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Assessing Impacts as Changes in Economic Output Anand Patwardhan Upasna Sharma

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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).

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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

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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

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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)

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Output variables being studied VariableSpecific Variable Spatial resolution Temporal resolution Length of the record Sample of Districts Agricultural production Paddy output (‘000 tons) DistrictAnnual (8 districts) (4 districts) 12 districts on the East coast of India Fish catchMarine fish catch (tons) DistrictMonthly1978 to coastal districts of Tamil Nadu Electricity consumption Units of electricity consumed by different categories of users Sub-district (for one district) Monthly1988 to 2000Nellore (Andhra Pradesh)

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Linkage between spatial extent and administrative units for reporting of data StatesThe country divided into 28 states and 7 union territories. DistrictsThe district is the principal subdivision within the state. There were 593 districts in India according to Census, 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 Sub-districts: Tehsils / talukas / mandals Districts in India are subdivided into taluqs or tehsils, areas that contain from 200 to 600 villages. Hierarchy of Administrative Units in India 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

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Descriptive statistics for paddy output Dsitrict Mean (‘000 tons /year) Standard. DeviationMaxMinRange Balasore Cuttack Puri Ganjam East Godavari Krishna Guntur Nellore Chengalpattu Tanjavur Ramnathpuram Tirunelveli

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Plots of Time Series of Paddy Output for Some Districts Tanjavur East GodavariNellore Balasore

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Secular Trend in the Data DsitrictTrend Coefficient Balasore9.78 Cuttack8.22 Puri7.80 Ganjam10.55 East Godavari24.52 Krishna25.07 Guntur27.24 Nellore19.34 Chengalpattu13.17 Tanjavur15.49 Ramnathpuram9.53 Tirunelveli7.05 All values are significant at 1% level of significance

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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 few events.Districts with many events

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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

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Results: Districts With Many Events StateDistrictKS test statistic D OrissaBalasore OrissaCuttack0.5152* OrissaPuri0.5565* Andhra PradeshKrishna0.6800** Andhra PradeshNellore0.5056** Tamil NaduTanjavur0.7097*** *** denotes = 0.01 (one-tailed) ** denotes = 0.05 (one-tailed) * denotes = 0.10 (one-tailed) : Level of significance

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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

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StateDistrictCyclonic event year (s) Event year residual 95% confidence limit OrissaGanjam22/9/ /8/ Andhra Pradesh East Godavari7/11/ /10/ Andhra Pradesh Guntur19/11/ /11/ Tamil NaduChengalpattu28/11/1966 4/11/ /10/1982 1/12/ Tamil NaduRamnathpuram24/11/ Tamil NaduTirunelveli17/12/ Figures in bold are statistically significant at 5% level of significance. Results: Districts With Few Events

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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.

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Year to Year Changes in Agricultural Output for Nellore District

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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

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Results of ANOVA for effect of cyclones on agricultural output StateDistrictANOVA(F statistic) KS test statistic D OrissaBalasore2.56* OrissaCuttack2.75*0.5152* OrissaPuri * Andhra PradeshKrishna2.65*0.6800** Andhra PradeshNellore5.24**0.5056** Tamil NaduTanjavur6.33***0.7097*** *** denotes = 0.01 ** denotes = 0.05 * denotes = 0.10

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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.).

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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 Impact Recovery Cyclonic event Nov-85

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