015, Journal of Labor Economics.

[15]  E. Duflo,et al.  How Much Should We Trust Differences-in-Differences Estimates? , 2001 .

[16]  Henry S. Farber,et al.  Is Tomorrow Another Day? The Labor Supply of New York City Cabdrivers , 2005, Journal of Political Economy.

[17]  Jonah E. Rockoff,et al.  Does Menstruation Explain Gender Gaps in Work Absenteeism? , 2010, The Journal of Human Resources.

[18]  David S. Lee.  Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects , 2005 .

[19]  A. Ichino,et al.  Time Allocation and Task Juggling , 2014 .

[20]  J. George,et al.  Work Interrupted: A Closer Look at the Role of Interruptions in Organizational Life , 2003 .

[21]  David Card,et al.  Management Practices, Workforce Selection, and Productivity , 2016, Journal of Labor Economics.

[22]  R. Thaler,et al.  Labor Supply of New York City Cabdrivers: One Day at a Time , 1997 .

[23]  Juanjuan Meng,et al.  research platform to scholars worldwide. New York City Cabdrivers ’ Labor Supply Revisited: Reference-Dependent Preferences with Rational-Expectations Targets for Hours and Income , 2008 .

[24]  Eleanor O' Neill,et al.  The cost of not paying attention , 2017 .

[25]  E. Fehr,et al.  Do Workers Work More If Wages are High? Evidence from a Randomized Field Experiment , 2005 .

[26]  A. de Grip,et al.  The Effects of Training on Own and Co�?Worker Productivity: Evidence from a Field Experiment , 2011, SSRN Electronic Journal.

[27]  Rachel T. A. Croson,et al.  Gender Differences in Preferences , 2009 .

[28]  Marie D. Connolly.  Here Comes the Rain Again: Weather and the Intertemporal Substitution of Leisure , 2008, Journal of Labor Economics.