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Using the WACC methodology to improve the assessment of projects in the french farming industry. Empirical evidences from farm's results of Isère

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par Anaël BIBARD
Grenoble Graduate School of Business - MBA 2012
  

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4.2 Calculation of the Historical Results Using the Data from CERFRANCE Isère

The objective of these calculations is to see if the hypothesis of the traditionalist's approach is verified in agriculture: does the performance of the firm increases with leverage? Therefore, the results of firms divided in 5 groups of leverage were analyzed (group 1 high leverages, group 5 lowest leverages. See section 3.2.4 page 31 for details).

4.2.1 Normality of the Datasets

Figure 14 presents the distribution of values for ROE to compare it with a normal distribution. The distribution does not fit with the normal distribution line looking at the histogram, because the central values have an abnormally high frequency. This impression is confirmed by the KolmogorovSmirnov test, because the P-Value is lower than 0.000. We can reject with 99% confidence that ROE comes from a normal distribution. The Shapiro-Wilk test cannot be performed because the sample is bigger than 2000 values.

Figure 14: histogram of frequency for ROE

The distributions of ROE have been tested for each year's separately, and within each year's for each
production either. Over the 25 tests, ROE could come from a normal distribution only for dairy

Dstrbuon
Normal

producers and cattle producers in 2010. Therefore, only the Kruskal-Wallis and Mood's Median tests were used to analyze ROE, and the results of the Anova's were not presented.

Figure 15 presents a histogram of the frequency of the distribution for ROA to compare it with a normal distribution. From a graphic perspective, it seems that ROA is closer than ROE to a normal distribution, as the normal distribution line is closer to the frequency histogram. However, the Kolmogorov-Smirnov test P-Value was lower than 0.000 as well, therefore we can also reject at 99% confidence that ROA comes from a normal distribution.

-3 -1 1 3 5 7

Figure 15: Histogram of frequency for ROA

Same as the ROE, the distributions of ROA have been tested for each year and within each year for each production separately. All the tests performed reported the same results: ROA does not come from a normal distribution at 99% confidence. Therefore, only the results of the Kruskal-Wallis and Mood's Median tests were discussed for ROA.

4.2.2 ROE

4.2.2.1 ROE Tested by Time and Specialization

The dataset have been tested first to see if time had an effect on ROE. The results of the Mood's
median and Kruskal-Wallis tests were quite logical: as someone could have predicted, time has a
significant effect on the ROE, with really low P-Values. The years 2006 and 2009 were two years of

isra A

bad results for farmers in Isère. The price of agricultural commodities soared in 2007 and 2008,
explaining that these two years were better. The effect was similar in 2010, when commodity prices

Dstributon

recovered from the severe fall of 2009. .

Mood's Median Test for ROE by YEAR Total n = 2840

Grand median = 0,0583012

YEAR

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

2006

568

326

242

0,0195117

0,00305438

0,041546

2007

568

245

323

5

0,0910952

55

0,0759334

0,110756

2008

568

250

318

0,0837851

0,065602

0,0957051

2009

568

328

240

0,0176906

0,00193535

0,0337506

2010

568

271

297

0,0734001

0,0512998

0,091961

Test statistic = 46,0986 P-Value = 2,3492E-9 Kruskal-Wallis Test for ROE by YEAR

YEAR

Sample Size

Average Rank

2006

568

1275,31

2007

568

1547,93

2008

568

1517,84

2009

568

1287,11

2010

568

1474,32

Test statistic = 57,0073 P-Value = 1,2328E-11

Figure 16: Box and whisker plots with median notch for ROE by year (means are shown with a red cross)

Figure 16 presents the box and whisker plots with median notch at 90% confidence intervals, showing that the results of the years 2006 and 2009 were significantly lower than in 2007, 2008 or 2010. Therefore, in order to isolate the time effect, ROE has to be analyzed year by year.

ROE has been analyzed by specialization:

- Dairy production,

- Cattle ranching,

Bnd

- Grain production,

- Diversified production.

Mood's Median Test for ROE by SPECIALIZATION Total n = 2840

Grand median = 0,0583012

SPECIALIZATION

Sample Size

n<=

n>

Median

90,0% lower CL

90,0% upper CL

Dairy

705

326

379

0,0747834

0,0595245

0,0896643

Cattle

225

154

71

-0,00274151

-0,0266764

0,0162487

Grains

630

299

331

0,0769898

0,0493805

0,103964

Diversified

1280

641

639

0,0580287

0,0458927

0,0699453

Test statistic = 36,2307 P-Value = 6,69281E-8

Dairy
Cattle
Grains
Diversified

 

Figure 17: Box and whisker plots with median notch for ROE by specialization (means are shown with a red cross)

The Mood's median test for ROE by specialization illustrates that the specialization has a significant impact on the ROE, with a really low P-Value illustrating that we cannot reject the hypothesis that the medians are different at 99% confidence. The cattle production has a median ROE lower than the other productions. The P-Value for the Kruskal-Wallis test is 3,96451E-8, giving the same results. Therefore, ROE was tested separately for each specialization and each year, in order to isolate the effect of time and specialization.

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