Abstract
This paper examines how government alerts about a potential dam rupture affect Brazilian municipalities’ local economic and financial conditions. The dam collapses in Mariana (2015) and Brumadinho (2019) revealed the extent of losses that environmental disasters can cause in terms of human lives, flora, and fauna. This paper investigates the effects of government alerts regarding dams’ structural vulnerability on nearby municipalities. We focus on municipalities with dams classified as structurally vulnerable but ended up not collapsing. This approach disentangles the actual economic effects of the dam rupture from the information value of the government alert. These notifications have a detrimental impact on municipalities’ local economic and financial conditions: local consumption declines, labor markets concentrate on less industrialized sectors, and financial development decreases. We also find that the government alerts diffuse to neighboring municipalities with a magnitude inversely proportional to the neighboring municipality’s distance to the notified dam. Our findings highlight the importance of incorporating negative externalities associated with potential environmental disasters into mining companies’ costs as a way to mitigate disasters.
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A chronology of the significant tailings dam failures can be seen here.
In the Mariana dam tragedy, the mud spread across the valley and traveled more than 650 km in the Doce River watercourse until it reached the Atlantic Ocean. In Brumadinho, the waste traveled 10 km downhill until it reached the Paraopeba River. Until 2022, there are 16 municipalities with inappropriate water supply systems, and the Vale Company, which was the company responsible for the collapsed dam, is in charge of water distribution for irrigation and human and animal consumption in these municipalities.
In October 2019, a judicial term created three groups to inspect mining dams with technical consultancy support. The first two groups were in charge of auditing dams in the state of Minas Gerais, and the third focused on other states. Only 50% of all dams in Brazil, excluding those in Minas Gerais, were audited in the latest report of Brazil Mining Agency (see page 5).
This bank is part of the S1 segment of Central Bank, which comprises the country’s most important financial institutions in terms of GDP. Moreover, more than 80% of all lending comes from these S1 banks, according to the Banking Report or Relatório de Economia Bancária of the Central Bank of Brazil (see page 212 in here). The bank has a significant share of credit and debit transactions in all 5,570 Brazilian municipalities. Additionally, it leads in terms of local credit concessions in many of these municipalities.
These job market data covers formal labor relationships in Brazil, which have some working guarantees such as unemployment insurance and Employee’s Severance Guarantee Fund (FGTS). The data is at the labor relationship level over time (employee-employer-time). We aggregate the data at the municipality-time level, taking into account the location of the employer’s plant.
The volume of contaminants released by the Brumadinho dam was lower than that poured by the Fundão dam (Mariana disaster). However, the Brumadinho collapse was much more significant in human lives. The mud wave created by the dam rupture killed more than 259 residents, and 11 people went missing, which is significantly more than the Mariana disaster (19 people dead). There was a chaotic situation in the region, with search teams looking for survivors in the mud that had engulfed the area. The lack of a contingency plan to aid the residents of affected municipalities was evident. The human impact of the Mariana and Brumadinho disasters can be viewed here and here.
There are also critical negative externalities that are important to take into account in dam tailing disasters. Nishijima and Rocha (2020) use the case of the breakdown of Mariana’s tailings dam to analyze the effect on dengue cases. They find that the accident had a statistically significant impact on dengue indicators. Moreover, such diseases can reduce labor productivity and influence income inequalities (Madsen 2016). Such an externality is vital to consider when measuring the risks of dam structures and their potential adverse effects on the population in case of collapse.
The tailing dam company must handle the stability declaration twice per year, and authorities may request additional reports based on the dam’s condition.
Rupture is imminent in Barragem Sul Superior (Barão de Cocais), B3/B4 (Nova Lima) e Forquilha III (Ouro Preto).
The dams’ names are: Jacobina Mineração e Comércio Ltda – Barragem 01 (Bahia) and Barragem 02 (Bahia); Mundo Mineração Ltda - Barragem II Mina Engenho (Minas Gerais) and Barragem Mina Engenho (Minas Gerais); Mineração Serra Grande S.A. (Goiás).
The Barragem B2 is ranked as with the emergence of level 2, meaning that there is an uncontrolled anomaly in the dam. Details can be found in this link at Art. 27, §\(1^{\circ }\).
Two dams included in the National Dam Safety Policy did not submit the regulatory documentation required to assess dams’ structural vulnerability. We could not locate specific information regarding these two dams, such as a description of the current risk category and potential damage.
The downstream method is expanded in stages, throughout its useful life, with the addition of dikes that sit on the reservoir’s edge. Overlapping dikes gradually widen the centerline. We did not find information about the building method of the Mina Oeste dam (Somisa), which was deemed structurally vulnerable in September 2019.
According to the Resolution 4,553 of January 30, 2017, enacted by the Brazilian National Monetary Council (CMN), the S1 segment comprises universal banks, commercial banks, investment banks, foreign exchange banks, and federal savings banks with either of the following characteristics: (i) size equal to or greater than 10% of Brazil’s GDP; or (ii) relevant international activity, regardless of the size of the institution.
There is an ongoing change in the payment means in Brazil. Households have been replacing cheques for credit and debit cards in economic trades. The default risk in cheques and convenience are key factors behind this trend, as cards facilitate day-to-day purchases. Moreover, this helps prevent theft since a personal password secures any transaction. Additionally, the Central Bank of Brazil launched the Brazilian Instant Payment Scheme (PIX) in 2020, a 24-hour instant payment system. Using a QR Code or entering information such as a cell phone number, email address, or taxpayer’s identification, the PIX enables faster, cheaper, and more accessible financial transactions.
The risk category considers the probability of an accident, the dam’s technical characteristics, state of conservation, and the Plan of Dam Safety. More information click here (pg. 4, in Portuguese).
The regulatory agency releases information about the number of potentially affected downstream populations (in terms of thousands of human lives). We report sub-sample averages in Table 2. We obtain this information in https://www.gov.br/anm/pt-br > Barragens > Sistema integrado de Gestão de Barragens (SIGBM) - Versão Pública > Classificação > Choose a Dam (Dados Cadastrais) > 7 - Dano Potencial Associado > Número de pessoas possivelmente afetadas a jusante em caso de rompimento da barragem. Unfortunately, the data across different dams are not standardized, and we had to pre-process this information individually for each dam.
According to the AMN, the potential environmental risk is ranked as: (a) insignificant (code 0) – the affected downstream area of the dam lies uncharacterized by its natural conditions, and the structure stores only Class II 0 waste – Inert, according to the NBR 10004 of ABNT NBR 10004; (b) lowly significant (code 2) – there are not relevant environmental interest or protected areas as defined in specific legislation, except permanently protected areas, and only stores Class 11 waste 8-Inert, according to the NBR 10004 of ABNT NBR 10004; (c) significant (code 6) – the affected downstream area of the dam has an area of relevant environmental interest or protected spaces as defined in specific legislation, except permanently protected areas, and only store waste Class 118 - Inert, according to the NBR 10.004 of ABNT NBR 10004; (d) highly significant (code 8) – the dam stores rejects or solid waste classified in Class IIA - Non-Inert, according to the NBR 10004 of ABNT NBR 10004. (e) highly significant aggravated (code 10) – the dam stores rejects or solid waste classified in Class I - Dangerous, according to the NBR 10004 of ABNT NBR 10004. For more information, click here (pg. 43, in Portuguese).
According to the AMN, the socio-economic impact is classified as: (a) non-existing (code 0); (b) low identity (code 1) – there is a small concentration of facilities residential, agricultural, industrial or relevant infrastructure socio-economic cultural in affected downstream areas of the dam; (c) medium (code 3) – there is a moderate concentration of facilities residential, agricultural, industrial or relevant economic or cultural infrastructure in affected downstream areas of the dam; (d) high (code 5) – there is a high concentration of facilities residential, agricultural, industrial or relevant economic or cultural infrastructure in affected downstream areas of the dam. For more information, click here (pg. 43, in Portuguese).
For example, Resolution 13/2019/ANM/MME established precautionary regulatory measures to ensure the stability of mining dams, particularly those constructed or raised using the “upstream” or other undeclared construction methods. Additionally, authorities demanded that a new conduct term establishing reparation and compensation policies be signed.
Moments after the dam in Brumadinho collapsed, the terms “death in Brumadinho” and “how many people died” reached a peak of popularity on Google Trends. The terms “tailing dam” and “disaster” also experienced a surge in search volume.
According to the AMN, there is compiled data of dams’ stability conditions only since September 2018 (see here). Before that time, we needed to gather information for each dam manually. Part of them did not have information about the dam’s vulnerability before 2018.
For example, authorities in Rio Acima (MG) declared the local B2 Auxiliar dam as structurally vulnerable thrice. Numerous dams were declared twice as structurally vulnerable. Examples include: Vargem Grande and Capitão do Mato, both in Nova Lima (MG); Forquilha I, II, and III, all in Ouro Preto (MG); BR Ismael and Barragem 1, both in Poconé (MT); Bacia de Decantação - Planta I in Santana de Parnaíba (SP); P1-1 in Minas do Leão (RS); and Barragem CBC in Santana da Boa Vista (RS).
Even though profits can theoretically be negative or positive, our sample contains only two observations of negative profits for a single small bank. For comparability with the other empirical findings, we chose to drop these observations and maintain the logarithm transformation.
The use of states is neither a too narrow nor a too broad geographical circumscription. If the circumscription were too narrow, we would lack control municipalities to compare treated municipalities. In contrast, if the circumscription were too broad, we could be comparing municipalities substantially distinct from one another.
In the same line of reasoning, Mody et al. (2012) associate labor income uncertainty with higher household savings.
In the same vein, de Castro and de Almeida (2019) find that the effect of the Mariana disaster was significant in the state of Espírito Santo (18% drop in industrial production). This effect was less pronounced in the state of Minas Gerais than in Espírito Santo. The authors claimed that the most affected state was less economically dynamic. Nonetheless, the drop in industrial mineral extractive production in Minas Gerais and Espírito Santo was 16% and 25%, respectively. The authors employed a synthetic control methodology and demonstrated a significant impact on industrial activity in regions affected by environmental disasters, such as the Mariana disaster.
See Estado de Minas Gerais, which is the official medium that the Brazilian state of Minas Gerais publishes information for the society.
See Veja Magazine for details.
See this article for more information.
There are 33 municipalities downriver from Mariana’s dam collapse across two states. In the state of Minas Gerais (26 municipalities), the following municipalities share borders with the Doce River: Aimorés, Alpercata, Antônio Dias, Barra Longa, Belo Oriente, Conselheiro Pena, Coronel Fabriciano, Galiléia, Governador Valadares, Ipaba, Ipatinga, Itueta, Naque, Nova Era, Periquito, Ponte Nova, Resplendor, Rio Casca, Rio Doce, Santa Cruz do Escalvado, São José do Goiabal, São Pedro dos Ferros, Sem-Peixe, Timóteo, and Tumiritinga. In the state of Espírito Santo (7 municipalities): Baixo Guandu, Colatina, Linhares, Marilândia, Itaguaçu, Santa Teresa, Afonso Cláudio. Out of these, only the dam in Nova Era (Minas Gerais) has the dam named Barragem Mãe D’Água as a member of the National Dam Safety Policy. This dam was notified of a potential structural vulnerability in March 2019.
There are 15 municipalities downriver from the Brumadinho’s dam collapse (all in the state of Minas Gerais): Betim, Esmeraldas, Florestal, Fortuna de Minas, Igarapé, Juatuba, Maravilhas, Mário Campos, Papagaios, Pará de Minas, Paraopeba, Pequi, Pompéu, São Joaquim de Bicas, and São José da Varginha. Only two municipalities have dams that are part of the National Dam Safety Policy (and make part of our sample): (i) Betim, with the dam named Dique D, and (ii) Igarapé, with the dams Barragem B1 Auxiliar - Mina Tico-Tico and Barragem D2 - Mina Tico-Tico. None of these dams received alerts concerning their structural vulnerability from 2017 to 2019.
The mining industry is generally located in the countryside and placed in small/medium municipalities. As the mining activity demands skilled labor, the entrepreneur also looks for employees in the surrounding municipalities, increasing income in nearby municipalities. Sometimes, employees also prefer to live in surrounding municipalities because of lower living costs.
The mesoregions in Brazil are regional geographic divisions delineated by the Brazilian Institute of Geography and Statistics (IBGE). They are groups of municipalities with an urban center and several local peripheral members in the surroundings. The IBGE identifies local urban centers by considering the connection of nearby municipalities through relations of dependency and displacement of the population in search of goods and the provision of services and jobs.
All municipalities in a specific mesoregion are always members of the same state.
We need to fix exactly one notified neighborhood so that the definition of the dummy variable \(Post_{i,t}\) is well-defined. Otherwise, we could potentially have two notified neighborhoods with government alerts issued at different periods.
If some municipalities are close enough to each other, the workforce between these municipalities may be mobile. In this case, even though we are capturing this as an indirect effect, it could be perceived as a direct effect on the notified municipality, but with the workforce from the surroundings. While we cannot disentangle this direct effect due to the workforce mobility from our spillover estimates, our empirical tests show that geographical distance plays an important role in the degree to which neighboring municipalities receive spillover effects.
Disdier and Head (2008) surveyed the literature on distance effects on bilateral trade. They find that distance negatively affects trade and that this result has remained persistently high since the middle of the century. Geographical distance also affects firm-to-firm relationships. Cross-border alliances are more probable to occur between firms from shorter geographical distances (Jha et al. (2019)).
We use the Haversine great circle distance to account for the globe’s spherical format.
We also incorporate the second-order terms that are not collinear with the fixed effects that result from the third-order interaction.
These regions encompass inhabited areas within a radius of 10 km or 30 minutes from the tailing dam structure.
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We thank the two anonymous referees for the insightful comments. Thiago C. Silva (Grant no. 408546/2018-2) and Benjamin M. Tabak (Grant no. 310541/2018-2, 425123-2018-9) gratefully acknowledge financial support from the CNPq foundation.
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Silva, T.C., Muniz, F.J. & Tabak, B.M. The Impact of Government Disaster Surveillance and Alerts on Local Economic and Financial Conditions. Environ Resource Econ 84, 559–591 (2023). https://doi.org/10.1007/s10640-022-00736-4
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DOI: https://doi.org/10.1007/s10640-022-00736-4