Assessing Impacts as Changes in
Economic Output
Anand Patwardhan
Upasna Sharma
Stock Vs. Flow

Conventionally impacts of cyclones (or other climate
hazards) measured as changes in stocks of human,
social and economic capital.

Alternatively, they may be measured as changes in
flow of goods and services, (typically economic
output).
Motivation for assessing impacts in
terms of changes in flow variables

Provides information about length of recovery
period
 Relief vs. recovery debate in disaster mitigation.

Could be used to establish the validity or
explanatory power of measures of adaptive
capacity (generic or specific adaptation)

Distinguish between loss of capital assets vs. loss
of income for affected communities
Natural Hazard – Tropical Cyclones
Why tropical cyclones is a good starting point for
exploring the concept:

Bounded in space and time (unlike droughts).

Impact large enough to disrupt economic activity over
an area, and for a duration long enough that it may be
resolved / detected
What do we expect the data to reveal?

We expect a drop in the output of the affected economic
sector as a consequence of the event in relation to the
baseline

Event year output should differ from non-event year output

We expect to observe gradual recovery in the period
following the event

Confounding factors:



Secular change
Other, non-event related disturbances
Variability (signal to noise ratio problem)
Output variables being studied
Variable
Specific
Variable
Agricultural
production
Spatial
resolution
Temporal
resolution
Length of
the record
Sample of
Districts
Paddy output District
(‘000 tons)
Annual
1966 - 1994
(8 districts)
1966 - 2001
(4 districts)
12 districts
on the East
coast of
India
Fish catch
Marine fish
catch (tons)
District
Monthly
1978 to 1996 8 coastal
districts of
Tamil Nadu
Electricity
consumption
Units of
electricity
consumed by
different
categories of
users
Sub-district
(for one
district)
Monthly
1988 to 2000 Nellore
(Andhra
Pradesh)
Linkage between spatial extent and administrative
units for reporting of data
Considerations for selecting spatial unit/scale of analysis:
• The scale of hazard and its impact
• The scale at which the socio-economic data is reported
Hierarchy of Administrative Units in India
States
The country divided into 28 states and 7 union territories.
Districts
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 district was
approximately 4,000 square kilometers, and the average population
numbered nearly 1.73 million in the year 2001.
Sub-districts:
Tehsils / talukas
/ mandals
Districts in India are subdivided into taluqs or tehsils, areas that
contain from 200 to 600 villages.
Descriptive statistics for paddy output
Dsitrict
Mean
(‘000 tons
/year)
Standard.
Deviation
Max
Min
Range
Balasore
388.43
109.48
642.82
214
428.82
Cuttack
619.03
139.39
946.89
292.59
654.3
Puri
441.92
112.19
769.67
261.44
508.23
Ganjam
412.18
139.87
750.76
180.88
569.88
East Godavari
754.59
243.53
1211
391.5
819.5
Krishna
761.26
233.57
1173.8
427.8
746
Guntur
752.3
259.29
1180.6
395
785.6
Nellore
452.89
176.65
819.48
198
621.48
Chengalpattu
655.81
187.3
993.57
232
761.57
1323.45
291.82
1937.4
878
1059.4
Ramnathpuram
348.04
157.32
744
117.1
626.9
Tirunelveli
351.44
104.85
611.27
211
400.27
Tanjavur
East Godavari
94
92
90
88
86
84
82
80
78
76
74
72
70
68
66
800
600
400
'000 tons
94
92
90
88
86
84
82
80
78
76
74
72
70
68
66
300
'000 tons
400
200
19
66
19
69
19
72
19
75
19
78
19
81
19
84
19
87
19
90
19
93
19
96
19
99
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
'000 tons
700
0
1400
19
66
19
68
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
'000 tons
Plots of Time Series of Paddy Output for
Some Districts
2500
600
500
2000
1500
1000
200
100
500
0
Year
Year
Balasore
Tanjavur
1200
900
800
1000
700
600
500
400
300
200
0
100
0
Year
Year
Nellore
Secular Trend in the Data
Dsitrict
Trend Coefficient
Balasore
9.78
Cuttack
8.22
Puri
7.80
Ganjam
10.55
East Godavari
24.52
Krishna
25.07
Guntur
27.24
Nellore
19.34
Chengalpattu
13.17
Tanjavur
15.49
Ramnathpuram
9.53
Tirunelveli
7.05
All values are significant at 1% level of significance
Assessing Impact on Agricultural Output
Grouped districts into 2 categories based on the number of cyclonic events
that occurred during the study period:
Districts with many events
Districts with few events.
Nellore
Guntur
900
1400
800
1200
700
1000
'000 tons
500
400
800
600
300
400
200
200
100
Year
Year
94
19
90
92
19
19
86
88
19
19
82
84
19
19
78
80
19
19
74
76
19
19
70
72
19
19
19
19
66
68
0
0
19
66
19
68
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
'000 tons
600
Districts with many events
•Existence of secular trend in the agricultural output data
required de-trending the data
•Classified the residuals into those for event years and
those for non-event years
• Examined the difference in these two populations using
the non-parametric Kolmogorov-Smirnov (K-S) test
Results: Districts With Many Events
State
District
KS test statistic D
Orissa
Balasore
0.5417
Orissa
Cuttack
0.5152*
Orissa
Puri
0.5565*
Andhra Pradesh
Krishna
0.6800**
Andhra Pradesh
Nellore
0.5056**
Tamil Nadu
Tanjavur
0.7097***
*** denotes  = 0.01 (one-tailed)
** denotes  = 0.05 (one-tailed)
* denotes  = 0.10 (one-tailed)
: Level of significance
Districts with few events
Compared residual in event year, with residuals of baseline
years (after accounting for secular trend) by using the Student’s
t-test.
The hypothesis can be specified as:
Ho: Re = Rne
Ha: Re < Rne
Where Re denotes the residuals for the event years
and
Rne denotes mean of residuals for the non event years
Results: Districts With Few Events
State
District
Cyclonic
event year
(s)
Event year
residual
95%
confidence
limit
Orissa
Ganjam
22/9/1972
28/8/1974
-40.46
-70.13
-33.3237
Andhra
Pradesh
East Godavari
7/11/1969
16/10/1982
-93.269
99.1569
-26.4841
Andhra
Pradesh
Guntur
19/11/1977
12/11/1987
-214.66
-204.01
-18.5919
Tamil Nadu
Chengalpattu
28/11/1966
4/11/1978
18/10/1982
1/12/1996
-25.3307
34.22083
-368.062
-41.9519
-21.8977
Tamil Nadu
Ramnathpuram
24/11/1978
5.02648
-44.3477
Tamil Nadu Tirunelveli
17/12/1980
18.4607
-28.7653
Figures in bold are statistically significant at 5% level of significance.
Alternative approach for districts with
many events
 To account for secular trends, earlier approach focused on residuals after detrending
the data
Alternatively, we can look at year to year changes in output. Four kinds of changes in
output are possible:
Non-event to event change in output,
Event to non-event change in output,
Non-event to non-event change in output and
Event to event change in output.
We can postulate certain expectations regarding these changes in output:
Non-event to event change in output to be negative,
Event to non-event change in output to be positive,
Non-event to non-event and event to event change in output could either be
positive or negative
If there is a statistically significant increase / recovery in output after the event year,
then it provides for a more robust basis for attributing the decrease in output in the
event year to a cyclone.
Year to Year Changes in Agricultural Output for
Nellore District
Year
Rice_Output
1966
325
1967
250
1968
198
1969
306.2
1970
209.7
1971
293.8
1972
244.2
1973
271.5
1974
301.7
1975
387.1
1976
299.6
1977
335.8
1978
365.9
1979
491
1980
425
1981
518
1982
395.5
1983
527.6
1984
421
1985
545.4
1986
568
1987
617
1988
677.3
1989
657.5
1990
732.28
1991
623.47
1992
594.97
1993
819.48
1994
731.78
Change
-75
-52
108.2
-96.5
84.1
-49.6
27.3
30.2
85.4
-87.5
36.2
30.1
125.1
-66
93
-122.5
132.1
-106.6
124.4
22.6
49
60.3
-19.8
74.78
-108.81
-28.5
224.51
-87.7
Statistical Technique Used
To test whether there is a statistically significant difference
between the means of the three types of changes in output, we
use Analysis of Variance (ANOVA) technique.
Analysis of Variance (ANOVA)
The hypothesis can be specified as:
Ho: µne-e = µe-ne = µne-ne
Ha: µne-e, µe-ne, µne-ne are not all equal.
Where µne-e denotes the mean of non-event to event changes in output;
µe-ne, denotes the mean of event to non-event changes in output;
µne-ne denotes the mean of non-event to non-event changes in output
Results of ANOVA for effect of cyclones on
agricultural output
State
District
ANOVA(F
statistic)
KS test statistic
D
Orissa
Balasore
2.56*
0.5417
Orissa
Cuttack
2.75*
0.5152*
Orissa
Puri
1.35
0.5565*
Andhra Pradesh Krishna
2.65*
0.6800**
Andhra Pradesh Nellore
5.24**
0.5056**
Tamil Nadu
6.33***
0.7097***
Tanjavur
*** denotes  = 0.01
** denotes  = 0.05
* denotes  = 0.10
Observations

Impacts of a cyclone can be measured in terms of flow of goods and services
of the affected socio-economic sectors, if appropriate spatial and temporal
resolution is chosen.

This approach can provide new impact metric for linking generic adaptive
capacity to observable impacts at the ground level.

This not only a relatively low cost methodology but also uses in a different
manner the data for which well established reporting mechanisms already
exist.

Assessment of impacts through a different route which could act as a check on
biases and errors of the conventional impact assessment methods.

This methodology can be replicated for natural disasters other than cyclones
(for instance, floods, earthquakes etc.).
Work in Progress
Extend the work - Fisheries output (fish catch), Electricity Consumption
(much higher temporal resolution then the agricultural data)
Impact of Cyclone on Fisheries Output – Chengalpattu District
1600
1400
Cyclonic event
Nov-85
1200
1000
800
600
400
200
Month and year
Ju
n86
-8
6
pr
A
Fe
b86
85
ec
D
ct
-8
5
O
-8
5
ug
A
Ju
n85
A
pr
-8
5
0
Fe
b85
Marine fish catch in tons
