Effects of China’s Air Pollution and Income Growth onHealth Outcomes among the Elder Adults

Mingzhi Xu∗

UC Davis

Zhe Yang†

Jinan University

Preliminary and Incomplete

July 2017

Abstract

How does the health cost of air pollution in China compare to the health benefits of incomegrowth? Using each city’s historical concentration of industries, nationwide industry-specificgrowth in trade after China entered the WTO, and nationwide industry-specific pollutionintensity, we construct two shocks –an income shock and a pollution shock– and use themtogether to instrument income and air pollution. We find that income growth and air pol-lution affect different dimensions of health. Whereas air pollution significantly increases theprobability of hypertension and overweight, income growth significantly improves almost ev-ery other health outcomes. We also examine the extent of cross-city spillovers for income andpollution shocks. We conclude that the effective range at which the pollution shock affectsaerosol optical depth is about 400 km, whereas the effective range at which the income shockaffects annual per-capita household consumption is about 100 km.

Keywords: Health, Air Pollution, Trade.

JEL Code: I1, Q53, Q56

∗mzhxu@ucdavis.edu; Financial support from the China Scholarship Council is gratefully acknowledged.†Co-responding author: zheyang@ucdavis.edu

1 Introduction

Although China’s rapid income growth has improved its citizens’ health outcomes, the im-

provement has been partially offset by severe problems with air pollution (Ebenstein et al., 2015).

Evidence of the negative causal effects of China’s air pollution on life expectancy and infant mor-

tality is abundant (Chen et al., 2013; Bombardini and Li, 2016).

How does the health cost of air pollution compare to the health benefits of higher income? This

question is important to policy makers who are facing a trade-off between faster income growth

and cleaner air, especially in countries where air pollution is largely a by-product of economic

development. A clear answer to this question requires comparing the causal effects of air pollution

on health with those of income.

An ideal identification strategy for comparing the two effects directly is to conduct two ex-

periments simultaneously in the same sample, one for each effect. Using such a two-variable-

quasi-experimental design and two decades of Chinese mortality data, Bombardini and Li (2016)

found that a one standard deviation increase in the export-driven pollution shock increases infant

mortality by 2.2 deaths per thousand live births, and that this increase focuses on the cardio-

respiratory-related mortalities. In contrast, the effects of export-driven income shock are in the

opposite direction, of the same order of magnitude, and not always statistically significant.

In this paper, we apply Bombardini and Li (2016)’s two-variable-quasi-experimental design

to non-mortality health effects among older Chinese adults aged 45-75. We use cross-sectional

data instead of panel data due to the scarcity of long panel micro data on non-mortality health

outcomes in China. Our main data sets are the China Health and Retirement Longitudinal Study

(CHARLS) conducted in 2011, and NASA’s satellite-based air pollution assessment. We find that

air pollution and income operate in different dimensions of health outcomes: among the eight

health outcomes we measured, air pollution in China significantly increases hypertension and

overweight, whereas income growth significantly improves almost every health outcomes except for

hypertension and overweight. Specifically, a one standard deviation increase in the export-driven

air pollution increases the probability of hypertension and overweight by 6.8 and 11.2 percentage

points, respectively, but does not significantly affect other health outcomes. In contrast, a 10%

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export-driven increase in annual consumption per capita decreases the probability of self-rating

one’s general health to “poor” or “very poor” by 2.4 percentage points, decreases probability of

depression (CESD > 10/30) by 5.2 percentage points, decreases the number of difficulties in the

six activities of daily living (ADL) by 4.3 percentage points, increases the number of words recalled

by 0.26 out of 20 and the peak expiratory flow by 13 L/min, but it does not significantly affect

grip strength or the probability of hypertension or overweight.

To address the issue of spacial correlation between income or pollution shocks and actual income

or pollution, we allow shocks from one city (including non-sample cities) to affect the income and

pollution of nearby cities, with the magnitude of impact depending on distance categories. We

conclude that the effective range at which the pollution shocks affect aerosol optical depth (AOD)

is about 400 km, whereas the effective range at which the income shocks affect annual per-capita

household consumption (Log PCE) is about 100 km.

The contributions of our paper are fourfold. First, we apply Bombardini and Li (2016)’s two-

variable shift-share method in a cross-sectional dataset to non-mortality health outcomes. Second,

this paper reveals that China’s air pollution and income growth affect different dimensions of

health– air pollution affects hypertension and overweight, whereas income affects almost every

health outcomes except for hypertension and overweight. Third, we confirm that the conventional

method that instruments pollution but controls for income leads to results that are similar to those

using our method, which instruments both income and pollution. Fourth, we examine the extent

of cross-city spillovers regarding the effects of income shocks and pollution shocks. We estimate

that the spatial lag is about 400 km for pollution shocks and about 100 km for income shocks.

The rest of the paper proceeds as follows: Section 2 outlines our empirical approach. Section

3 describes the data and sample. Section 4 discusses the validity of our identification strategy.

Section 5 presents results. Section 6 gives conclusion.

2 Identification Strategy

Establishing causal effects of air pollution or income on health is challenging for two reasons:

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omitted variable bias and reverse causation. Omitted variable bias occurs if unobservable factors,

such as stressful life events, affect both income and health. Reverse causation occurs if a person’s

health status affects her income Smith (2004).

To solve these problems, we construct two export shocks at the prefecture level, one for income

and one for air pollution. Both shocks are Bartik (Bartik, 1991) type shift-share shocks that

combine a location-specific historical share of each industry and a nationwide shift of each industry.

This method have been widely used in the literature to construct exogenous shocks (Blau et al.,

2000; Aizer, 2010; Bertrand et al., 2013; Schaller, 2013; Dorn and Hanson, 2015; Shenhav, 2016)

. In this paper, we use a common “share” for both income shock and air pollution shock: the

historical industry composition by each city. The “shift”, however, differs by type of shock: for

the income shock, the shift is the rise of Chinese export to the United States by industry following

China’s entry into the World Trade Organization (WTO). For the air pollution shock, the shift

is the rise of the same export by each industry times pollution intensity per dollar of output in

corresponding industries.

Figure 1 illustrates the purpose of constructing these two shocks.

2.1 Within-City Exogenous Income Shocks and Air Pollution Shocks

For simplicity, we start by assuming no cross-city spillover effect, meaning that each city’s

historical industry composition affects its own income growth and air pollution. We use the

following formula to construct the exogenous shock on log-income for each city during the period

of 2003-2011:

∆Slog-incomec,2003→2011 = ln(1 +

∑ind

Eindc,2003

POPc,2003

• ∆TRADEind2003→2011

Y indChina,2003

)

where c denotes a city, POPc,2003 is the population of the city in 2003, Eindc,2003 is the number of

employment in industry ind in city c in 2003, Y indChina,2003 denotes the output of industry ind in

China in 2003, TRADEind2003→2011 denotes the increase in annual export volume from China to the

US in industry ind during the period of 2003-2011.

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The intuition of this formula is that for a city to receive high income shocks, it has to have a

historical focus on industries that will experience fast trade growth in the future.

We use a similar formula to construct the exogenous shock on total suspended particulate

(TSP) for each city during the period of 2003-2011:

∆Spollutionc,2003→2011 =

∑ind

(Y indc,2003 × γind) • ∆TRADEind

2003→2011

Y indChina,2003

where Y indc,2003 denotes the output of industry ind in city c in 2003, γind denotes average amount of

pollutant emitted per yuan of industry ind output in China in 2005. (Y indc,2003 × γind) denotes total

pollutant emitted from industry ind in city c in 2003.

The intuition of the pollution shock formula is that for a city to receive high pollution shocks,

it has to have a historical focus on high pollution industries, and those high pollution industries

will experience fast trade growth in the future.

2.2 Cross-City Exogenous Income Shocks and Air Pollution Shocks

To adjust for the spacial interaction between one city’s shocks and another city’s outcomes,

we relax the assumption to allow shocks to affect nearby cities. For simplicity, we assume that

each income shock has a “impact disk” of radius r1. Nearby cities within r1 kilometers from the

shock city receive the shock, while other cities outside the circle do not. For each receiver city, the

total shock it receives accounts for all individual shocks that happened within r1 kilometers of the

receiver city.

Because income and income shocks are in the form of per capita amount, We convert them

in to over-population amount before we sum up within each circle. As shown by the first and

second lines of equations below, the total over-population income shock a city receives is the sum

of all individual total over-population income shocks within the “impact disk”. The third line of

the equations states that mathematically it’s equivalent to that the per-capita income shock a city

receives is the population-weighted average of all individual per-capita income shocks originated

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within r1 kilometers from the city:

∆TOTAL-SHOCKlog-incomec,r1

=∑

d(c,c′)<r1

∆TOTAL-Slog-incomec′,2003→2011

∆SHOCKlog-incomec,r1

• (∑

d(c,c′)<r1

POPc′,2003) =∑

d(c,c′)<r1

(∆Slog-incomec′,2003→2011 • POPc′,2003)

∆SHOCKlog-incomec,r1

=

∑d(c,c′)<r1

(∆Slog-incomec′,2003→2011 • POPc′,2003)∑

d(c,c′)<r1POPc′,2003

where c’ is another city1 near the sample city c, d(c, c′) denotes the distance between city c and

city c’, r1 is a pre-assumed distance threshold which takes values among 800 km, 400 km, 200 km,

100 km, and 0 km (within-city only). Throughout this paper, the default value of r1 is 400 km.

For pollution shocks, we assume the radius of “impact disks” to be r2. Nearby cities within r2

kilometers from the pollution shock city receive the shock, while other cities outside the circle do

not. The total pollution shock a city receives is the sum of all individual pollution shocks originated

within r2 kilometers from the city (we do not have to convert from “per-capita” to over-population

for the pollution shocks because both the total pollution shocks and the individual pollution shocks

are defined in total rather than per capita terms):

∆SHOCKpollutionc,r2

=∑

d(c,c′)<r2

∆Spollutionc′,2003→2011

where r2 alternatively takes values among 800 km, 400 km, 200 km, 100 km, and 0 km (within-city

only), and can be different from r1. Throughout this paper, the default value of r2 is 100 km.

2.3 Baseline 2SLS Model

We use the following two-stage least square (2SLS) model to estimate the effects of income and

air pollution on health outcomes:

HEALTHi = α + β1Log PCEi + β2POLLUTIONc + θXi + δZc + εi

1In order to let the source cities be connected as an unbroken study area, we also construct shocks for citiesoutside of our main sample. Those cities provides data on industrial employment and population, but not healthoutcomes.

5

First Stage:

Log PCEi

POLLUTIONc

=

α1

α2

+

δ11 δ12

δ21 δ22

∆SHOCKlog-income

c,r1

∆SHOCKpollutionc,r2

+

θ1

θ2

Xi+

δ1

δ2

Zc+

ε1

ε2

where i denotes an individual, c denotes a city. HEALTH is either one of the eight health

outcome variables: self-rated general health status, CESD-10 score, hypertension measured by

biomarkers, overweight, number of difficulties in the activities of daily living, number of words

successfully recalled in the memory test, peak respiratory rate, and grip strength. Log PCE is the

log of annual per-capita household expenditure. This is a better measure of long-run income than is

current income for our sample because current income can be very volatile and may not include farm

production that is self-consumed in low-income rural settings (Deaton, 1997; Lee, 2009; Strauss

et al., 2010). POLLUTION is the aerosol optical depth measured by the MODIS instrument on

NASA’s Terra satellite. X includes region-by-rural fixed effects and gender-by-age-by-education-

by-rural fixed effects. Z includes average temperatures in January, average temperatures in July,

annual rainfall, and interview month dummies.

ε is the error term that represents unaccounted health determinants, such as culture differences

in diet, health behaviors, and geographic variations in climate and public health input. To adjust

for the potential spatial correlations in these health determinants, we cluster standard errors at

the province level. To address the concern that the extent of those spatial correlations may be

smaller or larger than the size of a province (or at the same size of a province but does not follow

the provincial boarders), we alternatively cluster standard errors at three different levels: city

(prefecture) level, province level, and super province level. We define super provinces by merging

25 sample provinces into 13 larger units2. In Section 5.5, we assess the robustness of our estimates

to clustering by looking at the stability of standard errors across different clustering levels.

The exclusion restriction condition assumes that the only channel by which our constructed

income shocks and pollution shocks can affect health outcomes is through income and pollution.

2The 13 super provinces are: Beijing-Tianjin-Hebei, Shanxi, Inner Mongolia, Liaoning-Jilin-Heilongjiang, Henan-Shandong-Jiangsu(north)-Anhui(north), Shanghai-Zhejiang-Jiangsu(south)-Anhui(South), Jiangxi, Fujian, Hunan-Hubei, Guangdong-Guangxi-Hainan, Sichuan-Chongqing, Yunnan-Guizhou and Shaanxi-Gansu-Ningxia.

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Violation of this assumption occurs if our constructed income shocks and pollution shocks are

correlated with health factors that are but not driven by income growth or air pollution.

A major threat to the exclusion restriction assumption is our lack of control on long-run his-

torical patterns of income growth, air pollution, and health, due to the cross-sectional nature of

our data. We assess the severity of this problem by running the regression below to see how our

instrument shocks predict past income and pollution, and then examining the robustness of our

2SLS results to controlling past income and pollution.

FACTORi = α + β1∆SHOCKlog-incomec,r1

+ β2∆SHOCKpollutionc,r2

+ θXi + δZc + εi

A lack of prediction on these factors by our constructed shocks (β̂1 = β̂2 = 0) is in favor of the

chance that the exclusion restriction holds.

3 Data and Construction of Measurement

3.1 Data Sources Description

3.1.1 Data for Pollution

The primarily main pollutants used in analysis are sulfur dioxide (SO2) and aerosol optical depth

(AOD)3, which sources from the satellite remotely sensed data by National Aeronautics and Space

Administration (NASA). The SO2 satellite data are collected from the satellite with Ozone Mon-

itoring Instrument (OMI, aboard NASAs EOS/Aura satellite, launched in July 2004), for each

month ranging from 2005 to 2015. AOD data are originally collected in the same way as SO2

and it measures the particles in the atmosphere (such as dust, smoke, pollution. We use AOD to

approximate4 PM2.5. A small value of AOD corresponds to an extremely clean atmosphere while

a large value indicates a very hazy condition. The time period of AOD data are from 2004 to 2015.

3We will also include nitrate dioxide (NO2) for the future study.4Since AOD is a direct measure of particles in the air, it is positively correlated with PM2.5. We will estimate

PM2.5 following Ma et al. (2016) in the future research.

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SO2 and AOD data are respectively recorded at 0.25o×0.25o and 0.1o×0.1o grid points, which are

further used to construct the pollution intensity for each 10km× 10km grids in China. We believe

these data obtained from NASA satellite are unlikely to be interfered by Chinese government, and

would provide a very good proxy for ground-level pollutants emissions. Figure 2 in the appendix

shows the cross-section variation for SO2 and AOD in October 2015.

3.1.2 Data for Population, Employment and Output

The population data comes from the 2005 China Census data for the year 2005, as well China

Statistics Year Book for the rest of the years. The former is used to create the population density

that is to match with the high-resolution pollution data via geographic coordinate system, while

the latter is used to construct the pollution and trade shock measures on a per-capita basis.

The prefecture-industry specific employment and output data sources from the Annual Survey

of Industrial Production (ASIP) conducted by China’s National Bureau of Statistics (NBS). The

dataset5 surveys manufacturing firms with annual revenues of five million RMB. The sample size

varies from 165,119 in 1998 to 336,768 in 2007. The export information uses data from China

Customs. Although exports are also reported in the ASIP data, we believe the Customs data to

be more accurate. To use this data, we aggregate the eight-digit HS product-level information

to match with the CIC industry codes. Unmatched industry or location codes correspond to a

negligible loss of exported value information in this period.

3.1.3 Other Sources

The construction of pollution intensity (employed to build pollution export shock) follows Bom-

bardini and Li (2016), and uses data from the World Bank’s Industrial Pollution Project System

(IPPS) as well as China’s Environment Yearbook. The IPPS provides us the pollution emission

intensity (on the basis of emission per value output) by each 4-digit SIC industries. We aggre-

gate the data to 4-digit CIC industries for pollutants6 sulfur dioxide (SO2) and total suspended

particles (TSP ).

5The detailed information regarding ASIP could refer to Brandt et al. (2014).6Intensity of total suspended particles is for AOD.

8

3.2 Construction of the Key Variables

3.2.1 Measurement of Health Outcomes

We derive eight health outcomes from the 2011 CHARLS data. The first outcome is a binary

variable indicating whether the self-rated general health status is “poor” or “very poor” rather

than “very good”, “good”, or “fair”. The second outcome is a binary variable indicating whether

the person scored over 10 on the 10-item CESD score for depression symptoms. The higher the

score, the poorer the mental health is. The maximum score of this test is 30. The third variable is a

binary variable indicating whether the average between the second and the third measure of systolic

pressure exceeded 140 mmHg, the average between the second and the third measure of diastolic

pressure exceeded 90 mmHg, or the person participated had ever been diagnosed of hypertension

by a doctor. The universe of this variable is biomarker takers who provided non-missing values

on hypertension, overweight, peak respiratory flow rate, and grip strength. The fourth variable

is a binary variable indicating whether the measured BMI exceeded 25. The fifth variable counts

the number of difficulties in activities of daily livings. The sixth variable counts the number of

words in total the respondent successfully recalled in two memory tests, one for immediate recall

and one for delayed recall. Each test required respondent to memorize 10 commonly used words.

The seventh variable is the peak respiratory flow rate for lung function. The eighth variable is the

average grip strength of two hands.

3.2.2 Measurement of Income (Log PCE) and Pollution (AOD)

We measure income using the natural log of household annual per capita expenditure from the

2011 CHARLS data. We prefer this measurement to current income because it is less volatile, is a

better indicator for long-run income, and contains self-consumed goods from the farm. To address

the zero values in reported expenditures, we add 365 yuan/year before we take the natural log,

by assuming that every person has a non-reported subsistent level of consumption that worths 1

yuan ($0.29 PPP) per day.

We measure air pollution using the average reading of aerosol optical depth (AOD) during

July–August 2011 from the MODIS instrument on NASA’s Terra satellite. The raw data was

9

collected monthly with a spatial resolution of 10km×10km. We aggregate this data up to the city

(prefecture) level by taking simple averages over all 10km×10km squares within each city. Figure

3 illustrates how we aggregate measurement of air pollution for Beijing and Shanghai.

3.2.3 Measurement of Historical Industry Composition, Trade Growth, and Industry-

Specific Pollution Intensity

We measure each city’s industry employment composition in 2003 and each city’s population

in 2003 using data from the China Statistical Yearbook. We measure each industry’s pollution

intensity (the amount of total suspended particulate emitted per yuan of output) using China’s

environment yearbooks published by Ministry of Environmental Protection7.

We measure the industry-specific growth of export from China to US by using data from the

UN-Comtrade database. The raw data on trade volumes are by commodity categories at the 6-digit

HS level. We aggregate them to the CIC 4-digit level.

3.3 Description of Constructed Exogenous Income Shocks and Pollu-

tion Shocks

Figure 4 shows the distribution of the income shock and the pollution shock. Both shocks were

standardized and adjusted for spatial spillovers using their default thresholds (100 km for income

and 400 km for air pollution).

Figure 4 displays two desirable patterns. First, the two shocks spread out in two dimensions

instead of being close to collinear. Cities like Guangzhou and Shenzhen receives high income shocks

but low pollution shocks, while cities like Shijiazhuang and Xuzhou receives low income shocks

but high pollution shocks. This pattern helps our identification because it suggests that the two

shocks are distinctive enough for us to isolate the income effects from the pollution effects.

The second desirable pattern is that the figure confirms our prior knowledge regarding which

Chinese city export which type of commodities to the US. For instance, Guangzhou and Shenzhen

7The environment yearbooks in China only provides pollution intensities in China at the 2-digit CIC level. Weupdate it to the 4-digit CIC level using data from the World Bank’s Industrial Pollution Projection System, whichprovides the relative intensity in the US of each 4-digit SIC industry within the corresponding 2-digit SIC industry.We convert the SIC industries to the CIC industries using cross-walks

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receive large income shocks but moderate pollution shocks. Their low pollution-income shock

ratio is consistent with the fact that cities in the Pearl River Delta exported a large amount of

electronic devices, which has a low pollution intensity. By contrast, Shijiazhuang, Anyang and

Xuzhou receive large pollution shocks but small income shocks. Their high pollution-income shock

ratio is consistent with the fact that cities in the northern part of the North China Plain hosted the

majority of China’s steel export, which has a very high pollution intensity. Cities like Kunming,

however, ranks low in both income shocks and pollution shocks, which is consistent with the fact

that cities in the hinterland have overall low trade volumes.

3.4 Description of Analysis Sample

We restrict our sample to respondents between the ages of 45 and 75 and those who provided

non-missing values on Log PCE, self-rated general health, CESD-10, number of difficulties in ADL,

and number of words successfully recalled in the memory test. Among the 14,317 CHARLS re-

spondents between the ages of 45 and 75, 10,050 (70.2%) provided non-missing values on Log PCE

and the four non-biomarker health outcomes –self-rated “poor” or “very poor” general health,

depression, (CESD> 10), number of difficulties in ADL, and number of words recalled. 9,550

(63.3%) provided non-missing values on Log PCE and all of the eight health outcomes. Respon-

dents in our sample are from 113 Chinese cities. Table 1 reports summary the statistics of our

analysis sample. We weight all statistics using individual nonresponse-adjusted weights to make

them representative of the population.

Table 1 compares two city groups – cities that received higher-than-median air pollution shocks

(Column 2) and cities that received lower-than-median air pollution shocks (Column 3). As shown

in Column 4, the high-pollution-shock cities on average are not statistically significantly different

from the low-pollution-shock cities in Log PCE, sex ratio, age composition, education composi-

tion, or urbanization as well as most of the health outcomes. They are significantly different,

however, in air pollution, hypertension rate, overweight rate, and average BMI. The result of this

comparison suggests a positive effect of air pollution on hypertension, BMI and overweight. We

could have done a similar experiment for the effect of income. However, we are unable to draw

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conclusions about the effect of income in that comparison because the high-income-shock cities

and the low-income-shock cities differ significantly in air pollution levels.

4 Validity of Identification

4.1 Evidence of First-Stage Power

For our first stage to have sufficient power, the constructed income shocks and pollution shocks

must be correlated sufficiently with Log PCE and AOD. In addition, the effects of these two shocks

need to be different enough, to the extent that the 2× 2 correlation matrix is statistically different

from not having full rank.

Figure 5 suggests a strong first-stage power. The top left graph displays a strong positive

correlation between the income shock and Log PCE. The bottom right graph shows a clear positive

correlation between the pollution shock and AOD. The other two graphs provide a much less clear

pattern about the correlation between the income shock and air pollution and the correlation

between pollution shock and income.

Table 2 confirms the pattern in Figure 5. Column 1 runs a regression of Log PCE on the income

shock, the pollution shock, and the same set of control variables in our baseline 2SLS regressions

in Section 5. The results show a significant and positive correlation between the income shock

and Log PCE, and a negative but statistically insignificant correlation between the air pollution

shock and Log PCE. Column 2 shows that air pollution measured by AOD has a positive and

significant correlation with the pollution shock, and a negative but insignificant correlation with

the income shock. The Weak ID F-statistic8 for this first stage stands at 16.5, greater than 7.0, the

Stock-Yogo critical value at 10% maximal IV size for weak identification tests in a two-endogenous-

two-instrument case. In all of these regressions, we include the same set of control variables in our

baseline 2SLS results in Section 5.

8It is a cluster-robust Kleibergen-Paap Wald rk F-statistic rather than the F-statistic based on Cragg-Donald.Because we cluster standard errors at the province level, the Cragg-Donald F-statistic is no longer valid for testingweak IV.

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4.2 Exclusion Restriction

Table A1 in the appendix assesses the extent to which our constructed income shocks and

pollution shocks correlate with other observed factors, a sign indicating the potential violation

of the exclusion restriction. Column 1 shows a strong correlation between the income shock

and the initial output per worker. Figure A1 in the appendix confirm this pattern. Therefore,

historically richer cities were more prepared to benefit from the coming fast trade growth. If initial

income benefits health independently from the effects of current income, our 2SLS estimates will

be positively biased on the effect of income on health. We assess the magnitude of this bias by

looking at how sensitive our results are to the inclusion of log output per worker in 2003 as a

control variable.

Table 5 shows that controlling for initial income has only a moderate impact on the point

estimates and the categories of statistical significance, although it substantially lowers the first-

stage F-statistic.

Column 2 in Table A1 shows that our air pollution shock IV does not significantly correlate

with initial levels of income or pollution. Figure A2 in the appendix supports this pattern. Table

A2 further confirms that controlling for initial level of air pollution makes small additional changes

on our 2SLS estimates. Because high-resolution satellite measurement on AOD was not available

in 2003, we use 2005 as the year for initial level of AOD. Correspondingly, we change the period of

IV shocks from 2003-2011 to 2005-2011. This decrease in the length of IV shock makes our estimate

inaccurate, making results in Column 3 and 4 in Table A2 less comparable to our baseline results

in Column 1. However, the similarity between Column 3 and 4 suggests that the additional change

made by including the initial level of AOD (2005) is moderate.

Table A1 also shows that our constructed pollution shocks and income shocks do not signif-

icantly correlate with rainfall, annual average temperature, and the attrition rate of biomarker.

The lack of correlation between instrument variables and these observable factors is consistent with

the exclusion restriction assumption, although it does not test the violation of assumption directly.

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5 Results

5.1 Baseline Results

Table 3 presents our baseline results. Each column runs a 2SLS regression of a health outcome

on two endogenous variables: Log PCE and AOD, which are instrumented by the income shock

and the pollution shock. Both shocks were standardized after adjusting for spacial spillovers.

The distance thresholds are 100 km for income shocks and 400 km for pollution shocks. The

control variables include (urban/rural)×(6 regions) dummies, interview month dummies, average

temperatures in January and July, annual precipitation, and the sex-by-age-by-eduction-by-rural

fixed effects. We measure age using age-in-year dummies. We measure education using five levels:

illiterate, can read or write, fished primary school, finished middle school, and finished high school.

In all regressions, the weak ID first-stage F-statistic is above the Stock-Yogo critical value of 7.03.

An important pattern in the results is that Log PCE and AOD affect different types of health

outcomes. AOD increases the probability of hypertension and overweight, whereas Log PCE

improves almost every other health outcomes. There is a lack of overlap in the dimension of health

between the income effects and the pollution effects. Therefore, a combination of income growth

and air pollution shifts health problems towards hypertension and overweight.

Table 4 shows that our 2SLS estimates are robust across specification of control variables. In

column 1, the regression controls no additional variables besides instrumenting Log PCE and AOD

by income and pollution shocks, yet the point estimates and standard errors resemble those using

a full list of control variables (Column 7). Column 5-7 shows that adding demographic controls

make little change to our 2SLS estimates.

5.2 Effective Ranges of Income Shocks and Pollution Shocks

We provide four robustness checks to confirm that the ranges of the income shocks and the

pollution shocks are indeed r1 = 100 km and r2 = 400 km, respectively. First, we show in Table

6 that the choices of r1 = 100 km and r2 = 400 km optimize the statistical power in the first

stage. Each column of Table 6 shows the 2SLS results and first-stage F-statistics using a different

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value of r1, the radius of impact disks for individual income shocks. Each row-block of the table

shows results using a different value of r2, the impact radius for individual pollution shocks. We

expect that a misspecification in radius would compromise the statistical power in the first stage.

This is confirmed in Table 6 as it shows that when we use implausibly small or large values for

income shock radius, for example, “own city” or 800 km, the first stages are never significant. In

contrast, the first stage become most significant at values of r1 and r2 that are close to (r1 = 100

km, r2 = 400 km). Specifically, the first-stage F-statistic attains its maximum at r1 = 100 km for

almost every candidate value of r2, and it reaches its maximum at r2 = 400 km for almost every

candidate value of r1.

Second, Table 6 shows that the 2SLS estimates do not change drastically if we replace (r1 = 100

km, r2 = 400 km) by nearby values of r1 and r2. For example, assuming we mistakenly chose

r2 = 400 km, whereas the true radius for air pollution shocks is only 200 km, this hypothetical

misspecification would only change our estimated effect of logPCE on portion of people reporting

poor or very poor health from -0.232(0.065) to -0.242(0.067), a difference that could hardly change

our conclusion.

Third, as shown in Table 7, the choices r1 = 100 km and r2 = 400 km won the “horse race”

in regressions where we predict income and pollution using income shocks and pollution shocks

assuming various radius of “impact disks”. In each regression, the interpretation of a coefficient

on a shock with a particular radius, for example, the coefficient on a pollution shock with r2 = 400

km, is the effect of moving the source of pollution shock across the 400 km distance mark, but

not across other distance marks: 800 km, 200 km, 100 km, or the border of the city. Our choice

of r2 = 400 km is supported if moving a pollution source across the 400 km mark significantly

increases actual air pollution, while moving the pollution source across other distance mark does

not significantly affect actual air pollution. In total, Table 7 provided four pieces of evidence which

together justify our choices (r1 = 100 km and r2 = 400 km). (1) The upper part of Column 1

shows that each extra one standard deviation of average income shock within 100 km increases

expenditure by 16.5%, keeping average income shocks within other radius constant. In contrast,

none of the average income shocks within other radius significantly affects Log PCE once the

15

average income shock within 100 km are fixed at constant. These results suggest that the spatial

lag from the income shock to Log PCE is about 100 km. (2) Similarly, the upper part of Column

3 suggests that the spatial lag from the income shock to type of heating source is 100 km. (3)

The lower panel of Column 2 suggests that the largest effect on AOD happens when we reduce the

distance from the pollution shock source across 400 km. (4) The upper part of Column 2 suggests

that the spatial lag from income shock to air pollution is 400 km. Weaving these four pieces

together, Table 7 provides an integrated narrative for the impact of income shock on air pollution:

an income shock increases Log PCE within 100 km from the shock, consequently improves the way

people heat their homes within the 100 km radius, causing a negative air pollution shock within

the 100 km radius, eventually reduces air pollution within about a 400 km radius.

Fourth, Table 8 shows that picking r1 = 100 km and r2 = 400 km leads to results that are very

close to the results generated by a model which uses shocks of all candidate ranges as instruments.

Column 1 repeats our baseline results in Table 3. Column 2 runs a 2SLS regression in which log

PCE and AOD are instrumented by 10 shocks. The 10 shocks are income shocks and pollution

shocks with each of the five ranges: within cities, within 100 km, within 200 km, within 400 km,

and within 800 km. Moving from r1 = 100 km and r2 = 400 to the 10-shock model makes only

small changes in point estimates and standard errors, although the Weak ID F-statistic shrink

below its critical value in some cases.

5.3 Alternative Research Design: Instrumenting either One of Income

or Pollution, with Control on the Other

Table 9 compares the results across OLS, “Instrument Log PCE only”, “Instrument AOD only”

and “Instrument both (baseline 2SLS).” The results display a very consistent pattern: the variable

that is not instrumented always has a coefficient that is close to the OLS coefficient, and that

the variable instrumented by the corresponding shock always has a coefficient that is close to

the baseline 2SLS coefficient. This pattern suggests that the conventional method which uses

exogenous measures of pollution, but controls for endogenous income is unlikely to lead to large

biases, although income and pollution are both endogenous may strongly correlated to each other,

16

especially in developing countries. The majority of previous studies on the health cost of air

pollution in developing countries (Chen et al., 2013; Greenstone and Hanna, 2014; Tanaka, 2015;

Arceo et al., 2016) use this approach, due to the scarcity of simultaneous exogenous pollution

shocks and income shocks in one sample. Our test suggests that the lack of exogenous variations

in income or consumption does not put a major threat to their identification.

To test whether the effects of income and pollution differ across gender, age group, education

level, and urban/rural setting, we include interactions between subgroup indicators and Log PCE

and AOD. Because these interaction terms are endogenous, we instrument them by corresponding

interactions between subgroup indicators and exogenous shocks. In total, these models contain

four endogenous variables– Log PCE, AOD, Log PCE×subgroup, and AOD×subgroup– and four

exclusive instrument variables– income shock, pollution shock, income shock×subgroup, and pol-

lution shock×subgroup. We also control for the interactions between the subgroup indicator and

each of the original control variables in our baseline specification as well as the subgroup indicator

itself.

Table 10 presents the first stage results of these subgroup-interaction models. In these four-by-

four coefficient matrices, the coefficients on the diagonals are always relatively large and statistically

significant, a desirable pattern that enables us to distinguish the effects of the four endogenous

variables from each other.

Table 11 presents the 2SLS results of these subgroup-interaction models. A clear pattern is that

the effect of air pollution on overweight is greater among disadvantaged groups: female, people

with less than nine years of education, and rural residents. These people are young during the

Great Chinese Famine and more likely to experienced food shortages than average people during

the famine. This pattern is consistent with the empirical findings that those who experienced more

severe food shortages during early childhood have a higher risk of diabetes and obesity (Painter

et al., 2005; Speakman, 2006; Wang et al., 2010).

5.4 Alternative Measure of Air Pollution

To address the concern that we may have mis-specified the model by choosing the wrong

17

measurement window of air pollution or wrong pollutant (total suspended particulate, which cor-

responds to AOD), we alternatively change the measurement window from the default two months

(July–Augest 2011) to two years (2010-2011), and alternatively use the concentration of sulfur diox-

ide (SO2) from the OMI instrument on NASA’s Aura satellite. For SO2, the instrument variable

is the exogenous SO2 shock, which is constructed using the same formula for AOD, but replacing

the industry-specific TSP pollution intensity by the industry-specific SO2 pollution intensity.

Table 12 compare results across different measurement widows and different pollutants. The

results shows that our results are robust to the length of measurement window for air pollution.

Our results are also robust to the pollutant measured are similar across all specifications, except

that using SO2 roughly doubles the estimated effect of air pollution on depression (CESD> 10).

This robustness to the choice of pollutant, however, does not tell us which pollutant is more

relevant to hypertension and overweight. An ideal identification strategy to find the answer is

a 2SLS model with three endogenous variables –Log PCE, AOD, and SO2– and three exclusive

instruments –the income shock, the AOD shock, and the SO2 shock. Unfortunately our data set

does not provide a strong enough first stage for us to distinguish between these three effects.

5.5 Other Robustness Checks

Table 13 compares the estimates across different level of clustering for standard errors. Overall,

the cluster level of standard errors does not affect our conclusions. The only exception is in the

regression of depression (CESD> 10), where air pollution does not significantly affect the proba-

bility of depression when standard errors are clustered at city or province level, but significantly

reduces the probability when we cluster standard errors at the super province level.

Another concern regarding the robustness of our results is that the estimates may be driven

mostly by cities in a specific geographic region, in particular the Pear River Delta, where the

income shocks are 3-6 standard deviations higher than the mean. We address this concern by

alternatively omitting each one of the seven geographic regions. Table 14 shows that our estimates

are robust to excluding any one of the seven specific regions, even the Pear River Delta.

18

6 Conclusions

China’s rapid economic growth in recent decades has been accompanied by severe problems

with air pollution. This paper investigates the extent to which the health gains from income

growth have been offset by the health cost from air pollution.

We begin by demonstrating how China’s entry into WTO induced regional variation in recent

changes in income and pollution as well as how we construct the income shocks and the pollution

shocks. We then use these two shocks together as instrument variables for income and growth and

air pollution in a 2SLS model.

We show that income and pollution affect different dimensions of health: out of the eight health

outcomes, income significantly improves all outcomes except for the probability of hypertension,

the probability of overweight, and grip strength, whereas air pollution significantly increases the

probability of hypertension and the probability of overweight.

We examine the extent of cross-city spillovers regarding the effects of income shocks and pol-

lution shocks. Our results confirm that the spatial lag is about 400 km for pollution shocks and

about 100 km for income shocks.

We show that the conventional estimates that instrument pollution but controls for income

resemble our estimates, which instrument both income and pollution, although air pollution in

China is closely associated with income growth.

One limitation of our research is the cross-sectional feature of our data. Compared with the

panel fixed effects model, our cross-sectional model needs a major extra assumption: there is no

time-invariant factor that affects either the income shock or the pollution shock, and at the same

time affects either log PCE, AOD, or health. Overcoming this problem requires long and nation-

ally representative panels on non-mortality health outcomes, which is scarce in China for the time

being. We will leave this to future researches.

19

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Shilu Tong, Jun Bi, Lei Huang, and Yang Liu, “Satellite-based spatiotemporal trends in

PM2. 5 concentrations: China, 2004–2013,” Environmental health perspectives, 2016, 124 (2),

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Tanaka, Shinsuke, “Environmental regulations on air pollution in China and their impact on

infant mortality,” Journal of Health Economics, 2015, 42, 90–103.

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Great Chinese Famine leads to shorter and overweight females in Chongqing Chinese population

after 50 years,” Obesity, 2010, 18 (3), 588–592.

22

Figure 1: Illustration of Identification Strategy

Income Shocks

Pollution Shocks

Income

PollutionHealth

Other Factors

23

Figure 2: Pollution in China in October 2015 based on NASA satellite data

(a) Aerosol Optical Depth AOD Distribution

(b) Sulfur Dioxide (SO2) Emission Distribution

24

Figure 3: Illustration of Aggregating Satellite-Based Measurement of Air Pollution to City Level

(a) Beijing

(b) Shanghai (c) Guangzhou

Note: Each square is a satellite measurement unit with a size of about 10 km × 10 km.

25

Figure 4: Distribution of Income Shocks and Pollution Shocks at City Level

Sources: China Statistical Yearbook, UN Comtrade Database.

26

Figure 5: Correlations between Constructed Shocks and Observed Values

Sources: Log PCE from 2011 CHARLS, AOD from MODIS on NASA’s Terra satellite, ChinaStatistical Yearbook, UN Comtrade Database.

27

Tab

le1:

Des

crip

tive

Sta

tist

ics

All

Low

Pol

luti

onH

igh

Pol

luti

onD

iffer

ence

Mea

n(s

e)Shock

Cit

ies

Shock

Cit

ies

(3)

-(2

)(1

)(2

)(3

)(4

)L

ogP

CE

8.76

(0.0

5)8.

76(0

.07)

8.76

(0.0

6)0.

00(0

.09)

Polluti

on

(AO

D,

Sta

ndard

ized)

0.26

(0.1

8)-0

.21(

0.19

)0.

79(0

.17)

0.9

9(0

.24)*

**

%F

emal

e51

.16(

0.52

)51

.03(

0.89

)51

.31(

0.44

)0.

29(0

.97)

Age

57.3

9(0.

18)

57.4

8(0.

30)

57.2

9(0.

20)

-0.1

9(0.

35)

%R

ura

l50

.18(

3.95

)49

.18(

6.34

)51

.31(

3.70

)2.

14(6

.95)

%F

inis

hed

Mid

dle

Sch

ool

40.4

0(2.

46)

41.5

7(4.

08)

39.0

7(2.

29)

-2.5

1(4.

47)

%“P

oor

”or

“Ver

yP

oor

”24

.38(

1.17

)25

.66(

1.83

)22

.94(

1.59

)-2

.72(

2.47

)%

CE

SD>

10/3

032

.59(

1.81

)34

.93(

2.61

)29

.94(

2.39

)-4

.98(

3.45

)%

Hyp

ert

ensi

on

38.9

0(1.

22)

36.9

4(1.

34)

41.2

0(1.

70)

4.2

5(1

.88)*

*B

MI

23.8

2(0.

18)

23.5

0(0.

19)

24.2

0(0.

21)

0.7

0(0

.26)*

*%

Overw

eig

ht

(BM

I>

25)

34.0

9(1.

98)

30.8

9(2.

16)

37.8

5(2.

44)

6.9

6(3

.01)*

*#

ofD

ifficu

ltie

sin

AD

L(0

-6)

0.25

(0.0

2)0.

27(0

.04)

0.22

(0.0

3)-0

.05(

0.05

)#

ofW

ords

Rec

alle

d(0

-20)

7.46

(0.1

3)7.

48(0

.22)

7.43

(0.1

6)-0

.05(

0.27

)P

eak

Res

pir

ator

yF

low

(100

L/m

in)

2.95

(0.0

6)2.

88(0

.10)

3.03

(0.0

7)0.

15(0

.13)

Gri

pStr

engt

h(k

g)31

.05(

0.38

)30

.50(

0.48

)31

.70(

0.58

)1.

21(0

.74)

Obse

rvat

ions

1005

051

5948

9110

050

Num

ber

ofC

itie

s11

357

5611

3

Not

es:

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

epro

vin

cele

vel

inp

are

nth

eses

.A

llst

ati

stic

sare

wei

ghte

dby

ind

ivid

ual

wei

ghts

ad

just

edby

non

resp

onse

for

the

gen

eral

qu

esti

onn

aire

orth

eb

iom

arke

rqu

esti

on

nair

e.“H

igh

poll

uti

on

shock

citi

es”

an

d“lo

wp

oll

uti

on

shock

citi

es”

ind

icate

wh

eth

erth

ere

spon

den

tli

ves

ina

city

that

rece

ived

abov

e-or

bel

ow-m

edia

nsh

ock

sin

term

sof

exp

ort

-dri

ven

tota

lam

bie

nt

part

icu

late

sw

ith

in400

km

rad

ius

from

the

city

du

rin

g20

03-2

011.

AO

Dd

enot

esst

and

ard

ized

aver

age

readin

gs

on

aer

oso

lop

tica

ld

epth

du

rin

gJu

lyan

dA

ugu

st2011

from

MO

DIS

on

NA

SA

’sT

erra

sate

llit

e.L

ogP

CE

den

otes

the

log

ofh

ouse

hol

dp

erca

pit

aan

nu

al

exp

end

iture

inC

hin

ese

yu

an

in2011.

Sam

ple

sare

rest

rict

edto

ages

45–75.

For

hyp

erte

nsi

on

,B

MI,

over

wei

ght,

pea

kre

spir

ator

yfl

owan

dgr

ipst

ren

gth

,th

esa

mp

lesi

zesh

rin

ks

from

10,0

50

to9,5

50.

Sou

rces

:2011

CH

AR

LS

,A

OD

read

ings

from

MO

DIS

onN

AS

A’s

Ter

rasa

tell

ite,

Ch

ina

Sta

tist

ical

Yea

rbook,

UN

Com

trad

eD

ata

base

.

28

Tab

le2:

Bas

elin

eF

irst

Sta

ge:

Eff

ects

ofIn

com

eShock

san

dP

ollu

tion

Shock

son

Log

PC

Ean

dP

ollu

tion

Log

(PC

E)

Pol

luti

on(A

OD

)(1

)(2

)In

com

eShock

(r<

100k

m)

0.21

(0.0

4)**

*-0

.09(

0.10

)P

ollu

tion

(AO

D)

Shock

(r<

400k

m)

-0.0

6(0.

04)

0.57

(0.1

0)**

*F

irst

Sta

geF

16.5

Sto

ck-Y

ogo

10%

crit

ical

valu

e7.

0O

bse

rvat

ions

1005

0

Not

es:

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

ep

rovin

cele

velin

pare

nth

eses

.*p<

0.10,

**p<

0.0

5,

***p<

0.0

1.

Log

PC

Ed

enote

sth

elo

gof

hou

seh

old

per

cap

ita

annu

alex

pen

dit

ure

inC

hin

ese

yu

anin

2011.

AO

Dd

enote

sst

an

dard

ized

aver

age

read

ings

on

aer

oso

lop

tica

ld

epth

du

rin

gJu

lyan

dA

ugust

2011

from

MO

DIS

onN

AS

A’s

Ter

rasa

tell

ite.

Con

trol

vari

ab

les

incl

ud

e(u

rban

/ru

ral)×

(6re

gio

ns)

du

mm

ies,

inte

rvie

wm

onth

du

mm

ies,

aver

age

tem

per

atu

res

inJan

uar

yan

dJu

ly,

annu

alp

reci

pit

atio

n,

sex-b

y-a

ge-

by-e

du

ctio

n-b

y-r

ura

lfi

xed

effec

ts.

Sto

ck-Y

ogo

crit

ical

valu

esare

at

5%

sign

ifica

nce

leve

lfo

r10%

max

imu

mIV

size

.S

amp

les

are

rest

rict

edto

ages

45–75.

Sou

rces

:2011

CH

AR

LS

,A

OD

read

ings

from

MO

DIS

on

NA

SA

’sT

erra

sate

llit

e,C

hin

aS

tati

stic

alY

earb

ook

,U

NC

omtr

ade

Dat

abas

e.

29

Tab

le3:

Bas

elin

e2S

LS:

Eff

ects

ofL

ogP

CE

and

Pol

luti

onon

Hea

lth

Outc

omes

Dep

enden

t→

“P

oor”

or

CE

SD

Hyp

ert

ensi

on

Overw

eig

ht

“V

ery

Poor”

>10/30

(BM

I>25)

(1)

(2)

(3)

(4)

Log

PC

E-0

.242

(0.0

67)*

**-0

.525

(0.0

97)*

**-0

.010

(0.0

51)

0.04

8(0.

093)

AO

D(S

tandar

diz

ed)

-0.0

05(0

.031

)-0

.051

(0.0

45)

0.06

8(0.

018)

***

0.11

2(0.

028)

***

Sam

ple

Mea

n0.

260

0.35

20.

374

0.32

6F

irst

Sta

geF

16.5

16.5

11.6

11.6

Observation

s10

050

1005

082

7382

73

Dep

enden

t→

#of

Diffi

cult

ies

#of

Word

sP

eak

Expir

ato

ryH

and

Gri

pin

AD

L(0

-6)

Reca

lled

(0-2

0)

Flo

w(1

00L

/m

in)

Str

ength

(kg)

(5)

(6)

(7)

(8)

Log

PC

E-0

.440

(0.1

37)*

**2.

60(0

.46)

***

1.34

(0.3

9)**

*3.

06(2

.39)

AO

D(S

tandar

diz

ed)

-0.0

26(0

.065

)-0

.33(

0.32

)-0

.01(

0.13

)-0

.01(

1.25

)Sam

ple

Mea

n0.

280

7.25

2.86

30.6

8F

irst

Sta

geF

16.5

16.5

11.6

11.6

Observation

s10

050

1005

082

7382

73

Not

es:

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

ep

rovin

cele

velin

pare

nth

eses

.*p<

0.10,

**p<

0.0

5,

***p<

0.0

1.

Log

PC

Ed

enote

sth

elo

gof

hou

seh

old

per

cap

ita

annu

alex

pen

dit

ure

inC

hin

ese

yu

anin

2011.

AO

Dd

enote

sst

an

dard

ized

aver

age

read

ings

on

aer

oso

lop

tica

ld

epth

du

rin

gJu

lyan

dA

ugust

2011

from

MO

DIS

onN

AS

A’s

Ter

rasa

tell

ite.

“Poor

or

very

poor”

ind

icate

sse

lf-r

eport

ing

on

e’s

gen

eral

hea

lth

as

“p

oor”

or

“ver

yp

oor”

rath

erth

an

“ve

rygo

od

”,“g

ood

”,or

“fai

r”.

Con

trol

vari

able

sin

clu

de

(urb

an

/ru

ral)×

(6re

gio

ns)

du

mm

ies,

inte

rvie

wm

onth

du

mm

ies,

aver

age

tem

per

atu

res

inJanu

ary

an

dJu

ly,

annu

alp

reci

pit

atio

n,

sex-b

y-a

ge-b

y-e

du

ctio

n-b

y-r

ura

lfi

xed

effec

ts.

Sam

ple

sare

rest

rict

edto

ages

45–75.

Sou

rces

:2011

CH

AR

LS

,A

OD

read

ings

from

MO

DIS

onN

AS

A’s

Ter

rasa

tell

ite,

Ch

ina

Sta

tist

ical

Yea

rbook,

UN

Com

trad

eD

ata

base

.

30

Tab

le4:

Rob

ust

nes

sto

Con

trol

Var

iable

s

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Depen

den

t=

“P

oor”

or

“V

ery

Poor”

(N=

10050)

Log

PC

E-0

.286

***

-0.2

07**

*-0

.219

***

-0.2

38**

*-0

.239

***

-0.2

34**

*-0

.242

***

(0.0

65)

(0.0

54)

(0.0

67)

(0.0

68)

(0.0

73)

(0.0

72)

(0.0

67)

Pol

luti

on(A

OD

,st

andar

diz

ed)

-0.0

34-0

.018

-0.0

20-0

.013

-0.0

09-0

.012

-0.0

05(0

.029

)(0

.029

)(0

.031

)(0

.032

)(0

.034

)(0

.033

)(0

.031

)1stStage

F(C

riticalV.=

7.0)

3.6

11.2

23.7

22.0

14.7

13.5

16.5

Depen

den

t=

Hypert

en

sion

(N=

8273)

Log

PC

E0.

022

0.04

90.

023

-0.0

08-0

.001

-0.0

03-0

.010

(0.0

59)

(0.0

53)

(0.0

71)

(0.0

67)

(0.0

57)

(0.0

52)

(0.0

51)

Pol

luti

on(A

OD

,st

andar

diz

ed)

0.05

2**

0.06

1***

0.05

5***

0.05

8***

0.06

1***

0.06

3***

0.06

8***

(0.0

22)

(0.0

13)

(0.0

13)

(0.0

14)

(0.0

19)

(0.0

17)

(0.0

18)

1stStage

F(C

riticalV.=

7.0)

2.9

8.8

14.9

13.0

10.0

9.8

11.6

Reg

ion

FE

XX

XX

XX

Reg

ion×

Rura

lF

EX

XX

XX

Inte

rvie

wM

onth

FE

XX

XX

Tem

per

ature

san

dR

ainfa

llX

XX

Sex

FE

,A

geF

E,

Educ

FE

XX

Sex×

Age×

Educ×

Rura

lF

EX

Not

es:

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

ep

rovin

cele

vel

inp

are

nth

eses

.*p<

0.10,

**p<

0.05,

***p<

0.0

1.

Th

eS

tock

-Yogo

crit

ical

valu

efo

rth

efi

rst-

stag

eF

-sta

tist

ics

=7.

03fo

r(K

1=2,

L1=

2)

at

10%

maxim

um

IVsi

zean

dat

5%

sign

ifica

nce

leve

l.L

og

PC

Ed

enote

sth

elo

gof

hou

seh

old

per

cap

ita

annu

alex

pen

dit

ure

inC

hin

ese

yu

anin

2011.

AO

Dd

enote

sst

an

dard

ized

aver

age

read

ings

on

aer

oso

lop

tica

ld

epth

du

rin

gJu

lyan

dA

ugust

2011

from

MO

DIS

onN

AS

A’s

Ter

rasa

tell

ite.

“Poor

or

very

poor”

ind

icate

sse

lf-r

eport

ing

on

e’s

gen

eral

hea

lth

as

“p

oor”

or

“ver

yp

oor”

rath

erth

an

“ve

rygo

od

”,“g

ood

”,or

“fai

r”.

Con

trol

Var

iab

les

inco

lum

n(7

)in

clu

de

(urb

an

/ru

ral)×

(6re

gio

ns)

du

mm

ies,

inte

rvie

wm

onth

du

mm

ies,

aver

age

tem

per

atu

res

inJan

uar

yan

dJu

ly,

annu

alp

reci

pit

atio

n,

sex-b

y-a

ge-

by-e

du

ctio

n-b

y-r

ura

lfi

xed

effec

ts.

Sam

ple

sare

rest

rict

edto

ages

45–75.

Sou

rces

:2011

CH

AR

LS

,A

OD

read

ings

from

MO

DIS

onN

AS

A’s

Ter

rasa

tell

ite,

Ch

ina

Sta

tist

ical

Yea

rbook,

UN

Com

trad

eD

ata

base

.

31

Table 5: Robustness to Controlling Initial Income level

IV = Shocks 2003-2011

Extra Control on Levels→ None LogGDP 2003

(1, Baseline) (2)Dependent = “Poor” or “V Poor”

LogPCE -0.242(0.067)*** -0.226(0.087)***AOD -0.005(0.031) -0.007(0.041)

1st Stage F 16.5 9.4CESD > 10/30

LogPCE -0.525(0.097)*** -0.555(0.146)***AOD -0.051(0.045) -0.056(0.064)

1st Stage F 16.5 9.4Hypertension

LogPCE -0.010(0.051) -0.015(0.059)AOD 0.068(0.018)*** 0.068(0.022)***

1st Stage F 11.6 7.2BMI> 25

LogPCE 0.048(0.093) 0.020(0.115)AOD 0.112(0.028)*** 0.107(0.036)***

1st Stage F 11.6 7.2# of Difficulties in ADL (0-6)

LogPCE -0.440(0.137)*** -0.475(0.166)***AOD -0.026(0.065) -0.036(0.086)

1st Stage F 16.5 9.4# of Words Recalled (0-20)

LogPCE 2.60(0.46)*** 3.17(1.00)***AOD -0.33(0.32) -0.17(0.44)

1st Stage F 16.5 9.4Expiratory Flow (100L/min)

LogPCE 1.34(0.39)*** 1.34(0.55)**AOD -0.01(0.13) -0.04(0.18)

1st Stage F 11.6 7.2Grip Strength (kg)

LogPCE 3.06(2.39) 3.57(3.19)AOD -0.01(1.25) 1.36(1.28)

1st Stage F 11.6 7.2

Notes: Standard errors are clustered at the province level in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.Log GDP denotes the natural log of output per worker at city level. Log PCE denotes the log of householdper capita annual expenditure in Chinese yuan in 2011. AOD denotes standardized average readings on aerosoloptical depth during July and August 2011 from MODIS on NASA’s Terra satellite. Control variables include(urban/rural)×(6 regions) dummies, interview month dummies, and sex-by-age-by-rural fixed effects. Samplesare restricted to ages 45–75. Sources: 2011 CHARLS, AOD readings from MODIS on NASA’s Terra satellite,China Statistical Yearbook, UN Comtrade Database.

32

Table 6: Robustness to Assumed Ranges of Income Shocks and Pollution Shocks

Pollution Dependent = General Health Being “Poor” or “Very Poor”Shock Income Shock RadiusRadius Own City 100km 200km 400km 800km

Own City

LogPCE -0.402 -0.349 -0.207* -0.194* -0.178**(0.486) (0.402) (0.107) (0.104) (0.072)

AOD0.259 0.198 0.038 0.024 0.005

(0.553) (0.401) (0.071) (0.073) (0.076)1st Stage F 0.2 0.1 0.4 1.0 0.8

100km

LogPCE -0.202*** -0.214*** -0.207*** -0.243** 1.327(0.075) (0.065) (0.070) (0.105) (8.179)

AOD-0.063** -0.059** -0.061** -0.049 -0.598(0.031) (0.027) (0.031) (0.041) (2.796)

1st Stage F 2.2 10.3 7.0 3.1 0.0

200km

LogPCE -0.226*** -0.232*** -0.207*** -0.227* -0.002(0.077) (0.065) (0.070) (0.121) (0.196)

AOD-0.025 -0.024 -0.029 -0.025 -0.065(0.030) (0.025) (0.022) (0.026) (0.044)

1st Stage F 2.6 17.5 15.8 4.3 0.5

400km

LogPCE -0.238*** -0.242*** -0.207*** -0.214 -0.151(0.075) (0.067) (0.079) (0.146) (0.106)

AOD-0.005 -0.005 -0.005 -0.005 -0.005(0.031) (0.031) (0.030) (0.030) (0.027)

1st Stage F 2.9 16.5 16.0 4.3 5.9

800km

LogPCE -0.254*** -0.255*** -0.207** -0.201 -0.198(0.083) (0.078) (0.091) (0.148) (0.140)

AOD0.019 0.020 0.014 0.014 0.013

(0.037) (0.032) (0.027) (0.021) (0.023)1st Stage F 3.1 13.1 12.4 4.2 4.7

Notes: Standard errors are clustered at the province level in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.Baseline Radius Specifications are marked in bold. Income shocks are averaged within each radius for eachcity. Pollution shocks are added up within each radius (including non-sample cities) for each sample city.Log PCE denotes the log of household per capita annual expenditure in Chinese yuan in 2011. AOD denotesstandardized average readings on aerosol optical depth during July and August 2011 from MODIS on NASA’sTerra satellite. “Poor or very poor” indicates self-reporting one’s general health as “poor” or “very poor” ratherthan “very good”, “good”, or “fair”. Control variables include (urban/rural)×(6 regions) dummies, interviewmonth dummies, average temperatures in January and July, annual precipitation, sex-by-age-by-eduction-by-rural fixed effects. Samples are restricted to ages 45–75. Sources: 2011 CHARLS, AOD readings from MODISon NASA’s Terra satellite, China Statistical Yearbook, UN Comtrade Database.

33

Tab

le7:

At

Whic

hD

ista

nce

sar

eIn

com

eShock

san

dP

ollu

tion

Shock

sE

ffec

tive

?

Log

(PC

E)

Pol

luti

on(A

OD

)%

Dir

tyH

eati

ng

(1)

(2)

(3)

Inco

me

Shock

(Ow

nC

ity)

-0.0

09(0

.016

)0.

032(

0.07

1)1.

79(2

.36)

Inco

me

Shock

(r<

100k

m)

0.16

5(0.

062)

***

0.01

1(0.

185)

-26.

28(1

0.18

)***

Inco

me

Shock

(r<

200k

m)

0.02

9(0.

043)

-0.0

66(0

.131

)3.

55(7

.75)

Inco

me

Shock

(r<

400k

m)

0.00

3(0.

056)

-0.3

65(0

.110

)***

0.48

(4.5

5)In

com

eShock

(r<

800k

m)

0.01

5(0.

048)

-0.1

45(0

.157

)-2

.11(

6.22

)A

OD

Shock

(Ow

nC

ity)

0.07

5(0.

016)

***

0.01

3(0.

046)

2.67

(2.1

1)A

OD

Shock

(r<

100k

m)

-0.0

04(0

.028

)0.

097(

0.06

4)6.

29(3

.83)

AO

DShock

(r<

200k

m)

-0.0

31(0

.048

)0.

257(

0.13

0)**

2.04

(5.9

3)A

OD

Shock

(r<

400k

m)

-0.1

60(0

.062

)***

0.55

8(0.

143)

***

3.48

(5.3

7)A

OD

Shock

(r<

800k

m)

0.15

8(0.

084)

*0.

111(

0.20

3)-8

.40(

9.96

)Observation

s10

050

1005

010

050

Not

es:

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

ep

rovin

cele

vel

inp

are

nth

eses

.*p<

0.1

0,

**p<

0.05,

***p<

0.0

1.

Inco

me

shock

sare

aver

aged

wit

hin

each

rad

ius

for

each

city

.P

ollu

tion

shock

sar

ead

ded

up

wit

hin

each

rad

ius

(in

clu

din

gn

on

-sam

ple

citi

es)

for

each

sam

ple

city

.L

og

PC

Ed

enote

sth

elo

gof

hou

seh

old

per

cap

ita

annual

exp

end

itu

rein

Ch

ines

eyu

an

in2011.

AO

Dd

enote

sst

an

dard

ized

aver

age

read

ings

on

aer

oso

lop

tica

ld

epth

du

rin

gJu

lyan

dA

ugu

st20

11fr

omM

OD

ISon

NA

SA

’sT

erra

sate

llit

e.D

irty

hea

tin

gin

Colu

mn

3re

fers

hea

tin

gu

sin

gh

ayor

coal.

Contr

ol

vari

ab

les

incl

ud

e(u

rban

/ru

ral)×

(6re

gion

s)d

um

mie

s,in

terv

iew

month

du

mm

ies,

aver

age

tem

per

atu

res

inJanu

ary

an

dJu

ly,

an

nu

al

pre

cip

itati

on

,se

x-b

y-a

ge-

by-e

du

ctio

n-

by-r

ura

lfi

xed

effec

ts.

Sam

ple

sar

ere

stri

cted

toages

45–75.

Sou

rces

:2011

CH

AR

LS

,A

OD

read

ings

from

MO

DIS

on

NA

SA

’sT

erra

sate

llit

e,C

hin

aS

tati

stic

alY

earb

ook

,U

NC

omtr

ade

Dat

abas

e.

34

Table 8: Robustness to Weakening Assumptions on the Effective Ranges of Shocks: Using Shocksat All Distances as Instrument Variables

Assumed Effective Ranges forIncome and Pollution Shocks

(100km, 400km) All Ranges(1, Baseline) (2)

Dependent = “Poor” or “V Poor”LogPCE -0.242(0.067)*** -0.254(0.065)***

Pollution (Standardized) -0.005(0.031) -0.017(0.020)1st Stage F 16.5 8.4

Dependent = CESD > 10/30LogPCE -0.525(0.097)*** -0.528(0.104)***

Pollution (Standardized) -0.051(0.045) -0.040(0.038)1st Stage F 16.5 8.4

HypertensionLogPCE -0.010(0.051) -0.009(0.049)

Pollution (Standardized) 0.068(0.018)*** 0.062(0.015)***1st Stage F 11.6 5.8

BMI> 25LogPCE 0.048(0.093) 0.034(0.079)

Pollution (Standardized) 0.112(0.028)*** 0.080(0.020)***1st Stage F 11.6 5.8

# of Difficulties in ADL (0-6)LogPCE -0.440(0.137)*** -0.455(0.129)***

Pollution (Standardized) -0.026(0.065) -0.048(0.040)1st Stage F 16.5 8.4

# of Words Recalled (0-20)LogPCE 2.60(0.46)*** 2.64(0.43)***

Pollution (Standardized) -0.33(0.32) -0.27(0.25)1st Stage F 16.5 8.4

Expiratory Flow (100L/min)LogPCE 1.34(0.39)*** 1.32(0.38)***

Pollution (Standardized) -0.01(0.13) -0.01(0.09)1st Stage F 11.6 5.8

Grip Strength (kg)LogPCE 3.06(2.39) 2.77(2.36)

Pollution (Standardized) -0.01(1.25) -0.32(1.18)1st Stage F 11.6 5.8

Notes: Standard errors are clustered at the province level in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.In Column (1), instrument variables include income shocks within 100 km and pollution (AOD) shocks within400 km. In Column (2), instrument variables include income shocks within own city, 100 km, 200 km, 400km, and 800 km, and pollution (AOD) within own city, 100 km, 200 km, 400 km, and 800 km. Income shocksare averaged within each radius for each city. Pollution shocks are added up within each radius (includingnon-sample cities) for each sample city. Control variables include (urban/rural)×(6 regions) dummies, interviewmonth dummies, average temperatures in January and July, annual precipitation, sex-by-age-by-eduction-by-rural fixed effects. Samples are restricted to ages 45–75. Sources: 2011 CHARLS, AOD readings from MODISon NASA’s Terra satellite, China Statistical Yearbook, UN Comtrade Database.

35

Tab

le9:

Com

par

ing

Res

ult

susi

ng

Mult

iple

-Quas

i-E

xp

erim

ent

wit

hth

atU

sing

Sin

gle-

Quas

i-E

xp

erim

ent:

Eff

ects

ofL

ogP

CE

and

Pol

luti

onon

Hea

lth

Outc

omes

Inst

rum

ent

Non

eIn

stru

men

tIn

stru

men

tIn

stru

men

tB

oth

(OL

S)

LogP

CE

On

lyA

OD

On

ly(2

SL

SB

ase

lin

e)(1

)(2

)(3

)(4

)D

epen

den

t=

“P

oor”

or

“V

Poor”

Log

PC

E-0

.000(0

.006)

-0.2

25(0

.063)*

**

0.0

00(0

.006)

-0.2

42(0

.067)*

**

AO

D-0

.031(0

.011)*

**

-0.0

37(0

.012)*

**

-0.0

06(0

.024)

-0.0

05(0

.031)

Depen

den

t=

CE

SD

>10/30

Log

PC

E-0

.020(0

.008)*

*-0

.521(0

.097)*

**

-0.0

20(0

.008)*

*-0

.525(0

.097)*

**

AO

D-0

.045(0

.019)*

*-0

.059(0

.020)*

**

-0.0

52(0

.039)

-0.0

51(0

.045)

Hypert

en

sion

Log

PC

E-0

.000(0

.007)

0.0

05(0

.044)

0.0

00(0

.006)

-0.0

10(0

.051)

AO

D0.0

42(0

.008)*

**

0.0

42(0

.008)*

**

0.0

68(0

.019)*

**

0.0

68(0

.018)*

**

Overw

eig

ht

(BM

I>25)

Log

PC

E0.0

41(0

.008)*

**

0.0

83(0

.068)

0.0

43(0

.008)*

**

0.0

48(0

.093)

AO

D0.0

51(0

.011)*

**

0.0

53(0

.012)*

**

0.1

12(0

.028)*

**

0.1

12(0

.028)*

**

Diffi

cu

ltie

sin

AD

L(0

-6)

Log

PC

E0.0

02(0

.009)

-0.4

32(0

.144)*

**

0.0

02(0

.010)

-0.4

40(0

.137)*

**

AO

D-0

.027(0

.020)

-0.0

39(0

.022)*

-0.0

27(0

.056)

-0.0

26(0

.065)

Nu

mbe

rof

Word

sR

ecall

ed(0

-20)

Log

PC

E0.2

7(0

.06)*

**

2.3

9(0

.46)*

**

0.2

6(0

.06)*

**

2.6

0(0

.46)*

**

AO

D-0

.00(0

.12)

0.0

6(0

.11)

-0.3

3(0

.24)

-0.3

3(0

.32)

Pea

kE

xpri

ato

ryF

low

(100L

/m

in)

Log

PC

E0.0

6(0

.02)*

*1.2

9(0

.43)*

**

0.0

6(0

.03)*

*1.3

4(0

.39)*

**

AO

D0.0

1(0

.07)

0.0

7(0

.06)

-0.0

2(0

.12)

-0.0

1(0

.13)

Gri

pS

tren

gth

(kg)

Log

PC

E0.6

7(0

.20)*

**

2.9

5(2

.60)

0.6

6(0

.20)*

**

3.0

6(2

.39)

AO

D0.0

7(0

.59)

0.1

8(0

.68)

-0.0

2(1

.15)

-0.0

1(1

.25)

Not

es:

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

ep

rovin

cele

vel

inp

are

nth

eses

.*p<

0.10,

**p<

0.0

5,

***p<

0.01.

Colu

mn

(1)

run

sO

LS

of

hea

lth

ou

tcom

eson

Log

PC

Ean

dA

OD

.C

olu

mn

(2)

inst

rum

ents

Log

PC

Eby

inco

me

shock

s,w

ith

ad

dit

ion

al

contr

ol

on

act

ual

pollu

tion

(AO

D).

Colu

mn

(3)

inst

rum

ents

AO

Dby

pol

luti

onsh

ock

s,w

ith

add

itio

nal

contr

ol

on

act

ual

Log

PC

E.

Colu

mn

(4)

inst

rum

ents

two

end

ogen

ou

sva

riab

les

(Log

PC

Ean

dA

OD

)usi

ng

two

inst

rum

ent

vari

able

s(i

nco

me

shock

san

dp

oll

uti

on

shock

s).

Oth

erco

ntr

ol

Vari

ab

les

incl

ud

e(u

rban

/ru

ral)×

(6re

gio

ns)

du

mm

ies,

inte

rvie

wm

onth

du

mm

ies,

aver

age

tem

per

atu

res

inJan

uar

yan

dJu

ly,

an

nu

al

pre

cip

itati

on

,se

x-b

y-a

ge-

by-e

du

ctio

n-b

y-r

ura

lfi

xed

effec

ts.

Log

PC

Ed

enote

sth

elo

gof

hou

seh

old

per

cap

ita

annu

alex

pen

dit

ure

inC

hin

ese

yu

an

in2011.

AO

Dd

enote

sst

an

dard

ized

aver

age

read

ings

on

aer

oso

lop

tica

ld

epth

du

rin

gJu

lyan

dA

ugu

st20

11fr

omM

OD

ISon

NA

SA

’sT

erra

sate

llit

e.“P

oor

or

very

poor”

ind

icate

sse

lf-r

eport

ing

on

e’s

gen

eral

hea

lth

as

“p

oor”

or

“ver

yp

oor”

rath

erth

an“v

ery

good

”,“g

ood

”,or

“fai

r”.

Sam

ple

sare

rest

rict

edto

ages

45–75.

Sou

rces

:2011

CH

AR

LS

,A

OD

read

ings

from

MO

DIS

on

NA

SA

’sT

erra

sate

llit

e,C

hin

aS

tati

stic

alY

earb

ook

,U

NC

omtr

ad

eD

ata

base

.

36

Tab

le10

:H

eter

ogen

eous

Eff

ects

by

Subgr

oups:

Fir

stSta

ges

Y=

“Poor

”or

“VP

oor

”L

ogP

CE

Poll

uti

on

LogP

CE×

AO

D×

(AO

D)

Su

bgro

up

Sub

gro

up

(1)

(2)

(3)

(4)

Su

bgro

up

=F

em

ale

Inco

me

Sh

ock

(r=

100k

m)

0.21(0

.04)*

**

-0.1

0(0

.10)

-0.0

0(0

.00)

-0.0

0(0

.01)

Pol

luti

on(A

OD

)S

hock

(r=

400km

)-0

.08(0

.04)*

*0.57(0

.10)*

**

0.0

1(0

.00)*

-0.0

1(0

.01)*

Inco

me

Sh

ock×

Fem

ale

0.0

1(0

.01)

0.0

2(0

.01)*

*0.22(0

.04)*

**

-0.0

8(0

.11)

Pol

luti

onS

hock×

Fem

ale

0.0

4(0

.02)

0.0

1(0

.01)

-0.0

6(0

.04)

0.60(0

.10)*

**

Wea

kID

K-P

rkW

ard

F-S

tat

(K1=

4,

L1=

4)

8.4

[Un

der

IDK

-Prk

LM

p-v

alu

e][0.002]

Su

bgro

up

=A

ge≥

60

Inco

me

Sh

ock

(r=

100k

m)

0.22(0

.04)*

**

-0.0

7(0

.11)

-0.0

0(0

.00)

0.0

0(0

.00)

Pol

luti

on(A

OD

)S

hock

(r=

400km

)-0

.04(0

.04)

0.55(0

.10)*

**

0.0

1(0

.00)*

-0.0

1(0

.01)

Inco

me

Sh

ock×

Age≥

60

-0.0

4(0

.03)

-0.0

5(0

.02)*

*0.19(0

.04)*

**

-0.1

2(0

.10)

Pol

luti

onS

hock×

Age≥

60

-0.0

5(0

.03)

0.0

6(0

.02)*

**

-0.1

0(0

.04)*

*0.64(0

.10)*

**

Wea

kID

K-P

rkW

ard

F-S

tat

(K1=

4,

L1=

4)

4.2

[Un

der

IDK

-Prk

LM

p-v

alu

e][0.001]

Su

bgro

up

=“

Did

Not

Fin

ish

Mid

del

School”

Inco

me

Sh

ock

(r=

100k

m)

0.20(0

.04)*

**

-0.0

4(0

.12)

0.0

0(0

.01)

-0.0

0(0

.01)

Pol

luti

on(A

OD

)S

hock

(r=

400km

)-0

.03(0

.04)

0.53(0

.10)*

**

0.0

1(0

.01)

-0.0

2(0

.01)

Inco

me

Sh

ock×

Did

n’t

0.0

0(0

.03)

-0.0

7(0

.07)

0.21(0

.04)*

**

-0.1

1(0

.11)

Pol

luti

onS

hock×

Did

n’t

-0.0

4(0

.04)

0.0

6(0

.04)

-0.0

9(0

.04)*

*0.62(0

11)*

**

Wea

kID

K-P

rkW

ard

F-S

tat

(K1=

4,

L1=

4)

7.2

[Un

der

IDK

-Prk

LM

p-v

alu

e][0.004]

Su

bgro

up

=R

ura

lIn

com

eS

hock

(r=

100k

m)

0.23(0

.04)*

**

0.0

9(0

.12)

0.0

1(0

.00)

-0.0

1(0

.01)

Pol

luti

on(A

OD

)S

hock

(r=

400km

)-0

.10(0

.05)*

0.64(0

.12)*

**

0.0

0(0

.00)

-0.0

1(0

.01)

Inco

me

Sh

ock×

Ru

ral

-0.0

2(0

.05)

-0.3

3(0

.18)*

0.21(0

.05)*

**

-0.2

4(0

.14)*

Pol

luti

onS

hock×

Ru

ral

0.0

6(0

.08)

-0.1

1(0

.14)

-0.0

4(0

.05)

0.56(0

.11)*

**

Wea

kID

K-P

rkW

ard

F-S

tat

(K1=

4,

L1=

4)

7.3

[Un

der

IDK

-Prk

LM

p-v

alu

e][0.024]

Not

es:

Eac

hp

anel

isa

set

offirs

t-st

age

regr

essi

on

sin

wh

ich

four

end

ogen

ou

sva

riab

les

are

inst

rum

ente

dby

fou

rex

ogen

ou

ssh

ock

s.S

tan

dard

erro

rsar

ecl

ust

ered

atth

ep

rovin

cele

vel

inp

aren

thes

es.

*p<

0.10,

**p<

0.0

5,

***p<

0.01.

Sto

ck-Y

ogo

crit

ical

valu

esfo

rth

eK

leib

ergen

-Paap

rkW

ard

F-S

tati

stic

sar

en

otav

aila

ble

inth

eca

seof

(K1=

4,

L1=

4).

Log

PC

Ed

enote

sth

elo

gof

hou

seh

old

per

cap

ita

an

nu

al

exp

end

itu

rein

Ch

ines

eyu

an

in2011.

AO

Dd

enot

esst

and

ard

ized

aver

age

read

ings

onaer

oso

lop

tica

ld

epth

du

rin

gJu

lyan

dA

ugu

st2011

from

MO

DIS

on

NA

SA

’sT

erra

sate

llit

e.C

ontr

ol

vari

able

sin

clu

de

(urb

an/r

ura

l)×

(6re

gion

s)d

um

mie

s,in

terv

iew

month

du

mm

ies,

aver

age

tem

per

atu

res

inJanu

ary

and

Ju

ly,

an

nu

al

pre

cipit

ati

on

,se

x-

by-a

ge-b

y-e

du

ctio

n-b

y-r

ura

lfi

xed

effec

ts,

inte

ract

ion

sb

etw

een

the

sub

gro

up

du

mou

ran

dall

of

the

vari

ab

les

ab

ove,

an

dth

esu

bgro

up

du

mm

y.S

am

ple

sar

ere

stri

cted

toag

es45

–75.

Sou

rces

:20

11C

HA

RL

S,

AO

Dre

ad

ings

from

MO

DIS

on

NA

SA

’sT

erra

sate

llit

e,C

hin

aS

tati

stic

al

Yea

rbook,

UN

Com

trad

eD

atab

ase.

37

Table 11: Heterogeneous Effects by Subgroups: 2SLS

“Poor” or CESD Hyper Difficulties # of Words Lung Grip“V. Poor” >10/30 tention BMI>25 in ADL Recalled Flow (kg)

(1) (2) (3) (4) (5) (6) (7) (8)By Gender

LogPCE-0.230*** -0.514*** 0.002 0.044 -0.429*** 2.08*** 1.37*** 3.66(0.068) (0.086) (0.054) (0.081) (0.133) (0.38) (0.42) (2.94)

AOD-0.026 -0.067 0.092*** 0.085*** -0.088 -0.23 -0.00 0.55(0.034) (0.043) (0.024) (0.024) (0.059) (0.22) (0.13) (1.50)

LogPCE×Female-0.022 -0.018 -0.032 0.005 -0.026 0.99* -0.05 -1.16(0.055) (0.078) (0.077) (0.075) (0.093) (0.54) (0.22) (2.01)

AOD×Female 0.041** 0.029 -0.047 0.051** 0.119** -0.22 -0.02 -1.00(0.019) (0.033) (0.030) (0.023) (0.055) (0.32) (0.06) (0.74)

[K-P rk LM p-value] [0.0018] [0.0018] [0.0012] [0.0012] [0.0018] [0.0018] [0.0012] [0.0012]K-P rk Ward F-Stat 8.4 8.4 5.9 5.9 8.4 8.4 5.9 5.9

By Age Group

LogPCE-0.215*** -0.460*** -0.057 0.024 -0.313*** 2.70*** 1.30*** 2.60(0.065) (0.093) (0.058) (0.094) (0.092) (0.46) (0.37) (2.21)

AOD-0.002 -0.035 0.080*** 0.116*** -0.033 -0.52 -0.05 0.26(0.031) (0.043) (0.024) (0.027) (0.058) (0.35) (0.14) (1.25)

LogPCE×(Age≥ 60)-0.106 -0.253 0.161 0.076 -0.484* -0.10 0.16 1.99(0.105) (0.189) (0.154) (0.095) (0.261) (0.51) (0.23) (1.88)

AOD×(Age≥ 60) -0.019 -0.064* -0.018 0.002 -0.025 0.55** 0.12 -0.38(0.028) (0.033) (0.038) (0.029) (0.067) (0.22) (0.10) (0.47)

[K-P rk LM p-value] [0.0012] [0.0012] [0.0005] [0.0005] [0.0012] [0.0012] [0.0005] [0.0005]K-P rk Ward F-Stat 4.2 4.2 2.8 2.8 4.2 4.2 2.8 2.8

By Education Level

LogPCE-0.189** -0.346*** 0.066 0.006 -0.230* 1.61*** 1.58*** 1.99(0.082) (0.103) (0.076) (0.108) (0.135) (0.61) (0.43) (2.95)

AOD0.001 -0.000 0.090*** 0.064 0.020 -0.40 -0.03 1.57

(0.028) (0.033) (0.032) (0.043) (0.062) (0.24) (0.19) (1.13)

LogPCE×(Edu< 9)-0.077 -0.279** -0.107 0.066 -0.310* 1.47** -0.33 1.10(0.089) (0.114) (0.098) (0.107) (0.163) (0.57) (0.28) (2.27)

AOD×(Edu< 9) -0.012 -0.084** -0.034 0.074** -0.077 0.15 0.03 -2.39***(0.030) (0.042) (0.030) (0.035) (0.051) (0.26) (0.14) (0.70)

[K-P rk LM p-value] [0.0038] [0.0038] [0.0018] [0.0018] [0.0038] [0.0038] [0.0018] [0.0018]K-P rk Ward F-Stat 7.2 7.2 4.9 4.9 7.2 7.2 4.9 4.9

By Rural/Urban

LogPCE-0.240*** -0.485*** -0.056 -0.115 -0.302*** 1.89*** 1.49*** 1.77(0.080) (0.127) (0.067) (0.104) (0.110) (0.44) (0.36) (1.47)

AOD-0.004 -0.026 0.052*** 0.049* -0.039 -0.01 0.10 -0.11(0.019) (0.034) (0.017) (0.026) (0.042) (0.19) (0.10) (0.83)

LogPCE×Rural0.030 -0.042 0.131 0.360*** -0.113 0.55 -0.55 1.08

(0.128) (0.174) (0.105) (0.103) (0.243) (0.92) (0.67) (2.44)

AOD×Rural 0.001 -0.039 0.027 0.096** 0.043 -0.65 -0.18 0.01(0.041) (0.063) (0.045) (0.046) (0.078) (0.41) (0.23) (1.31)

[K-P rk LM p-value] [0.0240] [0.0240] [0.0164] [0.0164] [0.0240] [0.0240] [0.0164] [0.0164]K-P rk Ward F-Stat 7.3 7.3 5.7 5.7 7.3 7.3 5.7 5.7

See notes in Table 10.

38

Tab

le12

:R

obust

nes

sto

Mea

sure

men

tof

Pol

luti

on

Mea

sure

men

tof

Poll

uto

inA

OD

(2M

onth

s)A

OD

(2Y

ears

)SO

2(2

Month

s)S

O2

(2Y

ears

)(1

,B

ase

lin

e)(2

)(3

)(4

)D

epen

den

t=

“P

oor”

or

“V

Poor”

Log

PC

E-0

.242(0

.067)*

**

-0.2

43(0

.071)*

**

-0.2

32(0

.057)*

**

-0.2

26(0

.055)*

**

Pol

luti

on(S

tan

dar

diz

ed)

-0.0

05(0

.031)

-0.0

05(0

.029)

-0.0

27(0

.046)

-0.0

23(0

.041)

1st

Sta

geF

16.5

16.8

18.4

15.3

Depen

den

t=

CE

SD

>10/30

Log

PC

E-0

.525(0

.097)*

**

-0.5

37(0

.105)*

**

-0.4

97(0

.074)*

**

-0.4

72(0

.068)*

**

Pol

luti

on(S

tan

dar

diz

ed)

-0.0

51(0

.045)

-0.0

47(0

.042)

-0.1

16(0

.057)*

*-0

.097(0

.049)*

*1s

tS

tage

F16.5

16.8

18.4

15.3

Hypert

en

sion

Log

PC

E-0

.010(0

.051)

0.0

12(0

.048)

-0.0

14(0

.078)

-0.0

29(0

.060)

Pol

luti

on(S

tan

dar

diz

ed)

0.0

68(0

.018)*

**

0.0

64(0

.019)*

**

0.0

81(0

.049)*

0.0

70(0

.028)*

*1s

tS

tage

F11.6

11.7

12.8

10.2

BM

I>25

Log

PC

E0.0

48(0

.093)

0.0

85(0

.075)

0.0

30(0

.128)

0.0

02(0

.094)

Pol

luti

on(S

tan

dar

diz

ed)

0.1

12(0

.028)*

**

0.1

06(0

.027)*

**

0.1

54(0

.083)*

0.1

33(0

.046)*

**

1st

Sta

geF

11.6

11.7

12.8

10.2

#of

Diffi

cu

ltie

sin

AD

L(0

-6)

Log

PC

E-0

.440(0

.137)*

**

-0.4

46(0

.145)*

**

-0.4

10(0

.119)*

**

-0.3

90(0

.113)*

**

Pol

luti

on(S

tan

dar

diz

ed)

-0.0

26(0

.065)

-0.0

24(0

.061)

-0.0

92(0

.109)

-0.0

77(0

.092)

1st

Sta

geF

16.5

16.8

18.4

15.3

#of

Word

sR

ecall

ed(0

-20)

Log

PC

E2.6

0(0

.46)*

**

2.5

1(0

.44)*

**

2.5

3(0

.50)*

**

2.5

8(0

.45)*

**

Pol

luti

on(S

tan

dar

diz

ed)

-0.3

3(0

.32)

-0.3

0(0

.27)

-0.2

3(0

.46)

-0.2

0(0

.37)

1st

Sta

geF

16.5

16.8

18.4

15.3

Expir

ato

ryF

low

(100L

/m

in)

Log

PC

E1.3

4(0

.39)*

**

1.3

4(0

.41)*

**

1.2

6(0

.35)*

**

1.2

3(0

.34)*

**

Pol

luti

on(S

tan

dar

diz

ed)

-0.0

1(0

.13)

-0.0

1(0

.12)

0.1

4(0

.21)

0.1

2(0

.17)

1st

Sta

geF

11.6

11.7

12.8

10.2

Gri

pS

tren

gth

(kg)

Log

PC

E3.0

6(2

.39)

3.0

6(2

.62)

2.5

7(2

.23)

2.4

1(2

.04)

Pol

luti

on(S

tan

dar

diz

ed)

-0.0

1(1

.25)

-0.0

1(1

.18)

0.8

9(1

.75)

0.7

6(1

.58)

1st

Sta

geF

11.6

11.7

12.8

10.2

Not

es:

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

ep

rovin

cele

vel

inp

are

nth

eses

.*p<

0.1

0,

**p<

0.05,

***p<

0.01.

Th

e2-m

onth

mea

sure

men

tw

ind

owin

clu

des

Ju

lyan

dA

ugu

stin

2011

.T

he

2-ye

arm

easu

rem

ent

win

dow

incl

ud

esall

month

sin

2010

an

d2011.

Contr

ol

vari

ab

les

incl

ud

e(u

rban

/ru

ral)×

(6re

gion

s)d

um

mie

s,in

terv

iew

mon

thd

um

mie

s,av

erage

tem

per

atu

res

inJanuary

and

July

,an

nu

al

pre

cip

itati

on

,se

x-b

y-a

ge-

by-e

du

ctio

n-b

y-r

ura

lfi

xed

effec

ts.

Sam

ple

sar

ere

stri

cted

toag

es45

–75.

Sou

rces

:2011

CH

AR

LS

,A

OD

read

ings

from

MO

DIS

on

NA

SA

’sT

erra

sate

llit

e,S

O2

read

ings

from

OM

We

onN

AS

A’s

Au

rasa

tell

ite,

Ch

ina

Sta

tist

ical

Yea

rbook,

UN

Com

trad

eD

ata

base

.

39

Table 13: Robustness to the Cluster Level of Standard Errors

Cluster Level of Standard ErrorsCity (Prefecture) Province Super Province

(1) (2, Baseline) (3)

Dependent = “Poor” or “V Poor”LogPCE -0.242(0.058)*** -0.242(0.067)*** -0.242(0.078)***

AOD (Standardized) -0.005(0.028) -0.005(0.031) -0.005(0.026)1st Stage F 22.0 16.5 7.7

Dependent = CESD > 10/30LogPCE -0.525(0.098)*** -0.525(0.097)*** -0.525(0.125)***

AOD (Standardized) -0.051(0.039) -0.051(0.045) -0.051(0.026)**1st Stage F 22.0 16.5 7.7

HypertensionLogPCE -0.010(0.057) -0.010(0.051) -0.010(0.022)

AOD (Standardized) 0.068(0.018)*** 0.068(0.018)*** 0.068(0.019)***1st Stage F 17.4 11.6 4.8

BMI> 25LogPCE 0.048(0.081) 0.048(0.093) 0.048(0.061)

AOD (Standardized) 0.112(0.023)*** 0.112(0.028)*** 0.112(0.027)***1st Stage F 17.4 11.6 4.8

# of Difficulties in ADL (0-6)LogPCE -0.440(0.127)*** -0.440(0.137)*** -0.440(0.182)**

AOD (Standardized) -0.026(0.053) -0.026(0.065) -0.026(0.046)1st Stage F 22.0 16.5 7.7

# of Words Recalled (0-20)LogPCE 2.60(0.54)*** 2.60(0.46)*** 2.60(0.34)***

AOD (Standardized) -0.33(0.32) -0.33(0.32) -0.33(0.29)1st Stage F 22.0 16.5 7.7

Expiratory Flow (100L/min)LogPCE 1.34(0.37)*** 1.34(0.39)*** 1.34(0.46)***

AOD (Standardized) -0.01(0.13) -0.01(0.13) -0.01(0.11)1st Stage F 17.4 11.6 4.8

Grip Strength (kg)LogPCE 3.06(2.15) 3.06(2.39) 3.06(2.60)

AOD (Standardized) -0.01(1.48) -0.01(1.25) -0.01(0.74)1st Stage F 17.4 11.6 4.8

Notes: * p < 0.10, ** p < 0.05, *** p < 0.01. Log PCE denotes the log of household per capita annual expen-diture in Chinese yuan in 2011. AOD denotes standardized average readings on aerosol optical depth duringJuly and August 2011 from MODIS on NASA’s Terra satellite. “Poor or very poor” indicates self-reportingone’s general health as “poor” or “very poor” rather than “very good”, “good”, or “fair”. Control variablesinclude (urban/rural)×(6 regions) dummies, interview month dummies, average temperatures in January andJuly, annual precipitation, sex-by-age-by-eduction-by-rural fixed effects. Samples are restricted to ages 45–75.Sources: 2011 CHARLS, AOD readings from MODIS on NASA’s Terra satellite, China Statistical Yearbook,UN Comtrade Database.

40

Tab

le14

:R

obust

nes

sto

Excl

udin

gSp

ecifi

cR

egio

ns

Excl

uded

Reg

ions

Non

eP

earl

Riv

erSic

huan

Yun-G

ui

Oth

erN

orth

&N

orth

Del

taB

asin

Pla

teau

Sou

thN

orth

wes

tea

st(1

)(2

)(3

)(4

)(5

)(6

)(7

)Y

=“

Poor”

or

“V

ery

Poor”

Log

PC

E-0

.242

***

-0.2

51**

*-0

.249

***

-0.2

44**

*-0

.208

***

-0.2

36*

-0.2

32**

*(0

.067

)(0

.069

)(0

.071

)(0

.068

)(0

.077

)(0

.135

)(0

.068

)

AO

D-0

.005

-0.0

050.

001

-0.0

07-0

.029

0.10

9-0

.008

(0.0

31)

(0.0

32)

(0.0

33)

(0.0

31)

(0.0

47)

(0.1

26)

(0.0

31)

1st

Sta

geF

16.5

15.5

16.3

16.6

14.6

4.1

15.0

Observation

s10

,050

9,78

39,00

99,60

66,68

05,93

09,24

2

Y=

Hypert

en

sion

Log

PC

E-0

.010

-0.0

12-0

.016

-0.0

090.

023

-0.0

24-0

.002

(0.0

51)

(0.0

54)

(0.0

54)

(0.0

54)

(0.0

81)

(0.0

61)

(0.0

50)

AO

D0.

068*

**0.

067*

**0.

072*

**0.

068*

**0.

085*

*0.

090

0.07

0***

(0.0

18)

(0.0

18)

(0.0

20)

(0.0

18)

(0.0

33)

(0.0

84)

(0.0

19)

1st

Sta

geF

11.6

10.8

11.2

11.1

14.7

2.0

10.9

Observation

s8,27

38,05

27,38

97,91

85,54

94,85

77,60

0

Not

es:

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

ep

rovin

cele

velin

pare

nth

eses

.*p<

0.10,

**p<

0.0

5,

***p<

0.0

1.

Log

PC

Ed

enote

sth

elo

gof

hou

seh

old

per

cap

ita

annu

alex

pen

dit

ure

inC

hin

ese

yu

anin

2011.

AO

Dd

enote

sst

an

dard

ized

aver

age

read

ings

on

aer

oso

lop

tica

ld

epth

du

rin

gJu

lyan

dA

ugust

2011

from

MO

DIS

onN

AS

A’s

Ter

rasa

tell

ite.

“Poor

or

very

poor”

ind

icate

sse

lf-r

eport

ing

on

e’s

gen

eral

hea

lth

as

“p

oor”

or

“ver

yp

oor”

rath

erth

an

“ve

rygo

od

”,“g

ood

”,or

“fai

r”.

Con

trol

vari

able

sin

clu

de

(urb

an

/ru

ral)×

(6re

gio

ns)

du

mm

ies,

inte

rvie

wm

onth

du

mm

ies,

aver

age

tem

per

atu

res

inJanu

ary

an

dJu

ly,

annu

alp

reci

pit

atio

n,

sex-b

y-a

ge-b

y-e

du

ctio

n-b

y-r

ura

lfi

xed

effec

ts.

Sam

ple

sare

rest

rict

edto

ages

45–75.

Sou

rces

:2011

CH

AR

LS

,A

OD

read

ings

from

MO

DIS

onN

AS

A’s

Ter

rasa

tell

ite,

Ch

ina

Sta

tist

ical

Yea

rbook,

UN

Com

trad

eD

ata

base

.

41

Figure A1: Correlation between Income Shocks and Initial Income

Source: Log(Employment/Worker) from the China Statistical Yearbook.

Figure A2: Correlation between Pollution Shocks and Initial Air Pollution

Source: AOD from MODIS on NASA’s Terra satellite.

42

Tab

leA

1:C

hec

kin

gfo

rE

xcl

usi

onR

estr

icti

on:

Cor

rela

tion

sb

etw

een

Inst

rum

ent

Var

iable

san

dO

ther

Fac

tors

Log

GD

PA

OD

Rai

nfa

llA

vera

geB

iom

arke

rin

2003

in20

05(c

m)

Tem

p.

(◦C

)A

ttri

tion

(1)

(2)

(3)

(4)

(5)

Inco

me

Shock

(r<

100k

m)

1.62

***

0.11

-7.9

80.

270.

19(0

.31)

(0.6

3)(2

0.26

)(1

.38)

(0.1

6)

Pol

luti

on(A

OD

)Shock

(r<

400k

m)

0.22

0.43

1.45

0.30

-0.0

1(0

.18)

(0.3

2)(4

.52)

(0.4

1)(0

.04)

[Joi

nt

p-v

alue]

[0.0

00]

[0.2

74]

[0.8

25]

[0.7

66]

[0.3

64]

Not

es:

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

ep

rovin

cele

vel

inp

are

nth

eses

.*p<

0.10,

**p<

0.0

5,

***p<

0.0

1.

Th

eP

oll

uti

on

shock

for

Colu

mn

2u

setr

ade

grow

thfr

om20

05-2

011

inst

ead

of20

03-2

011.

LogG

DP

den

ote

sth

elo

gof

ou

tpu

tp

erw

ork

erby

city

.L

og

PC

Ed

enote

sth

elo

gof

hou

seh

old

per

cap

ita

annu

alex

pen

dit

ure

inC

hin

ese

yu

anin

2011.

AO

Dd

enote

sst

an

dard

ized

aver

age

read

ings

on

aer

oso

lop

tica

ld

epth

du

rin

gJu

lyan

dA

ugust

2011

from

MO

DIS

onN

AS

A’s

Ter

rasa

tell

ite.

Con

trol

vari

ab

les

incl

ud

e(u

rban

/ru

ral)×

(6re

gio

ns)

du

mm

ies,

rain

fall

(not

inC

olu

mn

3),

aver

age

tem

per

atu

res

(not

inco

lum

n4)

inJan

uar

yan

dJu

ly,

inte

rvie

wm

onth

du

mm

ies,

an

dse

x-b

y-a

ge-

by-r

ura

lfixed

effec

ts.

Sam

ple

sare

rest

rict

edto

ages

45–75.

Sou

rces

:20

11C

HA

RL

S,

AO

Dre

adin

gsfr

omM

OD

ISon

NA

SA

’sT

erra

sate

llit

e,C

hin

aS

tati

stic

al

Yea

rbook,

UN

Com

trad

eD

ata

base

.

43

Tab

leA

2:R

obust

nes

sto

Con

trol

ling

Init

ial

Lev

els

ofIn

com

ean

dA

irP

ollu

tion

IV=

Sh

ock

s2003-2

011

IV=

Sh

ock

s2005-2

011

Extr

aC

ontr

olon

Lev

els→

Non

eL

ogG

DP

2003

Non

eL

ogG

DP

2005

AO

D2005

(1,

Base

lin

e)(2

)(3

)(4

)D

epen

den

t=

“P

oor”

or

“V

Poor”

Log

PC

E-0

.242(0

.067)*

**

-0.2

26(0

.087)*

**

-0.2

57(0

.061)*

**

-0.2

42(0

.068)*

**

AO

D-0

.005(0

.031)

-0.0

07(0

.041)

-0.0

17(0

.044)

-0.0

06(0

.073)

1st

Sta

geF

16.5

9.4

5.4

10.3

CE

SD

>10/30 Log

PC

E-0

.525(0

.097)*

**

-0.5

55(0

.146)*

**

-0.5

98(0

.134)*

**

-0.6

49(0

.138)*

**

AO

D-0

.051(0

.045)

-0.0

56(0

.064)

0.0

09(0

.065)

0.0

63(0

.118)

1st

Sta

geF

16.5

9.4

5.4

10.3

Hypert

en

sion

Log

PC

E-0

.010(0

.051)

-0.0

15(0

.059)

0.0

16(0

.051)

0.0

34(0

.071)

AO

D0.0

68(0

.018)*

**

0.0

68(0

.022)*

**

0.0

30(0

.041)

0.0

20(0

.062)

1st

Sta

geF

11.6

7.2

3.1

7.4

BM

I>25

Log

PC

E0.0

48(0

.093)

0.0

20(0

.115)

0.0

94(0

.073)

0.0

75(0

.078)

AO

D0.1

12(0

.028)*

**

0.1

07(0

.036)*

**

0.0

35(0

.035)

0.0

31(0

.042)

1st

Sta

geF

11.6

7.2

3.1

7.4

#of

Diffi

cu

ltie

sin

AD

L(0

-6)

Log

PC

E-0

.440(0

.137)*

**

-0.4

75(0

.166)*

**

-0.5

59(0

.217)*

*-0

.647(0

.224)*

**

AO

D-0

.026(0

.065)

-0.0

36(0

.086)

0.0

97(0

.105)

0.1

98(0

.161)

1st

Sta

geF

16.5

9.4

5.4

10.3

#of

Word

sR

ecall

ed(0

-20)

Log

PC

E2.6

0(0

.46)*

**

3.1

7(1

.00)*

**

2.5

7(0

.48)*

**

3.1

1(0

.79)*

**

AO

D-0

.33(0

.32)

-0.1

7(0

.44)

-0.1

9(0

.38)

-0.4

5(0

.59)

1st

Sta

geF

16.5

9.4

5.4

10.3

Expir

ato

ryF

low

(100L

/m

in)

Log

PC

E1.3

4(0

.39)*

**

1.3

4(0

.55)*

*1.8

4(0

.75)*

*2.0

7(0

.64)*

**

AO

D-0

.01(0

.13)

-0.0

4(0

.18)

-0.5

1(0

.36)

-0.8

4(0

.55)

1st

Sta

geF

11.6

7.2

3.1

7.4

Gri

pS

tren

gth

(kg)

Log

PC

E3.0

6(2

.39)

3.5

7(3

.19)

6.8

2(5

.32)

6.5

3(3

.82)*

AO

D-0

.01(1

.25)

1.3

6(1

.28)

-4.4

0(2

.86)

-6.6

2(3

.31)*

*1s

tS

tage

F11.6

7.2

3.1

7.4

Not

es:

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

ep

rovin

cele

vel

inp

are

nth

eses

.*p<

0.10,

**p<

0.05,

***p<

0.01.

Log

GD

Pd

enote

sth

en

atu

ral

log

ofou

tpu

tp

erw

orke

rat

city

leve

l.L

ogP

CE

den

ote

sth

elo

gof

hou

seh

old

per

cap

ita

an

nu

al

exp

end

itu

rein

Ch

ines

eyu

an

in2011.

AO

Dd

enote

sst

and

ard

ized

aver

age

read

ings

onae

roso

lop

tica

ld

epth

du

rin

gJu

lyan

dA

ugu

st2011

from

MO

DIS

on

NA

SA

’sT

erra

sate

llit

e.C

ontr

ol

vari

ab

les

incl

ud

e(u

rban

/ru

ral)×

(6re

gion

s)d

um

mie

s,in

terv

iew

month

du

mm

ies,

an

dse

x-b

y-a

ge-

by-r

ura

lfi

xed

effec

ts.

Sam

ple

sare

rest

rict

edto

ages

45–75.

Sou

rces

:2011

CH

AR

LS

,A

OD

read

ings

from

MO

DIS

onN

AS

A’s

Ter

rasa

tell

ite,

Ch

ina

Sta

tist

ical

Yea

rbook,

UN

Com

trad

eD

ata

base

.

44