metadata: authors: 0: author: Dasgupta,Susmita
1: author: Guha,Bansari
2: author: Wheeler,David
count: India
docna: 0: docna: Co-Location, Socioeconomic Status and
            Perceptions of Environmental Change in the Indian Sundarbans
docty: Policy Research Working Paper
subtopic: Global Environment,Educational Sciences,Hydrology,Environmental Strategy,Environmental Management,Environmental Governance,Brown Issues and Health,Pollution Management & Control
teratopic: Education,Water Resources,Environment
abstracts: cdata!: Research on the 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 perceptions of environmental problems. Regionally,
            collective action may be aided by common perceptions among
            local elites who communicate across village lines. This
            paper uses regression analysis to explore variations in
            environmental perceptions across classes and localities,
            using new survey data from the Indian Sundarbans. The paper
            finds that perceptions vary significantly across localities.
            Within localities, perceptions among elite households vary
            significantly more than perceptions among non-elite
            households. The results therefore favor locally-oriented
            collective action in the region, along with local governance
            that promotes non-elite participation.
geo_reg_key: 517198,517191,517212
repnme: repnme: Co-Location, Socioeconomic Status and
            Perceptions of Environmental Change in the Indian Sundarbans
display_title: Co-Location, Socioeconomic Status
            and Perceptions of Environmental Change in the Indian Sundarbans
listing_relative_url: /research/2019/06/31200025/co-location-socioeconomic-status-perceptions-environmental-change-indian-sundarbans
topicv3: Water m1326522 137,Environmental Governance m1326638 1783,Education m1327048 470,Environment, Natural Resources and Blue
            Economy m1327391 566,Water Pollution m1327598 1798
publishtoextweb_dt: 2019-06-26T00:00:00Z
historic_topic: Education,Water Resources,Environment
no_of_pages: 19
author_keys: 0: author_key: 95558
1: author_key: 1435462
model_extractions: Policy Research Working Paper 8917 Co-Location, Socioeconomic Status and Perceptions of Environmental Change in the Indian Sundarbans _Susmita Dasgupta Bansari Guha David Wheeler_ Development Economics Development Research Group & Environment and Natural Resources Global Practice June 2019
Policy Research Working Paper 8917

# **Abstract**

Research on the 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 perceptions of environmental problems. Regionally, collective action may be aided by common perceptions among local elites who communicate across village lines. This paper uses regression analysis to explore variations in environmental perceptions across classes and localities, using new survey data from the Indian Sundarbans. The paper finds that perceptions vary significantly across localities. Within localities, perceptions among elite households vary significantly more than perceptions among non-elite households. The results therefore favor locally-oriented collective action in the region, along with local governance that promotes non-elite participation.

This paper is a product of the Development Research Group, Development Economics and the Environment and Natural Resources Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
# **Co-Location, Socioeconomic Status and Perceptions of Environmental Change in the Indian Sundarbans**

Susmita Dasgupta Lead Environmental Economist, The World Bank Bansari Guha Consultant, The World Bank David Wheeler Senior Fellow, World Resources Institute _Key Words:_ Commons, Collective Action, Environmental Perceptions, Sundarbans, India _JEL Classification_ : Q01; Q23. Q57

- Authors’ names in alphabetical order.

Corresponding Author: Susmita Dasgupta. Lead Environmental Economist, The World Bank, Address: MSN: MC-3-308, The World bank, 1818 H Street, Washington DC, 20008, USA; Email: <u>sdasgupta@worldbank.org;</u> Tele phone: 1-202-473-2679; Fax: 1-202-522-2714 _Acknowledgment:_

**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.
# **1. Introduction**

Collective actions in the commons are complex (for example, see Wade 1987a: Wade 1987b; Ostrom, Walker and Gardner 1992; Baland and Platteu 1993; Bardhan 1993; Ostrom et al. 1999; Ostrom 2003). The role of interest group homogeneity/ heterogeneity in determining successes and failures of collective actions in commons has been studied over a long period of time, as heterogeneity makes it harder for communities to reach an agreement about the sharing of benefits and costs of collective action (examples include Kanbur 1992; Baland and Platteu 1995. Alesina, Baqir and Easterly 1999; Poteete and Ostrom 2004). However, research on the determinants of collective action in the commons generally focuses on interest-group heterogeneity in endowments, wealth, economic interests, culture, political convictions, gender, etc., implicitly assuming that groups perceive the same problems but have different priorities (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). 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 perceptions of relevant problems. Regionally, collective action may be aided by common perceptions among local elites who communicate across village lines. The case study on environmental perceptions in Indian Sundarbans presented in this paper is expected to shed light on patterns of perceptions differing with income, education and location of households, using new survey data.

The Sundarbans is home to some of the poorest and the most vulnerable communities of India. The region is currently threatened by several environmental factors related to climate change, including increased frequency and intensity of cyclonic storms (Bandyopadhyay et al. 2018), fluctuations in temperature and rainfall, and rising salinity as sea level rise continues 2
(Mukhopadhyay et al. 2018). These factors can produce stresses that directly affect household welfare, including reduced timber stocks, livestock, fisheries and soil fertility; crop damage from insect infestation and water pollution; reduced access to drinking water; and forced outmigration of family members (Dasgupta, Sobhan and Wheeler 2017; Dasgupta et al. 2017). For understanding patterns of environmental perceptions across households, we conducted a survey of 600 households in three dispersed localities in the Indian Sundarbans. For each household, the survey collected extensive information on socioeconomic status variables, livelihoods, migration behavior and perceived threats from climate-related factors. We assign households to the three localities and three socioeconomic status groups, from information about their asset ownership and the education of the household head. In the survey, households performed separate ranking exercises for perceived changes in general environmental conditions: rainfall, temperature and salinity; and livelihood-related conditions: timber stocks, livestock, fisheries and soil fertility, insect infestation of crops, water pollution, access to drinking water, and forced outmigration of family members. Using the ranks for each household, we compute its numerical scores for each factor within each change group: 5 for environmental conditions, 10 for livelihood-related conditions. For each change group, we compute correlation coefficients for all unique household pairs in the sample. Then we estimate regression models to test the effects of socio-economic status, ethnicity and location of households on inter-household correlations.

This paper’s principal objective is to determine the relationships linking co-location and socioeconomic status to perceived environmental changes, and the potential implications for local and regional environmental governance.

3
<!-- Start of picture text --> al |<br><!-- End of picture text -->

<!-- Start of picture text --> {}QD oi<br><!-- End of picture text -->
For each household, the survey collected extensive information on socioeconomic status. For this exercise, we focus on survey-based measures of household wealth, education and ethnicity. We index wealth by counting total asset possession from a possible set of 29 assets.<sup>3</sup> We assign households to three wealth categories using breaks in the distribution at ownership at 2 and 4 assets. The survey reports the education level of the household head in 8 categories.<sup>4</sup> We classify education levels in 3 categories: primary, secondary, and post-secondary.

In the survey, households performed separate ranking exercises for perceived changes in general environmental conditions and livelihood-related conditions. In particular, surveyed household representatives were asked to rank perceived changes in 2 categories: (1) five general environmental conditions (less rain, more rain, more salinity, more frequent droughts, more heat); (2) ten environment-related threats to livelihood (tree loss, livestock loss, crop damage, fish loss, water pollution, soil fertility loss, water loss, pests and insects, animal diseases, forced outmigration of prime-age workers).

For understanding the variation in rank assignments, we first computed the numerical scores for each factor within each change group (5 for general environmental conditions, 10 for livelihood-related conditions) using the ranks for each household. For each change group, we then computed correlation coefficients for all unique household pairs in the sample. Finally, we estimated regression models to test the effect of four categorical variables on inter-household correlations: (1) common locality; (2) common status in the highest socioeconomic group; (3)

> 3 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, Color television, Black 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.

> 4 Education levels queried were Pre-Primary (below 1), Primary (1-4), Upper Primary (5-8),  Secondary (9-10), Higher Secondary (11-12), Undergraduate (B.A/B.Sc/B.Com), Post-graduate (M.A/M.Sc/M.Com), Post-Masters (Phd/Voc/Doctor/Eng).

5
common status in the lowest socioeconomic group; and (4) common ethnicity. 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:

- (1)  ρ�� �β� � β�L�� � β�HS�� � β�L�� � β�E�� �ε��

where

- ρij = Correlation coefficient for individuals i and j

- Lij = 1 if individuals i and j have a common locality; 0 otherwise

- HSij = 1 if individuals i and j both have High SES, as previously defined; 0 otherwise

- LSij = 1 if individuals i and j both have Low SES, as previously defined; 0 otherwise

- Eij = 1 if individuals i and j have common ethnicity; 0 otherwise Ɛij = Random error term

The analysis was conducted for all pairs of household heads whose responses are recorded for questions on changes in general environmental conditions, changes in livelihood-related conditions, asset ownership, education and ethnicity. For estimation, spatial econometric estimation is not needed in this case, since the paired household observations are drawn from all three locations. However, we believe that error variance may not be independent of the distance between household heads in each pair. Accordingly, we augment standard OLS and robust estimators with a GLS estimator that incorporates standard errors for 10 clusters identified by relative distance.

# **3. Results**

As expected, households varied in the number of rank assignments of perceived changes in environmental conditions by category; some cited only one factor, while others ranked all factors. variables, livelihoods, migration behavior and perceived threats from climate-related factors. Tables 1 and 2 provide information on mean scores by locality and socioeconomic status (SES) group, and summary cross-correlations. For this tabulation, we divide the sample into three SES groups: Low, for households in wealth category 1 with a primary-educated head; High, for 6
households in wealth category 3 with a post-secondary-educated head; and Middle, for all other households.

Table 1a displays mean scores for the 10 environment-related threats to livelihood across the three localities and three SES groups. The intergroup correlation coefficients in Table 1b reveal substantial variation, both within and across localities, that is summarized in Table 1c. The distribution 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.

**Table 1a:  Livelihood-related change - Scores by locality and socioeconomic status Locality codes: 1 - Dwarir Jangle; 2 - Kumirmari; 3 - Kankandighi**

|Locality||1|||2|||3||
|---|---|---|---|---|---|---|---|---|---|
|SES|Low|Middle|High|Low|Middle|High|Low|Middle|High|
|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|
|CropDamage|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|
|Waterpollution|8.4|6.7|6.9|6.3|4.9|5.0|4.7|4.1|3.5|
|Soil FertilityLoss|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|
|**~~Table 1b: Corre~~**<br>Animal Diseases|**~~lation coef~~**<br>4.1|**~~ficients - L~~**<br>2.8|**~~ivelihoo~~**<br>2.4|**~~d-relate~~**<br>5.0|**~~d change s~~**<br>5.0|**~~cores~~**<br>4.0|4.0|4.8|4.7|
|<br>Out-Migration|<br>1.3|<br>2.1|<br>1.6|<br>4.0|<br>4.3|<br>5.7|3.3|5.2|4.7|

**Table 1b: Correlation coefficients - Livelihood-related change scores**

|Locality<br>||1<br>|||2<br>|||3<br>||
|---|---|---|---|---|---|---|---|---|---|
|SES|Low|Middle|High|Low|Middle|High|Low|Middle|High|
|Low|1.00|||||||||
|1<br>Middle|0.72|1.00||||||||
|High|0.54|0.97|1.00|||||||
|**Table 1c: Distrib**<br>Low|**ution of c**<br>0.33|**orrelation**<br>0.63|**coefficie**<br>0.66|**nts**<br>1.00||||||
|2<br>Middle|0.13|0.38|0.44|0.70|1.00|||||
|High|-0.20|0.38|0.53|~~7~~<br>0.60|0.79|1.00||||
|Low|0.41|0.44|0.32|<br>0.50|0.52|0.20|1.00|||
|3<br>Middle|0.04|0.45|0.46|0.61|0.64|0.58|0.72|1.00||
|High|0.09|0.35|0.33|0.60|0.64|0.45|0.82|0.94|1.00|
**Table 1c: Distribution of correlation coefficients**

|Min|P25|P50|P75|Max|
|---|---|---|---|---|
|-0.20|0.37|0.51|0.64|0.97|

Tables 2a-2c provide the same information for five changes in general environmental conditions. Here typical correlations are higher, although substantial variation is also evident.

**Table 2a:  General environmental changes - Scores by locality and socioeconomic status**

**Locality codes: 1 - Dwarir Jangle; 2 - Kumirmari; 3 - Kankandighi**

|Locality||1|||2|||3||
|---|---|---|---|---|---|---|---|---|---|
|**~~Table 2b: Correlatio~~**<br>SES|**~~n coeffic~~**<br>Low|**~~ients - Gen~~**<br>Middle|**~~eral env~~**<br>High|**~~ironmen~~**<br>Low|**~~tal change~~**<br>Middle|**~~scores~~**<br>High|Low|Middle|High|
|<br>Households|<br>23|<br>72|<br>70|<br>29|<br>124|<br>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|
|More Heat|8.4|7.8|7.8|8.1|8.8|8.9|8.9|8.3|7.9|

# **Table 2b: Correlation coefficients - Livelihood-related change scores- General environmental change scores**

|Locality|||1<br>|||2<br>|||3<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||SES|Low|Middle|High|Low|Middle|High|Low|Middle|High|
||Low|1.00|||||||||
|1|Middle|0.82|1.00||||||||
||High|0.29|0.78|1.00|||||||
||Low|0.60|0.57|0.33|1.00||||||
|2|Middle|0.65|0.67|0.45|0.96|1.00|||||
||High|0.69|0.64|0.33|0.99|0.98|1.00||||
||Low|0.59|0.53|0.28|0.99|0.98|0.99|1.00|||
|3|Middle|0.57|0.50|0.24|0.99|0.96|0.98|1.00|1.00||
||High|0.71|0.71|0.45|0.97|1.00|0.99|0.97|0.96|1.00|

8
**Table 2c: Distribution of correlation coefficients**

|Min|P25|P50|P75|Max|
|---|---|---|---|---|
|0.24|0.56|0.71|0.98|1.00|

Table 3 presents the distributions of correlation coefficients for the two change groups. Both sets 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 3:  Distributions of interpersonal correlation coefficients**

|Environmental<br>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|

Our regression results of estimated effects of four categorical variables: common locality; common status in the highest socioeconomic group; common status in the lowest socioeconomic group; and common ethnicity on inter-household correlations are presented in Table 4. Estimates indicate common locality has a highly-significant positive impact on the interpersonal correlation of scores 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.

9
# **Table 4:  Regression results - Determinants of common environmental perceptions**

Dependent variable: Inter-individual correlation coefficient

|<br>Individuals|Environm<br>to Li|ental Threats<br>velihood|<br>|General E<br>Con|nvironmental<br>ditions||
|---|---|---|---|---|---|---|
|Have Common:|OLS|Robust|GLS|OLS<br>|Robust|GLS|
|Locality|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|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Tables 5 and 6 display predicted correlations from the GLS regressions by locality of residence and SES.<sup>5</sup> 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 locality. Conversely, paired individuals with High SES in different localities 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.

> 5 Ethnicity has no meaningful effect on these results; the table entries are calculated for paired individuals who do not have common ethnicity.

10
**Table  5:  Livelihood-related changes:  Predicted interpersonal correlations by co-location and SES**

|Common<br>Locality|Low|Common SES<br>Intermediate|High|
|---|---|---|---|
|Yes|0.26|0.23|0.21|
|No|0.12|0.09|0.08|

**Table  6:  General environmental changes:  Predicted interpersonal correlations by co-location and SES**

|Common<br>Locality|Low|Common SES<br>Intermediate|High|
|---|---|---|---|
|Yes|0.41|0.38|0.31|
|No|0.32|0.29|0.21|

# **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. An extensive literature has studied 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 11
2017; Chakraborty et al. 2017; Liu and Mu 2017). The principal focus is transactional, on the implicit assumption that agents have common perceptions 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 perception. Within localities, collective action may be easier if elite and non-elite households have similar perceptions of critical environmental problems. At the same time, extensive research indicates that regional governance is dominated by economic and educational elites who communicate across village 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 perceptions of the critical problems. Several other possibilities emerge from this line of inquiry, depending on comparative conditions at the village and regional levels. In the weakest scenario, environmental governance is hindered by perceptual heterogeneity within and across villages. Local governance may be strengthened if village elite and non-elite households share common perceptions, while regional governance may dominate if village-level perceptions differ but elites share perceptions across villages.

We conducted a survey of 600 households in the Indian Sundarbans, which is threatened by several environmental factors related to climate change, and explored the impacts of location and socioeconomic status on patterns of perceptions of environmental change. Our findings have three noteworthy features. First, for the broad Middle group, we find a positive, highly-significant correlation of perceptions across all localities.<sup>6</sup> Common locality adds a highly-significant

> 6 These are the constant terms in the regressions.

12
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 perceptions of environmental change that become more varied as socioeconomic status increases. While our results suggest that the strongest perceptual foundations for action in the commons are local, they also indicate that disagreement about priorities may undermine the leadership potential of local elites. By implication, village-level governance based on widespread participation in decisionmaking 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.

# **References**

Adhikari, B. and Lovett, J.C., 2006. Institutions and collective action: does heterogeneity matter in community-based resource management?. _The Journal of Development Studies_ , _42_ (3), pp.426445.

Alesina, A., Baqir, R. and Easterly, W., 1999. Public goods and ethnic divisions. _The Quarterly Journal of Economics_ , _114_ (4), pp.1243-1284.

Araral Jr, E., 2009. What explains collective action in the commons? Theory and evidence from the Philippines. _World development_ , _37_ (3), pp.687-697.

Baland, J.M. and JP Platteau, 1993. _A multidisciplinary analysis of local-level management of environmental resources_ . Facultés Universitaires Notre-Dame de la Paix.

13
Baland, J.M. and Platteau, J.P., 1995. Does Heterogeneity hinder collective action?. Cahiers de la Faculte des Sciences Economoques Sociales No. 142, Namur, Belgium, Facultes des Sciences Economiques et Sociales, Facultés Universitaires Notre-Dame de la Paix.

Baland, J.M. and Platteau, J.P., 1999. The ambiguous impact of inequality on local resource management. _World development_ , _27_ (5), pp.773-788.

Bandyopadhyay, S., S. Dasgupta, Z. Khan and D. Wheeler. 2018. Cyclonic Storm Landfalls in Bangladesh, West Bengal and Odisha, 1877-2016 : A Spatiotemporal Analysis. Policy Research Working Paper No. 8316. World Bank, Washington, DC.

Bardhan, P., 1993. Symposium on management of local commons. _Journal of Economic Perspectives_ , _7_ (4), pp.87-92.

Bardhan, P. and Dayton-Johnson, J., 2000, May. Heterogeneity and commons management. In _Proceedings of the 8th International Conference on Common Property, IASCP. Bloomington, Indiana_ .

Beck, T. and C. Nesmith. (2001). Building on poor people’s capacity: The case of common property resources in India and West Africa. World Development, 29, 119–119.

Chakraborty, J., T. Collins, S. Grineski and A. Maldonado. 2017. Racial Differences in Perceptions of Air Pollution Health Risk: Does Environmental Exposure Matter? International Journal of Environmental Research and Public Health, 14(2): 116.

Chen, M. 2017. Job versus environment: an examination on the attitude of union members toward environmental spending, Environmental Economics and Policy Studies. 19(4): 761–788.

Dasgupta, S., Md. I. Sobhan and D. Wheeler. 2017. The Impact of Climate Change and Aquatic Salinization on Mangrove Species in the Bangladesh Sundarbans. Ambio. Vol. 46, No. 6, 680694.

Dasgupta, S., M. Huq, Md. G. Mustafa, Md. I. Sobhan and D, Wheeler 2017. The Impact of Aquatic salinization on Fish Habitats and Poor Communities in a Changing Climate: Evidence from Southwest Coastal Bangladesh”. Ecological Economics **_._** 139, pp 128-139 _._ Gaspart, F. 2003. The risk of resource misappropriation in community driven development. World Development, 31: 1687–1687.

Ghertner, D. 2011. Gentrifying the State, Gentrifying Participation: Elite Governance Programs in Delhi. International Journal of Urban and Regional Research. 35(3): 504–532.

Janmaimool, P. 2017. Investigating pro-environmental behaviors of well-educated people in Thailand. International Journal of Sociology and Social Policy, 37(13/14): 788-807.

Jones, R. and R. Dunlap. 1992. The Social Bases of Environmental Concern: Have They Changed Over Time? Rural Sociology. 57(1): 28-47 14
Kanbur, S.R. 1992. Heterogeneity, distribution, and cooperation in common property resource management. World Bank Policy Research Working paper 844. Washington, DC: World Bank.

Khan, S. 2008. Local Governments and Local Elites. Local Government Studies. 34(4): 509-528.

Kölle, F., 2015. Heterogeneity and cooperation: The role of capability and valuation on public goods provision. _Journal of economic behavior & organization_ , _109_ , pp.120-134.

Kurian, M. and T. Dietz. 2013. Leadership on the Commons: Wealth Distribution, Co-provision and Service Delivery. The Journal of Development Studies. Volume 49(11): 1532-1547.

Kurian, M. and T. Dietz. 2007. Hydro-logic: Poverty, heterogeneity and cooperation on the commons. New Delhi: Macmillan.

Liu, X. and R. Mu. 2016. Public environmental concern in China: Determinants and variations, Global Environmental Change, 37: 116-127.

Lucas, A.. 2016. Elite Capture and Corruption in Two Villages in Bengkulu Province, Sumatra. Human Ecology. 44: 287–300.

Marchiori, C., 2014. Inequality and rules in the governance of water resources. _Ecological economics_ , _105_ , pp.124-129.

Mukhopadhyay, A., D. Wheeler, S. Dasgupta, A. Dey and I. Sobhan. 2019. Mangrove Spatial Distribution in the Indian Sundarbans: Predicting Salinity-Induced Migration. Journal of Management and Sustainability **_._** Vol. 9, No. 1 Myers N. 1997. The world’s forests: Problems and potentials. Environmental Conservation. 23: 156–168.

Ostrom, E., Walker, J. and Gardner, R., 1992. Covenants with and without a sword: Selfgovernance is possible. _American political science Review_ , _86_ (2), pp.404-417.

Ostrom, E., 2003. How types of goods and property rights jointly affect collective action. _Journal of theoretical politics_ , _15_ (3), pp.239-270.

Ostrom, E., Burger, J., Field, C.B., Norgaard, R.B. and Policansky, D., 1999. Revisiting the commons: local lessons, global challenges. _science_ , _284_ (5412), pp.278-282.

Oyono, P. 2004. Institutional Deficit, Representation, and Decentralized Forest Management in Cameroon. World Resources Institute Environmental Governance in Africa Working Paper No. 15.

Palinkas, L., S. Horwitz, C. Green, J. Wisdom, N. Duan, and K. Hoagwood. 2015. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research.

15
Adm Policy Ment Health. 42(5): 533–544.

Piabuo, S., D. Foundjem-Tita and P. Minang. 2018. Community forest governance in Cameroon: a review. Ecology and Society 23(3): 34.

Poteete, A.R. and Ostrom, E., 2004. Heterogeneity, group size and collective action: The role of institutions in forest management. _Development and change_ , _35_ (3), pp.435-461.

Ruttan, L.M., 2008. Economic heterogeneity and the commons: Effects on collective action and collective goods provisioning. _World Development_ , _36_ (5), pp.969-985.

Somanathan, E., Prabhakar, R. and Mehta, B.S., 2007. Collective action for forest conservation: Does heterogeneity matter. _Inequality, Cooperation and Environmental Sustainability_ , pp.234245.

Vedeld. T. 2000. Village politics: Heterogeneity, leadership and collective action. The Journal of Development Studies. 36( 5): 105-134.

Wade, R., 1987a. The management of common property resources: Finding a cooperative solution. _The World Bank Research Observer_ , _2_ (2), pp.219-234.

Wade, R., 1987b. The management of common property resources: collective action as an alternative to privatisation or state regulation. _Cambridge journal of economics_ , _11_ (2), pp.95106.

Wan, C., G. Shen and S. Choi. 2018. Differential public support for waste management policy: The Case of Hong Kong. Journal of Cleaner Production. 175: 477-488 16
# **Appendix Table A1:  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<sup>7</sup>|
||General|
|**Religion**|Hindu|
||Muslim|
||Christian|
|**Education**|Highest level|
|**Occupation**|Agriculture|
||Fishing|
||Forestry|
||Service|
|**Income and Employment**|Permanent|
||Seasonal|
|**Migration**|Permanent<br>Seasonal|
|**Housing Details**|Housing type|
||Distance from amenities|
||Household assets|

> 7 OBC abbreviates Other Backward Caste, a term utilized by the Government of India.

17