Name: Upasna Sharma
Mentor: Prof. Anand Patwardhan
External Mentor: Dr. Mahendra Shah
Supervisor: Dr. Neil Leary
An empirical approach to assessing generic adaptive capacity to tropical cyclone risk in coastal districts of India
Abstract: Differences in the impacts of climate hazard (say tropical cyclone) across the exposed unit (say a district) are determined by many factors viz.
the severity of the hazard itself, the population that is exposed to the hazard, generic measures of coping capacity, and specific measures of coping capacity. The generic measures of coping capacity are indicators of level of development that would be pertinent in explaining differences in the outcome / impact due to the hazard. If the indicators of generic coping capacity do explain differences in outcome across different districts, then policy measures to improve these indicators would mean an enhancement in generic adaptive capacity of the district in coping with future climate hazard. The objective of this paper is to test the explanatory power of indicators of development (generic adaptive capacity) in explaining observed outcome (human mortality) in the different coastal districts of east coast of India. Human mortality is modeled as a function of hazard, exposure and adaptive capacity with the view to statistically detect adaptive capacity using historical empirical data. Multivariate statistical technique, multiple regression, has been used to analyze the data.
1. Introduction
Adaptation and adaptive capacity have become important in the context of climate risk because of the mediating role adaptive capacity plays in determining the vulnerability of a system to climate risk. Adaptation and an enhancement in adaptive capacity are expected to reduce vulnerability to climate risk (Smit et al, 2001, Adger et al, 2004,
Brooks and Adger, 2005). This implies that all other things being equal, a unit or system with greater adaptive capacity would be less vulnerable to climate risk. In this paper we attempt to examine whether this really holds true in the real life context.
Adaptive capacity is defined as the potential or capability of a system to adjust, via changes in its characteristics or behaviour , so as to cope better with existing climate variability, or with changes in variability and mean climatic conditions (Adger et al,
2003). An important point to note here is that adaptive capacity represents potential rather than actual adaptation as a system requires time to realize its adaptive capacity as adaptation (Brooks et al, 2005). The vulnerability
1
of a system to an extreme weather event such as a tropical cyclone occurring today would be a function of its existing adaptations and baseline or current ability to cope, which resulted from the past realization of the adaptive capacity, and not the future adaptations that may be realized from the enhancement of adaptive capacity in the present or in the future. In this paper
1 In this paper we consider vulnerability as an outcome rather than the intrinsic state of the system and therefore we define it in terms of the impact on the system caused by an interaction of the hazard (tropical cyclone), exposure and adaptive capacity of the system.
1

the focus is on the assessment of past realization of adaptive capacity, which along with the hazard and the exposure determined the level of outcome / impact for a system.
Climate change is expected to manifest as changes in nature and frequency of variability and extreme weather events. Hence, instead of mean changes in climate and weather parameters the focus of this paper is extreme weather events.
Assessment of adaptive capacity cannot be made in a general way. The assessment would be meaningful if it is made in the context of particular kind of hazard that the system/unit of analysis faces. The adaptation process is determined to a large extent by the nature of the hazard to which a system or population must adapt (Adger et al, 2004). For instance, consider two different climate related hazards – droughts and tropical cyclones. In case of drought the investment in the technology to effectively harness groundwater resources or surface water resources would be an important factor in determining the capacity of the system of interest to adapt to the drought. But in case of tropical cyclones, it is the investment in technology for forecasting of cyclones and systems for forewarning the populations at risk that would be an important factor in determining the capacity of the system at risk to adapt. In this paper the climate hazard that we are focusing on are the tropical cyclones experienced in the East coast of India.
For both the drought and the cyclone hazards discussed above, resources are required for developing appropriate technological intervention, but the technological capability required in both cases is very ‘specific’ to the type of hazard being faced. Such a capacity is defined as ‘specific adaptive capacity’. There are some factors that determine a system’s capacity to adapt to not just a single climate hazard but to a range of climate hazards and other environmental or economic shocks. Such factors are said to be representative of the ‘generic’ capacity to adapt and hence constitute the ‘generic adaptive capacity’. For example, greater wealth or income levels would mean greater availability of financial resources to invest in developing different adaptation options for a range of hazards that a particular society faces. Generic adaptive capacity is often related to the elements of human development including a variety of institutional, political and cultural factors (Adger et al, 2003).
It is relatively clear that specific adaptive capacity and the specific adaptation interventions would reduce the human mortality due to cyclones if implemented effectively. In case of generic adaptive capacity it is easy to conceptually understand that higher levels of development would mean greater ability to respond to a range of hazards.
But the question that needs to be examined is whether all aspects of development play a uniformly important role in reducing the level of outcome. Certain aspects of development may play a greater role in reducing the outcome due to the hazard. Hence in this paper the focus is on assessing whether the generic adaptive capacity played a role in reducing outcome / impact due to the hazard, the focus is not on specific adaptive capacity. We follow the approach that elements of generic adaptive capacity are related to the elements of human development and hence we take development indicators as a proxy for indicators of generic adaptive capacity.
2

The objective is to test the explanatory power of indicators of development (generic adaptive capacity) in explaining observed differential impacts / outcomes in the different coastal districts of east coast of India. If the indicators of generic coping capacity do explain differences in outcome across different districts, then policy measures to improve these indicators would mean an enhancement in generic adaptive capacity of the district in coping with future cyclones.
2. Research Design
This section discusses the research hypothesis, the spatial unit of analysis, the temporal scale, the variables of interest and the technique used for data analysis in this study.
2.1. Hypothesis and model
Different regions along the East coast of India experience different levels of impact due to tropical cyclones. Differences in impacts across the different regions is due to many factors viz.
the intensity of the cyclonic storm, the population that is exposed to the cyclone, generic measures of coping capacity in the district, and specific measures of coping capacity in the district. Therefore the impact /outcome due to a cyclone can broadly be modeled as function of hazard, exposure and adaptive capacity as in equation
1.
Oi = f (Hi, Ei, ACi)……………………….(1)
Where,
Oi: A measure of outcome / impact due to cyclones in district i,
Hi: A measure of the cyclone hazard in district i,
Ei: A measure of exposure for the district i,
ACi: A measure of adaptive capacity in district i,
We expect that the hazard, exposure and adaptive capacity together would be significant in explaining the differential outcome due to the hazard for the unit of analysis. Hence the hypothesis can be stated as
Ho: Indicators of hazard, exposure and generic adaptive capacity are significant in explaining the differences in outcome across districts
Ha: Indicators of hazard, exposure and generic adaptive capacity are not significant in explaining the differences in outcome across the districts
2.2. Spatial unit of analysis
The spatial scale selected for the study had to reflect scale at which the impacts of the cyclone are felt. Considering this, the spatial unit of analysis could be selected in two different ways. One way was to choose ‘coastal zone’ as the unit of analysis, for instance a 100 KM distance from the coastline could be considered the coastal zone. The second way was to take a coastal administrative unit say a district or taluks (subdistrict level), as the unit of analysis. For this study we chose a coastal administrative unit as our unit of analysis as opposed to a coastal zone. Choosing a coastal zone as a unit of analysis (say,
3

100 KM distance from the coastline) may have been useful as it would have been a spatially more uniform unit of analysis, when compared to an administrative unit as administrative units have differential areas and coastline length. Yet, we decided to keep an administrative as the spatial unit of analysis since most of the impacts and exposure and socio-economic data are reported for an administrative unit rather than a coastal unit.
Table 1 below presents the hierarchy of the administrative units that are there in India.
Table 1: Hierarchy of administrative units in India
States The country divided into 28 states in 7 union territories.
District The district is the principal subdivision within the state. There were
593 districts in India according to Census, 2001. The districts vary in size and population. The average size of a coastal district was approximately 6,000 square kilometers, average coastline length of
120 KM and average population between 25 lakhs to 35 lakhs in the year 2001.
Tehsils / talukas / mandals
Districts in India are subdivided into taluqs or tehsils or mandals , areas that contain from 200 to 600 villages.
The spatial scale of a district seemed more appropriate than that of a tehsil as most of the administrative functions reside at the district level and therefore the socio-economic data is more easily available at the district level, than the sub-district level.
A methodological issue that arose when keeping district as a unit of analysis was the changes in the district area over time, due to splitting of (or reapportioning of some area of the district) to form new districts. This changes the number of spatial units and the size of these units across different points of time. In a longitudinal study, this problem needs to be addressed, so that the unit of analysis remains uniform over the time period of the study. There are two approaches for addressing this issue. One can be called the gridding approach where the distribution of human population is converted from national or subnational administrative units to a series of georeferenced quadrilateral or grids, which remain same over time. The second approach is to reapportion the variable of interest, which is affected by the change in size of district over time (say population) on the basis of the area of the new set of districts. The third approach was to choose a base year and keep the district boundaries in the base year as the unit of analysis. For the subsequent years, the districts are mapped onto the districts of the base year, by aggregating the values of the variable of interest, across those districts which have been newly created and the existing districts from which the new districts have been split or reallocated. For instance, in the mid-90s Bhadrak was split from Balasore (which was one single district till then). According to this approach, the population figures of Bhadrak and Balasore for the years after they have been split would be added to get the figure corresponding to the district of the base year. For this paper the third approach was adopted to address the problem of change in the unit of analysis over time.
This study includes only the coastal districts of the East coast and not the West coast.
This is because out of the 8 cyclones that cross the Indian coastline per year on an
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average, majority of them cross the East coast. The districts included in the study are indicated in the map in figure 1 below.
Balasore
Cuttack
Puri
Ganjam
Srikakulam
Vizinagaram
Vishakahapatnam
East Godavari
West Godavri
Krishna
Guntur
Prakasam
Nellore Chengalpattu
South Arcot
Tanjavur
Pudukottai
Ramnathpuram
Tirunelveli
Kanyakumari
Figure 1: Locations of the districts included in the study on the map
2.3. Time-period of the study and the cyclonic events included in the study
The study includes the tropical cyclones that crossed the East coast of India during the period 1981 to 2000. Table 2 below gives a list of individual cyclones that have been included in the study.
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Table 2: The cyclonic events included in the study
Cyclonic event
Simpson
Severity
On Saffir On IMD scale 2
Districts affected
2000, November 26-30
1999, October 25-31 scale
Category 1 VSCS (I) South Arcot, Tanjavur
Category 4 Super CS Balasore, Cuttack and Puri
1998, November 13-16
1997, September 23-27
1996, November 5-7
1996, Nov. 28-Dec. 6
Category 2
Below
Category 1
Category 1
Category 1
VSCS(I)
CS
VSCS(I)
Vizianagaram, Vishakhapatnam
Vishkhapatnam, East Godavari,
West Godavari, Krishna, Guntur,
Praksama
East Godavari and West Godavari
Chennai, Chengalpattu, South
Arcot,
Balasore, Cuttack, Puri, Ganjam 1995, November 7-10 Category 1 VSCS(I)
1994, October 29-31
1994, October 29-31 and
1994, November 4-10
1993, December 1-4
Category 1
Category 1
VSCS(I)
VSCS(I)
Nellore, Prakasam, Guntur,
Krishna, West Godavari
Chengalpattu, Chennai, South
Arcot, Tanjavur, Pudukottai,
Ramnathpuram, Tirunelveli,
Kanyakumari
Category 3 VSCS(II) Chengalpattu, South Arcot,
Tanjavur
1992, November 11-17 Category 1 or below
1991, July 27-31 Below
Category 1
1991, November 11-15 Below
Category 1
1990, May 5-11
1989, November 1-9
1987, October 14-19
1987, Oct. 31-Nov. 3
DD
CS
Category 1 VSCS(I)
Kanyakumari, Tirunelveli,
Ramnathpuram, Chennai
Cuttack, Puri, Ganjam
Chengalpattu, Chennai, South
Arcot, Tanjavur
Srikakulam, Vizianagram,
Vishkhapatnam, East Godavari,
West Godavari, Krishna, Guntur,
Prakasam, Nellore
Category 3 VSCS(II) Nellore, Prakasam
Below CS Srikakulam, Vishkhapatnam, East
Category 1 Godavari, West Godavari,
Krishna, Guntur, Prakasam,
Nellore
Below
Category 1
SCS Vizianagram, Vishkhapatnam,
West Godavari, Guntur, Prakasam,
Nellore
2 The Indian Meteorological Department classifies the cyclones on the basis of wind speed as follows:
Deep Depression (DD): 28-33 knots; Cyclonic Storm (CS): 34-47 knots; Severe Cyclonic Storm (SCS): 48-
63 knots; Very Severe Cyclonic Storm I (VSCS(I)): 64-90 knots; Very Severe Cyclonic Storm II
(VSCS(II)): 91-119 knots; Super Cyclonic Storm (Super CS): 120 knots and above.
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1987, November 11-13 Below
Category 1
1986, October 8-14 Below
Category 1
1985, November 12-13 Below
Category 1
CS Vizianagram, Vishkhapatnam,
East Godavari, West Godavari,
Krishna, Guntur, Prakasam
Vishkhapatnam, East Godavari,
West Godavari, Krishna, Guntur
Chennai, Chengalpattu, South
Arcot, Tanjavur
1985, December 11-14 Below
Category 1
1983, December 21-23 Below
Category 1
SCS
D
Nellore
Tanjavur, Pudukottai, South Arcot,
Ramnathpuram
Some cyclonic events that occurred during the period 1981 to 2001 have not been included in the data set as the memoranda of damages for these events could not be obtained, especially in case of Orissa. Most of the excluded cyclonic events were not very severe events. From among the events that have been included in the dataset, in case of a few events, some districts that were affected could not been included in the dataset, because of non-availability of impact data for these districts. The table includes those affected districts for which the data were available. The analysis has been done for the set of districts as they existed in 1981. The data for districts that had boundary changes because they were split into more than one district after 1981, was adjusted so that the unit of analysis could remain uniform for the period of the study.
2.4. Variables of interest, indicators and source of data
This section describes the variables that were of interest in this study and the choice of indicators that were used as measures for these variables.
Outcome : There are a number of outcomes due to a cyclone such as human mortality, human morbidity, loss of live stock, damage to properties, damage to agricultural land etc. The outcome indicator chosen for this study is human mortality. This choice has mainly been guided by consistency in data availability on human mortality across different cyclones both temporally and spatially. For the other outcome indicators the data availability is sparse.
Given that an outcome (human mortality in this study) is caused due to the interaction of the climate hazard (tropical cyclone in this study), exposure and the adaptive capacity of the unit of analysis, the next issue that arises is how to characterize this interaction so that it best captures the process through which this interaction results in the outcome. If a conceptual model of the process through which impact is generated due to the hazard, existed, then one could identify the elements of the hazard, exposure and adaptive capacity that played a role in the generation of the impact and one could characterize the theoretical relationship between these elements. This represents the deductive research approach. Since such a conceptual model is not yet available in the existing literature one could use the inductive research approach in which statistical procedures are used to select indicators. It involves relating a large number of variables to vulnerability in order to identify the factors that are statistically significant. Theory consists of generalizations
7

derived by induction from data: in other words, the finding of patterns in data that can be generalized (Adger, 2004). One of the problems in applying the statistical approach in this study is the non-availability of data on many indicators of development (generic adaptive capacity) and exposure at the spatial resolution (district level) of this study.
Given that no theoretical conceptual model that characterizes the process of impact generation due to a hazard exists and given that data on all possible indicators of development and exposure are not available, the approach we followed for selecting indicators for this study was a mix of the inductive and deductive research approaches.
We first identified the variables of interest based on our broad conceptual understanding of the impact process. Then we scouted for data that is available at the appropriate spatial resolution (district level). Based on the data that is available at the district level we selected indicators that seemed relevant to our conceptual understanding of the variables that would be relevant in the impact process. We then used statistical procedures to identify the indicators that are really significant in explaining the impact. Table 3 summarizes the variables and indicators that we selected and the reasons why we selected them.
Table 3: Summary of data
Variable of interest
Outcome Mort
Hazard
Indicator
RA
Description of the indicator
Human Mortality in a district due to a particular
Reason for choosing the indicator
Most consistently reported outcome cyclone
Rainfall anomaly Most deaths happen due to drowning and wall collapse
Exposure RDOP,
UDOP
Rural density of population; and
Urban density of population
Links hazard to impact
Source of data for the indicator
‘Memorandum of
Damages due to
Cyclones’ 3
‘Memorandum of
Damages due to
Cyclones’ for the rainfall during the days of the cyclone.
ICRISAT
4
database for normal rainfall during the days of the cyclone.
Census of India
(1981, 1991 and
2001)
3 A document that summarizes the impacts of the cyclones at the district level, and is presented by the state government to the central government for the purpose of receiving financial assistance from the central government.
4 District Level Database of the International Crop Research Institute in Semi-arid Tropics (ICRISAT)
8

Adaptive capacity
RDDEN Road Density
(Total roadlength
(in Km) in a district divided by the area of the district)
LITRATE Literacy rate in the district
Represents the level of infrastructure development in the district
ICRISAT database and www.indiastat.com
WMXA
HHEAR
HHTA
Percentage of houses with wall material of the type X and A
(weak construction material)
Percentage of rural houses with electricity available to them
Percentage of houses with sanitation available to them
Represents general human capability
Proxy for income levels of people
(one would generally expect higher income level to have a better quality of housing)
Represents general level of development in the district
Represents general level of development in the district
Census of India
(1981, 1991 and
2001)
Census of India
(1981, 1991 and
2001)
Census of India
(1981, 1991 and
2001)
Census of India
(1981, 1991 and
2001)
Hazard : There are a number of characteristics associated with a hazard, which cause the outcome. For a tropical cyclone these characteristics are wind speed, central pressure, tidal surge and the rainfall associated with the cyclone. As a hazard indicator, ideally one would choose the event characteristic(s) that reflects the mechanism of impact. Since the outcome we are studying this paper is human mortality, we have used rainfall anomaly i.e. rainfall in excess of normal for the period of cyclone, as the indicator of the hazard.
The evidence from data source - memoranda of damages (mentioned in table 3) suggests that human mortality mostly occurs due to drowning, either in floods due to excess rainfall or due to tidal surge, and in some cases due to wall collapse. Data on tidal surge is not available. Hence rainfall anomaly seemed to be the most appropriate indicator for representing the severity of the hazard for the outcome indicator human mortality.
Exposure: The density of population (population per sq. Km) was chosen as the indicator of exposure to tropical cyclones. This choice of exposure indicator allows us to link hazard (tropical cyclone) to the outcome (human mortality). By definition a more densely populated area is more exposed to a cyclonic storm or flood. This is because even if the span of the cyclone/flood is small a more densely populated area would mean a larger number of people exposed to the hazard as compared to a less densely populated area.
The density of population has been considered separately for rural and urban areas, as there is a substantial difference between the densities of rural and urban areas. If we
9

aggregate the density across rural and urban categories and consider the total density of population of a district, then the picture about exposure of population may get distorted.
Generic adaptive capacity: There are numerous indicators of development that would play a role in determining mortality due to cyclone. We chose to test the explanatory power of the following indicators of development in explaining differences in mortality due to cyclones across districts.

Level of infrastructure development: The indicator we chose to represent the level of infrastructure development was road density i.e. total road-length in a district divided by the area of a district. We chose this indicator because a higher level of road density would mean access to roads to a larger proportion of population in the district. This can be crucial for evacuation as well as relay of messages to remote villages where the cyclone warning is relayed through messengers. Therefore one would expect that a high level of road density could play a role in reducing the mortality due to cyclones by providing ground for speedy evacuation and relay of cyclone warning.

Quality of housing stock: We expect quality of housing stock to have an effect on mortality because in cases where people are not able to evacuate, a low quality of housing stock would mean higher chances of damage to housing stock and hence higher chances of people being killed in wall collapses and due to exposure to natural elements during the cyclone. The indicator for the quality of housing stock is the percentage of houses in a district that are built of type X and A wall material, which is essentially the really low quality of wall material. Census of India gives the distribution of houses at the district and the sub district level by the material of wall and material of the roof and material of flooring. The houses are classified by 10 predominant materials of wall (ekra; wood; cement; burnt brick; stone; unburnt brick;
GI and other metal sheets; mud; grass leaves; reed; thatch; bamboo etc.; and others), 8 predominant materials of roof and 7 predominant materials of flooring. For making the dataset more manageable the wall materials were grouped into four major categories. These groupings are based on the classification given in the Vulnerability
Atlas of India (1997) published by the Building Material and Technology Promotion
Council (BMTPC) and these are as follows:
Category Wall Material
X Grass leaves, reeds, thatch and bamboo, GI and other metal sheets and
other materials.
A Mud, unburnt brick, stone, wood and Ekra.
B Burnt brick
C Concrete
 General human capability: Literacy rate was chosen as proxy indicator of general human capability and the ability of the people to comprehend risk that the cyclone warning messages try to relay. One would expect that a higher level of literacy would mean a better ability to comprehend risk that is conveyed in the cyclone warning messages and hence a greater willingness to take protective measures to prevent mortality due to cyclones. Rural and urban literacy rates were considered separately
10

as the coastal belt where the waning message for evacuation is sent by the district authorities is in the human settlements that are situated in about 20 kms inland from the coastline. With a few exceptions this entire coastal belt is rural. In most districts there is significant difference between rural and urban literacy rates. Hence rural and urban literacy rates were considered separately in this study.
 General development indicators: Two indicators of general development were selected. One was percentage of households in rural areas of the district with electricity available to them. The rural areas only were chosen for this indicator as in the urban pockets a very large proportion of households have electricity available to them. The second indicator chosen to represent general level of development was the percentage of household in a district having sanitation facilities available to them.
2.4. Technique for data analysis: Multiple Regression
Multiple regression has been used as statistical technique for investigating whether the differences in outcome (human mortality) due to cyclones across districts can be explained together by hazard, exposure and generic adaptive capacity. In the following equation human mortality (Mort) due to cyclones is regressed against the independent variables mentioned below.
Mort = b0 + b1 RA + b2 RDOP + b3 UDOP + b4 RDDEN + b5 WMXA + b6 LITR + b7
LITU+ b8 HHEAR + b9 HHTA + e …(2)
Where,
Mort: Human mortality due to a particular cyclonic event in the particular district that was affected.
RA: Rainfall anomaly during a particular cyclonic event in the particular district that was affected.
RDOP: Rural density of population in the affected district in the year of the cyclonic event.
UDOP: Urban density of population in the affected district in the year of the cyclonic event.
RDDEN: Road density in the affected district in the year of the cyclonic event.
WMXA: Houses that have wall material of type X and A (WMXA) as a percentage of total houses in the affected district in the year of the cyclonic event.
LITR: Rural literacy rate in the affected district in the year of the cyclonic event.
LITU: Urban literacy rate in the affected district in the year of the cyclonic event.
HHEAR: Percentage of rural households that have the electricity available in the affected district in the year of the cyclonic event.
HHTA: Percentage of households that have toilet (sanitation) facilities available in the affected district in the year of the cyclonic event. b0, b1, ….b9 are the coefficients of the independent variables. e: is the error term.
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3. Results and discussion
This section presents the results that were obtained after applying the multiple regression technique and a discussion on the possible inferences that can be drawn from the results.
Before discussing the results obtained from multiple regression, the descriptive statistics and the visual description of the data are presented to give an idea about the data on different variables.
3.1. Descriptive statistics and visual description of the data
As is evident from table 2, that Chennai had been affected in a number of cyclonic events and the outcome, exposure and adaptive capacity data for Chennai is available too. But we excluded Chennai from the dataset on which we ran the regression. This was done so that we could have comparable units of analysis and Chennai unlike other districts is a completely urban district and does not have rural urban mix. Table 4 presents the descriptive statistics of all the variables included in this study.
Table 4: Descriptive statistics of the variables
Variable Units Mean Standard Maximum Minimum Range
Mort 53.33
Deviation
150.58 960 1 959
RA
No. of persons mm 217.39 155.83 848.46 16.69 831.77
RDOP Persons/ sq. km
298.9
UDOP Persons/ sq. km
RDDEN km/ sq. km
3925
0.9193
WMXA Unit-less 61.44
LITR
LITU
Unit-less 41.40
Unit-less 61.97
HHEAR Unit-less 34.65
118.33
1421.96
0.51334
9.68
12.10
7.41
13.25
764
4823
2.2714
89.8
72.7
78.5
71.60
125
1160
0.3585
40.3
20
50
16.40
639
3663
1.9129
49.5
52.7
38.5
55.2
HHTA Unit-less 18.58 9.10 44.4 4.30 40.1
Figure 2 below presents a scatter-plot matrix of the variables used in the study to give a visual sense of the data.
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0 600 2000 40 80 50 75 10 40
Mort
RA
RDOP
UDOP
RDDEN
WMXA
LITR
LITU
HHEAR
HHTA
0 300 100 700 0.5
20 60 20 60
Figure 2: Scatter-plot matrix
3.2. Results of multiple regressions
We began by regressing mortality against all the independent variables using the ordinary least squares (OLS) regression. The results of this initial OLS regression are presented below in table 5.
Table 5: Results of the initial OLS regression
Multiple R
2
= 0.2583; Adjusted R
2
= 0.1778
F statistic: 3.211 on 9 and 83 DF, p value = 0.002198
Residual Std. Error: 58.01 on 83 df
Variable
Intercept
RA
RDOP
UDOP
Coefficient
115.9
0.1948***
0.03968
0.005884 p-value
0.3743
1.06e-05
0.1459
0.3560
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RDDEN
LITR
LITU
WMXA
-53.48*
2.105*
1.129
0.2553
0.0736
0.0698
0.6348
0.7986
HHTA 0.2258 0.8772
HHEAR -1.603* 0.0626
*** denotes

= 0.01; ** denotes

= 0.05; * denotes

= 0.10;

: Level of significance
Examination of the diagnostics revealed heteroscedasticity and non-normal residuals, though there was no auto-correlation.
Next we checked for outliers. Jackknife test for outliers detected the mortality figure in district East Godavari (1996 cyclone) and district Cuttack (1999 cyclone) to be the outliers.
After excluding the outliers we again regressed mortality against all the independent variables. The results of the OLS regression are presented below in table 6.
Table 6: Results of the OLS regression after excluding outliers
Multiple R
2
= 0.3262 Adjusted R
2
= 0.2484
F statistic: 4.195 on 9 and 78 DF, p value = 0.0001902
Residual Std. Error: 142.5 on 86 df
Variable Coefficient p-value
Intercept
RA
RDOP
UDOP
RDDEN
-111.1
0.2341***
-0.1878**
0.009996
-40.071
0.4057
7.11e-07
0.0336
0.1210
0.1974
LITR
LITU
WMXA
HHTA
3.878***
0.3248
0.5596
0.1803
0.00356
0.8946
0.5725
0.8998
HHEAR -2.029** 0.0187
*** denotes

= 0.01; ** denotes

= 0.05; * denotes

= 0.10;

: Level of significance
The adjusted R
2
(proportion of variation in response attributed to the independent variables) of the model improved significantly and overall model fit (F statistic for testing the significance of all predictors) was significant at 1% level of significance. Among the individual variables, RDOP, which was not significant earlier, became significant, but road density which was significant earlier is not significant after removing the outliers.
Examination of the diagnostics still revealed the heteroscedasticity and non-normal residuals, though there was no auto-correlation.
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To deal with heteroscedasticity we transformed the dependent variable. The Box-Cox method suggested a log transformation. We took a log of mortality and regressed it against all the independent variables. The results of the OLS regression are presented below in table 7.
Table 7: Results of the OLS regression after log transformation of the response
Multiple R
2
= 0.4718 Adjusted R
2
= 0.4108
F statistic: 7.741 on 9 and 78 df, p value = 4.776e-08
Residual Std. Error: 1.201 on 78 df
Variable
Intercept
Coefficient
-2.611 p-value
0.3557
RA
RDOP
UDOP
RDDEN
LITR
LITU
WMXA
HHTA
0.007133***
-0.00355*
0.0000027
-1.1800*
0.04627*
0.01130
0.03923*
0.02767
2.56e-11
0.0568
0.9878
0.0787
0.0938
0.8275
0.0640
0.3621
HHEAR -0.00237 0.9095
*** denotes

= 0.01; ** denotes

= 0.05; * denotes

= 0.10;

: Level of significance
The adjusted R
2
and the overall model fit (F statistic for testing the significance of all predictors) improved significantly. Among the individual independent variables, in addition to RA, RDOP and LITR (which were significant in the previous regression),
WMXA also became significant and so did RDDEN. Examining the diagnostics revealed the residuals were homoscedastic (constant variance), were uncorrelated and were normally distributed.
3.2.1. The most parsimonious model
Next we used the stepwise regression method to arrive at the most parsimonious model.
The stepwise regression method suggests the model with following variables:
Log(Mort) = b1 RA + b2 RDOP + b3 RDDEN + b4 LITR + b5 WMXA + e …(3)
The co-efficient estimates of the step-wise regression are presented below in table 8.
Table 8: Results of the stepwise regression
Multiple R
2
= 0.4607 Adjusted R
2
= 0.4278
F statistic: 14.01 on 5 and 82 df, p value = 6.824e-10
Residual Std. Error: 1.183 on 82 df
Variable Co-efficient estimate p-value
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Intercept
RA
RDOP
RDDEN
-1.06023
0.007104 ***
0.003234 **
-1.099621 **
0.3376
3.57e-12
0.04541
0.01582
LITR 0.055427 *** 0.00454
WMXA 0.02396* 0.09289
*** denotes

= 0.01 ** denotes

= 0.05 * denotes

= 0.10

: Level of significance
Examination of the diagnostics revealed that the residuals were homoscedastic (constant variance), were uncorrelated but were not normally distributed.
To ensure the stability of the estimates of the co-efficients of the independent variables that we obtained in Table 8, we also estimated the co-efficients using robust regression.
Table 9: Results of the robust regression
Variable Co-efficient estimate t-value
Intercept
RA
-1.2697
0.0072
-1.1585
8.2506***
RDOP
RDDEN
-0.0033
-1.10928
-2.0478*
-2.4848**
LITR 0.0570 3.0019**
WMXA 0.0255 1.8075*
*** denotes

= 0.01 ** denotes

= 0.05 * denotes

= 0.10

: Level of significance
On comparing table 8 and 9 it is clear that estimates of the co-efficients of the independent variables are not very different from each other and hence they are robust.
Therefore the most parsimonious model that we obtain from this exercise is the following:
Log(Mort) = 0.0072 RA – 0.0033 RDOP – 1.10926 RDDEN + 0.0570 LITR + 0.0255 WMXA
+e …(4)
The co-efficient of RA has the expected positive sign i.e. greater RA is associated with greater human mortality. The co-efficient of RDDEN has the expected negative sign i.e. greater the road density, lower is the mortality. The co-efficient of WMXA also has the expected positive sign i.e. worse the quality of the housing stock, higher is the mortality.
From among the exposure variables RDOP and UDOP, it is RDOP which is significant, but it has sign opposite to that one would expect a priori. One would expect that the greater the density of population greater would be the mortality. In table 7, the UDOP has this expected + sign but it is not significant. A possible explanation for the negative sign of RDOP could be that generally, high density human settlements have better access to amenities such as roads and communications which play a vital role in the cyclone
16

warning message reaching the people in time and their timely evacuation. Far-flung rural areas are often low-density groups and hence they may have a lower chance of receiving the cyclone warning in time and hence evacuating in time. Maybe that is why greater mortality is associated with lower rural density of population.
The co-efficient of LITR also has sign opposite to that one would expect a priori. One would expect that greater the literacy rate lesser would be mortality. But positive sign of the co-efficient implies that higher levels of mortality are associated with higher levels of literacy. In table 7, LITU has the expected positive sign (though it is not significant). A possible explanation for the negative sign of the co-efficient of LITR could be that in rural areas, especially if there are homogeneous groups of people (because of occupation e.g. fishermen) living in a community, for their evacuation decision, people rely on the decision of the group and the village elders. A greater literacy rate in these communities may imply that the younger generation in these communities are moving out of their traditional occupations and exploring new avenues. In such a situation the norms that bound them to the decisions of the group get weakened. Hence they may not be taking a better decision regarding evacuation and they may no longer have access to traditional knowledge about forecasting cyclonic storms well in advance and hence take protective measures well in advance.
3.2.2. Predictive power of the model
To check the predictive power of the model, we partitioned the data into two parts, one consisting of two-thirds of the data and the other consisting of one third of data. We used two-thirds of the data to estimate the co-efficients of the independent variables of the best-fit model obtained above in equation 4. Using the co-efficients of independent variables obtained from regressing the two-thirds data, we predicted the response for the rest one third of the data. Then we plotted the predicted response (and its confidence intervals) and the actual observations for the response to see how close is the fit between the predicted response and the actual response. This plot is presented in figure 3 below.
17

0 5 10 15 20 25 30
Figure 3: Pot of predicted response and the actual observations of the response
In figure 3, the black solid line is the plot for actual observations of the response (log of mortality). The red dotted line is the plot of predicted response and the blue and green fine dotted lines are the confidence bands (intervals) for the predicted response. From the plot shows that there is a close fit between the predicted and the actual response. We also calculated the Root Mean Square Error for the for the fitted response (RMSE = 1.193) and the predicted response (RMSE=1.325). The RMSE of the predicted response seems comparable to the RMSE of the fitted response. Hence the predictive power of the model seems to be reasonably good.
3.2.3. Spatial differences in mortality
From the results obtained above we can infer that the hazard, exposure and adaptive capacity together play a role in determining the mortality due to a cyclone. Next we wanted to investigate whether there were differences in the indicators associated with the
18

districts that would explain the differences in mortality across the districts. Ideally we would have liked to study this difference for each district separately but we did not have enough data points for each district separately. Therefore instead of investigating differences across district, we decided to club together the districts of one state and study the difference in response that was attributable to a state. For this we introduce the categorical variable STATE in the regression equation. STATE could take any of these three values – Orissa (OR), Andhra Pradesh (AP), Tamil Nadu (TN).
We first simply added the categorical variable STATE in the regression equation as shown below:
Log(Mort) = b0+ b1 RA + b2 RDOP + b3 RDDEN + b4 LITR + b5 WMXA + b6
STATE + e …(4)
Table 10 presents the results of OLS regression with the categorical variable STATE added to the parsimonious model that we obtained previously (equation 4).
Table 10: Results of the regression with STATE added
Multiple R
2
= 0.473 Adjusted R
2
= 0.4269
F statistic: 10.26 on 7 and 80 df, p value = 4.156e-09
Residual Std. Error: 1.184 on 78 df
Variable
Intercept
RA
Co-efficient estimate p-value
-1.9023 0.1336
0.00709*** 5.02e-12
RDOP
RDDEN
LITR
WMXA
-0.0037**
-0.8225
0.0642***
0.03338
0.0268
0.19810
0.00196
0.03656
STATEOR -0.8176 0.18308
STATETN -0.3437 0.53115
*** denotes

= 0.01 ** denotes

= 0.05 * denotes

= 0.10

: Level of significance
In the above table 10, STATE does not turn out to be significant variable. Adding the
STATE variable in the regression equation means separate regression lines for all three groups (OR, AP, TN) but with same slope. The co-efficient of STATE represents simply the vertical distance between the two regression lines. Cleary, the same slope for all the three regression lines that this model assumes is not the correct assumption to made in this case. Therefore, next, instead of adding STATE in the regression equation we use the model form y ~ x * d which means separate regression line for each group (represented by the dummy variable d) and with different slopes.
The regression model therefore can be represented as follows:
Log(Mort) = (RA + RDOP + RDDEN + LITR + WMXA) * STATE + e …(5)
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Table 11 presents the results of the above regression model.
Table 11: Results of the regression with STATE as an interaction term
Multiple R
2
= 0.6516 Adjusted R
2
= 0.567
F statistic: 7.7 on 17 and 70 df, p value = 2.695e-10
Residual Std. Error: 1.029 on 70 df
Variable
Intercept
RA
RDOP
RDDEN
LITR
WMXA
Co-efficient estimate
-2.7957
0.01018***
-0.007703***
-0.67279
0.10253***
0.03207 p-value
0.2112
8.32E-12
0.000702
0.63702
0.000223
0.186243
STATEOR
STATETN
RA:STATEOR
-0.76550
5.62568*
0.002686
RA:STATETN -0.005590***
RDOP:STATEOR -0.002659
RDOPSTATETN 0.010988***
RDDEN:STATEOR -1.317000
RDDEN:STATETN -0.679454
0.840047
0.092851
0.611253
0.000903
0.80322
0.001893
0.633642
0.688845
LITR:STATEOR 0.108275
LITR:STATETN -0.128953**
WMXA:STATEOR -0.056615
0.227150
0.004242
0.435363
WMXA:STATETN -0.025262 0.495207
*** denotes

= 0.01 ** denotes

= 0.05 * denotes

= 0.10

: Level of significance
The results in table 11 clearly show that the STATE matters. For instance, the difference in rural literacy rate in Tamil Nadu and Andhra Pradesh do play a role in explaining differences in human mortality in these two states. Similarly, differences in the rainfall anomaly and the rural density of population also are significant in explaining differences in mortality across these two states. There does not seem to be a significant difference between Orissa and Andhra Pradesh or Orissa and Tamil Nadu. This may have happened because Orissa had very few data points (only 10) when compared to Andhra Pradesh and
Tamil Nadu.
3.2.4. Limitation of the study
The limitation of this study is that the initial model (in equation 2) does not include all possible indicators that would affect mortality due to a cyclone in a district. For instance it does not include variables that would capture the cyclone warning process, the number of cyclone shelters in a district etc. These variables could not be included as data on these
20

variables is not available either at the appropriate spatial scale or for the appropriate timeperiod or both.
A point that must be kept in mind while making inference from these results is that muticollinearity is present in the data. Multicollinearity increases the standard errors of the coefficients of the independent variables. The increased standard error could mean that coefficients of some independent variables may not be found to be significantly different from zero, whereas without multi-colinearity and therefore with lower standard errors these co-efficients might have been found to be significant too.
4. Observations and conclusions
From the above discussion it emerges that the indicators of development which cannot be linked to the cyclone hazard (percentage of households with electricity available and the percentage of households with sanitation available) do not turn out to be significant in explaining mortality due to cyclones, whereas the indicators of generic adaptive capacity which can be conceptually linked to the impacts generation process of the cyclone hazard, do turn out to be significant. This provides evidence for the claim that the assessment of adaptive capacity is meaningful in the context of a particular hazard and not in a general way. This also points to the need for developing a stronger conceptual model for the process through which a particular hazard would cause a particular outcome. Also an assessment of adaptive capacity does inform policy in identification of aspects of development (generic adaptive capacity), which play an important role in determining the outcome due to a particular hazard.
References:
Adger, W. N., S. R. Khan and N. Brooks, (2003). ‘Measuring and enhancing adaptive capacity’. Technical paper 7 of the Adaptation Policy Framework. http://www.undp.org/cc/apf_outline.htm
BMTPC (Building Material and Technology Promotion Council), 1997. Vulnerability
Atlas of India.
Brooks, N. and Adger W.N., (2005). ‘Assessing and Enhancing Adaptive Capacity.’ In:
Adaptation Policy Framework for Climate Change: Developing Strategies, Policies and
Measures, [B. Lim, E. Spanger-Siegfried, I. Burton, E. Malone, S. Huq, (eds.)]. UNDP-
GEF, Cambridge University Press, UK.
Brooks, N., W.N. Adger, and M. Kelly, (2005). ‘The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation.’
Global
Environmental Change Part A 15, 151-162.
Kelly, M., N. Brooks and W.N. Adger, (2004). ‘New indicators of vulnerability and adaptive capacity’ Tyndall Centre Technical Report 7. http://www.tyndall.ac.uk/publications/pub_list_2004.shtml
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Smit, B. and O. Pilifosova, (Coordinating Lead Authors), Burton, I., B. Challenger, S.
Huq, R.J.T. Klein, G. Yohe (Lead Authors), (2001). ‘Adaptation to Climate Change in the Context of Sustainable Development and Equity’ In: Climate Change 2001: Impacts,
Adaptation and Vulnerability, [J. J. McCarthy, O. F. Canziani, N. A. Leary, D. J.
Dokken, K. S. White, (eds.)]. Cambridge University Press, UK.
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