




































 

Ecology, Economy and Society–the INSEE Journal 3 (1): 47–67, January 2020 

 
RESEARCH PAPER 
 

Co-Location, Socioeconomic Status and Perceptions 
of Environmental Change in the Indian Sundarbans 
 

Susmita Dasgupta, Bansari Guha  and David Wheeler  
 
Abstract: Research on determinants of collective action in the commons generally 
focuses on interest-group heterogeneity, implicitly assuming that groups perceive 
the same problems but have different priorities. This paper changes the focus to 
the role played by perceptions themselves. Within localities, collective action may 
be easier if elite and non-elite households have similar assessments of 
environmental risks. Regionally, collective action may be aided by common 
assessments among local elites who communicate across village lines. This paper 
uses regression analysis to explore variations in environmental risk assessments 
across socioeconomic classes and localities, using new survey data from the Indian 
Sundarbans. We find that assessments vary significantly across localities. At the 
same time, assessments among elite households vary significantly more than 
assessments among non-elite households. Our results, therefore, favour locally-
oriented collective action in the region, along with local governance that promotes 
non-elite participation.  

Keywords: Commons, Collective Action, Environmental Perceptions, Sundarbans, 
India 

                                                           
 The World Bank, MSN: MC-3-308, The World Bank, 1818 H Street, Washington DC, 

20008, USA; sdasgupta@worldbank.org.  

 Sivnath Sastri College, 23/49 Gariahat Road, Kolkata 700029, India; 
bansari.guha@gmail.com 

 World Resources Institute; 10G Street NE #800, Washington DC 20002, USA; 
wheelrdr@gmail.com  

Authors confirm that there is no financial or personal relationship that could cause a conflict 
of interest regarding this article. The findings, interpretations, and conclusions expressed in 
this paper are entirely those of the authors. They do not necessarily represent the views of 
the International Bank for Reconstruction and Development/World Bank and its affiliated 
organizations, or those of the Executive Directors of the World Bank or the governments 
they represent.  

Copyright © Dasgupta, Guha and Wheeler 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.v3i1.88  

https://doi.org/10.37773/ees.v3i1.88


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

 

1. INTRODUCTION 

Collective action in the commons is a complex phenomenon that has 
received extensive attention in the literature (Wade 1987a; Wade 1987b; 
Ostrom, Walker and Gardner 1992; Baland and Platteu 1993; Bardhan 
1993; Ostrom et al. 1999; Ostrom 2003). Interest group diversity plays an 
important role in these studies, which typically find that heterogeneity 
makes it harder for communities to agree on arrangements for sharing the 
benefits and costs of collective action (Kanbur 1992; Baland and Platteu 
1995; Alesina, Baqir and Easterly 1999; Poteete and Ostrom 2004). Most 
research attributes the adverse impact of heterogeneity to the clash of 
interests among groups that are differentiated by economic status, gender, 
culture or political affiliation, while implicitly assuming that they perceive 
the same environmental problems (Baland and Platteu 1999; Bardhan and 
Dayton-Johnson 2000; Poteete and Ostrom 2004; Somanathan, Prabhakar 
and Mehta 2007; Ruttan 2008; Araral 2009; Marchiori 2014; Kölle 2015). 
For example, Bardhan and Dayton-Johnson (2000) cite two Indian cases 
(Jayaraman 1981; Easter and Palanisami 1986) in which wealth 
heterogeneity hinders collective action to develop and maintain irrigation 
systems, despite a common understanding that these systems would be 
beneficial.  

This paper drops that assumption and uses new survey data from the 
Indian Sundarbans to consider the role played by perceptions themselves, 
as revealed by residents’ assessments of the environmental risks faced by 
their communities. The stakes for collective action are significant: Within 
localities, collective action may be easier if elite and non-elite households 
have similar assessments. Regionally, collective action may be aided by 
common assessments of local elites who communicate across village lines.  

The low-lying delta region of the Sundarbans is inhabited by some the 
poorest and most vulnerable people in India, who are also among the most 
affected by growing threats from climate change: increased frequency and 
intensity of cyclonic storms (Bandyopadhyay et al. 2018); increasing 
fluctuations in temperature and rainfall; and rising salinity as sea level rise 
continues (Mukhopadhyay et al. 2018). Outmigration has increased as 
households suffer growing losses from climate-related degradation of 
timber stocks, livestock, fisheries, crops and water quality (Dasgupta, 
Sobhan et al. 2017; Dasgupta, Huq et al. 2017).  

In the face of these challenges, Sundarbans communities would 
undoubtedly benefit from collective action in the environmental commons 
– the set of environmental problems that are shared by numerous 
households. Effective mobilization requires cohesion, which, as previously 



[49] Susmita Dasgupta, Bansari Guha and David Wheeler 

 

noted, may be affected by differences in environmental risk assessment as 
well as negotiations among groups with different interests. 1 To address the 
risk assessment question, we conducted a survey of 600 households in three 
dispersed localities in Sundarbans. For each household, the survey collected 
extensive information on socioeconomic status, livelihoods, migration 
behaviour and perceived threats from climate-related factors. The paper’s 
principal objective is to determine the relationships linking co-location2 and 
socioeconomic status to environmental risk assessment, and the potential 
implications for local and regional environmental governance. The paper 
does not incorporate a study of inter-group negotiations, which would 
require an entirely different survey exercise. We acknowledge the possibility 
that higher average levels of assessed risks might enhance environmental 
governance by providing an additional incentive to negotiate. We believe 
that this may be a fruitful topic for future research. 

 

2. DATA AND METHODS 

2.1. Data 

The 600 households surveyed for the study are distributed across the Indian 
Sundarbans (Figure 1). We surveyed three localities (mouzas), one in North 
24-Parganas District (Dwarir Jangle, Sandeshkhali II Block), and two in 
South 24-Parganas District (Kumirmari, Gosaba Block; Kankandighi, 
Mathurapur II Block). The three mouzas were selected after village-level 
consultations to incorporate the range of current environmental risks and 
future threats identified by village residents. Within mouzas, households 
were selected from zones at progressively-greater distances from local rivers 
that are subject to annual flooding. Two hundred households were surveyed 
in each mouza from March 2016 to January 2017.  

 

 

                                                           
1 We draw a distinction between provision of a collective good through common effort, or 
mobilization, and provision of the good by an interested party. In small groups with 
considerable wealth inequality, Olson (1965) has noted the likelihood that self-interest may 
dictate the provision of a public good, if it is warranted by the individual’s expected gain. In 
the latter case, which does not entail mobilization, cohesion is not necessary. We are 
indebted to a reviewer for this clarification. 
2 The term co-location refers to the location of two parties in the same place. 



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

 

Figure 1: Survey mouzas in the West Bengal Sundarbans 

 

Source: Authors 

The survey employed maximum variation sampling (Palinkas et al. 2015) to 
incorporate a broad range of household social, economic, demographic and 
geographic characteristics.3 This approach is particularly useful for 
econometric analyses of relatively small datasets in cases where the core 
model variables are specified in advance. It focuses on capturing the 
marginal effects of variables over their full range. Random samples will tend 
to cluster in the mid-range with limited variation across observations, 
making it difficult to draw robust statistical inferences about marginal 
effects. Even with maximum variation sampling, the generalizability of the 
results will depend on the degree of overlap between samples drawn in this 
region and the value ranges of model variables in other regions. From this 
perspective, the broader range afforded by maximum variation sampling 
may yield more common-range observations than random sampling. 

This paper focuses on the relationship between socioeconomic status and 
differences in assessment of environmental risks. To measure differences in 
assessment, we asked survey households to rank the importance of changes 

                                                           
3 Sampling dimensions are tabulated in Appendix A. 



[51] Susmita Dasgupta, Bansari Guha and David Wheeler 

 

in variables grouped into two categories: general environmental conditions 
and conditions related to livelihood. The environmental risk variables were 
pre-determined in focus group exercises with villagers in the three mouzas. 
The categories were pre-designed for analytical purposes, so we did not 
expect survey respondents to provide any rationale for the groupings. In 
each category, we asked respondents to rank only those changes that they 
thought were significant. Some chose to rank changes for all variables in 
each category, while others limited their selection to small subsets. We did 
not ask about cyclonic storms, which affect the entire region and are 
sufficiently rare to make trend identification difficult for individual 
households. 

For general environmental conditions, we asked respondents to identify and 
rank the importance of significant changes in rainfall (lower or higher 
annual averages; more drought conditions), temperature (higher annual 
averages), and increases in salinity. For livelihood-related conditions that 
involve local environmental variables and associated resource stocks, we 
asked respondents to identify and rank the importance of changes in timber 
stocks4, livestock, fisheries, soil fertility, insect infestation of crops, water 
pollution, access to drinking water, and forced outmigration of family 
members.  

Using the ranks provided by a household, we computed numerical scores 
for variables within each change group: 5 variables for general 
environmental conditions and 10 for livelihood-related conditions. Among 
ranked variables in each group, the highest-ranking is arbitrarily assigned a 
score of 10; the next-ranked a score of 9, etc. Unranked variables were 
assigned scores of 0. For general environmental conditions we formed a 
dataset that included all pairs of households in our sample. These pairs were 
drawn from all mouzas to permit a robust analysis of the effect of proximity 
on assessments of environmental risks. We augmented the dataset for each 
pair of households by including their scores for the five general 
environmental variables. Then, for each household pair, we computed the 
correlation coefficient for the five scores. We repeated this exercise for the 
ten livelihood-related conditions. The result for each household pair was a 
scores correlation coefficient for general environmental conditions and 
another scores coefficient for livelihood-related conditions. The estimated 
correlation coefficients provided the dependent variables in our analysis of 
the relationship between socioeconomic status and differences in 
environmental risk assessment. 

                                                           
4 Respondents were asked to consider both the immediate village surroundings and nearby 
forest reserves. 



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

 

Table 1: Distribution of 
household asset counts 

Wealth 
Status 

Asset 
Count 

Sample 
Households 

Low 

0  4 

1 205 

2 144 

Medium 
3 104 

4  76 

High 

5  33 

6  16 

7  6 

8  4 

9  7 

 10  1 

 Total 600 

Source: Authors 

Table 2: Education status and reported education levels 

Education Status Survey Category Count 

Low 
Pre-Primary (below 1) 51 

Primary (1-4) 118 

Medium 
Upper Primary (5-8) 152 

Secondary (9-10) 60 

High 

Higher Secondary (11-12) 24 

Undergraduate (B.A/B.Sc./B.Com.) 35 

Post-graduate (M.A/M.Sc./M.Com.) 8 

Post-Masters (Ph.D./Voc./Doctor/Eng.) 1 

 
Total 449 

Source: Authors 

We developed our socioeconomic status indicators from survey-based 
measures of household wealth and 
education. We assigned high 
socioeconomic status (SES) to 
households whose wealth and 
education were both in the upper 
range. To ensure the clearest view of 
socioeconomic status effects, we 
assigned low SES to households 
whose wealth and education were 
both in the lower range. 
Identification of SES ranges, while 
analytically useful, have an 
unavoidably arbitrary component. 
We, therefore, chose break points in 
the data to ensure ample 
representation in each subgroup.  

We indexed wealth by counting total 
asset possession from a possible set of 29 assets.5 Table 1 displays our 
selection of break points and household representation in each range. 

The survey reports the education level of the household head in 8 
categories. Table 2 displays our selection of break points and household 

                                                           
5 We have used an unweighted count because we do not have reliable information on asset 
vintages or their market values. Assets queried were own car, taxi, auto-rickshaw, 
truck/small truck, bicycle, motor bike, gas stove, mixer, refrigerator/freezer, washing 
machine, iron, geyser, radio/cassette recorder, colour television, bBlack and white television, 
DVD player, land telephone, mobile, sewing machine, power generator (kerosene), power 
generator (solar), electric fan, air conditioner, personal computer, motor boat, row boat or 
sail boat, animal drawn cart, jewelry, other building excluding dwelling. 



[53] Susmita Dasgupta, Bansari Guha and David Wheeler 

 

representation in each range. The lower total representation for education 
(449, vs. 600 for wealth) reflects non-reporting by some survey households.  

2.2 Regression Model Specification  

Our research strategy posits that environmental risk assessments reflect a 
process of consensus formation that has received extensive attention in 
both the theoretical literature (Dyer et al. 2009; Hegselmann and Krause 
2002; Hoylst et al. 2001; Deffuant et al. 2000; Friedkin and Johnsen 1999; 
Lehrer and Wagner 1981; Wagner 1978) and empirical research (Estrada 
and Vargas-Estrada 2013; Aral and Walker 2012; Calvó-Armengol et al. 
2009; Denrell 2008; Yun et al. 2008; Childers and Rao 1992). In this 
research, individuals assign weight to both their own observations and the 
observations of others with whom they communicate. Residential proximity 
generally has a significant effect, because communications decline with 
distance. The same holds for the ―social proximity‖ provided by common 
cultural identity (e.g., ethnicity). However, the role of socioeconomic status 
is more controversial (Sun et al. 2019; Phillips and Zuckerman 2001; 
Aronson 1972; Goffman 1963). Numerous studies find that relatively high-
status individuals conform less to majority views than middle-status 
individuals. The rationale is that the relative security of high-status 
individuals makes them more willing to demonstrate divergence. For lower-
status individuals, however, the evidence is mixed. In some cases, lower-
status individuals also conform less than middle-status individuals. In other 
cases, they exhibit more conformity.  

These divergent findings have given rise to two views of the relationship 
between status and conformity. The ―U-shaped‖ model holds that both 
high- and low-status individuals conform less than middle-status 
individuals: the former because their position is secure, and the latter 
because they have little or nothing to lose in any case. The opposing model, 
which may have more relevance for developing countries, posits a 
monotone-increasing relationship between socioeconomic status and non-
conformity. In this view, low-status individuals, who may be at the survival 
margin, are unlikely to risk the costs that may accompany non-conformity.  

Our econometric modelling builds on this literature without taking explicit 
sides in the dispute, although our prior view was that the monotone-
increasing model seems more plausible for high-poverty areas like the 
Indian Sundarbans. We posited a model in which individuals’ assessments 
of environmental risks were formed from their own observations and those 
of individuals with whom they communicated. We indexed assessment 
similarity between two individuals using the correlation between their 
scores for the previously noted environmental change factors in our survey. 



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

 

For paired individuals from households i and j, we focused on three 
potential determinants of assessment similarity. The first was frequency of 
communication, which should be inversely related to the residential distance 
between the two households. Given that we cannot observe actual 
communications between surveyed individuals, we used residential distance 
as an index of the likelihood that they communicate. 

The second factor was ―social proximity‖. Ceteris paribus, we expect an 
individual to assign greater weight to the environmental risk assessments of 
individuals with the same group affiliation. For this exercise, we used a 
measure of common ethnicity to index social proximity. The third factor 
was socioeconomic status. As previously noted, social psychologists 
disagree that the relationship between socioeconomic status and non-
conformity is U-shaped or monotone-increasing.  

Our regression model tested the effects of four categorical variables on 
inter-household score correlations for environmental changes: (1) common 
mouza; (2) common status in the highest socioeconomic group; (3) 
common status in the lowest socioeconomic group; (4) common 
membership in the ethnic categories identified by the survey (scheduled 
caste, scheduled tribe, OBC).6  

We specified our regression model at the micro-level to capture the full 
range of individual variation in the sample, and estimated the following 
regression equation: 

ijijijijijij LSHSEL  43210  (1) 

Prior expectations: ,;;; 0000 4321   where, 

ij = Correlation coefficient of scores for environmental change factors 

  by paired individuals i and j 

ijL = 1 if individuals i and j are located in the same mouza; 0 otherwise 

ijE = 1 if individuals i and j have the same ethnic category; 0 otherwise 

ijHS = 1 if individuals i and j both have High SES, as previously defined;  

  0 otherwise 

ijLS  = 1 if individuals i and j both have Low SES, as previously defined;  

  0 otherwise  

ij = Random error term  

                                                           
6 OBC abbreviates Other Backward Caste, a term used by the Government of India. 



[55] Susmita Dasgupta, Bansari Guha and David Wheeler 

 

The SES dummy variables distinguish pairs of high-SES and low-SES 
individuals from others in the sample: cross-SES or middle-SES pairings. 
Our estimation results for HS and LS should be interpreted as deviations 
from the constant term, which absorbs the effect of the excluded pairings. 
Although we recognized the divergence of views in the literature, our prior 
expectation was that the deviation would be negative for high-SES pairs 
(indicating lower correlation) and positive for low-SES pairs.  

Spatial econometric estimation was not needed in this case, since the paired 
household observations were drawn from all three mouzas. However, we 
believe that error variance may not be independent of the distance between 
households in each pair. Accordingly, we augmented standard OLS and 
robust estimators with a GLS estimator that incorporates standard errors 
for 10 clusters identified by relative distance between paired individuals. 
Distances were calculated from the latitude and longitude recorded for each 
household. 

 

3. RESULTS 

Tables 3 and 4 provide information on mean variable scores by mouza and 
socioeconomic status (SES) group, as well as summary cross-correlations. 
Table 3a displays mean scores for the 10 environment-related threats to 
livelihood. As the table shows, there are substantial differences in mean 
scores across SES groups in each mouza. Nevertheless, the table also 
suggests more agreement within mouzas than across them. These patterns 
also characterize the correlation coefficients in Table 3b. The overall 
distribution of correlations (Table 3c) is roughly symmetric, with one 
negative correlation (-0.20), first, second and third quartile points at 0.37, 
0.51 and 0.64, respectively, and a maximum at 0.97. 

Tables 4a, 4b and 4c provide the same information for five changes in 
general environmental conditions. Here typical correlations are higher, 
although substantial variation is also evident. 

 

 

 

 

 

 



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

 

Table 3a: Livelihood-related change - Scores by mouza and socio-economic  
status 

Mouza (1) Dwarir Jangle (2) Kumirmari (3) Kankandighi 

SES L M H L M H L M H 

Households 23 61 46 3 15 3 20 43 22 

Change          

Tree Loss 7.2 7.4 7.7 6.0 5.2 5.0 7.8 7.2 7.4 

Livestock Loss 4.1 5.4 5.9 7.7 7.8 9.0 8.2 7.3 8.1 

Crop Damage 6.0 5.7 6.4 6.7 6.9 6.7 5.4 6.1 6.5 

Fish Loss  2.1 5.2 6.9 5.3 5.3 7.3 3.8 5.2 4.5 

Water pollution 8.4 6.7 6.9 6.3 4.9 5.0 4.7 4.1 3.5 

Soil Fertility Loss  5.0 3.5 2.5 4.0 4.3 3.3 6.2 4.6 5.5 

Water Loss 3.1 2.8 2.2 6.3 5.0 4.3 6.1 5.6 5.8 

Pests and Insects  4.7 2.9 1.9 3.7 5.6 4.7 5.6 4.7 4.2 

Animal Diseases 4.1 2.8 2.4 5.0 5.0 4.0 4.0 4.8 4.7 

Outmigration 1.3 2.1 1.6 4.0 4.3 5.7 3.3 5.2 4.7 

Notes: L indicates Low; M indicates Middle; H indicates High. 

Source: Authors 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3b: Correlation coefficients - Livelihood-related change scores 

M
o

u
za

 

SES 
(1) Dwarir Jangle (2) Kumirmari (3) Kankandighi 

L M H L M H L M  H 

(1
) 

L 1.00 
        

M 0.72 1.00 
       

H 0.54 0.97 1.00 
      

(2
) 

L 0.33 0.63 0.66 1.00 
     

M 0.13 0.38 0.44 0.70 1.00 
    

H -0.20 0.38 0.53 0.60 0.79 1.00 
   

(3
) 

L 0.41 0.44 0.32 0.50 0.52 0.20 1.00 
  

M 0.04 0.45 0.46 0.61 0.64 0.58 0.72 1.00 
 

H 0.09 0.35 0.33 0.60 0.64 0.45 0.82 0.94 1.00 

Notes: L indicates Low; M indicates Middle; H indicates High. 

Source: Authors 

Table 3c: Distribution of correlation coefficients 

Min P25 P50 P75 Max 

-0.20 0.37 0.51 0.64 0.97 

Source: Authors 



[57] Susmita Dasgupta, Bansari Guha and David Wheeler 

 

Table 4a: General environmental changes - scores by mouza and socio-
economic status 

Mouza (1) Dwarir Jangle (2) Kumirmari (3) Kankandighi 

SES L M H L M H L M H 

Households 23 72 70 29 124 42 29 65 31 

Change          

Less Rain 8.2 6.5 5.3 0.0 0.6 1.0 0.3 0.6 2.3 

More Saline 7.0 5.2 3.9 5.9 4.0 5.7 5.9 6.3 4.8 

More Rain 2.7 5.5 7.4 0.7 0.3 0.0 0.3 0.9 1.8 

More Frequent 
Drought 

1.7 2.6 3.0 0.0 0.0 0.0 1.0 1.4 1.2 

Higher 
Temperature 

8.4 7.8 7.8 8.1 8.8 8.9 8.9 8.3 7.9 

Notes: L indicates Low; M indicates Middle; H indicates High. 

Source: Authors 

Table 4b: Correlation coefficients - general environmental change scores 

M
o

u
za

 

SES 

(1) Dwarir Jangle (2) Kumirmari (3) Kankandighi 

L M H L M H L M H 

(1
) 

L 1.00 
        

M 0.82 1.00 
       

H 0.29 0.78 1.00 
      

(2
) 

L 0.60 0.57 0.33 1.00 
     

M 0.65 0.67 0.45 0.96 1.00 
    

H 0.69 0.64 0.33 0.99 0.98 1.00 
   

(3
) 

L 0.59 0.53 0.28 0.99 0.98 0.99 1.00 
  

M 0.57 0.50 0.24 0.99 0.96 0.98 1.00 1.00 
 

H 0.71 0.71 0.45 0.97 1.00 0.99 0.97 0.96 1.00 

Notes: L indicates Low; M indicates Middle; H indicates High. 

Source: Authors 

 

Table 4c: Distribution of correlation coefficients 

Min P25 P50 P75 Max 

0.24 0.56 0.71 0.98 1.00 

Source: Authors 

 

 



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

 

Table 5: Distribution of interpersonal correlation coefficients 

Environmental 
Change Factors 

N Min P10 P25 P50 P75 P90 Max 

Livelihood-
Related 23,871 -0.94 -0.30 -0.07 0.15 0.37 0.62 1.00 

General 109,278 -1.00 -0.40 -0.01 0.49 0.67 1.00 1.00 

Source: Authors 

Table 5 presents the distributions of correlation coefficients for paired 
individuals. The correlations for both change groups are distributed in the 
range [-1,1], with thousands of positive and negative values. Above the 
median, percentile points are substantially higher for the general 
environmental change factors. This is consistent with the cross-correlation 
patterns displayed for mean scores in Tables 1c and 2c. 

Table 6 reports our estimation results for the econometric model specified 
in equation (1) above. The dependent variable is the correlation coefficient 
of environmental change variable scores for paired individuals. The 
independent variables are categorical variables for common mouza; 
common status in the highest socioeconomic group; common status in the 
lowest socioeconomic group; and common ethnicity. 

The estimates indicate that common mouza has a highly significant positive 
impact on the interpersonal score correlations for both general 
environmental conditions and environment-related threats to livelihood. 
Conversely, high SES has a consistently negative impact on the correlation 
— marginally for threats to livelihood and with high significance for general 
environmental conditions. Low SES has a consistently positive impact that 
is marginally significant for threats to livelihood and highly significant for 
general environmental conditions. While common ethnicity has a positive 
impact, it is both small in size and statistically insignificant in all cases.7 

 

 

 

                                                           
7 To check the robustness of our results, we identified a separate group whose members 
have elite status in wealth or education, but not both. The econometric results for elite status 
are equally strong when a dummy variable was introduced for the group with partial elite 
status. These results are available from the authors on request. 



[59] Susmita Dasgupta, Bansari Guha and David Wheeler 

 

Table 6: Regression results - determinants of common environmental risk 
assessments (Dependent Variable: Inter-individual correlation coefficient) 

 Environmental Threats to 
Livelihood 

General Environmental 
Conditions 

 OLS Robust GLS OLS Robust GLS 

Individuals 
Have 
Common 
Mouza 

0.136 0.136 0.137 0.089 0.089 0.094 

 (29.76)** (28.32)** (5.49)** (27.25)** (26.99)** (4.21)** 

High SES -0.002 -0.002 -0.018 -0.080 -0.080 -0.074 

 (0.10) (0.09) (0.54) (3.95)** (4.22)** (2.59)* 

Low SES 0.029 0.029 0.027 0.040 0.040 0.031 

 (1.96) (2.08)* (1.41) (3.22)** (3.33)** (2.15)* 

Ethnicity 0.005 0.005 0.004 0.006 0.006 0.004 

 (0.94) (0.91) (0.23) (1.88) (1.88) (0.13) 

Constant 0.095 0.095 0.093 0.277 0.277 0.285 

 (28.86)** (33.29)** (7.84)** (123.40)** (123.36)** (9.84)** 

Obs 23,871 23,871 20,503 109,278 109,278 96,141 

R-squared 0.04 0.04 0.04 0.01 0.01 0.01 
Notes:  

1. Absolute value of t statistics in parentheses  

2. * significant at 5%; ** significant at 1%  

Source: Authors 

 

Tables 7 and 8 display predicted correlations from the GLS regressions by 
mouza of residence and SES.8 Middle SES is assigned to paired status for 
individuals who are neither high SES nor low SES. Both tables display the 
highest correlations for paired individuals with low SES in a common 
mouza. Conversely, paired individuals with high SES in different mouzas 
have the lowest correlations. Typical correlations are higher for general 
environmental changes than for livelihood-related changes, but livelihood-
related changes have greater relative effects. From highest to lowest case, 
the correlation falls by 69% for livelihood-related changes and 49% for 
changes in general environmental conditions. 

 

                                                           
8 Ethnicity has no meaningful effect on these results; the table entries are calculated for 
paired individuals who do not have common ethnicity. 



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

 

Table 7: Livelihood-related changes: Predicted interpersonal correlations by 
co-location and SES 

Common Locality 

Common SES 

Low Intermediate High 

Yes 0.26 0.23 0.21 

No 0.12 0.09 0.08 

Source: Authors 

Table 8: General environmental changes: Predicted interpersonal 
correlations by co-location and SES 

Common Locality 

Common SES 

Low Intermediate High 

Yes 0.41 0.38 0.31 

No 0.32 0.29 0.21 

Source: Authors 

4. DISCUSSION 

Cost-effective adaptation to climate-related changes will require increased 
public investment, but it will also depend on local support for appropriate 
collective action. There is extensive literature on the determinants of 
effective collective action at the village level, with a major focus on the 
problem of interest-group heterogeneity (Vedeld 2000; Kurian and Dietz 
2013). Relevant factors include differences in wealth, education and 
patterns of resource tenure and ownership (Beck and Nesmith 2001; 
Gaspart 2003). While such studies generally find that greater homogeneity is 
a positive factor, they have also explored important differences that are 
attributable to outside intervention modes, the presence of intermediating 
institutions, and the role of ―policy entrepreneurs‖ in forging and sustaining 
collective agreements among heterogeneous actors (Myers 1997; Kurian 
and Dietz 2007). More generally, the policy literature on commons 
problems explores the implications of heterogeneity in income, education 
and ethnicity for support for environmental regulation (Jones and Dunlop 
1992; Wang et al. 2018; Chen 2017; Janmaimool 2017; Chakraborty et al. 
2017; Liu and Mu 2017). The principal focus is transactional, on the implicit 
assumption that agents have common assessments of the problems but 
different priorities for addressing them. 

This paper attempts to contribute by addressing another aspect of 
heterogeneity that has received less attention in the literature: the role 
played by differences in environmental risk assessments. Within localities, 
collective action may be easier if elite and non-elite households have similar 
assessments of critical environmental problems. At the same time, extensive 



[61] Susmita Dasgupta, Bansari Guha and David Wheeler 

 

research indicates that regional governance is dominated by economic and 
educational elites who communicate across locality lines (Khan 2008; 
Ghertner 2011; Oyono 2004; Lucas 2016; Piabuo et al. 2018). By 
implication, regional environmental governance is likely to be more 
effective if elites in different localities have common assessments of the 
critical problems. Several other possibilities emerge from this line of 
inquiry, depending on comparative conditions at the local and regional 
levels. In the weakest scenario, environmental governance is hindered by 
heterogeneity of assessments within and across localities. Local governance 
may be strengthened, if local elite and non-elite households share common 
assessments; regional governance may dominate if local-level assessments 
differ, but elites share assessments across localities.  

We conducted a survey of 600 households in three mouzas of the Indian 
Sundarbans that is threatened by several environmental factors related to 
climate change and explored the impact of location and socioeconomic 
status on patterns of environmental risk assessment. Our findings had three 
noteworthy features: for the broad middle group, we found a positive, 
highly-significant correlation of assessments across all mouzas;9 common 
mouza adds a highly-significant increment to the correlation; another 
increment is added for low SES households — in contrast, high SES 
subtracts a significant increment. 

Given the complexity of collective actions in the commons, it is widely 
acknowledged that systems of governance need to be flexible to allow 
adaptation of management regimes to local conditions (Adhikari and Lovett 
2006). In the literature on the environmental commons, attention focuses 
primarily on class-related interests that affect the prospects for local or 
regional action. Our results suggest that socioeconomic status may also 
operate through assessments of environmental change that become more 
varied as socioeconomic status increases. While our results suggest that the 
strongest foundations for action in the commons are local, they also 
indicate that disagreements about environmental risks may undermine the 
leadership potential of local elites. By implication, village-level governance 
based on widespread participation in decision-making seems likely to 
promote the most effective environmental measures in the Indian 
Sundarbans. This is a key message for development partners, policy makers 
and practitioners working on management of environmental resources in 
the Indian Sundarbans.  

                                                           
9 These are the constant terms in the regressions. 



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

 

We should also note that our results may have broader geographic 
implications. Our research methodology can be replicated in other places 
and, as we have noted, our findings are consistent with previous research 
on consensus-building that finds more divergence among individuals with 
high socioeconomic status. Although more research is clearly desirable, we 
believe that these results may provide more relevant insights for collective 
action on environmental problems. 

 

ACKNOWLEDGEMENTS 

This research was conducted under the South Asia Water Initiative - 
Sundarbans Landscape administered by the World Bank. Authors are 
thankful to Swastika Bhattacharya for research assistance. 

 

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Table A: Survey dimensions for household selection 

Household Characteristic Dimensions 

Address 

Panchayat 

Village 

Within village 

Location 

Close to the river 

Intermediate location 

Interior village 

Ethnicity 

Scheduled caste 

Scheduled tribe 

OBC 

Other 

Religion 

Hindu 

Muslim 

Christian 

Other 

Education Highest level 

Occupation 

Agriculture 

Fishing 

Forestry 

Service 

Other 

Income and Employment 
Permanent  

Seasonal  

Migration 
Permanent  
Seasonal  

Housing Details 

Housing type 

Distance from amenities 

Household assets 

 

APPENDIX  

 

 

 


