1. Life cycle patterns, farm performance and structural change: an empirical research
Steven Van Passel
I’m working for the policy research centre for sustainable agriculture, this centre is a consortium between the university of Leuven and Gand;
I’m doing there model development and economic research!

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
The changing structure of the farm sector has important consequences for productivity and efficiency of farming, for the demand for government services and for the well-being of local communities. The structure of the agricultural sector and the structural change can influence final policy impact.
So this highlights the importance of investigating determinants of the agricultural sector and structural change.
In this research we will mainly focus on the interplay between farm aspects and farm performance and between farm performance and structural change.

4. Data FADN-data of 1018 Flemish farmers
Data available for the period
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)
The Belgian data are collected and managed by CLE, the centre of Agricultural Economics.

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
The performance of an enterprise can be defined in different ways, an important measure of performance is efficiency.
As in Farell, we define efficiency as the actual productivity level of a firm to his maximum productivity.
To measure efficiency we have to known the path of the production function.
The two most common and principal methods are DEA and SFA.
DEA uses lineair programming methods to construct a non-parametric piece-wise frontier over the data.
SFA uses econometric estimations of the frontier (so its parametric).
The disadvantage of SFA is that the researcher has to choose a functional form but SFA can disentangle inefficiency from random noise. An other advantage of SFA is that you can determine the properties of the efficiency estimates.
The error term V accounts for measurement error and random errors such as weather and luck, the error term u measures the technical efficiency.

6. Firm efficiency B A C Y F X
This figure shows the case for one output Y and one input X.
We observe for a farm the production C while this farm can reach a higher production level B indicated by the production frontier for the same amount of input.
So we observe C but we don not observe the production frontier F, as just mentioned we have to estimate this frontier using in this research the stochastic frontier approach. Estimated the frontier, we can easily calculate efficiency-estimates 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
The used model is called the random effects panel data formulation with time-invariant inefficiency. It’s important to notice that our efficiency estimates do not vary over time, we use nevertheless this method because using paneldata we estimate the firm specific technical efficiencies more consistently.
First we test the presence of inefficiency effects or not, we found that there is technical inefficiency.
An other important issue is the choice of the functional form. We tested the translog formulation versus the cobb douglas formulation. The hypothesis that the coefficients of the second order terms (in the translog formulation) were 0 was rejected, so we used the translog formulation.

8. Empirical model: results
Farm-aspects affecting efficiency
Age and efficiency
Growth and efficiency
The empirical results are presented in three sections:
The impact of farm aspects on efficiency;
Than the impact of age on farm efficiency will be analyzed in more detail
Finally the link between efficiency and growth will be analyzed.

9. This figure shows the frequency of the firm level technical efficiency estimates; we observe a wide range of the level of efficiency across all farms

10. First, get a closer look to the impact of education;
In our dataset we have 5 different education levels; diploma1 till diploma5; the highest education level is diploma1, in this estimation, this variable was the omitted variable. So we see that farmers with the highest level of education are more efficient than others.
Next, we have in our data three variables about succession; succession 1 means that there is a successor on the farm, succesor 2 means that’s not clear yet that there will be a successor or not. And successor 3 means that there is no succession, this is the omitted variable. Our results show that farms with succession or doubt about succession are more efficient than farms without successors.
The impact of age is negative indicating that older farmers are more inefficient than younger farmers, we come to this in more detail.
Solvency is measured as own capital divided by total capital. So a solvency of 0 means that all capital is financed with debts.
The results in this table indicating that farmers with low solvency rates (say more debts) are more efficient. This may look surprising but a possible explanation can be the fact that farmers with more repayment obligations are forced to work more efficiently

11. 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

12. 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
Is a farmer with no or little experience always more efficient than a farmer with say 10 year experience?
To study the impact of age on efficiency in more detail we expand our model with extra variables.

13. Impact of age on efficiency

15. Impact of age on efficiency
Age: 39 years
Solvency = 1
Diploma = 1
This figure shows the link between efficiency and age; so in general a farmer’s efficiency will increase till a certain age and after this critical age level the impact of age will go done.
In the case of a farm with a solvency of 1 (this means no debts) and the highest level of education the critical age is 39.

16. Impact of age on efficiency
Solvency = 0
Diploma = 1
Solvency = 1
No, it’s interesting to study to impact of solvency on this analysis,
Ceteris parabus the highest education level, we observe the two following curves for the two extreme cases where the solvency equals 0 or 1.
So you can see that the lower the solvency rate (so more debts) the impact of age on efficiency is higer. The critical age is also increasing.

17. Impact of age on efficiency
Diploma = 1 (high education)
Solvency = 1
Diploma = 5
(low education)
The same results can be found for the different education levels, ceteris paribus a solvency of 1.
We found a higher impact of age on efficiency for farmers with a higher level of education. The critical age increases with the education level.

18. Growth and efficiency Sample of 304 farms (1989-2002)
Calculation of efficiency of each farm during period
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%
The research question was:
Will the more efficient farms grow and will the more inefficient farms decline?
Meaning that farmers learn about their efficiency as they operate in the agricultural sector

19. Growth and efficiency
This figure shows the link between firm efficiency and firm growth.
Calculating the rank correlation we obtain a spearman’s rho of 0,20, and this correlation is significant
So although there are a lot of exceptions, in general this indicates that the more efficient farms will grow.
But further research here is necessary

20. 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

21. 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