metadata: authors: 0: author: Aragon,Fernando M.
1: author: Miranda Montero,Juan Jose
2: author: Oliva,Paulina
count: Peru
docna: 0: docna: Particulate matter and labor supply : the
            role of caregiving and non-linearities
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Pollution Management & Control,Air Quality & Clean Air,Labor Markets,Gender and Development,Brown Issues and Health,Global Environment
teratopic: Rural Development,Social Protections and Labor
abstracts: cdata!: This paper examines the effect of air
            pollution on labor supply in Lima, Peru. It focuses on fine
            particulate matter (PM2.5), an important pollutant for
            health according to the medical literature, and shows that
            moderate levels of pollution reduce hours worked for working
            adults. The research design takes advantage of rich
            household panel data in labor outcomes to address omitted
            variables and allows investigation of whether the response
            to air pollution is non-linear. The analysis finds that the
            effect of moderate pollution levels on hours worked is
            concentrated among households with susceptible dependents,
            that is small children and elderly adults, while the highest
            concentrations affect all households. This suggests that
            caregiving is likely a mechanism linking air pollution to
            labor supply at moderate levels. Further evidence of this
            mechanism is provided using DHS data on children morbidity
            for the same time period. Finally, no evidence is found of
            intra-household attenuation behavior. For instance, there is
            no re-allocation of labor across household members, and
            earnings decrease with air pollution.
geo_reg_key: 517191
repnme: repnme: Particulate matter and labor supply : the
            role of caregiving and non-linearities
display_title: Particulate matter and labor supply
            : the role of caregiving and non-linearities
listing_relative_url: /research/2016/04/26294380/particulate-matter-labor-supply-role-caregiving-non-linearities
topicv3: Air Pollution m1327122 1791,Environment, Natural Resources and Blue
            Economy m1327391 566,Gender m1327621 615,Labor Supply m1504055 2751,Health m1504661 3480
publishtoextweb_dt: 2016-04-28T00:00:00Z
historic_topic: Rural Development,Social Protections and Labor
no_of_pages: 46
author_keys: 0: author_key: 1148707
model_extractions: WPS7658 Policy Research Working Paper 7658 Particulate Matter and Labor Supply The Role of Caregiving and Non-Linearities _Fernando M. Aragon Juan Jose Miranda Paulina Oliva_ Environment and Natural Resources Global Practice Group April 2016
Policy Research Working Paper 7658

# **Abstract**

This paper examines the effect of air pollution on labor supply in Lima, Peru. It focuses on fine particulate matter (PM2.5), an important pollutant for health according to the medical literature, and shows that moderate levels of pollution reduce hours worked for working adults. The research design takes advantage of rich household panel data in labor outcomes to address omitted variables and allows investigation of whether the response to air pollution is non-linear. The analysis finds that the effect of moderate pollution levels on hours worked is concentrated among households with susceptible dependents, that is small children and elderly adults, while the highest concentrations affect all households. This suggests that caregiving is likely a mechanism linking air pollution to labor supply at moderate levels. Further evidence of this mechanism is provided using DHS data on children morbidity for the same time period. Finally, no evidence is found of intra-household attenuation behavior. For instance, there is no re-allocation of labor across household members, and earnings decrease with air pollution.

This paper is a product of the Environment and Natural Resources Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jjmiranda@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Particulate Matter and Labor Supply: The Role of Caregiving and Non-Linearities<sup>_∗_</sup> Fernando M. Arag ́on<sup>_†_</sup> Juan Jose Miranda<sup>_‡_</sup> Paulina Oliva<sup>_§_</sup> _JEL Classification_ : Q52, Q53, J31, J24 _Keywords_ : Labor supply; Air pollution; Heterogeneous Effects

> _∗_ We thank the Comit ́e de Gesti ́on de la Iniciativa de Aire Limpio para Lima y Callao for sharing their pollution data. We are grateful to Thiemo Fetzer, Joshua Graff-Zivin, Rema Hanna, and seminar participants at UBC and University of Washington for useful comments and suggestions.

> _†_ Department of Economics, Simon Fraser University; Email: faragons@sfu.ca

> _‡_ Environment and Natural Resources Global Practice, World Bank; Email: jjmiranda@worldbank.org

> _§_ Department of Economics, University of California, Santa Barbara and NBER; Email:

paulina.oliva@ucsb.edu
# **1 Introduction**

Existing evidence suggests that air pollution has negative effects on human health, especially for children and elderly adults.<sup>1</sup> A recent literature has also started to document negative effects among healthy adults in the form of reduction of labor productivity (Graff-Zivin and Neidell, 2012; Adhvaryu et al., 2014; Chang et al., 2014; Li et al., 2015) and hours worked (Hanna and Oliva, 2015).<sup>2</sup> These findings, from the U.S., India, and Mexico, point to changes in labor outcomes as relevant pollution externalities.

In this paper we focus on the causal relationship between air pollution and hours worked. The empirical analysis of this relationship is complicated by two empirical challenges. First, there could be omitted variables that affect both air pollution exposure and the outcome of interest; which may bias the estimated response to air pollution. Second, households may move in response to air pollution according to their specific vulnerability introducing sorting bias.

These challenges have been addressed in a previous study with the help of an exogenous and sudden change in air pollution in Mexico City in the early nineties (Hanna and Oliva, 2015). This research design, however, limits the extent to which one can explore non-linearities in relationship between air pollution and hours worked as well as the mechanisms at play. This paper takes advantage of panel data on hours worked from Lima, Peru, to address the empirical challenges described above while preserving enough exogenous variation in pollution exposure to uncover the non-linear relationship between pollution and hours worked.

By comparing the shape of the dose-response function across different household compositions, we shed light on how age-specific vulnerabilities to air pollution can result in different labor supply responses to air pollution across households. We also explore whether intra-household substitution in labor supply can dampen the effects at the household level. Finally, the research design also allows us to focus on a somewhat newly monitored pollutant, fine particulate matter (PM2 _._ 5), which has been shown to have some of the most adverse health effects in the medical literature (U.S. EPA, 2009), but for which there is relatively little economic research.<sup>3</sup>

> 1For a review of this literature see Currie et al. (2014) or Graff-Zivin and Neidell (2013). There is also evidence that pollution can affect human capital. For instance, several studies link pollution to poorer school and cognitive outcomes (Almond et al., 2009; Lavy et al., forthcoming), and to increases in school absenteeism (Ransom and Pope, 1992; Gilliland et al., 2001; Park et al., 2002; Currie et al., 2009a).

> 2Previous work using data from U.S. cities also finds a positive and significant correlation between air particulates and work loss (Ostro, 1983; Hausman et al., 1984).

> 3Most of the economic literature focuses on total suspended particulates (Chay and Greenstone, 2003; Sanders, 2012), carbon monoxide (Currie and Neidell, 2005; Currie et al., 2009a; Currie et al., 2009b), and ozone (Lleras-

2
The panel structure of our data allows us to address the two empirical challenges described above by controlling for an array of time-varying omitted variables and relying on within household comparisons. First, we account for a wide range of city-wide and local time varying omitted variables through the inclusion of week and municipality-by-year fixed effects. Second, we include household fixed effects in our estimation which rules out bias through time-invariant omitted variables. Importantly, since our estimates are identified out of the within household time variation in air pollution, they are also free of sorting bias provided that changes in air pollution exposure over time are uncorrelated with household specific vulnerability (Wooldridge, 2005).

We find that, even at moderate concentrations, air pollution reduces labor supply. The effect is concentrated among households with dependents more susceptible to pollution, i.e., small children and elderly adults. The magnitude is economically significant. For instance, an increase in PM2 _._ 5 of 10 _μ_ g/m<sup>3</sup> is associated to a reduction of almost 2 hours worked per week. The effect of air pollution on individuals with susceptible household members appears linear. In contrast, the effect of pollution on labor supply of workers in households without susceptible individuals is non-linear, and only appears to respond to high levels of pollution (above 75 _μ_ g/m<sup>3</sup> ). This observation points to the extensive margin as a potential source of non-linearities in the relationship between air pollution and labor supply: as pollution levels increase, the effects on labor supply expands beyond households with susceptible individuals to the rest of the population.

These findings are consistent with evidence suggesting that children and elderly adults’ health is more susceptible to air pollution, and thus may be affected at lower concentrations than healthy adults (U.S. EPA, 2009, Ch. 8). Our results suggest that the mechanism linking low levels of air pollution to labor supply is the increase in demand for caregiving: healthy adults reduce hours of work to take care of sick dependents. However, at higher concentrations, the link is more direct: pollution directly harms the health of those who participate in the workforce. To corroborate these findings, we use auxiliary data from the Demographic and Health Survey (DHS) on health outcomes for the same city and period. We find that PM2 _._ 5 is associated with an increase in respiratory diseases among small children.

Finally, we examine whether households respond to this shock by re-allocating caregiving

> Muney, 2010; Graff-Zivin and Neidell, 2012). Other studies focus on NO _x_ (Deschenes et al., 2012), SO2 (Hanna and Oliva, 2015) and aerosols (Jayachandran, 2009). Among the few studies focusing on PM2 _._ 5 are Zweig et al. (2014), Chang et al. (2014), Adhvaryu et al. (2014), Lavy et al. (forthcoming) and Li et al. (2015).

3
duties and labor supply among their members. For instance, a household may shift caregiving to workers with relatively worse earning opportunities to minimize the negative shock in earnings, and consumption. However, we find no evidence of intra-household reallocation of hours worked in response to air pollution shocks. There are no significant differences in the effect of pollution associated with age, gender, education, or position within the household. Consistent with the net reduction in hours worked within households, we find a negative effect of air pollution on earnings.

The rest of the paper is organized as follows. Section 2 provides background information on particulate matter and pollution in Lima city. Section 3 describes the data and discusses the empirical strategy. Section 4 presents the main results, while Section 5 concludes.

# **2 Background**

## **2.1 Fine particulate matter**

In this paper, we focus on the effect of fine particulate matter (PM2 _._ 5) on labor supply. This is motivated by the evidence linking it to respiratory and cardiovascular diseases.<sup>4</sup> PM2 _._ 5 is an air pollutant that consists of tiny particles less than 2.5 micrometers in diameter.<sup>5</sup> It can be produced by natural sources, like wildfires, but it largely comes from the combustion of fossil fuels and chemical reactions of air emissions. The concentration of PM2 _._ 5 in a given location is affected by proximity to its main sources but also by other local environmental factors, such as wind speed and direction, air temperature, humidity, precipitation and vegetation (Beckett et al., 2000; Hien et al., 2002; Janh ̈all, 2015). These factors create potential for seasonal and intra-urban variations in PM2 _._ 5 levels (Vecchi et al., 2004; Wilson et al., 2005).

Given their small size, PM2 _._ 5 can penetrate deep into the lungs and into the bloodstream (Bell et al., 2004). Moreover, it is harder to avoid than other pollutants since it can easily penetrate indoors (Thatcher and Layton, 1995; Vette et al., 2001). These features make it a particularly harmful pollutant (Bell et al., 2004; Pope III and Dockery, 2006).<sup>6</sup> A large medical literature finds evidence of a causal effect of short term exposure to PM2 _._ 5

> 4 However, we also explore other air pollutants such as PM10, nitrogen dioxide (NO2) and sulfur dioxide (SO2), see Section C.

> 5 Other types of particulate matter include PM10 and coarse particulate matter (PM2 _._ 5 _−_ 10).

> 6See U.S. EPA (2009) for a comprehensive review on health effects of particulate matter.

4
on cardiovascular and respiratory diseases, as well as an increase in mortality (U.S. EPA, 2009, Ch. 2).<sup>7</sup> These negative health effects are particularly important among susceptible populations, such as children, elderly adults, and people with pre-existing conditions like asthma and cardiovascular or lung disease. These populations are at increased risk for the detrimental effects of ambient exposure to particulate matter (U.S. EPA, 2009, Ch. 8). The effects are not necessarily immediate; several studies find a lag between exposure to particulate matter and hospital admissions. There is, however, no consensus on a priori lag times to use when examining morbidity. Some studies find short lags, 0 to 1 days, for cardiovascular diseases and for elderly patients with other diseases, and larger lags, from 3 to 5 days, for asthma hospital admissions.

# **2.2 Air pollution in Lima**

Lima is Peru’s capital and, with a population of more than 9 million, its largest city. It is also heavily polluted. For instance, during the period of this study (2007-2011), the average daily level of PM2 _._ 5 was around 45.6 _μ_ g/m<sup>3</sup> . This level is above the U.S. 24-hour standard of 35 _μ_ g/m<sup>3</sup> and is considered unhealthy for susceptible groups (U.S. EPA, 2012). In fact, in around 70% of weeks PM2 _._ 5 levels in Lima exceeded this threshold (see Figure 1).<sup>8</sup> The main source of air pollutants is exhaust from motor vehicles. According to some official estimates, this source accounts for more than 80% of total emissions in the city (CONAM, 2001). The rest is produced by point sources such as power plants and industrial sites.<sup>9</sup> There are several features relevant for empirical analysis. First, the distribution of PM2 _._ 5 has a wide support with common episodes of moderate to high concentrations (see Figure 2). This feature allows us to study the relationship between pollution and labor supply at different levels of PM2 _._ 5 and explore non-linearities in the dose-response function.

Second, there is intra-urban variation in PM2 _._ 5 levels. Areas in the north, center, and east side of the city have higher exposures to PM2 _._ 5 than areas in the south and closer to the sea shore (like Callao). This is likely driven by the presence of industrial sites in the east side and

> 7In contrast, the evidence linking other sizes of particulate matter to health problems is considered only suggestive or inadequate.

> 8Similarly, levels of other air pollutants such as PM2 _._ 5 and NO2 are above international standards. However, levels of SO2 are very low (DIGESA, 2012). See Figures A.1, A.2 and A.3 in the Appendix for distribution of other air pollutants.

> 9These estimates refer to total air emissions not only to PM2 _._ 5.

5
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ings, schooling, age, gender, etc. We use some these variables as controls or ancillary outcomes in our baseline regression.

To obtain measures of health, we use the Demographic and Health Survey (DHS) for 20072009.<sup>16</sup> This survey also includes geographical coordinates of each household’s residence. However, the location is randomly displaced by up to 3 kilometers. This introduces an additional measurement error. The DHS contains self-reported information on children’s health conditions in the last 2 weeks. Based on these data, we construct indicators of having an acute respiratory disease (i.e., cough accompanied by short breath), fever, diarrhea, or anemia.<sup>17</sup>

**Pollution data** We obtain daily measures of key pollutants such as PM2 _._ 5, PM10, nitrogen dioxide (NO2) and sulfur dioxide (SO2) for years 2007-2011. These measures provide average levels of pollutants for a 24 hour-period and were collected every 2-3 days each week.<sup>18</sup> The air pollution data come from five monitoring stations installed and operated by an office of the Ministry of Health (Direcci ́on General de Salud - DIGESA). The monitoring stations were located in the four cardinal points of the city plus one in the city center (see Figure 3).

Note that these monitoring stations were installed as part of the first project to systematically measure air quality in Peru.<sup>19</sup> We complement this dataset with data on monthly average temperature and humidity collected by SENAMHI.

**Matching labor, health and pollution data** To construct the measure of pollution for a given household, we first define the reference period for labor outcomes. This corresponds to the seven days prior to the interview date. We call this reference period week _t_ . Then we obtain the average level of a pollutant from monitoring stations in week _t −_ 1.<sup>20</sup> Thus, we use exposure to pollutants in the week before labor outcomes are realized. This responds to previous evidence

> 16We cannot use other years due to lack of geographical information. 17The ENAHO survey also contains self-reported information on health conditions, such as illnesses or accidents in the last 4 weeks. However, we do not use this information due to its longer time horizon, which difficult to match with environmental conditions, and because it does not distinguish between respiratory and non-respiratory diseases.

> 18The collection dates in a given week were randomly chosen. The collection process was done by trained personnel of the Ministry of Health using active air sampling procedures. The detailed data collection protocol is available at `http://www.digesa.sld.pe/norma_consulta/protocolo_calidad_de_aire.pdf` .

> 19As mentioned in Section 2.2, the meteorological agency (SENAMHI) started regular, hourly, collection of air quality indicators only since late 2011. We cannot use these new air quality data due to lack of georeferenced labor data for this period.

> 20We also examine the results using other lags and leads. We find that the only significant effect is obtained when using one-week lag. These results are available upon request.

8
suggesting a lag of a few days between exposure to pollution and health problems.<sup>21</sup> Finally, we take a weighted average of pollution levels of stations in the vicinity of the households. We use only data from stations within 8 kilometers of a household. Similar to previous work, we use an inverse distance interpolation method.<sup>22</sup> We trim some extreme and abnormal observations.<sup>23</sup> In particular, we drop observations with top 1% values of pollution and hours worked. We also drop observations from one station (located in the city center) for years 2007-2009 due to data collection problems and unusually volatile observations.<sup>24</sup> We use observations for this station for years 2010 and 2011 only.

Figure 3 displays the map of Lima city with the location of monitoring stations and census blocks both in the whole sample (yellow dots) and in the final sample used for this study (blue dots). Note that the final sample does not include observations located far from monitoring stations, especially in the north and east, but it is, otherwise, dispersed across the city.<sup>25</sup>

# **3.2 Empirical strategy**

The objective of the empirical analysis is to identify the causal effect of exposure to air pollution on labor supply. The first challenge that we face in achieving this is the likely presence of unobservable factors that may affect both pollution and number of hours worked. For instance, wealthier, better educated households may locate in less polluted areas. There could also be time-varying characteristics, such as seasonal variations in weather and health or local trends in labor markets and pollution levels that may create a spurious correlation between air pollution and labor supply.

Our empirical strategy addresses this potential source of endogeneity in two ways. First, we include a rich set of time-varying controls, such as temperature, humidity, week fixed effects and municipality-by-year fixed effects. Thus, our estimates are not subject to bias from any unobservable determinant of labor supply that is time varying but common across all households in Lima. As we control for municipality-by-year fixed effects, our estimates are also robust to the

> 21In the case of health outcomes, the reference period is two weeks before the interview date (i.e. weeks _t_ and _t −_ 1). In that case, we use measures of pollution in weeks _t −_ 1 and _t −_ 2.

> 22 We use the inverse of the Euclidean distance between the household and a monitoring station as weights.

> 23Using all available data produces similar, but less precise, results.

> 24According to communications with DIGESA’s personnel, from 2007 to 2009 this monitoring station was located in a place that did not fulfill the conditions stipulated in the data collection protocol. For this reason, the station was re-located in 2010.

> 25The final sample is not necessarily representative of Lima city given the changes made to the original sample, i.e., exclusion of areas further away from monitoring stations and non-panel observations.

9
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presence of local labor supply determinants that evolve slowly over time, such as gradual changes in labor markets or differential growth across municipalities. Second, we include household fixed effects in our baseline regression. This effectively controls for all time-invariant omitted factors that could bias our results such as demographics, preferences for air pollution, etc.

In addition to the classic omitted variables problem, which stems from the potential correlation between unobservable determinants of labor supply and air pollution, there could be bias in the OLS estimates stemming from heterogeneous responses to air pollution: correlated random coefficients. However, since our identification rests on within-household differences in air pollution exposure, the identification assumption we require is no correlation between household specific vulnerability and the changes in air pollution exposure experienced over time. As households in our sample do not move, it is likely that any changes in air pollution experienced will be orthogonal to household-specific vulnerability.<sup>26</sup> We estimate the following baseline regression: where the unit of observation is individual _i_ , in household _j_ , and _t_ is the reference week for labor outcomes, i.e., 7 days before the interview date. The main outcome is the number of hours worked, _hoursij,t_ although in some specifications we also use other outcomes, such as labor force participation or employment status. _PM_ 2 _._ 5 _j,t−_ 1 refers to the measure of fine particulate matter (PM2 _._ 5) to which the household was exposed in the week before labor outcomes were realized. Our main specification uses the average level of PM2 _._ 5, but we also use discrete measures, such as an indicator of whether the levels exceeded the U.S. standard (i.e. 35 _μ_ g/m<sup>3</sup> ) or a step function of the average PM2 _._ 5. Equation (1) includes household and week fixed effects, _αj_ and _ηt_ , and a set of time-varying fixed effects, weather and individual controls, **X** _ij,t_ .<sup>27</sup> We cluster the errors at the municipality level to account for spatial and serial correlation and use sampling weights in all estimations.<sup>28</sup>

> At the core of our analysis is the recognition that the effect of particulate matter on labor

> 26There might be, however, a problem of selection bias due to attrition from the panel sample. This could happen, for example, if households exposed to higher levels of pollution are more likely to re-locate and thus to drop from the panel sample. In Section 4.2 we test, and rule out, this form of selection bias.

> 27For a detailed list of control variables see notes of Table 2.

> 28We also check the robustness of our results to using Conley standard errors (see Table 4).

11
supply may be heterogeneous and uncovering systematic patterns for this heterogeneity can shed light on the mechanisms behind this relationship. As discussed in Section 2, some populations, such as small children and elderly adults, are more susceptible to health problems when exposed to particulate matter. These population groups are usually not part of the labor force thus pollution cannot affect their labor supply. However, it can _indirectly_ affect labor supply of other household members by increasing demand for caregiving: parents or older siblings may miss work to take care of a sick child or elderly relative. In that case, we would observe a negative effect of pollution on labor supply even for levels at which adult workers’ health is not affected. We examine this heterogeneity by splitting the sample between households with and without susceptible individuals. We classify a household as having a susceptible individual if at least one household member is a small child (5 years or younger) or an older person (75 years or older).

Table 1 displays the mean of key environmental and socio-economic variables for the whole sample and for the sample of households with and without susceptible individuals.<sup>29</sup> Note that there are not significant differences between both groups on exposure to pollutants or labor outcomes, such as participation rates, employment rates, or number of hours worked. There are, however, statistically significant differences in some observable characteristics such as poverty headcount, workers’ age, and household size.<sup>30</sup>

> 29See Table B.1 in Appendix for the mean of children’s health indicators.

> 30In Section 4.2 we examine the importance of these differences on explaining the results.

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Table 1: Mean of environmental and socio-economic variables

|||House|hold has|p-value mean|
|---|---|---|---|---|
|Variable|All|susceptib|le individual|comparison|
|||Yes|No|(2)=(3)|
||(1)|(2)|(3)|(4)|
|Pollution and weather|||||
|PM2_._5|45.5|46.0|45.2|0.446|
|PM2_._5 above 35 _μ_g/m<sup>3 </sup>(%)|71.1|71.5|70.8|0.750|
|PM10|80.3|79.5|80.8|0.666|
|SO2|20.0|20.8|19.5|0.247|
|NO2|24.0|24.1|24.0|0.849|
|Temperature (Celsius)|19.1|19.2|19.0|0.180|
|Humidity (%)|81.4|81.5|81.4|0.257|
|Individuals in working age|||||
|Labor force (%)|74.2|74.5|73.9|0.503|
|Individuals in labor force|||||
|Employed (%)|99.8|99.7|99.8|0.857|
|Hours worked|43.6|44.0|43.3|0.443|
|Has secondary job (%)|11.2|11.7|10.8|0.375|
|Is independent worker (%)|33.6|34.7|32.8|0.202|
|Earnings in last month|1067.2|1002.8|1111.2|0.008|
|Age|38.0|36.5|39.0|0.000|
|Schooling (years)|11.4|11.4|11.3|0.891|
|Is female (%)|45.4|45.8|45.0|0.312|
|Is household head (%)|35.7|32.4|37.9|0.000|
|Households|||||
|Poverty headcount (%)|15.5|24.5|10.2|0.000|
|Number of income earners|243.4|255.9|236.0|0.000|
|Household size|4.3|5.2|3.7|0.000|
|Nr. observations|5,218|2,167|3,051||

Notes: PM2 _._ 5, PM10, SO2 and NO2 are measured in _μ_ g/m<sup>3</sup> . These measures of pollution are 7-day averages for week _t −_ 1, where _t_ is the reference week for labor outcomes. Temperature and humidity are monthly averages. Earnings are measured in Nuevos Soles (PEN). Susceptible individuals include children 5 years and younger, and seniors 75 years and older. Number of observations refer to individuals in the labor force. Column 4 displays p-values of mean comparison tests. Means are obtained using sampling weights.

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# **4 Results**

## **4.1 Main results**

Table 2 presents the main results. We split the sample between individuals in households with and without susceptible individuals and use two alternative measures of exposure to pollution: average weekly PM2 _._ 5 and an indicator of PM2 _._ 5 exceeding the U.S standard (35 _μ_ g/m<sup>3</sup> ). In both cases, there is a negative and significant relationship between exposure to PM2 _._ 5 and hours worked but only for individuals in households with susceptible individuals. For individuals in households without susceptible individuals, there is no significant linear relationship. To the extent that the set of covariates and fixed effects control for relevant omitted variables, we can interpret these results as the causal effect of PM2 _._ 5 on labor supply.

The magnitude of the effect is economically relevant. A reduction of PM2 _._ 5 of 10 _μ_ g/m<sup>3</sup> is associated with an increase of 1.9 hours of work per week for workers in households with susceptible individuals.<sup>31</sup> This group represents a sizable proportion of the population: around 40 percent. Given the observed levels of pollution in Lima, our results imply that, for individuals with susceptible dependents, a reduction of average PM2 _._ 5 to levels compliant with U.S. standards would increase hours worked by almost 7 hours a week.<sup>32</sup> Our results suggest heterogeneous effects by presence of susceptible individuals. To formally test for the statistical difference in coefficients across demographic groups, we estimate a model using the whole sample and include a full set of interactions with an indicator for households with susceptible individuals. The results, shown in third row in Table 2, suggest that the magnitude of the effect of PM2 _._ 5 on hours worked is indeed significantly larger for individuals in households with susceptible individuals.

We interpret this result as evidence that a mechanism linking pollution to labor supply is caregiving: workers may reduce hours of work to take care of sick dependents, whose health is more susceptible to air pollution. This is a plausible explanation given previous findings of the negative effect of air pollution on health, specially of children and elderly adults.

> 31At average values, this result implies a pollution elasticity of labor supply of -0.167. As a comparison, Hanna and Oliva (2015) find SO2 pollution elasticities of labor supply ranging from -0.138 to -0.172 for Mexico city. In contrast, Chang et al. (2014) find no effect of PM2 _._ 5 on labor supply among pear packers in California. This insignificant result may be due to relatively lower concentrations of pollutants and a non-linear relationship between pollution and labor supply. The average PM2 _._ 5 in their context is 10.06 _μ_ g/m<sup>3</sup> while in the Peruvian case the average is 45.6 _μ_ g/m<sup>3</sup> .

> 32This figure is obtained from Column 3 in Table 2.

14
Table 2: Main results: effect of PM2 _._ 5 on hours worked

|||Hours|worked||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|PM 2.5|-0.192***|-0.039|||
||(0.046)|(0.050)|||
|PM 2.5 above|||-6.817***|-0.107|
|35 _μ_g/m<sup>3</sup>|||(2.279)|(1.635)|
|Diference|-0.144**||-6.388**||
|(1)-(2) or (3)-(4)|(0.071)||(2.924)||
|Household has|Yes|No|Yes|No|
|susceptible individuals|||||
|Observations|2,167|3,051|2,167|3,051|
|R-squared|0.429|0.447|0.429|0.447|

Notes: Robust standard errors in parentheses. Standard errors are clustered at the municipality level. * denotes significant at 10%, ** significant at 5% and *** significant at 1%. Baseline specification includes household, week and municipalityby-year fixed effects, characteristics of individual (gender, age, age<sup>2</sup> , schooling, schooling<sup>2</sup> , type of household member, indicator for having a second job, indicator of being independent worker), and monthly temperature and humidity. Third row displays difference of estimates for both samples obtained from a model with full interaction terms. PM 2.5 is average PM2 _._ 5 in week _t −_ 1, where _t_ is the week of reference of labor outcomes. PM 2.5 above 35 _μ_ g/m<sup>3</sup> is an indicator equal to 1 if average PM2 _._ 5 in week _t −_ 1 exceeded the U.S. standard.

15
We further examine the validity of this interpretation by exploring the effect of PM2 _._ 5 on children’s health.<sup>33</sup> To do so, we use data on self-reported children morbidity from the DHS. Our main outcome is the presence of cough accompanied with short breath. This is a common indicator of acute respiratory disease. As a falsification test, we also estimate the effect of PM2 _._ 5 on other diseases, not linked to short term exposure to air pollution, such as fever, diarrhea, and anemia. Our empirical specification is similar to (1) but there are some changes due to the limitations of the DHS dataset.<sup>34</sup> In particular, we include as covariates month and year fixed effects, weather controls, and children and mother’s characteristics, but do not include household, week, or municipality-by-year fixed effects.<sup>35</sup> As measures of exposure to pollution we use an indicator of average PM2 _._ 5 exceeding the U.S. standard and the log of average PM2 _._ 5.<sup>36</sup> Table 3 displays the results. Consistent with the existing literature, we find a positive and significant relationship between exposure to PM2 _._ 5 and our indicator of acute respiratory disease, but there is no significant relationship with other non-respiratory diseases. The magnitude of this relationship is sizable. According to our results, reducing PM2 _._ 5 to the compliance level with the U.S. standards would lead to a 9 percent reduction in the incidence of acute respiratory diseases. This represents almost half the average incidence.

# **4.2 Additional checks**

Our identification assumption would be violated in the presence of confounding, unobserved, omitted determinants of labor supply that vary over time within census blocks. In other words, if there were omitted variables that have not been fully accounted for by the rich set of fixed effects (week and municipality-by-year). We address this concern in two ways. First, we add a richer set of time-varying controls, namely, quarter-by-year fixed effects and non-parametric trends interacted with observable characteristics, such as poverty status, number of income earners, and worker’s age. The results, however, are similar (see column 1 in Table 4). Second,

> 33We are, however, unable to examine the effect on adults and the elderly due to data limitations. The DHS only covers morbidity of children 5 years and younger, while the ENAHO’s health data covers a long reference period and does not distinguish respiratory diseases.

> 34The main limitations are that (1) the DHS does not have a panel sample, (2) covers fewer years (2007 to 2009), (3) the sample is not distributed over the whole year, and (4) household’s locations are randomly displaced. These features prevent the use of household or week fixed effects, and difficult matching the household to a given municipality.

> 35See notes of Table 3 for additional estimation details.

> 36Results are similar using a more flexible specification with a step function of PM2 _._ 5 similar to the one used in Section 4.3, see Table B.2 in the Appendix.

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Table 3: PM2 _._ 5 and children’s health

||Cough and<br>short breath<br>(1)|Fever<br>(2)|Diarrhea<br>(3)|Anemia<br>(4)|
|---|---|---|---|---|
|Panel A|||||
|PM 2.5 above|0.093**|0.039|0.025|-0.001|
|35 mg/m3|(0.047)|(0.054)|(0.034)|(0.074)|
|Observations|712|712|712|492|
|R-squared|0.053|0.060|0.067|0.238|
|Panel B|||||
|ln(PM 2.5)|0.073*|0.008|0.007|-0.054|
||(0.042)|(0.043)|(0.035)|(0.056)|
|Observations|712|712|712|492|
|R-squared|0.053|0.060|0.067|0.240|

Notes: Robust standard errors in parentheses. Standard errors are clustered at the survey block level. * denotes significant at 10%, ** significant at 5% and *** significant at 1%. All regressions are estimated using OLS and include month and year fixed effects, individual controls (age and gender of child, mother’s age, indicators of household wealth, indicator for a smoking mother), and logs of monthly temperature and humidity. PM 2.5 refers to average PM2 _._ 5 in weeks _t −_ 1 and _t −_ 2, where _t_ is the period of 7 days prior to interview. Note that reference period for morbidity questions is weeks _t_ and _t −_ 1. Panel A uses an indicator of PM 2.5 being above the U.S. standard, while Panel uses log of PM 2.5.

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we examine the relationship between _contemporaneous_ exposure to pollution and hour worked. Note that an important omitted variable is local level of traffic. High traffic could raise air pollution and, by increasing transportation costs, also reduce labor supply. This alternative explanation would imply a contemporaneous relation between these two variables. However, the relationship is small and statistically insignificant (see column 2 Table 4).<sup>37</sup> Columns 3 through 5 in Table 4 check the robustness of our results to alternative specifications. Results are robust to using non-linear functions of weather, a narrower definition of susceptible population, and using a log-linear specification. In column 6, we also test alternative variance structures, and estimate standard errors using the procedure suggested by Conley (1999).<sup>38</sup> A key finding is that PM2 _._ 5 has different effects on hours worked in households with and without susceptible dependents. As discussed in Section 3.2, there are some observable differences between these two types of households. Households with susceptible dependents tend to have more income earners, relatively younger workers, and be slightly poorer. A relevant concern is that these differences, not the presence of susceptible individuals, are driving the heterogeneous results. This could happen, for instance, if poverty, worker’s age or household size influence how pollution affects labor supply.

We examine this alternative explanation by estimating a model using individuals in both types of households and adding interactions of PM2 _._ 5 with an indicator of having a susceptible individual and other observable characteristics (see Table B.3 in the Appendix). This specification allows us to examine other sources of heterogeneous effects of air pollution. Results, however, confirm the original findings: PM2 _._ 5 only seems to reduce hours worked among individuals in households with susceptible individuals.

Finally, we examine sample attrition as a source of selection bias. Note that, by using a panel sample and household fixed effects, we reduce concerns of residential sorting bias, i.e, bias due to systematic household differences that are correlated with exposure to pollution. However, households may drop from the panel sample in a systematic way. For example, wealthier, better educated, households may be more able to re-locate in response to changes in air pollution, and thus be more likely to drop from the panel sample. To the extent that

> 37Results are also insignificant when using average PM2 _._ 5 in weeks _t_ + 1 or _t_ + 2.

> 38 We use the STATA ado file _reg2hdfespatial_ developed by Fetzer (2014) and based on Hsiang (2010). We use a 1 year time lag and a distance cutoff of 8 kilometers.

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Table 4: Additional checks

||||Hours w|orked|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A. Households|with susceptib|le individual|s||||
|PM 2.5|-0.152***||-0.188***|-0.161**||-0.167***|
||(0.053)||(0.052)|(0.062)||(0.049)|
|Contemp.||-0.042|||||
|PM 2.5||(0.046)|||||
|Log(PM 2.5)|||||-7.478***<br>(1.746)||
|Observations|2,167|2,245|2,167|1,817|2,167|2,167|
|R-squared|0.436|0.422|0.430|0.425|0.429|0.202|
|B. Households|without susce|ptible individ|uals||||
|PM 2.5|-0.019||-0.038|-0.053||-0.027|
||(0.063)||(0.053)|(0.044)||(0.035)|
|Contemp.||0.004|||||
|PM 2.5||(0.045)|||||
|Log(PM 2.5)|||||-0.501<br>(1.614)||
|Observations|3,051|3,078|3,051|3,401|3,051|3,051|
|R-squared|0.451|0.432|0.447|0.447|0.447|0.178|
|Model|Additional<br>time-varying<br>controls|Contemp.<br>PM 2.5|Quadratic<br>polynom.<br>weather|Suscept.=<br>children<br>under 5|Semi-log<br>specif.|Conley<br>S.E.|

Notes: Robust standard errors in parentheses. Standard errors are clustered at the municipality level, except in Column 6 in which they are estaimted using the procedure proposed by Conley (1999). * denotes significant at 10%, ** significant at 5% and *** significant at 1%. All regressions include the same controls as the baseline specification (see notes of Table 2. Panel A and B use two different samples. Unless specified, PM 2.5 refers to average PM2 _._ 5 in week _t −_ 1, where _t_ is the week of reference of labor outcomes. Column 1 adds quarter-by-year fixed effects and year fixed effects interacted with indicators for being poor, having a number of income earners above the median, and above-median age. Column 2 uses the contemporaneous value of PM2 _._ 5, i.e., the average value during the reference week for labor outcomes. Column 3 adds temperature and humidity squared to the baseline regression. Column 4 defines as susceptible individuals only children 5 years and younger. Column 6 estimates model without sample weights.

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attrition is correlated with hours worked, this behavior would bias our results. We test for attrition bias following the procedure suggested by Verbeek and Nijman (1992), and described in Wooldridge (2002, Ch.17.7.2). This requires adding to the baseline regression an indicator equal to 1 if the household drops from the panel sample in the next period. Under the null hypothesis that attrition is not systematic, this additional explanatory variable should not be significant. We find that indeed this variable is insignificant and thus we fail to reject the null hypothesis. This result weakens concerns of attrition bias being a relevant issue (see Table B.4 in the Appendix).

# **4.3 Non-linearities**

The previous results provide evidence of the _average_ effect of PM2 _._ 5 on labor supply. However, these effects could be different at higher levels of pollution. This could happen, for instance, if health problems only become severe enough to require hospitalization or preclude work when pollution is sufficiently high. It could also be that different demographic groups react to different “critical” levels of air pollution.

To examine non linearities, we estimate the baseline model (1) replacing the main explanatory variable with a step function of PM2 _._ 5. In particular, we estimate the following model: where _PM_ 2 _._ 5<sup>_k_</sup> _j,t−_ 1<sup>isanindicatorequalto1ifaveragePM2</sup><sup>_._5inweek</sup><sup>_t −_1isinbracket</sup><sup>_k_.39</sup> Similarly to the baseline results, we split the sample between individuals in households with and without susceptible dependents.

Figures 4a and 4b display the estimates of _βk_ for both samples.<sup>40</sup> There are two important observations. First, for individuals with susceptible dependents (i.e., small children and elderly adults) the relationship between pollution and labor supply seems to be linear. The effect is negative and statistically significant even at moderate levels (i.e., around average). However, for individuals without susceptible dependents, the relationship is non-linear. At moderate levels

> 39We define the following brackets _k_ = _{_ 0 _−_ 35 _,_ 35 _−_ 45 _,_ 45 _−_ 55 _,_ 55 _−_ 75 _,_ 75+ _}_ . We define these brackets based on the breakpoints used in the U.S. Air Quality Index (U.S. EPA, 2012) and the constraint of having enough observations in each bracket.

> 40See Table B.5 in Appendix for the regression estimates.

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(i.e., below 75 _μ_ g/m<sup>3</sup> ), there is no effect of PM 2.5 on hours worked. But, at higher levels (above 75 _μ_ g/m<sup>3</sup> ) the effect becomes negative and significant.

We interpret these results as evidence that, at moderate levels, the mechanism linking pollution to labor supply is caregiving. But, at higher levels the link is more direct: pollution may reduce labor supply by harming workers’ health. This interpretation is consistent with existing epidemiological evidence suggesting that higher levels of pollution are required to affect the health of non-susceptible populations.<sup>41</sup> Moreover, this finding points to the extensive margin as a potential source of non-linearity in the relationship between air pollution and labor supply: as pollution levels increase, the effects on labor supply expands beyond households with susceptible individuals to encompass the rest of the population.

# **4.4 Attenuation behavior**

An important question is whether households attenuate the negative, short-term, impact of air pollution. The previous results suggest that households are unable to fully attenuate the negative effect of pollution on hours worked. However, they could try to reduce the negative impact on income.

A possible strategy would be to reallocate caregiving duties to household members with relatively worse labor opportunities (or a comparative advantage on caregiving). This would attenuate the negative shock on labor supply of main providers, and the associated reduction on earnings.

To illustrate this argument consider this simple model. There is a unitary household with two individuals _i_ = _{_ 1 _,_ 2 _}_ . Each individual has 1 unit of time that can be sold to labor markets, _Li_ , at wage _wi_ or used as domestic work, _hi_ , to provide a household service, namely, care for dependents.<sup>42</sup> The minimum amount of care that the household must provide is _s_ . For simplicity, we assume that the technology that transforms domestic work into caregiving is defined by _f_ ( _h_ 1 _, h_ 2) = _h_<sup>_ρ_</sup> 1<sup>_h_</sup> 2<sup>1</sup><sup>_−ρ_</sup> , with _ρ ∈_ (0 _,_ 1).<sup>43</sup> Household utility, _U_ ( _c_ ), depends of total consumption, _c_ .

The household’s allocation of domestic work, and thus choice of each individuals’ labor

> 41For example, the U.S. EPA considers 24-hour levels of PM2 _._ 5 between 35-55 _μ_ g/m3 as unhealthy for sensible populations, and between 55-150 _μ_ g/m<sup>3</sup> as unhealthy for the general public. Above that concentration, PM2 _._ 5 becomes very unhealthy or even hazardous (U.S. EPA, 2012).

> 42The model does not include leisure. However, the results including a labor-leisure trade-off are identical. 43 Results are similar using other homothetic functions.

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<!-- Start of picture text --> 10<br>5<br>0<br>5<br>-10<br>-15<br>-20<br>35-45 45-55 55-75 75+<br>PM 2.5<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>5<br>0<br>5<br>-10<br>-15<br>-20<br>35-45 45-55 55-75 75+<br>PM 2.5<br><!-- End of picture text -->
supply, is obtained by solving the following problem: In this framework, pollution affects household’s decisions by affecting dependents’ health and increasing minimum caregiving needs, _s_ . Using standard methods, we obtain that _h_ 1 = _sk_<sup>_ρ−_1</sup> and _h_ 2 = _sk_<sup>_ρ_</sup> where _k ≡ w_<sup>_<u>w</u>_</sup> 2<sup><u>1</u></sup> 1 _−ρ_ _<u>ρ</u>_<sup>.Thus,anincreaseinpollutionwouldincreasedomesticwork</sup> ( _h_ 1 _, h_ 2) and decrease labor supply of both workers.

It is easy to show that if _k_ = 1 the effects of pollution on domestic work and labor supply,<sup>_<u>dh</u>_</sup> _ds_<sup>_<u>i</u>_</sup> and<sup>_<u>dL</u>_</sup> _ds_<sup>_<u>i</u>_respectively,arethesameforbothindividuals.However,if</sup><sup>_k>_1,then</sup><sup>_<u>dh</u>_</sup> _ds_<sup><u>2</u></sup><sup>_>_</sup><sup>_<u>dh</u>_</sup> _ds_<sup><u>1</u></sup><sup>_>_0.</sup> This implies that an increase of air pollution would reduce labor supply of worker 2 _more_ than for individual 1.<sup>44</sup> Condition _k >_ 1 could happen if individual 1 earns a higher wage, _w_ 1 _> w_ 2, or if individual 2 has an advantage in providing caregiving, _ρ <_ 1 _/_ 2.

The model suggests that, in the presence of heterogeneous workers, households could minimize the negative shock of pollution by re-allocating domestic duties, and labor, among its members. This implies that the negative effect of air pollution on hours worked would be _larger_ for individuals with relatively lower wages.

To examine this possible attenuation behavior, we estimate the baseline model (1) adding interactions of _PM_ 2 _._ 5 with several factors associated with wage differentials such as age, gender, education, role as head of household, or being an independent worker.<sup>45</sup> Similarly to the baseline results, we split the sample between individuals in households with and without susceptible dependents.

We find, however, no evidence of significant heterogeneous effects of pollution driven by these characteristics (see Table 5).<sup>46</sup> One possible interpretation is that other constraints preclude using within-household reallocation of caregiving as a way to attenuate this shock. For example,

> 44 This results depends on the substitutability of _h_ 1 and _h_ 2. If both are perfect substitutes the effect is even more evident. In that case, pollution would only reduce _L_ 2 and have no effect on _L_ 1.

> 45We check that these variables are indeed significantly correlated with differences in earnings. These results are available upon request.

> 46See Table B.6 in the Appendix for results using the sample of households without susceptible individuals.

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there may be a limit on how many hours of work a worker can miss and thus additional caregiving would have to be shared between household members.

Table 5: Exploring attenuation behavior

||||Hour<br>|s worked<br>|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|PM 2.5|-0.185***|-0.190***|-0.175***|-0.132*|-0.199***|-0.195***|
||(0.039)|(0.063)|(0.043)|(0.070)|(0.058)|(0.042)|
|PM 2.5 _×_ worker|0.025|0.028|-0.004|-0.058|0.022|0.062|
|characteristic|(0.062)|(0.068)|(0.083)|(0.069)|(0.084)|(0.078)|
|Worker|Is house-|Is female|Under|Complete|Indep.|Has job|
|characteristic|hold head||25 years|secondary|worker|contract|
|Household has<br>susceptible indiv.|yes|yes|yes|yes|yes|yes|
|Observations|2,167|2,167|2,167|2,167|2,167|2,167|
|R-squared|0.422|0.422|0.422|0.423|0.430|0.422|

Notes: Robust standard errors in parentheses. Standard errors are clustered at the municipality level. * denotes significant at 10%, ** significant at 5% and *** significant at 1%. All regressions include household, week and municipality-by-year fixed effects, and the same individual and household controls as the baseline specification (see notes of Table 2) plus interactions of PM 2.5 with workers’ characteristics. Sample includes only workers in households with susceptible individuals.

An alternative explanation is that households might have other, more effective, attenuation strategies. For example, workers could use their vacation or sick days. In that case, air pollution would reduce hours worked, but have a smaller, or even negligible, effect on income. This strategy would be limited, however, to workers with such job benefits. We indirectly examine this alternative explanation by studying the effect of air pollution on earnings.

To do so, we estimate the baseline regression (1) using the log of monthly earnings as outcome variable.<sup>47</sup> To capture possible heterogeneous effects by access to employee benefits, we distinguish workers with and without job contracts. We consider a worker to have a job contract if she is a dependent worker with an indefinite or fixed-term contract. Only workers

> 47Note that, in contrast to labor outcomes, the period of reference for earnings is the 4 weeks prior to the interview (i.e., weeks _t_ to _t −_ 3). Thus the reference period for our measure of exposure to PM2 _._ 5 is weeks _t −_ 1 to _t −_ 4. Given the length of the period, using average PM2 _._ 5 would mask episodes of high pollution. For that reason, we use instead the share of weeks in the reference period in which average PM2 _._ 5 exceeded the U.S. 24-hour standard. We also use month instead of week fixed effects, and relax the model by using household plus year fixed effects without municipality-by-year fixed effects. Results adding municipality-by-year fixed effects are similar but noisier (see Table B.7 in Appendix).

24
with these types of contracts are entitled to paid vacation days, and other employee benefits such as medical insurance and paid sick leave.<sup>48</sup> In the sample, only 30% of workers have a job contract.

Table 6 presents the results. There are three important observations. First, consistent with imperfect attenuation, PM2 _._ 5 is associated with a (marginally) significant reduction in earnings for the average worker (column 1).<sup>49</sup> Second, the reduction in earnings occurs mainly in households with susceptible individuals. This suggests that the observed reduction in earnings is due mainly to lost hours, and not a reduction in wages. The lack of an effect in wages could stem from either wage rigidity in the short run or an absence of productivity effects.<sup>50</sup> Finally, the reduction in earnings seem to occur mostly among workers _without_ job contracts (column 2). However, hours worked drop for both workers with and without contracts.<sup>51</sup> We interpret these findings as evidence that households mitigate the short-term negative impact of pollution on income mainly by using employee benefits, such as vacation or sick days. However, households without those benefits have limited ability to attenuate this shock.

> 48Workers without contracts include: independent workers (i.e, self-employed workers and business owners), workers in probationary period, apprentices and trainees, and dependent workers without a contract.

> 49We also estimate the results in this section by averaging earnings across household members or adding hours worked. Results are similar, although less precise as this further reduces the number of observations (Appendix Tables B.8 andB.9).

> 50The later explanation would be in contrast with evidence from U.S. that air pollution has a significant negative effect on labor productivity and wages among farmers and manual factory workers (Graff-Zivin and Neidell, 2012; Chang et al., 2014). As we do not have a direct measure of worker productivity, we cannot rule out the presence of productivity effects.

> 51The estimated effects of PM2.5 on hours worked for both groups are -0.195 and -0.133 respectively (see column 6 in Table 5).

25
Table 6: PM2 _._ 5 and earnings

||ln<br>|(earnings <br>|in last mo<br>|nth)<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|A. % weeks PM 2.5 above|-0.152*|-0.195*|-0.009|0.009|
|35 _μ_g/m<sup>3</sup>|(0.088)|(0.113)|(0.068)|(0.078)|
|B. % weeks PM 2.5 above||0.107||-0.045|
|35 _μ_g/m<sup>3</sup> _×_ has job contract||(0.132)||(0.100)|
|C. p-value H0: _A_+_B_ = 0||0.406||0.685|
|Household has|yes|yes|no|no|
|susceptible individuals|||||
|Observations|2,274|2,274|3,240|3,240|
|R-squared|0.600|0.603|0.625|0.631|

Notes: Robust standard errors in parentheses. Standard errors are clustered at the municipality level. * denotes significant at 10%, ** significant at 5% and *** significant at 1%. All regressions include household, month and year fixed effects, and the same individual and household controls as the baseline specification (see notes of Table 2). The reference period for explanatory variable “% weeks PM 2.5 above 35 _μ_ g/m<sup>3</sup> ” is weeks _t −_ 1 to _t −_ 4. Row C displays the p-value of the test _A_ + _B_ = 0, where A and B refer to the estimates in two rows.

# **5 Conclusion**

This paper examines the short-term effect of fine particulate matter on labor supply. This issue is important to assess the social cost of pollution and to inform the design of environmental policies.

Using the case of Lima, Peru, we find evidence of a significant and sizable negative effect. The effects are non linear and heterogeneous, affecting mostly individuals in households with small children and elderly adults.

Our findings shed light on the mechanisms linking pollution to labor supply. They suggest that, at moderate levels, caregiving is an important mechanism linking pollution to labor supply. However, at higher levels, pollution affects all individuals suggesting that, at these levels, the mechanism may be more direct: deterioration of workers’ health.

Importantly, our results also point out two important issues not discussed before. First, pollution can have redistributional effects. In our case, the brunt of the pollution externality, in terms of lower labor supply and lower earnings, is borne by households with small children 26
and elderly adults, and informal workers Second, households seem to have a limited ability to reduce the negative effect of this shock on their income by reallocating caregiving duties among members of the household.

There are, however, some important issues not addressed in this paper. First, while we examine the short-term effects of pollution we are unable to study possible long-term, cumulative, effects. Second, we do not estimate the effect on other outcomes that may affect household livelihood such as labor productivity, medical expenses, or school absenteeism. Previous studies suggest these are also relevant externalities. Exploring these issues warrants further research.

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