Tsukuba Economics Working Papers
No. 2024-001

Does Exposure to PM2.5 Increase the Likelihood of Early Retirement in Middle-Aged
Individuals? Evidence from Chinese Databy
Meiyi Zhuang
Xinyi Zhang
Hisahiro Naito

May 2024

UNIVERSITY OF TSUKUBA
Faculty of Humanities and Social Sciences
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPAN

Does Exposure to PM2.5 Increase the Likelihood of Early
Retirement in Middle-Aged Individuals? Evidence from Chinese
Data
Meiyi Zhuang ∗†Program in Economic and Public Policy
Graduate School of Humanities and Social Sciences
University of Tsukuba
Xinyi Zhang ∗‡Graduate School of Humanities and Social Sciences
University of Tsukuba
Hisahiro Naito ∗§Graduate School of Humanities and Social Sciences
University of Tsukuba
May 2024∗

The authors appreciate the comments from Taiyo Fukai, Kennnichi Kashiwagi, Yoshinori Kurokawa, Mohammad Abdul Malek, Midori Matsuhima,Yuko Nakano and Zheng Fei Yu. The authors are responsible for all
remaining errors. H.N. appreciate financial support from the university research fund and from Japan Society ofPromotion of Sciences KAKENHI Grant 23H00827.
†
Email: zmeiyi95@gmail.com‡
Email: mint.weixin12@gmail.com§
Corresponding author. Email: naito@dpipe.tsukuba.ac.jp. Address: Tennodai 1-1-1, Tsukuba City, Ibaraki
Prefecture, Japan, Postal Code 305-0032

Abstract
Health is one of the most critical factors that affects retirement behavior, and poor health maylead to early retirement among middle-aged and older adults. In China, where the population
is aging rapidly, early retirement has significant implications for the economy. Recent studies
have shown that air pollution, particularly PM2.5, can cause various illnesses, such as respiratory
diseases, cardiovascular diseases, high blood pressure, and diabetes. In this paper, we analyze
the effects of PM2.5 on the retirement and health of middle-aged and elderly people, assumingthat the effects of air pollution on retirement are highly nonlinear and different for farmers andnon-farmers. To control for potential endogeneity, we use 2SLS estimation.
The regression results for non-farmers show that higher PM2.5 concentrations increase theprobability of heart-related diseases and early retirement behavior. Specifically, we found that a
10 microgram/m3 (about one standard deviation) per cubic meter increase in PM2.5 concentration is associated with a 58% increase in the probability of heart-related diseases and a
57% increase in early retirement. This implies that roughly 12.1 million people could continue participating in the labor market if the government can reduce PM2.5 concentration by
10 microgram/m3 per cubic meter across the country. For farmers, we found that higher PM2.5
concentration is associated with a higher probability of lung-related diseases, but we did not findevidence that it increases early retirement. For both non-farmers and farmers, we did not findevidence that a higher PM2.5 concentration decreases financial wealth.
These findings suggest that higher air pollution deteriorates the health of non-farmers, increases the disutility of work, and induces early retirement but does not affect the financial
wealth of farmers and non-farmers.

1

Introduction

Many developed countries and some middle-income countries are facing problems related toaging, such as increasing social security expenditures. However, the negative effects of aging canpotentially be mitigated if older individuals continue to work. Therefore, retirement decisions
among the elderly have significant policy implications for the economies of those countries.
The decision to retire is complex and influenced by various factors, including income, family,
work opportunities, and leisure activities. Several empirical studies have highlighted the importance of health as a factor in retirement decisions, although there is no consensus on the size of its
effect(Bound, Schoenbaum, Stinebrickner and Waidmann, 1999; Dwyer and Mitchell, 1999; McGarry, 2004; Disney, Emmerson and Wakefield, 2006; Au, Crossley and Schellhorn, 2005; Jones,
Rice and Roberts, 2010; Bound, Stinebrickner and Waidmann, 2010; Blundell, Britton, Dias andFrench, 2023). If an individual’s health is poor, they might not be able to continue working as
they approach middle age. Furthermore, due to the decline of specific bodily functions, older
and middle-aged individuals may be more vulnerable to external factors than younger individuals(Cakmak et al., 2007; Xue et al., 2018).
In the literature on health economics and environmental sciences, air pollution has received
significant attention as an important factor affecting people’s health and mortality. Numerous
studies have shown that high levels of PM2.5 concentration, a common air pollutant, can cause
various illnesses, including respiratory diseases, cardiovascular diseases, high blood pressure, anddiabetes (Sun, Yue, Ying, Cardounel, Brook, Devlin, Hwang, Zweier, Chen and Rajagopalan,
2008; Andersen, Raaschou-Nielsen, Ketzel, Jensen, Hvidberg, Loft, Tjønneland, Overvad and
Sørensen, 2012; Liang, Zhang, Zhao, Ruan, Lian and Fan, 2014; Franklin, Brook and Pope III,2015; Lin, Guo, Zheng, Di, Liu, Xiao, Li, Zeng, Cummings-Vaughn, Howard et al., 2017). This isbecause the size of PM2.5 is very small and they do not stop at bronchus when they are inhaled.
They are absorbed in bloodstream and cause inflammation in various organs of human body.
According to statistics from the World Health Organization (WHO) on PM2.5 concentration
between 2000 and 2013, the annual average value of PM2.5 concentration in most urban areas
in China exceeds 40 µg/m3 . This is far beyond the WHO guideline value of 10.0 µg/m3 (World
Health Organization and European Centre for Environment, 2021). In 2015, seven of the world’s
1

top ten most polluted cities were located in China (Zhou et al., 2015). The severe situation of
air pollution in China unavoidably affects older people since they spend more time outside in
their lifetime. Furthermore, middle-aged and older adults are more susceptible to outdoor air
pollution compared to younger people (Cakmak et al., 2007; Xue et al., 2018).
On the other hand, China, which has the largest population in the world, is facing the challenge of an aging population (Fang et al., 2015). Over the past two decades (2001-2021), China’s
population has grown at an average annual rate of 0.53%. During this time, the proportionof adults aged 60 and older has increased by 5.44%, from 13.26% in 2010 to 18.70% in 2020(National Bureau of Statistics of China, 2011, 2021).
Due to the high level of PM2.5 and aging population in China and the potential impact ofhealth on retirement decisions, a relevant policy question is to what extent air pollution affects
people’s decision to retire. This research question holds significance not only for China but also
for other developing countries, particularly in Asia and Central Africa, where air pollution is asignificant problem.
From a theoretical standpoint, the effect of air pollution on retirement is not straightforward.
First, people’s health might be negatively affected by poor air quality, which can decrease productivity even when individuals are young (He, Liu and Salvo, 2019). If this is the case, it can
reduce the lifetime wealth of such individuals. When people’s lifetime wealth becomes lower,
they might not have sufficient financial resources to retire early. In such a scenario, air pollution
might not increase early retirement (income effect). Second, air pollution might not affect thehuman capital of young individuals but could increase the disutility of work at an older age.
Evidence shows that air pollution increases the probability of having chronic diseases such as
hypertension and diabetes (Liang et al., 2014; Du et al., 2016). Thus, older people may experience significant physical and psychological difficulties in working if their health is negatively
affected (disutility effect). Therefore, the income effect and disutility effect work in opposite
directions. This suggests that it is important to examine empirically whether and to what extent
air pollution affects the retirement and health behavior of older people.
In this paper, we examine the effects of PM2.5 on the retirement and health of middle-aged
and elderly individuals. To achieve this, we combine satellite MERRA-2 data of PM2.5 and2

micro-data from CHARLS, utilizing city information from CHARLS and residential informationof respondents prior to the survey years.
In the literature, there are several studies that examine the effect of air pollution on labor
supply (Ostro, 1983; Hausman et al., 1984; Pönkä, 1990; Hanna and Oliva, 2015; Aragon et al.,2017; Huang et al., 2021; Li and Li, 2022; Wu et al., 2023). However, our study is different fromthe previous studies from several aspects. First, our study focus on the effect of air pollution on
retirement while previous studies focus on the labor supply. The retirement decision is a decision
to exit from labor market while the labor supply decision is a contemporaneous decision. Second,the effect of air pollution on retirement decision is highly nonlinear function of age while the labor
supply decision is not likely. In our specification, we allow the marginal effect of air pollution isa quadratic function of age. Third, the elderly people are more vulnerable to air pollution than
young people and the disutility of labor can be higher for old people than for young people. Thus,
it is not possible to extrapolate the estimated obtained by using the sample of young people tothe sample of old people. To the best of our knowledge, our study is the first paper to examine
the effect of air pollution on the retirement decision.
One concern of our estimates is that our estimates are biased due to hidden industry characteristics even when we use 2SLS estimation. We argue that our estimates are likely to biased
downward because our estimation results in Table 4-Table 9 show that as we add more citycharacteristics, the estimated coefficient becomes bigger. If our estimates are mainly driven byhidden city characteristics, the estimated coefficients should be smaller as we add more city characteristics as control variables. This indicates that the bias due to hidden city characteristics islikely to be very small.
The paper is organized as follows: Section 2 summarizes the related literature. Section 3
covers the model and the dataset. Sections 4.1 and 4.2 present the summary statistics andestimation results using the ordinary least squares (OLS) estimation method. In Section 4.3,
we present the regression results using the two-stage least squares (2SLS) estimation method.
Additionally, Section 4.4 discusses the potential mechanism, and Section 4.5 addresses the potential estimation bias due to migration. Section 5 provides a discussion of the results and draws
conclusions.

3

2
2.1

Literature Review
Health and Retirement

Various studies have examined the importance of health on retirement decision. The results ofnumerous earlier investigations have varied in this area. Some study emphasize the importanceof health on retirement decision while other studies argue that health is not an important factor.
According to a study by McGarry (2004), bad health has a considerably more significant
impact on the likelihood of continuing to work than financial factors, even when there are no
incentives. Dwyer and Mitchell (1999) found that health issues have a larger impact on retirement
planning than economic factors. Through a survey on older adults’ contentment with retirement,
Bender (2012) discovered that economic factors, health, and voluntary retirement are essential
drivers of older adults’ retirement satisfaction. Among these, economic factors are of utmost
importance.
Disney et al. (2006) examined how lousy health affects people’s decisions to retire in the
UK using the first eight waves of the British Household Panel Survey. Adverse personal healthshocks powerfully predict individual retirement behavior. Clarke et al. (2012) show that selfreported chronic health issues are linked to a reduced likelihood of continuing to work full-timeafter the age of 62. Men with high career aspirations but not working full-time beyond age 62
were less content with their lives despite controlling for various factors, including health. On theother hand, no such link was identified among women. De Wind et al. (2013) point out persons
in good health seek to maximize their quality of life. As a result, they may choose to retire
early. This also explains why several earlier quantitative research that looked at the association
between health and early retirement failed to find a significant one. Using cross-sectional datafrom Finland, Ilmakunnas and Ilmakunnas (2018) show that the actual retirement age is lessresponsive to health.
2.2

Air pollution and Health

Numerous studies in the past have shown that air pollution affects people’s health. Ying et al.
(2014) showed that long-term exposure to PM2.5 raises blood pressure by stimulating the sympathetic nervous system, which may result in hypothalamus inflammation, through research on
4

rats. Bell et al. (2014) show that there is a statistically significant link between hospitalization
for respiratory and cardiovascular conditions and PM2.5 levels. In addition, several other prevalent chronic diseases like diabetes and hypertension are linked to air pollution. Krämer et al.
(2010) find that air pollution from traffic is linked to a higher prevalence of type 2 diabetes inolder women.
Several studies examine the effect of air pollution for elderly people. Kan et al. (2008) showthat air pollution is a crucial aspect affecting older individuals’ health. Cakmak et al. (2007)
suggest that the very elderly are particularly susceptible to dying from air pollution. Xue et al.
(2018) show that respiratory illness in older individuals in Shenyang was substantially correlatedwith PM10 and NO2 levels.
2.3

Air Pollution and Labor Supply

As we just have discussed in the introduction, there are several studies that examine the effect of
air pollution on labor supply (Ostro, 1983; Hausman et al., 1984; Pönkä, 1990; Hanna and Oliva,
2015; Aragon et al., 2017; Huang et al., 2021; Gu et al., 2020; Li and Li, 2022; Wu et al., 2023).
For example, Hausman et al. (1984) find that, controlling for city-specific effects, one standard
deviation increase in particulates in the two weeks prior is associated with about a 10% increasein lost work days.
Hanna and Oliva (2015) examine the relationship between air pollution and labor supply.
They utilized a natural experiment that arose from the closure of a major refinery in Mexico
City to explore the causal relationship between pollution and labor supply. The refinery’s closure
resulted in a 19.7% reduction in pollution, as measured by SO2, in the surrounding neighborhoods. They found that the closure led to an increase in work hours per week of 1.3 hours,
representing a 3.5% increase.
Huang, Zhang, Chen and Ning (2021) analyze the effect of PM2.5 on the current labor
supply using the China Health and Retirement Longitudinal Survey (CHARLS). They pooled
four waves of data and examined the labor supply with the sample selection equation. 1 Note that1

To estimate the sample selection equation, it is necessary to have an exclusion variable that affects labormarket participation but not labor supply. In this study, the researchers used a dummy variable indicating
whether the respondent’s age was over 60, assuming that this variable only affected labor market participation.
However, since aging can impact both labor market participation and labor supply, this assumption may not hold.

5

retirement decision is qualitatively different from labor supply in several dimensions. Retirement
is the exist from the labor market while the labor supply is contemporaneous decision. Li andLi (2022) examine the effect of perceived air pollution on labor supply in China. Gu, Bian andElahi (2020) study the effect of air pollution on female labor supply in China.
3

Model and Data Set

3.1

Empirical Model

For modeling the retirement behavior, We believe that the marginal effect of PM2.5 is different
for the different age and it is a nonlinear function of age. Thus, we consider the following model:
Ypci = β0 + β1 (P M 2.5pc − P M 2.5) + β2 (Agepci − 45) × (P M 2.5pc − P M 2.5)
+ β3 (Agepci − 45)2 × (P M 2.5pc − P M 2.5) + β4 (Agepci − 45) + β5 (Agepci − 45)2
(1)

+ αX1pci + γX2pc + δX3p + εpci

where p is an index of provinces, c is an index of cities and i is an index of individuals. The variable
Ypci represents the outcome, which could be a dummy variable indicating retirement or chronic
diseases related to PM2.5 exposure of an individual i in city c. The independent variable P M 2.5pc
denotes the average PM2.5 concentration in the city where individual i resided between 2004 and2013. The national average of PM2.5 in China during the same period is denoted as P M 2.5. The
age of individual i is denoted as Agepci , while X1pci represents individual characteristics such as
gender, marital status, hukou, and year of schooling. In China, hukou is a legal document used torecord and retain basic information about the household population, divided into agricultural andnon-agricultural hukou. X2pc represents city characteristics, including GDP per capita, the shareof secondary and tertiary industries in GDP, average wage, total population, population density,and urban area share. X3p represents province characteristics, including GDP per capita, theshare of secondary and tertiary industries in GDP, total population, urban population density,and average education years.
Additionally, if air pollution is correlated with economic activity and labor demand, it could create an endogeneity
problem.

6

It is noteworthy that the conditional expectation of the derivative of Yic with respect toP M 2.5c − P M 2.5 is expressed as:∂Ypci= β1 + β2 (Agepci − 45) + β3 (Agepci − 45)2
E∂(P M 2.5pc − P M 2.5)(2)Thus, β1 represents the marginal effect of PM2.5 at age 45. The coefficients β2 and β3 determine
to what extent such a marginal effect of PM2.5 becomes a nonlinear function of age.
3.2

Data Set

We use China Health and Retirement Longitudinal Survey (CHARLS). We downloaded the dataand questionnaire from CHARLS’ official website on request. The World Bank, the National
Institute on Aging’s Behavioral and Social Research Division, the National Natural Science
Foundation of China, and Peking University all support CHARLS. A longitudinal study of adults
in China over 45 is called CHARLS. It seeks to comprehend the socioeconomic causes and effects
of aging. The study asks a wide range of questions about older adults’ social networks, economic
status, and physical and mental health.
From May 2011 to March 2012, the CHARLS Baseline Survey (2011 wave) was conductedacross the country in 28 provinces and municipalities. In 150 counties and districts, the survey
was conducted in 450 PSUs (village or community). Of the 450 PSUs, 52.67 % were rural and
47.33 % urban. The sample population was chosen as part of a stratified, multi-stage probabilistic
design (Zhao et al., 2013). All county-level units, except Tibet, were sampled using proportional
probability sampling (PPS), which was then stratified by region, county characteristics (urban
vs. rural), and gross domestic product (GDP) per capita. One of the residents who were older
than 40 and lived in the sampling household was chosen at random. The selected individual
became the respondent if they were 45 or older. 17,708 respondents in 10,257 households in 450
villages and urban communities, 150 districts, and 28 provinces made up this initial sample. In
addition, they include the sample whose age is 43 or older to increase the sample size. From July
2013 to January 2014, the second survey wave (2013 wave) was carried out. Up till now, datafrom four survey waves have been made available. 92 % of rural and 83 % of urban households
participating in the first wave completed at least one module in 2013 wave. 87 % and 86 % of7the households finished at least one module in waves 3 and 4, respectively. Compared to otherhousehold surveys, the success rate is high (Zhao et al., 2020).
To conduct a cross-sectional analysis, we use data from 2011 wave and 2013 wave. We use therespondents who answered both two waves. We choose the respondents whose age are between 43
and 78 in the 2011 wave. Additionally, those who have never worked before 2011 were excluded
from the data set.
Individuals often choose a retirement age based on their occupation. Unlike non-farmers,
farmers typically do not have a defined retirement age. To distinguish between farmers andnon-farmers, we classify individuals as farmers if they worked in agriculture in the previous
year or if their last job before retirement was in agriculture. Conversely, if an individual only
held non-farm jobs in the recent year or their last employment before retirement was outside of
agriculture, they are considered non-farmers.
Retirement refers to a person’s complete withdrawal from the labor market. If a person’sannual working time is less than 200 hours, we classify them as partially retired from the labormarket. If an individual is in partial retirement from the labor market in two consecutive waves,
we define them as retired.
For individual characteristics such as education, age, and marital status, and chronic diseases,
we use information from the 2013 wave.
Regarding PM2.5-related diseases, we define hypertension, diabetes, chronic lung diseases,
heart attack, and asthma as the chronic diseases related to PM2.5 (Andersen et al., 2011; Choi
& Cho, 2013; F. Huang et al., 2017; Yeatts et al., 2007). Since these are chronic diseases, we
use the second round survey. For example, if a respondent states that they have a heart-relateddisease in the 2nd round survey, we assume that this person developed a heart-related disease
and assign a value of one to the heart-related disease dummy variable.
Regarding PM2.5 concentration information, following the literature on the effect of PM2.5 on
health and other outcomes, MERRA-2 in the Goddard Earth Observing System Model (GEOS)
version 5.12.4 was used to generate PM2.5 data (Deschenes et al., 2020; Chen et al., 2022). The
use of Merra-2 is attractive from its longest span of the years covered. MERRA-2 is available
at a 50 × 60-km grid level for each month since 1980. MERRA-2 is the most recent iteration of8the Global Atmospheric Reanalysis for the Satellite Era published by NASA’s Global Modelingand Assimilation Office (GMAO). The collection includes assimilated aerosol diagnostics suchas complete extinction (and scattering) aerosol optical thickness (AOT) at 550 nm, the surface
mass concentration of aerosol components, and column mass density of aerosol components (black
carbon, dust, sea salt, sulfate, and organic carbon).
MERRA-2 provides the concentrations of five externally mixed aerosol species: dust(DUST),
sea salt (SS), black carbon (BC), organic carbon (OC), and sulfate (SO4). Following Buchard
et al. (2017), we used equation (3) to calculate the surface PM2.5 concentration at each city from2004 to 2013:
PM2.5c = DUSTc + SSc + BCc + 1.4 × OCc + 0.96 × SO4c
4
4.1

(3)

Results
Summary Statistics

VARIABLES
Individual Level Variables
Age
Hukou
GenderMarried Status
Years of Schooling
Educational Level
Annual Work Hour in 2011
Annual Work Hour in 2013
Retirement Dummy

Table 1. Summary Statistics
allfarmers
Mean
Sd
Mean
Sd
(1)
(2)
(3)
(4)

Hypertension
Diabetes
Lung Diseases
Heart Diseases
AsthmaThe Number of Five Chronic Diseases
The Net Wealth of HouseholdN

60.16 8.675
0.220 0.414
0.512 0.500
0.886 0.318
5.661 4.365
2.876 1.541
1,152 1,232
1,034 1,197
0.279 0.449
0.221 0.415
0.0487 0.215
0.0748 0.263
0.0963 0.295
0.0255 0.158
0.447 0.727
993.8 121,924
10,90460.68 8.606
0.0663 0.249
0.532 0.499
0.879 0.326
4.614 3.993
2.495 1.323
1,150 1,165
1,008 1,113
0.225 0.418
0.204 0.403
0.0372 0.189
0.0802 0.272
0.0867 0.281
0.0265 0.161
0.407 0.684-4,492 73,610
7,742

non-farmers
Mean
Sd
(5)
(6)
58.90 8.714
0.596 0.491
0.463 0.499
0.901 0.298
8.225 4.174
3.811 1.636
1,157 1,385
1,098 1,381
0.413 0.492
0.263 0.440
0.0769 0.266
0.0617 0.241
0.120 0.325
0.0231 0.150
0.543 0.812
14,425 194,295
3,162

Notes: The sample is individuals aged from 43 to 78 in 2011 wave and those who answered both 2011
and 2013 waves. Individual characteristics are taken from 2013 wave.

9

Table 2. Summary Statistics (2)All
Farmers
Mean
Sd
Mean
Sd
(1)
(2)
(3)
(4)

VARIABLES
City Level Variables
The City's Mean PM2.5 (2004-2013)
The Secondary Industry Share of GDP
The Tertiary Industory Share of GDP
Mean WageArea Size
GDP Per Capita
Population
Population Density
Province Level Variables
GDP Per Capita
Population
Urban Population Density
The Secondary Industry Share of GDP
The Tertiary Industory Share of GDP
Average Years of Schooling
N

38.02
0.495
0.369
44,407
21,66345,556
618.7
498.0

11.76
0.07920.0861
9,247
37,36038,665422.7
304.7

41,732 13,837
6,421 2,659
2,889 1,228
0.505 0.0428
0.389 0.0546
8.975 0.537
10,904

38.51
0.498
0.356
43,29022,27339,591
616.9
491.0

11.86
0.07590.0745
7,625
39,04831,085436.1
286.1

40,491 12,362
6,556 2,594
2,880 1,211
0.508 0.0369
0.383 0.0445
8.904 0.474
7,742

Fon-farmers
Mean
Sd
(5)
(6)
36.83
0.489
0.401
47,141
20,16960,159
622.9
515.3

11.41
0.08630.103
11,91732,82349,897388.1
345.6

44,770 16,529
6,091 2,786
2,910 1,267
0.498 0.0538
0.402 0.0720
9.149 0.635
3,162

In China, individuals are classified into either agricultural or non-agricultural hukou, depending on whether they live in rural or urban areas. While most individuals with agricultural hukou
live in rural areas, and those with non-agricultural hukou reside in cities, there are exceptions
to this rule. In our study, we found that that 78% of individuals lived in rural areas and madea living from agriculture. The male-to-female ratio in our sample was almost 1:1, and mostindividuals were married, reflecting the fact that our study focused on middle-aged and elderly
individuals. For years of education, we categorized education levels into nine categories based onquestionnaire responses. Then, we transform them to years of schooling. Notably, the educationlevel was generally low in our sample, consistent with the fact that most individuals were farmers.
In 2011, 4,428 individuals worked fewer than 200 hours per year, while 4,841 individuals
worked fewer than 200 hours per year in 2013. This finding may reflect the fact that moreindividuals retire as they age. One possible reason for the large number of individuals working
more than 2,000 hours per year is that many individuals in the study sample were farmers orheld both agricultural and non-agricultural jobs.

10Table 3. Summary Statistics (3)the Key Outcome Variables of Those Aged 45-50
farmers
non-farmers
Mean
Sd
Mean
Sd
VARIABLES
(1)
(2)
(3)
(4)
Retirement Dummy
0.0886 0.284
0.124
0.330
0.110
0.313
0.140
0.347
Hypertension
0.0251 0.156
0.0324 0.177
Diabetes
0.0418 0.200
0.0280 0.165
Lung Diseases
0.04590.209
0.0560 0.230
Heart Diseases
0.0175 0.131
0.00590 0.0766
AsthmaThe Net Wealth of Household-21,508 121,813-8,365 192,694
N
1,197
678

Columns (5)-(6) of Table 1 present the summary statistics for non-farmers. On average,
non-farmers are 59 years old, which is slightly higher than the average age of farmers. The
sample includes more non-agricultural hukou than agricultural hukou, although the difference
is not significant. Non-farmers have a significantly higher level of educational attainment thanfarmers. Additionally, the retirement rate of non-farmers is 41.3%, which is higher than farmers’
retirement rate of 22.5%. However, non-farmers have a higher prevalence of chronic diseases per
capita compared to farmers, and their net wealth is also higher.
For the analysis below, we estimate the effect of PM2.5 concentration on retirement of those
aged 45 using equation (1). Thus, it is useful to know the summary statistics of the outcome
variable of those who are close to age 45. To do so, we pick up individuals whose age is between
45 and 50 and calculate the mean and the standard deviation of the outcome variable. Table 3
shows the mean and the standard deviation of those individuals.

Figure 1 shows the map of average PM 2.5 concentration between 2004 and 2013. Figure 2
shows the histogram of the exposure of the average PM2.5 concentrations of all individuals usedin our sample. The histogram shows that the majority of individuals had PM2.5 concentrations
11

Figure 1: Concentrations of PM2.5 in China from 2004-2013

0

2

4

Percent
6

8

10

Figure 2: Histogram of PM2.5 Exposure in China

10

20

30
40
the concentration of PM2.5

5060

Notes: The unit of the horizontal axis is micrograms per cubic meter. The above histogram is constructed using
all individuals used in our regression. Thus, the unit of the histogram is an individual. The above histogramshows that most individuals are exposed to PM2.5 concentrations above 10 micrograms per cubic meter.

12

above 10 microgram, the WHO guideline value.
4.2

Results of OLS Estimation

Table 4 shows the estimation result of the regression using the retirement dummy as a dependent
variable. Panel A shows the results using farmers’ sample and Panel B show the the result
using non-farmers’ sample. Both Panel A and Panel B show that the effect of the retirement
is a quadratic function of age. Panel A shows that the estimated coefficient of PM2.5 on theretirement is 0.00093 and it is statistically insignificant. This estimated coefficient implies thata 10 microgram/m3 increase of PM2.5 only increase the probably of the retirement less than 1
percentage point. Thus, the size of the effect of PM2.5 exposure on the retirement for farmers is
economically and statistically very small.
Panel B of Table 4 displays the regression results for the retirement of non-farmers. The
estimated coefficient of PM2.5 is positive and statistically significant. Panel B shows that themarginal effect of PM2.5 concentrations is a quadratic function of age and highlights the importance of the heterogeneous nonlinear relationship between PM2.5 concentration, age, andretirement. In column (1) of Panel B, we control for individual characteristics such as gender,
years of education, hukou, and marital status. The estimated coefficient is 0.00454, indicatingthat a 10 microgram/m3 increase in PM2.5 concentration increases the probability of retirement
by 4.54 percentage points, and the estimated coefficient is statistically significant. In columns(2) and (3), we further include region fixed effects and city characteristics. In column (4),we add province-level covariates. The estimated coefficient is 0.00609, indicating that a 10
microgram/m3 increase in PM2.5 concentration increases the probability of retirement by 6.09
percentage points. Note that for those aged 45-50, the average retirement rate is 12 percent, asshown in Table 3. This implies that a 10 microgram/m3 increase in PM2.5 concentration increases
the probability of retirement by about 50 percent. Thus, Panel B of Table 4 shows that PM2.5
has a very large effect on retirement. The estimated regression result indicates that PM2.5 hasa more substantial effect on non-farmers than on farmers.

13

Table 4. The Effect of PM2.5 on Retirement (OLS)
Retirement Dummy
Dependent Variable
Variables
Panel A. Farmers' Sample
PM2.5 – meanPM2.5

(1)

(2)

(3)

(4)

0.000979(0.000968)-0.000285*(0.000160)

0.000848(0.00119)-0.000260(0.000164)

0.00169(0.00158)-0.000256(0.000164)

0.000931(0.00214)-0.000234(0.000166)

2.54e-06

1.71e-06

1.53e-06

1.42e-06(5.17e-06)-0.00243(0.00201)(5.08e-06)-0.00258(0.00205)(5.05e-06)-0.00267(0.00207)(4.98e-06)-0.00268(0.00211)

0.000583***0.000591***

0.000597***0.000595***(6.22e-05)
0.198
7,742

(6.25e-05)
0.212
7,742

(6.26e-05)
0.214
7,742

(6.30e-05)
0.220
7,742

0.00454**(0.00187)-0.000721**(0.000283)

0.00507**(0.00202)-0.000722**(0.000297)

0.00698***(0.00228)-0.000744**(0.000288)

0.00609**(0.00264)-0.000771***(0.000289)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45

1.83e-05**

1.85e-05**

1.93e-05**

2.03e-05**(8.38e-06)
0.0332***(0.00415)(8.87e-06)
0.0332***(0.00420)(8.51e-06)
0.0329***(0.00407)(8.53e-06)
0.0328***(0.00402)(Age – 45)2

-0.000135-0.000147-0.000144-0.000148(0.000112)
0.351
3,162

(0.000116)
0.358
3,162

(0.000112)
0.366
3,162

(0.000110)
0.369
3,162

✔

✔
✔

✔
✔
✔

✔
✔
✔
✔

(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2
R-squared
Observations
Panel B. Non-farmers' Sample
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)

R-squared
Observations
Control Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables

Notes: Clustering robust standard errors in parentheses assuming that the error terms arecorrelated within each city. PM2.5 is the average PM2.5 concentration in the past ten years.
meanPM2.5 is the national average of PM2.5 concentration in the past ten years. *** p<0.01, **p<0.05, * p<0.1.

14

Table 5. The Effect of PM2.5 on Having Each Disease (OLS): Farmers Sample(2)0.000450(0.000642)
1.77e-05(6.24e-05)

Lung
Diseases(3)
0.00261*(0.00140)-6.36e-05(0.000151)

Heart
Diseases(4)0.00296**(0.00142)-0.000188(0.000131)(5)0.00113(0.000763)-1.65e-05(9.98e-05)-2.43e-06-7.98e-07

3.56e-06

8.38e-06**

1.91e-06(4.71e-06)
0.0123***(0.00197)(1.82e-06)
0.00290***(0.000755)(3.78e-06)
0.00152(0.00172)(3.22e-06)
0.00724***(0.00158)(2.85e-06)-0.000529(0.000947)-0.000133**-6.14e-05***

4.59e-05-0.000128***

4.37e-05(6.12e-05)(2.27e-05)(4.62e-05)(4.06e-05)(2.76e-05)

✔
✔
✔
✔
0.051
7,742

✔
✔
✔
✔
0.011
7,742

✔
✔
✔
✔
0.032
7,742

✔
✔
✔
✔
0.064
7,742

✔
✔
✔
✔
0.015
7,742

Dependent Variables

Hypertension

Diabetes

Variables
PM2.5 – meanPM2.5(1)0.000640(0.00158)
3.67e-05(0.000153)(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2

AsthmaControl Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables
R-squared
Observations

Notes: Clustering robust standard errors in parentheses assuming that the error terms are correlated
within each city. PM2.5 is the average PM2.5 concentration in the past ten years. meanPM2.5 is thenational average of PM2.5 concentration in the past ten years. Column (1)-(5) show different estimation
results of different regression which use each type of disease dummy as a dependent variable *** p<0.01,** p<0.05, * p<0.1.

Tables 5 and Table 6 show the regression results regarding the effect of PM2.5 on the fivechronic diseases for the samples of farmers and non-farmers, respectively. We used chronic
diseases as the dependent variable to represent the health status of the samples because selfreported health status is always somewhat biased. Previous research has shown that people
frequently underestimate their health, particularly when filling out surveys and describing theirhealth negatively (Disney et al., 2006).
The dependent variable in the regression is the dummy variable for having each of the fivechronic diseases. Thus, the estimated coefficients show the effect of an increase in PM2.5 concentration on farmers aged 45. Columns (1) to (5) of Table 5 show the effect of PM2.5 onhypertension, diabetes, lung-related diseases, heart-related diseases, and asthma, respectively.

15

Table 6. The Effect of PM2.5 on Having Each Disease (OLS): Non-Farmers Sample
Hypertension

Diabetes

Lung
DiseasesHeart
Diseases

Asthma

(1)

(2)

(3)

(4)

(5)

0.00284

0.000242

0.000236

0.00415**

1.70e-05(0.00222)(0.00103)(0.00137)(0.00168)(0.000621)-0.000441

0.000169

1.85e-05-0.000415**

9.28e-05(0.000283)(0.000135)(0.000177)(0.000182)(7.62e-05)

1.89e-05**-5.98e-06

9.63e-07

1.38e-05**-2.94e-06(8.92e-06)(4.80e-06)(6.09e-06)(6.16e-06)(2.59e-06)

0.0191***0.00666***

0.000181

0.00857***

0.00201*(0.00333)(0.00162)(0.00208)(0.00200)(0.00104)-0.000272***-0.000118**

0.000114*-5.83e-05-7.80e-06(9.57e-05)(5.59e-05)(6.85e-05)(6.60e-05)(3.44e-05)

Individual Variables

✔

✔

✔

✔

✔

Region Fixed Effect

✔

✔

✔

✔

✔

Province Variables

✔

✔

✔

✔

✔

City Variables

✔

✔

✔

✔

✔

R-squared

0.076

0.028

0.037

0.094

0.022

Observations

3,162

3,162

3,162

3,162

3,162

Dependent Variables
Variables
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)

2

× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)

2

Control Variables

Notes: Clustering robust standard errors in parentheses assuming that the error terms are correlated
within each city. PM2.5 is the average PM2.5 concentration in the past ten years. meanPM2.5 is thenational average of PM2.5 concentration in the past ten years. Column (1)-(5) show different estimation
results of different regression which use each type of disease dummy as a dependent variable *** p<0.01,** p<0.05, * p<0.1.

Table 5 shows that, in terms of the size of the estimated coefficients, the level of PM2.5
concentrations has a relatively large effect on having lung-related diseases and heart-relateddiseases. For example, column (3) and (4) show that a 10 microgram/m3 increase in PM2.5
increases the probability of having lung-related and heart-related diseases for farmers aged 45 by
2.61 percentage points and 2.96 percentage points, respectively, and their effects are statisticallysignificant. Note that, in Table 3, the average probability of having lung-related and heartrelated diseases for those aged between 45 and 50 is 4.2% and 4.6%, respectively. Thus, a 10
microgram/m3 increase in PM2.5 increases the probability of having lung-related and heart-16related diseases by 62% and 64%, respectively, and these effects are not small.
Table 6 shows the effect of PM 2.5 on having each of the five diseases for the non-farmers
in the OLS estimation. Table 6 shows that except for heart-related diseases, the estimatedcoefficient of PM2.5 is economically very small and statistically insignificant. On the other hand,the effect of PM2.5 on having heart-related diseases is not small. Column (4) of Table 6 showsthat 10 microgram/m3 increase of PM2.5 per cubic meter increases the probability of having aheart-related disease by 4.15 percentage points. Table 3 shows that 5.6 percent of non-farmers
aged 45-50 have heart-related diseases. Thus, a 10 microgram per cubic meter increase of PM2.5
increases the probability of having heart-related diseases by 74 percent, which is not a small
number.
4.3

Results of 2SLS Estimation

In the previous subsection, we used OLS estimation. Note that PM2.5 can be correlated witheconomic activity, which can affect the retirement decision of workers directly. For example, inan area where economic activity is intensive, the demand for labor can be high, leading people
to delay their retirement decision. In such cases, we may underestimate the effect of PM2.5 on
retirement. To address this issue in our OLS estimation, we included several variables such ascity and province GDP, and GDP shares of specific sectors.
However, it remains possible that certain economic variables may be correlated with both
PM2.5 concentration and retirement. To address this potential issue, we use PM2.5 levels from30 years ago (1980) as an instrumental variable, and we interact it with age and age squared
while controlling for current industry structure and economic activities. The key assumption
underlying the use of this instrumental variable is that the PM2.5 levels from 30 years ago are
not correlated with current retirement behavior, once we account for current economic activities.
However, it’s worth noting that PM2.5 levels are influenced by various geographic factors suchas elevation and proximity to basins, as well as wind conditions. Therefore, current PM2.5
concentrations may still be correlated with past PM2.5 levels even after controlling for current
economic conditions.
Table 7 shows the results of the first stage regression in 2SLS estimation for the farmers’17

Table 7. First Stage Estimation of 2SLS: Farmers' Sample
Dependent Variable

PM2.5 – meanPM2.5(Age – 45)(Age – 45)2× (PM2.5 – meanPM2.5)× (PM2.5 – meanPM2.5)

Variables
PM2.5(1980)(1)
1.717***(0.0719)(2)-3.834**(1.626)(3)-85.81**(34.24)(Age – 45)
× PM2.5(1980)-0.000975(0.00101)

2.033***(0.0796)

4.541**(1.973)(Age – 45)2
× PM2.5(1980)

6.24e-05*-0.00389**

1.747***(3.33e-05)(0.00187)(0.117)(Age – 45)

0.0213(0.0214)-36.86***(1.790)-125.7***(40.84)(Age – 45)2

-0.00120*0.112***-28.91***(0.000687)(0.0389)(2.384)

✔
✔
✔
✔
195.7
7,742
0.978

✔
✔
✔
✔
439.0
7,742
0.952

✔
✔
✔
✔
435.5
7,742
0.929

Control Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables
F Statatiscs
Observations
R-squared

Notes: Clustering robust standard errors in parentheses assuming that the error terms are correlated withineach city. PM2.5(1980) is PM2.5 concentration in 1980 at the same city minus the national average of PM2.5
concentration in 1980. PM2.5 is the average PM2.5 concentration in the last 10 years (from the year of thesurvey) in which a respondent lived. meanPM2.5 is the national average of PM2.5 concentration in the last 10
years (from the year of the survey). F statistcs is F value of the null hypothesis that the coefficient of the threensrumental variables are equal to zero.

sample. In column (1), the dependent variable is the average PM2.5 concentration in a city from2004 to 2013 where the individual i lived minus the national average of PM2.5 in China from2004 to 2013. Our primary instrumental variable is the PM2.5 level in 1980 in a city where
the respondent lived from 2004 to 2013 minus the national average of PM2.5 in 1980. We also
have the interaction of this primary instrumental variable with demeaned age and its square asadditional instruments. In column (1), the estimated coefficient of our primary instrument is
1.71 and the standard error is 0.072. Thus, the t-value is more than 23, indicating that our
primary instrumental variable works very well.

18

Table 8. First Stage Estimation of 2SLS: Non-Farmers' Sample
Dependent Variable
Variables
PM2.5(1980)(Age – 45)
× PM2.5(1980)(Age – 45)2
× PM2.5(1980)(Age – 45)(Age – 45)2PM2.5 – meanPM2.5(Age – 45)(Age – 45)2× (PM2.5 – meanPM2.5) × (PM2.5 – meanPM2.5)(1)
1.685***(0.0903)-0.00194(0.00253)(2)-4.204**(1.838)2.008***(0.117)(3)-90.29**(40.26)1.644(3.513)3.79e-05-0.00199

1.891***(7.71e-05)
0.0206(0.0501)(0.00368)-35.03***(2.394)(0.172)-26.87(71.37)-0.000590

0.0327-33.04***(0.00150)(0.0749)(3.484)

✔
✔
✔
✔
120.2
3,162
0.967

✔
✔
✔
✔
311.7
3,162
0.933

✔
✔
✔
✔
280.3
3,162
0.894

Control Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables
F Statistics
Observations
R-squared

Notes: Clustering robust standard errors in parentheses assuming that the error terms are correlated withineach city. PM2.5(1980) is PM2.5 concentration in 1980 at the same city minus the national average of PM2.5
concentration in 1980. PM2.5 is the average PM2.5 concentration in the last 10 years (from the year of thesurvey) in which a respondent lived. meanPM2.5 is the national average of PM2.5 concentration in the last 10
years (from the year of the survey). F statistcs is F value of the null hypothesis that the coefficient of the threensrumental variables are equal to zero. *** p<0.01, ** p<0.05, * p<0.1.

The F-statistic for the null hypothesis that the coefficients of the three instrumental variables
are equal to 0 is 195.7, which is higher than the standard threshold of 10. Column (2) estimates
the second first stage equation of 2SLS, where the endogenous variable is the interaction of age
minus 45 and PM2.5 minus the national average of PM2.5. Again, the F-statistic for the null
hypothesis that the coefficients of all three instrumental variables are equal to zero is 439, which
is much higher than the standard threshold value. Column (3) estimates the third first stage
equation of 2SLS, where the endogenous variable is the interaction of the square of age minus
45, age minus 45, PM2.5 minus the national average of PM2.5. The F-statistic for the null
hypothesis that the coefficients of all instruments are equal to zero is 435.5.
19

Table 9. The Effect of PM2.5 on Retirement (2SLS): the Second Stage Results
Retirement Dummy
Dependent Variable
Variables
Panel A. Farmers' Sample
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)
Age – 45
Kleibergen-Paap Rank Wald
R-squared
Observations
Panel B. Non-farmers' Sample
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)
Age – 45
Kleibergen-Paap Rank Wald
R-squared
Observations
Control Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables

(1)

(2)

(3)

(4)

0.00165(0.00117)-0.000315**(0.000156)-0.00232(0.00203)
153.7
0.198
7,742

0.00145(0.00123)-0.000286*(0.000158)-0.00250(0.00206)
197.9
0.211
7,742

0.00195(0.00153)-0.000281*(0.000158)-0.00254(0.00207)
195.9
0.214
7,742

8.97e-05(0.00216)-0.000262(0.000159)-0.00251(0.00211)
211.2
0.220
7,742

0.00506***(0.00192)-0.000663**(0.000303)
0.0331***(0.00410)
99.63
0.351
3,162

0.00491**(0.00200)-0.000615*(0.000314)
0.0329***(0.00415)
103.2
0.358
3,162

0.00694***(0.00228)-0.000631**(0.000308)
0.0326***(0.00400)
157.3
0.365
3,162

0.00715**(0.00281)-0.000656**(0.000306)
0.0325***(0.00397)
137.8
0.369
3,162

✔

✔
✔

✔
✔
✔

✔
✔
✔
✔

Notes: Clustering robust standard errors in parentheses assuming that the error terms arecorrelated within each city. PM2.5 is the average PM2.5 concentration in the past ten years.
meanPM2.5 is the national average of PM2.5 in the past ten years. All specifications above
include the square of (Age – 45) and its interaction with (PM2.5 – meanPM2.5). To save thespace, we do not provide the estiamted coefficients of those variables, but those coefficients are
displayed in Table A1. *** p<0.01, ** p<0.05, * p<0.1.

Table 8 is the fist stage estimation of 2SLS for non-farmers’ sample. In all three cases, the Fvalue of the null hypothesis is above 100, which is higher than the standard threshold value 10.
Panel A of Table 9 shows the results of the second stage of 2SLS estimation for the sample
of farmers. The dependent variable is the retirement dummy. The first endogenous explanatory
variable is the average PM2.5 concentration in the past 10 years, which is the respondent’s
20

exposure to PM2.5 in the past 10 years, minus the national average of PM2.5 in the past 10 years.
The second and third endogenous explanatory variables are the interaction of the first endogenous
explanatory variable with age minus 45 and the square of age minus 45. In column (1), we include
three endogenous explanatory variables, age minus 45, its square, and individual characteristics.
In this column, the estimated coefficient of the average PM2.5 in the past 10 years minus thenational average of PM2.5 in the past year is 0.00165. Thus, a 10 microgram/m3 increase inPM2.5 increases the probability of retirement of farmers aged 45 by only 1.65 percent. However,
the estimated coefficient is not economically or statistically significant.
In column (2) of Panel A of Table 9, we additionally include region fixed effect. The estimatedcoefficient of the same explanatory variable in column (2) is 0.00145, which is neither economicallynor statistically significant. In column (3), the estimated coefficient of the same explanatory
variable is 0.00195. In column (4), the estimated coefficient of the same explanatory variable is
0.000089, which is practically equal to zero economically and is not statistically significant.
Regarding the effect of age on the retirement of farmers, Panel A of Table 9 shows that
retirement is a quadratic function of age, but the interaction term of age and PM2.5 is close to
zero. Thus, overall, we can conclude that the PM2.5 concentration does not significantly affectthe retirement decision of farmers.
Panel B of Table 9 shows the effect of PM2.5 concentration on retirement for non-farmers
in the second-stage result of our 2SLS estimation. The dependent variable is the retirement
dummy. From column (1) to column (4), Panel B shows that PM2.5 concentrations increases
the probability of retirement. The interaction term of demeaned age with PM2.5 and the squareof demeaned age with PM.25 show statistically and economically significant. This implies thatthe marginal effect of PM2.5 is a non-linear function of age.
In Panel B of Table 9, column (4) shows that a 10 microgram/m3 increase in PM2.5 increases the probability of retirement by 7.15 percentage points. Note that Table 3 shows thatthe average probability of retirement for non-farmers aged 45-50 is 12.4 percent. Thus, a 10
microgram/m3 increase in PM2.5 concentration increases the retirement of non-farmers who are
45 years old by 57.6 percent (= (0.0715/0.124) × 100). This implies that PM2.5 concentration
has a large effect on retirement.

21

In 2018, the number of individuals aged 40-49 in China was 228 million. The average shareof individuals not in agriculture, fishing, and forestry sectors was 74.2 percent. Assuming thisnational share applies to those aged 40-49, roughly 169 million individuals are non-farmers in thatage group. If China were to reduce the PM2.5 concentration level by 10 micrograms throughout
the country, according to column (4) of Panel B of Table 9, 12.1 million people could continue
participating in the labor market.
Panel B of Table 9 shows that the interaction term of demeaned PM2.5 and age minus 45
and its square are not zero. This implies that the marginal effect of an increase in PM2.5
depends on age. Figure 3 displays the marginal effect of a 10 microgram/m3 increase in PM2.5
on retirement at different ages in OLS and 2SLS estimations for non-farmers, controlling for
all level characteristics. Figure 3 shows that the marginal effects of PM2.5 concentrations are adecreasing function of age in both OLS and 2SLS estimation. Interestingly, the marginal effect of
PM2.5 concentration in 2SLS estimation at age 65 becomes almost equal to zero. This suggeststhat at age 65, most individuals are almost retired regardless of past exposure to PM2.5.
Table 10 shows the effect of PM2.5 concentration on having each of the five chronic diseases
for farmers. At first glance, the estimated coefficient of PM2.5 seems not to affect having chronic
diseases. However, a closer look shows that the estimated coefficient of PM2.5 in 2SLS estimationis quite similar to that in OLS estimation regarding the effect on lung-related disease. In Table
5, using OLS estimation, the estimated coefficient of PM2.5 concentration on having lung-related
and heart-related diseases is 0.00261, and it is statistically significant. In Table 10, using 2SLS
estimation, the estimated coefficient of PM2.5 concentration on lung-related diseases is 0.00236.
Thus, for lung-related diseases, the estimated coefficients are similar in both OLS and 2SLS
estimation. In the case of 2SLS, the standard error becomes larger, reducing the statisticalprecision of the estimate, but the estimates of OLS and 2SLS are very close. Note that from
Table 4, 4.18 percent of farmers aged 45 have lung-related diseases. Thus, Table 10 shows that a
10 microgram per cubic meter increase in PM2.5 increases the probability of having lung-relateddiseases by 56.4 percent (= (0.0236/0.0418) × 100).
Table 11 shows the effect of PM2.5 concentration on having each of the five chronic diseases for
non-farmers. Table 11 shows that a higher PM2.5 concentration increases the likelihood of having

22

Table 10. The Effect of PM2.5 on Having Each Disease (2SLS)The Second Stage Estimation: Farmers' Sample(2)0.000220(0.000685)
1.27e-05(7.40e-05)
0.00294***(0.000787)

Lung
Diseases(3)
0.00236(0.00161)-7.50e-06(0.000167)
0.00124(0.00178)

Heart
Diseases(4)0.00174(0.00142)-0.000112(0.000146)
0.00689***(0.00164)(5)0.00112(0.000885)-3.22e-05(0.000114)-0.000445(0.000941)

✔
✔
✔
✔
211.2
0.011
7,742

✔
✔
✔
✔
211.2
0.032
7,742

✔
✔
✔
✔
211.2
0.064
7,742

✔
✔
✔
✔
211.2
0.015
7,742

Dependent Variables

Hypertension

Diabetes

Variables
PM2.5 – meanPM2.5(1)-0.000530(0.00162)
2.67e-05(0.000161)
0.0124***(0.00201)
✔
✔
✔
✔
211.2
0.051
7,742

(Age – 45)
× (PM2.5 – meanPM2.5)
Age – 45

AsthmaControl Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables
Kleibergen-Paap Rank Wald
R-squared
Observations

Notes: Clustering robust standard errors in parentheses assuming that the error terms are correlated withineach city. PM2.5 is the average PM2.5 concentration in the past ten years.meanPM2.5 is the national averageof PM2.5 in the past ten years. Column (1)-(5) show different estimation results of different regressions
which use each type of disease dummy as a dependent variable. All specifications above include the squareof (Age – 45) and its interaction with (PM2.5 – meanPM2.5). To save the space, we do not provide theestiamted coefficients of those variables, but those coefficients are displayed in Table A2. *** p<0.01, **p<0.05, * p<0.1.

heart-related diseases. Column (4) shows that a 10 microgram/m3 increase of PM2.5 increases thelikelihood of having heart-related diseases by 3.25 percentage points. The estimated coefficientsare similar in both OLS and 2SLS. In OLS, the effect on having heart-related diseases is 0.00415
and in 2SLS it is 0.00325. Note that, for non-farmers, the average probability of heart-relateddiseases is 5.6 percent. Thus, a 10 microgram/m3 increase of PM2.5 concentration increases thelikelihood of having heart-related diseases by 58 percent.
Farmers tend to suffer from lung-related diseases. However, Table 9 shows that the retirement
behavior of farmers is not affected by a higher PM2.5 level, while the retirement behavior of nonfarmers is affected by it. One natural question would be why we observe such different behavior
regarding the response to PM2.5. Our conjecture is that since non-farmers have more wealth,they can choose to retire early when their health becomes bad. In fact, Table 2 shows that23

Table 11. The Effect of PM2.5 on Having Each Disease (2SLS)The Second Stage Estimatoin: Non-farmers' Sample(2)0.000795(0.00123)
0.000171(0.000164)
0.00666***(0.00158)

Lung
Diseases(3)0.000349(0.00151)
7.80e-07(0.000189)
0.000230(0.00208)

Heart
Diseases(4)0.00325*(0.00187)-0.000392**(0.000184)
0.00851***(0.00197)(5)-0.000521(0.000715)
0.000145(9.37e-05)
0.00184*(0.00106)

✔
✔
✔
✔
137.8
0.028
3,162

✔
✔
✔
✔
137.8
0.037
3,162

✔
✔
✔
✔
137.8
0.094
3,162

✔
✔
✔
✔
137.8
0.022
3,162

Dependent Variables

Hypertension

Diabetes

Variables
PM2.5 – meanPM2.5(1)0.00146(0.00272)-0.000504(0.000307)
0.0194***(0.00331)
✔
✔
✔
✔
137.8
0.076
3,162

(Age – 45)
× (PM2.5 – meanPM2.5)
Age – 45

AsthmaControl Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables
Kleibergen-Paap Rank Wald
R-squared
Observations

Notes: Clustering robust standard errors in parentheses assuming that the error terms are correlated withineach city. PM2.5 is the average PM2.5 concentration in the past ten years. meanPM2.5 is the national
average of PM2.5 in the past ten years. Column (1)-(5) show different estimation results of different
regressions which use each type of disease dummy as a dependent variable. All specifications above include
the square of (Age – 45) and its interaction with (PM2.5 – meanPM2.5). To save the space, we do not provide
the estiamted coefficients of those variables, but those coefficients are displayed in Table A3. *** p<0.01, **p<0.05, * p<0.1.

farmers’ net wealth (the balance of assets minus debt) is negative, while non-farmers’ net wealthis positive. Thus, our interpretation is consistent with the summary statistics.
4.4

A Potential Mechanism

As we discussed in the introduction, a higher PM2.5 level can affect the retirement decision
through two channels. The first channel is the disutility channel, where increased air pollution
leads to deteriorating health among workers, causing disutility of work to increase and inducing
early retirement. The second channel is the productivity channel, where reduced health lowers
the productivity of workers, decreasing their financial wealth. Since leisure is a normal good, alower wealth level could postpone a decision to retire.
To explore policy implications, it is important to identify the mechanism behind the outcome.
24

-.02

0

Marginal Effect of PM2.5
.02
.04
.06

.08

Figure 3: Marginal Effects of a 10 Microgram/m3 Increase of PM 2.5 on Retirement Probability
at Each Age

45

50

55
Age
OLS

60

65

2SLS

Notes: Each point denotes the marginal effect of 10 microgram/m3 increase of PM2.5 on the probability of retirement. A blue line with circles denote the marginal effect based on OLS estimation
and a red line with square denotes the marginal effect based on 2SLS estimation.

However, since the outcome is a combination of the two channels, it is difficult to infer whichchannel is at work. To gain insight, we regressed financial wealth (the value of assets minusdebt) on PM2.5. We used Equation (1) and set net wealth as the outcome variable. Table 12shows the estimation results, with columns (1) and (2) displaying the results using the farmers’
sample, and columns (3) and (4) displaying the results using the non-farmers’ sample
Columns (1) and (2) of Table 12 use OLS and 2SLS estimation for farmers’ sample, respectively. Both columns show that higher PM2.5 levels decrease net wealth, but the coefficient isvery small. For instance, column (2) demonstrates that a 10 microgram/m3 increase of PM2.5
concentration decreases net wealth by 0.11 Chinese yuan. However, the net wealth is already-4000 yuan, indicating that the estimated coefficient is negligible when compared to the average
PM2.5 value. Similarly, for non-farmers, both OLS and 2SLS show that the estimated coefficientof PM2.5 is positive but statistically insignificant, with a value of 0.0327. This implies that a 10
microgram/m3 increase of PM2.5 concentration increases net wealth by only 0.33 Chinese yuan,
25

Table 12. The Effect of PM2.5 on Net Wealth (OLS and 2SLS )Net Wealth
Dependent Variables
Farmers
Non-Farmers
SampleEstimation Method
Variables
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2OLS
(1)-0.0104(0.0130)0.00148(0.00131)2SLS(2)-0.000649(0.0144)0.000793(0.00145)

OLS(3)0.0336(0.0230)0.000830(0.00167)

2SLS(4)0.0327(0.0242)0.00170(0.00190)-4.03e-05-2.05e-05-1.74e-05-4.48e-05(3.85e-05)-0.0642***(0.0165)(4.34e-05)-0.0611***(0.0173)(5.16e-05)-0.0314(0.0254)(6.13e-05)-0.0343(0.0247)-0.000931*-0.00102**-0.000172-8.32e-05(0.000472)(0.000494)(0.000764)(0.000740)

✔
✔
✔
✔

✔
✔
✔
✔
211.2
0.209
7,742

✔
✔
✔
✔

✔
✔
✔
✔
137.8
0.100
3,162

Control Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables
Kleibergen-Paap Rank Wald
R-squared
Observations

0.209
7,742

0.100
3,162

Notes: Clustering robust standard errors in parentheses assuming that the error terms arecorrelated within each city. PM2.5 is the average PM2.5 concentration in the past ten years.
meanPM2.5 is the national average of PM2.5 concentration in the past ten years. *** p<0.01, **p<0.05, * p<0.1.

which is too small to be distinguished from zero.
Therefore, Table 12 shows that the effect of PM2.5 concentration on net wealth is practicallyzero, implying that the main channel through which PM2.5 concentration affects retirement isthe disutility channel through deteriorating health.
4.5

Potential Bias due to Internal Migration

One potential concern regarding our estimation results is the possibility of bias since a respondent’s current address may differ from where they lived in the past 10 years, potentially resulting
in a downward bias in our estimates. For example, a respondent may have recently relocated totheir current city due to asthma caused by air pollution in their previous city.
26

Table 13. The Effect of PM2.5 on Retirement : Robustness Checks
Using Province Level PM2.5 for Analysis : the Second Stage Results of 2SLS
Retirement Dummy
Dependent Variables
Variables
Panel A. Farmers' Sample
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)
Age – 45
Kleibergen-Paap Rank Wald
R-squared
Observations
Panel B. Non-farmers' Sample
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)
Age – 45
Kleibergen-Paap Rank Wald
R-squared
Observations
Control Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables

(1)

(2)

(3)

(4)

0.00132(0.00204)-0.000151(0.000318)-0.00365(0.00224)
50.40
0.192
7,742

-0.00116(0.00272)-0.000131(0.000299)-0.00344(0.00221)
54.79
0.210
7,742

-0.000409(0.00368)-0.000133(0.000302)-0.00345(0.00220)
54.31
0.212
7,742

-0.00100(0.00415)-9.98e-05(0.000300)-0.00348(0.00222)
72.32
0.219
7,742

0.00635**(0.00250)-0.000862**(0.000423)
0.0327***(0.00324)
45.97
0.351
3,162

0.00459(0.00287)-0.000808*(0.000442)
0.0325***(0.00346)
50.50
0.358
3,162

0.00834**(0.00350)-0.000818*(0.000446)
0.0321***(0.00329)
29.24
0.366
3,162

0.00783*(0.00462)-0.000858**(0.000428)
0.0321***(0.00315)
22.21
0.369
3,162

✔

✔
✔

✔
✔
✔

✔
✔
✔
✔

Notes: Clustering robust standard errors in parentheses assuming that the error terms arecorrelated within each province. PM2.5 is the average PM2.5 concentration in the past ten years
in each province. meanPM2.5 is the national average of PM2.5 in the past ten years. All
specifications above include the square of (Age – 45) and its interaction with (PM2.5 –meanPM2.5). To save the space, we do not provide the estiamted coefficients of those variables,
but those coefficients are displayed in Table A4. *** p<0.01, ** p<0.05, * p<0.1.

However, we argue that this possibility is likely to be very small. In our dataset, among
7,742 farmers, 99.01% of farmers (7,666 farmers) have been living in the current place since atleast 2004, and only 76 farmers moved to the current place after 2004. Similarly, among 3,162
non-farmers, 96.52% of non-farmers (3,045 non-farmers) have been living in the current place27

since at least 2004, and only 110 non-farmers moved to the current address after 2004. Thus,
the bias due to internal migration is likely to be small.
To further check the robustness of our estimation, we conducted a 2SLS estimation using
province-level PM2.5 concentration instead of city-level PM2.5 as the key explanatory variable.
We also used the province-level PM2.5 30 years ago as the instrumental variable, assuming that
respondents are likely to have moved within the same province when they relocated due to air
pollution. Moving across provinces involves higher psychological and search costs of finding a
new job, so individuals are more likely to move within the same province.
Table 13 presents the results of the 2SLS estimation of the effect of PM2.5 on retirement,using province-level PM2.5 levels as the endogenous explanatory variable instead of city-level
PM2.5 levels. The instrumental variable is also the province-level PM2.5 from 30 years ago. The
estimation results of 2SLS using province-level PM2.5 are quite similar to those using city-level
PM2.5, as shown in Table 9. For instance, in column 4 of Panel B of Table 13, the estimatedcoefficient of demeaned PM2.5 is 0.00783, while in column 4 of Panel B of Table 9, the estimatedcoefficient of demeaned PM2.5 is 0.00715.
Appendix A includes Table A5 and Table A6, which present the 2SLS estimation results ofthe effect of PM2.5 concentration on different types of diseases for farmers (Table A5) and nonfarmers (Table A6), using province-level PM2.5 concentration. The estimation results in Table
A5 and Table A6 are quite similar to those in Table 10 and Table 11. These tables suggest thatthe bias due to internal migration is likely to be very small.
However, one might argue that it is possible that those 186 respondents(76 farmers and 110
non-farmers) might have moved across provinces and those internal migration causes bias in ourestimates. To check such a possibility, we drop those 186 respondents, estimate our model using
the city level and compare the estimates with the estimates in Table 9. Table A7 shows theresult of 2SLS regression dropping 186 respondents. The key explanatory variable is the citylevel demeaned PM2.5 as in Table 9. The estimated coefficient of Table A5 is quite similar to
the estimated coefficients of Table 9. For example, in column (4) of Panel B of Table A7, theestimated coefficient of the demeaned PM2.5 is 0.00656 while in column (4) of Panel B of Table 9,
the estimated coefficient of the same variable is 0.00715. Thus, those two estimated coefficients28

are quite similar.
In summary, Table 13, Table A5, Table A6 and Table A7 indicate that the bias due to theinternal migration is not likely to affect our conclusion.
4.6

Other Potential Bias

One criticism of our estimation strategy using both OLS and 2SLS is that the concentration of
PM2.5 may be correlated with unobserved industry characteristics in each city, which could also
be related to the retirement patterns of the city. As a result, our estimates of the effect of PM2.5
may include not only the direct effect of PM2.5 concentration but also the indirect effect of theseunobserved industry characteristics. Since we only have cross-sectional data, it is difficult to
directly address this issue. However, we believe that if such a bias exists, it is likely to result inan underestimation of the effect of PM2.5.
In both OLS and 2SLS, as we move from column (1) to column (4) in Tables 4 and 9, we
include more province and city characteristics, which implies that we control for more industry characteristics. However, we observe that the estimated coefficient of PM2.5 in column (1)is smaller than the estimated coefficient in column (4). If the estimated coefficient of PM2.5
were mainly driven by unobserved industry characteristics of each city, then the estimated coefficients should decrease as we include more province and city characteristics as control variables.
Therefore, this suggests that even if there is an unobserved industry effect, it is likely that ourestimated coefficients of PM2.5 are underestimated rather than overestimated due to this bias.
5

Discussion and Conclusion

It is well-known that PM2.5 particles are very small and can penetrate deep into the humanbody beyond the lungs, entering the bloodstream and causing inflammation in various organs.
Medical literature has indicated that exposure to PM2.5 can lead to heart diseases, high bloodpressure, and other illnesses.
Previous studies on retirement behavior among middle-aged and older adults have mainly
focused on analyzing the effects of factors such as health status, financial status, and retirement
preparation on retirement. However, in our study, we aimed to investigate the impact of PM2.5
29

concentration on retirement behavior among middle-aged and older adults in China. This isparticularly crucial because China is known for its severe air pollution. Although there are manystudies that examine the effect of air pollution on labor supply, to the best of our knowledge,
this is the first paper to study the effect of air pollution on the retirement decision.
To achieve this, we divided the study sample into two groups: farmers and non-farmers. We
separately examined the effects of PM2.5 on their retirement behavior and health status.
Our study indicates that higher levels of PM2.5 concentration significantly increase the probability of retirement among non-farmers. Specifically, a 10 microgram/m3 increase in PM2.5 concentration (which is roughly equivalent to one standard deviation of PM2.5 concentrations) raises
the retirement probability of non-farmers aged 45 by 7.15 percentage points. Since the retirement
rate for non-farmers aged 45-50 is 12.4 percent, this means that a 10 microgram/m3 increase inPM2.5 concentration increases the retirement probability of non-farmers aged 45 by 57.6 percent. In contrast, we did not find any evidence that higher PM2.5 concentrations increase theprobability of early retirement among farmers. The estimated coefficients for farmers are statistically and economically insignificant. We also observed that large heterogeneous effect of PM 2.5
concentrations on the retirement behavior. As Figure 4 shows, the marginal effect of an increaseof PM2.5 concentration highly depends on age. At age 65, the marginal effect becomes almost
equal to zero.
Regarding the effect of PM2.5 concentration on diseases, we found that for non-farmers aged
45, a 10 microgram/m3 increase in PM2.5 concentration raises the probability of heart-relateddiseases by 58 percent. For farmers, a 10 microgram/m3 increase in PM2.5 concentration increases
the probability of both lung-related and heart-related diseases by 56 percent.
As discussed in the introduction, air pollution affects the retirement behavior of middle andelderly people through two mechanisms. The first mechanism is by decreasing productivity andfinancial wealth. A lower wage decreases the financial wealth of middle and elderly people,
and since leisure is a normal good, lower wealth due to air pollution is likely to decrease theprobability of early retirement. The second mechanism is by increasing the disutility of the labor
supply due to bad health caused by air pollution. Higher disutility due to air pollution is likely
to increase the probability of early retirement. Our regression on net wealth shows that air

30pollution does not decrease the net wealth of farmers and non-farmers, suggesting that the main
mechanism through which a higher PM2.5 concentration increases the probability of retirement
for non-farmers is likely the disutility channel.
Overall, our results are consistent with previous studies. Several recent studies have examined the effects of PM 2.5 on economic outcomes such as migration, obesity, and labor supply(Deschenes et al., 2020; Huang et al., 2021; Li and Li, 2022; Chen et al., 2022; Wu et al., 2023).
For example, Huang et al. (2021) show that 10 microgram/m3 decrease of PM2.5 increase labor
supply by 37.5 percent. Wu et al. (2023) shows that one percent increase of PM2.5 decrease theratio of employment to the whole population by 0.02018 unit. This can be translated into that10 microgram/m3 increase of PM2.5 concentration increase the employment to population ratio
45 percentage points. Additionally, studies in environmental science and health science havereported that higher air pollution in China affects the health status of Chinese people (Chenet al., 2013; Pui et al., 2014; Cakmak et al., 2007; Xue et al., 2018).
As the aging population in China continues to increase, the retirement decisions of Chinese
individuals have a significant impact on the economy. In 2018, the number of people aged 40-49
in China was 228 million, with an average of 74.2% working in non-agricultural, fishing, andforestry sectors. Assuming this national average applies to the 40-49 age group, approximately
169 million individuals in this group are non-farmers. Based on the findings in column (4) of
Panel B in Table 9, reducing PM2.5 concentration levels by 10 micrograms across China could
enable 12.1 million people to remain in the labor market.
China, South Asia, and Central Africa are among the most polluted regions in the world,with high population densities. Our study suggests that in these areas, many middle-aged andelderly individuals may have left the labor market due to air pollution. Therefore, future studies
should focus on quantifying the impact of air pollution on retirement decisions in developing
countries such as South Asia and Africa.
References
Andersen, Zorana J, Ole Raaschou-Nielsen, Matthias Ketzel, Steen S Jensen, Martin Hvidberg,
Steffen Loft, Anne Tjønneland, Kim Overvad, and Mette Sørensen (2012) “Diabetes incidence31and long-term exposure to air pollution: a cohort study,” Diabetes care, Vol. 35, pp. 92–98.
Aragon, Fernando M, Juan Jose Miranda, and Paulina Oliva (2017) “Particulate matter and labor
supply: The role of caregiving and non-linearities,” Journal of Environmental Economics andManagement, Vol. 86, pp. 295–309.
Au, Doreen Wing Han, Thomas F Crossley, and Martin Schellhorn (2005) “The effect of healthchanges and long-term health on the work activity of older Canadians,” Health economics, Vol.
14, pp. 999–1018.
Bell, Michelle L, Keita Ebisu, Brian P Leaderer, Janneane F Gent, Hyung Joo Lee, Petros
Koutrakis, Yun Wang, Francesca Dominici, and Roger D Peng (2014) “Associations of PM2.
5 constituents and sources with hospital admissions: analysis of four counties in Connecticut
and Massachusetts (USA) for persons greater than 65 years of age,” Environmental healthperspectives, Vol. 122, pp. 138–144.
Bender, Keith A (2012) “An analysis of well-being in retirement: The role of pensions, health,and ‘voluntariness’ of retirement,” The Journal of Socio-Economics, Vol. 41, pp. 424–433.
Blundell, Richard, Jack Britton, Monica Costa Dias, and Eric French (2023) “The impact ofhealth on labor supply near retirement,” Journal of Human Resources, Vol. 58, pp. 282–334.
Bound, John, Michael Schoenbaum, Todd R Stinebrickner, and Timothy Waidmann (1999) “Thedynamic effects of health on the labor force transitions of older workers,” Labour Economics,
Vol. 6, pp. 179–202.
Bound, John, Todd Stinebrickner, and Timothy Waidmann (2010) “Health, economic resources
and the work decisions of older men,” Journal of econometrics, Vol. 156, pp. 106–129.
Buchard, V, CA Randles, AM Da Silva, A Darmenov, PR Colarco, R Govindaraju, R Ferrare,
J Hair, AJ Beyersdorf, LD Ziemba et al. (2017) “The MERRA-2 aerosol reanalysis, 1980
onward. Part II: Evaluation and case studies,” Journal of Climate, Vol. 30, pp. 6851–6872.
Cakmak, Sabit, Robert E Dales, and Claudia Blanco Vidal (2007) “Air pollution and mortality

32

in Chile: susceptibility among the elderly,” Environmental Health Perspectives, Vol. 115, pp.
524–527.
Chen, Shuai, Paulina Oliva, and Peng Zhang (2022) “The effect of air pollution on migration:evidence from China,” Journal of Development Economics, Vol. 156, p. 102833.
Chen, Yuyu, Avraham Ebenstein, Michael Greenstone, and Hongbin Li (2013) “Evidence on
the impact of sustained exposure to air pollution on life expectancy from China’s Huai River
policy,” Proceedings of the National Academy of Sciences, Vol. 110, pp. 12936–12941.
Clarke, Philippa, Victor W Marshall, and David Weir (2012) “Unexpected retirement from fulltime work after age 62: consequences for life satisfaction in older Americans,” European Journalof Ageing, Vol. 9, pp. 207–219.
De Wind, Astrid, Goedele A Geuskens, Kerstin G Reeuwijk, Marjan J Westerman, Jan Fekke
Ybema, Alex Burdorf, Paulien M Bongers, and Allard J Van der Beek (2013) “Pathways
through which health influences early retirement: a qualitative study,” BMC Public Health,Vol. 13, pp. 1–9.
Deschenes, Olivier, Huixia Wang, Si Wang, and Peng Zhang (2020) “The effect of air pollution
on body weight and obesity: Evidence from China,” Journal of Development Economics, Vol.
145, p. 102461.
Disney, Richard, Carl Emmerson, and Matthew Wakefield (2006) “Health and Retirement in
Britain: A panel data-based analysis,” Journal of health economics, Vol. 25, pp. 621–649.
Du, Yixing, Xiaohan Xu, Ming Chu, Yan Guo, and Junhong Wang (2016) “Air particulate matterand cardiovascular disease: the epidemiological, biomedical and clinical evidence,” Journal ofthoracic disease, Vol. 8, p. E8.
Dwyer, Debra Sabatini and Olivia S Mitchell (1999) “Health problems as determinants of retirement: Are self-rated measures endogenous?” Journal of health economics, Vol. 18, pp.
173–193.

33

Fang, Evandro Fei, Morten Scheibye-Knudsen, Heiko J Jahn, Juan Li, Li Ling, Hongwei Guo,
Xinqiang Zhu, Victor Preedy, Huiming Lu, Vilhelm A Bohr et al. (2015) “A research agenda
for aging in China in the 21st century,” Ageing research reviews, Vol. 24, pp. 197–205.
Franklin, Barry A, Robert Brook, and C Arden Pope III (2015) “Air pollution and cardiovasculardisease,” Current problems in cardiology, Vol. 40, pp. 207–238.
Gu, Hejun, Fengqin Bian, and Ehsan Elahi (2020) “Effect of air pollution on female labor supply:
An empirical analysis based on data of labor force dynamic survey of China,” Social Work in
Public Health, Vol. 35, pp. 187–196.
Hanna, Rema and Paulina Oliva (2015) “The effect of pollution on labor supply: Evidence froma natural experiment in Mexico City,” Journal of Public Economics, Vol. 122, pp. 68–79.
Hausman, Jerry A, Bart D Ostro, and David A Wise (1984) “Air pollution and lost work.”
He, Jiaxiu, Haoming Liu, and Alberto Salvo (2019) “Severe air pollution and labor productivity:
Evidence from industrial towns in China,” American Economic Journal: Applied Economics,
Vol. 11, pp. 173–201.
Huang, Qiaolong, Yu Yvette Zhang, Qin Chen, and Manxiu Ning (2021) “Does Air Pollution
Decrease Labor Supply of the Rural Middle-Aged and Elderly?” Sustainability, Vol. 13, p.
2906.
Ilmakunnas, Pekka and Seija Ilmakunnas (2018) “Health and retirement age: comparison of
expectations and actual retirement,” Scandinavian journal of public health, Vol. 46, pp. 18–31.
Jones, Andrew M, Nigel Rice, and Jennifer Roberts (2010) “Sick of work or too sick to work?
Evidence on self-reported health shocks and early retirement from the BHPS,” Economic
Modelling, Vol. 27, pp. 866–880.
Kan, Haidong, Stephanie J London, Guohai Chen, Yunhui Zhang, Guixiang Song, Naiqing Zhao,
Lili Jiang, and Bingheng Chen (2008) “Season, sex, age, and education as modifiers of the effectsof outdoor air pollution on daily mortality in Shanghai, China: The Public Health and Air
Pollution in Asia (PAPA) Study,” Environmental health perspectives, Vol. 116, pp. 1183–1188.
34

Krämer, Ursula, Christian Herder, Dorothea Sugiri, Klaus Strassburger, Tamara Schikowski,
Ulrich Ranft, and Wolfgang Rathmann (2010) “Traffic-related air pollution and incident type2 diabetes: results from the SALIA cohort study,” Environmental health perspectives, Vol. 118,
pp. 1273–1279.
Li, Xiaoqin and Yonghui Li (2022) “The impact of perceived air pollution on labour supply:
Evidence from China,” Journal of Environmental Management, Vol. 306, p. 114455.
Liang, Ruijuan, Biao Zhang, Xiaoyi Zhao, Yanping Ruan, Hui Lian, and Zhongjie Fan (2014)“Effect of exposure to PM2. 5 on blood pressure: a systematic review and meta-analysis,”
Journal of hypertension, Vol. 32, pp. 2130–2141.
Lin, Hualiang, Yanfei Guo, Yang Zheng, Qian Di, Tao Liu, Jianpeng Xiao, Xing Li, Weilin Zeng,
Lenise A Cummings-Vaughn, Steven W Howard et al. (2017) “Long-term effects of ambient
PM2. 5 on hypertension and blood pressure and attributable risk among older Chinese adults,”
Hypertension, Vol. 69, pp. 806–812.
McGarry, Kathleen (2004) “Health and retirement: do changes in health affect retirement expectations?” Journal of Human Resources, Vol. 39, pp. 624–648.
National Bureau of Statistics of China (2011) “The 6th national population census.”(2021) “The 7th national population census.”
Ostro, Bart D (1983) “The effects of air pollution on work loss and morbidity,” Journal ofEnvironmental Economics and Management, Vol. 10, pp. 371–382.
Pönkä, Antti (1990) “Absenteeism and respiratory disease among children and adults in Helsinkiin relation to low-level air pollution and temperature,” Environmental Research, Vol. 52, pp.
34–46.
Pui, David YH, Sheng-Chieh Chen, and Zhili Zuo (2014) “PM2. 5 in China: Measurements,
sources, visibility and health effects, and mitigation,” Particuology, Vol. 13, pp. 1–26.

35

Sun, Qinghua, Peibin Yue, Zhekang Ying, Arturo J Cardounel, Robert D Brook, Robert Devlin,
Jing-Shiang Hwang, Jay L Zweier, Lung Chi Chen, and Sanjay Rajagopalan (2008) “Air pollution exposure potentiates hypertension through reactive oxygen species-mediated activationof Rho/ROCK,” Arteriosclerosis, thrombosis, and vascular biology, Vol. 28, pp. 1760–1766.
World Health Organization and European Centre for Environment (2021) WHO global air quality
guidelines: particulate matter (PM2. 5 and PM10), ozone, nitrogen dioxide, sulfur dioxide andcarbon monoxide: World Health Organization.
Wu, Beibei, Tiemei Yan, and Ehsan Elahi (2023) “The impact of environmental pollution onlabor supply: empirical evidence from China,” Environmental Science and Pollution Research,
Vol. 30, pp. 25764–25772.
Xue, Xiaoxia, Jianping Chen, Baijun Sun, Baosen Zhou, and Xuelian Li (2018) “Temporal trends
in respiratory mortality and short-term effects of air pollutants in Shenyang, China,” Environmental Science and Pollution Research, Vol. 25, pp. 11468–11479.
Ying, Zhekang, Xiaohua Xu, Yuntao Bai, Jixin Zhong, Minjie Chen, Yijia Liang, Jinzhuo Zhao,
Dongyao Liu, Masako Morishita, Qinghua Sun et al. (2014) “Long-term exposure to concentrated ambient PM2. 5 increases mouse blood pressure through abnormal activation of thesympathetic nervous system: a role for hypothalamic inflammation,” Environmental healthperspectives, Vol. 122, pp. 79–86.
Zhao, Yaohui, John Strauss, Xinxin Chen, Yafeng Wang, Jinquan Gong, Qinqin Meng, Gewei
Wang, and Huali Wang (2020) “China health and retirement longitudinal study wave 4 user’s
guide,” National School of Development, Peking University, pp. 5–6.
Zhao, Yaohui, John Strauss, Gonghuan Yang, John Giles, Peifeng Hu, Yisong Hu, Xiaoyan Lei,
Albert Park, James P Smith, and Yafeng Wang (2013) “China health and retirement longitudinal study–2011–2012 national baseline users’ guide,” Beijing: National School of Development,
Peking University, Vol. 2.
Zhou, Xinping, Yangyang Xu, Shuo Yuan, Cai Wu, and Hao Zhang (2015) “Performance and36potential of solar updraft tower used as an effective measure to alleviate Chinese urban haze
problem,” Renewable and Sustainable Energy Reviews, Vol. 51, pp. 1499–1508.

37

Appendicies

Table A1. The Effect of PM2.5 on Retirement (2SLS): the Second Stage Results
Dependent Variable
Retirement Dummy
Variables
(1)
(2)
(3)
(4)
Panel A. Farmers' Sample
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2
Kleibergen-Paap Rank Wald
R-squared
Observations
Panel B. Non-farmers' Sample
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2
Kleibergen-Paap Rank Wald
R-squared
Observations
Control Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables

0.00165(0.00117)-0.000315**(0.000156)

0.00145(0.00123)-0.000286*(0.000158)

0.00195(0.00153)-0.000281*(0.000158)

8.97e-05(0.00216)-0.000262(0.000159)

2.08e-06

1.30e-06

1.09e-06

1.29e-06(5.16e-06)-0.00232(0.00203)(4.98e-06)-0.00250(0.00206)(4.97e-06)-0.00254(0.00207)(4.88e-06)-0.00251(0.00211)

0.000586***0.000594***0.000600***

0.000595***(6.25e-05)
153.7
0.198

(6.28e-05)
197.9
0.211

(6.28e-05)
195.9
0.214

(6.33e-05)
211.2
0.220

7,742

7,742

7,742

7,742

0.00506***(0.00192)-0.000663**(0.000303)

0.00491**(0.00200)-0.000615*(0.000314)

0.00694***(0.00228)-0.000631**(0.000308)

0.00715**(0.00281)-0.000656**(0.000306)

1.57e-05*

1.38e-05

1.45e-05

1.54e-05*(9.17e-06)
0.0331***(0.00410)(9.52e-06)
0.0329***(0.00415)(9.30e-06)
0.0326***(0.00400)(9.23e-06)
0.0325***(0.00397)-0.000128-0.000132-0.000129-0.000132(0.000112)
99.63
0.351

(0.000115)
103.2
0.358

(0.000110)
157.3
0.365

(0.000109)
137.8
0.369

3,162

3,162

3,162

3,162

✔

✔
✔

✔
✔
✔

✔
✔
✔
✔

Notes: This is the detail version of Table 9.It shows the estimated coefficients of other variablesof the specifications presented in Table 9. Notes of Table 9 apply. *** p<0.01, ** p<0.05, * p<0.1.

38

Table A2. The Effect of PM2.5 on Having Each Disease (2SLS)The Second Stage Estimation: Farmers' Sample(2)0.000220(0.000685)
1.27e-05(7.40e-05)

Lungdiseases(3)
0.00236(0.00161)-7.50e-06(0.000167)

Heart
diseases(4)0.00174(0.00142)-0.000112(0.000146)(5)0.00112(0.000885)-3.22e-05(0.000114)-2.10e-06-3.96e-07

2.69e-06

5.61e-06

2.78e-06(5.02e-06)
0.0124***(0.00201)(2.10e-06)
0.00294***(0.000787)(4.19e-06)
0.00124(0.00178)(3.60e-06)
0.00689***(0.00164)(3.19e-06)-0.000445(0.000941)-0.000135**-6.38e-05***

4.99e-05-0.000115***

3.91e-05(6.21e-05)(2.34e-05)(4.86e-05)(4.16e-05)(2.75e-05)

✔
✔
✔
✔
211.2
0.051
7,742

✔
✔
✔
✔
211.2
0.011
7,742

✔
✔
✔
✔
211.2
0.032
7,742

✔
✔
✔
✔
211.2
0.064
7,742

✔
✔
✔
✔
211.2
0.015
7,742

Dependent Variables

Hypertension

Diabetes

Variables
PM2.5 – meanPM2.5(1)-0.000530(0.00162)
2.67e-05(0.000161)(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2

AsthmaControl Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables
Kleibergen-Paap Rank Wald
R-squared
Observations

Notes: This is the detail version of Table 10. It shows the estimated coefficients of other variables of thespecifications presented in Table 10. Notes of Table 10 apply. *** p<0.01, ** p<0.05, * p<0.1.

39

Table A3. The Effect of PM2.5 on Having Each Disease (2SLS)The Second Stage Estimatoin: Non-farmers' Sample(2)0.000795(0.00123)
0.000171(0.000164)

Lung
Diseases(3)0.000349(0.00151)
7.80e-07(0.000189)

Heart
Diseases(4)0.00325*(0.00187)-0.000392**(0.000184)(5)-0.000521(0.000715)
0.000145(9.37e-05)

1.98e-05**-6.74e-06

2.38e-06

1.25e-05**-5.10e-06(9.30e-06)
0.0194***(0.00331)(5.93e-06)
0.00666***(0.00158)(6.54e-06)
0.000230(0.00208)(5.94e-06)
0.00851***(0.00197)(3.20e-06)
0.00184*(0.00106)-0.000275***-0.000115**

0.000109-5.39e-05-5.86e-07(9.62e-05)(5.40e-05)(6.82e-05)(6.51e-05)(3.58e-05)

✔
✔
✔
✔
137.8
0.076
3,162

✔
✔
✔
✔
137.8
0.028
3,162

✔
✔
✔
✔
137.8
0.037
3,162

✔
✔
✔
✔
137.8
0.094
3,162

✔
✔
✔
✔
137.8
0.022
3,162

Dependent variables

Hypertension

Diabetes

Variables
PM2.5 – meanPM2.5(1)0.00146(0.00272)-0.000504(0.000307)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2(Age – 45)
× (PM2.5 – meanPM2.5)

AsthmaControl Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables
Kleibergen-Paap Rank Wald
R-squared
Observations

Notes: This is the detail version of Table 11. It shows the estimated coefficients of other variables of thespecifications presented in Table 11. Notes of Table 11 apply. *** p<0.01, ** p<0.05, * p<0.1.

40

Table A4. The Effect of PM2.5 on Retirement : Robustness Checks
Using Province Level PM2.5 for Analysis : the Second Stage Results of 2SLS
Retirement Dummy
Dependent Variables
Variables
Panel A. Farmers' Sample
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2
Kleibergen-Paap Rank Wald
R-squared
Observations
Panel B. Non-farmers' Sample
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2
Kleibergen-Paap Rank Wald
R-squared
Observations
Control Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables

(1)

(2)

(3)

(4)

0.00132(0.00204)-0.000151(0.000318)-0.00116(0.00272)-0.000131(0.000299)-0.000409(0.00368)-0.000133(0.000302)-0.00100(0.00415)-9.98e-05(0.000300)-1.14e-07-9.08e-07-9.84e-07-1.18e-06(6.56e-06)-0.00365(0.00224)(6.49e-06)-0.00344(0.00221)(6.45e-06)-0.00345(0.00220)(6.31e-06)-0.00348(0.00222)

0.000598***

0.000602***0.000606***

0.000605***(6.47e-05)
50.40
0.192
7,742

(6.54e-05)
54.79
0.210
7,742

(6.37e-05)
54.31
0.212
7,742

(6.38e-05)
72.32
0.219
7,742

0.00635**(0.00250)-0.000862**(0.000423)

0.00459(0.00287)-0.000808*(0.000442)

0.00834**(0.00350)-0.000818*(0.000446)

0.00783*(0.00462)-0.000858**(0.000428)

2.17e-05*

1.97e-05

1.98e-05

2.12e-05*(1.25e-05)
0.0327***(0.00324)(1.31e-05)
0.0325***(0.00346)(1.31e-05)
0.0321***(0.00329)(1.26e-05)
0.0321***(0.00315)-0.000121-0.000127-0.000120-0.000124(9.61e-05)
45.97
0.351
3,162

(0.000103)
50.50
0.358
3,162

(9.74e-05)
29.24
0.366
3,162

(9.23e-05)
22.21
0.369
3,162

✔

✔
✔

✔
✔
✔

✔
✔
✔
✔

Notes:This is the detail version of Table 13. It shows the estimated coefficients of other variablesof the specifications presented in Table 13. Notes of Table 13 apply. *** p<0.01, ** p<0.05, *p<0.1.

41

Table A5. The Effect of PM2.5 on Having Each Disease
Using Province Level PM2.5 for Analysis : the Second Stage Results of 2SLS
Farmers' Sample
Dependent variables

Hypertension

Diabetes

Variables
PM2.5 – meanPM2.5(1)-0.000704(0.00217)
0.000274(0.000296)(2)-2.45e-05(0.000889)
8.67e-05*(4.53e-05)(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2

Lung
Diseases(3)
0.00214(0.00159)-5.37e-05(0.000164)

Heart
Diseases(4)-0.000901(0.00162)
7.02e-05(0.000181)

Asthma(5)0.00170***(0.000661)
1.43e-05(9.86e-05)-9.88e-06-2.66e-06*

3.35e-06

1.45e-06

1.86e-06(9.73e-06)
0.0115***(0.00214)(1.53e-06)
0.00270***(0.000568)(3.90e-06)
0.00144(0.00157)(4.59e-06)
0.00618***(0.00171)(3.09e-06)-0.000645(0.000900)-0.000111*-5.67e-05***

5.11e-05-9.31e-05**

4.66e-05*(6.53e-05)(1.78e-05)(3.79e-05)(4.72e-05)(2.64e-05)

✔
✔
✔
✔
72.32
0.051
7,742

✔
✔
✔
✔
72.32
0.011
7,742

✔
✔
✔
✔
72.32
0.030
7,742

✔
✔
✔
✔
72.32
0.062
7,742

✔
✔
✔
✔
72.32
0.014
7,742

Control Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables
Kleibergen-Paap Rank Wald
R-squared
Observations

Notes: Clustering robust standard errors in parentheses assuming that the error terms are correlated withineach province. PM2.5 is the average PM2.5 concentration in the past ten years in each province. meanPM2.5
is the national average of PM2.5 in the past ten years. Column (1)-(5) show different estimation results ofdifferent regression which use each type of disease dummy as a dependent variable. *** p<0.01, ** p<0.05, *p<0.1.

42

Table A6. The Effect of PM2.5 on Having Each Disease
Using Province Level PM2.5 for Analysis : the Second Stage Results of 2SLS
Non-farmers' Sample
Lung
Diseases(3)-0.00172(0.00197)
0.000214(0.000166)

Heart
Diseases(4)0.00209(0.00273)-0.000227(0.000197)

Dependent variables

Hypertension

Diabetes

Variables
PM2.5 – meanPM2.5(1)-0.000739(0.00451)-0.000244(0.000171)(2)0.00308(0.00219)
0.000362**(0.000177)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45

1.36e-05***-1.29e-05*-5.17e-06

9.16e-06-3.96e-06(4.83e-06)
0.0181***(0.00233)(6.75e-06)
0.00643***(0.00154)(7.04e-06)-0.000197(0.00189)(6.89e-06)
0.00759***(0.00211)(4.87e-06)
0.00204*(0.00119)(Age – 45)2

-0.000233***-0.000110**0.000128*-2.78e-05-8.95e-06(6.17e-05)(5.00e-05)(6.68e-05)(6.91e-05)(3.87e-05)

✔
✔
✔
✔
22.21
0.075
3,162

✔
✔
✔
✔
22.21
0.029
3,162

✔
✔
✔
✔
22.21
0.037
3,162

✔
✔
✔
✔
22.21
0.093
3,162

✔
✔
✔
✔
22.21
0.022
3,162

(Age – 45)
× (PM2.5 – meanPM2.5)

Asthma(5)0.000263(0.00121)
0.000128(0.000139)

Control Variables
Individual Variables
Region Fixed Effect
Province Variables
City Variables
Kleibergen-Paap Rank Wald
R-squared
Observations

Notes: Clustering robust standard errors in parentheses assuming that the error terms are correlated withineach province. PM2.5 is the average PM2.5 concentration in the past ten years in each province. meanPM2.5
is the national average of PM2.5 in the past ten years. Column (1)-(5) show different estimation results ofdifferent regression which use each type of disease dummy as a dependent variable. *** p<0.01, ** p<0.05, *p<0.1.

43

Table A7. The Effect of PM2.5 on Retirement (2SLS): the Second Stage Results
Dropping Respondents who Moved to the Current Place after 2004
Dependent variable
Retirement Dummy
Variables
Panel A. Farmers' Sample
PM2.5 – meanPM2.5

(1)

(2)

(3)

(4)

0.00216**(0.00104)-0.000356**(0.000170)

0.00185(0.00126)-0.000319*(0.000172)

0.00217(0.00153)-0.000316*(0.000172)-3.47e-05(0.00219)-0.000294*(0.000173)

2.77e-06

1.82e-06

1.70e-06

1.87e-06(5.51e-06)-0.00265(0.00214)(5.30e-06)-0.00287(0.00215)(5.28e-06)-0.00289(0.00216)(5.19e-06)-0.00283(0.00220)(Age – 45)2

0.000597***0.000606***

0.000611***0.000606***

Kleibergen-Paap Rank Wald
R-squared
Observations(6.51e-05)
154.8
0.199
7,666

(6.48e-05)
212.2
0.213
7,666

(6.46e-05)
208.6
0.215
7,666

(6.50e-05)
214.1
0.221
7,666

0.00496**(0.00207)-0.000717**(0.000301)

0.00464**(0.00217)-0.000685**(0.000304)

0.00650***(0.00240)-0.000696**(0.000301)

0.00656**(0.00283)-0.000727**(0.000300)(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45

Panel B. Non-farmers' Sample
PM2.5 – meanPM2.5(Age – 45)
× (PM2.5 – meanPM2.5)(Age – 45)2× (PM2.5 – meanPM2.5)
Age – 45
(Age – 45)2
Kleibergen-Paap Rank Wald
R-squared
Observations
Control Variables
Individual variables
Region fixed effect
Province variables
City variables

1.75e-05*

1.60e-05*

1.67e-05*

1.78e-05*(9.19e-06)
0.0337***(0.00399)(9.31e-06)
0.0335***(0.00402)(9.17e-06)
0.0333***(0.00392)(9.13e-06)
0.0332***(0.00389)-0.000147-0.000148-0.000148-0.000152(0.000109)
96.55
0.351
3,052

(0.000112)
100.2
0.358
3,052

(0.000108)
149.1
0.365
3,052

(0.000107)
138.7
0.368
3,052

✔

✔
✔

✔
✔
✔

✔
✔
✔
✔

Notes: The sample is restricted to respondedents who lived even before 2004 in the current
place. Notes of Table 9 apply. *** p<0.01, ** p<0.05, * p<0.1.

44