The local economic impacts of air pollution and regulations: evidence from China

The local economic impacts of air pollution and regulations:

evidence from China

Ying Chen ^∗

London School of Economics December 2017

Work in Progress

Abstract

When countries start to clean up pollutions built up in the past, how do environ- mental regulations aect local growth prospects? I answer this question by analyzing the growth of Chinese cities in the decade after amendments to its national Air Pol- lution Prevention and Control Law (APPCL) in the late 1990s. I propose an original instrumental variable to measure cities' likelihood of hosting thermal power plants, one of the major contributors to China's ambient SO 2 levels to date. The variation in SO 2 levels at the time of new regulations across cities predicted by their respec- tive suitability to host thermal power plants is used to evaluate its causal eects on city economic outcomes. As the timing of APPCL coincides with hukou relaxation that allowed for greater labor mobility within the country, cities with worse air pol- lution lost substantial amount of high-skilled workers in the following decade, while their overall size remained roughly unaected. The manufacturing sector is heavily hit as more stringent regulations in more heavily polluted cities raised costs. Cities with 1% higher existing ambient SO 2 level grew 12% slower in overall manufacturing employment. The private industrial sector in these cities grew slower in terms of employment, capital, and output compared to their counterparts in cities with better air quality (and less regulation). Whereas for the state industrial sector, while its SO 2 polluting industries were negatively aected, capital ew to non-SO 2 polluting industries in cities more regulated. This state support helped non-SO 2 polluting rms in this sector to grew faster than their peers in cities less regulated in terms of output and value-added. But it did not create enough jobs to oset its city-wide losses from all other sectors.

∗

I am extremely grateful for the generous support and guidance from my advisors, J. Vernon Henderson and Olmo Silva. I

would also like to thank Steve Gibbons and Henry Overman, from whom I received valuable comments and feedbacks. I am solely

responsible for any and all errors.

1 Introduction

Environmental regulations have long been at the center of debate. On one hand, high levels of pollution are harmful to the human health and have irrevocable impact on climate. The long term benets of environmental regulations are well documented (Chay and Greenstone, 2003; Currie and Neidell, 2005; Schlenker and Walker, 2016). On the other hand, it is a major byproduct of human activities and production. Understanding the potential economic costs of pollution abatements, for example on employment and job transitions (Becker and Henderson, 2000; Walker, 2013), is crucial for designing better and more comprehensive regulatory policies.

This paper aims to help complement the existing discussion by providing evidence from the perspective of local economies. I examine how Chinese cities, which have been growing and urbanizing at unprecedented rates, respond to the national environmental regulations in curbing SO 2 emissions set out in the late 1990s.

Air pollution and its regulations play two major roles in the consideration of local economies.

Firstly, good air quality is often thought of as an aspect of local amenity that benets local economies. Places with better air quality have higher housing prices (Chay and Greenstone, 2005) and hence better local scal health and public provisions. Cities with higher amenity also tend to attract high-skilled human capital and rms (Glaeser et al., 2001). They also accumulate long-term growth advantage (Heblich et al., 2016). On the ip side, pollution abatements aiming to improve air quality have direct and indirect economic costs that are localized. Firms in heavily polluted and hence heavily regulated places face higher costs, because they must spend more resources on using cleaner technology (Roback, 1982). The impact of higher production cost is eventually borne by local workers. Alternatively, rms may move to places with less regulation, hurting local labor market at their origin Becker and Henderson (2000); Walker (2013).

How do these forces interact with each other and what are the net eects? How are the costs and benets of environmental regulations distributed spatially? China's heavy reliance on coal and emission of SO 2 make it a prime subject for studying these questions. It is one of the biggest coal consumers in the world, burning as much of it as the rest of the world combined since 2012 (US Energy Information Administration, 2014). As more of its cities reach the turning point of the environmental Kuznets curve (Zheng and Kahn, 2017), answering these questions in the Chinese context provides valuable insights for future policy designs both in China and for other emerging countries whose local economies are also going through fast industrialization and urbanization.

A wave of serious amendments of the Air Pollution Prevention and Control Law (APPCL) started in late 1990s marked China's eort to control and monitor air pollution. These policies set national and local ceilings of ambient SO 2 levels, forcing cities with higher existing pollution levels to curb SO 2 emission more strictly. While the stringency of regulation implementation is often dicult to measure (e.g., Greenstone and Hanna, 2014), this policy naturally sets a positive correlation between a city's existing level of SO 2 pollution and the strength of local implementation. The local ambient SO 2 levels in the starting year of this policy can then be used as a reasonable proxy for measuring degrees of implementation stringency. This claim is supported by statistical evidence. Another advantage of using local ambient SO 2 levels is that it also captures the air quality dimension of city amenity. In sum, higher ambient SO 2 levels in the late 1990s indicates stricter regulations over local SO 2 emission, as well as worse local amenity, all else being equal.

To formalize the above, I set out a simple Rosen-Roback style spatial equilibrium model

based on Moretti (2011) and Allcott and Keniston (2017). Under this framework, local growth is analyzed in terms of city employment and average wage levels. Workers respond to ambient SO 2 levels via two channels in the Roback terminology. The rst is the amenity channel.

Everything else being equal, workers are more likely to sort to places with better air quality (i.e., lower ambient SO 2 ). The second is through the productivity channel. SO 2 -polluting rms in high SO 2 cities will face stricter regulation and hence incur higher costs. This lowers the overall local productivity, making it less desirable for internal migrants. These two channels work in the same direction for city size and employment growth. Whereas for local wage levels, the productivity and amenity channels work in two opposite directions. Lower local productivity lowers wage, but lower amenity means that workers are compensated in terms of wage. The direction of wage change will depend on the relative strengths of these two forces.

The Chinese context is slightly more complicated for two main reason. Firstly, the timing of APPCL amendments coincide with a series of hukou reforms that allowed for greater labor mo- bility across places. This change in labor supply elasticity across cities during my study period aects the magnitudes of both the amenity and productivity channels. Secondly, the persistent existence of state-owned enterprises in the Chinese economy introduces a more complex picture.

While regulations are costly to regular private rms, their state-owned counterpart may receive favoritism and additional support from the state (e.g. Chen et al., 2017; Hsieh and Klenow, 2009). The analysis hence needs to allow for heterogeneous eects by private and state sectors.

Testing the above discussion empirically face the challenge that the variation in local am- bient SO 2 levels is not randomly distributed. A city's air quality is frequently correlated to its level of economic activity (Chay and Greenstone, 2003). Heavily polluted places tend to be more developed or industrialized, and may have endogenously distributed industry compo- sitions. Moreover, development and particularly urbanization can lead to further heavier air pollution (Zheng and Kahn, 2013). Therefore, comparing cities with non-randomly distributed air pollution levels and regulation intensity may lead to bias.

To address these endogeneity concerns, I construct a power plant suitability index to in- strument for ambient SO 2 levels at the time of APPCL amendments. Thermal power plants contribute to over 40% of China's SO 2 emissions (Lu et al., 2010). Two cities with compara- ble economic characteristics may dier substantially in their ambient SO 2 levels because one hosts a thermal power plant and one does not. However, actual power plant locations may still be endogenous for reasons such as local demand or political nepotism. So I borrow from the engineering literature and compute cities' suitability for hosting thermal power plants. This suitability index is a weighted sum of a given location's geographic characteristics, for example, elevation, slope, access to water, and so on. It comprehensively measures the likelihood of host- ing thermal power plants for a given location, which in this research is at the spatial accuracy of nine by nine square kilometers given currently available remote sensing data.

The suitability index strongly predicts the actual thermal power plant locations, through

which its predictive power of local ambient SO 2 levels is established. However, the exogeneity

of this instrument comes from the set of weights applied to each factor of its composition, rather

than the factors themselves. This set of weights measures each factor's importance for building

thermal power plants solely from the engineering considerations, while the factors themselves

could be correlated to economic outcomes of a location. Therefore, I further control for three

types of variables in the analysis. The rst type is a set of local economic characteristics

including existing transport infrastructures and share of employment in agriculture. They

control for initial dierences across cities. The second set includes other amenity measures that

are not factors in the computation of power plant suitability, such as annual average temperature

and wind speed. The last set of variables are components of the suitability index that could potentially have economic inuences themselves. For example, having access to water is crucial to building thermal power plants for cooling. Because proximity to waterways is frequently thought as a contributor to local economic growth, I include distance to large rivers and coasts as additional controls in the analysis. The inclusion of these three sets of variables minimizes threats to meeting the exclusion restriction.

The results based on census data show that a 1% higher SO 2 level in 1998 leads to a 3%

decrease in high-skilled population (with high school degrees and above) between 2000 and 2010. This eect is times larger for population with college degrees and above. As high-skilled population is a very small group, the overall city size decreases slightly but it is not statistically signicant. Cities with higher pollution do not attract migrants during this period of great internal mobility. Cities with higher ambient SO 2 at the time of APPCL amendments see a large reduction in manufacturing employment as a result of stricter regulation, but their overall GDP grow faster. Further examination with industrial survey data reveals that the APPCL amendments hurt mainly productivity in the private sector, resulting in loss of employment and capital outows. State-owned rms are less aected as support pour in in terms of large capital investments, keeping jobs at state-owned non-regulated (i.e., non-SO 2 polluting) industries and boosting output. Average wage level go up in more polluted and regulated cities, both to compensate for lower amenity and reect growth in the state-owned non-regulated sector.

We learn several facts from these results. Firstly, high-skilled population are very responsive to air quality, consistent with the literature. Secondly, environmental regulations negatively aect rms in the regulated sector, resulting in job losses in local labor market. In the Chinese context, however, this negative shock is borne mostly by the private sector, as the state pours in substantially higher capital to the non-regulated sector in more regulated places. Output in the state-owned sector leads overall growth in these cities. But the added employment in this sector does not counter job loss in the private sector.

2 Regulatory policies on SO ² trends and internal migration in China

2.1 SO 2 pollution trends and regulations

Sulfur dioxide (SO 2 ) is the major byproduct of burning fossil fuels such as coal by power plants and related industrial facilities. High levels of SO 2 in the air is harmful to human respira- tory system. It is also a primary precursor of acid rain that damages agricultural land, forests, buildings, and overall ecosystem. When interacted with other compounds in the atmosphere, it contributes to the formation of haze and particulate matter (US Environmental Protection Agency, 2017b,a). Given such adverse impacts of SO 2 pollution, most countries have been working on reducing its emission levels in the recent decades. Between 1990 and 2010, the EU 33 countries reduced their SO 2 emission from 27.7 to 7.2 million tons, and in the US the number went down from 23.3 to 7.7.

China, at the same time, has put forward various regulatory policies to curb its SO 2 emission through its APPCL amendments since the 1990s. However, such policies had been carried out intermittently, though out which period China's economy also grew at unprecedented rates. As a result, its SO 2 emission levels did not follow a steadily downward trend exhibited by the US and EU countries, and instead increased from 14.9 to 21.8 million tons between 1990 and 2010.

In Figure 1, I plot the year-to-year time trend of SO 2 emission levels in China, US, and EU 33

countries with 1990 as the base year. China's SO 2 emission levels uctuated over the course,

with the timing of such variation closely related to its regulatory policies and enforcement.

A national SO 2 emission limit was set for the rst time in China's 9th Five Year Plan (1996- 2000). With the goal of achieving its SO 2 reduction targets, the regional Two Control Zone (TCZ) policy was then proposed in 1995 and formally rolled out in 1998. While SO 2 emission was to be regulated nationally, the State Council specically designated 175 cities as either SO 2

or acid rain control zones for stricter scrutiny. These control-zone cities were identied based on their SO 2 emission records, annual and daily average ambient SO 2 concentrations at the time. They contributed to around 60% of SO 2 emission in China at the time (Hao et al., 2001).

Emission of SO 2 in China are driven primarily by heavy coal use. Hence, the detailed policy measures focused on the production and usage of coal. Coal mines of high sulfur content were to be shut down and new mines can only be commissioned for low-sulfur coal. Main coal users including coal-red power plants and factories in heavy SO 2 polluting industries were required to burn coal with low sulfur content, upgrade boilers and kilns, and treat euent gas. Such policy instruments added input and operation costs to related industries. World Bank (2003) shows that high-sulfur coal is 50% cheaper than low-sulfur coal. Upgrading equipments and treating pollutant can also add costs. For example, Becker and Henderson (2000) estimate such increase in average cost as high as 17% from the Clean Air Act in the US. In addition to the above requirement, emission charges were also levied on extremely heavy polluters, and in 2003 generalized to all discharges of pollutants (Goulder, 2005). And those with inecient technology were shut down. Most studies nd this policy to have eectively reduced SO 2 emission in China (Xu et al., 2004; Hering and Poncet, 2014; Tanaka, 2015). He et al. (2002) estimate that these measures reduced around 0.8 million ton SO 2 emission in the zoned cities per year, and 98 cities in 1999 (increased from 81 in 1997) met the national ambient air SO 2 concentration standard.

However, China's total SO 2 emission started to see its upward trend again in early 2000s (Lu et al., 2010), also indicated by Figure 1. This prompted the central government to continue to target SO 2 reduction in the 11th Five Year Plan (2006-2010). All existing regulatory terms were strengthened. Most emission reduction targets termed as expected in the 1995 APPCL amendments became compulsory, and their compliance linked directly to local political of- cers' promotion perspective (China Central Committee, 2006). SO 2 emission targets were set more specic for each province with emphasis on power sector and manufacturing rms in heavily polluting industries. Pollution levy tripled to 1.26 RMB/kg for SO 2 (Cao et al., 2009).

Additionally, all coal-red power plants are required to install the Flue-Gas Desulfurization (FGD) system SO 2 -scrubbing equipment and keep its operating rate above 90% of the time to avoid potential nes US Environmental Protection Agency and China State Environmental Protection Administration (2007). Current multi-disciplinary evidence suggests that this policy was eective in reducing SO 2 emission (e.g., Li et al., 2010; Xu, 2011; Schreifels et al., 2012;

Tan et al., 2017; Ma and Takeuchi, 2017). China's ocial SO 2 emission has since been on a general declining trend, as shown in Figure 1.

2.2 Regulations over internal migration

Shortly after the late 1990s APPCL amendments, China started relaxing its hukou policy

so internal migration became possible. China's hukou system works similar to an internal

passport system. When a Chinese citizen is born, he or she inherits a local citizenship tied

to his or her mother's hukou place of residence (as specic as to neighborhood subdistrict,

jiedao). This citizenship entitles the individual to either a rural or an urban status, which

is a direct inheritance of whether or not one's ancestors were peasants. This dichotomy of

legal status, along with locational dierentials, determine one's rights to housing, schooling,

job opportunities, health care, and even grain ratios prior to 1990s (Chan, 1994).

Because one's legal rights are so tied with his or her hukou location and status, China's internal migration is strongly restrictedly through the hukou system. Prior to 2000, changing one's hukou status or to move across locations legally was very dicult. The rare channels are through college education or job relocation within the government or large state owned enterprises, with high friction and monetary cost. People could otherwise move illegally as unregistered migrant or legally but temporarily, and in both cases they are entitled to zero or little local public provision at the receiving place (Au and Henderson, 2006b). Therefore, such inexibility hugely disincentives anyone for moving and local labor supply was largely inelastic.

The gradual hukou reform started in 2000, substantial relaxation (but not elimination) over hukou restrictions opened the door for internal migration(Chan, 2013). Existing literature com- prehensively studies eects of the hukou system in labor mobility and city sizes, documenting large internal movements of labor from rural to urban, as well as across urban areas (e.g., Au and Henderson, 2006b,a; Bosker et al., 2012).

The hukou relaxation allows for more elastic labor supply responses to local economic forces via both the productivity and amenity channels. However, it does not help distinguish which of the two channels dominate. The relative strengths of these two channels further depends on to which part of the environmental Kuznets curve that Chinese citizens belong.

3 Theoretical framework

The simple theoretical model presented here aims to formalize the relationship between city- level ambient SO 2 pollution and city growth prospects in the context of China's environmental regulation and imperfect labor mobility. It follows largely the works of Moretti (2011) and Allcott and Keniston (2017) under the Rosen-Roback spatial equilibrium framework. There are two cities, c ∈ {a, b} . Each city has two production sectors, j ∈ {m, s} . m represents the non-SO 2 polluting sector and s for SO 2 -polluting sector. Both goods are internationally traded and have exogenously determined prices P _m = 1 and P _s .

There are two time periods. t = 0 is the initial state, when there is limited labor mobility under hukou regulation, and there is no regulation over SO 2 polluting. Both cities are exactly the same except for ambient SO 2 levels, denoted by A _c . Let A _a,0 < A _b,0 , namely, city a has higher ambient SO 2 levels than city b . This dierence in A _c,0 is assumed to be exogenous here and will be instrumented for in the empirical exercise. In t = 1 , regulation over hukou is relaxed and labor now has more elastic yet still imperfect mobility. Environmental regulation is being implemented more stringently on cities with higher ambient SO 2 levels.

3.1 Production

Each sector j ∈ {m, s} has a composite rm employing N _jc workers and earning revenue R _jc = X _jc N _jc ^1−γ , where γ ∈ (0, 1) . Revenue productivity by denition is X _jc = P _j Ω _jc , price of the product times and physical productivity. Workers have homogenous skills and hence wage level is equalized within a city. Firms in a given sector have prot equation Π _jc = R _jc − W _c N _jc . Assume that workers are paid their marginal productivity, we have rm prot maximization that gives wage equation: W _c = (1 − γ)X _jc N _jc ^−γ . Rearrange to obtain labor demand by a typical rm in industry j and city c : N _jc = ( ^(1−γ)X _W

c

)

. Aggregating across industries to obtain

city-level labor demand N _c = N _mc + N _sc , which is in detail:

N _c = ( (1 − γ) W _c )

(X

1 γ

mc + X

1 γ

sc )

Taking logs (denoted with corresponding lower cases):

n _c = 1

γ ln(1 − γ) − 1

γ w _c + x e _c , where e x _c ≡ ln(X

1

mc

+ X

1

sc

) (1)

Firms can freely move across cities, so the inverse labor demand dierences across cities a and b is:

w _a − w _b = γ( x e _a − e x _b ) − γ(n _a − n _b ) (2) 3.2 Housing market

Following Moretti (2011), I assume that the number of housing units in city c equals to the number of workers with housing supply elasticity of k : p hc = kn c + k 0 . 1/k is thus the price elasticity of housing supply. This allows us to write housing price dierences across cities as population dierences:

p _ha − p _hb = k(n _a − n _b ) (3) 3.3 Consumers and workers

Individuals decide where to live and how much to consume by supplying 1 unit of labor. A representative individual consumes C im , C is , C ih units of non-SO 2 polluting goods, SO 2 polluting goods, and housing. Their utility also depends on their idiosyncratic tastes toward the local amenity levels, A _c E _ic . A _c is aected by the local SO 2 level. Individuals maximize their Cobb- Douglas utility:

U _ic = C _ih ^α C _is ^β C _im ^1−α−β A _c E _ic

subject to W _c − C _im − p _s C _is − p _hc C _ih ≥ 0 , with α, β, 1 − α − β ∈ (0, 1) .

The indirect utility is then u _ic = w _c − αp _hc − βp _s + a _c + e _ic plus some constant. Individual is indierent between cities a and b if u _ia = u _ib , which is equivalent to:

w _a − w _b = α(p _ha − p _hb ) − (a _a − a _b ) − (e _ia − e _ib ) (4) By assuming that e is distributed type I extreme value with scale parameter s ² where s ∈ (0, ∞) , we have e _ia − e _ib = s(n _a − n _b ) . As dened in Moretti (2011), s characterizes the degree of labor mobility. Larger s means that workers are more responsive to their location preferences and are less mobile. Further combining with equation 3, we obtain the inverse labor supply dierence between the two cities:

w _a − w _b = (αk + s)(n _a − n _b ) − (a _a − a _b ) (5) 3.4 Equilibrium

In the labor market equilibrium, we have the inverse city-level labor demand (equation 2) equals to supply (equation 5):

γ( x e _a − e x _b ) − γ (n _a − n _b ) = (αk + s)(n _a − n _b ) − (a _a − a _b )

This gives population dierences between a and b : n _a − n _b = 1

αk + s + γ [γ( e x _a − x e _b ) + (a _a − a _b )] (6) And the city-level wage dierence is given by substituting equation 5 into labor demand equation 2:

w _a − w _b = γ

αk + s + γ [(αk + s)( e x _a − x e _b ) − (a _a − a _b )] (7) As standard Roback models would suggest, both the population and wage dierences are deter- mined by the dierences in city-level productivity and amenity. A more productive city would attract more workers and raise the wage level, whereas a more polluted city (lower a ) would lose workers but compensate the residents with higher wage.

3.5 Changes over time

Next, we can write out population and wage dierences over time. Since there is no produc- tivity dierence between cities in period 0 by assumption, e x _a0 − e x _b0 = 0 . Furthermore, improving ambient air pollution usually takes a very long time. Assume that emission by period 1 is lower than it otherwise would have been without APPCL, but the actual ambient air quality did not improve, then the dierence in amenity between a and b persist: a _a1 − a _b1 = a _a0 − a _b0 . Figure 2 conrms this assumption by plotting the change of ambient SO 2 between 1998 and 2010 against its 1998 SO 2 level for each city. There is no improvement in air quality over this period.

The dierences over time for equations 6 and 7 are therefore:

∆(n _a − n _b ) = γ

αk + s ₁ + γ ( e x _a1 − x e _b1 ) − ( 1

αk + s ₁ + γ − 1

αk + s ₀ + γ )(a _b0 − a _a0 ) (8)

∆(w _a − w _b ) = γ(αk + s ₁ )

αk + s ₁ + γ ( e x _a1 − x e _b1 ) + ( 1

αk + s ₁ + γ − 1

αk + s ₀ + γ )γ(a _b0 − a _a0 ) (9) Due to strict hukou regulation in period 0, s ₀ will be relatively large so _αk+s ¹

_+γ is small.

Hukou relaxation in period 1 means that people are less bounded to their original locations, i.e., s ₁ < s ₀ . Also s > 0 by denition. Thus we have the following:

γ

αk + s 1 + γ < 1; γ(αk + s ₁ )

αk + s 1 + γ < 1; 1

αk + s 1 + γ − 1

αk + s 0 + γ > 0.

And by design we know (a _b0 −a _a0 ) > 0 . Therefore, the amenity channel is negative on ∆(n _a −n _b ) and positive on ∆(w _a − w _b ) . In other words, workers move away from heavy air pollution, and stayers are compensated by higher wage.

The signing of ( e x _a1 − e x _b1 ) , on the other hand, is less clear. The existing literature presents

mixed evidence. The conventional wisdom is that regulation imposes cost on rms and reduces

productivity. Greenstone (2002) shows that nonattainment counties under CAAAs lost more

jobs, capital stock, and output compared to attainment counties. Henderson (1996) and Becker

and Henderson (2000) document substantially less births of polluting rms in CAAA nonattain-

ment counties. Meanwhile, ample economic evidence also exist to suggest that environmental

regulations make rms more productive. Porter (1991) rst argues that environmental regula- tions in the US stimulate domestic innovation. This suggestion was later supported by empirical evidence in Jae et al. (1995) and Jae and Palmer (1997). Berman and Bui (2001) present evidence on oil renery productivity increase as a result of specic environmental regulations.

Levinson (2009) shows that technology advances, following pollution regulations in the US, con- tributed substantially to manufacturing output increase. Shapiro and Walker (2017) observe that by using inputs more eciently, rms increase productivity and reduce emission per unit output.

However, little evidence exists on this matter in the context of China. The singing of productivity change and its magnitude relative to the amenity eect will aect the general equilibrium population and wage change over this decade. Hence, I delay this discussion to the Results section.

3.6 Two-city to many-city

The discussion so far is based on the two-city scenario with one heavy polluted city and one cleaner city, whereas empirically in this paper there are 286 Han Chinese prefectures with continuous measures of ambient air pollution. In order to generalize the dierence between two cities' outcomes as a result of their dierence in initial levels of air pollution, we generalize to the many-city case by considering the expectation over all cities of the eects of varying initial air pollution levels. Let A _c ∈ {1, 0} denote city c 's amenity levels measured in air pollution, with 1 for high pollution levels and 0 for low. Let Y _c denote outcomes at the city level (e.g., population, wage). Equations 6 and 7 in the many-city case are therefore generalized to the dierence in average outcome between the high and low air pollution groups.

E [Y c |A c = 1] − E [Y c |A c = 0] (10) If initial air pollution levels are randomly assigned as in the model assumption, this observed dierence in outcome (i.e., equation 10) is the average treatment eect. However in reality, more polluted cities in the developing world tend to be manufacturing hubs or faster growing cities with high rates of urbanization. Equation 10 in fact can be written as:

E [Y c |A c = 1] − E [Y c |A c = 0] = E [Y 1c |A c = 1] − E [Y 0c |A c = 1] + E [Y 0c |A c = 1] − E [Y 0c |A c = 0], where E [Y 0c |A c = 1] − E [Y 0c |A c = 0] is the selection bias that makes the group of low pollution cities invalid counterfactuals for the group of high pollution cities. In the next section, I discuss my research design that circumvents this empirical challenge.

4 Research design and data

4.1 Power plant suitability as IV

To evaluate the impact of air pollution on economic activities for cities under the late 1990s SO 2 regulatory context, consider the following OLS specication:

ln(y _i,2010 ) − ln(y _i,2000 ) = ξ + ρ ln( SO 2 ) _i,1998 + φX _i ⁻¹ + _i (11)

where y i is a local economic outcome at the city level ( i ) such as population sizes, total employ-

ment, or average wage, SO 2 is the city-level ambient SO 2 level in 1998, and X _i is a vector of

city-level economic and climatic covariants. I picked SO 2 levels in 1998 as the baseline here as

anecdotal evidence show that it is the key year where serious regulatory measures were rolled out. I used 2000 ambient SO 2 levels as robustness check and it gives the same results. As laid out in Section 3, local economic outcome variables such as city sizes can be aected by local air pollution through two channels. The rst one is the rm productivity channel, where environmental regulations adds costs to rms proportional to concurrent local air quality. The second one is the amenity channel through which workers prefer less polluted cities, all else being equal.

While both mechanisms suggest that higher local air pollution will reduce city sizes and long-term growth, causally identifying such eects, even conditional on a comprehensive list of X _i 's, is econometrically challenging as variation in ambient SO 2 across cities is far from randomly distributed. For example, places that are more economically active tend to pollute more in aggregate (Chay and Greenstone, 2003), and thus are more likely to face stricter environmental regulation. These cities can dier in characteristics not observable to researchers such as institution quality that inuence both pollution level and economic prospects of cities.

The sorting of rms and workers, which is again frequently unobservable to researchers, may be correlated to both local air quality and future economic performance.

To address these endogeneity concerns, my research design uses power plant suitability to in- strument for its hosting city's level of SO 2 pollution, taking advantage of several useful attributes of coal-red power plants. The rst key attribute of coal-red power plants is its paramount importance in China's energy generation and coal consumption. Currently, over 70% of China's electricity production is still from coal sources, according to the International Energy Agency ¹ . Furthermore, around 50% of China's coal consumption is used in power plants, while industrial production accounts for another 40% and the rest is in domestic use or transportation (Lu et al., 2010). As the main contributor to SO 2 emission, coal-red power plants were hence the key target for China's SO 2 regulatory policies since late 1990s. Consequently, cities hosting coal-red power plants, and in particular rms in these places, came under scrutiny. In other words, rms are more likely to incur additional costs if they reside in the same city as coal-red power plants, all else being equal. Similarly, the marginal population sensitive to air pollution may sort away from highly-polluted cities for hosting coal-red power plants rather than other city characteristics.

However, it is still likely for unobservable factors correlated to actual coal-red power plant locations to aect the economic prospects of a city, its incumbent rms and residents. Actual power plant locations might be driven by demand forces. Electricity is produced and trans- mitted to its end users. Although demand does not completely determine the precise location of power plants, it still plays a part for transmission cost considerations as the cost goes up with longer transmission distance. Moreover given China's political system, it is possible that a coal-red power plant was strategically placed to favor the local politician for his or her future career or nepotism motivations. These places and their incumbent rms can both be aected by such potential unobserved factors.

Therefore in this paper, I borrow from the engineering literature an index of coal-red power plant suitability as the instrumental variable for a city's ambient SO 2 level in 1998.

This suitability index is a weighted sum of all relevant factors for building and operating coal- red power plants at a given location. Generally, the consideration comprises of the location's physical and biological characteristics such as topography, soil stability, land use, water bodies, fuel supply, and so on. Table 1 lists the detailed weights for each factor and their sub-categories.

1

Data sources: IEA statistics: China, People's Republic of: Electricity and Heat. Data accessed on August 16, 2017. The

percentage of electricity generated by coal in 1990, 2000, 2010, and 2014 are: 71%, 78%, 77%, and 73% respectively.

For example, elevation of the location has a weight of 0.06, where its values fall under four categories of suitability. When the location has elevation between 0-1km, it's given a reclassied value of 10; between 1-1.4km it's an 8; 1.4-1.8km gets a 4; above 1.8 is a 0. So the total contribution of elevation to the suitability index of this location is 0.06 times its reclassied value, the higher the more suitable. I collected information on all these variables listed in Table 1; reclassied their values and summed them up according to the assigned weights. This suitability index is therefore an aggregate of all these information, stipulating whether a given location is suitable for building coal-red power plants and how costly it will be from engineering perspectives.

In doing so, I collected the nest, publicly available remote sensing data for all the factors in Table 1, as well as air pollution measures. Detailed sources and resolution of these data are listed in Appendix Table A1. Since the suitability index is an weighted aggregate of all layers of satellite data, the nest spatial resolution is that of the coarsest layer 0.08 ^◦ × 0.08 ^◦ in this case. In other words, suitability index is computed for every 9×9 km ² of China. Figure 3a maps spatial distribution of the suitability index across Han China in blue, with gray lines tracing out prefecture boundaries. The darker the blue indicates greater suitability for building and operating coal-red power plants. Additionally, Figure 3b maps the distribution of ambient SO 2

density across China, with darker pixel indicating more severe SO 2 pollution in its area. Both parts of Figure 3 show the precise location of coal-red power plants built by 1998. They are geo-located and checked against Google Map Satellite imageries by SourceWatch Coal Issues ² . Visually, there is clear spatial overlapping patterns between power plant suitability and their actual locations, as well as the latter and SO 2 pollution.

4.2 Identifying assumptions and descriptive evidence

The link between this suitability index and local ambient SO 2 density is established through that it predicts coal-red power plant locations, which are the biggest contributor to SO 2

emission in China throughout the study period. As it captures the location's probability, rather than reality, of hosting coal-red power plants, it is more likely to meet the exclusion restriction than actual power plant locations. Furthermore, I include individual components of this index in the regression to address the concern that some of the individual components of this index may impact the location's economic outcomes on their own. Thus, the instrumental variable is eectively the set of weights from engineering perspectives for building power plants.

Because the analysis is carried out at the city level, I compute the city-level mean of suitability as the instrument. The rationale is that while the suitability of an individual pixel might be dicult to detect for engineers and planners back in the 90s, city-level comparison in terms of their respective mean suitability would have been more readily available information.

Ambient SO 2 density in 1998 is computed as zonal average inside city boundaries. Its variation across cities would rstly aect China's internal migration with hukou relaxation in 2000, and secondly the degree of scrutiny rms will face in the implementation of pollution regulating policies in the following years. To further mitigate bias, I use the decadal log- dierence of outcome variables, and include city-level economic and climatic characteristics prior to 1998 as covariates. Hence, conditional on pre-existing economic history and climate conditions, the two-stage least square retains only the variation in SO 2 density generated by variation in suitability for coal-red power plants. As a result, the estimate captures the causal eect of air pollution on outcome variables such as changes in city sizes, employment, and wage

2

Accessed in June 2017.

levels in the following decade, through both the amenity and productivity channels.

To formalize the above, the rst stage is as follows:

ln SO 2i,1998 = α + β Suitability i + γ X ⁻¹ i + ε _i (12) where suitability at the city-level is used and everything follows from equation 11. Speci- cally, the list of covariates includes: regional dummies for east and west China, dummy for being provincial capital or province-level city, number of road and rail rays in 1990, log census population in 1990, share of employment in manufacturing in 2000 (census), log distance to coast, log distance to nearest river, average precipitation rate, temperature, and wind speed in 2005. All the city-level economic variables are the same as Baum-Snow et al. (2017) and were kindly shared by the authors. The 2005 climatic measures are city-level zonal computations from various NASA projects as documented in Table A1. Although less ideal than measures from 1998, 2005 is the earliest year that these data are available for all of Han China.

Next, predicted SO 2 is used to estimate causal eects of the dependent variable:

ln(y _i,2010 ) − ln(y _i,2000 ) = η + κ ln SO c 2i,1998 + λ X ⁻¹ i + u _i (13) where everything follows from the OLS setting (equation 11) except the city-level ambient SO 2

is predicted by the rst stage (equation 12). The outcome variables examined in this paper are changes in city population and employment between 2000 and 2010, and employment in the utility and heavy manufacturing industries between 2000 and 2005. These are city-level measures aggregated up based on county-level censuses. While variables from census are able to tell a more complete story, I further complement the picture by computing city-level employment and wage variables for SO 2 and non-SO 2 polluting industries from the Annual Medium- and Large-enterprise surveys between 1999 and 2007. Again, the log changes of these variables between 1999 and 2007 are used to assess potential post-regulation impacts. Measures based on rm surveys miss out an important part of the picture small rms whose annual sales are less than 5 million RMB (Brandt et al., 2014). Some researchers argue that small rms are those most aected by these regulations SO 2 regulations (Gao et al., 2009). But these rm surveys allow for clearly identifying industries of high SO 2 emissions ³ and ownership status by paid-in capital that are not available in census data.

As mentioned before, using local ambient SO 2 level in 1998 as the key treatment variable captures variations of both potential channels: how strictly the regulations were implemented across cities, and how strongly local residents respond to air pollution once they can move.

Alternatively, I also run the above analysis using the Two Control Zone dummy as the treatment variable:

TCZ i,1998 = α + β Suitability i + γ X ⁻¹ i + ε i (14) ln(y _i,2010 ) − ln(y _i,2000 ) = η + κ[ TCZ i,1998 + λ X ⁻¹ i + u _i (15) Recall that although the late 1990s regulations applied to all of China, the TCZ cities were listed aside for stricter monitoring. This set of analysis complements the baseline as the predicted TCZ dummy captures directly the regulatory status. The list of TCZ cities are collected from the relevant State Council document (China State Council, 1998).

Tables 3 to 6 statistically establish the various linkages between power plant hosting, ambient SO 2 levels, suitability, and TCZ status at the city level. Table 3 rstly shows that cities hosting

3

These industries are identied based on 2-digit industry codes in reference to (Fioletov et al., 2016).

thermal power plants by 1998 pollute more, with and without conditional on city economic and climatic characteristics. Cities hosting thermal power plants and those with higher generating capacity by 1998 have higher ambient SO 2 in 1998 (columns 1-3), as well as higher extreme NO 2

levels in 2005 ⁴ (columns 4-6). NO 2 is another major pollutant of thermal power plants. It is much less harmful to human health and the environment, and rarely a target of environmental regulation. So their results further help show that indeed thermal power plants have paramount impact on the air quality of their hosting cities.

Next, Table 4 establishes the link between suitability index and thermal power plant hosting status. Columns 1-4 show that higher the mean or top 25% suitability of a city, the more likely it would have been hosting a thermal power plant by 1998, as well as one with higher generating capacity ⁵ . Columns 5 and 6 show a lack of such relationship in cities that started to host thermal power plants between 1998 and 2010. By 2010, all 286 cities in the sample have power plants. Therefore, all cities have suitable land to build power plants, and more suitable cities started hosting power plants earlier.

Table 5 provides support for using 1998 ambient SO 2 levels as the treatment variable to capture regulation stringency. Cities with higher ambient SO 2 levels in 1998 are more likely to be listed as TCZ cities that receive stricter regulation. This applies to both SO 2 and acid rain control zone statuses.

Finally, Table 6 presents the rst-stage results. City-level mean suitability is used as the instrumental variable and it is strongly predictive of ambient SO 2 in 1998, as well as NO 2 in 2005 as a robustness check.

5 Results

Next, the power plant suitability predicted 1998 ambient SO 2 level is used to evaluate the causal impact on various city economic outcomes. Firstly, tables 7 to 9 show results based on the 2000 and 2010 censuses to provide a comprehensive and representative picture. Tables 10 to 14 present results from 1999 and 2007 industrial surveys to illustrate how rms in dierent sectors (SO 2 versus non-SO 2 polluting) and by dierent ownership (privately versus state-owned) are aected by the regulations.

Table 7 shows how ambient SO 2 levels in 1998 aect dierent types of population. The overall net eect on city size is not statistically dierent from zero. As hukou relaxes, cities with 1% higher ambient SO 2 causes a 3.1% reduction in population with high school degrees and above, and a 9.2% decrease in population with college degrees and above. This is consistent with ndings in existing literature that high-skilled workers respond sensitively to amenity dierences such as air quality. High skilled workers account for a relatively small proportion of the overall population. The share of population with high school degree and above in an average city is 15% in 2000 and 22% in 2010, 3% and 8% for college and above. Hence the outow of high skilled workers do not show up in the change in overall city size. But this brain draining eect of air pollution poses long-term consequences to the local economy. The last two columns show results of migrant population. During this decade of great internal migration in China, cities with higher ambient SO 2 are not attractive to migrants.

Table 12 and 9 present results on employment and GDP that reect the impact of stricter regulations for cities with worse air quality. Table 12 shows that while the overall employment growth is not aected, cities under more stringent regulation grew about 10.8% slower in its

4

2005 is the earliest year that NO

measures are available with NASA.

5

I also tested the maximum and second maximum suitability value of a city. The relationships are the same.

manufacutring sector. Employment in the secondary industry (i.e., manufacturing, extractions, and utilities production) grew 11% slower, and that in the primary and tertiary industries grew slightly faster but are not statistically signicant. The GDP results, on the other hand, show overall faster growth and in the secondary industry for these cities, and a marginally statistically signicant 5% faster growth in wage. A reduction in employment with increase in output seems to suggest evidence for growing productivity in these cities with stricter regulations.

Without drawing conclusions too soon, I turn to industrial surveys to breakdown the eect of regulations on output, value-added employment, capital, and wage by SO 2 and non-SO 2

polluting industries, as well as by private versus state ownership status. The industrial surveys cover rms with above 5 million RMB in sales in the secondary industry, so the numbers should align roughly with the census results on secondary industry. Columns 1 and 4 in Table 11 show results on overall total output and value added. Their signs and magnitudes match well with those from the census secondary GDP measure. Breaking down output results by polluting types and ownership, we can see that faster output growth in cities with stricter regulation is driven by that in the state-owned rms and especially in the non-SO 2 polluting sector. The private sector, on the contrary, grew slower in heavily regulated cities.

In tables 12 and 13, I take a closer look at the two main factors of production, namely, labor and capital. The detailed breakdown by sector and ownership provide evidence for strong capital support from the government to state-owned rms and especially those in the non-SO 2

polluting industries. These rms also created more employment than those in cities with less regulation. However, employment and capital in private rms both experienced substantially slower growth with more regulation in SO 2 and non-SO 2 polluting sectors alike. Lastly, wage outcomes in table 14 show slower growth in the private sector and faster growth in the state, non-SO 2 polluting industries. It is also worth noting that privately owned rms in the non-SO 2

polluting industries tend to grow even slower than those in the SO 2 -polluting industries. This indicates that the government's favoritism helps state owned rms at the expense of the private sector. Overall, state support help save the city-wide output growth, but not the labor market outcomes.

Table 15 shows the OLS results for reference. Analysis based on OLS would have led to some dierent conclusions about the impacts of air quality in a climate of stricter regulations.

On the other hand in Tables 16 and 17, I use the two control zone status dummy and NO 2 level in 2005 as the treatment variables respectively. Both of these alternative treatment variables show patterns consistent with the main results, although the point estimates are less precise in some cases.

In Table 16, the TCZ dummy is set to 1 if the city had been listed as the key target city by the State Council in 1998, and 0 otherwise. This is a more direct treatment variable proxying for regulatory cost imposed on relevant industries and cities, however its binary nature is unable to capture for as much variation as a continuous variable such as the 1998 SO 2 level. Moreover, the TCZ status is not measuring regulation versus no regulation. Rather, it switches on for being on the State Council's list, potentially facing stricter enforcement and closer monitoring than the rest.

On the other hand, using NO 2 levels is similar to using the SO 2 measures and produces

similar results. Its benign nature allows for assuming away any direct amenity impact of air

pollution on city growth. However, it is almost always produced concurrently with SO 2 and

other harmful pollutants, discounting this advantage. Additionally, its earliest measures only

date back to 2005, which is not ideal.

6 Conclusion

Evidence presented show a few important facts for Chinese cities' growth potential. More polluted cities lose high-skilled human capital. Potential economic costs of environmental reg- ulation are signicant on privately owned rms in the SO 2 -polluting sector. They experienced slower growth in output, employment, and capital than their counterparts in cities less reg- ulated. These negative impacts are smaller for SO 2 -polluting but state-owned rms. While non-SO 2 polluting industries are not directly regulated by the APPCL amendments, govern- ment support in the form of large capital inows to cities with stricter regulation boosts faster growth in the state-owned rms in this sector. But this output enhancing favoritism occurs at the expense of privately owned, non-SO 2 polluting rms. Such government support compen- sated for the loss in output growth in the private sector, but do not make up for the loss of employment overall.

Produce and pollution rst, clean up later is a frequent choice made by policy makers.

Low eciency fuels are the cheaper way to generate energy and increase economic output, but the later cost of cleaning up is substantial. As shown in the case of China, heavily polluted cities face higher clean-up costs. Related industries have to stomach the negative shocks when regulation starts. Government eorts to counter the negative economic cost are expensive.

Such impacts are localized and can be long lasting.

The research design used here is applicable for future research. It can be extended to examine the impact of SO 2 pollution on other outcome variables. It can also be applied to answer research questions in other countries.

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7 Figures and Tables Figures

Figure 1: SO

emission trends, 1990-2015

0 50 100 150 200

1990 1995 2000 2005 2010 2015

Year

China USA EU 33

Data is converted to relative terms with respect to 1990 (=100).

Data sources: China Ministry of Environmental Protection; U.S. Environmental Protection Agency; European

Environment Agency. Accessed on August 9, 2017.

Figure 2: Ambient SO

change and power plant hosting

This regular convergence plot shows the persistency of SO

pollutions at the city level. Cities with high SO

pollutions remained highly polluted in the following decade. This plot also shows that this relationship is the

same for cities hosting thermal power plants early and those with newly added plants.

Figure 3: Spatial distributions of coal-red power plants by 1998, suitability index, and ambient SO

density

Suitability index not suitable 2 - 377 378 - 474 475 - 571 572 - 811

Prefecture boundary

#

* Power plants

(a) Coal-red power plant suitability and actual locations for those built by 1998

SO2 column mass density 0.30-4.00

4.01-9.48 9.49-16.75 16.76-26.60 26.61-44.36 Prefecture boundary

#

* Power plants

(b) Ambient SO

density and coal-red power plant locations for those built by 1998

Tables

Table 1: Detailed weights for suitability index construction Factor Sub-factor value Reclassify values

Elevation 0-1000 m 10

0.06 1000-1400 m 8

1400- 1800 m 4

> 1800 m 0

Slope 0-6 % 10

0.05 6-10 % 7

> 10 % 0

Road 0-500 m 0

0.08 0.5 - 10 Km 10

10-20 Km 7

20-40 Km 3

> 40 Km 0

Rail 0 - 500 m 0

0.14 0.5 - 10 Km 10

10-20 Km 7

20-40 Km 3

> 40 Km 0

Distance to urban area 0-10km 0

0.05 10-20km 10

20-50km 7

50-100km 4

> 100km 0

Distance to coal sources 0-5km 10

0.05 5-50km 5

> 50km 0

Cultivation Yes 5

0.04 No 10

Gas pipe line 0-500 m 0

0.08 0.5- 5 Km 10

5-10 Km 8

10-20 Km 6

20-40 Km 3

> 40 Km 0

Large river 0- 500 m 0

0.08 0.5 - 10 Km 10

10 - 20 Km 5

> 20 Km 0

Small river 0- 500 m 0

0.07 0.5 - 10 Km 10

10 - 20 Km 5

> 20 Km 0

Distance to earthquake spots 0-1km 0

0.05 > 1km 10

Distance to volcanoes 0-1km 0

0.02 > 1km 10

Distance to airelds 0-5km 0

0.05 > 5km 10

Distance to coal mines 0-5km 10

0.1 5-50km 5

> 50km 0

Distance to oil and gas elds 0-5km 10

0.08 5-50km 5

> 50km 0

Table 2: Summary statistics

Variable Mean SD Min Median Max

ln(SO2 density in 1998, city average) 0.33 0.93 -2.57 0.41 2.53

ln(NO2 density in 2005, city max) 13.72 0.50 12.90 13.69 14.71

1 if hosting thermal power plant by 1998 0.40 0.49 0.00 0.00 1.00

ln(thermal power generating capacity) 2.51 3.17 0.00 0.00 8.29

1 if hosting thermal power plants after 1998 & before 2010 0.36 0.48 0.00 0.00 1.00

suitability index, city average 4.63 0.51 2.68 4.60 5.96

suitability index, city 75th percentile 5.30 0.56 3.32 5.29 6.75

two-control zone city: SO2 zone 0.21 0.40 0.00 0.00 1.00

two-control zone city: acid rain zone 0.39 0.49 0.00 0.00 1.00

provincial capital or provincial-level city 0.09 0.29 0.00 0.00 1.00

total road and rail rays in 1999 4.48 2.58 0.00 4.00 13.00

share of agriculture employment in 2000 0.63 0.19 0.02 0.67 0.90

ln(distance to nearest coast in km) 5.18 1.86 -5.38 5.76 7.40

ln(distance to nearest river in km) 3.99 1.95 -1.26 4.66 6.47

mean city temperature in 2005 287.00 5.04 273.92 288.23 297.02

mean city wind speed in 2005 3.17 0.74 1.91 2.99 6.09

ln(population in 1990) 14.99 0.66 11.61 15.04 17.18

∆ log population, 2000-2010 0.06 0.11 -0.25 0.05 0.54

∆ log pop with high school degree and above, 2000-2010 0.48 0.15 0.12 0.47 1.01

∆ log pop with college degree and above, 2000-2010 0.97 0.20 0.41 0.97 1.51

∆ log migrant population, 2000-2010 0.69 0.37 -0.64 0.71 1.94

∆ log migrant (from other provinces) population, 2000-2010 0.55 0.49 -2.35 0.59 1.77

∆ log employment, 2000-2010 0.06 0.14 -0.38 0.04 0.63

∆ log manufacturing employment, 2000-2010 0.32 0.42 -0.67 0.32 1.72

∆ log primary employment, 2000-2010 -0.27 0.25 -1.35 -0.22 0.21

∆ log secondary employment, 2000-2010 0.45 0.37 -0.29 0.44 2.08

∆ log tertiary employment, 2000-2010 0.40 0.17 -0.28 0.39 1.42

∆ log GDP, 2002-2012 1.58 0.26 1.03 1.58 3.18

∆ log primary GDP, 2002-2012 1.09 0.35 -1.07 1.07 2.12

∆ log secondary GDP, 2002-2012 1.76 0.37 0.75 1.75 3.47

∆ log tertiary GDP, 2002-2012 1.54 0.28 0.92 1.52 2.90

∆ log average wage, 2000-2010 1.36 0.19 0.52 1.36 2.13

∆ log industrial output, 1999-2007 1.74 0.46 0.57 1.73 3.89

∆ log industrial output in SO2 industries, 1999-2007 1.80 0.51 0.42 1.78 4.27

∆ log industrial output in non-SO2 industries, 1999-2007 1.62 0.55 -0.22 1.63 3.73

∆ log industrial value-added, 1999-2007 1.80 0.51 0.39 1.83 3.89

∆ log industrial value-added in SO2 industries, 1999-2007 1.85 0.55 0.40 1.88 4.22

∆ log industrial value-added in non-SO2 industries, 1999-2007 1.71 0.63 -1.16 1.75 3.43

∆ log industrial output by private rms in SO2 industries, 1999-2007 3.04 0.93 -0.09 2.99 5.88

∆ log industrial output by private rms in non-SO2 industries, 1999-2007 2.65 0.77 0.69 2.57 5.88

∆ log industrial output by state rms in SO2 industries, 1999-2007 0.65 1.03 -3.31 0.75 3.85

∆ log industrial output by state rms in non-SO2 industries, 1999-2007 -0.39 1.38 -12.46 -0.19 2.85

∆ log industrial value-added by private rms in SO2 industries, 1999-200 3.09 1.01 0.23 3.08 7.03

∆ log industrial value-added by private rms in non-SO2 industries, 1999 2.76 0.85 -0.04 2.67 5.83

∆ log industrial value-added by state rms in SO2 industries, 1999-2007 0.67 1.09 -3.23 0.76 3.68

∆ log industrial value-added by state rms in non-SO2 industries, 1999-2 -0.25 1.41 -11.26 -0.07 3.98

∆ log industrial employment, 1999-2007 0.22 0.45 -0.65 0.12 2.33

∆ log employment in SO2 industries, 2000-2010 0.15 0.48 -1.00 0.07 3.26

∆ log employment in non-SO2 industries, 2000-2010 0.26 0.52 -1.43 0.18 1.88

∆ log industrial capital, 1999-2007 0.96 0.47 -0.32 0.95 3.16

∆ log industrial capital in SO2 industries, 1999-2007 1.03 0.58 -0.59 1.00 3.32

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