EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY 1

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1 EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY 1 Marcelo Caffera 2 Faculad de Ciencias Empresariales y Economía, Universidad de Monevideo, Absrac Unforunaely, he empirical lieraure on he enforcemen of indusrial emissions sandards refer o case sudies in he developed world, mosly he U.S. and Canada. There does no exis any example of his ype of empirical work for Lain America. In fac, Dasgupa, e al. (2001) and Wang e al. (2002) are he only examples of empirical sudies of effecs of inspecions and fines on polluion levels and he deerminans of he monioring and enforcemen aciviies of regulaors, respecively, for a less developed counry (China). This consiues a very imporan shorcoming because Lain America has a long radiion in waer polluion conrol laws, bu boh public opinion and papers ha have analyzed environmenal policy in he region have regarded hem as poorly enforced. Furhermore, many resources are being devoed o developing new regulaions and insrumens, bu no effor is being made o assess he effeciveness of he exising ones. This paper conribues o fill his gap by empirically esing he effec of inspecions and enforcemen acions of he municipal and naional governmens on indusrial plans emissions of BOD5 in Monevideo, Uruguay. Resuls sugges ha monioring and enforcemen aciviy by formal regulaors did no have an imporan deerren effec on repored BOD 5 levels and he compliance saus of he indusrial plans. However, I find ha he larger he hrea of being inspeced by he municipal governmen afer he end of he Indusrial Polluion Reducion Plan, he larger he level of repored BOD 5 by he plan for ha monh. This resul is consisen wih some resuls of he difference of means ess, which sugges he presence of under-reporing. The municipal governmen inspecions were an effecive way of discovering unrepored violaions, bu no enough o deer violaions. The low number of fines applied by regulaors during he period may explain his fac. 1 Ese rabajo recibió Mención Especial en el Premio Nacional de Economía Prof. Raúl Trajenberg Segunda Edición (2004) oorgado por la Faculad de Ciencias Sociales de la Universidad de la República. 2 I would like o hank Mónica González, Marcela Secco, María José Perez del Casillo, Enrique Garino, Hernán Mendez, Alicia Raffaele, Alejandra Beníez, Gerardo Sequeira, Rodrigo Gorriarán, Marisol Mallo, Francisca Pérez, Gerardo Balero, Alejandro Cendón, Marcelo J. Cousillas, Carlos Amorín Cáceres, Viviana Rocco, Juan Dubra, Hugo Roche and Fernando Borraz for heir help, commens and advice a differen seps of his research. address: marcaffera@um.edu.uy. 79

2 MARCELO CAFFERA 1. INTRODUCTION This paper is moivaed by he presen lack of formal economeric sudies evaluaing regulaors effeciveness in enforcing polluion regulaions in Lain America, and he deerminans of he allocaion of enforcemen acions among he regulaed plans. Effecively, he empirical lieraure ha deals wih hese wo issues unforunaely refers only o emissions of biological oxygen demand (BOD 5 ) and oal suspended solids by he US and Quebec pulp and paper indusry and air polluion from he US seel indusry [see (24), (10), (21), (15), (27), (18), (11), (16) and (34)]. In fac, Dasgupa, e al. (2001) and Wang e al. (2002) are he only examples of empirical sudies of effecs of inspecions and fines on polluion levels and he deerminans of he monioring and enforcemen aciviies of regulaors, respecively, for a less developed counry (China). 3 There does no exis any example of his ype of empirical work for Lain America. 4 This is a very imporan shorcoming because Lain America has a long radiion in waer polluion conrol laws based on uniform emissions sandards, bu boh public opinion and papers ha have analyzed environmenal policy in he region have regarded hem as poorly enforced [see (33), (13), (28) and (35)]. A he same ime, new regulaions for oher media (like air) and new incenive based insrumens are being developed and implemened in some pars of he region, bu no effor has been made o empirically es he capaciy o enforce hese new regulaions. In his respec, previous empirical analyses in he US, Canada and China are of lile guidance for a Lain American counry given he obvious differences in insiuional capaciies and even poliical sysems. This paper aims o sar filling his gap by, firs, empirically examining he deerminans of he allocaion of inspecions by he municipal and he naional governmen among indusrial plans in Monevideo, Uruguay, and hen by empirically esing he effec of monioring and enforcemen acions of boh he municipal and sae governmens on indusrial plan s emissions of BOD 5 in Monevideo, and heir probabiliies of being in violaion. 5 Also, a unique feaure of his research wih respec o pas empirical sudies is he availabiliy of four sources of informaion regarding levels of polluion. One is he level repored by indusrial plans, anoher is he level sampled by he municipal governmen, a hird is he level sampled by he naional governmen and he fourh is he level sampled by a privae consorium ha worked for he municipal governmen. This unique feaure allows me o perform difference of means ess as a simple way o explore he presence or absence of under-reporing. 3 There are a few oher examples of empirical analyses of informal and formal polluion regulaion in LDCs (see (29), (30) and (17)). Bu hese sudies, among oher, have significan differences in he qualiy of heir daa as compared o he above-menioned papers. 4 Exising works explore oher issues (see (3)), or have daa limiaions ha make hem no comparable (see (9), (6), (7), (12) and (14)). Firs, some of hem do no have informaion on emissions or formal regulaory measures or boh. Second, hey are all cross-secion sudies. See (5) for a deailed descripion of hese works and heir daa limiaions and differences. 5 BOD 5 is among he mos imporan polluans and is one of he wo polluans argeed by he municipal governmen and he Iner American Developmen Bank. I is also a polluan ha all plans emi and have o repor. 80

3 EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY 2. INSTITUTIONAL FRAMEWORK AND WATER POLLUTION REGULATION Boh he municipal governmen of Monevideo (Inendencia Municipal de Monevideo, IMM) and he naional governmen Environmen Office (Dirección Nacional de Medio Ambiene, DINAMA) have jurisdicion over indusrial waer polluion conrol in he ciy. The allocaion of responsibiliies beween hem can be summarized as follows. The IMM is responsible for monioring indusrial effluens and enforcing emissions sandards and he correc operaion of effluen reamen plans. I is also he regulaory insiuion o which he plans repor. The ask of he Naional Environmen Office (DINAMA), hrough he Division of Environmenal Conrol (DCA), is o issue he Indusrial Discharge Auhorizaion when i deermines ha a firm has a reamen plan ha enables i o comply wih he emission sandards. In oher words, he DCA is in charge of iniial compliance, while he IMM is in charge of coninuous compliance. This insiuional organizaion is resul of he hisorical evoluion of waer polluion legislaion. I was a he municipal level ha he firs regulaions concerning indusrial waer polluion appeared in he sixies, almos weny-five years before he creaion of he Minisry of he Environmen. 6 Bu he insiuional organizaion is also he resul of an informal agreemen beween he DINAMA and he IMM ha ook place in 1995, which was aimed a saving scarce monioring and enforcemen resources. Though he division of responsibiliies was clear in heory, coordinaion beween he wo offices remained poor in pracice. For example, he DINAMA coninued o monior plans even when hey were no invesing in heir reamen plans. Lasly, if we add ha he Minisry of he Environmen suffers imporan budge consrains ha preven he complee swapping of responsibiliies, i is very easy o undersand why he IMM coninues o play a role as significan as he DINAMA wih respec o indusrial waer polluion in he ciy of Monevideo. The DCA s saff is composed of only five persons, who are no only in charge of monioring and enforcing waer polluion legislaion, bu also he res of environmenal legislaion. Saffing is a bi beer a he Indusrial Effluens Uni of he IMM, where seven persons work, bu hey are only in charge of indusrial emissions in Monevideo. The polluion conrol insrumens used are uniform emission sandards. These are defined in erms of concenraions of polluans, no in erms of quaniies discharged. 7 No absolue legal limis are esablished on he quaniy of polluans o emi. Neverheless, raher han simply enforcing emission sandards, waer polluion conrol is cenered on he exisence and correc operaion of a reamen echnology. In fac, he legislaion does no sancion violaions o emissions sandards, only misoperaion of he reamen plan. 8 Fines are se as an increasing funcion of he number of violaions in record. This sysem requires he firms o supply a considerable amoun of informaion o he DINAMA in he applicaion process for a discharge permi (26). They provide informa- 6 Ordenanza sobre la Disposición de Aguas Residuales de los Esablecimienos Indusriales del Deparameno de Monevideo, Decreo N de la Juna Deparamenal de Monevideo, 1967, and Reglamenación de la Ordenanza sobre la Disposición de Aguas Residuales de los Esablecimienos Indusriales del Deparameno de Monevideo, Resolución N del Inendene Municipal de Monevideo, The only relaed regulaion saes ha he oal volume of effluen discharged mus no be greaer han 2.5 imes he average volume emied during an aciviy period for plans emiing o he sewage sysem, and 1.5 imes for plans emiing o waercourses. These measures would preven emporary overloads ha could produce permanen effecs on waercourses. The Decree also esablished ha discharge auhorizaions are condiioned on he capaciy of he municipal sewer, and ha he Minisry of he Environmen can mandae firms o conrol effluen volumes. 8 Decreo 253/79, Normas para prevenir la conaminación ambienal mediane el conrol de conaminación de aguas,

4 MARCELO CAFFERA ion regarding he producion process (including maximum daily and monhly producion, average waer consumpion, daily quaniies of inpus used), a descripion of he characerisics of effluens and solid wases generaed, informaion on condiions of recepor bodies a he poin of discharge, ime schedules for he consrucion of he reamen plan and a descripion of is operaion and mainenance. Recognizing very low compliance raes and he difficul economic siuaion of he indusrial plans of he ciy as a possible explanaion for hem, he municipal governmen of Monevideo implemened he Indusrial Polluion Reducion Plan ha relaxed he emissions sandards and esablished a ime schedule by which hey would converge again o he original levels. 9 The Plan was supposed o give he firms considerable ime o implemen changes in abaemen echnology. Saring on March 1 s 1997, he indusries had almos hree years before he sandards converged again o he original levels, in December 31 s A second observaion is ha regulaors recognized wool washing plans and anneries as he indusries facing he greaes difficuly in complying. These plans had laxer sandards in each period, and even more surprisingly, he BOD 5 sandards for hese wo ypes of plans emiing o municipal sewers converged o a value ha is higher han he original one (3,000 mg/l and 1,000 mg/l for wool plans and anneries, respecively, compared o 700 mg/l se by he Naional Decree 253/79). According o conversaions wih inspecors a he Deparmen of Environmenal Conrol of he DINAMA, hese inconsisencies have generaed problems in enforcemen because firms argue ha hey are complying wih municipal sandards while he DINAMA requires adjusmens o mee emission sandards se by he Naional Decree. 3. ACTUAL POLICY The sraegic plan ha guides he IMM conrol policy was oulined in he Urban Saniaion Direcor Plan (Plan Direcor de Saneamieno Urbano), in execuion since 1992 wih funds from he Iner-American Developmen Bank. In general erms, his is a plan for he exension of he municipal sewage sysem o several pars of he ciy. Concerning he waer polluion policy, hese works would reduce effluens discharged o ciy sreams by redirecing hem ino he sea hrough wo discharge pipes. Towards his objecive he IMM underook he hird sage of he Urban Saniaion Plan for he ciy of Monevideo (PSUIII). 10 This plan is key o undersanding he polluion conrol policy of he IMM and i needs o be aken ino accoun when inerpreing he empirical resuls in Secion 7 regarding he effeciveness of he emissions sandards enforcemen policy. The objecives of he PSUIII included he developmen of a Monioring Program for conrolling indusrial polluion and he qualiy of he ciy s waer bodies (see (25)). The Monioring Program was execued beween 1999 and 2001 by he privae consorium Muliservice-Seinco-Tahal (SEINCO). The major aciviies of he Monioring Program included inspecion, supervision and sampling of indusries o deermine level of compliance wih emissions sandards and esablishmen of he monioring frequency, duraion and procedure of emissions by indusry class Indusrial waer polluion in Uruguay is based on a sysem of self-reporing. Self-repors are sen o he Indusrial Effluens Uni of he IMM, alhough some plans send hem also o he Deparmen of Environmenal Conrol of he DINAMA volunarily. 9 Resolución Municipal N 761/96, Plan de Reducción de la Conaminación de Origen Indusrial, February 26h, Conrac signed in November 1996, Loan 948/OC-UR 82

5 EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY Repors include monhly levels of (1) producion, (2) ap and underground waer consumed, (3) energy consumed (elecriciy, wood, fuels), (4) number of employees and days worked, and (5) volumes of emissions and heir concenraions of polluans. Reporing periods are November - February, March - June and July - Ocober. Inaciviy periods should also be repored. Failing o send a repor on ime and in he correc form could lead o fines o he indusry and an observaion o he professional in charge. In heory, he plans have o send he repors wihin he wo weeks ha follow each reporing period. Bu acually his requiremen is no enforced. Two ypes of regular inspecions exis, wih and wihou effluen sampling. Sampling inspecions are hose in which he inspecors ake samples from he plan s effluens for laer analysis. These inspecions always include an evaluaion of he reamen plan performance as well as general quesions regarding he economic siuaion of he firm, including changes in levels of producion, or special evens ha could affec he effeciveness of he effluens reamen process. Non-sampling inspecions include he laer evaluaion and general quesions bu hey do no include a sample of he plan s effluens. Possible reasons for no sampling may be ha he plan is no working a he ime of he inspecion, or ha he plan is no discharging a he ime of he inspecion DATASET I use hree sources of informaion o consruc my daase. The core informaion comes from he Municipal Governmen of Monevideo. This is comprised of informaion regarding producion and polluion of indusrial plans, plus informaion regarding monioring and enforcemen aciviy of he IMM on hese plans. The informaion on producion and polluion is obained from he four-monh repors of he plans, described in Secion 3. The informaion on inspecions is comprised of he number of sampling and non-sampling inspecions done per monh per plan, and he resul of he sample in erms of mg/l of BOD 5. The informaion on fines levied by he IMM is comprised of he number of fines levied on each indusrial plan per monh and heir amouns. The sample period for all hese variables is July Ocober 2001, excep for fines, which is May Ocober My second source of informaion is he Environmenal Conrol Division (DCA) of he Minisry of he Environmen. This informaion includes number and resuls (in erms of BOD 5 mg/l) of sampling inspecions, and number of non-sampling inspecions. I also includes he oal number of compliance orders issued by he DCA. Pas he due dae, he DCA issues a noe communicaing o he firm ha i is poenially subjec o a fine due o non-compliance wih he previous order. I called his ype of acion fine hreas. Finally, in he case of fines, I have boh he number of fines per monh per plan and he amoun. The sample period for all he DCA variables is June Ocober Finally, my hird source of informaion is he privae parnership MULTISERVICE-SEINCO- TAHAL (SEINCO) ha was in charge of he Monioring Program ha he IMM implemened in 1999, financed by he IADB, menioned in Secion 3. This informaion is comprised of he number and resul of he sampling inspecions conduced by SEINCO. The period during which SEINCO inspeced plans was April Sepember This disconinuiy of discharges presens a problem for he DCA inspecors, who have very rigid ime schedules for inspecions in Monevideo because hey also have o inspec firms in he res of he counry. 83

6 MARCELO CAFFERA Table 1 gives a summary of all he informaion jus described. Table 1: Daa se descripion My daabase includes seveny-four (74) indusrial plans locaed in Monevideo. The selecion of hese 74 plans is no random. Firs, hey are all privaely owned plans. Public indusrial plans do no repor emissions o he IMM. Second, hey were seleced from a lis of indusrial plans ha were being sampled by SEINCO during he years 2000 and Mos of hese plans were also he ones ha were regularly inspeced by he IMM. From a maximum of eighy-seven plans, I excluded welve (12) plans ha repored less han six (6) imes during he hireen (13) reporing periods in my sample alhough hey were acive hroughou he 13 periods. From he remaining 75 I had o exclude one more because i was no reporing BOD 5 emissions; i repored only meals emissions. Consequenly, conclusions from my analysis mus be inerpreed according o his sample selecion bias. I can be said hough, ha he plans in he sample are responsible for more han 90% of he oal indusrial organic polluion in he ciy. I presen he descripive saisics for he inpu and polluion variables in Table 2 and he monioring and enforcemen variables in Table Table 2: Descripive Saisics for Inpu and Polluion Variables (Sample July Ocober 2001) Toal Poenial Observaions: 3, Descripive saisics for he levels of producion are no presened for space reasons. Also, gas and firewood consumpion are no included in he able. The IMM did no ask firms o repor gas consumpion before 2001, and in 2001 only one plan repored gas consumpion in wo reporing periods. The problem wih firewood is ha no all of he indusrial plans in he sample use firewood as an inpu and no all of hose who did no use i repored zero consumpion. Insead, a value was missing in he respecive cell. Given hese, I discarded hese wo variables from he analysis. 84

7 EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY The DCA inspeced a lo less han he IMM during he period July Ocober Ou of a oal of 760 inspecions by he wo regulaory offices on he seveny-four indusrial plans in he sample, he IMM conduced a oal of 549 inspecions while he DCA only performed 211. Furhermore, 401 of he IMM inspecions (73%) were sampling inspecions; while for he DCA sampling inspecions were 122 (58%). Table 3: Descripive Saisics for Monioring and Enforcemen Variables IMM and DCA (Sample July Ocober 2001) Toal Observaions: 4,736 Noes: (1) Observaions for fines levied by he IMM were available from May 1997 (3,996 observaions). (2) Saisics for amoun of fines are over he non-zero observaions (3) Dollars of Ocober Finally, i is ineresing o noe ha ha fines were very rarely levied in spie of exremely frequen repored and discovered violaions. I presen he descripive saisics of he variable exen of he violaion in Table 4. This variable is equal o emissions of BOD 5 (mg/l) minus he concenraion sandards se in he legislaion, censored a zero. I also presen he descripive saisics of a compliance saus variable equal o one if he plan repored a violaion and zero oherwise. The calculaions are done using he original sandards during he enire period and also using he laxer sandards of he Indusrial Reducion Plan during July December

8 MARCELO CAFFERA Table 4: Descripive saisics for violaions Violaions were frequen, even when measured as emissions in excess of he laxer sandards. Fory one percen (41%) of he repored BOD 5 levels were ou of compliance wih he emission sandards, and only weny six (26) plans of he oal sixy-nine (69) repored o be in violaion less han weny percen (20%) of he ime. The number of violaions as a percenage of he number repors never decreased below 25%, or 41% if we consider he original sandards. In spie of his, he IMM levied only 11 fines and he DCA only four fines during he same period. 5. MISSING VALUES As evidenced by Table 2, I have missing values (MV) in my panel. Observaions are missing eiher because a plan did no repor in a given period, in which case I have a missing value for he enire se of variables for ha period, or because he repor had missing values for one or a subse of variables. I call he firs case uni non-repor and he second case iem non-repor. There were a oal of sixy-wo (62) non-repors over a poenial 962 observaions (74 plans imes 13 reporing periods). Six of hese correspond o four plans ha ceased producion (for differen reasons). Twelve correspond o repored no-aciviy periods of hree differen plans. 13 Sixeen correspond o hree plans ha sared business in periods four, five and nine, respecively. The remaining weny-eigh correspond o random non-repors. There are several reasons for iem non-repors. One is ha some firms never repor a specific variable. Ohers repor a specific variable unsysemaically. For example, in he case of underground waer consumpion some firms repor zero consumpion in some periods and do no repor in ohers. 14 Finally, oher values appear o be randomly missing. Taking ino consideraion iem and uni non-repors here were a oal of 5,557 observaions missing for he inpus and polluion variables described in Table 2 plus he levels of producion re- 13 I reaed hese as missing values because in some cases he firms indicaed (usually in a leer o he Direcor of he Indusrial Effluens Uni of he IMM) ha hey were producing very low quaniies and herefore i was no worh reporing emissions. Even more, in one case he leer was followed by hree non-repors in he following periods wihou any clear informaion regarding he exac poin in ime in which producion re-sared. 14 Given he imporance of underground waer consumpion in he analysis I oped o fill-in hese missing values insead of discarding i as I did wih firewood. 86

9 EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY pored by he indusrial plans, ou of a oal of 40,924 possible observaions. In oher words, 13.6% of he daa se was missing. The problem wih MV is ha esimaion based only on he complee observaions (hose having no missing values) may bias parameer esimaes if he daa is no missing a random or he selecion rule is ignorable (see (23)). Verbeek and Nijman (1992a) proposed a formal es for ignorabiliy in linear regression models of panel daa. The es is worh performing because of he complexiies involved in esimaing a panel incorporaing he selecion rule. Neverheless, I canno perform he es because I have zero observaions for my balanced sub-panel. (I have no monh in which all he 74 plans repored). Consequenly, I proceed wih my unbalanced panel. This opion is jusified by hree reasons. Firs, and mos obvious, I have no choice, oher han o perform no esimaion a all. Second, ha i is fairly simple o conclude ha here exiss selecion bias in my daa se due o nonreporing. I have welve (12) observaions missing as a consequence ha he plans informed no aciviy or very low aciviy. Missing-ness is hen clearly relaed o he level of producion. The selecion rule is no independen, among oher possible hings, of he overall economic siuaion of firms or seasonaliy. These welve cases make my selecion rule no ignorable. Third, I do no hink his source of non-ignorabiliy of he selecion rule is imporan in erms of bias because in mos cases plans were acually no working and no emiing, as proved by inspecions performed in hose cases. If his is rue, and if I assume ha iem non-responses are missing a compleely a random, which I do, hen he missing observaions do no hide any unknown informaion. In spie of he fac ha I proceed wih an unbalanced panel, I impue for he iem non-responses before esimaing my parameers of ineres. The reason is ha iem non-responses accoun for 55.4% of he oal 5,009 observaions missing for he inpu and polluion variables. Several mehods are used in he applied lieraure and ohers are proposed in a more recen heoreical lieraure o deal wih missing values. The issue when selecing a mehod o deal wih missing values is ha some of hem (for example, impuing means) may reduce he efficiency of he final esimaors. A review of hese mehods, along wih a discussion of heir properies, can be found in (23) and (22). For he case of panel daa, a review of he lieraure of incomplee panels and selecion bias can be found in (36). There are basically wo crieria o follow when impuing values for iem non-repors: condiional mean impuaion and muliple impuaion (see (22)). Condiional mean impuaion mehods are based on (4), and (2). The basic idea is o use he informaion on he observed Xs or on he observed Xs and Ys o fill in missing values, correcing for he variances and covariances. Leas squares on he filled-in daa produce consisen esimaes assuming ha he iem non-responses are missing compleely a random (see (23)). Muliple impuaion is proposed as a way o handle he problem ha whaever he condiional mean impuaion procedure, esimaed sandard errors of he regression coefficiens from ordinary leas squares or weighed leas squares in he filled-in daa would end o be oo small, be- 87

10 MARCELO CAFFERA cause impuaion error is no aken ino accoun. (see (22), p. 1232). By muliple impuaion, basically, one impues m 2 values for each missing observaion o obain m differen daa ses. Wih each daa se one obains he desired esimaes and averages hem o obain a final parameer esimae and variance esimae ha correc for he underesimaion of variances produced by filling in missing observaions (see (31)). Boh condiional mean and muliple impuaion mehods were developed and applied for cases of cross-secion daa and herefore share a problem when applied o panel daa: i makes lile sense o fill in iem non-responses of one plan condiioning on informaion observed for he res of he plans, wih differen echnologies, managemen and oupu. I solved his problem by performing he impuaions wihin plans. This way I no only preserve beween-plan variabiliy, minimizing bias and variance problems for he final esimaes, bu I also use plan-specific informaion abou he missing values 15. Wihin-plan impuaion leaves aside muliple impuaion because his would produce m daa ses for each differen plan, and here is no clear way o handle all his informaion o obain he final panel esimaes. Consequenly, I use an ieraed Buck procedure wihin plan o impue for iem non-repors, in he spiri of he suggesion made by Beale and Lile. To perform his procedure I consruc he following variables for each plan: (1) WATER: Toal waer consumpion (in m 3 /monh) equals he sum of ap waer and underground waer consumed; (2) ENERGY EL*3.6 + FUEL*43,752.06: Toal energy consumpion in mega joules (MJ), where EL is he elecric energy consumed in Kwh/monh and FUEL is he quaniy of fuels consumed per monh in m 3 ; (3) LABOR equal o he oal number of days worked in he monh imes he oal number of employees in ha monh; (4) POLLUTION FLOW*BOD 5 *1000: Toal organic polluion discharged in (mg/day), where BOD 5 was already defined and FLOW is he average flow level of discharges, in m 3 /day; (5) PRODUCTION Quaniy produced by monh. 16 The original variables were fied using hese consruced variables. I esimaed he auxiliary linear regressions wih he variables in naural logarihm forms. These did no necessarily provide beer fis han auxiliary regressions wih variables in heir original form, bu hey are closer o he spiri of a Cobb-Douglas ype of producion funcion used ahead. 17 Finally, I do no use he monioring and enforcemen variables in his impuaion for wo reasons: firs, I conserve degrees of freedom in he auxiliary regressions wihin firms, and second, because i would be like cheaing o use hese variables o impue for he MV and hen use he resuling daa o es for heir effec on polluion. 15 An example of he laer is o use monhly volumes of effluens discharged divided by days worked in he monh o impue he monhly average effluen flow. 16 In weny-five cases his variable involved sandardizing unis of measure o be able o add differen producs. 17 A documen describing he disribuion of missing values per variable by indusrial plan, he processes followed o impue for iem non-responses in each plan, and he corresponding ieraion procedures is available from he auhor upon reques. 88

11 EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY 6.1. THE INSPECTION EQUATIONS 6. SPECIFICATION AND ESTIMATION ISSUES During he period under sudy boh he municipal (IMM) and naional governmen (DCA) office moniored indusrial plans in Monevideo. In fac, all seveny-four indusrial plans were inspeced a leas wo imes by he IMM. The DCA inspeced fify-eigh of hese same plans a leas once. The remaining sixeen plans were never inspeced by he DCA. Parallel monioring effors of regulaors were no coordinaed. The wo offices did no share informaion on monioring and enforcemen aciviies on a regular basis. Quie he conrary, informaion sharing was limied o specific and complicaed cases. In fac, he correlaion coefficien beween he number of inspecions of he wo offices across ime and plans is These argumens validae he chosen course of acion of esimaing separae inspecions equaions for he IMM, DCA and SEINCO. Apar from explaining he inspecion sraegies iself, he idea behind he esimaion of hese inspecion equaions is also o fi hem o obain probabiliies of being inspeced by each of hese insiuions ha can be used as insrumens for acual inspecions in he BOD 5, load and violaion equaions The IMM Inspecion Equaion Equaion 1 was esimaed for he IMM: 18 INSPIMM 3 γ i + γ INSPIMMCUM + γ INSPSEINCOCUM γ RF + γ DURINGPLAN 4 + γ γ FINEDIMMCUM i 1,...,74; July1997,... Ocober γ INSPIMMOTHERCUM 2 + γ STREAM + η 1 + γ VOL 5 1 (1) where INSPIMM is a dummy equal o one if plan i was inspeced by he IMM in monh. Firs of all γ i represen he plan-specific fixed effec. Is inclusion is he resul of a Hausman es ha compares he condiional (fixed-effecs) and he uncondiional logi esimaes. The Hausman chi-squared saisic was 98.7 clearly suggesing rejecing he null of common inercep in he favor of he alernaive of plan-specific fixed-effecs. Because fixed effecs are never acually esimaed in he condiional logi, he resuls of hese ess sugges he following rade-off. On he one hand, wihou esimaes for he fixed effecs I canno obain predicions for he probabiliies of inspecions. On he oher hand, if I specify an uncondi- 18 Table A.1 in he Appendix provides a lis of all he variables used in his chaper and heir definiions. 89

12 MARCELO CAFFERA ional logi o be able o predic probabiliies I do no recognize plan specific effecs and I obain inconsisen esimaes of my parameers. The chosen soluion was o esimae an uncondiional logi o predic he probabiliies and a condiional logi o inerpre he esimaed coefficiens. The wo models canno be specified idenically because he condiional (fixed-effec) logisic regression eliminaes any variable wihou wihin-plan variabiliy. Wha I presen here and in he following secions are he condiional logi models. I do no presen he resuls of he uncondiional models. In order o specify his inspecion equaion, firs I considered ha he sraegy of he IMM inspecors obeyed five rules, according o wha hey declared in inerviews. The firs one was a sample wihou replacemen rule. The IMM classified plans in Prioriy 1 and Prioriy 2 plans. Prioriy 1 plans (25 of he 74 plans in my sample) are he heavies polluers in erms of organic polluion and meals. They accoun for 80% of his polluion. Inerviewed inspecors declared ha hey ry o visi Prioriy 1 plans wice and Prioriy 2 plans once every six monhs. Bu he daa do no suppor his saemen. Therefore, in order o capure he sample-wihou-replacemen inspecion sraegy I included he number of inspecions performed in he plan during he las welve monhs (INSPIMMCUM -1 ) as explanaory variable. 19 The second rule menioned by IMM inspecors was ha plans wih worse compliance hisories and hose showing less cooperaion wih regulaors were inspeced more ofen. These plans were hose ha did no ake he promised measures o abae emissions or delayed hem. I included FINEDIMMCUM -1 o capure he level of cooperaion. 20 This variable measures he number of fines imposed by he IMM agains his plan in he las welve monhs; he more he cumulaive number of fines he less he cooperaion of he plan in he recen pas. 21 This level of cooperaion perceived by regulaors is no only a funcion of he recen formal hisory of he plan. I also depends on non-quanifiable facs on which inspecors based heir decisions. 22 Third, ciizens complains also riggered inspecions bu were no included as an explanaory variable because of he unavailabiliy of informaion abou hem. Neverheless, inerviewed inspecors declared ha mos of hese complains were no originaed by unusual levels of dis- 19 I ried he cumulaive number of inspecions performed in he las six monhs insead of welve monhs, bu he model performed beer wih welve monhs in erms of goodness of fi and boh he Akaike and Schwarz informaion crieria. 20 The inclusion of he number of deeced violaions in he las welve monhs did no improve he fi of he model. This resul is consisen wih he policy approach of he period. Effecively, during his period he enforcemen effors were no so much direced a enforcing sandards bu a decreasing emissions. Towards his end was ha he IMM implemened he Indusrial Polluion Reducion Plan relaxing emissions sandards. Also, he cumulaive amoun of fines was included insead of he cumulaive number. This did no change he resuls eiher. 21 I do no have informaion on inermediae enforcemen acions (e.g., compliance orders) issued by he municipal governmen of Monevideo, jus hose issued by he naional governmen office, DINAMA. 22 An example is he following: someimes inspecors are kep waiing a he plan enrance for he lengh of ime needed o make some quick cleanings and oher measures (like diluing) o comply wih he emissions sandards. This is more ypical in small plans, wih lesser ime of effluens reenion. Anoher example is he quickness of response o suggesed changes. I is worh noing ha his makes he effeciveness of waer polluion conrol very dependen on hose specific inspecors wih long experience in he job. In oher words, a good deal of he compliance hisory of plans is los when an inspecor reires or is appoined o anoher office. 90

13 EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY charges, bu from smells or illegal poins of discharge (e.g., srees, brooks) or when he public saniary sysem below he srees collapsed. Fourh, he failure o repor in subsequen periods also riggered inspecions according o he IMM inspecors. As a resul, he number of reporing failures in he previous wo reporing periods (RF ) was also included as an explanaory variable is anoher dummy variable equal o one in he monhs of hese wo years during which he IMM inspecors conduced special monioring campaigns due o he delay in he implemenaion of he Monioring Program by SEINCO. I is ineresing o noe ha IMM inspecors received exra IADB-financed paymens for hese campaigns. DURINGPLAN refers o he Indusrial Polluion Reducion Plan implemened from March 1997 o December This variable, a dummy equal o one during hese monhs, was included because he IMM could have changed is monioring sraegy given ha is objecive during hese monhs was o give more ime o plans o incorporae abaemen echnology. BOD 5 emissions sandard for plans emiing direcly ino waerways is 60 milligrams per lier (mg/l), while i is 700 mg/l for hose emiing ino he sewage sysem. A dummy variable indicaing wheher he plan was emiing direcly ino a waer body was also included o capure any possible effec of his on he probabiliy of being inspeced. This variable is STREAM, equal o one if he plan emis direcly ino a waer body. In addiion o he variables included o capure hese rules, oher variables were included o capure oher deerminans of IMM inspecions, for example, INSPIMMOTHERCUM -1. This variable measures he cumulaive number of inspecions performed by he IMM in he res of he plans. Inspecors knew ha he res of he plans were aware of inspecions performed a a specific plan, paricularly hose in he neighborhood, and ha his could have molded heir expecaions regarding a possible inspecion. On he oher hand, if he IMM monioring aciviies were affeced by imporan budge consrains, as hey acually were, he sign of his variable s coefficien would be negaive, indicaing ha he higher he number of inspecions performed on oher plans in he recen pas he smaller was he probabiliy of his plan being inspeced given he cos of monioring campaigns. Therefore, he sign of his variable s coefficien remains an empirical maer. Anoher imporan deerminan of IMM inspecions during par of he analyzed period was he implemenaion of he Monioring Program, financed by he Iner American Developmen Bank and in charge of he privae consorium SEINCO. This consorium conduced regular inspecions on indusrial plans during The IMM ook advanage of his siuaion, saving on monioring resources. INSPSEINCOCUM -1, he cumulaive number of inspecions performed by SEINCO on a plan in he las welve monhs, measures his effec. 23 In he firs six monhs of 1997 he IMM implemened a new enforcemen sraegy. I issued a fax o every plan in is daabase explaining he new four-monh Reporing Form forma and communicaed o he plans ha he municipal governmen was underaking a new plan for polluion conrol. For ha reason, in he firs reporing period I se he reporing failure hisory of every plan equal o zero as an indicaor ha a new enforcemen period had begun. 91

14 MARCELO CAFFERA Also, he Uruguayan indusrial secor wen hrough an imporan conracion during par of he analyzed period. In paricular, he indusry producion volume index dropped 8.6% on average in 1999 and 7.2% in (During 2000 i increased 2%). The conracion was larger as measured by he indusry real GDP: 23% beween 1996 and 2001, wih an average drop of 4% during he period and 8% during he period Alhough no recognized by auhoriies, as a consequence of his conracion, inspecors may have eased or loosened heir enforcemen pressure on plans, since i was precisely he difficul economic imes ha inspired he Indusrial Polluion Reducion Plan. I included he monhly level of he indusry producion volume index (VOL) o capure his possible effec. Finally, unusually high levels of repored polluion someimes riggered an inspecion, alhough very rarely according o UEI inspecors. One reason is ha obviously i canno be opimal for plans o repor peaks of heir emissions. Bu i is no easy o consruc a variable capuring he effec of unusual levels of repored emissions eiher. Many plans during he analyzed period sen heir repors in he final monh of he following reporing period. In oher words, regulaors were looking a a picure of he plan ha was a leas four monhs old. Oher plans repored immediaely afer he end of he reporing period. In shor, regulaors did no receive he informaion on emissions a he exac poin in ime in every period. This complicaed he possibiliy of consrucing a variable indicaing unusual level of emissions because i was impossible o know a wha poin in ime he regulaor was looking a he informaion so as o decide on an inspecion. For his reason, I oped o include no lagged indicaor of repored polluion. 25 The following variables had o be discarded in he condiional logi model, bu were included in he uncondiional specificaion used o fi he probabiliies of being inspeced. Firs, he prioriy group o which he plan belongs (PTY i, equal o 1 if he plan is a Prioriy 1 plan). Second, wo dummy variables: TANNERY for anneries and WOOL for wool washers.apar from classifying indusrial plans according o heir prioriy, he IMM also argeed anneries and wool washers. 26 The reason for his is ha he IMM, in accordance wih he IADB, argeed is conrol effors a wo polluans, Chromium and BOD 5. These wo indusries were he mos imporan sources of hese polluans, respecively. Finally, η is he error erm, assumed o be idenically and independenly disribued wih zero mean and o have a logisic disribuion. 24 The differences in he variaion of he volume index (consruced by he Naional Saisics Insiue) and indusrial GDP (consruced by he Cenral Bank) are due o differences in weigh of he differen secors in he consrucion of boh indexes. I chose he firs one because of monhly availabiliy. 25 In spie of his I ran a model wih he average BOD 5 level of he plan in he las six monhs as an explanaory variable. The resuling coefficien was exremely low ( ) and insignifican. The overall fi of he model increased merely as measured by he McFadden R square. 26 I included secor dummies in place of hese wo dummies o explore he resuls. The secor dummies were neiher significan nor did hey improve he fi of he model in he uncondiional regression. 92

15 EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY The DCA Inspecion Equaion The inspecion equaion proposed for he DCA was: INSPDCA α + α INSPDCACUM + α INSPDCAOTHERCUM i, i 1 i, 1 2 i, 1 + α EADCACUM + α VOL + α CARRASCO i, α STREAM + υ 6 i, i, i 1,...,74; July1997,... Ocober2001 (2) where INSPDCA is a dummy equal o one if plan i was inspeced in monh. αi represens he plan-specific fixed effec. Is inclusion is also he resul of a Hausman es. The value of he chi-squared in his case was The firs wo variables (INSPDCACUM and INSPDCAOTHERCUM) are defined exacly as INSPIMMCUM and INSPIMMOTHERCUM, and are included for he same reasons. EADCACUM -1 is he cumulaive number of compliance orders, fine hreas and fines issued by he DCA o he plan up o VOL and STREAM are he same variables included in he IMM inspecion equaion. Also, during 1999 he Naional Environmen Office (Direccion Nacional de Medio Ambiene, DINAMA) performed a special monioring campaign on hose plans in he basin of he Carrasco Sream. This campaign was he resul of an agreemen beween he DINAMA and a non-governmenal organizaion dedicaed o fighing polluion of his sream. I included he dummy CARRASCO1999 for his reason. Finally, I assume a logisic disribuion for he errors in his equaion for he same reason given for he IMM equaion. The DCA also argeed anneries and wool washers, bu, again, he corresponding dummies (TAN- NERY and WOOL) were dropped from he condiional logi due o perfec collineariy. Nowihsanding, hey were included in he uncondiional model used o fi he probabiliies The SEINCO Inspecion Equaion The specificaion of he SEINCO inspecion equaion is: INSPSEINCO β + β INSPSEINCOCUM + β INSPIMMCUM i, i 1 i, 1 2 i, 1 + β INSPDCACUM + β STREAM + δ 3 i, 1 4 i, i, i 1,...,74; April 1999,..., Ocober 2001 (3) 27 Separaing EADCACUM ino he cumulaive number of enforcemen orders, he cumulaive number of fine hreas and he cumulaive number of fines did no improve he resuls. 93

16 MARCELO CAFFERA β i represens he plan-specific fixed effec and was included afer running he corresponding Hausman es and obaining a value of 238 for he chi-squared saisic. INSPSEINCOCUM is he cumulaive number of SEINCO inspecions. INSPIMMCUM and INSPDCACUM were included o es how SEINCO used he informaion peraining o he monioring aciviy of he wo agencies o develop is own. Finally, according o inerviews, SEINCO also inspeced Prioriy 1 plans more frequenly and argeed anneries and wool washers because of heir imporance in erms of organic and meal polluion. Again, hese variables are no included in he specificaion above because his one corresponds o he condiional logi model, bu hey were included in he uncondiional model used o fi he probabiliies of being inspeced. 6.2 THE POLLUTION EQUATIONS 6.2.1The BOD 5 Equaion Equaion (4) is a linear polluion equaion in he spiri of Maga and Viscusi (1990), Laplane and Rilsone (1996) and Dasgupa, e al. (2001). I assumes a Cobb-Douglas echnology. + λ 10 ln( BOD5 + λ PINSPIMM 7 INSPIMMCUM + λ ) λ ln( P + λ ln( Energy EADCACUM q, ) + λ ln( Flow + λ PINSPDCA 1 ) + λ ln( Labor + λ INSPDCACUM λ 2 14 ) + λ TECH 1 DURINGPLAN ) + λ ln( Waer i + λ PINSPSEINCO λ 12 3 FINEDIMMCUM + µ + ν i 1,, 74; July 1997,, Ocober Equaion (4) develops from he idea ha he level of concenraion of organic polluion in a given monh, measured as Biological Oxygen Demand (BOD 5 ) in mg/l, is a funcion of wo ses of variables, one reflecing he marginal benefis of polluion (i.e., he value of he marginal produciviy of polluion) and anoher reflecing he marginal expeced cos of polluion. Marginal benefis of polluion are represened by he price of he final good ( P q ) and he inpu variables Labor, Waer, Energy and Flow. Marginal expeced coss of polluion are represened by he monioring and enforcemen variables. These are comprised of he probabiliies of being inspeced by he municipal and naional governmens (PINSPIMM and PINSPDCA ) and by he probabiliy of being inspeced by SEINCO (PINSPSEINCO ). These hree variables are included o capure he effec of fuure possible enforcemen acions due o oday s polluion decisions. They were obained by fiing he IMM, DCA and SEINCO inspecions equaions. Provided ha here is no conemporaneous correlaion beween he error erm in he polluion equaion ( v i, ) and he error erm in he inspecion equaions ( η i,, ν, δ ), hese fied values will be uncorrelaed wih v i,, and a leas squares esimaor will yield consisen esimaes of he parameers of he polluion equaion. 28 ) 1 (4) 28 Since he reduced form for he inspecion equaion is idenical o he (srucural) inspecion equaion, my esimaion procedure is he same as 2SLS bu in a sysem ha is no simulaneous. 94

17 EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY Bu polluion oday is also he resul of pas monioring and enforcemen acions. This is he reason for including he cumulaive number of inspecions performed during he las welve monhs by he municipal governmen (INSPIMMCUM) and he naional governmen (INSPDCACUM) and he cumulaive number of fines levied by he municipal governmen (FINEDIMMCUM) and he cumulaive number of inermediae enforcemen acions and fines levied by he naional governmen (EADCACUM). 29 Some cases in he previously cied lieraure include he conemporaneous number of inspecions or a dummy as an explanaory variable o indicae wheher he plan was inspeced in ha monh. Those who did no consider under-reporing o be an issue included i as anoher deerminan of polluion. These cases esimaed polluion and inspecions as joinly deermined in a sysem of wo equaions. Those who did consider he problem of under-reporing, like Shimshack and Ward (2002), did his as an imperfec and weak es for self-reporing accuracy. My approach was o use fied values obained from he inspecion equaions which would serve a he same ime as an economeric insrumen for acual inspecions and as a proxy for probabiliies of being inspeced. To es for he presence of under-reporing I used he informaion on BOD 5 samples by he IMM, DCA and SEINCO and conduc difference-of-means ess beween hese and he BOD 5 repored levels. The reason for including inermediae enforcemen acions apar from fines is ha wih only 15 fines in he whole period (despie frequen violaions) i is reasonable o conclude ha regulaors inended o reduce emissions via hese inermediae acions. These may have had heir own deerren effecs. This deerren effec could be explained because fines are no insananeously applied afer a violaion is repored or discovered by an inspecion. Insead, firms face an increasing probabiliy of being fined. Of course his probabiliy and he amoun of he fine is uncerain for he firms. However, firms learn by observing pas responses of regulaors o violaions. 30 Eigh firms modified heir reamen echnology during he period, eiher by consrucing nonexisen reamen plans or by significanly modifying exising plans. 31 I included he variable TECH, 29 Moneary fines were no he only penaly levied for no complying. Plans could also be emporarily closed. Bu neiher he municipal nor he naional governmen had rusworhy records of hese measures. Also, hese ypes of measures were as uncommon as fines during he period. Anoher form of penaly implemened was o make professionals in charge of reamen plans legally responsible for sending false repors. According o he IMM s Indusrial Effluens Uni Direcor, his was done as an explici enforcemen mechanism. The objecive was o persuade professionals abou he dangers of falsifying informaion and o ac on relucan plans hrough hem. According o his Direcor, his ype of expeced penaly may have had an imporan impac on emissions levels because plans relucan o decrease emissions may have encounered increasing difficulies in finding professionals in he marke who were willing o chea a heir own personal cos. Apar from is apparen effeciveness, his sraegy, which in a sense could be seen as a deviaion from he classical heoreical model of enforcemen, seems also opimal in erms of insiuional compaibiliy. High fines are rarely feasible o apply in less-developed counries where firms suffer from imporan cash flow consrains. These alernaive penalies are easier o apply because hey do no imply a cash paymen. A he same ime, hey do imply significan coss o he firm, eiher direcly (hrough closing) or indirecly (hrough he professionals incenives). Unforunaely, i was impossible o measure heir effecs. Finally, INSPSEINCOCUM (he cumulaive number of pas inspecions by SEINCO) was originally included in his model bu i was dropped due o is correlaion of 0.91 wih PINSPSEINCO. 30 I ran a version of his equaion separaing he cumulaive number of compliance orders, he cumulaive number of fine hreas and he cumulaive number of fines issued by he DCA. Resuls did no change. 31 One more plan incorporaed echnology he monh before he beginning of my sudy period and wo more during

18 MARCELO CAFFERA a dummy equal o one in he monh ha he plan incorporaed abaemen echnology and hereafer, o conrol he effec of changes in reamen echnology on BOD 5 levels. The las explanaory variable is DURINGPLAN. This variable is he same dummy ha was included in he IMM inspecion equaion. Is value is one during he monhs of he Indusrial Polluion Reducion Plan and zero aferwards. The idea of including his variable in he polluion equaion is o es for he presence of differen reporing and emiing behavior of plans during he plan. This is possible because during hese monhs emission sandards were laxer. Wih i he IMM inended o give plans ime o adop abaemen measures while a he same ime complying wih polluion regulaions. The inclusion of his variable measures he success of he plan. 32 The parameer µ i is a plan-specific effec. I chose a fixed-effecs model, as opposed o a randomeffecs model, because I am basing my inference on hese 74 specific plans, which were no randomly seleced from a large populaion and are responsible for around 90% of he indusrial emissions in he ciy. I did no perform formal ess for he uni effecs. To perform hese ess under he assumpion of non-spherical errors I would have o inver he variance-covariance marix of he errors and his is no possible when number of cross-secions (N74) ha is larger han he number of ime periods (T 52). In spie of his, I performed a Chow es assuming ha he errors were spherical. The es srongly suggesed rejecing he null hypohesis of common consan erms. 33 Finally, v i, is he sochasic disurbance. Following Park, he panel srucure of he errors can be: (1) Panel Heeroskedasic, (2) Conemporaneously Correlaed; and (3) Common Serially Correlaed or (4) Plan-specific serially correlaed. I have wo plans ha do no have conemporaneous (common) observaions and herefore I canno es he validiy of he assumpion of no conemporaneous correlaion of he errors ha underlies he applicaion of Arellano s robus sandard errors. Neverheless, his assumpion is jusified by he fac ha he unbalanced naure of he panel grealy diminishes he number of observaions o calculae he covariances σˆ ij. Given ha I have no observaions ha are common o all of he cross-secions, he esimaed residual covariance marix would be formed by emporally mismached sources. While his procedure is consisen (as he number of observaions wihin cross-secions approaches infiniy), i is no likely o be a good esimaor in his seing. The Durbin-Wason saisic of he original regression was This value suggesed rejecing he null hypohesis of non-auocorrelaion of he errors in favor of he alernaive of firs-order auocorrelaion. A classical Chow es exended for he case of N linear regressions, one for each plan, wih he resriced model being he pooled model íi ρ í i, 1 + ε i and he unresriced model being: íi ρ ií 1 + ε i was used o ess for plan-speficic versus common auocorrelaion 32 One cavea o his conclusion is sressed in he nex chaper. The afer-plan period coincided wih one of he mos imporan recessions of he Uruguayan economy in is enire hisory. As a resul, an inerpreaion of he success of he plan according o a posiive sign of he DURINGPLAN dummy could be misleading. 33 The unresriced model in his case is he FE model and he pooled model is he common-consan OLS model. The value of his saisic for his es was > F(73,2705]. 96

19 EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN MONTEVIDEO, URUGUAY of he errors. The value of F obained was The criical value for ends o one, herefore, he es suggess ha he null hypohesis of common auocorrelaion be rejeced in favor of he alernaive hypohesis of plan-specific auocorrelaion. Finally, I es for he presence of panel heeroskedasiciy wih hree differen ess: Barle, Levene and Brown-Forsyhe. The resuls of hese ess are presened in Table 6. All he ess in his able sugges rejecing he null hypohesis of panel homoskedasiciy in favor of he alernaive ha no all of he plan-specific errors variances are he same. Table 6: Tes for he Equaliy of Variances beween Residuals The naural esimaion approach would have been o use feasible generalized leas squares (FGLS), bu I canno esimae my model using FGLS because he error covariance marix is no inverible. The mehod chosen o avoid he singulariy of Óˆ and a he same ime o use he informaion of he 22 (74-52) plans ha I have in excess of T was o obain consisen poin esimaes of my parameers and hen calculae robus sandard errors for hese esimaes. 34 This mehod no only circumvens he impossibiliy of applying FGLS o produce consisen esimaes of he parameers bu i also allows me o draw correc inferences abou he coefficien esimaes. 35 Therefore, I firs ran a leas squares dummy variables (LSDV) model o obain residuals o ransform he daa as in Cochrane-Orcu. Wih he ransformed daa I run a second LSDV o esimae he parameers of he BOD 5 equaion and he LOAD equaion. Because my T is large (i.e., 52) his allows me o ge consisen esimaes of he fixed effecs. Wih he residuals of he second LSDV, I calculae Arellano s (1987) robus sandard errors. These assume no conemporaneous correlaion and are robus o panel heeroskedasiciy and serial correlaion. The reason for no calculaing Arellano s robus sandard errors wih he original daa and insead ransforming he model o eliminae auocorrelaion of he errors firs is ha his echnique assumes ha N is large and T is small and he asympoic resuls are derived as N. In my panel, alhough i is rue ha N>T, i is also rue ha T52 canno be considered small. Therefore, by ransforming he daa 34 I am graeful o Manuel Arellano for suggesing his o me via communicaion. 35 A considered bu discarded course of acion was Panel Correced Sandard Errors (Beck and Kaz, 1995 and Beck e al., 1993). I did no use Panel Correced Sandard Errors mainly for wo reasons. Firs, he moivaion of Beck and Kaz (1995) for suggesing hem was he overconfidence produced by Park s (FGLS) sandard errors, a poin already made by Freedman and Peers (1984). My moivaion here is somewha differen since I canno use FGLS in he firs place due o he fac ha N>T. Panel Correced Sandard Errors were developed for panels wih T>N. The second reason is an empirical one. I have wo plans (#52 and #72) ha did no have conemporaneous (common) observaions. Furhermore, he unbalanced naure of he panel grealy diminishes he number of observaions o calculae he covariances σˆ. In oher words, I canno calculae all σˆ o form Ωˆ. ij 97 ij

20 MARCELO CAFFERA o eliminae he serial correlaion of he errors firs I am aking ino consideraion s Arellano s (2003) cauionary noe ha when T is no small he robusness of his echnique o serial correlaion may decrease The Load Equaion The reason for esimaing a BOD 5 polluion equaion is ha emission sandards are defined in erms of concenraion of organic maer (as measured by BOD 5 ). Bu, in addiion, i is ineresing o es wheher he monioring and enforcemen sraegy of regulaors during he period had an effec on he oal organic load discharged by plans and o compare i wih he resuls obained wih he BOD 5 equaion. An ineresing issue ha may arise wih his comparison is wheher regulaors effeciveness is masqueraded by he diluion of effluens in clean waer, for example. The esimaed load equaion is specified exacly as he BOD 5 equaion excep for he obvious fac ha i canno include FLOW as an explanaory variable because LOAD is defined as BOD 5 imes FLOW. LOAD is hen measured in kg/day. The esimaion of he load equaion is performed exacly as he esimaion of he BOD 5 equaion The Violaion Equaion In order o es he effeciveness of regulaors regarding he compliance saus of plans I esimae a condiional fixed-effecs logisic model wih a dummy variable equal o one if he plan repored a violaion as a dependen variable. Violaions were defined wih respec o he laxer sandards during he Polluion Reducion Plan. The violaion equaion has he same explanaory variables as he BOD5 equaion, bu i has fewer observaions. Five plans were dropped from he sample because hey release effluens ino he soil and here are no sandards se for BOD5 in his case. Also, foureen addiional plans ha complied or did no comply in every monh of he period and herefore did no add any likelihood o he condiional model were also dropped from he sample. 7. RESULTS I firs presen he resuls of he under-reporing ess. Then I urn o he discussion of he resuls of he inspecion equaions esimaed for he IMM, DCA and SEINCO. Finally, I presen he resuls of he BOD 5, Load and Violaions equaions. 7.1 UNDER-REPORTING TESTS The firs naural quesion ha arises regarding he presence or absence of under-reporing is if here is any saisically significan difference beween he means of he BOD 5 levels sampled by he IMM, DCA, and SEINCO. In order o answer his quesion, I conduced wo difference-ofmeans ess. The firs uses all available observaions for each of he hree series and he second uses he common sample (composed only by 5 observaions). Fory-one plans were inspeced by 36 I hank Gabriela Sanromán for poining his ou o me. 98