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| sector | industry | description |
| Agriculture | Beef cattle | Beef cattle from feedlot operation and farming |
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| Pigs | Farmed and raised pigs | |
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| Poultry | Raised meat breed chicks (including chicken, duck, goose and turkey) and hatching egg breed chicks |
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| Sheep | Sheep farming for prime lambs, raw sheep milk and wool | |
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| Manufacturing | Bakery | Breads, cakes, pastries (including crumpets, doughnuts, slices, meat pies and pies) and biscuits |
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| Leather and leather products |
Tanning and fur dressing as well as manufactured leather (including machine belting, suitcases, handbags and wallets) |
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| Meat and meat products |
Processing (including slaughtering, boning, freezing, preserving and packing) meat products |
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| Oils and fats | Manufactured vegetable oils, fats, margarine, cooking oils and blended table or salad oils |
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| Other food products | Processed sugar, seafood, prepared animal and bird feed and snack foods |
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| Paper containers and products |
Cardboard, newsprint, paper pulp, paperboard, solid fibreboard sheets and wool pulp |
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| Soaps and other detergents | Manufactured soap productions, detergents, toothpastes, disinfectants, glycerine and candles |
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| Services | Accommodation, cafes and restaurants |
Accommodation (hotels, motels and other short- term accommodation), pubs, taverns, bars, cafes, restaurants and clubs |
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| Retail trade | Food, personal and household good and motor vehicle retailing (other than repairs) |
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| Road transport | Freight (including delivery service, long haulage and road freight service) and passenger (including long and short distance bus and taxi) |
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| Wholesale trade | Basic material (including farm produce, minerals, metals and chemicals), machinery and motor vehicle, and personal and household (including food, drink, tobacco, textile, clothing, footwear and household) good wholesaling |
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| Sources: ABS 1993, 2004. | ||
To make a suitable framework for the analysis of the effects of changes in economic variables on regional economies, the regions of interest have to be represented in a model. This requires the construction of a database representing the economy of the identified region. The regional databases for the four regions identified in chapter 4 are described below.
For each region the database comprises two major components. One component is the input-output table, which represents the structure of production and consumption of the region, and the other is the database relating to demographics and employment.
Input-output tables are the core data set for general equilibrium models. There are no existing regional input-output tables so they have to be constructed. The key information required to construct the regional input-output tables are production of goods and services. The values of production at the regional level are used to estimate the production shares of that region in the Australian state where it belongs. For the agricultural industries, the values of production in each of the four regions are estimated using the value of agricultural commodities produced from the ABS 2001 Census of Agriculture, available at the Statistical Local Area (SLA) level. The values of production for non-agricultural and food processing industries are not directly available from statistical data at the SLA level. They were estimated by using the shares of employment in these industries relative to the total employment in each region at the SLA level using ABS employment data.
Figure P shows the basic structure of the database representing transactions by economic agents. The columns identify the following agents:
The rows show the structure of the purchases made by each of the agents identified in the columns. Each of the C commodity types identified in the model can be obtained within the region, from other regions or imported from overseas. The source-specific commodities are used by industries as inputs to current production and capital formation, are consumed by households and governments, and are exported. Only domestically produced goods appear in the export column. Various types of commodity tax are payable on the purchases. As well as intermediate inputs, current production requires inputs of three categories of primary factor: labour (divided into O occupations), fixed capital and agricultural land. The other costs category covers various miscellaneous industry expenses.
Each cell in the input-output table contains the name of the corresponding matrix of the values (in some base year) of flows of commodities, indirect taxes or primary factors to a group of users. For example, V1BAS is a four-dimensional array showing the cost of C goods, from S source (domestic and imported), to I producers in R regions.
The make matrix shows the structure of production and sales of a commodity.
There are many regions across Australia reliant on stages of red meat production, from sheep and cattle farming to meat processing. Regions representing the different facets of the red meat industry in Australia were selected and identified using the ABS 2005-06 Agricultural Census (2008f) and 2006 Census of Population and Housing (2007). These statistical publications enabled small area regional analysis to be undertaken using data for statistical local areas (SLAs). Contiguous groupings of SLAs enabled areas dependent upon red meat producing and processing to be determined.
The sheep and cattle regions were identified by examining the value of production of cattle and calves or sheep and lambs slaughtered in each SLA, in conjunction with either total beef cattle numbers or lambs sold over the year. Lambs sold were selected to represent the sheep meat industry instead of total sheep and lamb numbers to ensure that a region reliant on the lamb industry was selected, rather than a region more reliant on wool.
On a state-by-state basis, the proportions of each SLA’s contribution to the state’s total, for each respective data series, were sorted into descending order. From this list, the SLAs contributing significantly to the state’s total were marked on a map. From these SLAs, several potential regions were then formed across Australia.
Unlike the previous region identification method, potential meat processing regions could not be identified from production values, because of data confidentiality issues. Instead, the proportion of workers employed in meat processing for each SLA was used. Intuitively, if a region processes a significant amount of red meat it would stand to reason that there would also be a high level of employment in the industry.
The identifying of potential meat processing regions was undertaken in a similar way to the other regions. On a state-by-state basis, each SLA’s proportion of employment in meat processing was sorted into descending order, with those with a high proportion being marked on a map. Contiguous regions across Australia were formed, with continual calculations of the proportion as new SLAs were included.