Inspections, pollution prices, and environmental performance: evidence from China

Ecological Economics 36 (2001) 487–498

ANALYSIS

Inspections, pollution prices, and environmentalperformance: evidence from China

Susmita Dasgupta a, Benoit Laplante a,*, Nlandu Mamingi b , Hua Wang a

a De6elopment Research Group, The World Bank, 1818 H Street, N.W. Washington, DC 20433, USAb The Uni6ersity of the West Indies, Bridgetown, Barbados

Received 16 November 1999; received in revised form 11 September 2000; accepted 18 September 2000

Abstract

In environmental economics, monitoring and enforcement issues have attracted relatively little research effort.Moreover, the bulk of the literature on these issues has been of a theoretical nature. Few have empirically analysedthe impact of monitoring and enforcement activities on the environmental performance of polluters. Moreover, allexisting studies have been performed in the context of developed countries. A purpose of the current paper is topartially fill this important gap by exploring the impact of both inspections and pollution charges on theenvironmental performance of polluters in China. While pollution charges represent an important pillar of theChinese environmental regulatory system, our results indicate that inspections dominate and better explain theenvironmental performance of industrial polluters. © 2001 Elsevier Science B.V. All rights reserved.

Keywords: Inspections; Prices; Environmental performance

www.elsevier.com/locate/ecolecon

1. Introduction

Since the beginning of the 1970s, governmentsof developed and developing countries have en-acted or amended a large number of environmen-tal laws and regulations often directed atcontrolling the industrial emissions of air andwater pollution. However, imposing a ceiling on aplant’s emissions does not necessarily imply that

emissions will fall and environmental quality im-prove. For the objectives of the regulation to beattained, the behaviour of the regulated commu-nity has to be monitored, and compliance withenvironmental standards has to be enforced.1

While a large amount of resources is typicallydevoted to designing environmental regulations,

1 We define monitoring as the process of verifying the firm’sstatus of environmental performance (e.g. compliance), andenforcement as the undertaking of actions (e.g. fines) to bringthe firm to improve its environmental performance (e.g. com-ply with environmental standards).

* Corresponding author. Tel.: +1-202-4585878; fax: +1-202-5223230.

E-mail address: [email protected] (B. Laplante).

0921-8009/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved.

PII: S 0921 -8009 (00 )00249 -4

S. Dasgupta et al. / Ecological Economics 36 (2001) 487–498488

and negotiating environmental standards with theregulated industries, it is generally acknowledgedthat resources devoted to monitoring and enforce-ment are insufficient relative to the complexityand extent of the task (Russell, 1990; O’Connor,1994).

While the monitoring of industrial pollutersand the enforcement of compliance are both cru-cial components of a credible regulatory program,monitoring and enforcement issues have attractedlittle research effort. In a recent survey of theenvironmental economics literature, Cropper andOates (1992) write: ‘‘The great bulk of the litera-ture on the economics of environmental regula-tion simply assumes that polluters comply withexisting directives.’’ Moreover, most of the litera-ture on these issues has been of a theoreticalnature.2

On the empirical front, two broad questionshave partially been addressed. A first questionpertains to the impact of the regulator’s behavioron the environmental performance of polluters. Inparticular, Magat and Viscusi (1990), Laplanteand Rilstone (1996) have examined whether or notinspections have an impact on the emissions (bio-logical oxygen demand (BOD) and total sus-pended solids (TSS)) of pulp and paper plants inthe United States and Canada, respectively. Ma-gat and Viscusi (1990) have shown that inspec-tions reduce permanently the level of emissions ofplants by approximately 20%. Laplante and Ril-stone (1996) have shown that not only inspectionsbut also the threat of inspections reduce emissionsby approximately 28%. These two analyses alsofind that inspections induce more frequent self-re-porting of emissions by the regulated plants.Hence, not only do inspections have an impact onthe actual level of emissions of the plants, butthey also provide regulators with a more accuratepollution profile of the plants. More recently,Nadeau (1997) has shown that inspections signifi-cantly reduce the duration of firms’ violation of

air pollution standards in the pulp and paperindustry in the United States. These results haveclear policy implications as to the importance of acredible monitoring strategy to create incentivesfor pollution control.

If inspections have an impact on the behaviorof polluters and partly explain improvements intheir environmental performance, a second ques-tion of interest thus pertains to the determinantsof the regulator’s monitoring strategy. Deily andGray (1991, 1996), Dion et al. (1998) have shownthat regulators are sensitive to local environmen-tal damages in their decision to inspect specificplants and, other things being equal, that greaterinspection effort is allocated towards those plantswhose emissions are likely to generate a higherlevel of damages. If economists have greatly criti-cized uniform environmental standards imposedon polluters, these papers show that the presenceof uniform standards does not necessarily implyuniform enforcement nor compliance with suchstandards. While this may be a desirable be-haviour, these authors also show that the moni-toring and enforcement activities of regulators arealso a function of variables pertaining to locallabour market conditions (such as unemploymentrate, and the importance of the plant in the locallabour market). Regulators thus appear to show areluctance to enforce standards in a way that mayhave an impact on employment. Dion et al. (1998)thus argues that both the public interest theory ofregulation and the economic theory of regulationcontribute to explaining the decision of regulatorsto monitor the environmental performance of reg-ulated plants.

Empirical studies on monitoring and enforce-ment issues have thus been of only recent interestand remain limited in numbers. Moreover, theyhave been performed only in the context of devel-oped countries: we are not aware of any empiricalstudy on the impact and determinants of monitor-ing and enforcement activities of an environmen-tal agency of a developing country at the plantlevel.3 The current paper is a first step toward

2 See Downing and Watson (1974), Harford (1978), Linderand McBride (1984), Russell et al. (1986), Beavis and Dobbs(1987), Harrington (1988), Malik (1990), Russell (1990). For arecent survey of the literature on monitoring and enforcement,see Cohen (1998).

3 Pargal and Wheeler (1996) have examined the impact ofnon-regulatory activities on environmental quality in Indone-sia.

S. Dasgupta et al. / Ecological Economics 36 (2001) 487–498 489

filling this important gap. Building upon Magatand Viscusi (1990), Laplante and Rilstone (1996),in this paper we analyse the impact of inspectionsand pollution charges on the environmental per-formance of industrial polluters in Zhenjiang,China. While inspections and pollution chargesjointly determine the expected penalty faced by afirm that fails to comply with the regulatory stan-dards, our results demonstrate that at the plantlevel, the variation in inspections is a better deter-minant of the firms’ environmental performancethan is the variation in pollution levies. This resultmay provide a key to China’s future quality ofenvironment.

In the next section, we briefly describe theinstitutional and regulatory context currently inplace in China. In Section 3, we present the modelwe purport to test. In Section 4, dataset andresults are presented. We briefly conclude in Sec-tion 5.

2. Context

2.1. (a) Growth and the en6ironment inZhenjiang, China

China’s industrial growth has been extremelyrapid. In the 1990s, industrial output increased bymore than 15% annually. Industry now accountsfor approximately 45% of China’s gross domestic

product, and has provided a source of rapidlyexpanding income. However, serious environmen-tal damage has accompanied this rapid growth. Alarge number of China’s waterways are near bio-logical death from excessive discharge of organicpollutants. In many urban areas, atmosphericconcentrations of pollutants such as suspendedparticulates and sulfur dioxide routinely exceedWorld Health Organization safety standards byvery large margins (Dasgupta et al., 1997; WorldBank, 1997). Chinese industry is a primary sourceof the environmental degradation noted in recentyears. China’s National Environmental ProtectionAgency (NEPA) estimates that industrial pollu-tion accounts for over 70% of the nation’s totalemissions of pollution (National EnvironmentalProtection Agency, 1996).

Zhenjiang, with a population of approximately3 million people, is an industrial city located onthe South Bank of the Yangtze River. It is di-rectly under the leadership of the Jiangsu provin-cial government. Given its location (Fig. 1), withNanjing lying west and Shanghai east, Zhenjianghas a key place in the economy of China and is animportant hub of communication on the lowerreaches of the Yangtze River. The Beijing-Shang-hai Railway passes through the city. With its port,railway and airports, Zhenjiang is a major landand water communications hub in China.

Zhenjiang’s industrial growth has been ex-tremely rapid during the period of China’s eco-nomic reform. Over the course of the last decade,Zhenjiang’s industrial output increased at an aver-age rate of 9% annually. The industrial sector isthe most important economic sector of Zhenjiang,employing a large percentage of the total laborforce. The industrial base is large and diversified.The Zhenjiang Economic Development Zone, cre-ated in October 1993, has already attracted over170 firms, including 80 foreign investments. State-owned enterprises do not dominate Zhenjiang in-dustry as private investments have considerablyincreased in the last decade. Given its importance,the rapid growth of the industrial sector has con-tributed significantly to improving living stan-dards. Average wage increased from Y 1373 in1986 to Y 3482 in 1993 (measured in constant Yof 1990). Zhenjiang’s ninth Five-Year PlanFig. 1. Zhenjiang and surroundings.


(1996–2000) calls for further development of thechemical, paper making, building material andaluminum processing industries. However, as aresult of this rapid expansion, environmentalquality — both air and water ambient quality —has significantly deteriorated. SO2 concentrationhas more than doubled between 1993 and 1997(from 0.022 to 0.052 mg/m3) and ambient TSSconcentration increased from 91 to 138 mg/l overthe same time period.

2.2. (b) En6ironmental protection in China4

Environmental protection in China became theresponsibility of the State in 1978 with Article 9of Chapter 1 of the Chinese Constitution:

The State ensures the rational use of naturalresources and protects rare animals and plants.The appropriation or damage of natural re-sources by any organization or individual bywhatever means is prohibited.

In accordance with the Constitution, the NationalPeople’s Congress, the highest legislative author-ity of China, adopted in 1979 (on a trial basis) theEnvironmental Protection Law (EPL). The EPLwas officially enacted in 1989. The purpose of theLaw is clearly stated in its Article 1:

This law is formulated for the purpose of pro-tecting people’s environment and the ecologicalenvironment, preventing and controlling pollu-tion and other hazards, safeguarding humanhealth and facilitating the development of so-cialist modernization.

The EPL provides the basic principles governingthe prevention of pollution and environmentalprotection and imposes criminal responsibility forserious environmental pollution (Article 43):

If a violation of [EPL] causes a serious environ-mental pollution accident, leading to grave con-sequences of heavy losses of public or privateproperty or human injuries or deaths of per-sons, the persons directly responsible for suchaccident shall be investigated for criminal re-sponsibility according to law.

An important pillar of China’s pollution regula-tory system is a pollution levy implemented na-tionally in 1982. Article 18 of the EPL specifiesthat:

In cases where the discharge of pollutants ex-ceeds the limit set by the state, a compensationfee shall be charged according to the quantitiesand concentration of the pollutants released.

Note that the levy system formally requires that afee be paid by any enterprise only on the quantityof effluent discharge that exceeds the legal stan-dard. Furthermore, the pollution levy is actuallypaid only on the pollutant that exceeds its stan-dard by the greatest amount, and not on all thepollutants that exceed the standards. Nationalregulations thus stipulate that a pollution levy LjM

be paid by a factory j emitting N pollutantswhere:

LjM=Max[Lj 1, Lj 2,…, LjN ] (1)

where

Lji=ri�hji−h i*

h i*n

Wj ; i=1,…,N (2)

where Lji is the estimated levy to be paid by plantj on pollutant i (i=1, …, N); ri is the nationallevy rate for pollutant i ; the pollution levy isfurther a function of the firm’s industrial sector ofactivity; hji is the discharge concentration of pol-lutant i by firm j ; h i* is the national legal dis-charge of pollutant i ; Wj is the wastewaterdischarge volume by plant j.The pollution levysystem in place in China is therefore not a truePigovian charge scheme (National EnvironmentalProtection Agency, 1992). Given its characteris-tics, it may also fail to provide strong incentivesfor pollution control.

4 This brief overview does not intend to provide a thoroughdescription of environmental protection in China. For a com-prehensive overview of environmental legislation and institu-tions in China, see Mei (1995), Sinkule and Ortolano (1995).


Fig. 2. Field inspection procedures

Environmental protection in China is generallydecentralised. The effective implementation of en-vironmental laws and regulations, including theimplementation of the pollution levy, is in largepart the responsibility of local people’s govern-ments. Article 16 of Chapter 3 of the EPL indeedstates that:

the local people’s governments at various levelsshall be responsible for the environmental qual-ity of areas under their jurisdiction and shalltake measures to improve the quality of theenvironment.

As a result, Environmental Protection Bureaus(EPB) have been created at all levels of localgovernments, from provinces and counties. Wang

and Wheeler (1996) have shown that effectiveimplementation of the pollution levy at theprovincial level to be a function of provincialincome and education: the higher the level ofincome and education, the higher the effectivelevy.

Zhenjiang Environmental Protection Bureau(ZEPB) is thus at the apex of decision-makingand interagency coordination on environmentalpolicies in Zhenjiang. At a lower level, there areenvironmental protection bureaus in all the vari-ous districts of the municipalities. These district-level EPBs are responsible for many activitiespertaining to the actual implementation of theenvironmental regulations. These activities includethe collection of pollution charges and non-com-pliance fees, the monitoring of air and water


ambient quality, and the monitoring and inspec-tion of industrial facilities. As indicated in Fig. 2,field inspections in Zhenjiang (as in all other EPBin China) follow a precise procedure. Apart fromregular inspection activities, note that complaintsmade by citizens regarding environmental inci-dents may give rise to field inspections. All Chi-nese citizens have the right to file complaints onpollution matters, and these have to be filed anddealt with in a very precise manner. ZhenjiangEPB is entitled to bring cases to court on behalfof the public. If the polluter is found at fault,various administrative penalties or warnings maythen be imposed. These may also include the needfor the polluter to install treatment facilities.

3. Model

The general model we wish to test, for both airand water pollution, is similar in nature to the onedeveloped by Magat and Viscusi (1990), Laplanteand Rilstone (1996). An important difference,however, is the inclusion of the pollution levy as adeterminant of environmental performance. Boththe probability of inspection and effective pollu-tion levy determine the expected penalty for non-compliance with the existing regulatory standards.

While we are generally interested in estimatingthe impact of ZEPB’s inspections on the pollutionemissions of the regulated firms, a difficulty is thatinspections at any given time may themselves be afunction of pollution at time t (that is, the regula-tor may observe pollution at time t, and thendecide whether or not to inspect at time t). Inother words, inspections may themselves be en-dogenous and correlated with the same variableswhich determine current pollution levels. If this isthe case, least-square estimates will be biased ingeneral. In fact, both of the above analyses haverejected the hypothesis that inspections were ex-ogenous. As a result, Magat and Viscusi (1990)have included in their analysis a vector of onlypast inspections. Laplante and Rilstone (1996) onthe other hand have prefered to estimate an in-spection equation and to re-estimate their basicinspection model by instrumental variables usingexpected inspections as instruments. Interestingly,

these last authors have found that the probabilityof an inspection in any given period is a decreas-ing function of past inspections — the regulator’smonitoring strategy thus being akin to one ofsampling without replacement. Given these re-sults, the basic system of equations of interest inthis paper is of the following nature:

INSPit=c+a1 Time+a2 LcINSPi,t−1

+a3 Lcci,t−1+a4 Pi,t−1+di+6it (3)

Pit=c+Xitb+Zig+d1 INSPit+d2 LcINSPi,t−1

+d3 Lcci,t−1+d4 Lei,t−1+d5 Pi,t−1+ai

+uit (4)

where i=1, 2, 3, …, N stands for plants; t=1, 2, 3, …, T stands for time; INSPit representsinspections performed at plant i at time t ;LcINSPi,t−1 is cumulative inspections performedat plant i up to time t−1; Lcci,t−1 is cumulativecomplaint targeted at plant i up to time t−1; Lcwand Lca will stand for water and air pollution-re-lated complaint, respectively; Pit is pollution emis-sions produced by plant i at time t. In the currentpaper, water pollution is measured by dischargesof total suspended solids (TSS) and chemical oxy-gen demand (COD). Air pollution is measured bydischarges of total suspended particulates (TSP).5

In all cases, the endogeneous variable Pit is mea-sured as the level of discharges relative to theirrespective standards. Hence, from the point ofview of the environmental regulator, the variableof interest is presumed to be the impact of itsmonitoring strategy on the compliance status ofthe regulated plants with the existing environmen-tal standards; Pi,t−1 is the lagged pollution vari-able; Lei,t−1 is the one-period lagged effectivelevy. In the case of water, the effective levy ismeasured as the ratio of water levy charged to theamount of wastewater discharged. In the case ofair pollution, the effective levy is measured as theratio of air levy charged to total discharge of airpollutants; Xit is a matrix of time-varying vari-ables consisting of the following variable: Emp

5 While air and water ambient quality may not be solelymeasured by the ambient concentration of TSP and TSS–COD, respectively, limited data availability restricts us to theuse of those parameters.


(number of employees); and dummy variables toindicate the nature of ownership of the plant:State (state owned enterprise) or Coll (collectivelyowned enterprise); Zi is a matrix of dummies toindicate a plant’s industrial sector of activity. Forwater pollution, Zi includes textile, petrol, to-bacco, food, beverage, paper and chemical; for airpollution, it includes petrol, coal, construction,paper and chemical. di and ai are firm-specificeffects while 6it and uit are the usual error terms.

Eq. (3) and Eq. (4) in the current paper differmarkedly from the models presented in Magatand Viscusi (1990), Laplante and Rilstone (1996).The difference pertains to the inclusion of twoimportant variables: citizens’ complaints and pol-lution levy. Both of these variables were absentfrom previous analyses.

For the purpose of estimation, we have as-sumed that: (1) the firm specific-effects are ran-dom; (2) the two error terms are uncorrelated andwell behaved; (3) the lagged pollution variable isuncorrelated with the error term in the inspectionequation and correlated with the correspondingerror term in the pollution equation; (4) all theright-hand side variables in the inspection equa-tion are doubly exogeneous — that is, uncorre-lated with the firm specific effects as well as withthe error term.

The system of Eq. (3) and Eq. (4) is recursiveand dynamic. Since the model is recursive, we canproceed with the estimation equation by equation(see Lahiri and Schmidt, 1978). Since the pollu-tion equation is the one of interest in the currentpaper, we transform Eq. (4) into the followingequation in order to eliminate the individualeffects:

V−1/2Pit=V

−1/2c+V−1/2Xitb+V

−1/2Zig

+V−1/2 INSPitd1

+V−1/2 LcINSPi,t−1d2

+V−1/2Lcci,t−1d3+V

−1/2 Lei,t−1d4

+V−1/2Pi,t−1d5+V

−1/2eit (5)

where eit is the new error term — that is the sumof the firm random specific effects (ai) and theregular error term (uit). The matrix omega is theappropriate matrix to eliminate the plants’ indi-

vidual effects. It is constructed as follows (see, forexample, Ahn and Schmidt, 1995, p. 18):

V−1/2=QV+uPV (6)

with

PV=IN� lT lT’ /T (7a)

QV=IN−PV (7b)

u2=s2

u

s2u+Ts2

u

. (7c)

For any integer m, let lm be an m× l vector ofones. The idempotent matrix Q6 transforms theoriginal variables into deviations from individualmeans and P6 transforms original variables into avector of individual means.

4. Dataset and results

4.1. Dataset

The dataset used in the current empirical analy-sis covers the period 1993–1997, with most obser-vations covering the period 1995–1997inclusively. As indicated in Table 1, most of thefirms belong to the manufacturing sector. A fur-ther breakdown indicates that the timber process-ing, the food processing and the petroleumprocessing industries represent the largest numberof sectors in our dataset with 17.2, 15.6 and 10.2%of the plants.

Table 2a and b describe the characteristics ofthe water (Table 2a) and air (Table 2b) pollutiondischarges of the firms in our dataset. In bracketsis the number of firms on which the entry for eachcharacteristic has been computed. With respect towater pollution, it can be noted that the averagedischarges of TSS and COD fell significantly overthe period covered by the dataset. Despite thisreduction, the number of firms paying waterlevies, and therefore not complying with the stan-dards, has remained relatively constant over theperiod 1995–1997. Hence, while deviation fromthe standards may have been reduced over time,compliance with the standards, in aggregate, ap-pears not to have changed significantly. A similartrend can be observed in Table 2b for TSP. Note


Table 1Number of firms, size, and ownership

1994 1995 1996 19971993

97 577Number of firms 55698 640Firms in manufacturing sector (%) 82 82 74 71 74

78 420Total c of employees 204 17680 202 201 980 217 175

Ownership (as % of firms)State owned 88 88 25 29 26

12Collective 10 67 62 652 7 80 8Joint-venture

Ownership (as % of output)92 4194 46State owned 49

6Collective 5 48 41 37Joint-venture 30 12 13 12

Size (as % of firms)9 3Large 45 4

42 1938 19Medium 17Small 4851 76 76 78

Size (as % of output)47 2938 31Large 31

Medium 3731 31 30 3216 3821 39Small 36

Table 2(a) Water discharge characteristics. (b) Air discharge characteristicsa

1994 1995 1996 19971993

(a)A6erage discharges (kg/year)

229 538 (96)TSS 88 532 (388)250 174 (97) 71 683 (391) 47 861 (530)392 089 (96) 83 869 (507) 73 271 (528)378 921 (97) 48 591 (626)COD

A6erage concentration (mg/l)TSS – – 127 (364) 130 (366) 99 (503)

– 365 (381) 355 (406)– 280 (507)COD

68 42 43 4568Proportion of firms paying le6y (%)

(b)A6erage discharges (ton/year)

1334 (97) 243 (577) 216 (556)1665 (98) 136 (640)TSP

A6erage concentration (mg/m3)– 589 (343)– 514 (228)TSP 572 (211)

40Proportion of firms paying le6y (%) 30 24 25 23

a The numbers in brackets refer to the number of firms in the dataset that is used to calculate the level of discharges.


Table 3Number of inspections

19961993 1994 1995 1997

1 0By national level 10 010 23 32By provincial level 2 4

3677 4080By city level (ZEPB) 1045 52551089

Table 4Number of complaints

19961993 1994 1995 1997

7873Water-related complaints 417 13139 195Air-related complaints 16328 26

that the proportions of firms violating the airpollution concentration standards appear to besignificantly lower than the number of firms violat-ing the water pollution standards.

Finally, in Table 3 and Table 4, we present theaggregate number of inspections and complaintsover the period of observation. Note the significantincrease in the number of inspections over time. Asexpected, most of these inspections appear to havebeen performed by the environmental protectionbureau at the city level. Complaints appear toconcern mainly air pollution issues.

4.2. Results

Given the presence of endogeneous variables inthe pollution equation (Eq. (4)), the general methodof moments (GMM) is an appropriate method ofestimation to obtain consistent and efficient esti-mates. Thus, the GMM is applied to Eq. (5). Theinstruments being used are (Q6V, P6V, P6Z)6 wherethe matrix V consists of X, LcINSPi,t−1, Lcci,t−

1, Let−1, Time and the constant term. GMM re-sults from Eq. (5) for water pollution are presentedin Table 5, and for air pollution in Table 6.

First, as Table 5 and Table 6 indicate, the number

of observations used is not the same across equa-tions as a result of missing values in some variables.Second, all equations pass the test of overidentify-ing restrictions; that is the instruments used arevalid. Third, for both water and air pollution, thelagged dependent variable is a relatively goodpredictor of current pollution emissions. This vari-able partly reflects the fact that the installation ofemissions control equipment is typically a processthat requires a long time. To this extent, the laggedpollution variable could also be interpreted as aproxy for changes in the production technology. Asimilar result was obtained by Magat and Viscusi(1990), Laplante and Rilstone (1996): their resultswere, however, somewhat stronger than those ob-tained here. This may be explained by a rapidlychanging and growing industrial sector in China.Fourth, water and air-related complaints do notappear to have a significant impact on pollution.Fifth, state and collectively owned enterprises ap-pear to exacerbate water pollution, at least as faras COD is concerned.

Results of interest concern the impact of inspec-tions and pollution levies. Inspections performedby the Zhenjiang EPB do have a statisticallysignificant negative impact on both water (mea-sured by TSS and COD discharges) and air pollu-tion. The estimations reveal that over the period ofanalysis, inspections reduce water pollution by

6 See Ahn and Schmidt (1995) for a thorough discussion oninstruments.


approximately 1.18 and 0.40% for TSS and COD,respectively, and air pollution by approximately0.34%.7 These results are lower than those ob-served in the pulp and paper sector in Canadaand the United States where inspections wereshown to reduce water pollution by 28 and 20%,respectively. Observe, however, that the pollutionlevy does not have a statistically significant impacton pollution. This result differs somewhat consid-erably from the result obtained by Wang andWheeler (1999). In that paper, the authors analysethe impact of the effective levy on pollution emis-sions in China and found the pollution levy tohave a statistically significant impact on pollution

Table 6Determinants of air pollutiona

TSP

INSP −0.0328** (0.022)LcINSP 0.0032 (0.621)Lca 0.0100 (0.843)

−0.1603 (0.168)Le0.3423** (0.040)Lagged dependent variable

−0.0002 (0.499)EmpState −0.1381 (0.499)Coll 0.0177 (0.945)Petrol −0.4518*** (0.092)Coal −0.1059 (0.749)Construction 9.0519* (0.000)

−0.7835* (0.007)PaperChemical −0.8535* (0.002)Test Overid. Restr. 7.3977 (0.286)

396c of observations

a Note: GMM is the method of estimation. GMM instru-ments are provided in the text. Variables are defined as in thetext. Test Overid. Rest. tests the validity of overidentifyingrestrictions. Numbers in brackets are P-values. Robust stan-dard errors are used throughout. (*), (**) and (***) meansignificant at the 1, 5 and 10% level, respectively.

Table 5Determinants of water pollutiona

CODTSS

−0.0500***INSP −0.0850**(0.100) (0.033)

LcINSP 0.0092 (0.195) 0.0176 (0.131)−0.2293 (0.340) −0.0433 (0.780)Lcw0.0026 (0.419)Le −0.1511 (0.236)

0.6800* (0.000)0.9964** (0.036)Lagged dependentvariable

Emp −0.0008**−0.0004 (0.211)(0.020)

0.3800 (0.415)State 1.3950***(0.056)

Coll 0.2137 (0.712) 0.7462** (0.032)7.4556 (0.158)Textile 4.7110 (0.140)

Petrol −4.0784 (0.150) −0.7307**(0.020)

Tobacco −1.1711*** −54.0391(0.062) (0.155)−2.0485**Beverage 0.4573 (0.545)(0.038)

−0.4784 (0.264)Food −0.8097 (0.112)−0.9390 (0.252)Paper 0.8722 (0.673)−0.6761 (0.351)Chemical −1.4565*

(0.002)Test Overid. Restr. 7.0918 (0.131) 1.9825 (0.852)c of observations 649 736

a Note: GMM is the method of estimation. GMM instru-ments are provided in the text. Variables are defined as in thetext. Test Overid. Rest. tests the validity of overidentifyingrestrictions. Numbers in brackets are P-values. Robust stan-dard errors are used throughout. (*), (**) and (***) meansignificant at the 1, 5 and 10% level, respectively.

emissions. These authors were unable to includeinspections in their analysis. The results obtainedin the current paper indicate that main determi-nant of the expected penalty function are inspec-tions and that variation in inspections dominatevariations in pollution levy as determinants ofenvironmental performance by industrial pollutersin China. The difference in result pertaining to theimpact of the pollution levy may also be explainedby the relatively small variation in effective pollu-tion levy in Zenjiang compared to the study byWang and Wheeler (1999) who used plant-leveldata across China. Our results also demonstratethat complaints do impact significantly inspec-tions. Given the impact of inspections on pollu-tion, citizens’ complaints therefore do have apositive and important impact on pollutioncontrol.

5. Conclusion

Monitoring and enforcement issues have beenthe object of a very limited number of empiricalanalyses at plant level. In developing countries,7 See Appendix A for the calculation of these percentages.


the difficulties of accessing data may explain thisstate of fact. Given the very limited number ofempirical analyses of the nature conducted in thispaper, we hope to have provided a better understanding of the impact of the regulator’s monitor-ing and enforcement activities on the behavior ofindustrial polluters. While inspections and pollu-tion charges jointly determine the expected penaltyfaced by a firm that fails to comply with theregulatory standards, our results demonstrate thatat the plant level, the variation in inspections is abetter determinant of the firms’ environmentalperformance than is the variation in pollutionlevies. This result may provide a key to China’sfuture quality of environment.

Whether or not these results support that addi-tional resources be devoted to monitoring activitiescannot strictly be addressed with the data currentlyavailable. Indeed, in order to do so, there would bea need to compare the costs of performing addi-tional inspections with the net benefits of reducedpollution emissions. However, given the high levelof air and water pollution currently observed inZhenjiang, given the impact that these may havesolely on health and productivity loss, it is reason-able to estimate that inspections do increase socialnet benefit in Zhenjiang.8 The results, however, dosuggest that informed citizens can have an impor-tant impact on pollution (via inspections). Das-gupta and Wheeler (1997) have analysed thedeterminants of citizens’ complaints in China andfound that they were an increasing function of thelevels of income and education. Hence, this wouldsuggest a greater role for the regulator to embarkon information and education policies.

Further research in this field will prove to becrucial and provide key policy implications. Inparticular, it does appear of interest to examinemore thoroughly the determinants of the monitor-ing activities performed by the environmental reg-ulator as well as the impact of various enforcementactions on the environmental performance of pol-luters. These remain the object of continued re-search.

Acknowledgements

We wish to thank very sincerely Zhenjiang Envi-ronmental Protection Bureau for allowing us accessto the data necessary to perform the analysispresented in this paper. We also wish to thank verysincerely Professor Genfa Lu (Department of Eco-nomics, Nanjing University) for his generous col-laboration. We also thank participants of theworkshop held in the Department of Economics ofNanjing University for their comments and sugges-tions. Our sincere thanks to Craig Meisner for hisresearch assistance. We finally thank the WorldBank Research Department for its financial sup-port. All remaining errors remain our own. Thefindings, interpretations, and conclusions expressedin this paper are entirely those of the authors. Theydo not necessarily represent the views of the WorldBank, its Executive Directors, or the countries theyrepresent.

Appendix A

These percentages are elasticities and have beencalculated as follows:

e=b*X(Y(

where b is the coefficient of inspection in a givenequation and X( is the mean for the inspectionvariable (INSP) over all observations in a givenequation. That is:

X( =%i=1

N %t=1T Xit

N*T

with t standing for time (t=1, …, T or t=1993, …, 1997 that is, T=5), and i representingfirms (i=1, …, N) Y( is the mean of the pollutionvariable (TSS or COD or TSP) over all observa-tions in a given equation. That is:

Y( =%i=1

N %t=1T Yit

N*T,

where i and t are defined as above.

8 Dasgupta et al. (1997) provide estimates of abatementcosts and health damages associated with air pollution inChina.


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