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| ABARE fishery surveys | ||
| As outlined in the Fisheries Management Act 1991, an objective of the Australian Fisheries Management Authority is to maximise the economic returns to the Australian community from the management of Australian fisheries. As part of monitoring the performance of AFMA against this management objective, each year ABARE conducts economic surveys of selected Commonwealth fisheries. The data obtained from these surveys are used to calculate performance indicators such as net returns, which can be then used to assess whether or not a fishery is being managed efficiently. Net returns are defined as the long-run profits from a fishery after all costs have been met. These costs include fuel, crew costs, repairs, depreciation, the opportunity cost of capital and the opportunity cost of family and owner labour. Although net returns do not provide an indication of the potential returns available from a fishery in the long run, a time series of net returns may indicate the direction in which returns in a fishery are heading. For instance, a fishery in which estimated net returns are regularly close to zero or negative is probably not being managed effectively. A positive trend, however, may suggest a fishery is approaching a point of maximum economic yield (MEY) — the level of effort at which the profits in a fishery are maximised. The net returns of a fishery can be calculated by summing the net returns of each boat in the fishery. The net return of each boat can be defined as: |
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NR = R – [OC + (d+r) K] – M |
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| NR net returns R total cash receipts attributable to the fishery, excluding any receipts from leasing licences or quota OC total operating cash costs less interest paid, less expenditure on leasing licences or quota, less licence fees and levies K value of capital associated with vessel (depreciated replacement value) d depreciation rate for vessel r real interest rate M costs of managing the fishery. Operating costs include day-to-day expenses such as fuel, crew costs, repairs, administration, gear, etc. The value of these expenses is usually obtained directly from fishers’ accounts. Note that both operating costs and receipts exclude any income or costs from leasing in or leasing out quota and licences. These are excluded because the amount fishers pay or accept for leasing quota and licences represents the expected future profits which can be generated from the quota or licence. Therefore, if leasing were included as revenue or costs, double counting would occur and estimates of net returns would be incorrect. Depreciation takes into account the decline in the value of capital over time through wear and tear and obsolescence. Depreciation expense is not consistently identifiable in fishers’ accounts, so ABARE calculates the depreciation of boats based on a capital inventory list collected during the surveys. The opportunity cost of owner and family labour is estimated during interviews. Often owners and their families are involved in the operation of a boat, either as skippers and crew or onshore as account and shore managers. While some will be paid the market value for their labour, some will not be paid at all and others paid very high amounts through ‘director’s fees’ or ‘management fees’. ABARE survey officers ask survey respondents what is the market value of each owner and family labour, and this amount is then considered as a cost. The opportunity cost of capital is the return that would have been earned if the capital was invested elsewhere, rather than invested in fishing capital. |
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| Relative standard errors | ||
| ABARE surveys a fraction of the vessels in a fishery to estimate financial performance. Estimates derived from these vessels are likely to be different from those which would have been obtained if information had been collected from a census of all vessels. How closely the survey results represent the population is influenced by the number of vessels in the sample, the variability of vessels in the population and most importantly the design of the survey and the estimation procedures used. To give a guide to the reliability of the survey estimates, measures of sampling variation have been calculated. These measures, expressed as percentages of the survey estimates and termed ‘relative standard errors’, are given next to each estimate in parentheses. In general, the smaller the relative standard error, the more reliable the estimate. |
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| Use of relative standard errors | ||
| These relative standard errors can be used to calculate ‘confidence intervals’ for the survey estimate. First, calculate the standard error by multiplying the relative standard error by the survey estimate and dividing by 100. For example, if average total cash receipts are estimated to be $100 000 with a relative standard error of 6 per cent, the standard error for this estimate is $6000. There is roughly a two in three chance the ‘census value’ (the value that would have been obtained if all boats in the target population had been surveyed) is within one standard error of the survey estimate. There is roughly a nineteen in twenty chance the census value is within two standard errors of the survey estimates. Thus, in this example, there is approximately a two in three chance that the census value is between $94 000 and $106 000, and approximately a nineteen in twenty chance that the census value is between $88 000 and $112 000. |
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| Comparing estimates | ||
| When comparing estimates across groups or years it is important to recognise the differences are also subject to sampling error. As a rule of thumb, a conservative estimate of the standard error of the difference can be constructed by adding the squares of the estimated standard errors of the component estimates and then taking the square root of the result. For example, suppose the estimates of total cash receipts were $100 000 in one year and $125 000 in the previous year — a difference of $25 000 — and the relative standard error is given as 6 per cent for each estimate. The standard error of the difference can be estimated as: |
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| so the relative standard error of the difference is: | ||
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($9605/$25 000) x 100 = 38% |
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| It should be noted that there may be changes in the population of a fishery from one year to the next. If these population changes are substantial, differences in estimates may be caused more by the changes in population than by changes in the variables themselves. | ||
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