key: cord-0631343-tv2xbi2q authors: Hinke, Stephanie von; Sorensen, Emil N. title: The Long-Term Effects of Early-Life Pollution Exposure: Evidence from the London Smog date: 2022-02-23 journal: nan DOI: nan sha: 9af15f40ce73c62249ef81be2d3c3f547d9cd4a1 doc_id: 631343 cord_uid: tv2xbi2q This paper uses a large UK cohort to investigate the impact of early-life pollution exposure on individuals' human capital and health outcomes in older age. We compare individuals who were exposed to the London smog in December 1952 whilst in utero or in infancy to those born after the smog and those born at the same time but in unaffected areas. We find that those exposed to the smog have substantially lower fluid intelligence and worse respiratory health, with some evidence of a reduction in years of schooling. There is a growing literature on the contemporaneous effects of exposure to air pollution on individuals' human capital and health outcomes (for a review see e.g., Graff Zivin and Neidell, 2013) . There is relatively little empirical evidence, however, on the much longerterm and cumulative effects of early-life pollution exposure, despite the fact that the earlylife environment has been shown to be crucial in shaping individuals' health and economic outcomes in older age. The literature on the so-called "Developmental Origins of Health and Disease" (DOHaD) hypothesis -proposing that circumstances early in life can have life-long, potentially irreversible impacts on individuals' health and well-being -explores the importance of the prenatal as well as early childhood environment and has mainly focused on the longer-term effects of (adverse) nutritional, health and economic environments (for a review, see e.g., Almond and Currie, 2011a; Almond and Currie, 2011b; Almond et al., 2018; Conti et al., 2019) . 1 The relative lack of studies on the very long-term effects of early life pollution exposure is likely to be at least partially driven by a general absence of high quality historical pollution data. Indeed, most studies that explore the effects of early-life pollution exposure investigate the immediate effects on child birth outcomes (see e.g., Chay and Greenstone, 2003; Currie and Neidell, 2005; Almond et al., 2009; Currie, 2009; Jayachandran, 2009; Currie and Walker, 2011; Knittel et al., 2016; Sanders and Stoecker, 2015; Arceo et al., 2016; Hanlon, 2018; Jia and Ku, 2019; Rangel and Vogl, 2019) , with only few exploring potential effects in childhood or early adulthood (see e.g., Reyes, 2007; Bharadwaj et al., 2017; Almond et al., 2009; Sanders, 2012; Black et al., 2013; Isen et al., 2017) and even fewer focusing on outcomes in older age (Bharadwaj et al., 2016; Ball, 2018a) . As such, ignoring potential long-term effects 1 For example, research has explored the importance of maternal physical health (Behrman and Rosenzweig, 2004; Almond, 2006; Almond and Mazumder, 2005) , maternal mental health (von Hinke et al., 2019) , maternal health behaviours (Nilsson, 2017; von Hinke et al., 2014) , maternal nutrition (van den Berg et al., 2021) , the economic environment (Van den Berg et al., 2006; Banerjee et al., 2010) , the early life health environment (Bleakley, 2007; Case and Paxson, 2009; Cattan et al., 2021) , or the home environment (Carneiro et al., 2015) . of pollution may lead to a substantial underestimation of the total welfare effects caused by exposure to environmental toxins. We overcome the lack of historical pollution data by relying on reduced form analyses. More specifically, we examine the effect of early life exposure to the London smog: a severe pollution event that affected London residents between 5 and 9 December 1952. Although pollution levels in London are currently much lower than in the 1950s, the high levels recorded at the time are similar to the levels currently reported in industrialising economies such as India and China, so our study is relevant in particular to those settings. During the smog, pollution from residential and industrial chimneys, vehicle exhausts and coal burning became trapped under a layer of warm air due to a thermal inversion, which caused a thick smog to form over London. We investigate the long-term effects of exposure to this smog event by studying individuals' human capital and health outcomes in older age. The data we use is the UK Biobank: a large population-based cohort of approximately 500,000 individuals living in the United Kingdom. It includes rich data on individuals' later life health and economic outcomes, linked to administrative records. Using participants' eastings and northings of birth, our identification strategy exploits spatio-temporal variation in exposure to the London smog across birth dates and locations using a difference-in-difference approach. In other words, we compare individuals who were exposed to the smog in early life to those living in unaffected regions as well as to those conceived after the smog, whilst controlling for local area-specific trends in the outcomes of interest across birth cohorts. This paper has three main contributions. First, most of the literature that explores the effects of pollution exposure focuses on child birth outcomes. Whilst it is important to better understand the effects on, e.g., infant mortality, it is one of the most extreme consequences of exposure to environmental toxins. Indeed, those who survive pollution exposure may be affected in other ways, with potential scarring reducing individuals' human capital and health potential. We investigate the effects on years of schooling, fluid intelligence, respiratory disease, and COVID-19 hospitalisations/mortality. Finding strong evidence of such longer-term effects would therefore indicate that any pollution impacts are much larger than what would be suggested by the literature focusing on the effects of pollution on birth outcomes, such as infant mortality. Second, we provide new empirical evidence in support of the DOHaD hypothesis. There is a large and growing literature in economics estimating the causal developmental origins of later life economic and health outcomes. These have shown the consequences of many adverse circumstances, but generally lack evidence on the longer-term effects of pollution exposure. 2 We contribute to this literature by exploring the very long-term effects of early life exposure to pollution, investigating individuals' outcomes at age ∼60. Our identification is similar to Bharadwaj et al. (2016) and Ball (2018a) , who also focus on the long-term effects of the London smog on asthma and employment outcomes respectively. In fact, to our knowledge, these are the only two studies that investigate the effects of early-life pollution exposure on individuals in older age. An additional advantage of our data and setting is that it allows us to identify the gestational ages that are most sensitive to pollution. 3 This builds on studies examining the long-term effects of other relatively short-term events, such as the Ramadan (see e.g. Almond and Mazumder, 2011) and the Dutch Hunger Winter (Lumey et al., 2011; Bijwaard et al., 2021) . In addition, our setting allows us to provide evidence on the human capital and health effects of pollution in a high pollution setting that is similar to current pollution levels in several industrialising countries, where evidence on the effects of pollution 2 The literature focusing on the contemporaneous effects of pollution exposure mainly shows large impacts on respiratory and cardiovascular disease as well as mortality, but also on brain health and cognitive decline (see e.g., Zhang et al., 2018; Bishop et al., 2018) . The literature suggests that high pollution concentrations affect lung function and cause irritation and inflammation of the respiratory system. Small pollution particles can penetrate deeply into the lung tissue and interfere directly with the transfer of oxygen to the blood. Both the elderly and the young are at increased risk of air pollution; the latter because their organs are still developing (EPA, 2021) and because they inhale more air per body mass than adults (Laskin, 2006) . In addition, because small particles can be passed through the placenta to the developing foetus, this can directly affect the oxygen available to the foetus, and with that, its development. 3 With just 42 individuals exposed in utero and 15 in infancy in Bharadwaj et al. (2016) , sample sizes do not allow for the analysis of trimester-specific effects. Although Ball (2018a) uses large samples, the analyses use individuals' year of birth, implying it is not possible to look at gestation effects. An advantage of our data is that we have large samples as well as information on the year and month of birth. This means we have more power to detect even relatively small effects on later life outcomes, and we are able to explore the importance of exposure at different gestational ages. remains limited (Greenstone and Jack, 2015) . Our third contribution is that we explore heterogeneity of treatment effects with respect to three important sources of variation. First, we build on a recent literature in social science genetics, directly modelling human capital and health outcomes as a function not only of individuals' environments ('nuture'), but also of their genetic predisposition to these outcomes ('nature') as well as the 'nature-nurture' interaction. This acknowledges the major role that genetic variation has been shown to play in shaping individuals' life outcomes (see e.g. Turkheimer, 2000; Polderman et al., 2015) , and allows nature and nurture to interact and jointly contribute to individuals' human capital and health formation, as highlighted in the medical (see e.g., Rutter, 2006) as well as economics and social science literature (see e.g., Cunha and Heckman, 2007) . Indeed, finding evidence of such 'gene-environment interplay' provides a strong argument against ideas of genetic (or environmental) determinism. As such, we examine whether -and to what extent -one's genetic variation can protect against, or exacerbate, the effects of such adverse events. There is a large literature investigating the importance of gene-by-environment interactions (G × E), with relatively recent contributions from economics and social science (see, for example, Biroli, 2015; Bierut et al., 2018; Barth et al., 2020; Ronda, 2020) . However, most existing studies tend to use endogenous environments, where it is not always clear how to interpret the main effects as well as the G × E interaction effect . 4 We address this issue by exploiting the London smog as a natural experiment, ensuring that the environment is orthogonal to observed and unobserved individual characteristics. Hence, we add to only a handful of relatively recent studies that exploit exogenous variation in the environment within a G × E setting, allowing us to identify the causal environmental impact within a G × E framework. 5 4 Indeed, the coefficient on the genetic component may partially capture environmental circumstances due to 'genetic nurture' (that is: parental genotypes can shape the offspring environment, and since the offspring's genetic variation is inherited from the parents, this may partially capture such environments; see e.g., Belsky et al., 2018; Kong et al., 2018) , and the coefficient on the environmental circumstances may pick up variation driven by genetics due to gene-environment correlation (that is: the fact that individuals with a genetic predisposition to a specific trait can be more commonly found in certain environments). 5 Other studies that exploit exogenous variation in the environment include, e.g., Fletcher (e.g., 2012 ), Schmitz and Conley (2016a) , Schmitz and Conley (2016b), Fletcher (2018) , Barcellos et al. (2018) , Pereira The second source of heterogeneity we explore is gender. Since the literature suggests that male foetuses are generally frailer than female foetuses, we explore whether the long-term effects of pollution differ by gender due to either scarring or differential selection. Finally, we investigate whether there is a social gradient in the effect of pollution exposure. For this, we characterise the local area that individuals are born in with respect to the social class and run our analysis separately for individuals in high and low social class areas. Our findings indicate large effects of both prenatal and childhood smog exposure on later life fluid intelligence and -to a slightly lesser extent -years of education. We also find a robust increase in the probability of being diagnosed with respiratory disease, but no differences in rates of COVID-19 hospitalization or mortality. Furthermore, these effects are generally larger for individuals exposed in the first and second trimester of pregnancy, with overall reduced effect sizes for those exposed in the last trimester. Our heterogeneity analysis shows that the negative effects of being exposed to the smog prenatally and in early childhood are generally stronger for those with a high genetic predisposition to the outcome. For respiratory disease, for example, this suggests that the respiratory health of individuals who are genetically predisposed is more vulnerable to severe pollution events compared to the health of individuals how are not genetically predisposed. Furthermore, we show that the effect on years of schooling is driven by women, both for prenatal and early childhood exposure, whereas there is no clear gender-difference in the longer-term effects on fluid intelligence or respiratory disease. Using the gender ratio as the outcome, we find no evidence of gender differences in survival, suggesting that the differential gender effects are driven by scarring rather than selection. Finally, we find a strong social gradient in long-term pollution effects, with individuals born in lower social class areas (as proxied by a high proportion of the population being either in semi-skilled or unskilled occupations) being substantially more affected; similar to e.g., et al. (2020) , and Biroli and Zünd (2021) , with Muslimova et al. (2020) exploiting exogenous variation in both genetic variation and environmental circumstances. See Pereira et al. (2021) for a recent review of this literature. Jans et al. (2018) . This in turn suggests either that the higher social classes were better able to avoid highly polluted areas, or that the health stock of lower class individuals is simply more vulnerable to adverse early life shocks. As Londoners at the time were not aware of the potential health risks of (severe) pollution, and there is little evidence of avoidance behaviour in the early 1950s, the former is perhaps less plausible, though we cannot say this with certainty. The rest of the paper is structured as follows. Section 2 provides the background to the London smog and Section 3 describes the data used in our analysis. We set out the empirical strategy in Section 4, and discuss the results in Section 5. We explore the sensitivity of our findings in Section 6 and conclude in Section 7. On 4 December 1952, an anticyclone led to a temperature inversion over London, causing the cold air to be trapped under a layer of warm air. The resulting fog, in combination with higher than usual coal smoke (due to the slightly colder temperature at the time) from residential and industrial chimneys, the pollution from vehicle exhausts (e.g. steam locomotives, dieselfuelled buses) and other pollution (e.g. coal-fired power stations), formed a thick smog. 6 With very little wind, it was not dispersed and led to an unprecedented accumulation of pollutants over the next five days, from 5-9 December 1952. Wilkins (1954) discusses the severity of the London smog in terms of changes in concentrations of black smoke and sulphur dioxide (SO 2 ), with the historical measurements from that paper presented in Figure 1 . 7 This shows two interesting features. First, there is a rapid rise in both black smoke and SO 2 concentrations between 5 and 9 December, with average concentrations rising to three to four times their usual level, after which they returned to 6 The coal that was used domestically immediately after the war was of poor quality, with increased amounts of sulphur dioxide compared to the better quality coals that were mainly exported to pay off World War II debts. 7 Both black smoke and SO 2 are released into the atmosphere via fuel combustion, such as coal burning. pre-smog levels. Second, there is substantial regional variation in pollution within London, indicated by the grey dashed lines, each representing a different measurement station. Note, however, that the black smoke concentrations shown here are likely to be underestimated. Indeed, the smoke filters that measured the pollution were so overloaded that concentrations were more likely to be around 7-8 mg/m 3 in the worst polluted areas of London (Warren Spring Laboratory, 1967) . Table I in Wilkins (1954) while deaths are digitised from Table VIII in Logan et al. (1953) . Levels of smoke and sulphur dioxide were measured at the time. However, as discussed in Wilkins (1954) , it is likely that there were increases in tar, carbon monoxide (due to severe traffic congestion), carbon dioxide (due to a strong correlation with sulphur dioxide) and sulphuric acid (due to the oxidation of sulphuric dioxide). Although Londoners were used to such smogs, the one in December 1952 was worse than any event Londoners had experienced before. Due to the dramatically reduced visibility, all public transport other than the London Underground was suspended, most flights to London Airport were diverted, ambulance services stopped, and -with its penetration into indoor areas -concerts, theatres and cinema screenings were cancelled. Outdoor sporting events were also cancelled (see, e.g., BBC, 1952) . Despite this, Londoners got on with everyday life, potentially since the health consequences of extreme pollution were unknown. However, medical statistics that were published in the following weeks showed a substantial increase in mortality, with an estimated 4,000 deaths caused by the smog. Indeed, the right vertical axis of Figure 1 presents the daily number of deaths over the period of the smog, depicted as the solid black line. This shows around 300 daily deaths before the smog, increasing to ∼900 at its peak, after which is reduced; a similar inverse U-shaped pattern as the pollution data (Logan et al., 1953) . Subsequent calculations showed that 90% of the excess deaths were among those aged 45 and over (Ministry of Health, 1954) . There was also an increase in mortality among newborns and infants, as well as foetal loss (Hanlon, 2018; Ball, 2018b) , but these capture a relatively small proportion of the total increase. However, also in the months after the London smog, mortality exceeded normal levels. 8 About half of all excess deaths were attributed to bronchitis or pneumonia, with other increases observed in respiratory tuberculosis, lung cancer, coronary disease, myocardial degeneration and other respiratory disease (Logan et al., 1953) . Our primary dataset is the UK Biobank, a prospective, population-based cohort that contains detailed information on the health and well-being of approximately 500,000 individuals living in the United Kingdom. Recruitment and collection of baseline information occurred between 8 Although an initial government report suggested these deaths were caused by influenza, there was no influenza outbreak in 1952, and Bell et al. (2004) find that only an extremely severe influenza epidemic could account for the excess deaths during this period. More recent analysis indeed suggests that the smog caused up to 12,000 deaths (Bell and Davis, 2001). 2006 and 2010, when participants were 40-69 years old. The data include information on demographics, physical and mental health, health behaviours, cognition, and economic outcomes, obtained via questionnaires, interviews, and measurement taken by nurses. It has also been linked to GP and hospital records, as well as the National Death Registry. Furthermore, samples of blood, urine and saliva have been collected, and all individuals have been genotyped. Bycroft et al. (2018) give a detailed description of the sample. We are interested in the long term economic and health consequences of short term variation in the early-life pollution environment. We focus on a range of outcomes, informed by previous literature on the effects of pollution. First, we build on the literature that shows medium-to-long term effects of early life pollution exposure on economic outcomes (see e.g., Almond et al., 2009; Ball, 2018a) , investigating the effects on educational attainment and fluid intelligence. Educational attainment is defined based on individuals' qualifications 9 , and fluid intelligence is a score based on problem solving questions that require logic and reasoning ability, independent of acquired knowledge. Next, we build on the literature showing pollution effects on individuals' health (see e.g., Currie and Walker, 2011) , and explore the effects on respiratory disease. The contemporaneous effects of air pollution on respiratory disease are well-known. Less is known, however, about the potential long term effects of early life exposure. Indeed, since air pollution disproportionally affects individuals with compromised lung function, and much of the burden in adulthood is believed to be due to poor development (rather than accelerated decline) in lung function (see e.g. Lancet, 2019), early life pollution is a natural exposure to consider in the development of respiratory disease. We create a dummy variable to indicate whether the individual has been diagnosed with respiratory disease from the administrative hospitalisation data and mortality records that have been merged into the UK Biobank, distinguishing between chronic and acute respiratory conditions. 10 Furthermore, due to the links between 9 Table A .1 in Appendix A shows the mapping between qualifications and years of education, using a similar definition as in, e.g., Rietveld et al. (2013) , Okbay et al. (2016) , and Lee (2018) . 10 The hospitalisation data include all diagnoses in ICD-10 coding. We use ICD-10 J00-J99 to identify respiratory disease as diagnosis or cause of death. ICD-10 [J40-J47] and [J09, J1, J20-J22] are used to respiratory disease and severe COVID-19 (Aveyard et al., 2021) , we additionally use a binary indicator for being hospitalised with, or having died from, Using participants' eastings and northings of birth, we assign each individual one of the 1472 Local Government Districts of birth across England and Wales. 12 This spatial information, in combination with temporal information on individuals' year-month of birth, allows us to identify individuals who were exposed to the smog at different time points during the intrauterine and early childhood period. We split our sample along the time dimension by considering whether the prenatal period precedes, overlaps, or follows the smog event on December 5-9th, 1952. This allows us to define three groups: (i) those exposed to the smog during childhood (i.e., those born before the smog), (ii) those exposed to the smog in utero, and (iii) those conceived after the smog event and therefore not exposed. 13 We split our sample along the spatial dimension by identifying the geographical areas in and around London that were exposed to high pollution during the smog. To do so, we overlay the reduced visibility and sulphur dioxide measurements from Wilkins (1954) onto a district-level shapefile. This is shown in Figure 2 , where the solid black outlines indicate the areas with high and low reduced visibility and the dotted outline indicates the area with high sulphur dioxide measurements. 14 We define "high exposure" districts as identify chronic and acute respiratory conditions, respectively. 11 We use ICD-10 emergency codes U071 and U072 to identify COVID-19 related hospitalisations and deaths. 12 Our districts are defined based on the 1951 shapefiles from Vision of Britain (Southall and Aucott, 2009) . 13 Note that we do not observe gestational age at birth. Hence, we assume that the prenatal period cover the nine months before the year-month of birth. The exact birth date cutoffs are as follows. Exposed in childhood: 1950-Dec to 1952-Nov. Exposed in utero: 1952-Dec to 1953-Aug. Not exposed: 1953-Sep to 1956-Dec. We drop those born after December 1956 for two reasons. First, depending on their month of birth in 1957, individuals may have been directly affected by an educational reform -the raising of the school leaving age -which has been shown to have affected individuals' longer-term education as well as health outcomes (see e.g. Harmon and Walker, 1995; Davies et al., 2018) , though note that the evidence on the health effects are more mixed (see e.g., Clark and Royer, 2013) . Second, the first Clean Air Act allowed local authorities to create Smoke Control Areas; areas that prohibited all smoke emissions. The first orders of such Smoke Control Areas were announced in 1957 (Fukushima, 2021) . By dropping all births in 1957 onwards from our analysis, we avoid our estimates potentially capturing reductions in pollution due to the Smoke Control Areas. 14 The sulphur dioxide boundary, based on Wilkins (1954) , shows measurements from different stations with limited geographical coverage, resulting in a boundary with a sharp border,while the visibility boundary is based on observations recorded by the Meteorological Office at 9am and 6pm throughout the smog event those that experienced severe reductions in visibility (i.e., overlap with the two inner solid boundaries in Figure 2 ) and/or experienced high sulphur dioxide measurements (i.e., overlap with the dotted boundary in Figure 2 ). Districts that only overlap with the outer solid boundary, indicating the mildest reduction in visibility, are classified as "low exposure". In our main analysis, we do not distinguish between the high and low exposure districts but instead refer to them jointly as "treated" districts. We compare these treated districts to a set of "control" districts that are defined as other urban districts in England and Wales with a population density exceeding 400 individuals per km 2 . In our robustness checks, we explore the sensitivity of our results to control districts with different population densities, to excluding the "low exposure" districts, to assigning exposure based on individuals' reported birth locations, as well as by defining control districts as other major cities in England and Our sample selection process is as follows: we only consider the subsample of individuals born in the years 1950 to 1956, and restrict the sample to those born in either treated or control districts. Furthermore, we follow the (genetics) literature and restrict our sample to those of white European ancestry. 16 This leaves us with between 26,805-65,060 participants for the main analysis, depending on the outcome of interest. Given the potential importance of the weather for exposure to smog, we merge in an auxiliary dataset on ambient temperature, sunshine, and rainfall. These data are available from the MET Office in the form of an interpolated grid of measurements (MET Office, 2022). We use a grid resolution of 25km and assign measurements to individuals by linking their location of birth, as measured in eastings and northings, to its nearest grid point. We merge in the weather data at individuals' birth location for the period of the smog. (Wilkins, 1954) . 15 The control districts used in the main analysis are shown on a map in Figure A .1, Appendix A, and colourised according to their population density. The data on population density is from Vision of Britain (Southall, 2011) . Ball (2018a) argues that the only other city with unusually high pollution at the time of the London smog was Leeds. We therefore drop Leeds in all our analyses. All city boundaries are defined according to 1951 district shapefile. 16 Because genetic variation differs by ancestry, this accounts for population stratification; a form of genetic confounding. We discuss the genetic data as well as its interpretation in more detail in Appendix D. The geographic boundaries of the London smog based on the maps in Wilkins (1954) . The solid black outlines show the areas with reduced visibility. The inner boundaries experienced a more severe reduction in visibility. The dotted outline shows the area with high sulphur dioxide measurements. The map classifies the districts into 'high exposure' (dark gray), 'low exposure' (light gray), and 'unexposed' (white) districts. City of London is approximately at the center of the map. For ambient temperature, we assign the minimum temperature measured during the smog, while for sunshine and rainfall, we use the average. The weather measurements capture additional local conditions that, linked to individuals' eastings and northings of birth, vary within districts. 17 Table 1 presents the descriptive statistics, showing that 44% of the sample is male and individuals, on average, have 13.3 years of education. Individuals' fluid intelligence is a continuous score, standardised to have mean zero and unit variance. 9.2% of our sample has been diagnosed with respiratory illness; for 7.6%, this is an acute condition, and for 1.5%, it is chronic. Finally, 0.7% of our sample has been either hospitalized or has died with COVID-19 as primary or secondary cause. 17 Figure A .2 in Appendix A shows the monthly time series of weather conditions (temperature, sunshine and rainfall) for our sample of interest, distinguishing between the districts that are exposed to the London smog (treated) and the control districts. As mentioned in the introduction, this shows a slightly lower temperature in exposed districts at the time of the smog. However, the difference between treated and control districts is minimal (less than 0.5 • C). Hence, these do not suggest any notable differences in weather conditions between the district types. Columns: (1) sample mean, (2) sample standard deviation, (3) number of observations. The availability of the variables varies and hence also the number of observations in column (3). To investigate the long term effects on human capital and health outcomes of early life pollution exposure, we exploit spatio-temporal variation in the exposure to the London smog across birth dates and locations using a difference-in-difference approach. We distinguish between those born inside and outside of the exposed London area (spatial variation) while also considering the timing of birth relative to the smog event (temporal variation). Our main specification is: where Y ijt denotes the outcome of interest for individual i, born in district j at year t. Thus, α j denote district fixed effects, and γ t are year of birth fixed effect. We additionally include administrative-county-specific time (year-month) trends, denoted by τ k t. 18 The vector X i in- 18 We do not include district-specific trends in our main analysis, as with over 1400 districts, some only include few individuals. Instead, with ∼230 administrative counties, we observe a larger number of individuals in each administrative county-year and we include trends specific to these geographical regions. In our sensitivity analysis in Appendix B, however, we show that our results are generally robust to including administrative county-specific year (as opposed to year-month) trends, district-specific year or year-month trends, and not including any trends. cludes weather conditions during the smog, gender, and month-of-birth dummies to account for weather effects, gender differences, and seasonality in the outcome. The indicators E IU i and E CH i are dummy variables that are equal to one for individuals who are exposed to the London smog in utero and in early childhood (i.e.,