Is a Word to the Wise Sufficient? Evidencefrom China’s ”Invite-to-talk”

Environmental Policy

Yue Fang∗

Thomas Lyon†

July 2, 2018 – Version 1.0aVery Preliminary

Abstract

Developed countries like the United States typically have mature for-mal environmental regulation systems, but in developing countries for-mal regulations are often absent or only exist on paper because of sub-optimal enforcement. In this paper, we focus on China's new “invite-to-talk” policy, a mechanism that allows the Ministry of EnvironmentalProtection to give local government officials verbal warnings in case ofserious environmental violations. Regression analyses show significantimprovement in environmental quality after local officials are “invited-to-talk,” and subsequent slowdowns in economic growth. The analy-sis in this paper also reveal the interplay between China's unique po-litical economic dynamics and pollution abatement in the absence ofstrict, consistent environmental regulatory enforcement. The resultsshow that being “invited-to-talk” may affect government officials' ca-reer prospects and motivate them to take solid actions.

∗Ross School of Business, University of Michigan. ryfang@umich.edu. Correspondingauthor. We thank seminar participants at the University of Michigan, the Duck Family Grad-uate Workshop, and AERE 2016 Annual Summer Conference for useful comments. All errorsremain ours.

†Dow Chair of Sustainable Science, Technology and Commerce. Ross School of Busi-ness, University of Michigan. tplyon@umich.edu.

2

1. Introduction

Pollution is often an unavoidable by-product of industrial development. Along

with its economic take-off over the past 30 years, China has also suffered a

staggering downgrade of environmental quality. Environmental pollution,

especially air pollution, began attracting public attentions in China. Accord-

ing to Pew Center’s Spring 2015 Global Attitudes Survey, air pollution has

overtaken income inequality and crime to become the second most con-

cerned issue among the Chinese public.

To combat the rising level of pollution, China introduced a policy called

“environmental invite-to-talk (Chinese:Yuetan)”. In case of a serious envi-

ronmental violation, the Ministry of Environmental Protection (MEP) gives

responsible local officials (usually the mayor of the prefecture city) a warn-

ing, requiring him or her to travel to Beijing for what is presumably a sharply

critical conversation with senior Party officials. The new policy went into ef-

fect in 2008. Implementation began in earnest starting after the MEP's Pro-

visional Invite-to-talk Regulations went into effect in 2014 and further accel-

erated after the new Environmental Protection Law passed in 2015 (Yang et

al. (2015)). The latter is said to be the most rigorous environmental law in

the history of the People's Republic of China. It gives the MEP more legal

power to punish environmental violations and violators, including closing

down polluting firms, and confiscating polluting facilities or equipment.

The ”invite-to-talk” policy imposes no legally binding constraints on the

city or government official involved. It could easily be seen as a form of

“cheap talk” that would not be expected to have any empirical impact. How-

ever, under China's political system, being invited to talk may harm a gov-

ernment official's chance of climbing the hierarchy of the Party system. Un-

like a democratic country, under China's political system, the Department

of Organization of the upper level Committee of the Chinese Communist

3

Party determines government officials’ assignments. Prior work has found

that local officials’ chances of promotion are enhanced when they make in-

vestments in local transportation infrastructure but not when they invest in

local environmental improvement (Wu et al. 2012). In recent years, how-

ever, the Department of Organization has expressed that it will exercise veto

power over officials’ promotion decisions on environmental grounds and

hope they can thereby impose more stringent mandates on pollution emis-

sions (Ran (2013)). In this emerging situation, even though the invite-to-talk

system lacks coercive power, it may still motivate government officials to in-

vest more in pollution control, as they are concerned about the negative im-

pact of an invite-to-talk record on their career prospects.

The contribution of this research is twofold. First, it expands our under-

standing of the range of instruments that comprise non-conventional and

informal regulation, which is an active stream of research initiated by Par-

gal and Wheeler (1996). When the textbook model of optimal market-based

regulation is absent, informal regulations, such as community pressure and

public “naming and shaming” become more vital. In a country like China

where community building is suppressed, community pressure is not al-

ways reflected in environmental decisions. As stated by Van Rooij (2008),

when aligned with industrial interests, state institutions restrict rather than

support citizen action. Nevertheless, our empirical results demonstrate that

this specific type of non-conventional regulation works under a bureaucratic

system which rarely takes preferences and inputs from the public into con-

sideration. It expands the scope of non-conventional regulation to include

scenarios where democracy and the interaction between regulatory agen-

cies and civil society are absent and sheds light on environmental gover-

nance in China.

Our second contribution is to political economy research. Some previous

literature, such as Zhou (2004), focuses on the role of the Chinese promotion

4

mechanism, characterized as a “promotion tournament model,” in achiev-

ing China’s remarkable rate of economic growth. In this model, the Chinese

central government creates a “tournament competition” among local may-

ors using relative performance as the guideline for promotion and demo-

tion. This incentive design is a double-edged sword: on one hand, promo-

tion tournaments serve as an incentive system inducing Chinese local of-

ficials to boost economic growth; on the other hand the system also leads

to many profound and pressing problems by inducing opportunistic and

short-term behavior, such as aggressive industrial development that com-

promises environmental quality Zheng, Kahn, Sun and Luo (2014). In this

paper, following the promotion tournament model's line of reasoning, but

factoring the “invite-to-talk” policy into the promotion likelihood function,

we re-examine the dynamics of the trade-off between GDP growth and envi-

ronmental quality in government officials' calculations. We find the “invite-

to-talk” policy is a significant signal of government concern about pollution,

and this forces us to reevaluate some of the conclusions established in the

previous literature.

The remainder of the paper is organized as follows. Section 2 introduces

some background information that is essential to understand this paper but

may not be familiar to readers, including an introduction to key terminolo-

gies and an explanation of the Chinese political system that affects politi-

cians' careers. Section 3 presents the framework that informs our research

questions and lays out the hypotheses to be tested. Section 4 presents the

construction of the data and summary statistics. Section 5 presents the main

results. Section 6 concludes.

2. Background

5

2.1. Environmental Regulations and Enforcement in China

In most developed countries, environmental regulations are formalized in

statutory law and enforced through a systematic and well-documented pro-

cess that imposes penalties on non-compliant facilities. By contrast, envi-

ronmental control in developing countries is much less thorough and con-

sistent. (Blackman, 2010). Even when countries have environmental statutes

that are modeled on those of developed nations, their implementation is

typically weak. Penalties may be poorly designed, and monitoring and en-

forcement are often inadequate. In China, for example, inspections by envi-

ronmental authorities often lead to an increase in emissions at the facilities

they visit because of the perverse structure of penalties for high emissions

levels (Lin, 2013). In such a setting, where formal regulation is not func-

tioning well, informal regulation can help to fill the gap: local communities

trade off the costs and benefit of pollution, and use social pressure as lever-

age against pollution (Pargal and Wheeler, 1995).

To a significant extent, China resembles what has been described in the

Indonesian informal regulation case in Wheeler et al. (1997) , where formal

regulations are inadequate and feeble in enforcement. As early as 1983, the

Central Government of China announced that environmental policy had be-

come a state policy. A number of subsequent efforts have been devoted

to environmental legislation and environmental awareness education: for

example, China's environmental protection law was passed in 1989, and a

much stricter amendment went into effect in 2015. However, in practice, law

enforcement is far from being satisfactory. Although the central government

issues fairly strict regulations, the actual monitoring and enforcement re-

mains compromised since local governments have greater interests in eco-

nomic growth. Enforcement and implementation of the law may be foiled

by a lack of capacity and by conflicts of interest. Because the environmental

6

protection agencies in each location are affiliated to local government and

under direct leadership of local officials, local political cadres can easily in-

terfere with environmental law enforcement (Zhang and Cao (2015)).

All of these factors are analogous to the Indonesian case in Wheeler et

al. (1997) : both the Chinese and the Indonesian government lack an effec-

tive means of combating pollution through formal regulatory instruments.

However, the two situations are not identical. One key difference between

China and Indonesia is that although activist groups in China can obtain

environmental information and participate in environmental governance,

they are not allowed to bring pollution-related lawsuits against the govern-

ment Zhang and Cao (2015). China also lacks an electoral system that allows

the public to have input into the optimal level of pollution. Rather, since the

career prospects of Chinese officials are determined by their superiors in the

CCP Department of Organization, the preference of the Department of Or-

ganization is the most important factor that directs the trade-off between

pollution and economic development. Although the differences between

China and Indonesia are significant, the inherent logic behind environmen-

tal governance is analogous. First, in both countries, the optimal amount

of pollution is determined by local variables that dictate marginal benefits

and costs: in Indonesia,these variables are determined by local commu-

nities' characteristics and preferences, while in China these variables are

determined by officials' career concerns. Secondly, in both countries, the

marginal cost of pollution is rising over time. In Indonesia, local commu-

nities display stronger distaste for pollution as its level goes up. In China,

under the new evaluation standards for officials, excessive pollution is in-

creasingly likely to impair officials' chance of advancement. These special

institutional settings set China apart from countries with strict law enforce-

ment or a mature civil society and therefore require us to start our analysis

from the rational calculation of government officials.

7

2.2. Hierarchy and Duality in the Organization of the

Chinese Government

Currently, the organization of the Chinese government is structured in a hi-

erarchy of five different levels: central government, provincial (including

directly-controlled municipalities like Beijing or Shanghai), prefectural, county,

and township. The structure is graphically illustrated in Figure 1. Each level

in the hierarchy is responsible for supervising and leading the work carried

out by lower levels of administrative strata. For each level of local govern-

ment, there are two leading government officials: the head of government,

such as the governor, mayor or magistrate; and the Party Secretary. De-

pending on the administrative level of the place they are governing, govern-

ment officials carry administrative rankings, too. They are: national (corre-

sponding to central government leaders), provincial-ministerial (leaders of

provinces and direct-controlled municipalities), bureau-director (leader of

prefectures), division-head level (leader of counties), and deputy sub-levels

between any two of these levels. There are some exceptions to the general

rule. For provincial capitals and some prefectural cities of economic im-

portance (officially referred to as “cities specifically designated in the state

plan”) the leaders of these cities enjoy vice-provincial-ministerial ranking.

The impact of the “invite-to-talk” policy on these places may differ.

In Mao's era, the Chinese Communist Party exerted direct leadership

over the state. After Deng's reform and opening, Party control became slightly

less obvious and direct. The Communist Party nominates a single mayoral

candidate, and the legislative branch, the People's Congress, which is un-

der the Party's tight control, approves the nomination. At the same time,

the head of the Communist Partys local authority, the Secretary of the Chi-

8

nese Communist Party, still plays an active role in policy-making. This situ-

ation leads to a duality in the organization of almost every level of the Chi-

nese government. This duality entails at least apparent conflict on a legal

and practical level: on any given issue, who is the ultimate decision-maker?

Formally, on the prefecture level, mayors are in charge of administrative af-

fairs. However, real authority in the Chinese system remains under the con-

trol of the Chinese Communist Party. Although not explicitly documented,

it is universally understood that under China's current one-party system,

the Secretary of the CCP is more powerful than his/her mayoral partner on

the same administrative level. As an old slogan goes: the Party leads every-

thing. In reality, the mayor is often responsible for “practical” matters, such

as municipal construction, while the Secretary arbitrates “big issues”, such

as guidelines for economic development or organizational issues. Because

of the special party-government relationship, moving from being the mayor

to the party secretary at the same administrative level is considered a pro-

motion. In fact, for Chinese officials, this is the most common ascending

career path. Understanding the administrative hierarchy helps us to trace

the career trajectories of Chinese officials, and will be crucial for the empir-

ical analysis to follow.

2.3. The Promotion Rules in the Chinese System

Under China's political system, the Department of Organization of the di-

rect upper level Committee of the Chinese Communist Party determines

government officials’ assignments. The Central Committee of the CCP ap-

points the officials of provinces, direct-controlled municipalities, and some

major cities (so-called provincial-level and vice-provincial cities). Provin-

cial governments appoint the officials of prefecture-level cities. Although

9

the Central Government does not directly appoint prefectural and county

level officials, it can ensure that its policy focus can be correctly enforced

by setting up appropriate selection rules for provincial-level officials, whose

performances are the summation of lower-level officials' performances in

their administrations. In the past, GDP was the main criterion in evaluating

lower-level officials' performance. However, in the wake of rising environ-

mental awareness and increasing pressures from the public, many regions,

such as Yunnan Province and Guangdong Province, have expressed that they

would exert veto power over officials promotion decisions on environmental

issues. In this situation, even though the invite to talk system lacks coercive

power, it may still motivate government officials to devote attention to pol-

lution control out of career concerns if they expect their performance on

environmental well-being will be used for assessment.

2.4. Measures to Combat Pollution after Invite-to-talk

As revealed in news reports, municipal governments are increasingly taking

measures to mitigate pollution. One type is the use of direct administrative

commands. For example, according to Liu (2015), a Chinese official author-

itative news website, after being invited-to-talk the City of Linyi established

a “prefecture-wide pollution control task force”, enacted a municipal 3-year

Plan of Pollution Mitigation, and closed down 57 highly polluting firms. A

second type of measure is municipal direct investment in pollution abate-

ment, such as building sewage disposal works, deploying sprinklers, and

installing public bicycles, to achieve structural improvements in the longer

run.

Due to government officials' promotion incentive, they often need to re-

spond to pressure from superiors immediately in order to stay in their good

graces; sometimes the measures can be so drastic that they even go beyond

10

legal boundaries. These aggressive measures are referred to as “campaign-

style law enforcement” in China. For example, according to news reports, af-

ter receiving a warning from the Ministry of Environmental Protection, the

city of Shangqiu closed down almost all restaurants in town, claiming that

oil smoke in cooking caused a deterioration in air quality.

One practical problem with measuring direct municipal investment is

the absence of and/or inaccuracy of data. Although indicators such as pro-

cessing capacity for polluted water are available in Government Yearbooks,

according to news reports they are not reliable because many of the water

treatment plants are built solely to fulfill requirements on paper and are not

actually in operation. Thus, in this paper, we will infer meaningful changes

in investment or facility operations from short-run changes in GDP growth

rates or industrial electricity consumption to trace changes in pollution abate-

ment after an official is invited-to-talk. If the “invite-to-talk” policy indeed

generates strong pressure for environmental law enforcement, we may be

able to observe a decline in economic growth in subsequent periods.

3. Empirical Strategy and Theoretical

Hypotheses

From the discussion above, being invited to talk gives government officials

a warning about environmental governance and posed pressure on the like-

lihood of promotion. Government officials have incentive to improve en-

vironmental equality and re-balance environmental quality and economic

growth. We hypothesize that invite-to-talk could force government officials

to reduce pollution, slow down economic growth and put more attention to

environmental protection in their future work. We are also curious about

11

the determinants of invite-to-talk, and whether being invite-to-talk hurts

the chance of promotion. We develop three hypotheses as follows.

Environmental quality improvement

Because being invited to talk signals unsatisfactory current conditions, gov-

ernment officials have incentives to improve environmental quality after an

invitation. Thus, we assume that environmental quality improvement en-

sues after the official responsible for the prefecture is invited to talk. Also,

we expect that the effectiveness of any improvement depends on the cur-

rent situation of the mayor: if the mayor is an acting mayor whose seat is not

yet “stable” enough, the risk of failing environmental tests would have more

serious ramifications, such as failing to ever obtain a full mayorship. Thus

we have the following hypothesis.

Hypothesis 1. The invite-to-talk system is effective in pollution abatement.

Being invited-to-talk rises the environmental awareness of government offi-

cials and forces them to give environmental protection higher weight in the

public policy bundle.

To test the effectiveness of the ”invite-to-talk” policy on pollution abate-

ment, we add post-invite-to-talk binary variables to conduct difference-in-

difference estimation to capture the policy’s effect using pollution moni-

toring data and visibility data. We use environmental protection related

word count and frequency in the local government’s Report on the Work

of the Government, which is the summary of past year’s work and the fo-

cus of the next year’s plan, as a proxy for government officials' environmen-

tal awareness and environmental policy concentration and observe whether

they change after invite-to-talk. The details about Report on the Work of the

Government and the construction of the variable is in Section 4.6. The re-

12

gression equations are as follows:

Pollutioni,t = β0 + β1I(Post− IV T )i,t +&β2I(ActingMayor)i,t+

β3I(Post− IV T )i,t ∗ I(ActingMayor)i,t + σI(Y eart) + ρI(Regioni) + εi,t(1)

Pollutioni,t is the pollution level of prefecture i at week t. I(Post− IV T )i,t is

a dummy variable that equals 1 if the government official of prefecture i was

invited to talk at time (t-1). I(ActingMayor)i,t is the dummy variable indicat-

ing that the mayor was an acting mayor in prefecture i at time t. β3I(Post −IV T )i,t ∗ I(ActingMayor) is an interaction term. I(Y eart) and I(Regioni) are

year and region fixed effects.

The following equation is used to detect the change in environmental

awareness:

Count/Freqi,t = β0 + β1I(Post− IV T )i,t + β2GDPPCi,t + β3I(V iceProv)i,t+

β4EduLeveli,t + σI(Y eart) + ρI(Regioni) + εi,t

(2)

Counti,t and Freqi,t are count and frequency (as a percentage of the total

number of words) in the government report of prefecture i in year t. GDPPCi,t

is the per capita GDP. We include this variable because we expect that afflu-

ent regions have greater environmental awareness (Selden, 1994). EduLeveli,t

is the educational attainment of the incumbent mayor to test whether better-

educated officials are more likely to care about environmental quality.

Rebalancing environment and economic growth

As we argued in Section 2.3, being invited-to-talk sends a signal that the

government official puts too much weight on GDP growth and too little on

environmental quality. Therefore, the government official will slow down

13

economic growth to improve the environment. The model in the Appendix

illustrates the process in detail. Therefore, we have the third hypothesis:

Hypothesis 2. Being “invited-to-talk” negatively affects local economic de-

velopment.

It is worth mentioning that there are long-standing doubts about the trust-

worthiness of Chinese GDP data, as discussed by Rawski (2001). Although

recent studies show that the gap between reported GDP growth and actual

GDP growth is rather small (Mehrotra and Paakkonen (2011) and Clark et

al. (2017)), to be prudent we use some alternative measures–growth rates of

industrial electricity consumption and regional firm revenues– to conduct

robustness checks. Because industrial electricity consumption is calculated

by utility companies and is not used for government officials’ assessment,

it is less subject to political manipulation. As pointed out by Li Keqiang,

then-Secretary of the Communist Party of Liaoning Province and currently

Premier of China, ”GDP data of Liaoning showed traces of artificial modifi-

cation. So we prefer to track economic trends of Liaoning using other indica-

tors: railway freight, electricity consumption and new loans.” The regression

is specified as:

yi,t = β0 + β1I(Post− IV T )i,t + β2IV Ti + σI(Y eart) + ρI(Provi) + εi,t (3)

where yi,t is GDP or electricity consumption growth rate of year t. I(Y eari) is

an indicator that whether the prefecture has been invited-to-talk. I(Provi)

and I(Y eart) are the province and year fixed effects.

Chance of promotion

Because the Chinese government claims to have begun putting more weight

on the environment in evaluating officials' performance, being “invited to

talk” could be viewed as a stigma and hurt government officials' chances

of advancement. In the theoretical model in the Appendix, a simple model

14

is presented that predicts government officials will tighten pollution abate-

ment enforcement after such an invitation. If pollution is reduced through

blunt instruments such as direct cuts to production, this will also cause eco-

nomic growth to slow down. Therefore, we have the following three hy-

potheses:

Hypothesis 3. Being invited to talk has a negative impact on officials' politi-

cal careers and reduces the likelihood of promotion.

Our econometric strategy is to employ Logit and Cox proportional haz-

ards regression model to estimate the likelihood of career advancement for

government officials and to test whether it is influenced by being invited to

talk. More specifically, we regress a binary variable indicating a promotion

on the ”invite to talk” dummy variable and other control variables that are

believed to be relevant to officials' career prospects, such as their educa-

tional attainment, personal traits (age and gender), and local GDP growth

(Zhou (2004)). The basic regression equation is set up as:

I(Promotion)i,t = β0 + β1I(IV T )i,t + β2GDPGrowthi,t + β3EduLeveli + β4Agei

+β5I(V iceProv)i + σY eart + ρRegioni + εi

(4)

where I(IV T )i,t is a dummy variable indicating whether government official

i was invited to talk in year t (whether he/she has been invited to talk during

his/her tenure in the Cox model without the subscript t). GDPGrowthi,t is

the percentage growth of GDP during the term of office of official i of year t.

Agei,t is the age of the official i at year t. I(V iceProv)i is a dummy variable

whether the prefecture has vice-provincial status, as we expect the higher

administrative level of vice-provincial cities gives them additional protec-

tion against environmental law enforcement. Genderi equals 1 if the mayor

is female. EduLeveli is the educational attainment of the mayor. it is possi-

15

ble that the chance of promotion varies over time. To address this issue. year

fixed effect Y eart is added to the Logit model.

In the survival model, T is defined as a continuous random variable denot-

ing the number of months from the beginning of the official’s term to pro-

motion. Time as it passes is denoted by t in the model, irrespective of calen-

dar time. Therefore, all officials start their terms at t=0. The problem that the

chance of promotion varies over time is naturally solved in survival model.

The Cox proportional hazards model assumes the following regression equa-

tion:

λ(t) = λ0(t)exp[β1I(IV T )i + β2AvgGDPGrowthi + β3EduLeveli + β4Agei

+β5I(V iceProv)i + β6EduLeveli]

(5)

where λ0(t) is the baseline hazard function. AvgGDPGrowthi is the aver-

age GDP growth over the official’s term. Agei is the age at the beginning of

his/her office. It is worth noting that our data end at 2016 so corrections are

applied to address right-censoring.

Besides Equation (1)-(5), we are also curious what factors predict whether

the official is invited to talk. There are anecdotes that polluted places are

more vulnerable, but it is likely that the criteria are not all objective. We set

up the following equation:

I(IV T )i,t = β0 + β1RankAQIi,t + β2GenderMayori + β3EduLeveli

+β5I(V iceProv)i + εi(6)

where I(IV T )i,t represents whether the prefecture is invited at year t. RankAQIi,t

represents the national rank of AQI: higher rank means worse air quality.

EduLeveli is the educational attainment of official i. I(V iceProv)i is a dummy

variable whether the prefecture has vice-provincial status. It is possible that

16

officials with higher administrative rank are more less likely to be caught. We

employ both linear probability and Logit model to estimate this equation.

4. Data and Summary Statistics

Our analysis covers the period from 2010 to early 2016, which covers the on-

set of invite-to-talk policy and the end year of available data.

4.1. Invite-to-talk Data

Our “invite-to-talk” dataset was mainly obtained through Regulations on

Open Government Information of the People's Republic of China (similar to

FOIA in the US) from the Ministry of Environmental Protection. It includes

the names of prefectures and government officials in charge, together with

the date of and reason for being invited to talk. We supplemented the data

with news coverage and government newsletter reports from the Ministry of

Environmental Protection. In our data period, 47 government officials from

46 prefectures had been invited.

Both mayors and deputy mayors appeared in the sample of officials invited

to talk. We did not find publicly available information about the require-

ment of precisely which officials should attend meetings. Observed varia-

tion may reflect the attitude of the top official of the region involved, or may

simply be a matter of personal time constraints. In regression results un-

reported in this paper, we find no difference in impact depending on which

official attended the meeting. Therefore we ignore this detail throughout the

remainder of the analysis.

17

4.2. Pollution Data

China began collecting daily air quality data in 2000, and publishing it in the

official website of the Ministry of Environmental Protection. The data con-

tain the following indicators: PM10, PM2.5, sulfur dioxide, nitrogen oxides,

carbon monoxide (CO), and ozone. Based on these indicators, an Air Quality

Index (AQI) is constructed, with higher AQI suggesting worse air quality.

Air pollution monitoring sites are owned and operated directly by the

central government. Manipulating results is not uncommon and has been

reported multiple times. Chen et al. (2012) studies the credibility of official

air quality results during the period 2000-2009. Using visibility data from

weather stations, which are less likely to be manipulated, they find that there

are signs of distortion when there are political needs, such as when a city is

contesting for a model city award. However, generally speaking, there is a

significant correlation of AQI with two alternative measures of air pollution.

Therefore, in this paper, we still use the official AQI measure but we also use

both official pollution data and visibility data for robustness checks. The

summary statistics are reported in the upper half of Table 2.

4.3. Visibility Data

Visibility data are taken from the National Oceanic and Atmospheric Admin-

istration (NOAA). The reason NOAA records Chinese data is that, under an

international treaty on weather information exchange, governments of all

member countries share weather data with other countries and thus have

incentives for cooperative monitoring. The basic unit of the dataset is indi-

vidual weather stations. The weather stations monitor visibility and other

weather readings that could affect visibility, such as barometric pressure

(ground and sea level), wind speed, precipitation, and temperature at least

18

daily. The data include more than 1,000 weather stations in China, covering

the majority of Chinese prefecture-level cities. Since Chinese prefectures are

very large in area and contain vast rural subdivisions, we only select stations

within 50 mile radius of city centers. When there are multiple stations within

this range, we average all observations. The summary statistics are reported

in the upper half of Table 2.

4.4. Chinese officials data

Data on Chinese officials are mainly taken from CSMAR (a reputed Chi-

nese research data provider) Chinese Provincial and Prefecture Level Offi-

cial Data, which include name, educational attainment (bachelors, masters,

doctoral degree), ancestral home, birthplace, and date of birth for each of-

ficial. We supplement the CSMAR data with data from the Government and

Party Leader Database of People's Daily, which were taken down using web-

scraping techniques. Investigation into the data reveals that officials can be

uniquely identified by DOB, birth place and name. A unique ID is created to

identify each individual. By tracing each person in the temporal dimension,

we can profile the trajectory of each person's political career, including her

or his previous and next positions.

One issue that remains is how to determine whether officials get promoted

after their current terms. In this research, promotion is defined as one of

the following two events: (1) an official is lifted to a higher administrative

position (such as the head of a county becoming the head of a prefecture-

level city). Due to the limitation of the dataset, which only covers heads of

provinces and prefecture level cities but not other positions of the same ad-

ministrative level, such as promotion from mayor to the Chairman of the

Standing Committee of the Provincial People's Congress, there might be

some omissions; (2) transfer to a more important position on the same level,

19

such as a mayor becoming a prefectural Secretary of the CCP (the most com-

mon situation). As discussed in Section 1, since the Secretary of the CCP is

more powerful than the mayor, this is considered a promotion. Our dataset

shows this to be the most common scenario of promotion. Summary statis-

tics are reported in Table 1.

4.5. Work Report Data

As a measure of democratic supervision and an embodiment of the prin-

ciple of transparency in politics, delivering a periodic, summary speech to

the legislature by the head of the government has been a long-standing tra-

dition in many countries that uphold or allegedly uphold democratic val-

ues. For example, the President of the United States presents the State of

the Union address every year after inauguration, summarizing the govern-

ment’s accomplishments of the past year, as well as laying out the agenda for

the coming year, to a joint session of the United States Congress. The coun-

terpart in China is called Report on the Work of the Government (Zhengfu

Gongzuo Baogao, hence force Government Work Report or “the report”). As

its name implies, the Government Work Report encapsulates the key accom-

plishments of the last year and envisions future objectives. At the beginning

of every year, the heads of each level of government (central, provincial, pre-

fectural and county) read the reports to the People's Congress of the respec-

tive level, submit them to the People's Congress for review and approval

and later publicize them to the public. Because the reports are meant to

be comprehensive and receive significant attention, they also have certain

value for publicity and signaling by officials. They encompass the most im-

portant information that government officials want to convey to the public,

to the media and to their superiors. Also because of these functions, the re-

ports are something government officials treat seriously. Therefore, the texts

20

of work reports allow researchers to distill government foci for the year to

come. By looking at the frequency and the share of keywords, we can obtain

an idea of the relative importance of each aspect of public policy. Here we

are mainly interested in changes in the frequency of words in the “environ-

mental lexicon” before and after the government official received a verbal

warning. The lexicon that we will use in our analysis is manually selected

from environment-related, top 200 notional words by frequency. It consists

of the following words:

Environmental Protection Pollution Ecology(ical)

Water Quality (Pollution caused) haze PM 2.5

Dirty water Exhaust gas Energy consumption

Energy-saving Emission reduction Sulphur dioxide

Air Quality PM 10 (inhalable particles) Pollution emittance

5. Results

In this section, we apply the methodology described above and present re-

sults for each of our hypotheses. Table 2 presents the changes in the AQI

after an official was invited to talk (Equation 1), which provide an answer

to Hypothesis 1. Column (1)-(4) use standard diff-in-diff method. Column

(1) and (3) controls for year fixed effect. Column (2) and (4) includes year

and month fixed effects. Prefecture fixed effect is always controlled in (1)-

(4). Pre-trends are added to the specification in (3) an (4). The post-IVT

dummy variable is consistently positive and significant at 0.01 level for all

specifications, suggesting that the policy improves visibility and is effective

21

in reducing pollution. Acting ∗ Post is insignificant, implying that acting

mayors are more not more likely to control pollution after being invited to

talk. All pre-trend terms are not significant, eliminating the concern that

prefectures may anticipate being invited to talk and and take precautionary

action to clean the air beforehand. As pointed out by Bertrand, Duflo and

Mullainathan (2004), they find that the regression equation is subject to a

possibly severe serial correlation problem, and conventional DID standard

errors severely understate the true standard deviation of the estimators. To

address this concern, we employ a simple yet effective method suggested by

them and report the results in Column (5)1. The coefficient is slightly smaller

in magnitude but the significance level and sign remain the same. This sug-

gests that the DID result is not plagued by dynamic panel problems.

To eliminate concerns about using official environmental monitoring data,

we next replace government data with weather station visibility data, which

only serve scientific research purposes and thus are less vulnerable to po-

litical manipulations, in order to conduct a robustness check. The results

are displayed in Table 3. Column (1)-(4) use standard diff-in-diff method.

Column (1) and (3) controls for year fixed effect. Column (2) and (4) in-

cludes year and month fixed effects. Prefecture fixed effect is always con-

trolled in (1)-(4). Pre-trends are added to the specification in (3) an (4). All

three specifications control for meteorological variables that can affect vis-

ibility, namely precipitation, wind speed, dew point and barometric pres-

sure. Coefficients on the post-IVT dummy variable are consistently positive

and significant at the 0.01 level for all specifications, suggesting that the pol-

icy improves visibility and is effective in reducing pollution. The results are

1They suggest a simple solution: collapsing the time series information into a “pre” and“post” period and run the equation (1) on this averaged outcome variable in a panel oflength 2. Since not all invite-to-talks occur in the same period in our data, we first regressthe dependent variable on state fixed effects, year dummies, and any relevant covariates.Then we divide the residuals of the treatment states only into two groups: residuals fromyears before the change, and residuals from years after the change

22

consistent with the findings generated using official air quality data in Table

2.

Table 5 presents the changes in word count and word frequency in gov-

ernment work reports after an “invite-to-talk”. Columns (1)-(3) use environ-

mental word count as the dependent variable, while Columns (4)-(6) use the

frequency of “environmental words” as a fraction of the number of words in

the entire report. Columns (2) and (5) include province fixed effects, while

columns (3) and (6) include prefecture fixed effects. Year fixed effects are

included in all specifications. It can be seen that post-IVT carries a positive

and significant sign for all specifications, suggesting that officials put more

weight on environmental regulations after being invited to talk. Combined

with results in Table 6, this implies that government officials now pay more

attention to environmental quality relative to GDP growth.

The results that test Hypothesis 2 are presented in Table 6 (Equation 3).

Since most cities with invite-to-talk dummy variables equal to 1 are from

provinces with high growth rates, province-level fixed effects are always in-

cluded. Column (1) and Column (2) use official GDP growth rate as the de-

pendent variable, while Column (3) and Column (4) use the growth rate of

industrial electricity consumption. All specifications include year fixed ef-

fects. It is clear that there are declines in the growth rate of electricity con-

sumption after an “invite-to-talk”, but the effect on the GDP growth rate is

lower in (1) and is not significant when we control for prefecture fixed ef-

fects in Column (2). Either data-faking or economic structural change could

be responsible for this discrepancy.

To test Hypothesis 3, both the Logit model (Column 1-2) and the Cox sur-

vival model (Column 3-4) are employed. The Logit regression are conducted

on government official-year level. In the survival regression, we model....

23

We include vice provincial status, educational attainment, GDP growth rate,

gender, and age as control variables. The control variables are their respec-

tive values at year t in the Logit regression and the averages over the officials’

years in office in the survival model. Because environmental quality as a fac-

tor in official evaluation became much stricter after 2014, we regress the full

sample and post-2014 subsample separately. The results are shown in Table

7. It can be seen that, after controlling for educational attainment, aver-

age GDP growth, city status indicators, and age, being invited to talk does

not seem to affect the chances of promotion for the full sample. The reason

could be that post-invite-to-talk improvement saved officials' political ca-

reer. It is also possible that the invite-to-talk regulation lacks coercive power

to punish violators.

We turn next to the determinants of an invitation to talk. We use AQI

rank since we expect that the MEP uses rank to select target prefectures. It

can be seen from Table 4 that AQI rank is a significant variable in determin-

ing whether a prefecture gets a verbal warning from the Ministry of Envi-

ronmental Protection. We expect vice-provincial status is positive and sig-

nificant, since the higher administrative level of vice-provincial cities gives

them additional protection. However, vice-provincial status does not in-

crease the chance of a local official being invited to talk. The results suggest

that the standards of invite-to-talk are rather fair.

6. Conclusion

Our regression analyses confirm three hypotheses. First,the invite-to-talk

policy is effective in pollution reduction. The result is robust when we re-

place less reliable official pollution data with weather station visibility read-

ings. Second, economic growth, measured by GDP and electricity consump-

tion growth rates, slows down after the head of the municipality has been in-

24

vited to talk. Third, although being invited-to-talk does not appear to change

officials' chances of promotion, post-2014 samples suggest that the policy

may have already started to affect officials' career prospects.

These results present a consistent story about the political economy be-

hind the invite-to-talk policy. By inviting officials who failed the environ-

mental test to talk, the central government sets up a new rubric to evalu-

ate government officials, since being invited-to-talk might be viewed as a

stigma and affect government officials' career prospects. Driven by the new

rule, government officials re-adjust the trade-off between pollution and eco-

nomic development to maximize the likelihood of promotion, putting more

weight on environmental protection.

The post invite-to-talk pollution control is effective: both official pollu-

tant monitoring data and weather station visibility readings show signs of

improvement. As suggested in recent news reports and confirmed by Ta-

ble 6, post invite-to-talk pollution control is often achieved by slowing eco-

nomic development.

The full-sample regression results in Table 7 suggest that being invited-

to-talk does not hurt mayors' chances of promotion for both the sample

before and after 2014, when the new environmental regulations came into

effect. The reason could be that officials are pardoned for improvements in

environmental quality afterwards, or the DEP cannot do anything to punish

them. If the latter is true, we would suspect that the policy will lose its power

in the long run.

A Appendix: A Simple Theoretical Model

In this section, we try to construct a theoretical model to illustrate some of

the key features of the gaming process. The key ideas are somewhat similar

25

to Jia (2012), although we approach the modeling slightly differently. There

are several key points the model captures. First, we assume that govern-

ment officials try to solely maximize their likelihood of promotion. Saying

that implies that government officials do not directly take pecuniary bene-

fits, although their actions are not always purely altruistic. Second, unlike

their American counterparts, city leaders in China a have greater impact in

influencing their cities' economic activities and industrial mix. The most

commonly used leverages are offering land subsidies and tax reductions to

attract investments. Officials can also shut down firms they do not like or do

not need anymore. In our model, for simplicity's sake, we assume there is

no case-by-case bargaining; instead, local government in city i sets up a tax

rate ti for all firms.

Assume that there are only two cities competing for investment. There is

only one industry with unit number of firms and uniform production tech-

nology, but different levels of pollution. Because both cities attract invest-

ment and can utilize external labor force, wage w and interest rate r are as-

sumed to be exogenous and productivity is determined by the natural en-

dowment of the city where the firm locates, γ. Therefore, the indirect pro-

duction function is f(γ, w, r). Pollution level conforms to distribution G(·)on [0,1].

Mayors can impose ”pollution tax” on emissions, ti. ti does not have to take

the form of real taxation. Instead, it can be interpreted as the ”punishment”

corresponding to each level of pollution. Mayors strive to maximize their

probability of promotion, denoting as U = U(∫f(γ),

∫dG(·), c). The first

term in the bracket denotes the sum of total output. The second term is the

sum of pollution generated from all firms that choose to locate in city i. The

third term is connections of the government official with superiors in city

i. Ceteris paribus, connections with superiors can increase the likelihood of

promotion:

26

∂U1

∂c=

∂U

∂∫f(γ)

∂c> 0,

∂U2

∂c=

∂U

∂∫dG(·)∂c

> 0.

For individual firm with pollution level p locating in city i, profit π can be ex-

pressed as:

πi = f(γ)− tipi

Assume that city 1 has a better natural endowment, i.e. γ1 > γ2, a firm will

choose city 1 if possible. So the first cut-off that decides which firms will

locate in city 1 is determined by:

π1 = f(γ1)− t1p = f(γ2)− t2p

which is equivalent to: [ pc1 =f(γ1)− f(γ2)

t1 − t2]

The second cut-off that governs which firms will choose to invest in city 2

will be determined by:

π = f(γ)− t2p ≥ 0

which is equivalent to:

pc2 =f(γ2)

t2

We have the following proposition about the government official's reaction

when pollution carries more weight in promotion evaluation.

Proposition A.1. U2() < U2() implies t2 < t2, viz. when the pollution level

carries more weight in the likelihood of promotion function, the optimal tax

rate for the government official increases.

Proof. The derivative utility function of the mayor in city 2 with respect to

27

the pollution level t2 is:

∂U2

∂t2= U1f(γ12)[

f(γ1)− f(γ2)t1 − t2

− f(γ2)

t2]′ + U2[G(

f(γ1)− f(γ2)t1 − t2

)−G(f(γ2)t2

)]′

= U2[f(γ1)− f(γ2)(t1 − t2)2

G′ +f(γ2)

t22G′] + U1f(γ2)[

f(γ1)− f(γ2)(t1 − t2)2

− f(γ2)

t22

] = 0

(7)

After the new ”invite-to-talk” policy was implemented, U2() < U2() for any

value.

Now we prove that it shall lead to a bigger t1 > t:

If t1 < t1, let z =f(γ1)− f(γ2)

(t1 − t2), then z > z. From (1), we know that:

U2 =U1f(γ1)(1− z)

G′(z) ∗ z2

f(γ1)− f(γ2)

U2 =U1f(γ1)(1− z)

G′(z) ∗ z2

f(γ1)− f(γ2)

Since G’ is an increasing function, G′(z) > G′(z), also (1 − z) < (1 − z) and

z2 > z2 , so we have U2|G(1)−G(z) < U2|G(1)−G(z), consistent with U2() < U2().

Similarly, we can prove that t2 < t2.

28

Figure 1: Levels of Chinese Administrative Regions

29

Table 1: Summary Statistics

Variable Definition Obs. Mean Std. dev

Pollution atrributes

AQI Air Quality Indicator, by month/year by city 16,114 76.59 37.48

Visibility Visibility, by month by city 10,395 10.17 4.47

Pressure Historical sea level/Station pressure, by month by city 9930 974.67 99.81

Precipitation Rain, snow, sleet, or hail that falls to the ground, by month by city 10,394 0.111 0.157

Wind Speed Wind speed, by month by city 10,397 4.46 1.78

Official and city attributes

Education achievement (mayor) =1 if primary school, =2 middle school, =3 high school, 350 3.12 0.64

=4 bachelor’s, =5 master’s, =6 doctoral, by year and by city

Gender (mayor) =1 if female 924 0.069 0.254

Age (mayor) Age of the mayor at the beginning of office 915 49.30 3.62

Promotion (mayor) =1 if official was promoted 921 0.356 0.48

GDP growth GDP growth rate, by year by city 1114 0.122 0.526

GDP growth GDP growth rate, by year by city 1114 0.122 0.526

Power consumption growth rate Power consumption growth rate, by year by city 1114 0.109 0.076

Count of environmental words Total count of words from the ”environmental lexicon” 1591 53.05 18.88

Frequency of environmental words Ratio of total count of words from the ”environmental lexicon” as total number of words 1591 0.0059 .0017

Firm attributes

Total Income Total operational income (in millions. Currency: CNY) 27,925 2204.32 18038.79

Privately Owned =1 if the firm is privately owned 27,925 0.6844 0.465

Manufacturing =1 if the firm is a manufacting firm 27,925 0.763 0.425

*This table explains the meaning of variables in this paper and reports the summary statistics.

30

Table 2: The Effectiveness of “Invite-to-talk” Policy

(1) (2) (3) (4) (5)VARIABLES AQI AQI AQI AQI AQI

Post-IVT -4.000*** -2.916** -3.935** -2.680** -6.212***(1.535) (1.231) (1.591) (1.280) (1.681)

Acting*Post -0.310 -0.0179 -0.317 -0.0411(2.781) (2.123) (2.782) (2.125)

Acting Mayor -0.345 -1.055 -0.347 -1.062(0.937) (0.776) (0.938) (0.776)

Pre-trend 1 0.705 -0.587(4.759) (4.115)

Pre-trend 2 0.621 5.307(5.090) (4.362)

Constant 61.14*** 75.10*** 61.14*** 75.11***(0.644) (1.429) (0.644) (1.429)

Observations 16,114 16,114 16,114 16,114 2379R-squired 0.401 0.585 0.401 0.585 0.035Time FE Year Year and Month Year Year and MonthPref. FE X X X XModel Spec. DID DID DID DID BDM Residual

This table reports the change in air quality before and after the official got invited to talk.

Dependent variable is AQI (air quality index). Column (1)-(4) use standard diff-in-diff

method. Column (5) reports estimate obtained from Bertrand, Duflo and Mullainathan

(2004). Pre-trends are added to the specification in (3) an (4). IVT is a dummy variable in-

dicating that whether the prefecture had been invited to talk. Post-IVT is a dummy variable

that equals to 1 after the prefecture is invited to talk. Acting denotes whether the mayor is

an acting mayor. Acting*Post is the interaction term between Acting and Post-IVT.

31

Table 3: Robustness Test: Replacing Official Monitoring Data with Weather Sta-

tion Observations

(1) (2) (3) (4)VARIABLES Visibility Visibility Visibility Visibility

Temperature 0.106*** 0.0912*** 0.106*** 0.0912***(0.00579) (0.00622) (0.00579) (0.00622)

Precipitation 0.273 -0.929*** 0.275 -0.927***(0.173) (0.170) (0.173) (0.170)

Dew Point -0.0931*** -0.161*** -0.0931*** -0.161***(0.00459) (0.00520) (0.00459) (0.00520)

Air Pressure -0.0694*** -0.0258*** -0.0693*** -0.0258***(0.00642) (0.00771) (0.00642) (0.00771)

Post-IVT 0.331** 0.326** 0.321** 0.310**(0.137) (0.129) (0.140) (0.131)

Acting Mayor 0.186* 0.144 0.189* 0.148(0.0970) (0.0919) (0.0972) (0.0921)

Acting*Post IVT 0.0782 0.308 0.0764 0.306(0.364) (0.343) (0.364) (0.343)

Pre-trend 1 -0.437 -0.454(0.446) (0.425)

Pre-trend 2 0.167 0.0171(0.437) (0.417)

Constant 76.39*** 35.10*** 76.35*** 35.15***(6.479) (7.734) (6.480) (7.735)

Observations 9,284 9,284 9,284 9,284R-squared 0.768 0.798 0.768 0.798Time FE Year Year and Month Year Year and MonthPref. FE X X X X

This table reports the change in air visibility before and after the official got

invited to talk. Dependent variable is visibility, defined as visible distance in

miles. Post-IVT is a dummy variable that equals to 1 after the prefecture is in-

vited to talk. Acting denotes whether the mayor is an acting mayor. Acting*Post

is the interaction term between Acting and Post-IVT. Precipitation denotes the

volume of rain fall in milliliter. Temperature is the day temperature. Pressure

denotes the average air pressure during daytime. Pre-trends are added to the

specification in Column (3) and (4).

32

Table 4: Determinants of Invite-to-talk

(1) (2)VARIABLES IVT IVT

Rank AQI 6.12e-05** 0.00371**(2.59e-05) (0.00164)

Gender Mayor -0.00676 -0.631(0.0116) (1.025)

Edu Level Mayor -0.0115*** -0.748***(0.00438) (0.279)

Vice Provincial City -0.00286 -0.267(0.0121) (1.045)

Constant 0.0431** -2.580**(0.0183) (1.268)

Method Linear LogitObservations 2,015 2,015R-squared 0.009 0.046Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

This table reports the determinants that predict whether

an official will be invited to talk. Dependent variable is

the invite-to-talk dummy variable. Column (1) uses linear

probability model. Column (2) presents Logit regression

estimates.

33

Table 5: Changes of Word Count and Frequency of Environmental Lexicon after IVT

(1) (2) (3) (4) (5) (6)Dep. Var Count Count Count Freq. Freq. Freq.

Post IVT 12.06*** 9.918*** 7.187*** 0.00108*** 0.000892*** 0.000780***(2.353) (2.176) (2.669) (0.000208) (0.000197) (0.000243)

IVT 6.812** 4.618* 0.000606** 0.000467*(2.999) (2.728) (0.000265) (0.000248)

GDP Per Capita 0.651*** 0.628*** 0.261 6.05e-05*** 4.33e-05*** -1.86e-07(0.113) (0.114) (0.570) (1.00e-05) (1.04e-05) (5.19e-05)

Vice Provincial City 2.852 4.707** -6.09e-05 -3.03e-05(2.232) (2.083) (0.000198) (0.000189)

Edu. Level Mayor 0.251 0.925 0.356 -2.45e-05 1.60e-05 -5.98e-05(0.742) (0.694) (0.806) (6.57e-05) (6.30e-05) (7.35e-05)

Constant 43.27*** 41.61*** 44.70*** 0.00505*** 0.00501*** 0.00538***(2.532) (2.349) (3.263) (0.000224) (0.000213) (0.000297)

Year FE X X X X X XGeo. FE Prov. Pref. Prov. Pref.Observations 1,477 1,477 1,477 1,477 1,477 1,477R-squared 0.099 0.276 0.589 0.115 0.251 0.571

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

This table reports the changes in word count and frequency (defined as the fraction of the ar-

ticle) of words in the “environmental lexicon”. IVT is a dummy variable indicating that whether

the prefecture had been invited to talk. Post-IVT is a dummy variable that equals to 1 after the

prefecture is invited to talk.

34

Table 6: GDP and Industrial Power Consumption after IVT

(1) (2) (3) (4)VARIABLES GDPGrowth GDPGrowth PowerConsump% PowerConsump%

IVT -0.0779** 0.00263 -0.207 -0.132(0.0303) (0.0192) (0.197) (0.199)

Post IVT -0.104*** 0.00382 -0.329*** -0.225**(0.0155) (0.0100) (0.103) (0.106)

Constant 0.133*** 0.207*** 0.153*** 0.260***(0.00234) (0.00324) (0.0154) (0.0338)

Year FE X XProv. FE X X X XObservations 1,698 1,698 1,649 1,649R-squared 0.166 0.674 0.144 0.155

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

This table reports the changes in GDP growth rate and industrial power consumption

growth rate after the official was invited-to-talk. IVT is a dummy variable indicating

that whether the prefecture had been invited to talk. Post-IVT is a dummy variable

that equals to 1 after the prefecture is invited to talk.

35

Table 7: Effect of IVT on Officials’ Chance of Promotion, Full sam-

ple

(1) (2) (3) (4)Variable Promo. Promo. Promo. Promo.

All Years After 2014 All Years After 2014IVT 0.372 0.387 -0.185 -0.357

(0.330) (0.641) (0.299) (0.465)Vice Prov. -0.560* -0.625 -0.731* -0.466

(0.340) (0.781) (0.396) (1.018)Edu. Level 0.232** 0.565** 0.254** 0.179

(0.107) (0.257) (0.101) (0.180)GDP Growth -1.225 -2.068 4.150*** -1.009

(1.068) (1.450) (1.071) (1.968)Gender 0.101 0.426 0.125 -0.159

(0.259) (0.557) (0.246) (0.427)Age 0.0714*** 0.0839* -0.00101 0.0513

(0.0194) (0.0498) (0.0178) (0.0323)Constant -6.734*** -9.035***

(1.171) (2.873)Observations 2,504 755 698 375Year FE X XModel Logit Logit Survival Survival

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

This table reports the determinants that predict whether an of-

ficial will be promoted. Dependent variable is the promotion

dummy variable. Column (1) and (2) use Logit model with year

FE. Column (3) and (4) presents survival analysis regression esti-

mates. GDP Growth denotes the average rate of GDP growth dur-

ing the official's tenure for (3) and (4). Age denotes the age of the

official in each observed year in Column (1) and (2) and the age at

the first year in office. Vice Prov. denotes whether the prefecture

is a vice-provincial city.

36

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