SIMULATION MODEL BASED ON IACS DATA; ALTERNATIVE APPROACH TO ANALYSE SECTORAL INCOME RISK IN AGRICULTURE

We develop a static simulation model to analyse income losses and income risks at aggregated agriculture sector level. Our empirical case study is based on farm level records for direct payments claims (IACS data) and covers the period 2010–2011. Using Monte Carlo simulations, we investigate the impact of different levels of risk on income trends. Results show that 80% of farms are extremely dependent on direct payments. Farm production types highly supported by direct payments consequentially fall into the low-risk group. Results show that a significant share of income loss at sector level is carried by small farms (by economic class). Average probability of larger losses at the sector level ranges between 2% and 64%. Our results also indicate that larger farms often have better risk-return ratios and thus face lower relative income risks.


Issue Date:
Apr 15 2016
Publication Type:
Journal Article
DOI and Other Identifiers:
doi: 10.15414/raae/2016.19.01.56-64 (Other)
Record Identifier:
https://ageconsearch.umn.edu/record/254151
PURL Identifier:
http://purl.umn.edu/254151
Published in:
Review of Agricultural and Applied Economics (RAAE), Volume 19, Number 1
Page range:
56-64
Total Pages:
9
JEL Codes:
R52; R58; H41
Note:
http://roaae.org/issue/review-of-agricultural-and-applied-economics-raae-vol-19-no-12016/?article=simulation-model-based-on-iacs-data-alternative-approach-to-analyse-sectoral-income-risk-in-agriculture




 Record created 2017-04-01, last modified 2018-01-23

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