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Gender Gaps in Birth Weight Across Latin America: Evidence on the Role of Air Pollution

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Abstract

Recent estimates indicate that more than 100 million people in Latin America and the Caribbean are exposed to air pollution levels exceeding World Health Organization guidelines. Air pollution persists because of a development process centered around high rates of urbanization and congestion, geographically concentrated industrialization, and biomass burning. This paper focuses on a relatively understudied consequence of this pollution-intensive development process: its gender impact. The analysis provides systematic evidence across the region on the impact of in utero exposure to air pollution on infant health and well-being, a period when the medical literature suggests male fetuses are more delicate than female fetuses. Health at birth is known to have long-term consequences, so this investigation seems warranted and aids the understanding of future gender gaps in socioeconomic development. The empirical analysis combines satellite and survey data from three countries in the region: Bolivia, Colombia, and Peru. Based on sibling comparisons, the analysis finds that a 10% increase in pollution exposure in utero reduces the male–female birth weight gap by approximately 50 g. This weight reduction is equivalent to the impact of smoking five cigarettes a day (versus none) during pregnancy.

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Notes

  1. For example, several cities have implemented integrated air quality management plans and have made sectoral investments, such as sustainable urban transport.

  2. Making choices to avoid pollution depending on the gender of their future child could be another channel. This type of behavioral response can be plausible in some contexts, but corroborating evidence does not seem widespread.

  3. Even in the epidemiology literature, evidence for developing countries, and for Latin America in particular, is limited. A recent meta-analysis of 1628 studies in the region found that most of the evidence is concentrated in a few cities (Fajersztajn et al. 2017).

  4. The evidence in the economics literature for Latin America is even scarcer. We are aware of only a few papers studying pollution exposure in utero: for Mexico (Arceo et al. 2015), Chile (Miller and Ruiz-Tagle 2018), and Uruguay (Balsa et al. 2014). There are also few economic studies for Latin America that focus on the effects of pollution later in life (see for instance, Miller and Vela 2013).

  5. For example, Almond et al. (2009) and Black et al. (2013) study nuclear disasters in Ukraine and Norway, respectively. Nilsson (2009) investigates the long-term impact of banning leaded gasoline in Sweden during the 1970s. Sanders (2012) studies reductions in U.S. pollution caused by the recession of the early 1980s. Isen et al. (2017) examine the U.S. Clean Air Act of the 1970s and use restricted access data on adult earnings by county and date of birth.

  6. Other satellite measures of aerosol are also available. Total Ozone Mapping Spectrometer (TOMS) data are available since the 1970s but has been discontinued. Ozone Monitoring Instrument (OMI) data are more accurate than TOMS, but start in 2005. However, MODIS has high spectral resolution, which enables it to detect clouds and aerosols better than previous satellite-based instruments.

  7. Also, some ground stations have the advantage of monitoring additional pollutants (i.e., ozone, sulfur, and nitrogen oxides), but this is often not the case in many developing countries.

  8. Further, while ground-based stations measure only dry particles, satellite-based measures cannot distinguish water vapor from other particles.

  9. The name of the aerosol data used is MODIS/Terra Aerosol Optical Thickness Daily L3 Global 0.05Deg CMA (MOD09CMA). Other MODIS products include aerosol measures over water based on MODIS Aqua satellite.

  10. See https://modaps.modaps.eosdis.nasa.gov/services/about/products/c6-nrt/MOD09CMA.html.

  11. The MOD09CMA data can take values slightly higher than 5000 because of the processing.

  12. Available at http://www.who.int/phe/healt_topics/outdoorair/databases/cities/en/.

  13. Particulate matter can be of different sizes, but the most common particle sizes are PM10 and the finer PM2.5.

  14. A more detailed validation of AOD, also relying on WHO data, is presented in Gendron-Carrier et al. (2018). Based on several linear models, those authors show that AOD tracks particulate matter measures relatively closely even in a simple model without additional controls. In general, they obtain R-squares of over 0.75. Though their analysis can provide a basis for translating the AOD measure into PM10, it does not fit our data closely, probably because we use different versions of MODIS AOD data (the L3 data used here have been processed further for use in climate modeling). Instead, we rely on a coarser rule of thumb to translate AOD to PM10 measures. In Fig. 2, the left axis shows the raw AOD measure, whereas the right axis shows the AOD transformation to PM10. The two axes can be used to coarsely translate between both pollution measures. For reference, the horizontal line in the figure gives WHO’s recommended maximum annual average PM10 exposure level of 20 mg/m3.

  15. Not all of this variation is used in this paper, as the DHS data are not available for all municipalities/districts and all time periods.

  16. To construct the figure, we first calculated monthly pollution averages in each country so that the box plot captures only time variation in pollution and not geographic variation.

  17. Median is chosen to better capture first moment of distribution in a context in which frequency of observation may be low.

  18. The following steps were used in preparing the maps: first, each child in the birth records of the DHS was assigned the pollution level in the municipality where the mother resided during pregnancy; then, after each child’s pollution exposure was determined, we calculated the average municipal exposure for children in the survey.

  19. One caveat of this decision is that we implicitly select the sample by relative unresponsiveness to pollution shocks in terms of geographic mobility.

  20. Consequently, the expected change in the health measure for girls and boys would be equal to (0.01 ∗ β1 ∗ δ)) and (0.01 ∗ (β1 + β3) ∗ δ), respectively.

  21. When interpreting the results in Table 1, it is important to take into account that the satellite-based pollution measures used in the regressions should be interpreted as logarithms. Given that the model is linear-log, for a 1% change in pollution, the effect is calculated as the coefficient on pollution divided by 100. For a 10% change in pollution, the effect is calculated as the coefficient on pollution divided by 10.

  22. In all models, the male dummy is positive and significant, suggesting that males have a higher birth weight by approximately 150 to 200 g.

  23. To check for robustness of the results and comparability across samples, we run the estimations from columns (1), (2), and (3) using the restriction of families that have more than one child. Results are comparable in magnitudes but sometimes not statistically significant.

  24. Temperature inversions are climatologically phenomena that are independent of short-run variations in local pollution as well as local economic activity.

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Authors and Affiliations

  1. IDB Invest, Washington, DC, USA

    Gabriela Aparicio

  2. Inter-American Development Bank, Washington, DC, USA

    María Paula Gerardino

  3. Duke University, Durham, NC, USA

    Marcos A. Rangel

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  1. Gabriela Aparicio
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  2. María Paula Gerardino
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  3. Marcos A. Rangel
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Correspondence to María Paula Gerardino.

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Aparicio, G., Gerardino, M.P. & Rangel, M.A. Gender Gaps in Birth Weight Across Latin America: Evidence on the Role of Air Pollution. J Econ Race Policy 2, 202–224 (2019). https://doi.org/10.1007/s41996-019-00043-z

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