




































 

Ecology, Economy and Society–the INSEE Journal 3 (2): 33–58, July 2020 

 
RESEARCH PAPER 
 

Revisiting the Economic Costs of Arsenicosis: A PSM 
Approach 
 

Sanjana Chakraborty  and Vivekananda Mukherjee  
 
Abstract: The present paper uses the propensity score matching (PSM) method to 
calculate the economic loss of arsenicosis-affected households. In contrast to prior 
studies, whose estimates of income loss were limited to labour-market sources, the 
PSM method controls for labour market and other sources of income, as well as 
demographic and educational factors, to identify losses from social discrimination. 
It first establishes that arsenicosis-affected households are subject to social 
discrimination, and then shows that this leads to a significant loss of expenditure. 
Second, it proves that overlooking social discrimination leads to an 
underestimation of income loss. The results have important implications, both for 
understanding the plight of arsenicosis-affected households and for cost–benefit 
calculations in the adoption of policies for fighting arsenic contamination. 

Keywords: Arsenicosis, Economic Cost, Propensity Score Matching, Social 
Discrimination 

 

1. INTRODUCTION 

Arsenic is a metalloid that is found throughout the Earth’s crust and in soil, 
sediments, water, air, and living organisms. The estimated global prevalence 
of arsenic in soil is 5 µg/kg (microgram per kilogram), but concentrations 
may vary considerably in different geological regions; the variations range 
from 0.1 to 4,000 µg/kg. Across different countries, arsenic is used as a 
drug as well as a poison. 

                                                           
 Department of Economics, Jadavpur University, Kolkata 700032, India; 
sanjanadrc@yahoo.co.in.  

 Department of Economics, Jadavpur University, Kolkata 700032, India; 

mukherjeevivek@hotmail.com.  

Copyright © Chakraborty and Mukherjee 2020. Released under Creative Commons 
Attribution-NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

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.v3i2.134  

https://doi.org/10.37773/ees.v3i2.134


Ecology, Economy and Society–the INSEE Journal [34] 

 

Arsenic can enter the human body directly through the consumption of 
contaminated drinking water and indirectly through the food chain, if 
arsenic-contaminated groundwater is used in agricultural production. 
Arsenic consumption leads to health hazards such as skin, lung, liver, 
bladder, and kidney cancer. It can harm the central and peripheral nervous 
systems as well as the heart and blood vessels. It may also cause birth 
defects and problems in the reproductive system. Arsenic-related health 
hazards may be classified into two types: (i) acute: including gastrointestinal 
discomfort, vomiting, diarrhoea, convulsions, coma, and ultimately death; 
(ii) chronic: including skin lesions (arsenicosis or arsenical dermatitis) that 
are characterized by hyper-pigmentation, hyperkeratosis, and hypo-
pigmentation. Acute toxicity, however, is infrequent. Arsenic’s effects on 
the victim are not immediately perceptible. Gradual arsenic poisoning from 
drinking tainted water can take years to ravage the body, producing pigment 
patches and scaly skin; swollen limbs and joints; and tumorous growths on 
limbs and feet. By the time even early symptoms are visible, the person is 
often riddled with cancer. Thus, arsenic has a cumulative toxic effect that 
destroys the human body, leading to inevitable death. Since rural areas, 
especially in developing countries, are seldomly supplied with treated 
surface water, people living there face a much higher threat of developing 
arsenicosis. 

While arsenic contamination has been reported in 38 countries in the world 
(Chakraborti et al. 2009), Asian countries have been affected more than 
others. Globally, the countries most affected by arsenic contamination are 
all from Asia: India, Bangladesh, China, and Taiwan, with India having the 
highest and Taiwan having the lowest incidence. The Ganga–Meghna–
Brahmaputra (GMB) plain of India and Bangladesh is the worst affected 
area in the world, giving cause for alarm. More than 500 million people 
living in the GMB plain may potentially be at risk from groundwater arsenic 
contamination (Nahar, Hossain, and Hossain 2008; BGS and DPHE 2001). 

Arsenicosis affects the health and well-being of both the affected 
individuals and their families (Hanchett 2004; Roy 2008). The people 
affected by arsenic poisoning suffer from symptoms like gradual loss of 
energy, physical weakness, loss of appetite, lethargy, sleeplessness, 
diminishing ability to work, gastrointestinal distress, burning sensations, and 
gradually progressive debilitation, which lead to an inability to work, and 
loss in income. It also leads to strained relations within the family and social 
stress. When the symptoms of arsenicosis become more evident, various 
agonizing social issues often arise, such as affected persons being fired from 
their jobs, children being debarred from their schools, girls being denied 
marriage, and married women facing marital problems and even divorce. 



[35] Sanjana Chakraborty and Vivekananda Mukherjee 

 

The affected people are often socially boycotted and ostracized from social 
functions as they are mistakenly considered to be contagious. These social 
barriers create economic challenges for the affected people and their 
families (Becker 1971).1 

Although arsenicosis cannot be cured, it can be adapted to and mitigated. 
The medical expenditures borne by affected households to treat arsenicosis 
are an example of adaptation. There are mitigation approaches that the 
affected persons can take as well: (i) using arsenic-safe water sources such 
as dugwells and deep tubewells that are regularly monitored for arsenic, 
piped water projects, etc., and (ii) improving one’s nutritional status by 
adopting a healthy diet with plenty of vitamins such as A, C, and E 
(Quamruzzaman et al. 2003; Milton 2003).2 The government may undertake 
policies to promote and complement these mitigation efforts. For example, 
it may spread awareness about arsenic-safe sources of drinking water and 
nutritional foods to combat arsenicosis. It may also undertake piped water 
projects and mark arsenic-safe tubewells.3 The economic impact of 
arsenicosis on a household, therefore, depends on the adaptation and 
mitigation efforts of the household as well as various social and policy 
parameters. 

While several studies have investigated arsenic contamination, most of them 
have explored its geological aspects, the related health problems, and 
technologies for its removal. Studies like Mandal et al. (1996), Chowdhury et 
al. (1999), Chowdhury et al. (2000), Chakraborti et al. (2002, 2003, 2009), 
and Ahmed et al. (2006) have mapped the spatial spread of arsenic risk 
zones in West Bengal, India, and in Bangladesh. These studies have also 
evaluated the scale of the problem in terms of the at-risk population. The 
geological aspects of groundwater arsenic contamination have been studied 
by Harvey et al. (2002, 2005), Akai et al. (2004), Acharyya and Shah (2007), 
Biswas et al. (2012), and Planer-Friedrich et al. (2012). Studies on 
epidemiological issues and adverse health effects related to chronic arsenic 
toxicity include Datta (1976), Milton (2003), Chakraborti et al. (2004), Kapaj 
(2006), Ghosh (2008), Vahter (2009), and Rahman et al. (2009). Substantial 
literature is also available on mitigation strategies and technologies for 
arsenic removal (Misbahuddin and Fariduddin 2002; Hossain et al. 2006). As 
mentioned before, besides its chronic effects, groundwater arsenic 

                                                           
1 A publication by the World Health Organization points out that the social problems arising 
from groundwater arsenic contamination increase the pressure on the economies of the 
affected areas (Curry et al. 2000). 
2 Malnutrition slows down the elimination of toxic arsenic from the body and aggravates 
arsenicosis. 
3 Arsenic-safe tubewells are coloured green and unsafe tubewells are coloured red. 



Ecology, Economy and Society–the INSEE Journal [36] 

 

contamination creates widespread social and psychological problems for the 
affected people and their families. Over the last decade, a number of 
studies, mostly in Bangladesh, have highlighted the social, socio-cultural, 
socio-economic, psychosocial, and mental health effects of arsenic 
poisoning (Curry et al. 2000; Barkat 2004; Hadi and Parveen 2004; Hanchett 
2004; Hassan et al. 2005; Ahmad et al. 2007; Nahar, Hossain, and Hossain 
2008; Brinkel et al. 2009; Sarker 2010; Mahmood and Halder 2011; Sultana 
et al. 2012; Syed et al. 2012). However, there is a dearth of such valuable 
studies that examine the social dimension of arsenic contamination in the 
Indian context. 

Studies evaluating the economic cost of arsenic contamination are limited, 
but designing any arsenic mitigation programme would require cost 
estimates. This study attempts to derive such an estimate of the costs 
involved. Studies like Khan (2007), Roy (2008), Mahanta et al. (2016), and 
Thakur et al. (2019) have measured the arsenic-affected households’ 
willingness to pay for mitigation projects by estimating their losses from 
labour market participation and remedial medical expenditures. 
Additionally, affected households also experience losses due to the social 
discrimination they face. The methodologies adopted by Khan (2007), Roy 
(2008), Mahanta et al. (2016), and Thakur et al. (2019) overlook this 
dimension of income loss. The present paper attempts to fill this gap.  

This paper accounts for the income loss of arsenic-affected households 
from reduced labour market participation as well as social discrimination. 
First, it shows that calculating the economic cost based on the labour 
market alone would lead to underestimation, as such calculations will not 
account for losses caused by social discrimination. Then, it estimates the 
labour supply behaviour and spending behaviour of the households facing 
social discrimination. It shows that in the presence of sufficiently strong 
social discrimination, households may work more, and when the labour 
market effects are controlled, such households spend less than other 
households. 

This theory is supported by survey data collected in 2005–06 from arsenic-
affected and unaffected households in Kolsur village in North 24 Parganas 
district, West Bengal, India. Our estimation of the economic cost compares 
the incomes of arsenic-affected households and observationally similar 
households which differ only in terms of the presence of arsenicosis. The 
observational match has been found using the propensity score matching 



[37] Sanjana Chakraborty and Vivekananda Mukherjee 

 

(PSM) method pioneered by Rosenbaum and Rubin (1985).4 With PSM, we 
control for the labour market and other sources of earning like working on 
their own land or on land owned by others, the household’s per capita 
working days per month in different seasons (summer, winter, and 
monsoon), adaptive expenditure to avoid labour market loss, and a host of 
other demographic and educational factors. We show that social 
discrimination exists and estimate the income loss that it creates. The 
results show that the measure of income loss per capita derived by Roy 
(2008) based on labour market outcomes alone, in a similar geographical 
region and during a similar time period, is an underestimation.5 

The paper contributes to the literature by providing a broader measure of 
the economic costs of arsenicosis by including the costs of the social 
discrimination faced by arsenic-affected households. It argues that the 
economic costs are greater than any estimate generated based on labour 
market outcomes alone. The results of the paper are important from a 
policy point of view, as the suggested method of economic cost calculation 
may make viable several arsenic removal programmes that are stalled due to 
unfavourable cost–benefit calculations. The paper also highlights the 
importance of policies in eliminating the social discrimination against 
arsenic-affected households, as it leads to significant economic losses for 
them. 

The paper is organized as follows: Section 2 presents a theoretical model to 
illustrate the way arsenicosis affects a household’s labour supply, income, 
and expenditure. It also shows that considering only labour market effects 
underestimates the welfare loss of arsenic-affected households and why a 
technique like PSM is required for this purpose. Sections 3 and 4 describe 
the methodology and data collection, respectively. Section 5 analyses the 
data and discusses the results. Section 6 presents the conclusions. 

 

2. THEORETICAL FOUNDATIONS 

Consider a representative arsenic-affected household that derives its utility 
from the consumption of commodities and leisure. The expenditure on 
commodities is represented by x . The time endowment of the household is 

represented by )A(T  hours, out of which L hours is consumed as leisure. 

                                                           
4 See Caliendo and Kopeinig (2008), Imbens and Wooldridge (2009), Heinrich et al. (2010), 
and Kassie et al. (2010) for recent reviews of the literature. 
5 Our result is not directly comparable to those of either Mahanta et al. (2016) or Thakur et al. 
(2019) due to geographic and temporal dissimilarities. 



Ecology, Economy and Society–the INSEE Journal [38] 

 

The utility function of the household is: )L,x(UU  , which is maximized 

by the choice of }L,x{ subject to the budget constraint: 

)A(M)L)A(T(wx   (1) 

In Equation (1), w  stands for the market wage rate and )A(M  stands for 

the non-labour income of the household. In our model, 0A  is an index 

of the severity of arsenicosis in the household. A high value of A  implies a 

high severity of arsenicosis. We assume that 0 )A(T that is, the more 

severely a household is affected with arsenicosis, the less time it has both 

for work and leisure. We also assume that 0 )A(M , that is, the greater 

the severity of arsenicosis in a household, the lower the non-labour income 

of the household. We treat )A(M  as an indicator of the social capital 

enjoyed by the household. We assume that the lower non-labour income of 
an affected household reflects the social discrimination it faces.  

Assuming the existence of an interior solution }L)A(T,x{ ** 00   to 

the household’s utility maximization problem, the equilibrium amount of 
labour supply and the equilibrium expenditure of the household are written 
as:  

)]A(M),A(T,w[L)A(TL **   (2) 

and 

)A(MwLx **   (3) 

respectively. Since leisure is a normal good, by income effect it follows 

that 0




T

L*

, 0




M

L*

 and 0








)A(T

T

L

A

L **

.  

Substituting }L)A(T,x{ ** 00   in the utility function above, the indirect 

utility function of the household is defined as: 

)]A(L),A(x[U)A(v **  (4) 

Proposition 1: The welfare loss of households due to arsenicosis calculated 
based only on labour market outcomes leads to underestimation. 

Proof: Applying the first-order conditions of the household’s utility 
maximization problem from Equations (3) and (4), we derive: 

)]A(M)A(Tw[
x

U

A

v










 (5) 



[39] Sanjana Chakraborty and Vivekananda Mukherjee 

 

The first term of the RHS of Equation (5) captures the loss in welfare due 
to the labour market effect, and the second term captures the loss due to 
lack of social capital, which is due to social discrimination.  

From the assumptions of the model, we know 0




x

U
, 0w , 0 )A(T  and 

0 )A(M . Therefore, the RHS of Equation (5) is negative. Hence, the 

statement of the proposition follows.  

Proposition 1 justifies the use of the PSM method for estimating the 
welfare loss of a representative arsenic-affected household. The method 
also captures the loss of income that arises due to the erosion of the social 
capital of such households, which previous studies have failed to capture. 

Proposition 2: (a) 
A

L*




≥ or < 0 if and only if )A(T  ≥ or < 

]
M

L
)A(M

T

L
)A(T[

**









 ; 

 (b) (i) if ]
M

L
)A(M

T

L
)A(T[)A(T

**









 , 0





A

x*

  

 (ii) if ]
M

L
)A(M

T

L
)A(T[)A(T

**









 , 

A

x*




≥ or < 0 

if and only if 
A

L
w

*




≥ or < )]A(M[  . 

Proof: From (2) we obtain: 

]
M

L
)A(M

T

L
)[A(T)A(T

A

L ***














;  

and from (3) we obtain: 

)A(M
A

L
w

A

x **










 

The statement of the proposition follows.  

As the incidence of arsenicosis escalates in a household, the endowment of 
time and social capital shrinks. In terms of labour supply, this evokes two 
kinds of reactions from the household. First, a direct effect of the reduction 
of time endowment )A(T  through which its supply of labour falls. Second, 

an indirect effect ]
M

L
)A(M

T

L
)A(T[

*
*

*









  through which the change in 



Ecology, Economy and Society–the INSEE Journal [40] 

 

income causes a decline in consumption and leisure but an increase in 
labour supply. The household’s supply of labour falls if the direct effect 
dominates the indirect effect. For such households, consumption 
expenditure falls as well. However, if the indirect effect dominates, the 
labour supply rises, i.e., the household works more. If social discrimination 

is strong, i.e., )A(M  has a large magnitude, the indirect effect would also be 

strong. If it is strong enough, as Proposition 2 suggests, it is perfectly 
possible that an arsenic-affected household works more than an arsenic-
unaffected household, and its consumption expenditure is more than that 
of an arsenic-unaffected household. However, if the labour supply remains 
unchanged or rises, social discrimination would imply a fall in the 
consumption expenditure of arsenic-affected households. 

Earlier studies like Khan (2007), Roy (2008), Mahanta et al. (2016), and 
Thakur et al. (2019), which do not consider the theoretical possibility of 
social discrimination and do not apply the PSM method as their empirical 
methodology, found that the labour supply and, consequently, the welfare 
of affected households, declined due to arsenicosis. The data and the 
methodology applied in this paper yielded a different result, which we 
explain at the end of Section 4. 

 

3. EMPIRICS: METHODOLOGY 

Impact evaluations (causal inferences) are fundamentally based on 
randomized experiments. In the case of successful randomization, the 
treated or affected group and the untreated or control group are identical 
with respect to all observed and unobserved characteristics except the 
treatment status. The two groups are thus totally interchangeable. Although 
a perfectly randomized experiment is considered a theoretically ideal, in 
practice, randomization is mostly infeasible, specifically in social science 
experiments or social behavioural research studies. Under such 
circumstances, non-experimental methods are used. But the non-random 
observational studies or non-experimental methods usually suffer from a 
selection bias (a bias in the sample selection). Techniques like multivariate 
regression analysis, instrumental variable method, quasi-experimental 
method, and PSM are used to minimize the selection bias. In this paper, we 
use the PSM method. 

The propensity score )}X(P{  is defined as the probability of participating 

in a programme given the observed set of variables X. Unlike with 
randomization, in PSM, the two groups are identical only in their observed 
characteristics. Dealing with multiple covariates, confounding background 



[41] Sanjana Chakraborty and Vivekananda Mukherjee 

 

covariates, and observed background characteristics is too troublesome due 
to both computational and data problems. So, Rosenbaum and Rubin 
(1985) suggested the use of a propensity score, which acts as a balancing 
score. When the set of variables to match is large, the applied matching 
procedure based on this unidimensional balancing score is known as PSM. 
In other words, PSM refers to the pairing of the treatment and control or 
comparison units with similar values on the propensity score, possibly 
discarding all unmatched units (Rubin 2001). 

3.1. Evaluation framework and matching basics 

Inferences about the impact of the treatment on the outcome of an 
individual involves speculation about how this individual would have 
performed without the treatment. The standard framework to discuss this 
problem is the potential outcome approach or Roy-Rubin model (or Rubin 
causal model). In the case of a dichotomous/binary treatment, the 
treatment indicator 1iD  if an individual i  receives the treatment or is 

affected and zero otherwise. The framework assumes that there are two 

potential outcomes 1Y  and 0Y  corresponding with ( 1iD ) and ( 0iD ). 

Importantly, a unit i  can only be in one state (either 1iD  or 0iD ) at 

one point of time, so only one of the two outcomes ( 1Y  or 0Y ) is 

observed. If, say, 1Y  is observed, the unobserved outcome 0Y  is taken as 

the “missing link”. The unobservable outcome of this framework is termed 
as “counterfactual” since it is counter to fact (Winship and Morgan 1999), 
i.e., what the outcome would be without the programme at the same point 
of time. Hence, we need a comparison group that will allow us to attribute 
any change in the treatment group to the programme to gauge causality 
properly. The PSM method matches the treatment and the 
comparison/control group based on their observed characteristics and 
investigates the impact of the treatment. 

In the present study, we consider the incidence of arsenicosis as the 
treatment. The outcome variables are per capita monthly household income 
and per capita monthly household expenditure. The use of the PSM 
method ensures that any selection bias is minimized with regard to 
observable characteristics. 

In applying PSM, we adopt the three-step analytic process described by 
Guo and Fraser (2010). The steps are as follows: 

 

 



Ecology, Economy and Society–the INSEE Journal [42] 

 

Step 1: Selection of background covariates 

The first is to search for and select the best background covariates that 
could be causing an imbalance between the treated and control groups. The 
treatment group (arsenic-affected group) and the control group (arsenic-
unaffected group) are compulsorily made to share a similar set of 
background covariates.  

Step 2: Calculation of propensity scores 

The propensity scores are calculated for each household using binary 
logistic regression or a binary discrete choice model (Gujarati and Sangeetha 

2007). Denoting the binary treatment condition as iD , we define 1iD  if a 

household is in the treatment condition (arsenic-affected) and 0iD  if a 

household is in the control condition (arsenic-unaffected).  

The vector of the conditioning variables is denoted as iX  and the vector of 

the regression parameters is denoted as i . The binary logistic regression 

depicts the conditional probability of receiving the treatment as follows: 

iiii

ii

XX

X

iii
ee

e
)D(E)X|D(P











1

1

1
 (6) 

Note that this is a nonlinear model, as iD  is not a linear function of the 

vector of conditioning variables iX . But the transformed equation through 

the logit function (i.e., the natural logarithm of odds or ]
)D(P

)Di(P
log[

i1
 

becomes a linear function of iX . The logit model being estimated is 

specified as:6 

iiei X)
p

p
(logL 



1

 (7) 

where P  denotes )D(P i . The propensity score is the estimated value of 

iP = )D(P i . As PSM is applied for the comparison of the outcomes of the 

two groups, the two assumptions mentioned below need to be satisfied. 

Assumption 1: iD s are independent over all i .  

The non-randomized data of this study ensure that Assumption 1 holds. 

                                                           
6 Alternatively, a probit model or a discriminating analysis can be used for the same purpose. 



[43] Sanjana Chakraborty and Vivekananda Mukherjee 

 

Assumption 2: Conditional independence assumption (CIA): 

X|D)(Y),(Y 10 . 

Assumption 2 implies that given a set of observable covariates X , which 
are not affected by the treatment, potential outcomes are independent of 
the treatment status. That is, it is assumed that the outcomes )(Y),(Y 10  are 

independent of the treatment status conditional on X . In this study, we 
assume that the CIA holds. 

As we have considered several background covariates, there is a problem of 
multidimensionality. To reduce this multidimensionality, the propensity 
scores are estimated. The background covariates are described in Section 4. 
The questionnaire based on which the data on the selected background or 
confounding covariates was collected is given in Appendix 1. After the 
propensity scores are calculated, we use them to match the two groups. 

Step 3: Matching 

After the propensity score estimation, the PSM is implemented. We 
construct matched pairs between the two types of households, i.e., the 
arsenic-affected households (treatment group) and the arsenic-unaffected 
households (control group) based on the maximum closeness of their 
propensity scores. Before matching, we must satisfy the following 
assumption. 

Assumption 3: Common support or overlap condition: 110  )X|D(P . 

To identify the common support region, we adopt the “minima and 
maxima criterion” approach, which eliminates all observations whose 
propensity scores are smaller than the minimum and larger than the 
maximum in the opposite group. Observations that lie outside the 
described region are discarded from the analysis. 

In the present study, we applied the most straightforward, traditional pair-
wise matching method (Rubin 1973), nearest neighbour (NN) matching, 
where households from the comparison group are chosen as a matching 
partner for the treated household that is closest in terms of the propensity 
score. NN matching is of two types: “with replacement” and “without 
replacement”. In the former case, an untreated/control household can be 
used more than once as a match, whereas in the latter case, it is considered 
only once (i.e., once a treated case is matched to a non-treated case, both 
cases are removed from the pool). In the present study, the sample size of 
the control group is large, so we use NN matching without replacement.  



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

 

Formally, if iP  and jP  represent the respective propensity scores of the 

treated and control participants, and if 1I  and 0I represent the respective 

sets of treated and control participants, a neighbourhood )P(C i  contains a 

control participant j  (i.e., 0Ij ) as a match for the treated participant i  

(i.e., 1Ii ) if the absolute difference in propensity scores is the smallest 

among all possible pairs of propensity scores between i  and j , as: 

0Ij||,PP||min)P(C jii    

Once j  matches to i , j  is removed from 0I  without replacement. If for 

each i  there is only a single j  found to fall into )P(C i , then the NN 

matching is 1-to-1 matching. If for each i  there are n  participants found 

to fall into )P(C i , then the NN matching is 1-to- n  matching. Note that 

here we are not imposing any kind of restriction on the distance between 

iP  and jP  as long as j  is the NN to i  in terms of the estimated 

propensity score. Even if ||PP|| ji   is large (i.e., j ’s propensity score is 

vastly different from i ’s), j  would still be considered a match to i . To 

overcome this problem, the following condition is derived, where the 
absolute distance between the propensity scores of the two participants will 
be less than the pre-specified tolerance for matching or a calliper (  ) 
satisfying: 

0Ij,||PP|| ji   (8) 

Rosenbaum and Rubin (1985) suggest that the size of the calliper be 0.25 of 
the standard deviation of the sample estimated propensity scores ( p )(i.e., 

p.  250 ). This variety of NN matching, which has been adopted in the 

present study, is known as calliper matching. Here, we are using NN 
matching without replacement within a calliper. By adopting this method, 
we get a subsample of the control participants and treated participants 
whose propensity scores have been matched. 

Step 4: Post-matching analysis 

After the matched treatment and control groups are formed, we compare 
the average of the outcome variables for the two groups and check if they 
are significantly different or not. The questionnaire about the outcome 
variables is presented in Appendix 1. 

 



[45] Sanjana Chakraborty and Vivekananda Mukherjee 

 

Table 1: Descriptive statistics and comparison of the outcome variables of arsenic-
affected households and arsenic-unaffected households  

Outcome 
variables 

Arsenic-affected  
(no. of observations = 374) 

Arsenic-unaffected  
(no. of observations = 392) 

Mean 
difference  

Mean Min Max Mean Min Max 

Per capita 
monthly 

household 
income 
(INR)  

1,475.52 
(495.15) 

500 4,000 
2,187.11 
(832.20) 

833 6,000 

 
-711.59* 

Per capita 
monthly 

household 
expenditure 

(INR)  

1,300.81 
(277.97) 

677 3,725 
1,730.33 
(555.28) 

677 5,033 

 
-429.52* 

Note: Standard deviations are in parentheses and *, **, *** denote a 1%, 5%, and 
10% level of significance, respectively. 

4. DATA 

The data used in the empirical analysis are drawn from a household 
database created by School of Environmental Studies, Jadavpur University 
(SOES, JU), using a primary survey during 2005–2006 in the arsenic-
affected village of Kolsur located in Deganga block, North 24 Parganas 
district (West Bengal, India). The database was a complete enumeration of 
1,000 households in the village. Of these, 374 households were arsenicosis 
affected, and the rest (i.e., 626 households) were unaffected. In our study, 
we included all the 374 affected households in the treatment group. The 
control group was constituted by unaffected households living within a 
radius of 6–9m from each of the affected households. Selected in this way, 
there were 392 households in the control group. Both the treatment and 
control households were deliberately chosen from the same geographical 
area so that they did not differ much in terms of socio-economic and 
demographic characteristics, and the unobserved heterogeneity of the 
households was not too vast. All the households in both the groups had the 
same source of drinking water, which was arsenic-affected. We have 
collected household data on two outcome variables: per capita monthly 
income and per capita monthly expenditure (both in Indian National Rupee 
[INR]).7 The descriptive statistics and the comparison of the outcome 
variables are presented in the Table 1. 

                                                           
7 INR 1 = $0.02 on 4 May 2005. See Pound Sterling Live (2005). Roy (2008) also uses the 
same exchange rate. 



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

 

In both the outcome variables, on average, the control group performs 
better than the treatment group (Table 1). They earn more and spend more. 
They differ significantly in terms of their per capita monthly income and 
per capita monthly expenditure. On average, the arsenic-affected 

households and the arsenic-unaffected households earn ₹1,475.52 and 

₹2,187.11 per month per capita, respectively, and spend ₹1,300.81 and 

₹1,730.33 per month per capita, respectively (see Table 1). The standard 
deviations of both the income and expenditure variables are high, with 
income showing more variation than expenditure. 

We consider three different sets of background covariates for our study. 
The first set relates to the demographic characteristics of the households 
and contains variables like religion, percentage of male and female 
members, and percentage of people of working age (15–64 years old) in the 
household. The second set relates to the income as well as medical 
expenditure of the households. This includes whether the household earns 
farm or non-farm income. Under farm income, whether the income is 
earned by working on one’s own land or on land owned by others is 
considered. Other variables included are the household’s per capita working 
days per month in different seasons (summer, winter, and monsoon) and 
the household’s per capita monthly medical expenditure (INR), which we 
use to approximate the adaptive measures taken by the household to avoid 
possible labour market loss. The third set includes variables that capture the 
level of educational attainment in the households. This includes the 
percentage of household members having attained middle-school education 
(passed class 8), higher-secondary education (passed class 12), and graduate 
education, and the percentage of household members who are literate. Land 
being the major asset in rural areas, we also include the per capita land 
ownership of households in terms of katha8 to record the wealth of the 
households.9 We describe the data on the background covariates in Table 2. 

There are more female members in arsenic-unaffected households 
compared to arsenic-affected households (significant at 5% level). Among 
the income as well as medical expenditure–related variables, the treatment 
group and the control group are significantly different on three counts.  

                                                           
8 1 katha = 0.017 acre. 
9 We have not included the source of drinking water as a background covariate since in our 
sample, as arsenic-contaminated ground water is consumed both by the treatment (arsenic-
affected) households and the control (arsenic-unaffected) households. Therefore, it is not a 
source of variation in explaining the observed incidence of arsenicosis. We thank one of the 
referees for bringing up this issue. 



[47] Sanjana Chakraborty and Vivekananda Mukherjee 

 

Table 2: Descriptive statistics and comparison of the background covariates of 
arsenic-affected households and arsenic-unaffected households 

Background variables 

Arsenic-affected (no. of 
observations = 374) 

Arsenic-unaffected (no. 
of observations = 392) 

Mean 
difference 

Mean Min Max Mean Min Max 

Religion dummy 
(Hindu = 1, Muslim 

= 0) 

0.40 
(0.49) 

0 1 
0.48 

(0.50) 
0 1 -0.08 

% of males in the 
household 

36.07 
(14.13) 

0 75 
37.61 

(12.83) 
0 66.67 -1.54 

% of females in the 
household 

33.70 
(12.86) 

0 75 
36.87 
(12.97) 

0 66.67 -3.17** 

% of household 
members of working 

age (15–64 years) 

74.75 
(20.55) 

33.33 100 
75.73 

(22.37) 
0 100 -0.98 

Working on own land *  
0.47 

(0.50) 
0 1 

0.48 
(0.50) 

0 1 -0.01* 

Working on others’ 
land *  

0.23 
(0.42) 

0 1 
0.19 

(0.39) 
0 1 0.04*** 

Other source of 
income (non-farm)  

0.60 
(0.49) 

0 1 
0.53 

(0.50) 
0 1 0.07 

Per capita working 
days p.m. in summer  

9.70 
(3.42) 

3.33 30 
9.76 

(3.20) 
4.33 15 -0.06 

Per capita working 
days p.m. in winter  

9.71 
(3.44) 

3.33 30 
9.76 

(3.20) 
4.33 15 -0.05 

Per capita working days 
p.m. in monsoon 

9.53 
(3.51) 

0 30 
9.66 

(3.21) 
3.7
5 

15 -0.13 

Per capita monthly 
household medical 
expenditure (INR)  

24.02 
(21.16) 

10 225 
13.56 
(6.54) 

4 50 10.46* 

Middle school (up to 
class 8) (%) 

75.64 
(24.39) 

0 100 
77.44 

(24.42) 
0 100 -1.8 

Higher secondary 
(%) 

1.85 
(7.34) 

0 50 
1.90 

(7.80) 
0 50 -0.05 

Graduate (%) 
1.01 

(5.32) 
0 50 

0.76 
(4.70) 

0 50 0.25 

Literate persons (%) 
72.96 

(23.55) 
0 100 

75.24 
(24.19) 

0 100 -2.28 

Per capita land 
ownership (in katha) 

6.87 
(7.36) 

0.5 55.5 
5.78 

(5.73) 
0.67 40 1.09* 

Note 1: Standard deviations are in parentheses and *, **, *** denote a 1%, 5% and 
10% level of significance, respectively). 

Note 2: * implies Yes = 1, No = 0. 

Note 3: p.m. = per month. 

 



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

 

There are more households in the control group who work on their own 
land compared to the treatment group (at a 1% level of significance). But 
there are more households in the treatment group who work on others’ 
land compared to the control group (significant at a 10% level). Again, the 
per capita monthly medical expenditure of the arsenic-affected households 
is more compared to that of the arsenic-unaffected households (significant 
at a 1% level), which confirms our apprehension that the per capita 
monthly medical expenditure essentially reflects the adaptive expenditure of 
the arsenic-affected households from possible labour market loss. The two 
groups are not significantly different in any other aspect except that the 
treatment group has more per capita ownership of land than the control 
group (significant at a 1% level). The similarity in the number of per capita 
working days across the groups shows (Proposition 1) that the direct and 
indirect effects of arsenicosis almost balance each other out, which indicates 
the social discrimination of the arsenic-affected group. 

 

5. ANALYSIS AND RESULTS 

We performed the PSM analysis using the Stata 12 software. Through PSM, 
we first paired the households in the treatment group with the households 
in the control group who observationally possess similar characteristics. To 
do this, we identified the common support region between the treatment 
and control groups in terms of their propensity scores. PSM can be 
performed only on the observations in the common support region 
between the two groups. From the propensity score calculations, we found 
329 households of the treatment group (i.e., arsenic-affected group) and 
392 households of the control group (i.e., arsenic-unaffected group) in the 
common support region. The rest (45 households) of the treatment group 
were out of the common support region. The 329 households (on support) 
of the treatment group were matched with exactly 329 households (on 
support) of the control group. According to the methodology described 
above, we used the standard deviation of the propensity scores (estimated 
as 0.26) to calculate the value of the calliper as 0.0656. 

The differences in the mean outcomes of the matched pairs were 
interpreted as the impact of the treatment, i.e., the economic loss suffered 
due to arsenicosis. Table 3 describes the results of the comparison of the 
outcome variables of the two groups. From Table 3, it can be observed that 
the mean of the monthly household per capita income of the 329 

households of the treatment group is obtained as ₹1,485.98 per month per 

capita, while for the 329 households of the control group it is ₹2,174.17 per 



[49] Sanjana Chakraborty and Vivekananda Mukherjee 

 

Table 3: Comparison of the outcome variables of matched households (no. of 
observations = 329) 

Outcome 
variables 

Arsenic-affected Arsenic-unaffected Mean 
difference 

S.E. T-stat Mean Min Max Mean Min Max 

Per capita 
monthly 

household 
income 
(INR) 

1,485.98 
(515.64) 

640 4,000 
2,174.17 
(888.33) 

1,000 6,000 -688.20* 70.38 -9.78 

Per capita 
monthly 

household 
expenditure 

(INR) 

1,309.06 
(309.42) 

677 3,725 
1,751.85 
(596.65) 

765 5,033 -442.78* 46.05 -9.61 

Note: Standard deviations are in parentheses and *, **, *** denote a 1%, 5% and 10% 
level of significance, respectively).  

month per capita. The former is lower than the latter by ₹688.20 (equivalent 
to $13.76) per month per capita, which turns out to be significant at a 1% 
level. We can take this figure as a measure of welfare loss per capita due to 
arsenicosis on account of social discrimination.10 Note that this figure is 
more than the income loss figure calculated by Roy (2008), which stands at 

₹350 (equivalent to $7).The difference arises because while Roy (2008)11 
calculates the income loss which arises only due to adverse labour market 
outcomes, our figure captures the income loss from non-labour sources like 
social discrimination as well. Therefore, the empirical findings of the paper 
confirm that the figures provided by Roy (2008) underestimate the 
economic loss due to arsenicosis as claimed in Proposition 1 stated in 
Section 2. 

We have also compared the per capita average monthly household 
expenditure for both the treatment and control groups. While the mean of 
the per capita monthly household expenditure of the treatment (arsenic-

affected) group was ₹1,309.06, the same for the control (arsenic-unaffected) 

group was ₹1,751.85; this is a difference of ₹442.78 (equivalent to $8.86) 
per month per capita, which is significant at a 1% level. Since we have 
controlled for the labour market effects (and other kinds of wealth effects 

                                                           
10 The treatment and control groups of our study are matched in terms of their hours of 
labour supply in different seasons of the year and their adaptive medical expenditure. This 
controls for labour market effects on the household income. 
11 More recently, Mahanta et al. (2016) and Thakur et al. (2019) as well. 



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

 

like land holding) in our empirical study, according to Proposition 2 derived 
in Section 2, this can happen only if social discrimination against arsenic-
affected households exists.12 

 

6. CONCLUSIONS 

The present paper proves two points. First, social discrimination exists 
against arsenicosis-affected households and, consequently, these households 
suffer from significant economic losses, both in terms of per capita income 
and per capita consumption. Second, the existence of social discrimination 
leads to an underestimation of income loss when calculated only based on 
labour market sources. The results have important implications for cost–
benefit calculations in the adoption of policies tackling the problem of 
arsenicosis. 

While prior studies that estimate economic loss due to arsenicosis 
concentrate only on loss of labour income due to illness, the present paper 
constructs a theoretical model to argue that such a method underestimates 
the true economic loss of these households as they also face social 
discrimination. We argue that the PSM method, when applied properly, 
offers a way to estimate the loss from non-labour market sources like social 
discrimination. In this study, we apply the method on a dataset collected 
from a household survey in an arsenic-affected village in West Bengal, 
India, to estimate the income and consumption loss due to social 
discrimination. We then compare the estimates with a previous study by 
Roy (2008) during a similar time period to show the extent of possible 
underestimation. On a policy front, the paper highlights two aspects. First, 
since the paper shows that social discrimination against arsenic-affected 
households exists and causes them significant losses in income and 
expenditure, there should be policies to build awareness against the social 
discrimination faced by such households. Second, the cost–benefit analysis 
conducted for projects aimed at solving the arsenic problem should take 
into account the losses from social discrimination, which are significantly 
high, as we show in this paper. Projects that were previous considered 

                                                           
12 This argument follows from the fact that the differences in labour supply between the 
treatment and control households across all seasons were not significantly different 
(statistically) from zero (Table 2), and that there is no evidence in our survey that there exists 
more than one wage rate in the market (the labour supply on one’s own land also fetches the 
market wage rate [in the sense of opportunity cost]). Moreover, our PSM study with per 
capita working days per month in the three seasons as an outcome variable showed that the 
treatment households supplied significantly more labour compared to their matched control 
households in the sample. We report the results in Appendix 2 of the paper. 



[51] Sanjana Chakraborty and Vivekananda Mukherjee 

 

unviable because of an unfavourable cost–benefit analysis may become 
viable in this new method. 

The study suffers from an important limitation. Social discrimination is an 
unobserved factor in this study, which causes significant per capita income 
and expenditure differences between the treatment and control group 
households. A direct measure of social discrimination would have made the 
results robust. The present study faces data limitations on this count. This 
work can be updated with a more recent and comprehensive database. The 
impact of various policy interventions on the arsenic-affected households 
can also be studied. These remain as future research agenda. 

 

ACKNOWLEDGEMENTS 

The authors would like to thank two anonymous reviewers for their helpful 
comments. 

 

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[57] Sanjana Chakraborty and Vivekananda Mukherjee 

 

APPENDIX 1 

Table A.1: The questionnaire 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Name of the head of the family 

Family 
size 

Male Female Total  Religion dummy 

    Hindu (1) Muslim (0) 

No. of literate persons  Percentage of literate persons 

Percentage of males in the household Percentage of females in the household 

Working in own land Working on others 
land 

Other source of income (non-
farm) 

Yes (1) No (0) Yes (1) No (0) Yes (1) No (0) 
 

House-
hold 

members 

Education level Education 
level (in %) 

Age 
(years) 

Percentage 
of 

household 
of working 
age range 

Middle 
school (up 
to class 8) 

(1) 

Higher 
secondary 

(2) 

Graduate 
(3) 

(1) (2) (3)   

1.         

2.         

3.         

4.         
 

Total land ownership of the household (katha)  

Per capita land ownership of the household (katha)  

Total monthly household income (₹)  

Per capita monthly household income (₹)  
 

Household working days per month in different seasons 

Summer  Winter  Monsoon  

Household per capita working days per month in different seasons 

Summer  Winter  Monsoon  

 

Total monthly household expenditure (₹)  

Per capita monthly household expenditure (₹)  

Total monthly household medical expenditure (₹)  

Per capita monthly household medical expenditure (₹)  

 

 



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

 

APPENDIX 2 

Table A.2: Comparison of labour supply of matched households (no. of 
observations = 329) 

Outcome 
variables 

Arsenic-affected Arsenic-unaffected  Mean 
difference  

S.E. T-
stat Mean Min Max Mean Min Max 

Per capita 
working 
days per 
month in 
summer  

9.76 
(3.49) 

3.33 30 
9.21 

(2.95) 
4.33 15 

0.55** 0.25 2.19 

Per capita 
working 
days per 
month in 

winter  

9.77 
(3.50) 

3.33 30 
9.21 

(2.94) 
4.33 15 

0.57** 0.25 2.24 

Per capita 
working 
days per 
month in 
monsoon  

9.59 
(3.58) 

0 30 
9.10 

(2.94) 
3.75 15 

0.49 0.26 1.94 

Note: Standard deviations are in parentheses and *, **, *** denote a 1%, 5% and 
10% level of significance, respectively. 

 


