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Life cycle patterns, farm performance and structural change: an empirical research

Life cycle patterns, farm performance and structural change: an empirical research. Steven Van Passel. Outline. Objectives Data Empirical model Results Conclusion. Objectives. To measure farm performance in Flanders: measuring firm-efficiency

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Life cycle patterns, farm performance and structural change: an empirical research

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  1. Life cycle patterns, farm performance and structural change: an empirical research Steven Van Passel

  2. Outline • Objectives • Data • Empirical model • Results • Conclusion

  3. Objectives • To measure farm performance in Flanders: measuring firm-efficiency • To test the impact of firm aspects on firm-efficiency: • Impact of age • Impact of succession • Impact of education • Impact of solvency • To test the link between firm-efficiency and firm-growth on farm-efficiency

  4. Data • FADN-data of 1018 Flemish farmers • Data available for the period 1989-2002 • 8926 observations •  unbalanced panel data • To measure the link between efficiency and growth, we use a balanced data set of 304 Flemish farms (4256 observations)

  5. Empirical model • Farm performance  measuring firm-efficiency • Measuring production frontier • Data envelopment analysis (DEA) • Stochastic frontier approach (SFA) • Aigner, Lovell & Schmidt(1977) and Meeusen & Van de Broeck(1977) introduced: Yit = α + f(xit , β) + vit - ui

  6. Firm efficiency Y F B A C 0 X

  7. Empirical model • Random effects panel data formulation with time-invariant inefficiency • Inefficiency or not (ui = 0?) • Cobb-Douglas versus translog functional form • Predictions of firm-level technical efficiencies (Battese & Coelli, 1988) • To analyze the impact of firm-specific factors on efficiency, we enlarge the stochastic production function with firm-aspects

  8. Empirical model: results • Farm-aspects affecting efficiency • Age and efficiency • Growth and efficiency

  9. Impact farm aspects on efficiency • Farm managers with a high education level are more efficient than managers with lower education levels; • Older managers are less efficient; • Farms with a successor are more efficient than farms without a successor; • Farmers with high solvency are less efficient

  10. Age and efficiency • Age has an inverse impact on efficiency • What about experience? • Link solvency and age? • Link education and age? • Expand our model with extra variables

  11. Impact of age on efficiency

  12. Impact of age on efficiency Age: 39 years Solvency = 1 Diploma = 1

  13. Impact of age on efficiency Solvency = 0 Diploma = 1 Solvency = 1

  14. Impact of age on efficiency Diploma = 1 (high education) Solvency = 1 Diploma = 5 (low education)

  15. Growth and efficiency • Sample of 304 farms (1989-2002) • Calculation of efficiency of each farm during period 1989-1996 • Farm-Growth = farm size(2002) – farm size (1997) • Farm-growth ~ farm efficiency ? • 10 farms with highest growth in farm size  average efficiency of 85,2% • 10 farms with highest decline in farm size  average efficiency of 73,5%

  16. Growth and efficiency

  17. Conclusion • Measuring farm performance as efficiency, we observe a wide range in the level of technical efficiencies across all farms • Higher education levels, presence of a successor and low solvency rates have a positive impact on farm-efficiency Impact of age on efficiency • First a positive impact (‘learning effects’) • After certain age decreasing impact • High solvency rates, low education decrease this critical age level

  18. Conclusion • More efficient farmers have in general a significant higher growth • Further research • Constructing a growth model by incorporating firm aspects as size, succession besides efficiency  growth = f(size,succession,education,efficiency) • Problem: correlation between efficiency and those other aspects

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