Skip to main content

Estimation of damage to human health due to forest burning in the Amazon


Forest burning stands as a highly used practice in the Brazilian Amazon Forest. Burning trees is the cheapest way to expand agricultural frontiers in the Amazon region. Nevertheless, the smoke generated in this process can produce undesirable negative effects, in particular health-induced problems by the polluted air. This study aims to investigate whether these effects on the health of the region’s population are statistically significant, using as variables mortality and morbidity rates associated with respiratory diseases. The estimated morbidity model was used to estimate the health damage to those people who became ill only by the fire.

This is a preview of subscription content, access via your institution.


  1. 1.

    A contingent valuation technique may be adopted by asking people directly about their willingness to pay or accept variation in the risk to life or the incidence of an ailment. Or more commonly, by observing the wage variation regarding the variation of risk in the occupation.

  2. 2.

    We would like to thank the referee for this suggestion.

  3. 3.

    Following the referee’s suggestion, we also estimate the same models that appear in Table 1 using the morbidity caused by waterborne diseases (Cholera, intestinal infections, typhoid fever, Poliomyelitis, Shigellosis, Shistosomosis, and Amoebic dysentery) as a dependent variable. In all regressions, the independent variable, fogo, does not contribute to increase morbidity. Following the referee’s rationality, we can infer that our results are not spurious. The results of these estimations are available upon request.

  4. 4.

    Under certain conditions, the central limit theorem guarantees that the estimate distribution converges towards normal. In the case of this distribution, the mean value coincides with the most probable value, which is the mode.


  1. Ardila S, Queiroga R, Vaugham WJ (1998) A review of the use of contingent valuation methods in project analysis at the Inter-American Developing Bank. Report of Vanderbilt University, Nashville

    Google Scholar 

  2. Breusch TS, Mizon GE, Schmidt P (1986) Efficient estimation using panel data. Econometrica 57(3):695–700

    Article  Google Scholar 

  3. Cornwell C, Rupert P (1988) Efficient estimation with panel data: an empirical comparison of instrumental variables. J Appl Econ 3:149–155

    Article  Google Scholar 

  4. Externe E (1998) Externalities of fuel cycles: economic valuations—an impact valuations approach.

  5. Hausman J (1978) A specification test in econometrics. Econometrica 46(6):1251–1271

    Article  Google Scholar 

  6. Hausman JA, Taylor W (1981) Panel data and unobservable individual effects. Econometrica 49(6):1377–1398

    Article  Google Scholar 

  7. Heintz RJ, Tol RSJ (1996) Secondary benefits of climate control policies implications for the global environment facility. CSERGE Working paper GEC 96-17, London

    Google Scholar 

  8. Homma AK (1993) A Dinâmica dos Desmatamentos e das Queimadas na Amazônia: Uma Análise Microeconômica. Anais do XXXI Congresso Brasileiro de Economia e Sociologia Rural 1:663–676

    Google Scholar 

  9. IBGE Instituto Brasileiro de Geografia e Estatística (1987) Diagnóstico Ambiental da Amazônia Legal

  10. Judge G, Hill C, Griffiths W, Lee T, Lütkepohl H (1982) Introduction to the theory and practice of econometrics. Wiley, New York

    Google Scholar 

  11. Johnston J, Dinardo J (1997) Econometric methods. McGraw-Hill International Editions

  12. Markandya A (1998) The valuation of health impacts in developing countries. Planejamento e Políticas Públicas 18:119–143

    Google Scholar 

  13. Nepstad D (1999) Large-scale impoverishment of Amazonian forests by logging and fire. Nature 398:505–508

    Article  Google Scholar 

  14. Nepstad DC, Moreira AG, Alencar AA (1999) A Floresta em Chamas: Origens, Impactos e Prevenção de Fogo na Amazônia. Brasília: programa Piloto para a Proteção das Florestas Tropicais do Brasil

  15. Ostro DB (1987) Air pollution and morbidity revisited: a specification test. J Environ Econ Manage 14:87–98

    Article  Google Scholar 

  16. Ostro DB (1983) The effects of IR pollution on work loss and morbidity. J Environ Econ Manage 10(2):371–382

    Article  Google Scholar 

  17. Pearce D (1998) Valuing statistical lives. Planejamento e Políticas Públicas 18:69–118

    Google Scholar 

  18. Seroa da Motta R, Mendes APF (1995) Custos de Saúde Associados à Poluição do Ar no Brasil. Pesquisa e Planejamento Econômico 25(1):165–198

    Google Scholar 

  19. Seroa da Motta R, Ortiz RA, Ferreira SF (2000) Health and economic values for mortality cases associated with air pollution in Brazil. Expert workshop on assessing the ancillary benefits and costs of greenhouse gas mitigation strategies. Washington, DC, March 27–29

  20. Thomas V (1985) Evaluating pollution control: the case of São Paulo. J Dev Econ 19:133–146

    Article  Google Scholar 

Download references


We are thankful for helpful comments received from an anonymous referee and the editor for helpful comments on this work. All errors are our own.

Author information



Corresponding author

Correspondence to Paulo R. A. Loureiro.

Additional information

Responsible editor: Junsen Zhang



Appendix 1 Descriptive statistics of the variables of the model

Listed below are the basic statistics for the variables used in the model. As the database was structured as panel data, a slightly more complex presentation was needed for the statistic description of the variables. Had the variables been described in a trivial manner, either by a time series or cross-section, information related to the data sample would be contained in the very first line, where the designation “overall” appears, for which statistics are calculated for the entire data set, and for which differences among groups are not considered. In this case, the moments are described by the means \(\overline{\overline x} \) and \(\overline{\overline y} ,\) in both i and t, for the dependent and independent variables and the total sum of the squares is given by:

$$S^{o}_{{xx}} = {\sum\limits_i {{\sum\limits_t {{\left( {x_{{it}} - \overline{\overline x} } \right)}{\left( {x_{{it}} - \overline{\overline x} } \right)}\prime \quad {\text{and}}\quad S^{o}_{{xy}} = } }} }{\sum\limits_i {{\sum\limits_t {{\left( {x_{{it}} - \overline{\overline x} } \right)}{\left( {y_{{it}} - \overline{\overline y} } \right)}} }} }$$

As the data structure of the model is presented in panel data form, further analysis is required. The term “within” describes the situation in which the means are broken down by group with regard to the entire sample. Thus, instead of \(\overline{\overline x} \) and \(\overline{\overline y} ,\) we have \(\overline{x} _{i} \) and \(\overline{y} _{i} \) considered only in t for each group i; and the sum of the squares and of the cross products are given by the following expressions:

$$S^{w}_{{xx}} = {\sum\limits_i {{\sum\limits_t {{\left( {x_{{it}} - \overline{x} } \right)}{\left( {x_{{it}} - \overline{x} _{{_{i} }} } \right)}\prime \quad {\text{and}}\quad S^{w}_{{xy}} = } }} }{\sum\limits_i {{\sum\limits_t {{\left( {x_{{it}} - \overline{x} _{{_{i} }} } \right)}{\left( {y_{{it}} - \overline{y} _{i} } \right)}} }} }$$

Lastly, definitions may be made considering the differences among groups. In this case, the term “between” is used. Here, the sum of the squares and the cross products may be expressed as follows:

$$S^{b}_{{xx}} = {\sum\limits_i {T{\left( {\overline{x} _{i} - \overline{\overline x} } \right)}{\left( {\overline{x} _{i} - \overline{\overline x} } \right)}\prime \quad {\text{and}}\quad } }S^{b}_{{xy}} {\sum\limits_i {T{\left( {\overline{x} _{i} - \overline{\overline x} } \right)}{\left( {\overline{y} _{i} - \overline{\overline y} } \right)}} }$$

Table 5 lists the basic statistics of the variables, taking into consideration what was outlined above. Naturally, the blank spaces for the means, with regard to the sample groups “within” and “between,” are there to save space. As there are 261 groups (districts), we would need the same number for these measurements.

Table 5 Descriptive statistics of the variables

Appendix 2. Database

Regarding the health data, in this study, we have used annual hospital treatment values, average in-patient treatment value, and the average in-patient treatment period related to respiratory illnesses (CID-10 X). The sample covered all districts in the Amazon region. The data also refer to the public health services, extracted from DATASUS, the Ministry of Health’s information service.

The estimation of the area burned used in this study was obtained from the so-called hot pixels. These hot pixels represent satellite-observed fire outbreak points. This information was processed by the Wood Hole Research Center.

With regard to the population data used in this study, a series for this variable had to be constructed. This was due to the fact that there were no annual data available for the districts, as this information is only available for those periods in which the Brazilian Institute of Geography and Statistics (IBGE) census was conducted. The procedure used in this case was to take the district population data for the 1991–1997 period and determine the remaining periods by interpolation based on a linear growth rate, determined by the district population values for the 2 years indicated above.

As information regarding district income was also not available for the period, the real lumber production value (logs), and the real agricultural production value (temporary planting) were tested as proxies for this variable. Another possible proxy for municipal income is the use of a cattle-raising activity index, which is directly related to the growth in the region’s cattle herd. All these data were constructed from information obtained from the Sistema IBGE de Recuperacão Automatica (SIDRA) system of the Agricultural Census, maintained by the IBGE. The municipal income data for all municipalities were only for those years for which a demographic census was conducted.

Data regarding unpaved roads were generated from a joint study conducted by Institute of Applied Economic Research (IPEA) and IBGE (IBGE 1987). Sawed lumber production was obtained from the Institute of Environmental Research for Amazônia (IPAM) database.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Cardoso de Mendonça, M.J., Sachsida, A. & Loureiro, P.R.A. Estimation of damage to human health due to forest burning in the Amazon. J Popul Econ 19, 593–610 (2006).

Download citation


  • Amazon
  • Fire
  • Respiratory diseases

JEL Classification

  • C33
  • D63
  • Q2