Brief description
NRRA requested surge team to develop a means to produce a defensible, evidence-based estimate of total economic impact of disasters for the recent flood events in Northern NSW and southern Queensland in February and March 2022 . Although the primary objective was to be quantify the total economic impact for the entire flood event, the team developed an approach that would enable estimation of value at risk and value impacted at higher spatial resolution or granularity that could also be used to inform policy and program design.The aim was to develop a minimum viable product (MVP) for immediate use by the NRRA.
An approach was developed to estimating economic value at risk for administrative areas and then use flood extent to calculate percent of each administrative area impacted by the hazard, to estimate economic value impacted. Gross Value Added (GVA) by each administrative area (ASGS SA2 level) was selected as the headline economic value at risk figure. The GVA for each administrative area can be aggregated to a headline figure of total economic impact. The approach was intended to be rapid and robust, and to provide a preliminary view of direct impacts soon after a disaster occurs.
The outputs include:
A) An Economic value-at-risk data stack (including a range of economic data - jobs, productivity by worker, property reconstruction value, inter-regional and sectoral economic activity and built environment data)
B) A calculation sheet to calculate value impacted based on value at risk (GVA) and input parameters to quantify hazard within each administration unit
Both outputs are contained within a Microsoft Excel document.
Lineage
1. Data collection• Discussion around potentially relevant data with ABS and GA
• Subsequent requests for data from data providers GA and ABS
2. Data wrangling, cleansing, integration, formatting, etc.
• Data stack architecture built in r and reproduced in Excel format
• Fully integrated data structure for all of Australia by SA2 by year produced from all available data (note that not all geographies or years were available for all metrics)
• Creation of full data list including source, type, description, geography, time period(s), and format(s) of data included in the data cube
3. Identification of Gross Value Added (GVA) as headline ‘economic value at risk’ indicator
• GVA represents the value of goods and services produced minus intermediate consumption (i.e. the value of inputs into their production such as raw materials, rent, and labour costs).
• GVA per worker per industry was only available at the LGA level. Using the count of workers per industry per SA2, an estimate of total GVA per industry per SA2 was calculated using the LGA GVA per worker data applied to all SA2s within the given LGA. Where SA2s were split between LGAs, a weighted average GVA per worker by industry was calculated, with weighting related to the percentage of SA2 land area in the relevant LGAs.
• GVA informs the calculation of Gross Regional Product, but excludes the influence of taxes and subsidies.
4. Selection and analysis of relevant data
• Calculation of GVA per industry per SA2
• Selected data taken from most recent year available
5. Integration of ‘% of impacted land’ as an input function
6. Input flood extent table by % of each SA2 to calculate annual economic value at risk in $m (real)
Data time period: 2011-01-01 to 2021-01-01
Subjects
2022 |
Applied Economics |
Banking, Finance and Investment |
Commerce, Management, Tourism and Services |
Econometrics |
Economics |
Environmental Science and Management |
Environmental Sciences |
Economic Models and Forecasting |
Economic impact |
Economic value at risk |
Environment and Resource Economics |
Environmental Impact Assessment |
Flood impact |
Gross Value Added |
Investment and Risk Management |
Northern Rivers |
Surge |
flood |
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Identifiers
- Local : 102.100.100/441772
- DOI : 10.25919/7rqg-8t34
