ESTIMATING INCOME RISK AT THE PIG SECTOR LEVEL

The paper presents a possible theoretical approach how income risk could be indirectly analysed at the sector level. This is an important step in the early development stage of eventual policies dealing with income issues. In such circumstances one should have reliable information about the characteristics of income risk faced by different groups of farms in relation to their economic size and income structure. From an information viewpoint this is very demanding and is lack of information that is often the main obstacle for such preliminary analysis. The main assumption in the approach presented is that appropriate accounting data at the farm level are not available, as the most common approach to estimate income variability per farm. The approach presented utilizes different sources of information, such as data at the farm level, national statistics and analytical models, in order to support the simulation process and to give greater insight into income losses at the sector level. The annual subsidy application is crucial information for each farm in the sector from which information about the main production activities could be gathered. On this basis, and with the support of other data sources, income structure for each farm analysed is reconstructed. To imitate income risk, potential from Monte Carlo Simulations is utilised. Possible different risks are entered as uncertain variables and are supported by different uncertain distributions, representing possible states of nature. In the current development stage they are mainly based on triangular random distributions. In such a manner income risk is simulated at the farm level; however results are summarised and presented for group of farms. Regarding this assumption, it is an example of a bottom-up approach. The tool developed is tested on data from the pig sector in Slovenia. The subsequent results suggest that this could be a useful approach for rough estimation of income risk and points out some limitations and drawbacks that could be further improved.