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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
∗[email protected]; Financial support from the China Scholarship Council is gratefully acknowledged.†Co-responding author: [email protected]
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.
3
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.
6
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.
7
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
10
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
11
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.
12
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.
13
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
14
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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Ebenstein, Avraham, Maoyong Fan, Michael Greenstone, Guojun He, Peng Yin, and
Maigeng Zhou, “Growth, pollution, and life expectancy: China from 1991–2012,” The Amer-
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dynamics,” 2009.
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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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Schaller, Jessamyn, “For richer, if not for poorer? Marriage and divorce over the business cycle,”
Journal of Population Economics, 2013, 26 (3), 1007–1033.
Shenhav, Na’ama, “Essays on Gender Gaps and Investments in Children.” PhD dissertation,
University of California, Davis 2016.
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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