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Labor market effects of dirty air. Evidence from administrative data

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Abstract

We study the impact of air pollution on labor supply and wage compensations in Italy. Matching administrative data on the universe of Italian dependent employees in the private sector with \({\hbox {PM}}_{10}\) concentrations and weather data at monthly frequency, we exploit exogenous variation in wind speed to instrument for endogenous air pollution exposure. We find that a one standard deviation increase in \({\hbox {PM}}_{10}\) level, leads to a 7.3% higher probability of sick leave and to an earning loss of 0.83 euros/worker/month. These figures generated total social excess expenditures of 755 million euro during the period 2011–2016 considering a pollution threshold set by the World Health Organization and extending the effects to the total workers population. The heterogeneity analysis shows that the impacts are larger for workers in constructions and services, for white and blue collars and for females and foreign workers, while we find no impact on managers. The sick wage received by exposed workers is not always aligned to the pollution exposure actually faced by different worker categories.

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Notes

  1. For sick leaves longer than 20 days, the compensation increases to 66.6% of forgone salary, for a maximum period of 180 days for each calendar year.

  2. While allowing for an easier computational setting and preserving precision in assigning pollution exposure, this procedure does not allow to fully capture potential individual-level confounding factors that may affect our outcomes of interest in each cell. A fruitful approach to overcome this limitation was employed, among others, by Isen et al. (2017) and Currie et al. (2015), who control for micro-level heterogeneity through a compositional adjustment procedure. Replicating this approach however would have required a richness of information available at cell level that we could not exploit both for data availability reason and privacy limitations in importing these data according with the rules of the VisitInps research program.

  3. Specifically, CAMS data combine observations from past and current satellite instruments and computer models for reanalysis. It is worth noting that mere satellite observations often have gaps due to instrumental failures or clouds obscuring the view, and inconsistencies may occur because of a difference in resolution between instruments. CAMS data do not suffer from this limitation.

  4. The air quality standards are set by the European Commission for each pollutant. For PM10, the daily concentration is 50 \(\upmu\)g/m\(^{3}\). For further details, see https://ec.europa.eu/environment/air/quality/standards.htm.

  5. Our baseline results are clustered at the municipality level, following the level of aggregation of our pollution “treatment” assignment (Bertrand et al., 2004).

  6. When clustering on provinces, we obtain a s.e. of 0.131, which is slightly larger than the one obtained with clusters on municipalities (s.e.: 0.092). Importantly, both inferences lead to obtain coefficients statistically significant at 1%.

  7. It is worth noting that our 2SLS estimates deliver a Local Average Treatment Effect (LATE), in which the ‘compliers’ are workers in windy cities. While using high frequency data there could be cities where wind speed is almost zero in specific time intervals (days or hours) with no effect on pollution dispersion, this is not the case in our setting because we never observe wind speed dropping to zero using monthly wind data. Our analysis considers a period of six years during which all municipality-month pairs are affected to some extent by wind, hence we can plausibly interpret our LATE as an average treatment effect (ATE). With this assumption, we thus elaborate our back-of-the-envelope calculations based on the full sample.

  8. According to recent INPS estimates, the total social welfare expenditures for sick leaves amounted to 2.8 billion/year in 2018. Source: Senate Hearing held by the INPS President in September 2018, available at: http://www.senato.it/application/xmanager/projects/leg18/attachments/documento_evento_procedura_commissione/files/000/000/291/Memorie_INPS.pdf.

  9. We cannot test the dispersion effect for non-particle pollutants such as SO\(_2\), NO\(_2\) or O\(_3\) as these pollutants were not fully available at the time of writing.

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Acknowledgements

The realization of this project was possible thanks to the sponsorships and donations of the “VisitInps Scholars” program. We thank the staff of Direzione Centrale Studi e Ricerche at INPS for their support with the data and the institutional setting. The findings and conclusions expressed are solely those of the authors and do not represent the views of INPS. The authors are grateful to Tito Boeri (Bocconi University), Luca Citino (Bank of Italy), Matthew Gibson (Williams College), Andrea Piano Mortari (CEIS Tor Vergata) and Paolo Naticchioni (INPS) and two anonymous referees for their valuable comments. The authors declare that they have no material, financial or other non-academic interests that relate to the research described in this paper.

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Authors and Affiliations

  1. DISES, Department of Economic and Statistical Sciences (DISES), University of Naples “Federico II”, DG Studi e Ricerche-INPS, CSEF, Naples, Italy

    Edoardo Di Porto

  2. DiSSE, Department of Social Sciences and Economics, Sapienza University of Rome, P.le Aldo Moro 5, Rome, Italy

    Joanna Kopinska

  3. CEIS Tor Vergata, Rome, Italy

    Joanna Kopinska & Alessandro Palma

  4. Gran Sasso Science Institute (GSSI), Via Francesco Crispi 7, 67100, L’Aquila, Italy

    Alessandro Palma

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  1. Edoardo Di Porto
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Correspondence to Alessandro Palma.

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Appendix

Appendix

1.1 Additional tables

See Tables 11 and 12.

Table 11 IV estimates of the effect of \({\hbox {PM}}_{10}\) with standard errors clustered on provinces
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Table 12 First stage estimates with standard errors clustered on provinces
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Di Porto, E., Kopinska, J. & Palma, A. Labor market effects of dirty air. Evidence from administrative data. Econ Polit 38, 887–921 (2021). https://doi.org/10.1007/s40888-021-00231-x

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