




































Ecology, Economy and Society–the INSEE Journal 1 (1): 43–65, April 2018 

 
RESEARCH PAPER 

 

Health Damages from Air Pollution: Evidence from 
Opencast Coal Mining Region of Odisha, India 
 

Tapaswini Nayak  and Indrani Roy Chowdhury   
 
Abstract: The present study examines the productive day‘s loss of local 
communities in the opencast coal mining region of Angul (Talcher) district in 
Odisha, due to respiratory illness, by using the dose-response function model. The 
productive day‘s loss is estimated in terms of restricted activities or work days lost, 
due to severe respiratory illness (RI), induced by air pollution. Health diaries are 
analyzed through the seasonal household survey to predict the likelihood of RI-
related sickness (in terms of the restricted days) of the residents of the mining 
region (due to air pollution). Poisson and negative binomial regression are fitted for 
the purpose of count data analysis. The regression result confirms that there is a 
positive and significant relationship between the level of air pollution (respiratory 
suspended particulate matter (RSPM)/particulate matter less than 10 g/m3(PM10) 
and RI-related sick days, depicting that a reduction in air pollution level (PM10) 
may cause a reduction in expected number of RI-related sick days in the coal 
mining region. Further monetary welfare gain from avoiding the RI-related sick 
days are estimated for the population of Talcher coal mining area, Odisha.   
Keywords: Air pollution; respiratory illness; health diaries; dose-response function; 
count data regression. 

 

1. INTRODUCTION 

Many developing economies with abundant mineral reserves are trapped in 
a low-level equilibrium, with abject poverty, unemployment, ill health, and 

                                                        
 Department of Economics, Kendrapara Autonomous College, Odisha. India 754240; 

tapaswininayak82@gmail.com  

 Centre for the Regional Development, School of Social Sciences, Jawaharlal Nehru 
University (JNU), New Delhi, India-110067; indranirc1@gmail.com 

Copyright © Nayak and Roy Chowdhury 2018. Released under Creative Commons 
Attribution-NonCommercial 4.0 International licence (CC BY-NC 4.0) by the authors.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 
Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581-6152 (print); 2581-6101 (web). 

DOI: https://doi.org/10.37773/ees.v1i1.9  

https://doi.org/10.37773/ees.v1i1.9


Ecology, Economy and Society–the INSEE Journal [44] 

Figure 1: Mineral Growth Trend in Odisha (in last 10 years) (Production in 
million tons) 

 
Source: Odisha Economy survey, 2014-15 

poor education. This is the well-documented ‘resource curse‘ or ‗paradox of 
plenty‘ (Sachs and Warner 1997, 2001). Balancing efficient utilization of 
mineral resources with ensuring equitable and sustainable development and 
safeguarding the local environment and societal well-being is often a 
challenge.  
 

In the post-liberalization period, many Indian states are encountering a 
voracious appetite for energy and infrastructure. The intense interstate 
competitions trigger the demand for energy and infrastructure, which is 
needed to attract investment to boost growth. This appetite has led to the 
rampant extraction and utilization of mineral resources in these states. 
Mining activities are often accompanied by a drastic jump in mineral rich 
pollution load (both air and water pollution), widespread soil degradation 
and forest cover loss (State of Environment Report 2008; David Stuligross 
1999).  

The state of Odisha is mineral-rich. In the post-reform period, Odisha and 
other mineral-rich states have been struggling to answer a difficult question: 
should they intensify their extraction of mineral resources in their quest for 
a high rate of growth at the cost of their environment? 

Odisha is blessed with an abundance of mineral reserves — 24 per cent of 
India‘s coal reserves, 17 per cent of iron ore, 98 per cent of chromite, 51 
per cent of bauxite, 35 per cent of manganese, and 92 per cent of nickel ore 
(Odisha Economic Survey 2014–15). The state has the second largest 
reserve of coal in India. The share of mining in Odisha‘s gross state 
domestic product (GSDP) has escalated significantly to 7 per cent of GSDP 
in 2014–15 (Odisha Economic Survey 2014–15). The state ranks first in 



[45] Tapaswini Nayak and Indrani Roy Chowdhury 

Figure 2: Production of Major 
Minerals in Odisha, 2013-14 (in Rs. 
lakh) 

 
Source: Odisha Economic survey, 
2014-15 

country in terms of value of coal reserves and extraction. The state has 
witnessed an unprecedented increase in coal production from 8 million tons 
in 2003–04 to 60 million tons in 2013–14. Figures 1 and 2 depict the 
production recent growth trends of major mineral resources in Odisha. 

Coal alone accounted for 70 per cent 
of the mineral royalty received by the 
state for each of the past four years 
(Odisha Economic Survey 2014–15).  

Odisha is one of the richest 
biodiversity regions in South-East Asia 
(State of the Environment Report 
Odisha 2007). In terms of mineral 
deposits and production, the state 
occupies a vital position in the country 
(Murthy and Rao 2006; Economic 
Survey of Odisha 2014–15). The 
strong performance of the mining 
sector in terms of both production and 

value has thus resulted in a significant growth of the state‘s average GSDP 
(Economic Survey of Odisha 2014–15). The investment share of the mining 
sector in total outstanding investments makes Odisha the fifteenth highest 
recipient of foreign direct investment (FDI) among all Indian states. The 
mining revenue receipt has been steadily increasing over the past decade 
(Odisha Economic Survey 2014–15).  

A report by the State of Environment (2008) has declared all the coal belts 
of the state most unsafe for human health and living conditions. The 
neighbourhood of the coal belt witnesses alarmingly high concentration of 
suspended particulate matter (SPM) and respirable particulate matter (RPM) 
exceeding the national standard level by several folds. Moreover, WHO 
(2002) suggests that coal mining process which release toxic pollutants can 
adversely affect the air quality in the surrounding environment. 

Coal is an important source of fuel and power and is therefore critically 
important for the growth and development of a transition economy like 
Odisha‘s. Moreover, coal has the highest forward linkage with a number of 
other industries. Therefore, extraction of coal is an attractive option for a 
state for the creation of direct and indirect employment opportunities, 
foreign exchange earnings through FDI, generation of mineral royalties, tax 
revenues, etc. However, coal production is treated as ‗dirty industry‘ as it is 
the most polluting natural resource (Elizabeth and York 2012; Michelle 
2014). Apart from the direct impact of the occupational hazards of the coal 
miners, coal mining activities, particularly open cast coal mining, imposes 



Ecology, Economy and Society–the INSEE Journal [46] 

highly negative environmental externalities (through air pollution, water 
pollution, contamination and loss of fertility of land, and forest loss and 
degradation) and health externalities (in terms of poor quality of health 
among the local communities, living in the proximity of opencast coal 
mining region). Mining and extraction activities create irreversible damages 
to the local environment, having long-run future consequences. From 
mining to combustion of coal, all the intervening processes release various 
toxic pollutants (such as RSPM (PM10)), carbon dioxide sulfur dioxide (SO2) 
which adversely affect the air quality in the surrounding environment 
(WHO 2002). These huge amounts of wastages and pollutants (along with 
fly ash, coal dust, and heat) have dangerous health impact for the human 
beings living in the neighbourhood. Thus, from the policy perspective, or 
for state planning, it poses a serious challenge, in terms of a trade-off 
between the overall growth and development of the state vis-a-vis 
environmental degradation and health concerns of the local communities. 
Given the economic importance of coal, for a state like Odisha, forgoing 
the mineral extraction activities implies forgoing immediate opportunities 
for the entire state seems exorbitantly costly at the present stage of its 
development. As a consequence, the environment and health concerns of 
local communities in the mining region remain on the back burner. 

Opencast coal mining leads to more severe air pollution than underground 
mining (Ghose and Banerjee 1995). All the mining processes, starting from 
drilling to transportation and screening, are the major sources of such 
emissions (Pathak et al. 2007). Air pollution in opencast coal mines is mainly 
due to the toxic emission of particulate matter and gases including methane, 
sulfur dioxide, and oxides of nitrogen. These air pollutants reduce air 
quality and ultimately affect people around mining areas (Nanda and Tiwari 
2001). The major air pollutants produced by opencast coal mining are SPM 
and RPM (Sinha and Banerjee 1997; CMRI 1998). Mine fire is also 
considered as a major source of air pollution in some of the coalfields. The 
problems may have local, regional, and global impacts. Dust generated by 
opencast coal mining also affects agricultural productivity (Mishra 2008). 
Several research findings (Gupta 2006; Chowdhury and Imran 2010) show 
high concentrations of lower atmospheric pollution, especially 
RSPM(PM10), adversely contribute to human morbidity, increase respiratory 
syndromes, and reduce lung functions. Longer term exposure to air 
pollution (PM10) may lead to irritation, asthma, high blood pressure and 
heart diseases (Pope et al. 1995, 2007). High concentration of RSPM 
increase respiratory diseases such as chronic bronchitis and bronchial cases, 
while gaseous emissions contribute towards global warming, acid rain, and 
ground-level ozone besides causing health hazards to the exposed 



[47] Tapaswini Nayak and Indrani Roy Chowdhury 

population (Pathak et al. 2007). Therefore, health risk and morbidity due to 
air pollution is high where opencast coal mining is predominant. Pattanayak 
et al. (2011) also observe that in Keonjhar, Odisha, local communities are 
vulnerable to diseases such as acute respiratory illness (RI), gastric, 
tuberculosis, and skin diseases, due to high exposure to toxic elements. All 
these health problems no doubt have economic costs arising from the 
expenses incurred in treating the diseases and productive days lost (Ostro 
1994; Banerjee 2001). 

Traditionally, opportunity costs of human health was interpreted solely on 
account of the individual‘s physical well-being without considering the 
environmental and social backgrounds around him (Rapport and Mergler 
2004). From the 1970s, there has been a departure from the traditional 
belief, and ‗health‘ is recognized as a precious asset in itself and an 
absolutely essential ingredient for human development and productivity 
(Grossman 1972). Good health with mental well-being is termed both a 
‗resource‘ and ‗means‘ for stimulating economic development, and an 
outcome of economic development (Brundtland 2002); the WHO (2005) 
argues that ‗quality of health‘ is related not only to freedom from diseases 
but also associated with a healthy and hygienic environment. 

Therefore, this paper attempts to explore the linkages between health and 
the environment in the opencast coal mining area in Angul district of 
Odisha. This linkage paves the pathway for the significance of 
environmental quality in determining the quality of health and well-being. 
Given the enormous economic cost involved in terms of productive days 
lost and treatment of RI-related diseases caused by coal-mining activity, it is 
critically important to estimate the health impact of air pollution in the 
opencast coal mining region. 

This paper is organized as follows. Section 2 reviews the literature. Section 
3 explains the dose-response methodology that depicts the association 
between human health and air pollution. Section 4 presents the household 
survey design. Section 5 describes the econometric methodology. Section 6 
presents the empirical estimation of RI-related sick days and discusses the 
regression results. Section 7 discusses the evaluation of the welfare gain of 
air pollution reduction. Finally, Section 8 presents the main findings and 
conclusions. 

 

 

 

 



Ecology, Economy and Society–the INSEE Journal [48] 

2. LITERATURE REVIEW 

The seminal work by Grossman (1972) pioneered the formulation of a 
theoretical model of household production function. He analyses the 
demand for health by developing human capital model, where he suggests 
‗health‘ as a durable human capital stock and medical care as an important 
factor in its production. Cropper (1981) added pollution as a crucial 
ingredient and calculated the health damage reduction from the reduction in 
the air pollution level. Ostro (1994) suggests the a three-step method of 
calculating the health benefits of reducing air pollution. The first is to find 
out the dose-response relationship that shows the health outcome of air 
pollution. The second is to set the target group population, who are 
victimized, and exposed to the specific air pollution effect. The third is to 
calculate the change in air pollution level by implementing a specific 
pollution-reducing technique. 

The more recent studies have attempted to explore a relationship between 
air pollution and the occurrence of illness by using dose-response and 
damage functions, while some economists have calculated the health 
damage cost of air pollution levels using one of three methods: cost-of-
illness (COI), dose-response, and contingency valuation (Alberini et al. 1997; 
Ostro 1994; Krupanik 2000). In the case of South Asia, Murty et al. (2003) 
and Gupta (2008) calculated the health benefits from the reduction in urban 
air pollution level in the Indian states. Mishra (2012) estimated the damage 
to human health and agricultural productivity loss from pollution induced 
by coal-mining activities in Odisha. Chaulya (2003) shows that the annual 
average SPM and RSPM (PM10) concentration in the Ib valley coalfield of 
Odisha exceeds the standards set in the Indian National Ambient Air 
Quality Standards (NAAQS) protocol for both residential and industrial 
areas. The problem is not limited to these areas alone; air ambient quality is 
critical in the state‘s entire mining belt. 

The literature on estimating the morbidity and health impact costs are 
mostly confined to urban air pollution, vehicular pollution, etc. There are 
some studies on the health impact of air pollution in the cement industry 
(Bogahawatte 2008) and iron mining (Pattanayak et al. 2011), and of the 
occupational hazards (i.e., direct impact) of coal mining (Sarkar et al. 2004), 
but there are only a few studies to the best of our knowledge on the health 
impact of air pollution induced by opencast coal mining in Odisha (Mishra 
2012; Singh et al. 2010; Hota and Mishra 2010). Most studies address the 
health concerns from air pollution and the pollution load in coal mining 
areas, but they do not address many important questions. 



[49] Tapaswini Nayak and Indrani Roy Chowdhury 

Table 1: Categorisation of Ambient Air Quality Status with respect to criteria 
pollutants based on prescribed standard and exceedance factor 

Pollution level Residential/Industrial 

RSPM(PM10) (µg/m3) PM2.5 (µg/m3 ) 

Low (L) 0-30 0-20 

Moderate (M) 30-60 20-40 

High (H) 60-90 40-60 

Critical (C) >90 >60 

Source: State Pollution Control Board, Bhubaneswar 

Therefore, this study tries to bridge the research gap by examining the 
association between the air pollution level and negative health outcomes 
(we consider RI for this study)1 in the opencast coal mining region (Angul-
Talcher) of Odisha. This study also tries to analyse the economic impact of 
negative health effects by considering the loss of wages of productive days 
for the study region. 

We raise some unanswered questions. (1) To what extent does the coal 
mining activities contributed to the health-related well-being of the local 
community in the mining region and to their environment? (2) Does the 
critical ambient air quality near the opencast coal belt matter to the health 
of the neighbouring community? (3) How do the socio-economic factors 
affected the health stock of the local communities in the neighbourhood of 
coal mining areas? (4) How many productive days are lost by the local 
communities due to RI related diseases on account of such negative 
externalities ? 

Against this backdrop, the study area is located in a coal mining district 
Angul of Odisha, in the eastern part of India. The state has two coalfields 
namely- Talcher coalfield (in Angul district) and lb River coalfield (in 
Jharsuguda district).The coalfield of Ib valley is comparatively new 
compared to that of Talcher coal field(Directorate of Mines, Government 
of Odisha, Bhubaneswar). The older the coal mines project, the more is its 
environmental impact. The numbers of active opencast coal mine projects 
are also larger in Talcher coal field area than the IB valley coal field. As per 
the latest report by Central Pollution Control Board (CPCB 2009) on 
Comprehensive Environment Assessment of Industrial Clusters, Talcher 
coal filed region of Odisha has secured seventh position as one of the most 

                                                        
1 RI includes all the common acute syndromes of respiratory diseases such as: bronchial 
asthma, cough with mucus, and sputum production, respiration allergy to dust, allergic 
cough, running nose, headache, flu, and fever, chest tightness, pain, and acute bronchitis. 
These symptoms are identified as commonly reported respiratory illness after consulting the 
specialist doctors from the Mahanadi Coal Field Hospital, located in the study region. 



Ecology, Economy and Society–the INSEE Journal [50] 

critically polluted industrial cluster in India.2 Keeping in mind of all these 
perspectives we select the Angul-Talcher coal mining region as our study 
region3. 

Table 2: Ambient Air Quality of the study region 2015-16 

Month 

Station 1 
(MCL) 

Station 2 
(TTPS) 

Station 3 
(NALCO Nagar, 

Angul) 

Station 4 
(Industrial Estate, 

Angul) 

Average Average Average Average 

RSPM 
(PM10) 

PM2.5 
RSPM 
(PM10) 

PM2.5 
RSPM 
(PM10) 

PM2.5 
RSPM 
(PM10) 

PM2.5 

January 171 130 154 122 137 98 137 94 

February 186 99 165 115 117 80 108 80 

March 161 87 136 76 129 65 118 74 

April 177 105 91 53 108 57 92 51 

May  149 93 123 79 97 80 95 50 

June 140 87 71 44 78 38 84 47 

July 126 72 56 34 80 47 88 53 

August 100 57 61 34 79 39 65 49 

September 87 39 70 51 100 49 96 57 

October 133 48 95 75 91 52 98 54 

November 135 75 126 68 105 57 105 54 

December 134 79 164 86 107 54 105 56 

Average 
Annual 

140 81 111.08 69.75 102 57 99.25 60 

Source: CPCB (2009) 

 

3. DEFINING THE RELATIONSHIP BETWEEN HUMAN 
RESPIRATORY HEALTH AND AIR POLLUTION: DOSE- 
RESPONSE FUNCTION 

Often, the relationship between air pollution and premature mortality is 
studied by using time-series analysis of daily observations of number of 
deaths and the pollution level. These studies capture the effects of the 
short-term exposure to pollution on the probability of dying. But it is 
expected that people, who are in a weak physical state or who have a history 
of chronic diseases are more susceptible to contact recurring diseases due to 
the high concentration of pollution in the environment and are therefore 
supposed to be the most vulnerable. These types of exposure can be 
captured through the ‘dose-response function‘. If there is a cause of high 

                                                        
2 The concentration of PM10 in air is very high for the study region which often exceeds the 
prescribed level (by Central Pollution Control Board) by two folds. See the table no (1) and 
(2) for detail about the severity of air pollution. 

3 See Figure 3 for the district wise extraction and value of coal in Odisha in recent year. 



[51] Tapaswini Nayak and Indrani Roy Chowdhury 

Figure 3: District wise break-up of extraction and value of coal in Odisha, 
2013-14 (Quantity in lakh MT and value in crore of rupees) 

 
Source: Odisha Economy survey, 2014-15 

concentration of pollutants in the surrounding environment, then it may 
trigger some negative health outcome (i.e., negative externalities of 
environmental pollution) as its effect. This association between cause and 
effect has been known as ‗dose-response function‘.4 

This function captures a range of responses towards the respiratory illness 
of different individuals due to different degree of outdoor pollutants, when 
other confounding socio-economic factors and the level of indoor pollution 
are controlled. This function therefore relates the importance of a ‗Dose‘ 
(that is the intensity of air pollution to which the local people are exposed) 
to the ‗responsiveness‘ of the individuals (the response may be any kind of 
morbidity due to the respiratory diseases, stress, and even death or 
mortality). The degree of impact can be captured as the proportion or the 
days of sickness or restricted activity days due to respiratory illness 
syndromes (H).This method estimates the partial derivative (or the slope of 
b) of ‗Dose-Response‘ function as given in the following equation (1). The 
purpose is to calculate the marginal effect of a change in occurrence of a 
given health outcome related to a change in air ambient quality (A) with 
other confounding factors. 

The next step by Ostro (1994) is to calculate the numbers of people who 
are supposed to be exposed to the particular air pollution effect (i.e. 
POPi).Then how many productive days (restricted activity days or sick 
days), they are losing because of the exposure. Finally, the last step is to 

                                                        
4See Ostro (1983, 1987, 1994), who estimated the ‗Dose-Response Function‘ to find out the 
impact of air pollution on morbidity and found that PM10, affect both the restricted activity 
days and work days lost. 



Ecology, Economy and Society–the INSEE Journal [52] 

calculate the welfare gain due to a unit change or reduction in ambient air 
quality (dA). 

Thus the dose-response function can be expressed as (following Ostro 
1994): 

 dAPOPbdH ii

**      (1) 

Where,
idH  = change in individual‘s risk of health outcome i 

b = slope from the ‗dose-response‘ function. 

iPOP  = targeted population at risk of health impact i 

dA  = change in ambient air quality under consideration 

 

4. SURVEY DESIGN 

The present study is based on the primary survey at the household level, 
though pollution data are collected from the secondary sources such as 
State Pollution Control Board, Odisha and Regional Pollution Control 
Board, Angul. To understand the severity of air pollution on the health 
outcome of the local residents we first conducted a pilot survey followed by 
five rounds of group discussions during September 2015. Baseline 
information was collected from that pilot survey from some randomly 
selected households in the vicinity of Mahanadi Coal Field Limited. The 
baseline information did confirm the severity of health problem in the 
region. To capture the exposure of air pollution on human health (for those 
who are residing near the coal field area) through ‗dose-response‘ method, 
we required background information on the health status of individuals 
residing in the study region along with other environmental and socio-
economic information. The pilot survey helped us to design a well-
structured questionnaire for the final survey. We conducted the survey 
during September 2015 to January 2016, involving (210) households of the 
10 neighbouring villages, of Angul-Talcher opencast coal mining region of 
Mahanadi Coal Field Limited (MCL). Following Krupanick (2000), Gupta 
(2008), Murty et al. (2003), Adhikari (2012) and Imran et al. (2010), the 
survey kept track of weekly health diary of the selected households for six 
weeks extended over two seasons. We followed two stage stratification, for 
selecting sample (following Gupta 2006 and Adhikari 2012). The reason for 
following a two stage stratification sampling procedure is to capture 
precisely the local people‘s exposure to air pollution and the consequent 
adverse health impact. In the first stage of stratification, the villages within a 
four-kilometre radius from the air pollution monitoring station were 
identified (and a total 10 villages were selected. We then randomly selected 



[53] Tapaswini Nayak and Indrani Roy Chowdhury 

21 households from each village. Thus a total of (21 x 10 = 210) 
households from 10 villages and an aggregate of 855 members were 
surveyed5. Out of 855 household members 254 members were suffering 
from RI symptoms during the first round of the survey. Given the seasonal 
differentiates in air quality levels, we collected health diary information on 
254 household members repeatedly for six weeks during two different 
seasons (i.e. October or post-monsoon and December or winter). The 
health diaries covered the RI-related health status, of mitigating 
expenditure, chronic illness etc. Therefore the survey resulted in a pooled 
data, containing 1524 (= 254 x 6) observations. But by using the Stata 
software 12.0, we found only 932(restrictive sample) observations having RI 
diseases, sick days, and mitigating expenses over the entire 6 weeks. Given 
that the present study is focused on the number of RI-related sick days, so a 
total of 932 observations on the basis of availability of full information 
could only be taken for empirical investigation. 

 

5. EMPIRICAL ESTIMATION OF RI-RELATED SICK DAYS 

Our goal is to estimate the number of sick days or restricted activity days of 
a person suffering from respiratory diseases. This dependent variable 
counts the duration of sickness episode which is related to the particular 
respiratory syndromes (associated with air pollution) based on one week 
recall period6.The dependent variable counts how many days in a week 
(based on one week recall period), an individual is unable to work or 
restricted from other activities due to respiratory illness. As our dependent 
variable i.e., ‗sick days‘ is finite number of non-negative integers (small 
discrete values, say 0 1 2, 3, 4, …) or count values, assuming 0 values in 
several observations, we need a probability distribution that takes care of 
this count data. We used the Poisson and Negative Binomial regression to 
model the count outcomes i.e., the negative health impacts of air pollution 
(captured by the number of restricted activity days) as experienced by the 
respondent in a given time period. 

                                                        
5 The sampling framework takes into account the randomly selected households from each 
of the 10 villages, which are located within a stipulated area i.e., 4 km radius surrounding the 
station. So the selection bias is likely to be minimum and therefore can be comfortably 
ignored. Here proximity is used to identify the neighbourhood areas which are very close 
within the radius of 4 km from the opencast coal mines. 

6 For the primary survey we collected information regarding the prevalence of RI-related 
diseases and the mitigating expenditure incurred by the household and therefore we set the 
recall period of one week. This is deliberately done to extract correct information on the 
mitigating expenditure data. 



Ecology, Economy and Society–the INSEE Journal [54] 

Given that Poisson regression is the starting point of count data analysis, 
we initially fitted the Poisson regression to the number of sick days by using 
the method of Pseudo Maximum Likelihood Estimation or Quasi- 
Maximum likelihood estimation. It is possible to write the probability of 
number of sick days(Y) for a respective individual, using the Poisson 
regression model as: 

 Prob. ....7,...23,1,0,
!













it

it

itit

it

it
it y

y

ey

x

y
Y

it  (2) 

 where
x

i e   

Here ity denotes the RI-related sick days or restricted activity days (related 

to air pollution level) for an individual i at time period t. ‗e‘ is the exponential 
function of 

itity  , the mean value of the number of sick days due to 

pollution.   is the vector of regression coefficients while itx  is the vector 

of independent variables. And itu is the unobserved error term that may 

affect the individual health stock. 

The estimated mean value is therefore: x

t e
ˆˆ  . A log transformation gives 

the equation: xi  ˆˆln    

As the count data variance usually exceeds the mean, the Poisson model has 
its limitation arising from the property of equi-dispersion. Alternatively we 
also run the negative binomial model (NBM) as an alternative model which 
accounts for the overdispersion in count data, when the conditional 
variance surpasses the conditional mean. The only difference between two 
is that NBM has an ancillary parameter (α) to model the overdispersion. 

5.1. Choice Variables 

The vector of explanatory variables comprises of daily PM10 data obtained 
from the nearest air pollution monitoring station from the respondents' 
residence. The study has narrowed down the ‗daily (PM10)‘ as the single-
most important indicator for air pollution in the coal belt, as the exposure 
to PM10 is considered to be most damaging impact on respiratory health 
(Gupta 2006; Chowdhury and Imran 2010).7  

                                                        
7  Several research findings show high concentrations of lower atmospheric pollution 
especially RSPM (PM10) contribute to human morbidity, increase respiratory syndromes, and 
reduced lung functions (Gupta 2006; Chowdhury and Imran 2010). The secondary data on 
air pollution shows that in open cast coal mining areas in Odisha, the PM10 level is 



[55] Tapaswini Nayak and Indrani Roy Chowdhury 

Table 3: Example of Variables and Categories Used 

Coal 
mining 

externalities 
(Due to air 
pollution) 

Defining 
variables 

Original Questions 

Health 

Adult outdoor 
exposure and 
workplace 
information 

Based on the questions regarding workplace 
location, number of working hours/day, 
affected by coal fumes/dust, loss of 
income/day if work lost due to RI related 
sickness  

Children 
outdoor 
exposure 

Based on questions regarding number of 
hours spent daily in school/college/other 
places and therefore whether exposed to coal 
fumes/dust or not. 

Occurrence of 
respiratory 
illness 

To capture the respiratory health status- 
different types of RI related diseases and its 
frequency of occurrence in daily and weekly 
basis, based on the recall period in the 
previous week and in last year were asked. 

Health effects Status of health with the level of air pollution 

Health cost 
Income lost due to loss of 
productive/restricted workdays   

Annual income Income calculated from different sources 

Per capita 
income 

Households total income from different 
sources divided by the number of household 
members 

Indoor pollution 
Captured by  the questions  on the nature of 
fuel use, use of chimney/exhaust fan in 
kitchen, disposal of solid waste? 

Ownership 
status 

Different types of assets from different 
sources 

Source: Author‘s own survey schedule 

We control for a bunch of variables8 including pre-existing illness (from 
previous week),  chronic diseases,9 exposure to  outdoor  pollution  or  coal 

                                                                                                                            
alarmingly high and often exceeds the national prescribed level by Central Pollution Board 
by several fold. 

8 Table 3 defines the main variables and its original question which were asked at the time of 
survey. 

9 When one individual has been suffering from the diseases like asthma, blood pressure, 
heart diseases, and tuberculosis for more than 1 year, then that person is to be treated to 
have a chronic illness. The chronic disease of an individual describes his health stock. If an 
individual has been suffering from a chronic disease then it is expected that he is more 



Ecology, Economy and Society–the INSEE Journal [56] 

Table 4: Descriptive Statistics of Independent Variables 

Variable Description Obs. Mean Standard 
Dev. 

Minima Maxima 

PM10 
Average 
Weekly PM10 
(ug/m3) 

6 148.68 13.79 132.5 167.5 

Age 

Age of 
Household 
Members in 
Years  

932 38.40    20.36 1 82 

Edu_inyears 

Education in 
Years of 
Schooling 
(for illiterate) 

932 6.58  4.08 0 15 

HH_size 
Household 
Size  

210 4.06   1.15 1  8 

HH-Income 
Household 
Income in 
Rupees. 

932 19062 12564 1500 80000 

PCI 

Per capita 
Income in 
Rupees. 
(Household 
Income 
divided by 
Number of 
household 
Members) 

932 4806 3094.07 700 20000 

Source: Primary Survey, 2015-2016 
 

dust, 10  smoking etc., to avoid overestimation of pollution exposure on 
health effects of the respondents. Besides these individual characteristics 

                                                                                                                            
vulnerable to air pollution exposure and is therefore supposed to have higher number of 
illness related sick days. This is used as control variable in our econometric analysis, which 
captures the occurrence of chronic illness among the individuals. It assigns a value ‗1‘ if the 
respondent reports a chronic diseases (as mentioned above) otherwise it assigns a value ‗0‘. 

10As discussed, coal is one of the most polluting natural resources (because it emits toxic 
pollutants such as carbon dioxide, sulfur dioxide, respiratory suspended particulate matters 
etc. to atmosphere). The brittle nature of coal creates coal dust in the coal mining activities 
including loading unloading, transportation etc. In the open cast coal mining neighbourhood 
villages, the suspended coal dusts often create haze in the atmosphere which often interrupts 
visibility. Given these facts it is evident that regular exposure of the air borne contaminants 
due to coal and coal dust may trigger respiratory illness. It is anticipated that more outdoor 
exposure may increase the probability of illness. The exposure to coal dust is a dummy 
variable which assigns 1 if the representative individual is exposed to outdoor coal dust (or 
stay in outdoor more than 1 hour a day), otherwise it is 0 (zero). 



[57] Tapaswini Nayak and Indrani Roy Chowdhury 

Table 5 Poisson Regression Model 

Dependent Variable 
(sick days) 

Poisson 

Independent variables Coefficient(P-value) Standard Error 

PM10 0.0059(0.001)*** 0.1907 

Age -0.0004(0.887) 0.0033 

Age square 6.2307(0.989) 0.0004 

Gender -0.061(0.178) 0.0454 

HH size 0.021(0.191) 0.0156 

Exposed_coaldust -0.057(0.178) 0.0426 

Awar_polutnhelth -0.139(0.001)*** 0.0436 

Asthma_cronic 0.123(0.001)*** 0.0378 

B.P -0.004(0,383) 0.0478 

Indiv_smoking 0.105(0.079)* 0.0604 

Fuel_coaldumy 0.071(0.081)* 0.0413 

Prevsweek_ill 0.123(0.001)*** 0.0370 

Prevsweek_expend. -0.052(0.146) 0.0361 

Exercise -0.028(0.539) 0.0466 

PCI 0.042(0.215) 0.0343 

Constant_ -3.260(0.001)*** 0.9934 

Log pseudo likelihood -1914.7761  

Wald ch2(df) (15) 95.28  

Prob.>chi2 0.000  

Pseudo R2 0.024  

No of observation 932  

Source: Authors  
Note: ***, ** and * indicate significance at 1%, 5% and 10% level 
respectively. 
 

(like age, gender, education, awareness about adverse health effect of 
pollution, per capita income level, and exercise habits) and household 
characteristics  (household  size  and  medium  of  cooking,  fuel  or  indoor 
pollution), that may affect the health outcomes are controlled in the 
regression analysis.11 

 

 

                                                        
11  We have presented the descriptive tables of independent variables in table 4, which 
includes only the continuous and categorized independent variable‘s descriptive statistics. 
We have not included some independent variables viz., gender, chronic disease, exposure to 
coal dust, or outdoor pollution, smoking habit, awareness regarding effect of pollution on 
health, coal as a fuel, exercise and previous week illness and illness related expenditure‘s 
descriptive statistics as these variables has taken as dummy variable in regression model. 



Ecology, Economy and Society–the INSEE Journal [58] 

6. REGRESSION ANALYSIS 

The regression result of the Poisson and NBMs are given in Tables 5 and 6, 
which display the regression coefficients, standard errors, and probability 
values (z>p) for each explanatory variable. 

In table (5) and (6), both the regression model (Poisson and NB) present 
approximately similar results, because the ‗α‘ value (the parameter which 
accounts for overdispersion) is very small (i.e., - 0.0103) in NBM. The only 
difference is that the Poisson model gives the robust standard error (as it is 
based on pseudo maximum likelihood method and the NBM accounts the 
overdispersion as it has one ancillary parameter i.e., α. 

The regression results confirms our hypothesis that there is a positive and 
significant relationship between the level of air pollution (RSPM/PM10) and 
RI-related sick days. The coefficient of PM10 (0.0059) is positive and 
statistically significant at 1 per cent level of significance (p-value is 0.001 or 
equivalently t-value is 4.22) in the Poisson model. The positive coefficient 
of pollution variable depicts that a reduction in air pollution level (PM10 
level) will cause a reduction in expected number of RI-related sick days. The 
coefficient of awareness regarding pollution related health impact is 
negative and significant at 1 per cent level; showing a decrease in sick days 
due to the rise in awareness level. The coefficient of chronic diseases 
asthma is positive and significant at 1 per cent level, thereby presenting an 
increase in sick days as the chronic asthma increases. The other socio-
economic and control variables such as individual smoking habits, coal-
fuel12 and illness in previous week have positive coefficients as expected 
and are also statistically significant.13 

The coal as a daily household fuel has been controlled for indoor air 
pollution. This variable is statistically significant and positive impact on 
aggravating the respiratory illness and its related sick days. The individuals 
who have active smoking habits are more susceptible to the exposure of air 
pollution and thus respiratory illness related sick days increases for them. 

                                                        
12 The frequency of using ‗coal‘ as a cooking fuel contributes to the level of indoor air 
pollution. The study assumes it as a dummy variable giving the value 1 if a representative 
household uses coal for their cooking regularly. Here, we consider if a household uses coal 
as a fuel for cooking more than 20 times in a month, then we take the value 1 and it takes 0 
otherwise. 

13  We have checked the multicollinearity and heteroscedasticity test for the model after 
running the regression and found these problems among the independent variables are not a 
serious concern for the model. The Wald chi2 (15) is 95.28 with p-value 0.000, which 
indicates the model is significant as a whole. 



[59] Tapaswini Nayak and Indrani Roy Chowdhury 

Table 6 Negative Binomial Regression Result 

Dependent Variable (sick 
days) 

Negative Binomial 

Independent variables Coefficient(P-value) Standard Error 

PM10 0.0059 (0.000)*** 0.1942 

Age -0.0004(0.891) 0.0034 

Age square 4.5107(0.992) 0.0004 

Gender -0.061(0.186) 0.0463 

HH size 0.021(0.200) 0.1595 

Exposed_coaldust -0.057(0.185) 0.4341 

Awar_polutnhelth 0.139(0.001)*** 0.0444 

Asthma_cronic 0.123(0.001)*** 0.3855 

B.P -0.004(0,390) 0.4875 

Indiv_smoking 0.105(0.084)* 0.6159 

Fuel_coaldumy 0.071(0.091)* 0.0421 

Prevsweek_ill 0.123(0.001)*** 0.0377 

Prevsweek_expend. -0.052(0.151) 0.3686 

Exercise -0.028(0.555) 0.0475 

PCI 0.042(0.220) 0.0349 

Constant_ 3.266(0.001)*** 1.0110 

Lnalpha : -4.567SE(1.4013) 

Alpha: 0.0103SE (0.1455) 

LR test of alpha=0 
Chibar2(01) Prob.>chibar2 

54.43 (0.000) 

Log likelihood : -1914.5084 

LRchi2(df): (15) 85.98 

Prob.>chi2 0.000 

Pseudo R2 0.022 

No of observation 932 

Source: Authors  
Note: ***, ** and * indicate significance at 1%, 5% and 10% level respectively 

The presence of respiratory illness in the previous week among the 
individual increases the risk of experiencing more RI-related sick days. 

 

7. WELFARE GAIN (IN MONETARY TERMS) OF THE 
REDUCTION IN RI-RELATED SICK DAYS 

To estimate the monetary benefits from the reduction in RI-related sick 
days, we calculate the marginal effect from the Poisson regression. Marginal 
effect defines the reduction in RI-related sick days due to the fall in PM10 
levels in air. 



Ecology, Economy and Society–the INSEE Journal [60] 

Following Gupta (2006), Chowdhury and Imran (2010) and Adhikari 
(2012), monetary benefits from reduction in RI-related sick days can be 
expressed as: 

 
7

365
10  PM      (3) 

Where   is the coefficient value of PM10 from Poisson regression model (β 

= 0.0059) 

λ is the predicted value of (H) or RI sick days from the Poisson model (λ = 
0.0205) 

(λ= 0.0205, is the predicted value of sick days from the Poisson model, 
which is calculated by the help of post-regression analysis command in 
Stata-12.0) 

∆PM10 is the change in the level of PM10 from the current to the safe 
standard prescribed level, (the average current level PM10 in two seasons is 
159 µg /m3 and the prescribed standard level by Odisha Pollution Control 
Board (OPCB) is 90.4 µg /m3 for residential area.) 

(β * λ) is the marginal gain from Poisson estimation or (0.0059 * 0.0205 = 
0.00012) 

The Poisson regression result shows that 1 µg /m3 reduction in PM10 results 
in a marginal benefit of 0.00012(days) for a representative household 
member in a week. In order to calculate the welfare gain in sick days for a 
representative household member by reduction in PM10 to a safe level, we 
have to multiply the marginal benefit value by ΔPM10 i.e., 0.00012 * 68.6 = 
0.00823 as gain in reduction in sick days for a representative household 
member. The annual benefit in monetary terms or welfare gain can be 

calculated by multiplying this value 0.00823 with the 
7

365  i.e., 0.4292 as 

annual monetary benefit for reduction in sick days due to reduction in PM10 
level or improvement of PM10. 

Putting the values in equation (3) 

0.0059 * 0.0205 * 68.6 * 52.14 = (0.43) days per year for a representative 
household member. 

Here: β = 0.0059, λ = 0.0205, ΔPM10 = 159 µg /m3 - 90.4 µg /m3 = 68.6 µg 

/m3 and 14.52
7

365
 . 



[61] Tapaswini Nayak and Indrani Roy Chowdhury 

The calculated average wage (per monthly) of working individual is Rs. 275 
from working group sample from the survey data. Thus the annual 
monetary gain for a representative household member is 0.43 * 275= 
Rs.118.25 by avoiding the RI-related sick days due to reduction in PM10 
level. By extrapolating for the total population of Talcher coal mining area, 
the monetary welfare gain from avoiding the RI-related sick days are 
estimated as Rs. 64, 95,51,5.66 per annum. 

However our study presents lower bound estimation of monetary welfare 
gain (of health improvement) from the reduction in air pollution in the 
opencast coal mining of MCL of Talcher area. The study has the limitation 
of not capturing the averting expenditure of air pollution and the 
opportunity cost of time spent for seeking care. It poses a serious constraint 
to gather information on these variables from the survey because of the 
heterogeneity of the respondents (covering children, women, and old 
persons other than the working people). Moreover the region is endowed 
with some active public health centres run by MCL, offering some basic 
health services to the local community. The out of pocket expenditure 
which was captured during the survey was mainly the expense incurred by 
the residents from the private consultation over and above the free services 
they can avail from primary healthcare centres (PHC). The compulsion to 
visit the private practitioner was mainly due to congestion, long waiting 
time, recurrence, lack of doctor, non-availability of medicine etc., as 
reported in the survey. 

 

8. CONCLUSION 

For developmental processes, the economic importance of coal is 
undoubtedly very high, being the cheapest source of energy. However coal 
is the most polluting natural resource; from production to combustion it 
leads to the emissions of various harmful gases including methane (CH4), 
sulfur dioxide (SO2), and nitrogen oxides (NOx), carbon monoxide (CO), 
particulate matter, various toxins, etc. Besides coal dust, coal wastes, and 
coal particles are produced during the mining process, soot is released 
during the transportation of coal. It is evident from various studies that all 
these pollutants can cause severe and potentially deadly respiratory and lung 
problems, and cardiovascular diseases. Thus, apart from the direct impact 
of the occupational hazards of the coal miners, coal mining activities 
(particularly opencast coal mining), imposes highly negative environmental 
externalities (through air pollution, water pollution, soil contamination and 
fertility loss and forest loss and degradation) and health externalities (in 
terms of poor quality of health among the local communities, living in the 



Ecology, Economy and Society–the INSEE Journal [62] 

proximity of opencast coal mining region). Although there is huge 
economic cost involved in tackling these environmental and health 
externalities,14 it is seldom addressed seriously at the policy level or in the 
implementation stage. Thus the pricing of coal involves huge cross-
subsidies in India from the unaccounted environmental and health 
damages. Even the conservative attempt of imputing these negative 
externalities in the pricing of coal would have increased the price of coal 
several folds. 

This primary concern of this paper has been on the impact of air pollution 
on respiratory health (in terms of their number of sick days or restricted 
days due to RI) among the residents of some coal mining regions of 
Odisha. The attempt is to capture the partial impact of air pollution 
(triggered by coal production) on respiratory health damages. Given that 
the level of PM10 is always at an alarming level in the Angul-Talcher coal 
mining region, it is natural to expect that the respiratory health outcome 
would be very pervasive and critical in the proximity of the coal field. It is 
evident from the literature that the critical air pollution level (particularly 
concentration of PM10 in air) affects the respiratory health status (lung and 
chest problems, cough, bronchial asthma, bronchitis, TB and other lung 
related diseases) of the residents in the proximity of the coal field. 

Our empirical exercise predict the likelihood of RI-related sickness (in 
terms of their restricted days/sick days) of the residents near the open cast 
coal mines (due to air pollution). We find there is a strong and direct 
relation between acute respiratory disease related sick days or restricted 
activity days and the level PM10 (air pollution) in Angul-Talcher coal mining 
area. The positive value of the coefficient of pollution variable depicts that 
a reduction in air pollution level (PM10 level) causes a reduction in expected 
number of RI-related sick days. We find that a representative individual in 
the Angul-Talcher region can save 0.43 days or Rs. 118. 25 by avoiding the 

                                                        
14 See Epstein et al. (2011, 92). At the Center for Health and the Global Environment, 
Harvard Medical School, Dr. Paul Epstein and others found that accounting for the full 
costs of coal would double or triple its price. The study, released in the Annals of the New 
York Academy of Sciences, tallied the economic, health, and environmental costs associated 
with each stage in the life cycle of coal—extraction, transportation, processing, and 
combustion—and estimated those costs, which are borne by the public at large, at USD 
175–500 billion annually. Air pollutants from combustion accounted for USD 187.5 billion, 
mercury impacts as much as USD 29.3 billion.  The report estimated the public health 
burden in Appalachian communities at USD 74.6 billion a year, and that death, injury, and 
increased healthcare costs constituted much of the impact. The study discussed several other 
impacts that are not easily quantified: coal mining and processing releases heavy metal toxins 
and carcinogens into water supplies; coal miners die at work or are injured; and coal mining 
societies are impacted. 



[63] Tapaswini Nayak and Indrani Roy Chowdhury 

RI-related sick days, if there is a reduction of 68.6 µg /m3 of PM10 per year. 
This is calculated by taking the difference between the average current level 
of PM10in the two seasons (i.e. 159 µg /m3) and the standard level (i.e., 90.4 
µg /m3) as prescribed by the OPCB. Thus the monetary welfare gain from 
avoiding the RI-related sick days for the entire population of Talcher coal 
mining area are estimated to be Rs. 64, 95,515. 66 per annum. Although the 
study derives the lower bound estimates of monetary gain due to the 
reduction in air pollution, the figures bears some serious policy concerns. 

 

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