Commute duration and health: Empirical evidence from Brazil

There have been many empirical studies associating commuting time and health outcomes in the last few decades. Their general conclusion is that commuting and health are negatively related. The validity of their findings, however, is questionable, given their lack of good identification strategies to correctly account for omitted variables. In this paper, we analyze this relationship using a large and unique nationally representative sample of Brazilian individuals, coupled with the use of propensity score matching techniques, and the application of an exhaustive set of standard falsification tests and sensitivity analysis that may prevent one from claiming a causal link between the two variables. Our results indicate that individuals with more than one hour of commuting appear to have statistically higher probability, ranging from 1.9 to 4.6 percentage points, of reporting bad health status when compared to a person whose commuting time is less than one hour.

[1]  A. Ichino,et al.  From Temporary Help Jobs to Permanent Employment: What Can We Learn from Matching Estimators and Their Sensitivity? , 2006, SSRN Electronic Journal.

[2]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[3]  Anders Karlström,et al.  Is sick absence related to commuting travel time? - Swedish Evidence Based on the Generalized Propensity Score Estimator , 2009 .

[4]  D. Rubin,et al.  Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score , 1985 .

[5]  E. Theurl,et al.  Stress perception and commuting. , 2009, Health economics.

[6]  Mônica Viegas Andrade,et al.  O Efeito da distribuição de renda sobre o estado de saúde individual no Brasil , 2005 .

[7]  Jeffrey A. Smith,et al.  Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators? , 2000 .

[8]  R. Jackson,et al.  Urban Sprawl and Public Health: Designing, Planning, and Building for Healthy Communities , 2004 .

[9]  Laurie Pickup,et al.  Commuting — a further stress factor for working people: evidence from the European Community , 1988, International archives of occupational and environmental health.

[10]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[11]  J. Heckman,et al.  Longitudinal Analysis of Labor Market Data: Alternative methods for evaluating the impact of interventions , 1985 .

[12]  A. Bauman,et al.  Health benefits of cycling: a systematic review , 2011, Scandinavian journal of medicine & science in sports.

[13]  S. Kasl,et al.  Self-ratings of health: do they also predict change in functional ability? , 1995, The journals of gerontology. Series B, Psychological sciences and social sciences.

[14]  M. Hamer,et al.  Active commuting and cardiovascular risk: a meta-analytic review. , 2008, Preventive medicine.

[15]  Erika Sandow.  On the road: social aspects of commuting long distances to work , 2011 .

[16]  M. Emran,et al.  Access to Markets and Rural Poverty: Evidence from Household Consumption in China , 2013, Review of Economics and Statistics.

[17]  Petra E. Todd,et al.  Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme , 1997 .

[18]  G. W. Imbens.  Sensitivity to Exogeneity Assumptions in Program Evaluation , 2003 .

[19]  Y. Benyamini,et al.  Self-rated health and mortality: a review of twenty-seven community studies. , 1997, Journal of health and social behavior.

[20]  E. Oster,et al.  Unobservable Selection and Coefficient Stability: Theory and Validation , 2013 .

[21]  Richard Wener,et al.  Running for the 7:45: The effects of public transit improvements on commuter stress , 2003 .

[22]  Mitch Renkow,et al.  Commuting, Migration, and Rural‐Urban Population Dynamics , 2000 .

[23]  Christopher R. Taber,et al.  Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools , 2000, Journal of Political Economy.

[24]  Joshua D. Angrist,et al.  Mostly Harmless Econometrics: An Empiricist's Companion , 2008 .

[25]  G. Imbens,et al.  Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization , 2001, Health Services and Outcomes Research Methodology.

[26]  General health perceptions , 1978 .

[27]  R. Novaco,et al.  Commuting Stress, Ridesharing, and Gender: Analyses from the 1993 State of the Commute Study in Southern California , 1994 .

[28]  Abba M Krieger,et al.  Causal conclusions are most sensitive to unobserved binary covariates , 2006, Statistics in medicine.

[29]  James J. Heckman,et al.  Alternative methods for evaluating the impact of interventions: An overview , 1985 .

[30]  R. Nayga,et al.  Food away from home expenditures and obesity among older Europeans: are there gender differences? , 2012 .

[31]  Jungyul Sohn.  Are commuting patterns a good indicator of urban spatial structure , 2005 .

[32]  H. White.  A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity , 1980 .

[33]  A. Lewbel,et al.  Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models , 2012 .

[34]  J. Björk,et al.  Relationship between commuting and health outcomes in a cross-sectional population survey in southern Sweden , 2011, BMC public health.

[35]  D. Rubin,et al.  Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome , 1983 .

[36]  D. Rubin.  Multiple imputation for nonresponse in surveys , 1989 .