





































Ecology, Economy and Society–the INSEE Journal 5 (2): 43-66, July 2022 

RESEARCH PAPER 

Discounting Disaster: Land Markets and Climate Change 
in the Indian Sundarbans 

Sumana Bandyopadhyay, Sunando Bandyopadhyay, Susmita Dasgupta***, 
Chinmoyee Mallik****, David Wheeler***** 

Abstract: Data scarcity has hindered studies on the impacts of climate change on 
land prices in the coastal regions of developing countries. Focused on the Indian 
Sundarbans, this paper is at the forefront of such research. Market conditions in 
the region feature unregulated transactions, unenforced zoning, and a lack of 
disaster insurance. For many residents with hereditary land ownership, stark 
poverty eliminates any risk buffer provided by savings or other non-essential liquid 
assets. Using new household surveys and environmental data, our study 
hypothesizes that salinization and cyclone strikes have already adversely affected 
land prices. We quantify such impacts using a georeferenced panel of 342 salinity 
monitoring stations and a spatial raster database on all cyclonic storm strikes since 
1970. Our econometric results reveal highly significant negative impacts for both 
factors. We use the regression results to predict land prices for the most and least 
favourable environmental conditions recorded in our database. The results show 
that these climate change–related conditions account for spatial differentials greater 
than an order of magnitude in land prices. Such extreme risk differentials suggest 
high financial and fiscal stakes, underscoring the critical importance of 
appropriately targeted adjustment policies.  

Keywords: Indian Sundarbans; land transactions; environmental variables; tropical 
cyclones; salinity; coastal erosion 

 

 

 Department of Geography, University of Calcutta, India. sumona_bm@yahoo.com  

 Department of Geography, University of Calcutta, India. sunando@live.com  

*** Development Economics Research Group, World Bank, USA. sdasgupta@worldbank.org  

**** Department of Rural Studies, West Bengal State University, India. 
chinmoyeemallik@gmail.com  

***** World Bank, USA. wheelrdr@gmail.com 

Copyright © Bandyopadhyay, Bandyopadhyay, Dasgupta, Mallik and Wheeler 2022. Released 
under Creative Commons Attribution © NonCommercial 4.0 International licence (CC BY-

NC 4.0) by the authors.  

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

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

DOI: TBA 

mailto:sumona_bm@yahoo.com
mailto:sunando@live.com
mailto:sdasgupta@worldbank.org
mailto:chinmoyeemallik@gmail.com
mailto:wheelrdr@gmail.com


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

 

1. INTRODUCTION 

This paper uses 2016–17 household surveys and environmental data to 
investigate the impact of climate change on land prices in the Indian 
Sundarbans coastal region. Coastal studies relating land prices to climate 
factors are plentiful in developed countries, particularly the United States 
(US). However, until recently, data scarcity has hindered similar research in 
the coastal regions of developing countries. To our knowledge, this paper 
represents the first such assessment. 

The Sundarbans is a UNESCO heritage mangrove forest that extends 
across the India–Bangladesh border at the mouth of the Ganga–
Brahmaputra–Meghna river basin. The incidence of poverty is strikingly 
high in the Sundarbans, and poor people in the region rely mostly on 
natural resources for their livelihoods. Salinization is spreading inland, and 
tidal surges and cyclones—to which the region is prone—are increasing in 
number and severity (Dasgupta and Wheeler 2018). The combination of 
poverty, topography, salinization, and the increasing risk of inundation have 
created conditions that will become widespread as sea-level rise and climate 
change continue (Dasgupta et al. 2016). 

Our study uses household data drawn from a georeferenced survey of land 
transactors, which include information on plot sizes and prices. We quantify 
environmental conditions using two data sources: (i) a georeferenced panel 
of salinity measures drawn from 342 monitoring stations in the Indian 
Sundarbans region and (ii) a spatial raster database that incorporates 
information on all cyclonic storms that have struck the region since 1970. 

Using these data, we estimate an econometric model that relates land prices 
to the effects of salinization, cyclonic storm intensity, and inundation risk 
from proximity to the coastline. In theory, one would expect salinization to 
reduce land prices through its impact on soil fertility. Land prices should 
also be lower in areas prone to cyclonic storm damage as well as those 
susceptible to inundation. We use the regression results to predict land 
prices for cases where salinization, cyclonic storm intensity, and inundation 
risk are at their least and most favourable levels in the sample.  

Overall, our results suggest that climate change–related conditions account 
for spatial differentials greater than an order of magnitude in Sundarbans 
land prices. We believe that these results can help inform a key policy 
debate on whether to compensate residents in threatened coastal regions 
for their constantly escalating losses from salinization and inundation risk as 
the sea level rises. The answer may vary with local conditions. However, the 
urgency of the debate is affected by the scale of the financial and fiscal 
stakes, which, as our results suggest, is quite large.  



[45] S. Bandyopadhyay, S. Bandyopadhyay, S. Dasgupta, C. Mallik and D. Wheeler 

 

 

The paper is organized into five sections. Section 2 reviews the relevant 
literature, while Section 3 introduces our household survey dataset and 
climate-related databases. Section 4 specifies and estimates our econometric 
model and uses the results to predict land prices. Finally, Section 5 explores 
the policy implications and concludes the paper. 

 

2. PREVIOUS RESEARCH 

As the sea level rises, flooding increases, and salinity spreads inland, land 
markets in coastal communities will adjust to the uncertainty surrounding 
the timing and intensity of future changes. In the US, Nakanishi (2016) has 
shown that natural disasters make land prices more volatile and increase 
average property values in safer areas. Other studies, mostly from the US, 
have found that property values are lower in flood zones (Dubé, 
AbdelHalim, and Devaux 2021; Bakkensen and Barrage 2021; Kousky et al. 
2020; Florax, and Rietveld 2009; Bin, Kruse, and Landry 2008; Daniel, Bin 
and Kruse 2006; Harrison, Smersh, and Schwartz 2001; Donnelly 1989; 
Shilling et al. 1985) or subject to time discounting1 that depends on the 
incidence of past floods (Ratnadiwakara and Buvaneshwaran 2020; Beltrán, 
Maddison, and Elliott 2019; Atreya and Ferreira 2014; Atreya, Ferreira, and 
Kriesel 2013; Bin and Polasky 2004; Bartosova et al. 2000) and the existence 
of mandatory flood insurance programmes (Frazier, Boyden, and Wood 
2020; Atreya and Czajkowski 2019; Speyrer and Ragas 1991). Other studies 
have shown that, despite disaster risks, a region’s high property values may 
be sustained by its natural environmental advantages (Fu and Nijman 2021; 
Wu, Chen, and Liou 2021; Beltrán, Maddison, and Elliott 2018; Atreya and 
Czajkowski 2016; Bin and Kruse 2006; Eves 2004).  

Studies based in the coastal areas of developing countries can provide a 
useful extension of the literature because market conditions differ sharply 
from those in their Western counterparts. In the Indian Sundarbans, land 
transactions are virtually unregulated, zoning is not enforced, private and 
public disaster insurance is non-existent, and for many residents whose land 
ownership is hereditary, stark poverty eliminates any risk buffer provided by 
savings or other liquid assets that are not essential for survival. In short, the 
Sundarbans experience exemplifies unconstrained risk adjustment in land 
markets under rising environmental stress.  

In economic theory, the price of a land parcel is generally assumed to reflect 

 

1 'Time discounting' means the lowering of property prices following a flood event. The 

duration (or 'time') of the price lowering (or 'discounting') varies due to several factors. 



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

 

the present value of its expected future rent flow at the prevailing discount 
rate (Hoover and Giarratani 1999).2 For the Sundarbans, climate-related 
factors are expected to be significant in this context. Salinization is 
hypothesised to reduce land rent through its impact on soil fertility 
(Dasgupta et al. 2017). Land rent is expected to be lower in areas prone to 
cyclonic storm damage as well as those susceptible to inundation and tidal 
flux. The impacts of salinization, past storm intensity, and inundation risks 
will depend on their roles in the formation of transactors’ expectations 
about the severity of future conditions.  

To date, empirical work on the land market impact of climate-related risks 
has focused mainly on coastal areas in the US, where storm damage and 
inundation risk have been afforded more attention than salinization 
(Dachary-Bernard et al. 2019; McAlpine and Porter 2018; McNamara and 
Keeler 2013; Lichter and Felsenstein 2012; Bin et al. 2010; West et al. 2001; 
Yohe et al. 1996). 

 

3. DATA 

Our data are drawn from six widely dispersed villages (mouzas) in the 
Gosaba, Kultali, and Sagar community development (CD) blocks of the 
Indian Sundarbans (Figure 1). As Table 1 shows, the three blocks are 
roughly comparable in area and population density. 

3.1 Land Transactions 

Data collection in the Sundarbans presents unique challenges because 
resident communities are isolated and public records are incomplete. 
Formally, a land transaction involves the execution of a legal deed by the 
seller in favour of the buyer. The deed is supposed to be filed at the local 
public office after the payment of registration charges, which are dependent 
on the transaction price. In practice, the formal process ratifies transactions 
by wealthier households while informal transactions prevail among poor 
landowners who cannot read or understand formal contract measures or are 
unwilling to register because of the fees involved.  

 

 

2 This prevailing view abstracts from numerous potentially qualifying factors, including 
regulations, local customs, and speculation. For additional discussion, see Buurman (2001). 



[47] S. Bandyopadhyay, S. Bandyopadhyay, S. Dasgupta, C. Mallik and D. Wheeler 

 

 

Figure 1: Locations of Transacting Households, Indian Sundarbans  

Note: Red points denote sampled households with village (mouza) names. Study 
area blocks are shown in yellow. Green and grey /yellow colours denote forested 
and reclaimed Sundarbans, respectively. 
Source: Field Survey using the Global Positioning System. Base map prepared 
from Resourcesat-2 images for 2015. 

The household survey for this research was conducted in the Sundarbans 
from 1 October 2016 to 15 January 2017, with additional visits to verify and 
clean the data from March to May 2017. The six sampled villages all have 
populations near 10,000, according to 2018 estimates, based on the 2001–
2011 growth rate. In each village, the survey team identified land 
transactions that had taken place between 2006 and 2016 (inclusive) with 
the help of the village elders and village surveyors (amins). Initial 
conversations with the village elders identified families who had made 
transactions.3 All identified households were surveyed and additional 

 

3 Each transaction involves a buyer and a seller, but the final dataset incorporates only one 
observation per transaction to avoid duplicated measures for model variables. The survey 
team used data for the first transactor identified, so the database includes both buyers and 
sellers. 



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

 

households were identified as transactors over the course of the survey. 
Cross-checks were performed with the village amins to validate plot sizes, 
locations, and ownership. We tested and modified the household survey 
instrument in a pilot study, and the full survey was then conducted for the 
456 identified transacting households (Figure 2).  

Table 1: Statistics of the Studied Community Development Block, Indian 
Sundarban 

Block Households 
(Number) 

Population 
(Number) 

Area 
(km2) 

Population 
Density 

(Persons/km2) 

Gosaba 58,197 246,598 296.7 831.1 

Kultali 45,099 229,053 306.2 748.1 

Sagar 43,716 212,037 282.1 751.6 

Source: Census of India (2011) 

Among the 456 land parcels identified by the survey, 78% are solely used 
for cultivation, 15% are partly occupied by housing, and 7% are commercial 
properties. Figure 2 provides summary information on prices, parcel areas, 

and timing. Real unit land prices are calculated at ₹2,017 per hectare, using 
the World Bank’s annual gross domestic product (GDP) deflator.4 The 
sample yields a roughly balanced representation for eight price categories, 

from prices below ₹1,000 per hectare (10.5% of transactions) to prices 

above ₹100,000 per hectare (13.4% of transactions). Similarly, transaction 
parcels are distributed in an approximately balanced manner across seven 
ranges, from parcels below 2 hectares (12.7%) to those above 60 hectares 
(7.9%). The transaction timing is widely spaced. 

3.2 Climate-related Variables 

In this section, we use two variables – cyclone strikes (frequency and 
intensity) and salinity – to attempt an econometric analysis, where salinity is 
used as a cross-sectional variable. 

 

 

 

4 The World Bank’s GDP deflator for India is the annual price index for goods and services. 
We have used it to adjust the year-to-year comparison of land market values for price 
inflation. 



[49] S. Bandyopadhyay, S. Bandyopadhyay, S. Dasgupta, C. Mallik and D. Wheeler 

 

 

Figure 2: Transaction Characteristics of the Sampled Households, Indian 
Sundarbans — Land Prices (A), Parcel Areas (B), Transactions by Block (C), and 
Transactions by Year (D) 

Source: Primary survey, 2016-17 

3.2.1 Cyclone Strikes 

Cyclonic storms regularly strike the coastal region of the Sundarbans from 
May to December. In the northern Bay of Bengal, recent research has 
found significant increase in the intensity of cyclones with the acceleration 
of global warming (Bandyopadhyay et al. 2021; Mishra 2014; Krishna 2009). 
Considering the coastlines of Odisha, West Bengal, and Bangladesh during 
1877–2016, Bandyopadhyay et al. (2021) reported a notable increase in 
storm landfalls in the Sundarbans region between 1961 and 2016. This 
implies that the impact of cyclone strikes on land prices must have 
increased in recent years. This section incorporates the increased frequency 
and intensity of Sundarbans cyclone strikes into a spatial index for the 
econometric analysis, which assigns the greatest weight to recent strikes.  

During a cyclone’s passage, the damage caused by a few hours of battering 
by waves, winds, and storm surges can equal many years’ worth of fair-
weather depreciation. The damage inflicted on the region is well-
documented (ADB-GoO-WB 2013; EM-DAT 2019; NIDM 2014; Khalil 
1993). To illustrate, Cyclone Sidr struck the Sundarbans region of 
Bangladesh in November 2007, causing 3,406 deaths and economic losses 
of US$ 1.68 billion (GoB 2008). Cyclone Aila struck the Indian Sundarbans 
in May 2009, causing 100 deaths and losses above US$ 1.05 billion (IMD 
2013; Mallick et al. 2011; GoWB 2009; IAA 2009). In the wake of such 
destruction, Dasgupta and Wheeler (2018) find large coastal population 



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

 

displacement effects. 

As noted in Section 1, damage from cyclone strikes may play a significant 
role in the determination of land prices in the Sundarbans. Introducing this 
variable into the econometric work requires constructing a damage measure 
based on the historical record. In this paper, we incorporate the impact of 
cyclone strikes using a georeferenced panel database of past cyclonic storms 
(Bandyopadhyay et al. 2021) and the methodologies of Dasgupta and 
Wheeler (2018) and Dasgupta et al. (2022), which compute cyclonic storm 
intensities in a multi-stage exercise.  

First, we assemble complete georeferenced records for cyclonic storms in 
the studied region. For the period from 1970 to 2016, we use track data of 
storms above the wind speed of 33 knots (62 km per hour), available from 
the International Best Track Archive for Climate Stewardship (IBTrACS, 
version 3.9). This data source is maintained by the Global Data Center for 
Meteorology, operated by the United States National Oceanic and 
Atmospheric Administration (NOAA, 2018). To check for missing data, we 
also employ georeferenced storm track information from the India 
Meteorological Department (IMD). We exclude all storms rated as tropical 
depressions because their maximum wind speeds fall below 34 knots, which 
limits their potential for causing serious damage. Winds above 33 knots 
reach gale force and a Beaufort Scale value of 9, when trees start to break 
off and walking become difficult. The analysis uses two commonly available 
measures of cyclonic storm strength: (i) maximum wind speed, measured in 
knots, and (ii) primary impact zone, measured by the radial distance 
between a storm’s centre and the outer boundary of its maximum wind 
speed zone. 

For each storm, we compute the primary impact zone along its track 
(Bandyopadhyay et al. 2021). Using a methodology from the United States 
National Hurricane Center (USNHC 2018), we compute wind speed at each 
point after landfall as a function of wind speed at landfall and elapsed time 
after landfall.5 We derive wind damage potential using a standard 
exponential formulation (HRC-AOML 2018).6  

 

5 In the USNHC model, the ratio (wind speed to wind speed at landfall) decays exponentially 
with time after landfall. The absolute value of the exponential parameter is a positive 
function of wind speed at landfall (i.e., the rate of decay is greater for storms with higher 
initial wind speeds). 
6 In our computation, wind damage potential is proportional to the square of wind speed, 
which is measured in knots (kt). Wind damage potential is therefore expressed in kt2. 



[51] S. Bandyopadhyay, S. Bandyopadhyay, S. Dasgupta, C. Mallik and D. Wheeler 

 

 

Figure 3: Cyclone Intensity Indices (CII), Indian Sundarbans  

Note: CII are dimensioned in kt2. The coastline is indicated by the dotted blue line; 
unclassified land areas are shown in grey; and black boundaries depict the study 
areas of Sagar, Kultali, and Gosaba. 

Source: IBTrACS data from NOAA (2018) 

Next, we compute historical storm damage potentials using high-resolution 
spatial population data from the CIESIN (2019) Gridded Population of the 
World (GPW, version 4). These data have resolutions of 30 arc seconds 
(approximately 1 km at the equator). Using a geographic information 
system (GIS), we overlay each GPW point with all historical cyclone impact 
zones to identify the cyclones that have affected the point. Thus, for each 
GPW point, we generate a time series of cyclones, with impact years and 
estimated wind damage potentials (dimensioned in kt2).  

Finally, we divide the historical storm data into three 15-year periods: 1970–
1984, 1985–1999, and 2000–2014. For each period, we compute the mean 
wind damage potential for each GPW point. Then, we combine the mean 
wind damage potentials for the three periods into an overall storm intensity 
index (dimensioned in kt2) using weights computed by Dasgupta and 
Wheeler (2018) and Dasgupta et al. (2022) from regression analyses of 



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

 

historical impacts on population displacement in the region.7 Figure 3 maps 
the cross-sectional cyclone intensity index (CII) that we use for the 
econometric analysis in this paper.  

3.2.2 Salinity 

Water salinity in the Indian Sundarbans is rising as climate change affects 
river flows and the sea level. Salinity is already near marine levels in 
southern areas, with measures of 30 parts per thousand (ppt) or higher. By 
2050, regional salinity will intensify considerably, with many northern areas 
also surpassing 30 ppt (Dasgupta et al. 2022; Mukhopadhyay et al. 2019; 
Dasgupta et al. 2017). These changes are expected to reduce the value of 
agricultural land in the affected areas. Figure 4 overlays Figure 1 with local 
enlargements that display marine and riverine encroachments in two study 
areas during the past century. These changes have brought the shoreline 
much closer to many households, with direct salinization effects in 
Beguakhali (Sagar) and longer-term effects from rising riverine salinity in 
Dayapur (Gosaba).8  

Our database for econometric estimation includes land transactions in the 
years 2006–2016. However, water salinity monitoring data are only available 
for 2012–2015. Matching the two datasets by year would limit our 
econometric database to 4 of the 11 years in our land transaction sample. 
Accordingly, we incorporate salinity as a cross-sectional variable based on 
monitoring data for the same period. Since the salinity observations are 
incomplete, we perform interpolations on a spatial panel database of 
readings for 342 monitoring stations in the Indian Sundarbans provided by 
WWF International (2019) (Figure 5). This is an unbalanced panel, with 
many time-series observations from some monitoring locations and sparse 
observations from others. Table 2 displays the available observations by 
month and year.9 

 

7 We use storm intensities for previous periods because the results in Dasgupta and Wheeler 
(2018) indicate that expectations about cyclone strikes in an area adjust to the historical 
pattern with long lags. 
8 Prawn cultivation in saline water for export may have increased land salinity and depressed 
land values near prawn farms in the Sundarbans sampled households (Ghoshal et al. 2019). 
However, none of the land clusters used for the present analysis is located close to an 
aquaculture or prawn farm whose saline operations could influence land prices. In general, 
prawn farming is conducted only on the outer sides of main embankments in creek-adjacent 
areas of sections in the southern Indian Sundarbans. Farmlands are usually not converted for 
aquaculture. 
9 As Table 2 shows, monitoring stations have operated with different frequencies during the 
sample period. These differences are mainly related to operations and maintenance problems 



[53] S. Bandyopadhyay, S. Bandyopadhyay, S. Dasgupta, C. Mallik and D. Wheeler 

 

 

Figure 4: Coastal and Riverine Encroachment on Two Study Villages, Indian 
Sundarbans  

Note: Red points denote sampled households with village (mouza) names. Study 
area blocks are shown in yellow. Green and grey /yellow colours denote forested 
and reclaimed Sundarbans, respectively. 

Source: Field Survey using the Global Positioning System. Land extents extracted 
from 1922 Survey of India topographical maps, 1967 Corona space photos, and 
2015 Resourcesat-2 satellite images. 

To fill in the panel, we estimate an interpolation model that incorporates 
fixed effects (FE) for time (by month and year) and location (by monitoring 
station). The model controls for average differences in salinity at different 
monitoring stations while incorporating the annual trend and seasonal 
fluctuations that affect all stations concurrently. The model is specified as 
follows: 

 

under typical conditions in the Sundarbans. Observations in the database have been 
recorded for cases where monitors met the required technical specifications during the 
periods of operation. 



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

 

 
(1) 

where Sit equals salinity (ppt) at monitoring location i in period t; DSj 
represents the monitoring dummy variable (1 for monitoring location j and 
0 otherwise), DMk equals the month dummy variable, y stands for the year 
(2012, …, 2015), and εit is the stochastic error term. 

Table 2: Salinity Monitoring Observations by Month and Year, Indian Sundarbans 

 

Month 2012 2013 2014 2015 

January - - 64 64 

February 40 58 158 64 

March - 132 72 48 

April - 122 72 48 

May 2 58 144 60 

June 10 52 64 76 

July 8 52 56 68 

August - 52 56 44 

September 2 46 40 62 

October 8 52 64 56 

November 36 56 64 56 

December 150 96 64 56 

Source: WWF International (2019)  

We use regression predictions to fill in the missing observations for the 342 
monitoring stations in all 12 months for the years 2012–2015. Using actual 
and interpolated observations for 2015, we choose the peak month of May 
for our cross-sectional salinity index. 

 

4. RESULTS AND DISCUSSION 

Our econometric model includes FE for the three CD blocks in order to 
incorporate the impact of unobserved factors such as attractiveness for 
tourism and differential soil fertility. We include FE for the observation 
years as well as interaction terms that allow for land price dynamics in the 
different blocks. We estimate a log-linear model since the distribution of 
land prices is highly skewed but approximately log-normal. This approach 
minimizes outlier effects while preserving the information in extreme 



[55] S. Bandyopadhyay, S. Bandyopadhyay, S. Dasgupta, C. Mallik and D. Wheeler 

 

 

observations.10 We control for plot size since extensive research has 
documented a significant negative relationship between plot size and land 
price.11 

Figure 5: Salinity Estimates in the Indian Sundarbans, May 2015 

 

Note: Salinity values are in parts per thousand (ppt) 
Source: WWF International (2019) 

We specify the following estimation model:12 

 

 

10 We believe that this approach is superior to truncating highly skewed distributions to 
eliminate arbitrarily identified outliers. For further discussion, see Tukey (1977). 
11 For a summary of past research, see Lin and Evans (2000). 
12 We have also experimented with a measure of local land erosion risk, proxied by the 
change in the distance to the nearest riverine shoreline from 1967 to 2015. This measure has 
positive and negative values for accretion and erosion, respectively. Because it proved 
insignificant in all estimates, we excluded it from the final specification of the estimation 
model (2).  



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Expectations: α1 > 0; α2, α3, and α4 < 0 
(2) 

For household i and period t, ln Xit equals the log land transaction price; Di 
represents the distance from the coastline (see Figure 3); Ci indicates past 
and expected future wind damage from cyclonic storms; Si represents 
salinity of the nearest monitoring location; Ni indicates the size of the 
transacted plot; Bj equals the CD block dummy variables for Gosaba (1), 
Kultali (2), and Sagar (3); yt stands for the observation year; DYk equals the 

dummy variables for years (2006, …, 2016); and Ɛit is the stochastic error, 
subject to spatial and temporal autocorrelation.  

Land transactions are recorded by year from 2006 to 2016; we convert 

prices per hectare to ₹2,017 using the India GDP deflator in the World 
Bank’s World Development Indicators. For Di, we compute the distance 
from each household location to the nearest point on a coastal polyline 
constructed by the authors. As shown in Figure 3, the polyline follows the 
outer coastlines of the southernmost Sundarbans islands, with direct linear 
segments between islands. Ci for a household is the nearest point in the 
spatial raster of past storm severity indicators described in Section 3.2.1 
(Figure 3). Si for a household is the estimated reading for May 2015 from 
the nearest water salinity monitor described in Section 3.2.2 (Figure 5). We 
use this as our proxy variable, following the finding of Dasgupta et al. (2017) 
that land salinity in this coastal region is strongly predicted by proximate 
water salinity. The size of each transacted plot is drawn from our survey 
data. 

Table 3 reports our results for land prices in recorded transactions. To test 
for robustness, we use alternative estimators that incorporate different 

assumptions about the structure of the stochastic error term (Ɛit) in the 
model. These techniques produce the same point estimates for model 
parameters, but their differing estimates of standard errors (and the 
accompanying t statistics) may lead to very different inferences about the 
statistical significance of model variables. We replicate the point estimates 
in columns 1–3 to aid the interpretation of the t statistics. In this case, we 
find that our model is robust to the changes. We include results for 
ordinary least squares (OLS), standard errors adjusted for 71 clusters of 
household groups (hamlets), and a spatial heteroscedasticity and 
autocorrelation consistent (HAC) estimator that incorporates both spatial 
and temporal autocorrelation (Hsiang 2010, 2020).  

 

 



[57] S. Bandyopadhyay, S. Bandyopadhyay, S. Dasgupta, C. Mallik and D. Wheeler 

 

 

Table 3: Regression Results: Land Price Versus Climate-related Variables 

Independent 
Variables 

(1) OLS (2) Cluster 
SE 

(3) Spatial 
HAC 

 

Salinity −0.238 
(4.36)** 

−0.238 
(2.09)* 

−0.238 
(3.49)** 

 

Cyclone strike intensity −0.004 
(2.58)* 

−0.004 
(3.50)** 

−0.004 
(3.23)** 

 

Plot area −0.023 
(9.39)** 

−0.023 
(5.65)** 

−0.023 
(4.80)** 

 

D (Kultali) −327.206 
(2.22)* 

−327.206 

−1.93 

−327.206 
(2.67)** 

 

D (Kultali) × year 0.163  

(2.21)* 

0.163  

−1.93 

0.163 (2.67)** 
 

D (Sagar) −356.766 
(2.58)* 

−356.766 
(2.85)** 

−356.766 
(2.50)* 

 

D (Sagar) × year 0.176 (2.56)* 0.176 (2.84)** 0.176 (2.49)* 
 

Constant 23.975 
(6.39)** 

23.975 
(5.63)** 

23.975 
(7.16)** 

 

Observations R 
squared 

456  

0.3 

456  

0.3 

456  

 

R-squared 
 

Notes: Dependent variable: log land price 
 The absolute value of t statistics are in parentheses.  
 * = 5% significance level and ** = 1% significance level. 
 a Dummy variable results for 2007–2016 are excluded. 
 OLS: ordinary least squares; HAC: heteroscedasticity and autocorrelation 

consistent; D: Distance from shoreline 
 

4.1 Fixed Effects and Collinearity 

Preliminary estimates show that collinearity between the CD block FE and 
Di, our measure of distance from the coastline, is too great for independent 
parameter estimation. We choose to retain the block FE since it may absorb 
the effects of factors other than distance from the coastline. We exclude the 
dummy variable for the Gosaba block to avoid total collinearity, so the two 
blocks’ results should be interpreted as deviations from the result for 
Gosaba. The FE estimates for Kultali and Sagar are both negative and 
significant, with a somewhat larger estimated effect for Sagar. Since the 



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Sagar block is closer to the ocean, this may partly reflect the distance from 
the coastline. However, other factors may also be involved. To cite one 
possibility, Figure 5 shows that salinity measures for the Sagar region are 
both sparse and low. These may not adequately represent salinity for Sagar, 
particularly in Beguakhali village (Figure 1), because it is close to the open 
ocean. Part of the negative result for Sagar may represent an adjustment for 
this factor.  

The positive, significant interactions of the two block dummy variables with 
observation years suggest that exogenous trends have reduced the FE 
differences from Gosaba during the sample period. We incorporate the full 
set of yearly dummy variables in all regressions, but the results are trendless 
and insignificant. We therefore exclude them to make Table 3 easier to read. 

4.2 Results for Environmental Risks and Plot Size 

We find the expected signs and high significance in all cases: land 
transaction price decreases with salinity and cyclone intensity index (CII). 
Despite the sample correlation of salinity and CII, their independent 
covariation with land price is sufficient to yield consistently high 
significance for OLS, cluster standard errors (SE), and spatial HAC. We 
find that land price falls significantly with plot size, as reported in most of 
the empirical literature on land price determination in developed countries. 

While we incorporate adjustments for spatial and temporal autocorrelation, 
our estimates only reflect land values revealed by transactions. An extensive 
body of literature has studied the problem of estimation bias when samples 
are truncated because some potential transactions are excluded due to 
mismatches between buyers and sellers (Bishop et al. 2020; Gatzlaff and 
Haurin 1997, 1998; Gatzlaff and Ling 1994; Munneke and Slade 2000). In 
our case, the most likely source of truncation bias is inherited land with very 
high salinity and/or cyclone risk, for which non-market factors may create a 
reservation price that exceeds very low offers from buyers. Our area 
controls for salinity and cyclone risk are the best available, but their spatial 
resolution may not capture the full range of local variations, including 
extremely low values. Where those occur, buyer/seller mismatches may 
preclude any transactions. If our survey database excludes properties with 
the lowest valuation in high-risk areas, our sample transactions will 
overstate market valuations in those areas. By implication, our estimates 
may be downwardly biased, understating the marginal impact of salinity and 
cyclone strike risk on land prices. Our estimated parameter values should 
therefore be regarded as conservative. 

 



[59] S. Bandyopadhyay, S. Bandyopadhyay, S. Dasgupta, C. Mallik and D. Wheeler 

 

 

4.3 Assessing Impact Magnitudes 

Our econometric results suggest that salinization and cyclonic storm 
damage play significant roles in determining land prices in the Indian 
Sundarbans. However, their empirical importance hinges on actual impact 
magnitudes. As noted earlier, the Sundarbans case differs markedly from 
previously studied Western cases because transactions are unregulated, 
zoning is not enforced, private and public disaster insurance does not exist, 
and poverty eliminates any financial risk buffer for many households. To 
assess impact magnitudes, we use our regression results to predict land 
prices for the most and least favourable environmental conditions recorded 
in our database.  

Table 4 shows that the Sagar block exhibits the most favourable 
environmental conditions in terms of salinity (17.8 ppt) and CII (1,365), 
while the Gosaba block has the least favourable conditions (salinity of 29.3 
ppt and CII of 1,876). We use our econometric results to predict the 
associated land prices in 2016, with dummy variable controls for Sagar and 
Gosaba.13 Our results show that the environmental variables have very large 
effects under the prevailing market conditions in the Sundarbans. Point 
estimates for land prices under the most and least favourable conditions are 

₹86,622 and ₹3,997, respectively. We augment the comparison with lower- 
and upper-bound predictions using a forecast SE, which shows that the two 
ranges are far from overlapping. 

Summing up, within our Sundarbans sample, the point prediction for land 
price under the most favourable environmental conditions is nearly 22 
times the point prediction under the least favourable conditions. By 
implication, land prices in areas that are currently least affected will fall 
sharply as continued sea-level rise and storm intensification drive those 
areas toward the current worst-case values. 

 

 

 

13 Here, it is useful to recall the interpretation of block dummy variables with the 
Gosaba dummy excluded. The results for Sagar and Kultali are differences from 
the FE for Gosaba. To predict for Sagar, its dummy variable is set at 1 while the 
dummy for Kultali is set at 0. To predict for Gosaba, the dummies for both Sagar 
and Kultali are set at 0. We also include the interactions of block dummy variables 
with observation years in the predictions. 



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

 

Table 4: Predicted Land Prices Under the Most and Least Favourable 
Environmental Conditions, Indian Sundarbans 

Environmental 
Conditions 

Value Block Predicted Land Price (₹2,017) 

Point −1.96 SE +1.96 SE 

Most 
favourable 

Salinity 
(ppt) 

17.8 Sagar 
 

 6,622 

 

 2,550 

 

230,519 

Cyclone 
intensity 
index 
(CII) 

1,365 Sagar 

Least 
favourable 

Salinity 
(ppt) 

29.3 Gosaba 
 

 3,997 

 

 1,389 

 

 11,502 

Cyclone 
intensity 
index 
(CII) 1,876 Gosaba 

Note: ppt stands for parts per thousand 
Source: Compiled from NOAA (2018), WWF International (2019), and primary 
surveys 

5. POLICY IMPLICATIONS AND CONCLUSION 

We believe that these results can help address an important policy question 
for threatened coastal regions: should residents be compensated for the 
ever-increasing losses from salinization and inundation risk as the sea level 
rises? For wealthier households or businesses whose acquisition of coastal 
land has already benefited from deep risk discounts, there is no apparent 
rationale for additional compensation. However, our results suggest that 
poorer residents with inherited coastal land will face steep depreciation of 
their primary asset as ocean encroachment continues. 

Some form of means-tested compensation may be warranted, but its form 
will be critical. For example, periodic compensation payments in situ would 
inevitably rise until they become fiscally unsustainable. In contrast, one-time 
compensation could be affected by public land purchases from poorer 
households at above-market prices, followed by the proscription of 
settlement or auction resale at a loss under the condition of caveat emptor. 
Whatever measures are considered, the risk differentials revealed by our 
results indicate that the financial and fiscal stakes are quite high.  



[61] S. Bandyopadhyay, S. Bandyopadhyay, S. Dasgupta, C. Mallik and D. Wheeler 

 

 

This econometric exercise for the Indian Sundarbans has afforded the 
opportunity to study unconstrained risk adjustment in land markets under 
rising environmental stress. The extreme risk-based price differentials 
highlight the critical importance of appropriately targeted adjustment 
policies for this climate-vulnerable coastal region as well as those of other 
developing countries.  

ACKNOWLEDGEMENTS 

This research was conducted under the South Asia Water Initiative – Sundarbans 
Targeted Environmental Studies of the World Bank. We thank Prativa Karmakar, 
Debarpita Banerjee, Binay Krishna Pal, and Jaydip De for their help at various 
stages of the work.  

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