Wes I q oq POLICY RESEARCH WORKING PAPER 1909 Capital Market Responses to Capital wzrkets do responJ to information about a frm s Environmental Performance environmental performance in Developing Countris *and if properly informed, may provide appropriate financial and reputational incentives Susmita Dasgupta for pollution control. Peri-aps Ben6it Laplante more resources shoLld be Nlandu Mamingi used for disseminatinc tirT- specific information ahoa,rt environmental perfrm lance to allow all stakeholCel s ,r make informed decisions The World Bank Development Research Group April 1998 POi Icy RESEARC H 'QORKING, PAIPER 1909 Summary findings Firms in developing countries are often said to have no the announLcemenit of iei-m-specific envirormITeor1al news. incentives to invest in pollution control because they They show t1lat: typically face weak monitoring and enforcement of eCapital markc-ts react pos:tivel (the firms market environm ental regulations. But the inability of formal value increases) ro tie announcemenit of rewards and institutions to coontrol pollution through fines and explicit recognition of superior environmenrta: penalties may not be as serious an impedirnent to performance. pollution control as is generally argued, contend * They react necgat:vely (the firns' vale decreases) to Dasgupta, Laplante, and Mamingi. citizens' complaimns. Capital markets may react negatively to news of Enviroinmelltal regulators in developi:ng count1ries adverse environmental incidents (such as spills or could (1) harness mlarket forces he introduci:g structured violations of permits) as well as positively to the progr:ams to release f-rm-specific inforrnati.)n aboet announcement that a firm is using cleaner technologies. environtrrienral ncrformarnce, and (2) empower The authors assess whether capital markets in and SMaell(:.de2-S thrOLg`1 crl - acllral Argentina, Chile, Mexico, and the Philippines react to education progralmris. This paper-a product of the Development Research Group-is part of tie group s ongo1ing xwork on inderiai pollution and also to study whether capital markets in developing counitries can proi-Lde ,nccnltives needed for pollutioni control. The study was funded by the Bank's Research Support Budget under the research project "Incentives for P)oluotionl Control: 'The Role of Capital Markets" (RPO 680-76). Copies of this paper are available free from the World Bank, 18 1.8 H Srtreet NW, Washington, DC 20433. Please contact Roula Yazigi, room \IC2-622, telephon,o D2.)-473-7176, fax 2d2-f2.)-3230, Internet address rvazigiCa worldhank.org. April 1998. (36 pages) The Policy Research 'Working Paper Series dlsseminates the fin-,diZgs of l,ork 11 'lr 'o '!' ' t'ec 'ha r' a de-elopment issiues. Aln objective of the series is tO get the findinigs out quickly. *s' it t,;r""l -Cn'''7tiI'Ž or;e feesstho; 'ik papers carn the narmes of the authors anzd should be cited accordingyb. The indoi gs. 'i- is. 0!l O)i 0i: paper are entirely thcose of the autho,s. The;' do not necessarily represent the lO /(t "'h N i" ' iWnkBa. its 1i'leo : ' i, '''''< j coo0ntr:es they represent. Produced bv the Policv Research Dlsseminralon (Clorer CAPITAL MARKET RESPONSES TO ENVIRONMENTAL PERFORMANCE IN DEVELOPING COUNTRIES Susmita Dasgupta Benoit Laplante Nlandu Mamingi The World Bank Development Research Group Acknowledgments Our most sincere thanks to Maria Teresa Correa who for eight months relentlessly collected the vast amount of information necessary to conduct the current analysis. We would also like to thank the following individuals for their collaboration and support: in Argentina, Hugo Medina and Osvaldo Mignini; in Chile, Carlos Parra; in Mexico, Alejandro Ritch and Ricardo Rivera; in the Philippines, Sergio Marquez and Socorro Clemente for allowing us access to the appropriate stock market data and answering our numerous questions. We also thank Miodrag Deric and Craig Meisner for their research assistance. Finally, we thank the participants at the Canadian Environmental and Resource Economists Workshop (October 1997), Randy Wigle, and David Wheeler, for their generous comments. 2 Executive summarv It is generally said that firms in developing countries do not have incentives to invest in pollution control effort because of the weak monitoring and enforcement of the environmental regulations. This argument assumes that the environmental regulator is the only agent that can penalize the firm for a lack of pollution control effort, or reward the firm for good environmental performance or innovation in environmental technologies. It ignores that capital markets may react negatively to the announcement of adverse environmental incidents involving specific firms (such as violation of permits, spills. etc.) or positively to the announcement of greater pollution control effort such as the adoption of cleaner technologies. Hence, the inability of formal institutions in developing countries to provide incentives for pollution control effort (via the traditional channel of fines and penalties) may not be as serious an impediment to pollution control as is generally argued. Capital markets, if properly informed, may provide the appropriate financial and reputational incentives. In this paper, we assess whether or not capital markets in Argentina, Chile, Mexico, and the Philippines react to the announcement of firm-specific environmental news. We show that capital markets react positively (increase in firms' market value) to the announcement of rewards and explicit recognition of superior environmental performance; we also show that capital markets react negatively (decrease in firms' value) to citizens' complaints. An immediate policy implication from the current analysis is that environmental regulators in developing countries may explicitly harness those market forces by introducing structured programs of information release on firms' environmental performance, and empower communities and stakeholders through environmental educationLprograms. At the margin, less resources should be devoted to the enforcement of regulations and more to the dissemination of information which allows all stakeholders to make informed decisions. These results may also shed some new light on the pollution haven hypothesis. A large number of studies have examined the potential impact of environmental regulations on international competitiveness. Many of these have concluded that pollution intensive firms have not invested or relocated in developing countries to benefit from lower environmental standards and/or poor enforcement of environmental regulations. Hettige et al. (1992) observes that "one possibility is that the expected profitability of investment in pollution-intensive sectors has also been affected by growing concern over legal liability or reputational damage" (p. 480). To the extent that capital markets may reward firms with good environmental performance and penalize firms with poor environmental performance, the potential reaction of capital markets may explain that the pollution haven hypothesis has so far not found empirical support. 3 1. Introduction Thouch environmental regulations have now been in use for more than 20 years, it is increasingly recognised that their efficacy in controlling pollution emissions has been dampened by a lack of appropriate monitoring and enforcement. Resources devoted by various regulatory agencies to the monitoring of emission standards have typically been characterized as insufficient.' Moreover, when compliance with the standards is found to be lacking, it is generally acknowledged that fines and penalties are too low (compared to pollution abatement costs) to act as effective deterrents. In a recent study of environmental regulations in East Asian countries, O'Connor (1994) writes: 2 In several of the countries studied here, the monitonng problem is compounded by weak enforcement. In short, when violators of standards are detected, if penalised at all they often face only weak sanctions. (...) polluters are exempted from fines either on grounds of financial hardship or because the violators wield undue political influence. Perhaps the most pervasive problem is that, even when fines are levied, they are frequently so low in real terms that they have little if any deterrent value. In virtually all the countries studied, there remains considerable room for improvement on the enforcement front. (p. 94) It is indeed generally said that firms in developing countries do not have incentives to invest in pollution control effort because of weak monitoring and enforcement of the environmental regulations. This argument however assumes that the environmental regulator is the only agent that can penalise the firn lacking pollution control effort, or reward the firm for good environmental performance or innovation in See Russell (1990). 2 Those being Japan, Korea, Taiwan, Thailand, and Indonesia. 4 environmental technologies. It ignores that capital markets may react negativelv to the announcement of adverse environmental incidents (such as violation of permits, spills, court actions, complaints, etc.) or positively to the announcement of greater pollution control effort such as the adoption of cleaner technologies. The impact of firm-specific environmental news on market value may work its way through various channels: a high level of pollution intensitv may signal to investors the inefficiency of the firm's production process; it may invite stricter scrutiny by environmental groups and/or facility neighbours; it may result in the loss of reputation, goodwill, etc. On the other hand, the announcement of a good environmental performance or of the investment in cleaner technologies may have the opposite effect: lesser scrutiny by regulators and communities (including the financial community), greater access to international markets, etc.3 Hence, the inability of institutions in developing countries to provide incentives for pollution control effort via the traditional channel of fines and penalties may not be as serious an impediment to pollution control as is generally argued. Capital markets, if properly informed, may provide the appropriate reputational and financial incentives. A limited number of papers have analyzed the reaction of capital markets to environmental news in Canada and the United States. These studies have generally shown 3 See Porter and Van Linde (I1995) and Klassen and McLaughlin (1996) for more details. 5 that firms suffer from a decline in market values -upon announcetnent of adverse environmental news.4 In this paper, we assess whether or not capital markets in Mexico, Chile, Argentina, and the Philippines react to the announcement of firm-specific environmental news. To our knowledge, the current analysis is the first of this nature performed in developing countries. Even in those countries where it is generally argued that the environmental regulations suffer from poor implementation, we show that capital markets react negatively (decrease in firms' value) to citizens' complaints targeted at specific firms. We also show that markets react positively (increase in firms' market value) to the announcement of rewards and explicit recognition of superior environmental performance. An immediate policy implication from the current analysis is that environmental regulators in developing countries may explicitly harness those market forces by introducing structured programs of information release on firms' environmental peformance, and empower communities and stakeholders through environmental education programs.5 These results may also shed some new light on the pollution haven hypothesis. A large number of studies have examined the potential impact of environmental regulations on intemational competitiveness.6 Many of these have concluded that pollution intensive In the United States, these studies include, among others, analysis of the reaction of markets to releases of the Toxics Release Inventory (Hnamilton (1995) and Konar and Cohen (1997)). Lanoie and Laplante (1994) analyze the reaction of capital markets to environmental news in Canada. For a survey of these studies, see Lanoie, Laplante and Roy (1997). We know of at least two such programs currently in place in developing countries: in Indonesia (PROPER Prokasih) and the Philippines (Ecowatch). Similar programs are currently being developed in Mexico and Colombia. For further details. see Afsah et al. (1996). See for example, Jaffe et al. (1995), Kolstad and Xing (1994), Levinson (1992), Low and Yeats (1992), Stewart (1993), Tobey (1990), Walter (1992), and Wheeler and Moddy (1992). 6 firms have not invested or relocated in developing countries to benefit from lower environmental standards and/or poor enforcement of environmental regulations. Hettige et al. (1992) observes that "one possibility is that the expected profitability of investment in pollution-intensive sectors has also been affected by growing concern over legal liability or reputational damage" (p. 480). Where traditional tools and actions may have been unable to create incentives for pollution control, our results give some support to this point of view to the extent that capital markets may reward firms with good environmental perfornance and penalize firns with poor environmental performance. In the next section, we describe our dataset. In Section 3. we brieflv describe the event-study methodology used in this analysis to measure the reaction of capital markets to environmental news (both positive and negative news). Results are presented in Section 4. We briefly conclude in Section 5. 2. Dataset The countries retained in this study - Argentina, Chile, Mexico, and the Philippines - are countries where stock markets are believed to work reasonably well, where market capitalization is relatively high and increasing over time (Table 1), and where market concentration is not an impediment to conducting event-study analyses (Table 2).7 Alhough market concentration may appear to be high, note that the IFC General Indexes represent only a fraction of total market capitalization. Actual market concentration is lower than suggested in Table 2. 7 TABLE 1 Capitalization of the stock market of Argentina, Chile, Mexico, and the Philippines, 1990-1994 (in million of U.S. dollars) Market 1990 ] 1991 | 1992 | 1993 | 1994 Argentina 3 268 18 509 18 633 r 43 967 36 864 Chile 13 645 27 984 29 644 44 622 68 195 Mexico 32 725 98 178 139 061 200 671 130 246 Philippines 5 927 10 197 13 794 40 327 55 519 Source: International Finance Corporation, Emerging stock markets factbook, 1995. TABLE 2 Market Concentration in the IFC General Indexes, End - 1994 Market IFCG Index share of total 10 largest stocks' share of market capitalization total market capitalization Argentina 50.9 41.7 Chile 66.1 46.4 Mexico 63.9 33.8 Philippines 54.4 44.3 Source: Intemational Finance Corporation, Emerging stock markets factbook, 1995. For each country, we selected a newspaper which has a large circulation and is of particular interest to the business community.8 Environmental news were collected in each of the countries over the period 1990-94 inclusively. Once these news were In the United States, the Wall Street Journal is generally the preferred source of information for conducting event-study analyses. In Argentina, environmental news were collected from the newspaper La Nacion (daily circulation of approximately 250 000; ranks 3rd in Buenos Aires); in Chile, we used El Mercurio (daily circulation of approximately 200 000; ranks 3rd in Santiago); in Mexico City, we used Excelsior (daily circulation of 200 000; ranks 7th in Mexico City); finally, in the Philippines, news were collected from the Manila Bulletin (daily circulation of 300 000; ranks 3rd in Manila). All newspapers were available from the Library of Congress for most of the period 1990-94. Information from missing issues was obtained directly from the publishers of the papers in the respective countries. 8 collected, we identified those articles involving firms traded in local capital markets. As shown in Table 3, the number of environmental news (i.e. newvsclips) collected in each country is relatively large (a total of 7 354 environmental news were collected over the period 1990-94), with Mexico alone representing 47.5% of the total number of news. The number of environmental news is also relatively constant over the period of analysis. Approximately 20% of the news involve specific firms, traded and non-traded. As expected, the number of news involving publicly traded companies is relatively small in all countries. However, publicly traded companies represent a much larger share of the number of companies cited in environmental news than their relative numbers in the economy. This may be explained by their generally larger size, thus being of greater scrutiny. TABLE 3 Number of news (1990-1994) 1990 1991 1992 1993 1994 Argentina Total number of environmental news 201 189 168 198 170 With name of non-traded companies 28 32 48 33 27 With name of publicly traded companies 0 0 2 13 15 Chile Total number of environmental news 309 285 293 282 272 With name of non-traded companies 29 48 43 22 32 With name of publicly traded companies 4 25 34 36 16 Mexico Total number of environmental news 625 707 759 613 618 With name of non-traded companies 161 143 118 73 88 With name of publicly traded companies 14 25 7 10 8 Philippines Total number of environmental news 317 309 334 265 266 With name of non-traded companies 54 47 44 47 55 With name of publicly traded companies 8 8 4 9 12 9 IEnvironmental news were divided into two groups: positive (e.g. rewards, investment in pollution control, etc.), and negative (e.g. spills, complaints, warnings, etc.). The sample set is described in Table 4. As can be observed, Chile registered 53 events (environmental news) involving 17 publicly traded firms over the period 1990-94; 20 of those events were positive while 33 were negative. Argentina registered 20 events (5 positive and 15 negative) involving 11 firms. The Manila Bulletin reported 18 events (10 positive and 8 negative) with 10 firms. Finally, the Mexican sample consists of 35 events (of which only 4 were positive) involving 10 publicly-traded firms firms. Observe that the number of events in Table 4 is smaller than the number of news (with name of publicly traded companies) in Table 3. This is the case since a significant number of newsclips is simply a repetition or follow-up on an initial event and does not provide any additional information to what is already known. In most cases, we have included in our dataset only the announcement of the initial event. Table 4 Description of data set Country Name of firm' Sector of activitv Nature and Number of Events Positive Negative Argentina Astra Oil l Ipako Oil 1 2 Perez Oil 0 2 YPF Oil 1 4 Celulosa Pulp and paper I 0 Telefonica Telephone 0 Colorin Chemical 0 2 Indupa Chemical I 0 Molinos Rio Food 0 Sevel Metal 0i Siderca Metal 0 1 Total 11 firms 6 sectors 5 15 10 Table 4 (continued) Countrv Name of firm Sector of activity Nature and Number of Events Positive Negative Chile Endesa Electric 3 4 Chilgener Electric 4 4 CMPC Pulp and paper 2 I CAP Metal 3 4 Volcan Building material 0 Minera Investment 0 1 Vapores Transportation 0 1 Emos Water 3 I Puerto Water 0 1 Victoria Fabric 0 1 lansa Food I Molymet Metal I Coloso Fishery 0 5 Iquique Fishery 1 5 Lirquien Building material 0 I Chilectra Electric I I Eperva Fishery I 0 Total 17 firms 10 sectors 20 33 Mexico Cydsasa Pulp and paper, oil 1 3 Grupo Maya (A) Cement 0 6 Grupo Maya (B) Cement 0 4 Tolteca (Tolmex) Cement 0 Met-Mex Penoles (A) Mining 1 6 Met-Mex Penoles (B) Mining 0 3 Femsa Food I 0 Grupo Vitro Manufacture I 0 GC3 Cement 0 _ Kimberiy y Clark Pulp and paper 0 2 Grupo Bimbo Food 0 2 Telefonos de Mexico Communication 0 2 Total 10 firms 8 sectors 4 31 Philippines Apex Mining Mining 0 1 Atlas C. Mining Mining I 0 Ayala Land, Inc. Property 0 O Benguet Mining 3 2 Jolibee Food I 0 Lepanto Mining 0 Manila Mining Mining I 0 Mondragon Trading 0 1 San Miguel Food 4 I Robinson Land Property I 0 Total 10 firms 5 sectors 10 8 Complete names of firns appear in Appendix 1. 11 III. Event-study methodology The event-study methodology is used in this studv to examine the reaction of investors to positive and negative news (also called events).9 The methodology is based on the assumption that capital markets are sufficiently efficient to evaluate the impact of new information (events) on expected future profits of the firms. It involves the following steps: (1) identification of the events of interest and definition of the event window'o; (2) selection of the sample set of firms to include in the analysis;' (3) prediction of a "normal" return during the event window in the absence of the event; (4) estimation of the abnormal return within the event window, where the abnormal return is defined as the difference between the actual and predicted returns; and (5) testing whether the abnormal return is statistically different from zero. Several methods may be used to obtain to estimate abnormal returns: the single-index model (constant mean return model), the market model and the capital asset price model (CAPM) are the most widely used. The market model assumes a linear relationship between the return of any security to the return of the market portfolio: Rif = a i +,8 i R,,, + e,, (1) with E(ej,,) = O and Var(ej,)=-C 9 For more details, see MacKinlay (1997). to The event window consists of the day where the event occured (day 0) and some days before and after the event. Firms may be excluded if simultaneous events are occuring within the event window. 12 where t is the time index. i = 1.2,r N stands for securitv, R,, and R,,,, are the returns on security i and the market portfolio respectively during period t, and e,, is the error term for security i. Equation (1) is generally estimated over a period which runs between 120 and 210 days prior to the event up to 10 days prior to the event. The event window is defined as the period from 10 days prior to the event to 10 days after the event. With the estimates of a, and A3 from equation (1), one can predict a "normal" return during the days covered by the event window. The prediction error (the difference between the actual return and the predicted normal return), commonly referred to as the abnormal return (AR), is then calculated as: (2) ARi, = R,, -c-, R,, Under the null hypothesis. the abnormal returns will be jointly normally determined with a zero conditional mean and conditional variance o2 (AR,,): 2 = 0-2 + I[1 + (R,,,, 2R)2 (3) C7 (AR,, e, where L is the estimation period length (i.e. number of days used for estimation) and R,,, is the mean of the market portfolio. With L large, o-2 (AR,,) ao e. 13 For each individual event, one can estimate the abnormal returri and relevant test statistics at each instant in time within the event window. However. in order to draw overall inference on the abnormnal return observations for the event(s) of interest, one can also aggregate the abnormal returns. For any given subset of N events (or securities), the sampled aggregated abnorrnal returns (AAR,) at each instant t within the event window is computed as IN (4) AAR, = , E ARj, For large L, the variance is 1N (5) VAR(AAR, )= To test for the significance of AAR, a Z (or t) test can be derived. In order to test for the persistence of the impact of the event during a period (7, - T, ), the abnormal return can be added to obtain the cumulated abnormal returns (CAR, (T,, T2 )) for security i over the period (T2 - T (6) CAR,(,T2) = ARj, T,= 14 where Tu < < t < T s Th E event window, and 7I and T, are the lower and upper limits of the event window, respectively. Asymptotically (as L increases) the variance of the cumulative abnormal return for security i is (7) 0j (T,,T2 (T2 - E + 1) . To test the null hypothesis of zero cumulative abnormal return. one can formulate a Z test as CAR, (T,, T )- V(O,Ci (T,, T, ): (8) Z = CAR 2 AT(Ol) (0.2 (T ,T))1 An aggregation of interest can also be performed across both time and events. In that scenario, the average cumulative abnormal return is defined as: (9) CAAR( , T2 ) = - E CAR, ( T;, T2) where N is the number of events. The variance of CAAR is (10) var(CAAR(,T2))= N-2 c(T,T2) Under the null hypotheses that the abnormal returns are zero, 15 (11) ~~~~CAAR(T,,.T,) (var(CAAR(I;,T, )))2 As pointed by MacKinlay (1997, pp. 24), this distributional result is asymptotic with respect to the number of securities N and the length of estimation window L. In the next section, we present results obtained from using the single-index model (constant mean return model).'2 IV. Empirical Results We apply the event-study methodology to the environmental events collected in each of the country over the period 1990-94. While various subsets of firms can be presented (e.g. by countries, by industrial sectors, etc.), each of those subsets contains a relatively small number of firms, and results in each subset are typically driven by changes in the market values of a limited number of firms. Hence, for the purpose of the analysis, we first present the results obtained at the most disaggregated level, i.e. the firm level. This is more likely to indicate the nature of the events to which capital markets 12 The single-index model is a particular case of the market model described above. Where market retuns were available, we also obtained results using the market model. Results were similar to those presented here. In fact, Henderson (1990) points out that the three estimating methodologies yield results of similar nature. 16 appear to be more sensitive. In Table 5 and 7. we indicate the nature of events for which statistically significant increases or reductions in market values are observed.'3 With respect to positive news. it is of extreme interest to note in Table 5 (and Appendix 2) that out of the 13 events for which statistically significant increases in market values are obtained, 8 of them involve the report of an agreement with the regulator or the explicit recognition by the regulator of a superior environmental performance. That a firn reports an investment in pollution control (or compliance with standards) does not appear to impact capital markets. Markets appear to react to the recognition of such investment or performance by the authorities. For those events, market values increase by more than 20% over the entire event window. 13 Complete statistical results are presented in Appendix 2 and 3. Where the length of estimation period is too short, we combine days prior to the event window with post event period starting 30 days after the event window. 17 Table 5 Positive events (* indicates a statisticallv significant increase in market value) ARGENTINA Name of Date Nature of Event Companvy Astra 3/15/94 Investment in environmental protection. Ipako 2/7/93 Investment in environmental protection. YPF 12/24/94 Investment to save birds. Celulosa 8/3/92 Investment in manufacturing recyclable papers. Indupa 2/7/93 Company action: agreement with government for environmental performance improvement. CHILE Endesa 1/31/92 Investment in pollution abatement. 9/6193 Court verdict: positive for the company. 8/8/94 Investment in environmental protection. Chilgener 1/9/90 * Pollution abatement: agreement between company and government. 8/5/90 Pollution abatement announcement. 11/9/93 * Government action: agreement approved by the President of Chile. 6/23/94 Company action: declaration of technical aspects of the agreement. CMPC 2/26/92 Investment in water pollution abatement. 1/7/94 * Investment realization: recycling plant to be inaugurated by the president of Chile. CAP 8/15/92* Court verdict: investment in pollution abatement. 10/2/92 Investment action: use of equipment for pollution control. 1 1/8/92 * Government action: recognition of the company's investment in pollution control equipment. Emos 4/16/92 Investment in construction of a waste water treatment plant. 2/24/93 The treatment plant will start working from March 15. 8/11/93 President of Chile will officially inaugurate the plant. lansa 9/26/93 * Investment in water pollution abatement. Molymet 10/11/93 Pollution treatment plant inaugurated by the President of Chile. Iquique 8/11/92 Investment in pollution abatement. Chilectra 5/29/93 Company reward for environmental performance. Eperva 7/1/94 Self impact assessment of environment. 18 Table 5 (continued) ~~~ ~~MEXICO Cvdsasa 5/11/92 Investment in improvement of environment. Apenol 7/10/93 * Announcement: existence of pollution control equipment. Femsa 9/14/91 Agreement with government on pollution abatement. Vitro 4/18/91* Investment in environmental projects. PHILIPPINES Atlas 10/20/90 The companv has a representation project since 1970. Benguet 12/28/92 Government action: mandatory environmental guarantee fund for the company. 7/19/93 * Government action: Reward (trophy) for reforestation program. 2/6/94 Investment in environmental protection. Jolibee 6/28/94* Investment in recyclable paper. Manila Mining 4/17/92 * Compliance certified by the Environmental Regulatorv authority of Philippines. San Miguel 11/5/90 * Investment in waste water treatment plant. 2/10/91 * Government action: praise company for having environmental concern. 9/14/91 Company action: implementation of reforestation project. 6/8/93 Announcement: new waste water treatment plant. As indicated in Section 3, it is possible to pool together events and test for the statistical significance of the average abnormal return for the events thus pooled. Given the nature of the results on individual stock markets, it is of interest to test if government actions (e.g. agreements and awards) as a whole are statistically significant. In Table 6, we have grouped together these government actions and treated them as a single set of events. As can be observed, government actions as a whole are mildly statistically significant on day +1. However, the difference between government actions and other 19 positive events fail to be statistically significant. This may be explained by noting in Table 5 that 3 individual government actions failed to be statistically significant.'4 Table 6 Government actions vs Other positive events'5 Dav -1 Day 0 Day +1 Window Government actions AAR CAAR AAR CAAR AAR CAAR CAAR 5.080 23.805 -10.627 13.177 14.420. 27.615 9.574 (0.650) (0.904) (-1.360) (0.509) (1.846) (1.020) (0.267) All other positive events -2.156 -10.583 -0.846 -11.457 -1.625 -15.488 17.245 (0.176) (-0.247) (-0.069) (-0.255) (-0.133) (-0.330) (0.308) Government actions Vs All other positive events 7.236 34.387 -9.781 24.634 16.045 43.103 -7.670 0.499) (0.696) (-0.674) (0.475) (1.106) (0.796) (-0.115) These results give some support to public information programs whereby the regulator rates and releases not only bad environmental performance but also superior performance. The results indicate that such recognition does not solely limit itself to an increase in reputation but also has a positive financial impact on the firm (through an expected increase in demand brought about by the enhanced reputation, or reduction in expected costs, e.g. lesser scrutiny by environmental groups, communities, and regulators). 14 In Argentina: Indupa (2/7/93). In Chile: Emos (8/1 1/93) and Molymet (10/1 1/93). In these last two events, it was announced that the President of Chile would inaugurate a plant (as opposed to approving an investment or agreement). 15 For Government actions and All other positive events, the sampled aggregate abnormal return (AAR) is computed for day - 1, 0, and + 1. The average cumulative abnormal return (CAAR) is computed for day -10 up to the day. For the event window, the average cumulative abnormal return is calculated over the period -10 to + 10. Within brackets is the value of the Z statistics. For Government actions Vs All other positive events, the AAR is here defined as the difference between the AAR for Government actions and the AAR for All other positive events. The Z statistics is defined accordingly. ".", "c", and "*" means significant at the 10%, 5% and 1% level respectively (one tailed-test). 20 With respect to negative events (Table 7), we obtain statistically significant decreases in market values especially when it is reported that governments or citizens have complained about the pollution record of the firm, and not when court actions or fines are reported. Table 7 Negative events (* indicates a statistically significant reduction in market value) _ ARGENTINA Name of Date Nature of Event Company Ipako 10/16/92* Government action: warning about pollution problem. 9/9/93 Accident. Perez 5/2/93 Government action: warning for oil spill. 12/12/94 Accidental oil spill. YPF 1 1/7/93* Environmental problem (birds killed). 11/30/93 * Citizens complaint. 1/24/94 Government action: warning. 8/10/94 Oil spill to river. Colorin 8/2/93 Suspicious transfer of solid waste. 1 1/2/94 * Government deadline to companv. Molinos 9/30/93 Government action: fine. Sevel 8/2/93 Government Court action against co. Siderca 11/2/94 Government action: warning. CHILE Endesa i/19/92 * Government complaint. 9/29/92 * Warning from environment ministry. 2/7/93 President's advice on pollution improvement. 4/21/93 * Citizens protests against company. Chilgener 7/13/90 Government complaint. 1/19/92 Government complains on bad environmental performance of the company. 4/8/92 * Environmental accident. 4/16/92 Court action by citizens. CMPC 9/30/92 * Citizens complain about solid waste pollution. CAPC 4/2/91 Air polluter. 6/27/92 Court action by citizens. 8/8/92 Grace period granted to curb water pollution. 8/12/92 Government supports court action. Volcan 12/2/93 Govemment black list of polluters. 21 Table 7 (continued) Minera 9/2/91 Court action. Vapores 6/6/92 Company is fined by government. Emos 10/17/93 Accident: drinking water contamination. Puerto 7/23/92 * Government complains about health hazard in the vicinity of the company. Victoria 12/2/93 Government black list of air polluter. lansa 5/29/93 One of the plants ordered to shutdown. Coloso 4/1/92 Government action: fine. 12/2/93 Government action: company shutdown for few hours. 2/5/94 Court action:fine. 3/11/94 Government action: company shutdown. 3/18/94 Citizens complaint: accident. Iquique 4/1/92 * Government action: fine. 12/21/93 Government action: fine. 2/5/94 Court action: fine. 3/10/94 Government action (Company closed for 72 hours). 3/11/94 Court action for bad smell problem. Lirquien 7/15/92 Government black list of air polluter. Chilectra 7/11/92 Citizens complain against company expansion. Molymet 1/19/92 1Governnent complaint: company major air polluter. PHILIPPINES Apex 4/24/91 * Government action. Ayala 12/8/94 * Government warning. Benguet 3/21/90 Government action: penalty. 3/23/90 Workers dismissals. Lepanto 10/22/90 Pollution problem resulting in death and illness. Mondragon 10/11/94 Complaint by citizens about tree cutting. Robinson Land 6/15/94 Government action: company shutdown. San Miguel 10/7/94 Oil spill. MEXICO Cydsasa 2/16/90 Spill causing death and injury. 3/19/92 Black list of air polluter for company's subsidiary. 10/9/92 Government action: environrmental audit. Grupo Maya (A) 10/4/90 NGO's black list of air polluter. 3/12/91 Company relocation requested by Citizens. 3/15/91 Government action: warning. 9/20/91 * Citizens complaint. 1 1/27/91* (1 1/25/94): Citizens and ecologists complaint. 7/29/92 * Citizens complaints. 22 Table 7 (continued) Grupo Mava (B) 3'12/91 Company relocation requested bv Citizens. 3,/15/91 Governrent action: warning. 9/20/91 * Citizens complaint. 11/27/91* (11/25/94): Citizens and ecologists complaint. Tolteca 10/14/90 NGO's black list of air polluter. 2/13i92 Temporary and partial shutdown. Met-Mex 3/22/91 Citizens complaints. Penoles (A) 6/4/91 Company pollution bad record pointed bv a Senator. 819/91 * Government action: company temporarily shutdown. 3/2/94 Accident: citizens complaint. 3/4/94 Pollution control equipment investigation. 8/27/94 Relocation of 300 families living in the vicinity of the co. Met-Mex 3/22/91 Citizens complaints. Penoles (B) 6/4/91 * Company pollution bad record pointed by a Senator. 3/4/94 Pollution control equipment investigation. Cementos de 5/25/92 Government action: warning about environmental Chiguagua performance. (GC3) Kimberly Clark 5/21/92 * Government action: fine for water pollution. Grupo Bimbo 3/19/92 * Black list of air polluter. 2/14/93 Government action: initiate court action. Telefonos de 5/21/19 Government action: warning about tree cutting. Mexico 6/9/94 Government action: fine. Given the nature of these results, we have pooled together government and citizens' complaints and tested whether or not they had a statistically significant differential impact on market values when compared to all other negative events. Results in Table 8 indicate that they strongly do. 23 Table 8 Complaints Vs All other negative events16 Dav -1 I Dav 0 Day +1 Window Complaints (Government and Citizens) AAR CAAR AAR CAAR AAR CAAR CAAR -1.405 T30.209* 3.137 -27.331* -1.244 -24.473- -36.014- (-0.343) (-2.335) (0.767) (-2.014) (-0.304) (-1.727) (-1.921) All other negative events -2.751 T -1.274 [ 0.524 [ -1.489 1 2.889 ] 2.680 1 1.1687 (-0.988) (-0.146) (0.190) (-0.162) (1.047) J (0.280) J (0.092) Complaints Vs All other negative events 1.347 -28.934 [ 2.613 -25.842- -4.133 -27.152. -37.182* (0.273) j(-1.853) (0.530) (-1.578) (-0.838) (-1.587) (-1.643) We mav interpret this result by noting that the filing of a complaint can provide unanticipated news to markets leading them to expect further actions, yet unknown, to be undertaken. Reductions in market values range on average from 4% to 15%. These losses are much greater in magnitude than any losses observed in previous studies conducted in developed countries. "7 V. Conclusion In this paper. we have shown that despite a generallv acknowledged poor enforcement of environmental regulations, capital markets in Argentina, Chile, Mexico and the Philippines appear to react to the announcement of environmental events involving publicly traded companies. While fines and penalties used by the environmental agencies of these countries may have fallen short of creating incentives for pollution control, capital markets have penalised firms suffering from adverse environmental events, and rewarded firms with positive environmental news. While we 16 See Foomote 15 for details of computation. 17 See Lanoie et al. (1997) for more details. 24 are certainlv not arguing that strong enforcement of regulations should be abandoned and that markets (firns. consumers, communities) be left to themselves to negotiate and induce pollution abatement from polluters (not all firms mav be responsive to public release of their environmental performance), these results suggest that in numerous circumstances market forces (even in developing countries) have not remained idle upon receiving signals of the environmental performance of firms. These results indicate that at the margin, environmental regulators should devote less resources to the enforcement of regulations, and more to the collection, analysis, and dissemination of appropriate, reliable, and timely information. Further research in this area will indicate whether or not our findings can be generalised, as well as providing a greater understanding of the mechanisms which underpin the reaction of capital markets. Moreover, whether or not firms have "voluntarily" undertaken pollution abatement activities seeking the obtention of the reward, and whether or not adverse market reaction has lead firms to subsequently invest in pollution control is a further issue of investigation. It is indeed currently beyond the realm of our possibilities to comprehensively address this issue as it requires a vast amount of firm-level data that is not currently available for the countries studied here. From an anecdotal point of view however, it is interesting to note, among others, that after Chilgener (Chile) had released a cloud of toxic air pollution over Santiago and suffered a loss of 5% of its market value Is 8 Konar and Cohen (1997) have shown that firms that have suffered the largest reduction in market value following the release of the TRI in 1989 have subsequently invested most in pollution abatement. 25 in April 1992. it announced on September 25 1992. an investment of 115 million dollars to control air pollution. 26 Appendix 1 Complete name of companies in sample set ARGENTINA Astra: Astra Compania Argentina de Petroleo Ipako: Ipako Industria Petroquimica Perez: Perez Compane YPF: Yacimientos Petroliferos Fiscales Celulosa: Empresa Celulosa Argentina Telefonica: Empresa Telefonica de Argentina Colorin: Colorin lndustriai de Material Sintetico Indupa: Indupa Molinos Rio: Molinos Rio de la Plata Sevel: Sevel Argentina Siderca: Siderca CHILE Endesa: Empresa Nacional de Electricidad Chilgener: Chilgener CMPC: Compania Manufacturera de Papetes v Cartones CAP: Compania de Acero del Pacifico Volcan: Compania Industrial el Volcan Minera: Compania Minera Tamaya Vapores: Compania Sud Americana de Vapores Emos: Empresa Metropolitana de Obras Sanitarias Puerto: Empresa Portuaria Puchoco Victoria: Fabrica Victoria de Puente Alto Iansa: Industria Azucarara Nacional Molymet: Molibdenos y Metales Coloso: Empresa Pesquera Cotoso Iquique: Pesquera Iquique Lirquien: Vidrios y Planos Lirquien Chilectra: Chilectra Eperva: Empresa Pesquera Eperva 27 Appendix I (continued) MEXICO Cydsasa: Celulosa y Derivados Grupo Maya: Grupo Empresarial Maya Tolteca (Tolmex): Cementos Tolteca Met-Mex Penoles: Empresa Metalurgica Met-Mex Penoles Femsa: Fomento Economico Mexicano Vitro: Grupo Vitro GC3: Cementos de Chiguagua Kimberly Clark: Kimberly y Clark de Mexico Bimbo: Grupo Bimbo Telmex: Telefonos de Mexico PHILIPPINES Apex Mining: Apex Mining Company Atlas C. Mining: Atlas Consolidated Mining & Development Corporation Ayala Land: Ayala Land Benguet: Benguet Corporation Jolibee: Jolibee Corporation Lepanto: Lepanto Consolidated Mining Company Manila Mining: Manila Mining Mondragon: Mondragon International Philippines San Miguel: San Miguel Corporation Robinson Land: Robinson Land Corporation 28 Appendix 2 Reaction of Market to Positive News I ARGENTINA day -I day 0 day + I Event window Astra 3/15/94 2.651 2.705 -0.476 2.229 -1.355 0.874 -7.626 (1.017) (0.328) (-0.183) (0.258) (-0.520) (0.097) (-0.639) Ipako 2/7/93 -4.107 2.266 -2.819 -0.553 -0.825 -1.378 19.965 (-0.534) (0.093) (-0.366) (-0.054) (-0.107) (-0.052) (0.566) YPF 1224/94 -4.573 -4.714 -2.279 -6.933 -0.346 -7.339 -7.695 (-0.169) (-0.123) (-0.084) (-0.149) (-0.013) (-0.136) (-0.127) Celulosa 8/3/92 -2.462 -10.117 0.696 -9.421 0.696 -8.725 -9.984 (-0.425) (-0.546) (0.119) (-0.485) (0.119) (-0.430) (-0.372) Indupa 2/7/93 -1.106 11.735 -5.145 6.589 0.855 7.444 18.187 (-0.157) (0.528) (-0.732) (0.283) (0.122) (0.306) (0.565) CHILE Firmns Date AR CAR AR CAR AR CA4R CAR Endesa 131/92 0.873 2.428 1.029 3.457 -0.861 2.596 8.568 (0.327) (0.288) (0.386) (0.391) (-0.323) (0.281) (0.700) 9/6/93 -0.426 -0.367 -0.031 -0.397 -0.096 -0.493 0.530 (-0.318) (-0.087) (-0.023) (-0.090) (-0072) (-0.106) (0.086) 8/8/94 -0.019 0.839 -0.486 0.353 -1.497 -1.145 -2.388 (-0.015) (0.213) (-0.391) (0.085) (-1.203) (-0.265) (-0.419)_ Chilgener 1/9/90 0.347 6.899 0.596 7.495 1.588 9.083 21.290* (0.146) (0.917) (0.251) (0.950) (0.668) -(1.102) (1.953) 8/5/90 -3.626 -12.180 -4.386 -16.566 -2.500 -19.066 -21.697 ( ] 350) (-1.434) (-1.633)) (-1.860) (-0.931) (-2.049) (-1.863) 11/9/93 2.746 * 7.624- 0.943 8.567* 0.250 8.817* 25.443** (1.780) (1.563) (0.611) (1.674) (0.162) (1.650) (3.599) 6/23/94 -1.510 -8.549 -1.711 -9.843 -1.343 -8.753 -23.820 (-0.654) (-0.943) (-0.746) (- t.124) (-0.586) (-1.245) (-2.267) CMPC 2/26/92 1.401 3.346 2.560 5.906 -0.604 5.302 0.755 (0.699) (0.505) (1.222) (0.850) (-0.288) (0.731) (0.144) 1/7/94 -2.523 4.475 1.957* 6.431* 2.980** 9.412t* 25.915' (-2.188) (1.227) (1.697) (1.681) (2.584) (2.356) (4.903) CAP 8/15/92 -3.077 -5.639 3.597. -2.042 0.260 -1.783 0.094 (-1.387) (-0.803) (1.621) (-0.277) (0.117) (-0.232) (0.009) 10/2/92 0.448 (-2.033) 1.430 -0.603 -0.745 -1.344 0.808 (0.261) (-0.375) (0.833) (-0.106) (-0.433) (-0.277) (0.103) 11/8/92 -0.105 2.095 1.544 3.640 2.850' 6.489- 21.613** (-0.095) (0.420) (0.979) (0.730) (1.807) (1.301) (2.991) The cumulative abnormal return for day -1, 0 and +1 is computed for day -10 up to the specified day. For the event window. the cumulative abnormal return is calculated over the period - 10 to + 10. Within brackets is the value of the Z statistics. ".", "", and "" means significant at the 10%, 5% and 1% level respectively (one tailed-test). 29 Appendix 2 (continued) Emos 4/16/92 -9.544 -13.429 -0.453 -13.884 -2.58 -27.684 -27.684 (-1.797) (-0.799) (-0.085) (-0.788) (-1.215) (-1.137) (-1.137) 2/24/93 1.131 -1.194 -0.385 -1.578 -1.137 -2.175 -12.693 (0.257) (-0.086) (-0.087) (-0.108) (-0.258) (-0.178) (-0.629) 8/11/93 -0.024 -0.169 -0.024 -0.193 -0.024 -0.217 0.919 (-0.006) (-0.012) (-0.06) (-0.015) (-0.006) (-0.227) (0.051) lansa 9/26/93 -0.727 9.881' -1.626 8.255 0.170 8.425 21.265** (-0.345) (1.483) (-0.772) (1.182) (0.081) (1.155) (2.203) Molymet 10/11/93 -5.500 -15.168 -1.409 -16.577 -1.409 -17.986 -35.849 (-0.704) (-0.614) (-0.180) (-0.634) (-0.180) (-0.664) (-1.000) Iquique 8/11/92 -5.947 -4.452 -0.437 -4.889 -4.603 -9.492 -13.421 (-1.293) (-0.306) (-0.095) (-0.320) (-1.001) (-0.596) (-0.638) Chilectra 5/29/93 -1.026 4.499 -1.039 3.460 -0.822 2.368 8.440 (-0.500) (0.533) (-0.506) (0.387) (-0.401) (0.371) (0.897) Eperva 7/1/94 -2.284 3.093 -4.802 -1.709 -7.642 -9.352 11.877 (-0.491) (0.210) (-1.031) (-0.111) (-1.642) (-0.580) (0.557) _____ _ MEXICO Cydsasa 5/11,/92 -0.361 -10.654 -0.3975 -10.783 -1.729 -10.912 -12.558 (-0.129) (-1.363) (-0.052) (-1.299) (-0.052) (-1.259) (-1.109) Apenol 7/10/93 1.603 0.927 9.979** 10.905* -1.997 8.909- 11.397 (0.806) (0.147) (5.018) (1.653) (-1.004) (1.293) (1.241) Femsa 9/14/91 -0.872 -3.102 -2.967 -6.068 1.254 -4.814 -13.125 (-0.247) (-0.278) (-0.840) (-0.518) (0.355) (-0.393) (-0.817) Vitro 4/18/91 4.863** 11.703* -4.213 7.490 -1.922 5.498 -8.386 (2.533) (1.943) (-2.212) (1.186) (-1.046) (0.833) (-0.936) 0 X PHILIPPINES _ -_ _ Atlas 10/20/90 0.142 0.419 -1.078 -0.658 0.142 -0.517 -10.746 _________ _ _ (0.045) (0.042) (-0.342) (-0.063) (0.045) (-0.047) (-0.945) Benguet 12/28/92 -0.071 0.049 -8.404 -8.356 -0.071 -8.426 -16.287 __________ ___X____ (0.015) (0.003) (-1.773) (-0.531) (-0.015) (-0.513) (-0.750) 7/19/93 -0.111 7.769 -0.111 7.657 7.581- 15.238 42.271* (-0.020) (0.441) (-0.020) (0.415) (1.303) (0.790) (1.656) 216/94 -0.107 -3.926 -0.107 -4.033 -0.107 -4.141 -9.660 (-0.019) (-0.224) (-0.019) (-0.219) (-0.019) (-0.216) (-0.389) Jolibee 6/28/94 0.032 -9.049 0.032 -9.017 4.032- -4.985 -14.616 (0.010) (-0.910) (0.010) (-0.868) (1.282) (-0.458) (-1.014) Manila 4/17/92 29.086** 20.201 -8.606 11.595 40.753** 52.347** 107.786** Mining (5.211) (1.145) (-1.542) (0.526) (7.302) (2.708) (4.214) San 11/5/90 1.843 18.210* 0.353 18.563** -1.097 17.466* 20.663* Miguel (Q.696) (2.199) (0.135) (2.138) (-0.419) (1.926) (1.722) 2/10/91 3.688 33.578** 4.651* 38.234** -2.738 35.496** 48.323** E________ ________ (1.244) (3.582) (1.571) (3.889) (-0.924) (3.457) (3.557) 9/4/91 -0.342 -7.808 -0.342 -8.150 -1.268 -9.418 -12.389 (-0.120) (-0.867) (-0.120) (-0.862) (-0.445) (-0.954) (-0.949) 6/8/93 -4.008 -43.761 -5.875 -49.636 -5.262 -54.894 -97.839 (-0.059) (-0.204) (-0.087) (-0.211) (-0.078) (-0.234) (-0.315) 30 Appendix 3 Reaction of Market to Negative News' ARGENTINA day -I day 0 day + I Event window Firms Date AR CAR AR. CAR AP C.AR CA.R Astra 9/10/93 -1.057 8.415 -1.969 6.447 -0.864 5.583 4.333 (-0.385) (0.743) (-0.717) (0.708) (-0.315) (0.587) (0.344) Ipako 10/16/92 -21.038** -20.897 0.664 -20.143 28.381 8.238 50.549 (-3.902) (-0.967) (0.098) (-0.893) (4.171) (0.350) (1.621) 9/9/93 3.037 - 13.871 -0.167 -14.038 0.1 80 -13.858 -20.347 (0.646) (-0.889) (-0.035) (-0.944) (0.038) (-0.850) (-0.944) Perez 5/2/93 -1.706 1.876 -0.003 1.873 2.491 4.364 18.290 (-0.374) (0.130) (-0.001) (0.124) (0.547) (0.277) (0.876) 12/12/94 -0.053 0.255 1.439 1.694 0.580 2.274 -14.778 (-0.021) (0.031) (0.556) 1(0.197) (0.224) (0.254) (-1.245) YPF 11/7/93 1.057 -10.942* 2.224 -8.718 * 1.978 -6.740 -8.499 (0.600) (-1.963) (1.262) (-1.491) (1.122) (-1.104) (-1.052) 11/30/93 -0.306 - 10.723* 1.519 -9.204- -1.102 - 0.305* - 14.820* (-0.171) (-1.890) (0.847) (-1.547) (-0.614) (-1.658) (-1.803) 1/24/94 -1.631 -0.973 -0.710 -1.683 1.564 -0.119 7.406 (-0.964) (-0.182) (-0.420) (-0.300) (0.924) (-0.020) (0.955) 8/10/94 -0.052 -0.522 -0.250 -0.773 -0.647 -1.420 -1.477 (-0.028) (-0.090) (-0.136) (-0.300) (-0.352) (-0.223) (-0.175) 5/15/94 2.692 7.326 2.924 10.250 5.306 15.556 15.461 (0.948) (0.816) (1.030) (1.089) (1.343) (1.582) (1.189) Color 8/2/93 -5.761 5.786 0.211 5.977 0.211 6.208 15.708 (-0.744) (0.240) (0.028) (0.237) (0.028) (0.235) (0.450) 11/2/94 -0.26-1 -16.840 -3.039 -19.880- -0.261 -20.141 -37.418* (-0.056) (-1.146) (-0.654) (-1.290) (-0.056) (-1.251) (-1.757) Molymos 9/30/93 2.852 7.673 6.798 14.471 -2.159 12.311 34.425 (0.926) (0.788) (2.208) (1.417) (-0.701) (1.154) (2.440) Sevei 8/2/93 -3.061 -6.476 -1.092 -7.568 -0.061 -7.628 -5.440 (-1.107) (-0.741) (-0.395) (-0.825) (-0.022) (-0.796) (-0.429) Siderca 11/2/94 2.997 -5.423 1.236 -4.186 -0.167 -4.353 -5.854 (1.394) (-0.790) (0.575) (-0.587) (-0.078) (-0.585) (-0.594) CHILE Endesa 1/19/92 -2.112 -13.831 * -2.326 -16.157* -2.362 - i8.519* -9.370 (-0.794) (-1.920) (-0.870) (1.831) (-0.888) (-2.009) (-0.768) 9/29/92 -4.603** -12.720 1.0401 -11.680 -2.356- -14.035 -4.419 (-2.612) (-0.793) (0.590) (-0.756) (-1.337) (-0.724) (-0.547) 2/7/93 -1.139 2.971 -0.817 2.154 -0.315 1.893 5.112 (-0.698) (0.575) (-0.500) (0.398) (-0.193) (0.325) (0.683) 4/21/93 1 .505 -1.635 1.837 0.201 -2.000- -1.799 -12.281** l_________ (0.980) (-0.337) (1.196) (0.040) (-1.302) (-0.338) (-1.745) Thecmulative abnormal return for day -I, 0 and +I is computed for day -10 up to the specified day. For the event window, the cumulative abnormal return is calculated over the period - 10 to + I10. Within brackets is the value of the Z statistics. "", "*", and "**" means significant at the 10%, 5% and 1% level respectively (one tailed-test). 31 Appendix 3 (continued) Chilgener 7/13/90 1.305 -1.052 0.294 - 0.759 4.524 3.765 1.667 ____ __ (0.479) (-0.122) (0.108) (-0.084) (1.663) (0.399) (0.134) 1/19/92 -1.556 -9.914 -0.306 -10.220 -0.306 -10.525 -7.082 (-0.507) (-1.022) (-O.100) (-1.004) (-O.100) (-0.990) (-0.504) 4/8/92 -8.325* -7.054 5.689 -1.365 -5.316* -6.681 -6.534 (-2.841) (-0.761) (1.941) (-0.140) (-1.814) (-0.658) (-0.487) 4/16/92 1.285 -12.290. 2.612 -10.308 0.712 -9.595 12.009 (0.432) (-1.373) (0.878) (-1.045) (0.239) (-0.931) (-0.881) CMPC 9/30/92 -0.041 -9.023* -2.891 -1 1.921 * 0.018 - 11.903* -1.349 _ _ (-0.026) (-1.805) (-1.833) (-2.274) (0.012) (-2.174) (-0.186) CAPC 4/2/91 4.021 5.704 -1.145 4.559 -2.165 2.394 -7.426 (1.682) (0.754) (-0.479) (0.575) (-0.906) (0.289) (-0.678) 6/27/92 0.025 -0.668 0.025 -0.644 1.087 0.444 -1.021 (0.009) (-0.074) (0.009) (-0.068) (0.378) (0.045) (-0.078) 8/8/92 0.472 1.946 -0.384 1.562 -0.925 0.637 2.716 (0.209) (0.272) (-0.170) (0.258) (-0.408) (0.081) (0.262) 8/12/92 -0.944 -0.284 -1.825 -2.109 -0.201 -2.310 2.973 (-0.419) (-0.040) (-0.810) (-0.282) (-0.089) (-0.296) (0.288) Volcan 12/2/93 -2.862 -28.589 2.138 -26.451 1.900 -24.551 -33.202 (-0.357) (-1.128) (0.267) (-0.995) (0.237) (-0.884) (-0.904) Minera 9/2/91 -0.477 -2.374 -0.477 -2.850 -0.477 -3.327 -3.942 (-0.171) (-0.270) (-0.171) (-0.309) (-0.171) (-0.345) (-0.309) Vapores 6/6/92 -1.498 -3.135 0.926 -2.209 0.911 -1.298 0.807 (-0.593) (-0.393) (0.367) (-0.115) (0.361) (-0.148) (0.070) Emos 10/17/93 -0.148 -1.471 -0.148 -1.619 -0.148 -1.767 -5.799 (-0.038) (-0.119) (-0.038) (-0.125) (0.038) (-0.131) (-0.324) Puerto 7/23/92 -0.374 -5.464- -2.160 -7.624- -0.738 -8.362 - 16.892* (-0.208) (-1.473) (-1.203) (-1.343) (-0.411) (-0.963) (-2.054) Victoria 12/2/93 -9.895 -42.389 -13.272 -55.661 -10.848 -66.508 -86.081 (-0.502) (-0.680) (-0.673) (-0.851) (-0.550) (-0.974) (-0.953) lansa 5/29/93 0.500 0.015 0.498 0.513 0.042 0.555 3.279 (0.242) (0.002) (0.241) (0.081) (0.020) (0.072) (0.346) Coloso 4/1/92 6.961 35.171 -2.988 35.174 -0085 32.089 32.052 (2.165) (3.459) (-0.932) (3.017) (-0.026) (2.881) (2.243) 12/2/93 0.256 16.630 4.359 20.989 0.256 21.245 44.995 (0.087) (1.777) (1.472) (2.138) (0.087) (2.072) (3.317) 2/5/94 0.086 -3.492 -4.460- -7.952 -4.914. -12.628 -15.746 (0.028) (-0.357) (-1.440) (-0.774) (-1.510) (-1.177) (-1.109) 3/11/94 -4.860. 1.273 0.140 1.413 0.140 1.533 -12.670 (-1.545) (0.128) (0.045) (0.135) (0.045) (0.143) (-0.879) 3/18/94 ~ 0.139 0.741 0.139 0.880 -3.808 -2.928 -13.210 (0.044) (0.074) (0.044) (0.084) (-1.211) (-0.269) (-0.916) 32 Appendix 3 (continued) Iquique 4,1,'92 -0.032 13.750 21.632 35.382 -17.838** 17.543 19.676 (-0.07) (0.955) (4.753) (2.344) I (-3.919) (1.113) (0.943) 12/21/93 3.895 15.384 0.124 15.507 11.151 26.659 35.137 (0.779) (0.996) (0.025) (0.957) (2.283) (0.916) (1.569) 2/5/94 0.086 25.987 -0.017 25.971 -0.017 25.954 16.726 (0.028) (1.666) (-0.003) (1.587) (-0.003) (1.519) (0.740) 3/10/94 -0.032 18.820 -0.094 18.725 -0.032 18.694 52.526 (-0.006) (1.177) (-0.019) (1.123) (-0.006) (1.073) (2.279) 3/11/94 -0.147 7.126 -0.085 7.042 -3.209 3.832 40.314 (-0.029) (0.443) (-0.017) (0.417) (-0.631) (0.217) (1.729) Lirquien 7/15/92 -2.509 -23.458 27.491 4.033 0.600 4.633 6.302 (-0.121) (-0.358) (1.325) (0.059) (0.029) (0.064) (0.066) Chilectra 7/11/92 -0.207 -7.201a 1.065 -6.136 1.133 -5.003 -1.204 (-0.132) (-1.391) (0.651) (-1.130) (0.693) (-0.882) (-0.160) Molymet 1/19/92 -3.140 -40.617 -9.390 -50.007* -4.029 -54.036* . 1 I 943** (-0.378) (-1.545) (-1.130) (-1.814) (-0.485) (-1.877) (-2.939) MEXICO Cydsasa 2,'6/90 -1.661 4.582 0.254 4.448 -0.134 3.928 -1.842 (-0.733) (0.605) (0.112) (0.610) (-0.059) (0.567) (-0.178) 3/19/92 1.591 3.058 1.565 4.623 1.146 5.768 6.671 (0.676) (0.411) (0.665) (0.392) (0.487) (0.707) (0.618) 10/9/92 0.104 11.788 0.104 11.892 -0.396 11.146 13.082 ________ _______ (0.040) (1.414) (0.040) (1.394) (-0.154) (1.290) (1.110) Grupo 10/4/90 -0.176 6.264 -0.176 6.088 -0.176 5.912 7.347 Mava (A) (-0.045) (0.505) (-0.045) (0.468) (-0.045) (0.435) (0.409) 3/12/91 -0.209 3.875 1.220 5.095 0.073 5.168 29.874 _________ (-0.053) (0.308) (0.307) (0.387) (0.018) (0.376) (1.641) 3/15/91 1.222 5.624 0.075 5.699 -0.207 5.492 30.213 (0.308) (0.448) (0.019) (0.432) (-0.052) (0.399) (1.660) 9/20/91 -1.269 -11.604* -1.269 -12.873* -1.269 - 14.141 * -24.845** (-0.675) (-1.953) (-0.675) (-2.066) (-0.675) (-2.173) (-2.885) 11/27/91 -1.041 -14.545** -1.041 -15.586** -0.295 -15.881** -27.475** (-0.566) (-2.500) (-0.566) (-2.554) (-0.160) (-2.492) (-3.259) 7/29/92 -1.170 -26.986* -1.171 -28.409* -1.423 -31.854 * -52.891 ** (-0.297) (-2.069) (-0.297) (-2.063) (-0.361) (-2.079) (-2.926) Grupo 3/12/91 2.737 14.242 1.268 15.511 -0.121 15.390 59.367 Maya(B) (0.954) (1.569) (0.442) (1.630) (-0.042) (1.548) (4.514) 3/15/91 1.257 13.579 -0.132 13.448 -0.132 13.316 63.416 _________ _________ (0.438) (1.480) (-0.046) (1.412) (-0.046) (1.338) (4.818) 9/20/91 -1.386 -12.392. -1.748 -14.140. 0.069 -14.4109 -30.332** (-0.525) (-1.484) (-0.662) (-1.615) (0.026) (-1.539) (-2.507) 11/27/91 -2.688 - I 6.099* -1.591 -16.193* -0.094 - 16.632* -29.371 ** (-1.075) (-1.835) (-0.636) (-1.942) (-0.038) (-1.870) (-2.564) Tolmex 10/14/90 4.594 6.162 9.798 15.961 0.417 16.378 30.047 (1.658) (0.703) (3.536) (1.737) (0.151) (1.706) (2.366) 33 Appendix 3 (continued) MetMEx 3/22/91 4.142 20.674 0.119 20.793 -0.710 20.084 37.335 (A) (1.992) (2.789) (0.057) (3.104) (-0.341) (3.143) (3.917) 6/4/91 -0.008 23.669 -0.52] 23.149 10.044 33.193 29.115 (-0.004) (3.370) (-0.240) (3.213) (4.623) (4.411) (2.925) 8/9/91 -9.677** -3.142 -5.239** -8.388 -0.088 -8.476 - 15.193- (-4.237) (-0.445) (-2.343) (-1.131) (-0.039) (-1.094) (-1.482) 3/2/94 -0.765 1.088 -0.113 0.975 0.107 1.081 0.812 (-0.105) (0.047) (-0.016) (0.040) (0.015) (0.043) (0.024) 3/4/94 -0.134 0.882 0.086 0.968 -0.795 0.173 0.599 (-0.018) (0.038) (0.012) (0.040) (-0. 10) (0.007) (0.018) 8/27/94 0.141 6,067 -0.923 5.144 -0.289 4.854 7.850 (0.020) (0.268) (-0.129) (0.217) (-0.040) (0.196) (0.239) MetMEx 3/22/91 -2.662 -8.572 3.480 -5.092 9.577 4.485 -16.531 (B) (-0.284) (-0.289) (0.371) (-0.164) (1.022) (0.138) (-0.385) 6/4/91 -8.985 -28.811 -13.064. -41.875. -0.161 -42.036 -43.385 (-0.936) (-0.949) (-1.361) (-1.316) (-0.017) (-1.264) (-0.986) 3/4/94 -0.187 18.743 0.279 18.556 0.046 18.835 25.107 1 (-0.021) (0.655) (0.031) (0.618) (0.005) (0.601) (0.605) GCG 5/25/92 -3.168 -12.765 9.937 -2.828 -1.820 -4.648 -8.458 (-0.937) (-1.193) (2.938) (-0.252) (-0.538) (-0.397) (-0.546) Kirnber 5/21/92 0.560 -6.951 -0.565 -7.516 -0.192 -7.708 -55.103** (0.308) (-1.210) (-0.311) (-1.217) (-0.106) (-1.225) (-6.618) Bimbo 3/19/92 1.630 -8.763 * 1.972 -6.792 -0.301 -7.092 -22.521** (0.942) (-1.603) (1.140) (-1.184) (-0.174) (-1.184) (-2.842) 2/14/93 -0.655 4.452 0.861 5.313 -4.139 1.174 -89.247* (-0.761) (0.141) (0.086) (0.160) (-0.414) (0.034) (-1.950) Telmex 5/21/93 -0.761 -1.361 -0.436 -1.797 0.883 -0.915 - 10.272. (-0.455) (-0.257) (-0.261) (-0.324) (0.527) (-0.158) (-1.339) 6/9/94 -0.953 -3.065 1.044 -2.021 -1.148 -3.169 -9.840- (-0.508) (-0.340) (0.556) (-0. 324) (-0.611) (-0.487) (-1.453) PHILIPPINES Apex 4/24/91 0.263 -9.810 -14.023* -23.832 0.263 -23.564 -40.704 (0.035) (-0.408) (-1.844) (-0.935) (0.035) (-0.895) (-1.168) Ayala 12/8/94 0.024 1.752 -4.201* -2.449 4.436 1.986 9.238 (0.008) (0.187) (-1.415) (-0.249) (1.494) (0.193) (0.679) Benguet 3/21/90 -2.217 1.752 -2.275 -0.524 2.664 2.140 3.615 (-0.451) (0.113) (-0.463) (-0.032) (0.542) (0.126) (0.161) 3/23/90 2.634 -1.119 0.134 1.515 0.134 1.649 2.990 (0.538) (-0.072) (0.027) (0.102) (0.024) (0.105) (0.133) Lepanto 10/22/90 3.388 -3.298 3.273 -0.025 6.391 6.366 5.917 _(1.412) (-0.435) (1.364) (-0.003) (2.664) (0.766) (0.538) Mondrag 10/11/94 -0.284 -5.824 2.841 -2.983 -0.284 -3.268 3.057 on _ (-0.087) (-0.564) (0.870) (-0.275) (-0.087) (-0.289) (0.204) San 10/7/94 0.342 3.589 0.342 3.931 0.342 4.273 4.810 Miguel _ (0.129) (0.427) (0.129) (0.446) (0.129) (0.461) (-0.395) Robinson 6/15/94 -1.389 -2.605 1.127 -1.417 -0.139 -1.617 -5.332 Land (-0.373) (-0.221) (0.303) (-0.120) (-0.037) (-0.125) (-0.397) 34 References Afsah, S., Laplante. 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