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Journal of Health Economics 42 (2015) 90–103

Contents lists available at ScienceDirect

Journal of Health Economics

journa l h om epa ge: www.elsev ier .com/ locate /econbase

nvironmental regulations on air pollution in China and their impactn infant mortality

hinsuke Tanaka ∗

ufts University, United States

r t i c l e i n f o

rticle history:eceived 27 January 2014eceived in revised form 24 February 2015ccepted 26 February 2015vailable online 6 March 2015

EL classification:56

1853

12

a b s t r a c t

This study explores the impact of environmental regulations in China on infant mortality. In 1998, theChinese government imposed stringent air pollution regulations, in one of the first large-scale regulatoryattempts in a developing country. We find that the infant mortality rate fell by 20 percent in the treatmentcities designated as “Two Control Zones.” The greatest reduction in mortality occurred during the neonatalperiod, highlighting an important pathophysiologic mechanism, and was largest among infants bornto mothers with low levels of education. The finding is robust to various alternative hypotheses andspecifications. Further, a falsification test using deaths from causes unrelated to air pollution supportsthese findings.

© 2015 Elsevier B.V. All rights reserved.

13

eywords:nfant mortalityir pollutionnvironmental regulationhina

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. Introduction

There is little disagreement that air pollution poses a major envi-onmental risk to human health. Improved air quality worldwides correlated with the amelioration of numerous health problems,ncluding respiratory infections, cardiovascular diseases, and lungancer.1 Developing countries rank highest in air pollution world-ide, and children under age five in these countries are considered

o be the most vulnerable population. Elevated air pollution isenerally beyond the scope of individual control and falls to theublic sector. However, environmental regulations on air pollution

re extremely contentious. This was evident at the 2009 Unitedations Climate Change Conference, also known as COP15, held inopenhagen, where many developing countries refused to commit

∗ Correspondence to: The Fletcher School, Tufts University, 160 Packard Avenue,edford, MA 02155, United States. Tel.: +1 6176274619.

E-mail address: Shinsuke.Tanaka@tufts.edu1 A sizable literature documents health risks associated with air pollution expo-

ure. See, for example, Schwartz et al. (1996), Levy et al. (2000), Samet et al. (2000),hay and Greenstone (2003a), Currie and Neidell (2005), and Currie et al. (2009),nd Arceo et al. (2012).

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ttp://dx.doi.org/10.1016/j.jhealeco.2015.02.004167-6296/© 2015 Elsevier B.V. All rights reserved.

hemselves to a legal framework for reducing pollution emissions.heir opposition is largely due to the prevailing concern that theconomic costs associated with pollution abatement may outweighhe health benefits. Accordingly, air pollution regulations are stillare in developing countries, and whether, and to what extent,nvironmental regulations on air pollution lead to health benefitsemains an important question yet to be answered.

In this paper, we examine the effect of environmental regula-ions pertaining to air pollution on infant mortality in China. Ashina’s economy continued to grow at unprecedented rates for the

ast several decades, ambient air quality deteriorated to one of theorst levels in the world due to its heavy reliance on coal-fired

nergy generation. In 1998, the Chinese government imposed strin-ent regulations on pollutant emissions from power plants, in onef the first attempts on such a large scale in developing countries.his so-called the Two Control Zone (TCZ) policy designated nearly75 prefectures exceeding the nationally mandated pollutiontandards as the TCZ. In these areas, the power industry, which con-

ributed more than 90 percent of air pollution, was forced to reducemissions and install new pollution control technologies, while alsohutting down a massive number of small, inefficient power plants.or our purpose, the TCZ policy provides a quasi-experimental

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The current research design has several advantages over previ-ous studies. First, this study focuses on infants, not only because

system. Second, even if pollution is successfully reduced to some extent, infant mor-tality may not fall if a concave relationship between mortality and pollution levelleads to low marginal pollution effect at high concentration levels. Third, magni-tudes of impacts in reducing air pollution should be greater if people have limitedaccess to medical services, initially have lower health status, and/or have limitedknowledge in avoiding pollution.

3 Since the most heavily affected industry is the power industry, which was adriving force behind China’s rapid economic growth, the findings in this studyaccordingly highlight the important tradeoffs among economic growth, environ-mental quality, and human health. See Tanaka et al. (2014) for the impact of theenvironmental regulation on industrial performance.

4 A relatively smaller-scale air quality regulatory regime, targeting a differentindustry, in another developing country, can be found in the Indian transportationsector, which was mandated to use compressed national gas vehicles in Delhi duringworking hours. Kumar and Foster (2007) show its effect on respiratory health.

S. Tanaka / Journal of Heal

nvironment, wherein the intensity of exposure to the regulationsan be defined by the TCZ regulatory status, and we are able to com-are changes in infant mortality rate (IMR) before and after the pol-

cy reform, between the cities assigned and not assigned as the TCZ.To implement the analysis, we draw IMR data from the Chi-

ese Disease Surveillance Points (DSP) system that collected birthnd death registrations for 145 nationally representative sites from991 through 2000. IMR, defined as the number of infant deathsnder age one per 1000 live births in a given year, is available forach DSP site by year level, linked with detailed information onirth characteristics and parental attributes. We match this dataseto the TCZ regulatory status assigned to individual cities, based onhe governmental report, and thereby estimate the treatment effectf the regulations.

We find that the air pollution regulations led to significanteductions in infant mortality. The difference-in-differences esti-ates suggest that the regulations have led to 3.29 fewer infant

eaths per 1000 live births than would have occurred in the absencef the regulations. This corresponds to a 20 percent reductionn IMR. 63 percent of the reduction in infant mortality occurreduring the neonatal period, highlighting an important pathophysi-logic mechanism, and the greatest reduction of mortality occurredmong children born to mothers with low educational levels.

A major methodological challenge, however, is that the TCZ des-gnation rule may not be orthogonal to unobserved characteristicshat contribute to reductions in air pollution and infant mortal-ty. The present study conducts a number of robustness checksnd a falsification test to address this issue. First, we confirm thathe TCZ status has little association with changes in observableovariates, assuring that there is no systematic difference in con-urrent trends in observable characteristics between the TCZ andon-TCZ cities. Although this is not a direct test of the exclusionestriction, since it requires that TCZ status not be correlated withrends in unobservable factors, this result leads us to believe thathe treatment effect is less likely to be confounded by differentialrends in unobservable factors as well (Altonji et al., 2005). Second,he regression is also directly adjusted for differential pre-existingrends in mortality, yet the estimates are essentially unaffected.astly, the policy had no impact on infant deaths due to acciden-al causes. The absence of a causal mechanism linking air pollutiono these causes of death serves as falsification evidence, suggest-ng that differences in access to or quality of medical services andechnologies cannot be the sources of bias. Overall, there is no evi-ence that the estimates are driven by inappropriate identificationssumptions, leading us to believe that the treatment effect basedn the TCZ status is indeed not spurious but causal.

This study makes three major contributions to the existing liter-ture. First, by exploiting regulation-induced changes in air quality,t addresses a policy-relevant question: to what extent do envi-onmental regulations in developing countries lead to reductionsn infant mortality? Several prior studies have focused on vari-tion in air quality induced by recession (Chay and Greenstone,003a), weekly fluctuations (Currie and Neidell, 2005), wildfiresJayachandran, 2009), or wind directions (Luechinger, 2014). Chaynd Greenstone (2003b) provide compelling evidence for the link-ge between the Clean Air Act of 1970 and infant mortality in the

.S. It remains to be determined, however, whether, and how effec-

ively, environmental regulations can improve human health ineveloping countries.2 A recent study by Greenstone and Hanna

2 Extrapolating evidence in developed countries to developing countries is dif-cult for a number of reasons. First and foremost importantly, we do not knowhether environmental regulations in developing countries had any impact in

educing pollution due to weaker implemental mechanisms and an enforcement

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nomics 42 (2015) 90–103 91

2011) examines regulations on air pollution and water pollutionn India since 1987. They find these regulations efficacious in reduc-ng air pollution, but such reductions led to modest and statisticallynsignificant reductions in infant mortality. Our study providesn interesting contrast, finding that infant mortality significantlyesponded to the environmental regulation. Further, the regulatione focus on targeted coal for energy generation, which is the major

ontributor to air pollution in many other developing countries asell, whereas Greenstone and Hanna (2011) focus on vehicularollution.3,4 The findings in this study accordingly present relevantstimates for the effect of environmental regulations in develop-ng countries implementing similar policies on coal in the powerndustry.

Second, the present study contributes to our understandingf the relationship between air pollution and infant mortality atreater concentration levels. Previous evidence is predominantlyerived from the United States or other developed countries, whereollution is relatively low.5 Since we know little about the shape ofhe dose–response relationship, it is consequently difficult to pre-ict the marginal impact of pollution reduction in the presence ofon-linear relationship.6 Air pollution in China is one of the highest

n the world. Its total suspended particulates (TSP) level in 1995 wasour times higher than the WHO standards, and four times higherhan the level in the United States in 1970, when the Clean Airct was amended, as examined in Chay and Greenstone (2003b).hus, estimates in China provide compelling evidence applicable tohe distinctive context of developing countries where air pollutionevels are relatively high.

Third, there is extensive literature showing differential patternsccording to socioeconomic status, yet it is still an open question aso whether air pollution also exhibits differential impact on infant

ortality (Currie and Hyson, 1999; Case et al., 2002; Jayachandran,009). While infants in poor countries are considered to be theost susceptible to the effect of pollution, not only because of

igh pollution levels but also because families lack the resourcesr knowledge necessary to avoid exposure, the impacts may bemall if air pollution does not have first-order effect on them.7 Thus,he present study helps identify vulnerable population in designing

5 Examining the pollution effect at low levels, especially levels lower than whats often considered to be the standard, is also an interesting question in itself.urrie and Neidell (2005) find that CO has significant impact on infant mortality

n California over the 1990s at relatively low levels.6 For example, Arceo et al. (2012) show a non-linear relationship between CO

nd health in Mexico. Evidence in developed countries may understate the impactf pollution reduction in developing countries if the marginal impact of pollution isigher at greater concentration levels.7 For example, people with poor health tend to stay indoor with little exposure

o pollution. Children from rich households, who tend to have better health, may bexposed more to pollution if they are more likely to have outside activities.

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pcttemissions for heating,15 whereas the acid rain control zones wereprimarily in the south, where heat, humidity, and solar radiationcombine to create high atmospheric acidity. Hence, acid rain in the

2 S. Tanaka / Journal of Heal

hey are particularly vulnerable to air pollution due to their weakespiratory system, but because focusing on infant mortality mit-gates complicating factors associated with adult mortality. Forxample, adult deaths correlate more closely to chronic diseaseonditions than to acute changes in air quality. In addition, adultsay migrate into less polluted areas. Addressing infant mortality

ircumvents these issues, if not completely, because it is relativelyess difficult to identify causes of death during the first year ofirth, and because migration rates are low for pregnant women and

nfants. Lastly, China is not only one of the most polluted countriesut also one of the first developing countries to regulate air pollu-ion on such a large-scale. It is evident that China serves as a rareesearch environment in which to assess the impact of environ-ental regulations at greater concentration levels.The rest of the paper is organized as follows. Section 2 provides

he historical background on air pollution and national air pollutionegulations in China. Section 3 describes the data and the descrip-ive statistics. Section 4 presents the econometric framework andts validity, and Section 5 presents empirical results. Section 6 con-ludes.

. Background on air pollution and regulations in China

.1. Brief history

China is infamous for its air pollution, due to emissions from aower sector that relies heavily on coal to generate electric power.s the world’s largest coal producer, China possesses abundantnd relatively cheap coal, which constitutes the country’s primarynergy resource endowment, accounting for 75.5 percent of totalnergy production in 1995 (National Bureau of Statistics of China,006). However, coal generally emits more pollutants than otherossil fuels. As China underwent rapid economic growth, total SO2missions increased from 18.4 million tons in 1990 to 23.7 millionons in 1995, and the ambient air pollution rose to levels detri-

ental to human health (State Environmental Protection AgencySEPA], 1996).

Fig. 1 illustrates the world distribution of TSP (Panel A) and SO2Panel B) concentration levels in 1995. The TSP level in Beijing, theapital city of China, was 377 �g/m3, almost four times higher thanhe WHO guideline of 90 �g/m3, and its SO2 concentration level was0 �g/m3, almost double the WHO guideline of 50 �g/m3 (WHO,002). SO2 is also an important precursor of acid rain. From the980s to the mid-1990s, the area of China experiencing acid rainxpanded by more than 1 million km2 (Yang and Schreifels, 2003).

During that decade, elevated air pollution gave rise to increas-ng public concern about adverse impacts on human health.8

n response, the Chinese government formulated a series of

8 It is generally known that the smaller a particulate, the more detrimental it iso health. For example, PM10 or PM2.5, the particles with a diameter of 10 or 2.5

icrometers or less, respectively, or toxic gas, such as SO2, are considered to behe most hazardous because, when inhaled, these particulate matters or gas canenetrate deep into the lungs and interfere with internal gas exchange. Further,aden et al. (2000) find that fine particles emitted from combustion sources (i.e.,otor vehicles or coal combustion) have a stronger association with mortality than

hose from non-combustion sources. Alternatively, SO2 becomes sulfuric acid whent interacts with water, which is the main component of acid rain that may have airect or indirect impact on health. Yet, epidemiological evidence of the impact ofO2 on mortality in developed countries is somewhat mixed. While Mendelsohn andrcutt (1979) show close associations between the two, SO2 is also considered a less

mportant determinant of mortality (Schwartz and Marcus, 1990; Nielsen and Ho,007). Hedley et al. (2002) is one of the few intervention studies that investigateshanges in SO2 caused by an overnight restriction on all power plants and roadehicles in Hong Kong using fuel oil with a sulfur content of more than 0.5 percent.hey found that the intervention resulted in an immediate reduction in ambientO2 concentrations and a reduction in death rates particularly due to respiratory

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nvironmental regulatory policies, the first version of whichas enacted in 1987, known as the Air Pollution Prevention andontrol Law (APPCL). This original law, however, failed to reduceir pollution, mainly because it excluded the power sector, theajor contributor of SO2 emissions (Qian and Zhang, 1998). Evenorse, SO2 emissions continued to surge, and areas affected by

cid rain expanded.APPCL was consequently amended in 1995. The major part of

he amendment was to include a section to regulate pollutant emis-ions and coal combustion, particularly regarding the usage of highulfur-content coal, at power plants (Hao et al., 2007). Although the995 APPCL still had a weak enforcement mechanism and limitedfficacy, a prominent feature of the amendment was to propose auture regional strategy, which would identify priority regions tomprove air quality and prevent the spread of acid rain.9,10

This was officially approved and implemented as the Two Con-rol Zone (TCZ) policy in January 1998 (State Council, 1998). Thisegislation designated prefectures exceeding nationally mandatedhresholds as either acid rain control zone or SO2 pollution controlone.11 Based on the records in preceding years12, prefectures wereesignated as SO2 pollution control zone if;

Average annual ambient SO2 concentrations exceeded the ClassII standard,13

Daily average concentrations exceeded the Class III standard, orHigh SO2 emissions were recorded.14

Alternatively, prefectures were designated as acid rain controlone if

Average annual pH values for precipitation were less than orequal to 4.5,Sulfate deposition was greater than the critical load, orHigh SO2 emissions were recorded.

In total, 175 prefectures out of 333 prefectures across 27rovinces were designated as TCZs. They accounted for 40.6 per-ent of its population, 62.4 percent of GDP, and 58.9 percent ofhe total SO2 emissions in 1995 (Hao et al., 2001). The SO2 pollu-ion control zone was concentrated in the north due to high SO2

nd cardiovascular reasons. See also Aunan and Pan (2004) and Matus et al. (2012)or health effects of air pollution in China.

9 Article 27 of the 1995 APPCL stipulates: “The environmental protection depart-ent under the State Council together with relevant departments under the State

ouncil may, in light of the meteorological, topographical, soil and other naturalonditions, delimit the areas where acid rain has occurred or will probably occurnd areas that are seriously polluted by sulfur dioxide as acid rain control areas andulfur dioxide pollution control areas, subject to approval by the State Council.”10 It is a standard practice of policy experimentation in China to implement strate-ies in a particular region or for a set of time period, attempting to demonstrate theirffectiveness before expanding their implementation to the entire nation.11 In this sense, the legislation can be considered to be parallel to the attainmentnd nonattainment county designation by the Clean Air Acts in the United States.12 The original document does not specify exactly which years of records they refero.13 According to the Chinese National Ambient Air Quality Standards (CNAAQS)or SO2, Class I standard designates an annual average concentration level notxceeding 20�g/m3, Class II ranges 20 �g/m3 < SO2 < 60 �g/m3, and Class III ranges0 �g/m3 < SO2 < 100 �g/m3. Cities should meet Class II, which is considered to be

ess harmful.14 The original document does not specify the levels of SO2 emissions that areonsidered to be “high.”15 See Almond et al. (2009) for the impact of heating policy, which created aiscrepancy in air quality north and south of the Huai River.

S. Tanaka / Journal of Health Economics 42 (2015) 90–103 93

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outh cannot necessarily be attributed to SO2 emissions travelingown from the north, but is rather due to local emissions. This isven more evident because acid deposition is the greatest in theummer, when wind direction is generally south to north.

The TCZ status enforced more stringent regulations mandatinghe use of less high-sulfur coal and the development of clean coalechnology. For example;

No new coal mines producing coal with a sulfur content higher

than 3-percent can be established, and existing mines that pro-duce such coal must gradually be shut down or reduce output.Construction of any new coal-burning thermal power plants inlarge and medium-sized prefectures is prohibited.

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orld distribution of the TSP in Panel A and SO2 in Panel B in 1995.

All new and renovated power plants are required to use coal withless than 1 percent sulfur content.Existing power plants using coal with sulfur content above 1percent are required to install flue gas desulfurization (FGD)equipment.

.2. Effectiveness of TCZ policy

Various studies have documented the effectiveness of TCZ reg-

latory actions in reducing pollutant emissions and improving airuality. For example at the national level, SO2 emissions fell from3.67 million tons in 1995 to 19.95 million tons in 2000, and theercentage of prefectures exceeding the Class II standard fell from

9 th Economics 42 (2015) 90–103

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Table 1Descriptive statistics of baseline sample.

Mean Std. Dev. Min. Max. Obs.

Panel A: Birth characteristicsIMR from death record 16.61 16.79 0 202.53 556IMR from birth record 9.62 13.43 0 133.18 550% of Boy 0.55 0.04 0.33 0.71 550% of January births 0.11 0.07 0 1 548Birth order 1.40 0.28 1 3.39 551

Panel B: Household characteristicsMother’s age 25.37 1.35 22.18 30.45 552Maternal education ≥H.S. 0.22 0.30 0 1 547% of Han 0.89 0.25 0 1 553

Panel C: District characteristicsTotal population 106,551 53,062 5267 251,707 556Number of infants 1,706 1,213 44 6,747 556% of city and above 0.46 0.50 0 1 553

Notes: Each column presents the variable mean, standard deviation, the minimumvDt

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4 S. Tanaka / Journal of Heal

4 percent in 1995 to 20.7 percent in 2000. By the end of 1999,ollieries producing more than 50 million tons of high-sulfur coalad been closed (Hao et al., 2001). By the end of 2000, the totalower capacity with FGD equipment exceeded 10,000 MW, andmall thermal power plants, with output capacity below 50 MW,ere actively shut down because they were relatively less efficient,ad high coal consumption rates, and emitted massive amounts ofollutants. This reduced raw coal consumption by10 million-tonsnd SO2 emissions by 0.4 million tons (Yang et al., 2002).

Importantly for our identification strategy, reductions in airollution were more remarkable in TCZ cities. Among TCZs, SO2missions fell by about 3 million tons, and about 71 percent of allactories producing over 100 tons of emissions per year reducedheir SO2 emissions to the standard between 1998 and 2000 (Het al., 2002). Between 1998 and 2005, the number of prefecturesn the SO2 pollution control zones meeting the Class II standardose by 12.3 percent, those meeting the Class III standard increasedy 4.2 percent, and those not meeting the Class III standard felly 16.5 percent. Within the acid rain control zone, the numberf prefectures meeting the Class II standard rose by 3.3 percent,hose meeting the Class III standard increased by 7.9 percent, andhose not meeting Class III decreased by 11.2 percent (Unitedations Environment Programme, 2009). Online Appendix III pro-ides available evidence on the effect of TCZ regulation on airuality.

. Data sources and descriptive statistics

.1. Data sources

Infant mortality. The micro-level data on infant mortality comerom the Chinese Disease Surveillance Points (DSP) system. TheSP covers 145 sites, primarily at the county-level,16 establishedn the representative sample of the national population. It reportshe censuses of death and birth registrations for the sampleopulation of 10 million residences (approximately 1 percent ofhe national population) in various geographic areas across 31rovinces, autonomous regions, and municipalities in China.

Overall, the original data record approximately 500,000 deathsfor all ages) and 1,000,000 births from 1991 through 2000, fromhich the dataset we obtained was aggregated to the DSP site

y year level.17 The birth record reports whether or not infantsied within the calendar year, and if they did, the cause of death,sing the International Classification of Disease, 9th Revision (ICD-) codes. For our purpose, we use the birth registration to measurehe infant mortality rates, because it additionally contains variableselevant for the analysis, i.e., the infant’s characteristics such as gen-er, the date of birth, birthweight, length of gestation period, and

irth order as well as maternal demographics such as age, educa-ion, and race. On the other hand, the death record is less useful inur analysis because it only reports the age in years and cause of

16 There are four main administrative divisions in China: the province, the prefec-ure, the county, and the township. The “county” level refers to districts, county-levelities, or counties. 17 out of 145 sites are at the prefecture level, while the rest aret one of the county-level divisions.17 Because the latest DSP data available at the city level end in 2000 and infor-ation in the subsequent years are unfortunately not accessible, the analysis

ecessarily ends then. Some evidence indicates that pollution has increased duringhe twenty-first century, after the coverage of our dataset, to bolster rapid domesticconomic growth, which almost negated the effect of TCZ policy. Thus, the effectf TCZ policy was indeed concentrated in the first several years, which our datasetovers but likely to have dissipated afterwards. Subsequent policies such as subsi-izing sulfur scrubber or other incentive programs to control SO2 emissions haveeen implemented since 2006. The effects of these policies are beyond the scope ofur paper.

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eath without information on birth date or family attributes. Fur-her details on how these 145 sites were selected, and how the dataere processed at each site can be found in Online Appendix I.

TCZ regulatory status. The TCZ regulatory status is reported inhe document “Official Reply to the State Council Concerning Acidain Control Areas and Sulfur Dioxide Pollution Control Areas,”ublished by the State Council in 1998. The assignments are pri-arily made at the prefecture level, while those in municipalities

re at the county level. The document lists the names of all pre-ectures that are designated as within the Acid Rain Control Zoner the SO2 Control Zone. Hence, we can merge this information tohe DSP sites, and those sites that are in the TCZ prefectures com-rise the treatment group in the analysis.18 In total, 61 of 145 DSPites are in the TCZ prefectures and thus comprise the treatmentroup, and 84 sites are in the non-TCZ prefectures, forming theontrol group. More detailed rules on matching DSP sites and TCZegulatory status are described in Online Appendix I.

.2. Descriptive statistics

Table 1 presents the descriptive statistics of the baseline samplen the pre-reform period (prior to 1995).19 The weighted average

eans of observations at the DSP site by year level are calculatedsing population as the weight. The table shows that the mean IMR

s 16.61 per 1000 live births, using the death record.20 As expected,

ithin a calendar year.21 It is worth emphasizing that since the

18 Because the regulatory actions were the same between the SO2 and acid rainontrol zones, we consider both areas as comprising one treatment group.19 We use pre-1995 as the baseline observations in case the APPCL amendmentad any contribution to pollution reductions. We address this issue by separatingontrolling for this period up to when the TCZ policy was implemented (i.e., yearsetween 1995 and 1997).20 By 2003, there was a concern that the DSP system might not comprise a rep-esentative sample of China, being more concentrated in developed and populousreas than rural ones. Indeed, according to the estimates by the World Bank, theverage IMR over the same period was 38.2 per 1000 live births. Adjustments haveeen made to the DSP system by extending the number of sites to 160 after our studyeriod. For the purpose of our study, the issue is less important, as we estimate theithin-city impacts.

21 Note that the definition of IMR will differ according to whether one is usinghe death record or the birth record. Using the death record, the number of infantsho did not make it to age one per 1000 live births is measured, whereas using

th Economics 42 (2015) 90–103 95

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ain analysis focuses on the relative changes in IMR, a question ofhether the levels of IMR in the birth record are underestimated

r not does not invalidate the use of the birth record, unless theata were intentionally manipulated to respond to the TCZ regu-

ation, which is unlikely due to the quality control enforced by thenstitution that has little to do with SEPA. If there is any concern

ith using the birth data, it is that the impact may be understatedf reductions in infant deaths are truncated at zero. As the tablehows, several DSP sites report no occurrence of infant deaths inoth death and birth records. This is not surprising, given that theyave a small number of births. To address this issue, we repeat theain analysis using the death record as a robustness check.

. Empirical framework

.1. Basic specification

The main objective of this study is to assess the effect of air pol-ution regulations on infant mortality. In an ideal research setting,he TCZ status is randomly assigned across cities, creating varia-ion uncorrelated with baseline characteristics. In the absence of

randomized controlled trial, we first use a simple difference-in-ifferences (DID) approach, based on the TCZ regulatory status;

jt = ̨ + �1(Tj × Postt) + ı1Xjt + �t + �j + εjt (1)

here Yjt is IMR in city j in year t, Tj is an indicator variable thatakes on the value one if city j was assigned as a TCZ in 1998,22 andostt is an indicator variable that takes on the value one for yearsn or after 1998.23 The city fixed effects, �j, control for the perma-ent heterogeneity across cities, whereas the year fixed effects, �t,ontrol for year-specific shocks that are common to both TCZ andon-TCZ cities. Xjt controls for an additional set of covariates thatapture birth, parental, and city characteristics at the city by yearevel. All standard errors are clustered at the city level, allowing for

n arbitrary correlation within cities over time.

The parameter of interest is �1, the reduced-form impact of airollution regulations on infant mortality, capturing the difference

he birth record, the number of infants who died within the same calendar year inhich they were born is measured. For example, if an infant is born on January 1st,

here is almost one year during which to record her death during the first year afterirth, whereas there is only a day in which to record the death of an infant born onecember 31st.

22 Note that the non-TCZ cities are not equivalent to “non-affected” cities. It isore appropriate to say that the regulations were more stringent in the TCZ cities,

elative to those in the non-TCZ cities. In such a context, it may be ideal for Tj toeasure a continuous intensity of exposure to the regulations, measured by either

er-capita amount of high-sulfur coal used or by per-capita amount of SO2 emis-ions in the baseline years. A similar strategy is used in Qian (2008), where she useshe amount of tea planted in each county in China to measure the impacts of relativeemale income on sex ratio, where women have comparative advantage in pickingea, and the price of tea was dramatically increased by the post-Mao reforms. Also,leakley (2007) uses the pre-treatment hookworm infection rate to measure theenefits of hookworm eradication on school enrollment. Unfortunately, data limi-ation makes this infeasible, and we decided to use a discrete choice of being in theCZ cities or not. This is still valid because the large amount of high-sulfur coal andO2 emissions was produced in the TCZ cities. The TCZ policy may also have reducedollution beyond TCZ cities, especially when non-TCZ cities are located near TCZities, either directly through the policy effect on even non-TCZ cities or indirectlyhrough reducing pollution that travels to non-TCZ cities. (Note that lifetime andravel distance of pollutants may be highly dependent on conditions, such as typef pollutants, temperature, weather, wind, etc.) However, reductions in pollution inon-designated areas, if any, would only understate our finding. Restricting the con-rol group to distance places would generate a concern about comparability acrosshe places. Instead, it is part of our important finding that the treatment effects existven among geographically similar sites.23 We later discuss the theoretical and empirical rationales of using 1998 as a cut-ff year for the post-reform period, rather than1995 when the APPCL was amended.e find that the results are robust to using 1995 instead.

ctveim

ostIt1aTTthtitt

nt

lJt

ndicates the timing of TCZ policy implementation in January 1998. Because eachbservation represents the annual average value, the solid vertical line is locatedetween 1997 and 1998 to clarify the timing of their implementations.

n the changes in IMR before and after the regulations, betweenhe cities that were and were not designated as a TCZ in 1998. Ifhe air pollution regulations contributed to significant reductionsn IMR in the TCZ cities relative to non-TCZ cities, �1 is expected toe negative.

.2. Validity of the identification assumptions

The key identification assumption for Eq. (1) to provide a causalnference is that the non-TCZ cities provide valid counterfactualhanges in infant mortality for the TCZ cities, had they not beenreated, conditional on covariates. Two potential hypotheses mayiolate this assumption: (1) there is a systematic difference in pre-xisting trends in mortality reductions, and/or (2) the TCZ statuss not orthogonal to factors explaining the reductions in infant

ortality in the post-treatment period.To examine the pre-existing trend, we plot the evolution of IMR

ver time between the TCZ and the non-TCZ cities in Fig. 2. Theolid vertical line indicates the timing of TCZ policy implementa-ion in January 1998.24 The figure provides graphical support thatMR trends were similar in the pre-intervention period; infant mor-ality fell between 1991 and 1993, stayed somewhat constant until996, and fell again in 1997 in both sets of the cities. A trend breakppears around 1998, when IMR continued to drop only among theCZ cities, while IMR stayed somewhat constant within the non-CZ cities. A similar drop in IMR is illustrated in an even-studyype analysis in Fig. 3. Notably, although the IMR was continuouslyigher in the TCZ cities than those in the non-TCZ cities before 1998,he reverse is true after 1998, which is commensurate with the tim-ng of the TCZ policy. A similar pre-trend between the two sets ofhe cities suggests that the post-trend would have been similar inhe absence of the regulations.

However, it may not be fully convincing that TCZ cities andon-TCZ cities would be similarly trended in the absence ofhe treatment. In light of this, we extend Eq. (1) by additionally

24 Because each observation represents the annual average value, the solid verticaline is located between 1997 and 1998 (since the TCZ policy was implemented inanuary, early in the year). This is to clarify the timing of the TCZ policy implemen-ation.

96 S. Tanaka / Journal of Health Economics 42 (2015) 90–103

Fig. 3. Event-study analysis of the effect on IMR. Notes: The figure plots thecoefficients and their associated 90% confidence interval based on a single regres-sion modifying Eq. (1) by interacting the TCZ and respective year dummies (insteadof a post dummy):

Y∑2000

w

cs

Y

waare

fissmlttpogabtiomttu

oaieial

Table 2Balancing test by the TCZ status.

Trend difference

(1) (2)All Years 1996–2000

Panel A: Birth characteristics% of Boy 0.004 −0.001

(0.009) (0.006)% of January births 0.001 0.005

(0.006) (0.005)Birth order 0.053* −0.007

(0.029) (0.024)

Panel B: Household characteristicsMother’s age 0.138 0.025

(0.113) (0.100)Mother’s education ≥H.S. 0.034 0.032

(0.045) (0.041)% of Han 0.038* 0.035

(0.021) (0.029)

Panel C: District characteristicsTotal population 1433 3171

(4132) (3719)Number of infants 139.5 124.2

(138.2) (109.5)

Panel D: Economic CharacteristicsGDP per capita 918.12(yuan) (740.88)Num. of hospitals −29.34(in units) (32.70)Water supply 0.109(100 million tons) (0.114)Electricity consumption 14.94(in kwh) (26.87)

Notes: Each entry reports the coefficient of the interaction term of TCZ status and apost dummy (=1 if year is 1998 or later) from a separate regression when using therespective variable as the dependent variable based on Eq. (1). All regressions areweighted by the number of population, and the robust standard errors, clustered atthe DSP site level, are reported in the parentheses. The sample includes all obser-vations between 1991 and 2000 in column (1), and observations between 1996 and2f

paicbgo1oom

rndaf

5

Following the identification strategies outlined above, we firstpresent the main impact of environmental regulations on IMR in

jt = ̨ +i=1992

�i(Tj × 1(year = i)) + ı1Xjt + �t + �j + εjt .

here 1(*) is an indicator variable taking on the value of one if year is i.

ontrolling for the location-specific linear trend using the followingpecification:

jt = ̨ + �2(Tj × Postt) + ı2Xjt + �t + �j + �j × t + εjt (2)

here � j × t absorbs long-term linear trend in IMR that may varycross DSP sites. The specification in Eq. (2) explicitly controls forny effects through differential trends across cities. Such rigorousegression-adjusted evidence below also shows that the treatmentffect is robust to DSP site-specific time trends.

Another concern is that the regulations’ effect may be con-ounded by other concurrent changes in policies or factors affectingnfant deaths. If the central government assigned the TCZ statusolely based on the nationally mandated standards, the designationhould be less likely to respond to demands from local govern-ents. However, because the TCZ status is based on air pollution

evel, and air pollution level is conditioned by various local fac-ors, it may still be possible that the TCZ status simply proxieshese other factors. For example, air pollution may be high in urbanlaces where the number of births is decreasing due to high levelsf women’s participation in the labor force or relatively more strin-ent enforcement of the one child policy. In this case, the negativessociation between the TCZ status and IMR would be confoundedy the quantity-quality tradeoff. To address this concern, we inves-igate whether the TCZ status has any association with changesn observable characteristics. Although this is not a formal testf exclusion restrictions, as the assumption states that the treat-ent status should not covary with unobservable characteristics,

he absence of significant correlation with observable characteris-ics suggests that there should not be significant correlations withnobservable variables either (Altonji et al., 2005).

Column (1) of Table 2 addresses potential differences in trendsf characteristics between the TCZ and non-TCZ cities, before andfter the policy change. Each entry reflects the coefficient of thenteraction term, �1, in Eq. (1) from separate regressions, when

ach characteristic is used as the dependent variable. Any signif-cant estimate indicates a systematic difference in trend patternsnd may confound the regulation effects. Strikingly, the table shedsight on little systematic difference in trends. Further, most of the I

000 in column (2). Economic characteristics are available only for urban areas androm 1996.

* Significant at p < 0.1 level.

oint estimates are substantially small and virtually indistinguish-ble from zero. Importantly, the TCZ status balances the trends inmportant predictors of infant deaths, such as mother’s age or edu-ational level as well as the percentage of male and share of Januaryirths. A low degree of correlation with local economic trends sug-ests that underlying economic shocks are less likely to be a sourcef bias.25 In column (2), the sample is restricted to a shorter period,996–2000, which serves as a robustness check, because a numberf policies are fixed under the 9th Five-Year Plan. In this case, nonef the variables presents significant differences in trends, whileany of the estimates become even smaller in magnitude.Overall, these findings provide compelling evidence that the TCZ

egulatory status is orthogonal to trends in observable determi-ants of infant deaths, strongly suggesting that the current researchesign is unlikely to be biased by changes in unobservable vari-bles. A falsification test and robustness checks below will provideurther support.

. Empirical results

25 We address channels through additional economic activities in Online AppendixI.

S. Tanaka / Journal of Health Eco

Table 3Main results on IMR.

(1) (2) (3) (4)

TCZ × Post −2.870** −3.592*** −3.205*** −3.287(1.103) (1.067) (1.087) (2.128)

Observations 1340 1281 1281 1281R2 0.415 0.438 0.442 0.552

Year fixed effects Y Y Y YDistrict fixed effects Y Y Y YHH controls N Y Y YDistrict controls N N Y YDistrict trend N N N Y

Notes: Dependent variable is number of infant deaths per 1000 live births, estimatedfrom the birth record. Robust standard errors are clustered at the DSP site level. Col-umn (1) includes only year and DSP sites fixed effects without additional covariates,while column (2) adds birth and parental characteristics (share of male, birth sharesin respective month, birth order, mother’s age, mother with high school degree ormore, Han), column (3) additionally includes DSP sites characteristics (number ofbirths, total population, rainfall), and column (4) additionally includes DSP-site spe-cific time trends. The point estimate based on the specification in column (1) usingthe same sample as in columns (2)–(4) is −3.306 (1.06).

**

tacipnewfi

5

mbEidbtitpmoiimcpttra

tiwsoc

om

rrCitittdlc

5

agbMrooaCTgAiRbowt1tions correlate to a lower incidence of low birthweight births by3.42 per 1000 live births.30 Columns (3) and (4) further examinethe corresponding impact on the length of gestation period and the

26 Note that we use the baseline estimate of IMR based on the death record (16.61per 1000 live births), as it captures the entire infant deaths per year.

27 Based on back-of-envelope calculation of elasticity using changes in air pollu-tion, our elasticity of changes in mortality rate relative to changes in pollution isabout 0.9. In economics studies, evidence in developing countries is still scarce, yeta recent study by Arceo et al. (2012) finds elasticity of 0.415 in Mexico, and otherstudies in U.S. provide elasticities of 0.284 (Chay and Greenstone, 2003a) or 1.827(Knittel et al., 2011). Thus, our estimates in China are still in the range of the lit-erature, though toward a higher end. Evidence on the dose–response relationshipbetween pollution and human health in the fields of medicine and epidemiology isin favor of greater elasticity at greater pollution levels.

28 For adults, air pollution is often linked to respiratory and cardiovascular disease,aggravation of asthma, heart disease, lung malfunction or cancer, stroke, and possi-bly carcinogenesis. For infants, high exposure to pollution in the neonatal period islikely to result in deaths by acute respiratory infections. Some recent studies showstrong impacts coming from maternal exposure.

29 See Behrman and Rosenzweig (2004), Case et al. (2005), and Almond et al. (2005)for costs and returns of low birthweight.

30 In theory, it is also possible to find lower birthweight when infant moralitydecreases, as marginal babies who would have died before are now born alive.This argument is particularly true in a case, for example, where improved medicaltechnology contributes to greater infant survival at the margin (or special health-care services for newborns at risk). In our case, infant mortality decreased due to

Significant at p < 0.05 level.*** Significant at p < 0.01 level.

he subsequent section. We then highlight the biological mech-nism underlying these results to illustrate that the effects areoncentrated during the neonatal period and induced by changesn internal causes of deaths, deaths potentially associated with airollution, and not due to external causes of deaths, ones obviouslyot associated with air pollution. Then, we present heterogeneity inffects depending on infants’ gender or mothers’ education. Finally,e present a number of robustness checks that support our mainndings.

.1. Main results on infant mortality

We present the DID estimates of the regulations’ effect on infantortality in Table 3. The dependent variable is IMR per 1000 live

irths for all causes of deaths. Column (1) provides the result fromq. (1) without any controls except city and year fixed effects,ndicating that the TCZ status is associated with 2.87 fewer infanteaths per 1000 live births. Column (2) controls for a number ofirth and parental characteristics, directly addressing a concernhat changes in infant mortality may be explained by changesn observable characteristics of samples that vary over time andhat are correlated with the TCZ status. Namely, it controls for theercentage of male births, share of births by month, birth order,other’s age, mother’s education, and percentage of Han. The share

f births in every month works similarly to birth month dummies,f the data were at the individual level, addressing the fact thatnfant deaths for those who were born in earlier of the year are

ore likely to be in the data. With these controls, the estimatedoefficients become larger and are statistically significant at the 1ercent level. Column (3) additionally controls for DSP site charac-eristics, in an effort to control for changes in total number of births,otal population, and precipitation rates. The estimated effects areobust to the inclusion of these covariates and remain significantt the 1 percent level.

A major concern with any non-experimental study is that thereatment group and control group are not comparable. Our studys no exception. In Column (4), we present results based on Eq. (2),

hich directly controls for linear time trends specific to the DSP

ites. While Eq. (2) represents our preferred specification through-ut the paper, it substantially reduces the degrees of freedom,ausing the standard error to double. However, the magnitude

iwsp

nomics 42 (2015) 90–103 97

f the point estimate is essentially unaffected, indicating that theain finding cannot be explained by differential trends.The estimate in column (4) represents approximately 20 percent

eductions in infant mortality over the three years in the post-eform period.26 As a comparison of this figure to the U.S. context,hay and Greenstone (2003b) find that the nonattainment status

n 1972 is associated with a 3–6 percent reduction in IMR in nonat-ainment counties from the previous year. This puts our estimatesn about a similar magnitude in terms of average annual reduc-ion in IMR. It is worth mentioning that these comparisons pertaino the effect of environmental regulation on infant mortality, buto not imply differences in marginal effect of air pollution. Due to

ack of reliable pollution data in our study area, it is not feasible toalculate elasticity of infant mortality in response to air pollution.27

.2. The biological mechanism

In an effort to identify the biological mechanism through whichir pollution affects infant mortality, we adopt two different strate-ies. The first examines whether the regulations are associated withirth outcomes, namely birthweight and length of gestation period.aternal exposure to pollution during pregnancy is considered to

etard fetal development, as absorbed pollutants limit nutrition andxygen flows to fetuses. This sometimes results in low birthweightr shorter gestation period, although exact channels or outcomesre not well-known (Dejmek et al., 1999; Perera et al., 1999).28

olumn (1) of Table 4 shows that those who were born in theCZ cities weighed more by 2.66 g, yet the estimate is not distin-uishable from zero. This result should be interpreted with caution.s shown in the table, the baseline birthweight was already high

n China, and parents can often intentionally adjust birthweight.ather, we should be more concerned about children born at lowirthweight, which is often considered to have a lasting impactn later health and socioeconomic status.29 In Column (2), hence,e evaluate the distributional effect on births at low birthweight;

he dependent variable is the number of births below 2500 g per000 live births. The result shows that the environmental regula-

mproved pre-natal environment, which is more likely to result in greater birth-eight. Our finding of positive effect on birthweight is indeed consistent with a

trong association between maternal smoking (which works in a similar way asollution exposure) and reduced birthweight.

98 S. Tanaka / Journal of Health Economics 42 (2015) 90–103

Table 4Identifying the biological mechanism.

(1) (2) (3) (4) (5) (6) (7)Birthweight Low birthweight Gestation period Short gestation Deaths w/in 1day Deaths w/in 28 days Deaths w/in 6 mo.

TCZ × Post 2.66 −3.42** −0.237 −2.59 −0.85* −2.08** −2.40**

(16.32) (1.70) (0.217) (3.17) (0.44) (0.85) (0.93)N 1249 1235 657 650 1295 1295 1295R2 0.78 0.66 0.44 0.23 0.43 0.48 0.50Pre-1995 mean 3313.2 15.76 39.09 26.47 2.88 6.59 8.00

[116.9] [15.47] [1.80] [118.5] [5.03] [9.35] [11.23]Year effect Y Y Y Y Y Y YDistrict effect Y Y Y Y Y Y YHH attributes Y Y Y Y Y Y YDistrict attributes Y Y Y Y Y Y Y

Notes: Dependent variables are; birthweight in gram in column (1), the number of infants born with less than 2500 g per 1000 live births in column (2), gestation period inweek in column (3), the number of infants born in less than 32 weeks per 1000 live births in column (4), IMR within one day in column (5), IMR within 28 days in column(6), and IMR within 6 months in column (7). The mean values of respective variable (weighted by total population) and their standard deviations in the square bracket arep

sllp

iaodtb2ompmmwGitp6di(rfia

oitiice

ctp

ma

aiIcttep

biohaci

mscvstmaF

ditioeand Leon, 1999; Woodruff et al., 2006).

rovided based on the observations before 1995.* Significant at p < 0.1 level.

** Significant at p < 0.05 level.

hare of gestation period below 32 weeks, the lowest one percentileevel. Neither estimate is significant, indicating that the impact onow birthweight is not driven by changes in length of gestationeriod.

The results above suggest that fetal exposure to pollution affect-ng fetal development appears to be a key. However, birthweightnd length of gestation period may not fully capture fetal devel-pment. Hence, we now explore the effect on deaths occurring atifferent time periods. Infant deaths occurring during the neona-al period (within 28 days after birth) are generally considered toe associated with poor fetal development (Chay and Greenstone,003a,b).31 Column (5) presents the impacts on infant deaths thatccurred within one day of birth. The point estimate is small yetarginally significant at the 10 percent level, implying that 26

ercent of overall reduction in IMR occurred within one day. Theagnitude is in line with Chay and Greenstone (2003b), who esti-ate that roughly 22 percent of overall infant deaths occurredithin one day. Yet this contrasts to the finding in Chay andreenstone (2003a), who attribute roughly 60 percent of overall

mpact to infant deaths within one day. Column (6) reveals thathe regulations are disproportionately more associated with therobability of death during the neonatal period, indicating that3 percent of the effect of the regulations on infant mortality isue to reductions in this period.32 This corresponds to the find-

ngs in Chay and Greenstone (2003a) and Chay and Greenstone2003b), whose 73–82 percent and 80 percent of infant mortalityeductions occurred in the same period, respectively. Overall, thesendings highlight weak fetal development via maternal exposures an important biological mechanism.

A related concern is that the regulations are confounded byther concurrent changes in factors contributing to the reductionsn infant mortality. For example, in response to high exposureo pollution in the TCZ cities, the local governments may havencreased healthcare spending, leading to an improvement in qual-

ty and/or quantity of healthcare services. Then, without directlyontrolling for a local health policy, a simple DID estimate wouldrroneously pick up effects through a healthcare policy, causing

31 According to the WHO, infant deaths during the neonatal period are also asso-iated with preterm birth, intrapartum-related complications, and infections, andhus we need caution in attributing it solely to fetal development, as there is also aossibility that post-natal exposure plays a role.32 Note that because the analysis is based on the birth record, we need to keep inind that deaths in a shorter period are more likely to be recorded and be cautious

bout the estimates. We appreciate this discussion by an anonymous referee.

m

da

coGsc

n upward bias. In order to rule out such a possibility, we exam-ne the regulations’ effect on infant mortality by cause of death.f maternal exposure is a primary channel, we expect to see largerhanges in mortality associated with prenatal disorders as opposedo postnatal causes. There is added significance to this analysis;he estimates are directly comparable to the falsification test thatxamines the effect on external causes of deaths unrelated to airollution.

Given that the exact etiology and pathology of diseases causedy fetal exposure to air pollution are not yet known, the most

mportant comparison is between internal and external causesf deaths. We compute IMR due to internal deaths to include allealth-related, non-accidental causes that are potentially associ-ted with air pollution, other than infant deaths due to externalauses of deaths, those that clearly do not pertain to air pollution:njury and poisoning (Chay and Greenstone, 2003a,b).

As predicted, Table 5 shows statistically significant effects onortality from health-related causes.33 When effects are estimated

eparately for four major health-related causes,34 the estimatesorrespond to a reduction of 49.7 percent in mortality from ner-ous system disorders and a reduction of 32.7 percent in circulatoryystem disorders. These findings are consistent with vast literaturehat also finds strong associations between these birth defects and

aternal smoking during pregnancy (which essentially works in similar mechanism as air pollution exposure) (see for exampleried, 1995; Brennan et al., 1999).

On the other hand, infectious, parasitic and respiratory diseasesid not have significant impacts. Low rates of respiratory diseases

ndicate that these are not typical causes of deaths for infants, par-icularly during the neonatal period, who spend most of their timesndoor, while these diseases have been found to be a major causef deaths for children or at youngest post-neonatal period (See forxample, Woodruff et al., 1997; Borja-Aburto et al., 1997; Bobak

Most importantly, we find no statistically significant effect onortality from external causes such as injury and poisoning. If

33 Over the study period between 1991 and 2000, 51 percent of infant deaths areue to diseases of the circulatory system, 19.4 percent come from nervous systemsnd sense organs, and 19 percent are due to external causes.34 Note that this exercise of separately estimating effects for different “internal”auses do not itself test whether the fundamental cause is due to air pollution orthers, as it is mentioned that the exact pathway is not known. Indeed, Chay andreenstone (2003a,b) do not disentangle effects among internal causes for this rea-on. We do this simply to highlight variation across disease types given our dataapacity to do so.

S. Tanaka / Journal of Health Economics 42 (2015) 90–103 99

Table 5Effects on IMR by cause of death.

(1) (2) (3)Pre-1995 mean mortality rate Estimated coefficients %� in mortality rate

All non-accidental causes 7.61 −2.82*** −37.07[11.28] (0.98)

All non-accidental causes disaggregatedNervous system disorders 1.63 −0.81*** −49.69

[3.85] (0.31)Circulatory system disorders 4.56 −1.49* −32.68

[7.18] (0.76)Infectious and parasitic diseases 1.15 −0.38

[2.49] (0.29)Respiratory system disorders 0.13 −0.03

[0.77] (0.13)

Accidental causesInjury and poisoning 2.01 −0.39

[7.21] (0.48)

Notes: All specifications include year fixed effects, DSP site fixed effects, household attributes, and district attributes. The number of observations is 1281. Standard deviationsare reported in the square brackets, and robust standard errors are reported in the parentheses.

*

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Table 6Heterogeneity in effect.

Dependent variable

IMR Low birthweight Short pregnancy

Boys −3.12*** −2.06 −0.677(1.20) (1.52) (3.47)

Girls −3.41*** −4.98** −3.11(1.18) (2.29) (3.60)

Mother’s education < H.S. −2.21 −3.85* −3.43(2.15) (2.16) (3.65)

Mother’s education ≥ H.S. 4.45 −4.94** −1.52(2.98) (2.29) (1.65)

Notes: Each cell reports the coefficient of interests and standard errors in the paren-theses from separate regressions for respective subsample. The dependent variablesare IMR, the number of infants born at less than 2500 g per 1000 live births, and thenumber of infants born in less than 32 weeks per 1000 live births, respectively.Low maternal education is defined as educational attainment less than high school,and high maternal education is equal to or above high school completion. Note thatthe analysis based on mother’s education inevitably drops deaths associated withmissing mother’s education.

*

to

lbhNTeatimpacted by air pollution initially. Alternatively, mothers withgreater educational attainment are more likely to know how to pro-tect their children from being exposed to pollution outside and/or

Significant at p < 0.1 level.*** Significant at p < 0.01 level.

ouseholds had had improved access to healthcare services, thereould likely have been reductions in infant deaths by external

auses as well.While this does not directly rule out the possibility that the reg-

lations spuriously pick up the effect through unobserved changeshat are correlated with internal causes of deaths but not withxternal ones, the finding suggests that the main results are notriven by any other channels, leading us to believe that the relation-hip is causal. We provide a number of further robustness checksn Section 5.4.

.3. Heterogeneity in the regulations effect

This part tests the hypothesis that the regulations on air pol-ution may have a heterogeneous impact on infant mortalitycross various subsamples. We first search for heterogeneity inhe treatment effects between boys and girls due to biologicallyased gender differences given that, in the literature, male fetusesre considered to be more physiologically sensitive than femaleetuses to environmental changes. On the other hand, heteroge-eous effects may also reflect gender discrimination, particularlyhen infant deaths occur after birth. For example, if boys were

nitially more likely to be protected from exposure to pollution orere more likely to receive medical treatments for health prob-

ems caused by pollution, then the effect of pollution reductionsould be more pronounced for girls. The first and second rows

n Table 6 report the estimated effects for boys and girls, respec-ively. The coefficients suggest that the effect on infant mortality isarger among girls than boys, yet the difference is not statisticallyignificant. This indicates that pollution reduction effect on infantortality was similar for boys and girls. In contrast, the impact

n birthweight is substantially higher for girls than for boys. Thenterpretation of the results requires a caution. On one hand, thevidence is consistent with the gender bias hypothesis in that girlsre more sensitive to changes in environment on a condition thatarents had a means to identify the gender of the fetus, whichhanged their behaviors. On the other hand, the finding is also

onsistent with the literature that female fetuses have a higherhreshold at which pollution leads to mortality than boys do, andhus only girls were affected when pollution decreased somewhatrom initially high levels to lower levels, which were still above the

eeU

Significant at p < 0.1 level.** Significant at p < 0.05 level.

*** Significant at p < 0.01 level.

hreshold for male fetuses. We do not find any significant effectsn the length of gestation period.

Next we explore whether the effect differs according to theevel of mother’s education. Such heterogeneity is likely to arise,ecause behavior is known to be an important determinant inealth production function when it comes to pollution (Zivin andeidell, 2009; Moretti and Neidell, 2011; Deschenes et al., 2012).35

he effect may be amplified among households with low lev-ls of maternal education for many reasons. For example, infantsnd fetuses of poorly educated mothers tend to have a lower ini-ial health endowment, making them more liable to be adversely

35 In empirical studies, Jayachandran (2009) finds greater wild fire air pollutionffect on low-income households in Indonesia. Arceo et al. (2012) finds that thestimated marginal effect of carbon monoxide is larger in Mexico compared to the.S. estimates, while the effect of PM10 is similar between the two countries.

1 th Economics 42 (2015) 90–103

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Table 7Robustness checks.

Specifications Coeff.

Use death record −3.45*

(1.84)Use 1996–2000 −3.57**

(1.44)Only districts and cities −3.62**

(1.64)Common support −3.24***

(1.12)Eliminate outliers −2.73***

(0.95)Include province × year effects −3.50***

(1.27)Control for 1995–1997 −4.08***

(1.56)Weight by number of birth −2.92**

(1.26)Use 1995 cut-off −2.92*

(1.61)Cluster at prefecture level −3.205***

(1.076)Only northern China −3.138*

(1.657)Only southern China −3.258**

(1.497)Use 1991–1997 sample −1.967

(1.901)

Notes: The table provides robustness checks of the main results to various otherhypotheses and specifications. See the text for their explanations.

* Significant at p < 0.1 level.** Significant at p < 0.05 level.

runn

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tp

00 S. Tanaka / Journal of Heal

ave more access to health services to treat their children. Fami-ies with high socioeconomic status also have a greater degree of

obility to better areas, while the poor may continue to be exposedo greater pollution. On the other hand, the effect may be smallermong poor households, if long-term exposure to pollution in poorreas allowed them to be more adept at keeping infants indoors.

The third row reports the estimated effects for the samplef households where maternal education was less than a highchool degree, and the fourth row for the sample of householdshere mothers attained at least a high school education. We find

hat the regulations’ effect is substantially higher among house-olds with low maternal education.36 The finding suggests thathe regulations’ effect on infant mortality should be stronger forhe low-socioeconomic families that are more vulnerable to theffects of air pollution.

.4. Additional robustness checks

The findings above leave little room for the scope of confound-ng factors. First, little association between TCZ status and trendsn observable characteristics limits the possibility that the mainesults erroneously reflect time trends that vary systematicallyetween the TCZ and the non-TCZ cities. Second, the consistencyf the regulations’ effect in both magnitude and statistical signif-cance when controlling for the set of key determinants of infant

ortality suggests that the estimated effects are robust to compar-sons with similar characteristics. Third, the absence of treatmentffect in the falsification exercise on external infant mortality pro-ides strong evidence that health care system reform or medicalechnology advancement cannot be a source of bias.

In this subsection, we extensively explore additional robust-ess checks to rule out other possible scenarios. First, we examinehether the finding is robust when using an alternative dataset.s discussed above, using the birth record, which reports theccurrence of deaths only within the calendar year, may result innderstating the effect, if the number of infant deaths is truncatedt zero. The death record allows us to compute IMR for all deathsccurring before age of one. As expected, the first row of Table 7hows that the size of the estimate becomes larger, though not sub-tantially different from the main result, indicating that the effects not sensitive to using the death record, while the main analysis

ay understate the overall impact, if any.Second, we confirm that the treatment effect is not driven by

ther national or local policy changes. In the second row, we restricthe sample to the years between 1996 and 2000, which correspondso the period of the 9th Five-Year Plan. The fact that the time periods shorter and falls within one policy regime helps reduce a set ofotential confounders in the pre- and post-natal health environ-ent other than pollution. Also, Table 2 shows that all observed

haracteristics had balanced trends during this short period. Thestimated effect is similar, suggesting that various other nationalr local policy changes should not confound the effect.

Third, despite evidence that the treatment effect is robust to het-rogeneous city-specific trends in IMR, as predicted in Table 3, thereay still be a concern that the TCZ status may be correlated with

dministrative divisions. For example, urban districts and county-evel cities may be more likely to be treated, whereas poor counties

ay be less likely to be assigned as TCZ. To address this issue, we

36 Note that, although neither of these estimates in themselves nor the differencesetween them are statistically significant, when we restrict the IMR to internalauses, the coefficient to low maternal education level is −4.40 and significantt the 5 percent level, whereas that to high maternal education is −0.34 and nottatistically significant.

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*** Significant at p < 0.01 level.

estrict the sample to only districts and county-level cities, mostlyrban areas, in the third row. The estimate is larger and remains sig-ificant at the 5 percent level, indicating that the treatment effect isot driven by simple comparisons between urban and rural areas.

A major concern with any non-experimental studies is the pos-ibility that omitted heterogeneity may give rise to a spuriouselationship. Controlling for the attributes, as in columns (2) and3) in Table 3, may not solve this issue when we cannot comparehe distribution of attributes across TCZ and non-TCZ cities. Weddress this issue in two ways. In the fourth row, we limit to thebservations under common support using the propensity score,hich potentially restricts the sample to DSP sites that have similar

bserved characteristics. In doing so, we first compute propensitycores of being TCZ based on households and DSP attributes usedn the main analysis. Then, we re-estimate the treatment effectssing observations only under the common support. Alternatively,

n the fifth row, we examine whether the main findings are robusto eliminating outliers. Specifically, we eliminate DSP sites whoseMR are above 99th percentile. The orders of the both magnitudesre similar.

Another concern is that there may be unobserved policy changeshat affected infant mortality. In addressing this, we control forrovince times year fixed effects in the sixth row. In this specifica-ion, the effect of the regulations on infant mortality is identifiedsing variation in regulatory stringency before and after 1998ithin the province, thus purging any potential effects resulting

rom any other policy changes at the provincial level. Further, inhe seventh row, we control for the years between 1995 and 1997,he intermediate period after the APPCL and before the TCZ pol-cy was in effect. Note that this additional variable controls for the

mmediate impacts of APPCL, if any, but does not directly rule outelayed impacts of APPCL after 1998. Both of the estimates are againnchanged.

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Next, we re-estimate the main analysis using different spec-fications. Namely, we weight the regressions using the numberf population aged 0 in the eighth row;37 we use 1995 as a cut-ff year instead of 1998 to incorporate all years after APPCL wasnacted in the ninth row;38 we cluster the standard error at therefecture level in the tenth row;39 we use only northern China inhe eleventh row; we use only southern China in the twelfth row.40

ll these estimates effects remain similar, showing robustness tolternative specifications.

Lastly, we repeat the main analysis using samples only in there-reform period in the last row. We use 1995 as a placebo cut-ff year, and thus years between 1995 and 1997 are defined aspost”-reform observations. The point estimate is lower and nottatistically indistinguishable from zero, reassuring that the trendsetween the TCZ and non-TCZ cites are similar in the pre-reformeriod.

Taken all above together, there is no evidence to indicate thathe main results are driven by inappropriate identification assump-ions, leading us to believe that the relationship is causal. Theollection of these robustness checks substantially limits the scopef omitted variables, leading us to believe that the main find-ngs substantiate the causal impact of environmental protectionn infant mortality.

. Conclusions

China suffers from notoriously bad air pollution, the healthffects of which have been of increasing public concern. In 1998,

37 This intends to adjust the regression weighted by the number of birth, wheree do not have information on actual numbers of birth.

38 The main analysis uses 1998 as a cut-off year for the post-reform period, ratherhan 1995 when the APPCL was amended, based on both theoretical and empir-cal rationales. For theoretical purposes, it is plausible to take two to three yearsefore the regulations are carried out to the full extent. Chay and Greenstone (2005)re based on a similar argument when they use 1975 nonattainment status as annstrumental variable in estimating the effect of 1970 Clean Air Act on housingrices between 1970 and 1980. In their context, the nonattainment status changesvery year. By using the mid-decade regulation, they also take into account a two-o three-year lag before the policy was fully executed. This is also relevant in ourontext because the regulations required power plants to alter the energy sourcesnd install costly technology (such as FGD). Informal conversations with officials atocal power plants provide anecdotal evidence to support this assertion. Time-lagsre also likely in China because it is common for the government to set policy targetsr guidelines, often very ambitious ones, without specifying the critical details untilater, thereby largely leaving implementation up to the local governments or indi-idual firms. Further, the 1995 amendment had a weak implementation mechanism,nd more drastic actions (such as shutting down numerous inefficient power plantsnd enforcing stringent air pollution regulations) were enforced only after the TCZolicy in 1998. The empirical rationale for the cut-off date is based on the findinghat the interaction term between the TCZ status and the post-1998 period betteralances important determinants of infant mortality, compared with using 1995 as

cut-off year, suggesting that the former is less likely to be confounded. Further,sing a 1998 cut-off year enables us to restrict the sample to observations between996 and 2000, where the interaction term is not correlated with any observableariables. Therefore, using 1998 better averts omitted variable bias.39 The main results are clustered at DSP level for two reasons. (1) It is conventionalo cluster the standard errors at the treatment-site level, and in our case, the treat-

ent status varies at the DSP level, not the prefecture level. There are cases that onerefecture accommodates multiple DSP sites that have different treatment status,

.e., one is a district or county-level city, while the other is a county. (2) Indeed,ost DSP sites are in different prefecture. Out of 145 DSP sites, there are only 12

ases that we observe multiple sites in a single prefecture (two of which are threeSP sites in one prefecture, and the rest of the cases include two DSP sites in onerefecture). This robustness check clustering at the prefecture level should thus beeen as accounting for serial correlations within prefecture.40 The northern and southern China is defined as prefectures accommodating SO2

ontrol zones (north) or acid rain control zone (south) (or more so if accommo-ating both). We eliminated Tibet Autonomous Region, Qinghai Province, Xinjiangutonomous Region, as they are not typically perceived as either northern or south-rn China.

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he TCZ policy, which went into effect in 1998, was one of theargest-scale air pollution regulatory schemes ever implementedn a developing country, imposing stringent regulations on pollut-nt emissions from power plants in cities exceeding the nationallyandated standards.The major objective of this paper is to test the hypothesis that

hese regulations led to reductions in infant mortality within theCZ cities subjected to particularly stringent regulations. Using theifference-in-differences approach, comparing changes in infantortality between the cities assigned and not assigned as the TCZ,

efore and after the policy reform, we find substantial impacts:nfant mortality decreased by 20 percent; a large fraction of theeduction occurred in the neonatal period, which can be attributedo fetal exposure; and infants of mothers with low levels of educa-ion benefited the most.

The set of falsification tests and robustness checks limits theole of omitted variables in biasing these estimates, leading uso believe that the linkage between the air pollution regulationsnd infant mortality reduction is causal. First, the estimates areobust to DSP site-specific trends in IMR. Second, we confirm thathe treatment effect is absent for infant deaths caused by externalauses, ruling out a potential mechanism through improved localealthcare system. Lastly, the estimates are robust to various alter-ative hypotheses, i.e., using the death record, controlling for otherolicies at national or local levels, and limiting to only urban areas.

The findings in this study have important implications for pol-cy. First, the question of whether, and to what extent, air pollutionegulations in developing countries can lead to reducing infantortality remains unanswered. This study highlights a significant

eduction in infant mortality correlating to air pollution reductions,n contrast to Greenstone and Hanna (2011) who find that air pol-ution reductions in India had only modest and insignificant impactn infant mortality. Our results substantiate compelling evidenceo support air pollution regulations in countries that suffer fromigh levels of air pollution. The size of the benefits itself may note enough to justify environmental protection without consider-

ng their costs. In the United States, the Clean Air Act Amendmentsre found to have caused distortions on productivity (Gollop andoberts, 1983; Barbera and McConnell, 1990; Greenstone et al.,012), firm’s location decisions (Henderson, 1996; Becker andenderson, 2000; List et al., 2003); employment (Greenstone,002; Deschenes, 2010; Walker, 2011, 2013); and foreign direct

nvestment inflows and outflows (Eskeland and Harrison, 2003;eller and Levinson, 2002; Hanna, 2010). On the other hand, Tanakat al. (2014) provide compelling evidence that polluting firms in theCZ cities substantially improved economic performance throughncreased market dynamics via the entry of more efficient firmsnd the exit of less efficient ones. In addition, costs of reducingollution are likely to be smaller under convex marginal cost func-ions. Therefore, our finding is likely to pass cost-benefit analysisnd suggest further implementation of air pollution regulations inimilarly polluted countries.

Second, while the precise mechanisms through which air pol-ution reductions lead to health benefits is not known, our findingsighlight substantial reductions in infant mortality during theeonatal period, shedding light on maternal exposure to pollu-ion as a potential pathophysiologic mechanism. This necessitatesdditional policy interventions to protect pregnant women againstnvironmental risks.

Third, our findings identify that children in households withow maternal education are particularly susceptible to fluctua-

ions in air quality. Although all individuals are potentially exposedo ambient pollution, the evidence indicates that socioeconomictatus cushions the effect of air pollution, either through behav-oral factors in avoiding pollution or socioeconomic factors such

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s increased access to medical care. As such, our findings provideustifications to interventions targeting low-income households,ncluding information provision about air pollution effect.

This study has clear policy implications for developing countriesn general: namely, while climate change does not currently appearo be a sufficiently strong motivation for these countries to embarkn more aggressive air pollution regulations, our findings leaveittle doubt that protecting the environment is vital to improvingomestic public health.

cknowledgements

I am indebted to Daniele Paserman, Dilip Mookherjee, Tavneeturi, and Wesley Yin for invaluable advice and feedback. I am alsorateful to Lucas Davis, Esther Duflo, Michael Greenstone, Hsueh-ing Huynh, Kelsey Jack, Ginger Zhe Jin, Hiroaki Kaido, Kevin Lang,driana Lleras-Muney, Michael Manove, Kenneth A. Rahn, Leenaudanko, Marc Rysman, Johannes Schmieder, Jeremy Smith, threenonymous reviewers, and seminar participants at Boston Univer-ity, Hiroshima University, Loyola Marymount University, Nationalniversity of Singapore, Tufts University, University of Washing-

on, 2010 NEUDC, and the 2011 Royal Economic Society for theiromments and suggestions. I also thank the Chinese Center for Dis-ase Control and Prevention for sharing the data. Financial supportrom the Institute for Economic Development at Boston Univer-ity, as well as Hewlett/IIE Dissertation Fellowship in Population,eproductive Health and Economics, is gratefully acknowledged.

ieshuang He provided excellent research assistance. All remainingrrors are my own.

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