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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( Télécharger le fichier original )par Anaël BIBARD Grenoble Graduate School of Business - MBA 2012 |
ConclusionThe WACC methodology tested in this research presents real limitations due to its underlying theories based on the market efficiency. This assumption holds only for the listed companies, introducing a bias in the calculation presented in this paper. However, the results obtained are consistent with the initial expectations regarding the relationship between leverage and financial performance or financial distress. Therefore, consultants should take more consideration for this methodology and use it in their feasibility studies. The other achievement of this paper concerns the discount rates actually used by practitioners. All signs clearly indicate that many of them use abnormally low actualization rates, ranging from 2.5 to 4.5%. The impact of these low actualization rates in valuation methods, such as the profitability method, can be really important. The NPV of a farm can be over-estimated by two to four using such discount factors! Some consultants use more appropriate rates, but it is far from being a generality according to the results of the survey. Then, it appears clearly that leverage has a positive impact on the financial performance of the farms of Isère. These expected results confirm that the WACC methodology should hold in the context of small and medium farming business. Therefore, consultants should consider the capital structure of the farms into consideration not only to avoid the risk of financial distress, but also to look for the optimal leverage, which appears to range between 40 and 60%. From the results presented in this paper, the 60-80% leverage group presented really good performances, but also more variability. Moreover, the bankruptcy risk was not studied because a sample of farms studied had to be constant. Therefore, it is safer to consider that the optimal debt level lies in the group 40-60%, may be closer to 60% regarding the good performance of the 60-80% group, particularly for the dairy specialization. Finally, the consultants of the CERFRANCE Isère do not need to wait for other researches on the field to modify their methods. Main recommendation is to increase significantly their actualization rates. On the other side, it must be acknowledge that further researches on the topic are necessary to improve the methodology and determine more precisely the hypothesis that should be taken, regarding the risk premiums and the beta for example. The productivity constraints of the consultants working in the different CERFRANCE militate in favor of a partnership with the engineering schools specialized in agriculture. This partnership, already implemented in some schools, could be the starting point of others researches about the financial performances of farming businesses. BibliographyAgreste. (2011 b). Agreste Primeur, France Métropolitaine, Recensement agricole 2010. Montreuil Sous Bois: Agreste. Agreste. (2011). Commerce extérieur agroalimentaire. Agreste Conjoncture , 2 (12), pp. 1-5. Agreste. (2010). Memento de la statistique agricole. Retrieved 03 23, 2012, from www.agreste.agriculture.gouv.fr: http://www.agreste.agriculture.gouv.fr/reperes/memento-de-lastatistique-agricole/ Agreste. (2011, September). Mémento de la statistique agricole. Retrieved May 28, 2012, from www.agreste.agriculture.gouv.fr: http://www.agreste.agriculture.gouv.fr/IMG/pdf_R8212C01.pdf Agreste. (2011 c). Premiers résultats en Isère. Agreste Rhône Alpes, Coup d'Oeil , 129, 4 p. Al-Rjoub, S., Varela, O., & Hassan, M. (2005). The size effect reversal in the USA. Applied Financial Economics , 15 (17), 1189-1197. Ambassade de France à Washington. (2009). Les Etats Unis en chiffre. Washington DC: République Française. AREC. (2011). la méthanisation en Poitou-Charentes - Enjeux et perspectives. Retrieved March 29, 2012, from www.arecpc.com: www.arecpc.com/f245_la_methanisation_en_poitou_charentes_enjeux_et_perspectives.html Barry, P. J. (1980a). Capital Asset Pricing and Farm Real Estate. American Agricultural Economics Association , 62 (3), 549. Barry, P. J. (1981). Capital Asset Pricing and Farm Real Estate: Reply. American Agricultural Economics Association , 63 (3), 580. Barry, P. J. (1980b). Financial Impacts of Government Support Price Programs: Comment. American Journal Of Agricultural Economics , 62 (3), 599-604. Bergstrom, G., Frashure, R., & Chisholm, J. (1991). The Gains from international small-company in Global Portfolios: Quantiative Strategies for Maximum Performance. Homewood: R.Z. Aliber and B.R. Bruce, Business One Irwin. Bhardwaj, R. K., & Brooks, L. D. (1993). DUAL BETAS FROM BULL AND BEAR MARKETS: REVERSAL OF THE SIZE EFFECT. Journal of Financial Research , 16 (4), 269-284. Binder, J. J. (1992). BETA, FIRM SIZE, AND CONCENTRATION. Economic Inquiry , 30 (3), 556-563. Birner, R., Davis, K., Pender, J., Nkonya, E., Anandajayasekeram, P., & Ekboir, J. (2009). From best practice to best lit: a framework for designing and anlyzing pluralistic agricultural advisory services worldwide. Journal of Agricultural Education and Extension , 15, 341-355. Borrel, B., & Hubbard, L. (2000). Global Economic Effects of the EU Common Agricultural Policy. Economic Affaris , 20 (2), 18-26. Brealey, R. A., Myers, S. C., & Allen, F. (2008). Principles of corporate finance (Ninth international edition ed.). New York: McGraw Hill. Breisinger, C., Ecker, O., & Al-Riffai, P. (2011). Economie du Printemps Arabe. De la révolution à la transformation et la sécurité alimentaire. Politiques alimentaires en perspective , 18, 2-4. Carter, C. (1981). Capital Asset Pricing and Farm Real Estate: Comment. American Agricultural Economics Association , 63 (3), 578. CCI Rhône Alpes. (2011). Les Chiffres Clés de l'Isère, édition 2010-2011. Grenoble: Chambre de Commerce et d'Industrie de Grenoble. Chambers, R. G. (1989). Insurability and Moral Hazard in Agricultural Insurance Markets. American Journal of Agricultural Economics , 85 (2), 604-616. Chan, K., & Chen, N.-F. (1988). An Unconditional Asset-Pricing Test and the Role of Firm Size as an Instrumental Variable for Risk. Journal of Finance , 43 (2), 309-325. Claus, J., & Thomas, J. (2001). ). Equity Premia as Low as Three Percent? Evidence from Analysts' Earnings Forecasts for Domestic and International Stock Markets. Journal of Finance , 56 (5), 1629- 1666. Clipici, E. (2011). Directions of Common Agricultural Policy. Agricultural Management , 13 (1), 173- 178. CNCER. (2012, February). CERFRANCE, conseil et expertise comptable. Retrieved June 30, 2012, from www.reseau-cerfrance.fr: http://www.reseau-cerfrance.fr/emploi-formation/?p=jeunes-diplomes Coface. (2011). Observatoire des défaillances. Retrieved May 28, 2012, from www.coface.fr: http://www.coface.fr/CofacePortal/ShowBinary/BEA%20Repository/FR_fr_FR/documents/observato ire/observatoire-des-defaillances.pdf Damgé, M. (2012, July 20). Sécheresse aux Etats-Unis, les cours des céréales flambent. Retrieved July 21, 2012, from www.lemonde.fr: http://www.lemonde.fr/economie/article/2012/07/20/secheresseaux-etats-unis-les-cours-des-cereales-flambent_1736460_3234.html?xtmc=cereales&xtcr=1 Damodoran, A. (2011, February 23). Equity Risk Premiums (ERP): Determinants, Estimation and Implication? Retrieved June 23, 2012, from Social Science Research Network: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1769064 Dimson, E., & Marsh, P. (2001). U.K. Financial Market Returns, 1955-2000. Journal of Business , 74 (1), 1-31. Direction Générale des Impots. (2006, November).
fiche descriptive 4166. Retrieved June 20, 2012, Dobson, P. (2012, May 25). French 10-Year Government yield drops to to record low 2,422. Retrieved June 03, 2012, from www.bloomberg.com: http://www.bloomberg.com/news/2012-05-25/french10-year-government-bond-yield-drops-to-record-low-2-422-.html Dongcheol, K. (1993). The Extent of Nonstationarity of Beta. Review of Quantitative Finance & Accounting , 3 (2), 241-254. Drew, M. E., & Veeraraghavan, M. (2003). Beta, Firm Size, Book-to-Market Equity and Stock Returns. Journal of the Asia Pacific Economy , 8 (3), 354-380. Elekes, A., & Halmai, P. (2009). The "Hottest Topic" of the budgetary review: Will the CAP survive? Intereconomics , 44 (5), 300-308. Evans, G. (2008, August 4). N.Z. Forecasts. Retrieved March 29, 2012, from www.bloomberg.com: http://www.bloomberg.com/apps/news?pid=newsarchive&sid=a4O5KrquIb90 FAO. (2012, February 23). FAOSTAT. Retrieved March 23, 2012, from faostat.fao.org: http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor FAO. (2012). Major food and agricultural commodities producers. Retrieved March 29, 2012, from www.fao.org: http://www.fao.org/es/ess/top/commodity.html?item=1012&lang=en&year=2005 Faure, G., & Compagnone, C. (2011). Les Transformations du conseil face à une nouvelle agriculture. Cahiers de l'Agriculture , 20 (5), 321-326. Faure, G., Desjeux, V., & Gasselin, P. (2011). Revue Bibliographique sur les recherches menées dans le monde sur le conseil en agriculture. Cahiers de l'Agriculture , 20 (5), 327-342. Fernandez, P. (2010, November 16). Retrieved June 30, 2012, from Social Science Research Network: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1473225 Gangwar, L., Singh, D., & Mandal, G. (2008). Economic Evaluation of Peach Cultivation in North Indian Plains. Agricultural Economics Research Review , 21 (1), 123-129. Gardner, B. L. (1994). Liberalization in New-Zealand agriculture: Discussion. American Journal of Agricultural Economics , 76 (5), 1053. Hanson, S. D., & Myers, R. J. (1995). Testing for a time-varying risk premium in the returns to U.S. farmland. Journal of Empirical Finance , 2 (3), 265-276. Harwood, J., Heifner, R., Coble, K., Perry, J., & Somwaru, A. (1999). Managing Risk in Farming: Concepts Research and Analysis. Agricultural Economic Report , 774. Hénin, F. (2011, August 30). Fin des quotas laitiers - PAC 2015 - Projet européen des JA - Elevagenet.fr. Retrieved March 31, 2012, from www.web-agri.fr: http://www.web-agri.fr/actualite-agricole/politique-syndicalisme/article/le-projet-europeen-de-ja-pour-installer-et-maintenir-du-lait-sur-tout-le-territoire-1145-73874.html Hénin, F. (2011, December 10). Impact de la fin des
quotas sur les accords de prêts aux éleveurs. Retrieved April
01, 2012, from
www.web-agri.fr:
http://www.web-agri.fr/actualite- Hénin, F. (2012, March 26). Réforme de la PAC - DPU - Verdissements. Retrieved March 27, 2012, from www.terrenet.fr: http://www.terre-net.fr/actualite-agricole/politique-syndicalisme/article/le-nouveau-calcul-des-paiements-directs-portera-sur-55-de-l-enveloppe-des-soutiens-publics-affectes-205-79048.html Holzman, & Jorgensen. (2001). Social Risk management: A New conceptual framework for social protection, and beyond. International Tax and public Finance , 8, 529-556. Howarth, R. (2000). The CAP: History and attempts at reform. Economic Affairs , 20 (2), 4. IMF.Stat. (2012, 03 23). Principal Global Indicators. Consulté le 03 23, 2012, sur www.principalglobalindicators.org: http://www.principalglobalindicators.org/default.aspx Israelsen, C. (2009). The Value Premium. Financial Planning , 39 (6), 108-110. Jegadeesh, N. (1992). Does Market Risk Really Explain the Size Effect? Journal Of Financial & Quantitative Analysis , 27 (3), 337-351. Johnson, M. R. (2002). Kansas's Agricultural Use Value Method. Assessment Journal , 9 (3), 23-29. Laur, P. (2010). Le Beta varie-t-il pour de bonnes raisons ? L'influence des caractéristiques spécifiques des firmes et du contexte macroéconomique sur le coefficient de risque systématique ß. Retrieved July 10, 2012, from www.vernimmen.net: http://www.vernimmen.net/ftp/Memoire_Pierre_Laur.pdf Le Boulanger, A. (2011, November 29). La PAC 2014 au coeur des crises. Retrieved January 15, 2012, from www.cerfrance.fr: http://www.cerfrance.fr/blog-agri/2011/11/29/la-pac-2014-au-coeur-descrises/ Le Monde.fr. (2007, February 27). L'inflation française est restée mesurée en 2006. Retrieved June 20, 2012, from www.lemonde.fr: http://www.lemonde.fr/economie/article/2007/01/12/l-inflationfrancaise-est-restee-mesuree-en-2006_854975_3234.html Leonard, S. (2009). U.S. Equity Returns After Major Market Crashes. Journal of Financial Planning , 22 (10), 84-94. Miranda, M., & Glauber, J. (1997). Systemic Risk, Reinsurance, and Failure of Crop Insurance Markets. American Journal of Agricultural Economics , 79, 206-215. Momagri. (2012b, June 26). Another CAP is possible.
Retrieved July 21, 2012, from Momagri. (2012, February 27). France is gradually losing its
place among major agricultural export powerhouses. Retrieved April 02,
2012, from
www.momagri.org: Moravcsik, A. (1999). Le grain et la grandeur : les origines économiques de la politique européenne du général de Gaulle (1ère partie). Revue Française de Science Politique , 49 (4-5), 507-544. Mundler, P., Labarthe, P., & Laurent, C. (2006). Les disparités d'accès au conseil: le cas de la région Rhône-Alpes. Economie Rurale (291), 26-34. Nartea, G., & Webster, P. (2008). Should farmers invest in financial assets as a risk management strategy? Some evidence from New Zealand. Australian Journal Of Agricultural & Resource Economics , 52 (2), 183-202. OECD. (2009). Managing Risk in Agriculture: a Holistic Approach. Paris: Editions OCDE. OECD. (2011). Risk management in agriculture. Retrieved November 10, 2011, from www.oecd.org: www.oecd.org/agriculture/policies/risk OECD-FAO. (2010). Agricultural Outlook 2010-2019. Marston Gate: OECD. Petit, M. (2006). L'exploitation agricole familiale: leçons actuelles de débats anciens. Cahiers Agricultures , 15 (6), 486-490. Pinson, V. (2012, March 22). Fin des quotas laitiers: quels enjeux pour les abattoirs? Retrieved April 01, 2012, from www.lequotidienlesmarches.fr: http://www.lequotidienlesmarches.fr/marches/findes-quotas-laitiers-quel-enjeu-pour-les-abattoirs-art329564-39.html Quiry, P., & Le Fur, Y. (2012). Finance d'Entreprise. Pierre Vernimmen 10ème édition. Paris: Dalloz. Rhein, E. (2009, September 11). The UE needs a competitive dairy production. Retrieved March 31, 2012, from rhein.blogactiv.eu: http://rhein.blogactiv.eu/2009/09/11/the-eu-needs-a-competitivedairy-industry/ Rickard, S. (2001). Time to scrap the CAP? Accountancy , 128 (1298), 83. Rose, F. (2008). A note on the role of investment capital in the US agricultural futures markets and the possible effect on cash prices. Groupe sur les céréales, les aliments du bétail et le sucre. OECD. Salomons, R., & Grootveld, H. (2003). The equity risk premium: Emerging vs Developed Markets. Emerging Markets Review , 4, 121-144. Sandrey, R. A., & Scobie, G. M. (1994). Changing international competitiveness and trade: Recent experience in New Zealand agriculture. American Journal Of Agricultural Economics , 76 (5), 1041. Saunders, C., Wreford, A., & Cagatay, S. (2006). Trade Liberalisation and greenhouse gas emissions: the case of the dayring in the European Union and New-Zealand. Australian Journal of Agricultural & Resources Economics , 50 (4), 538-555. Sharpe, W. F. (1964). Capital asset prices: a theory of market equilibrium under conditions of risk. Journal of Finance , 19 (3), 425-442. Shomir, S., Pat, O., & Jeong-gil, C. (2011). FURTHER INVESTIGATION ON THE VARIABILITY OF INDIVIDUAL STOCK BETA AND PORTFOLIO SIZE. International Journal Of Business, Accounting, & Finance , 5 (1), 91-102. Sminou, K. (2010). Stock Market and Agricultural Futures Diversification: An International Perspective. Journal Of Alternative Investments , 12 (4), 36-57. Spencer, D. (2003). An agreement to deal. Economist , 368 (8337), 65. Switzer, L. (2010). The behaviour of small cap vs. large cap stocks in recessions and recoveries: Empirical evidence for the United States and Canada. The North American Journal of Economics and Finance , 21 (3), 332-346.
terrenet.fr. (2012, January 31).
mobilisation de la filière betterave contre la fin des quotas de
production. Retrieved March 31, 2012, from
www.terrenet.fr:
http://www.terre- Waintrop, M. (2010, August 18). Pourquoi le lait Allemand
est-il moins cher que le Français. Retrieved March 15, 2012, from
www.la-croix.com:
http://www.la-croix.com/Actualite/S- WTO. (2011). International Trade Statistics 2011. Geneva: World Trade Organization. You, G. (2010, August 17). Institut de l'Elevage: une filière lait "low-cost". Retrieved March 20, 2012, from www.web-agri.fr: http://www.web-agri.fr/actualite-agricole/economie-social/article/lait-prixtroisieme-trimestre-filiere-allemande-low-cost-1142-64110.html Zaharia, I., Tudorescu, N., & Zaharia, C. (2009). Have the Cap Reforms Delivered Significant Changes. Economics, Management & Financial Markets , 4 (2), 107-110.
Appendix 3: Questionnaire L'objectif de cette enquête est d'avoir un aperçu des techniques utilisées par les conseillers d'entreprise agricole pour actualiser les flux. Les résultats de cette enquête seront analysés de manière anonyme, et seront utilisés dans le mémoire « L'utilisation du CMPC en vue d'optimiser l'évaluation du coût du capital dans les exploitations agricoles en France. Constatations empiriques à partir des résultats économiques d'exploitations Iséroises ». Une version électronique de ce mémoire pourra être distribuée aux personnes intéressées d'ici octobre.
Nom de votre compagnie: Age: Sexe: Question 1: Quelle est votre profession ? Question 2: Pouvez-vous décrire succinctement votre activité ? Question 3: Dans le cadre de votre profession, rédigez vous des études prévisionnelles pour vos adhérents afin d'estimer la rentabilité de leurs projets ?
Question 4: est-ce que vous utilisez des taux d'actualisation pour évaluer la valeur d'une entreprise agricole ? (actualiser un flux perpétuel: flux prévisionnel reproductible / taux d'actualisation)
Question 5: Est-ce que vous utilisez la méthode de la Valeur Actuelle Nette (VAN) pour estimer la rentabilité d'un projet ? (projet photovoltaïque ou méthanisation par exemple)
Question 6: comment choisissez vous un taux d'actualisation ? Cochez les méthodes utilisées dans votre structure.
Question 7: Dans quelle fourchette de pourcentage se situent les taux d'actualisation que vous utilisez principalement pour votre clientèle agricole ?
Question 8: Comment qualifieriez vous les méthodes utilisées dans votre structure pour comparer et évaluer des projets d'investissement ?
Question 9: Si l'on vient a vous proposer un outil pour estimer le coût moyen pondéré du capital des exploitations de votre région afin de l'utiliser comme taux d'actualisation, comment considéreriez vous cet outil ?
P-Value Appendix 4: Normality test of the datasets for ROE, extraction from statgraphics Uncensored Data - ROE Data variable: ROEc The Shapiro Wilk test for normality cannot be performed. Therefore we observed the data graphically, and considered that the data follow a normal distribution (density trace and histogram of frequency look normal). 2800 values ranging from -4,78806 to 4,81454 Fitted Distributions Normal mean = 0,0444249 standard deviation = 0,628166 The StatAdvisor This analysis shows the results of fitting a normal distribution to the data on ROEc. The estimated parameters of the fitted distribution are shown above. You can test whether the normal distribution fits the data adequately by selecting Goodness-ofFit Tests from the list of Tabular Options. You can also assess visually how well the normal distribution fits by selecting Frequency Histogram from the list of Graphical Options. Other options within the procedure allow you to compute and display tail areas and critical values for the distribution. To select a different distribution, press the alternate mouse button and select Analysis Options.
Tests for Normality for ROEc Den Test
Statistic Too much data The StatAdvisor This pane shows the results of several tests run to determine whether ROEc can be adequately modeled by a normal 03 distribution. The Shapiro-Wilk test is based upon comparing the quantiles of the fitted normal distribution to the quantiles of the data. The Shapiro-Wilk test was not performed because the sample size was greater than 2000.
Goodness-of-Fit Tests for ROEc Kolmogorov-Smirnov Test
The StatAdvisor This pane shows the results of tests run to determine whether ROEc can be adequately modeled by a normal distribution. Since the smallest P-value amongst the tests performed is less than 0,05, we can reject the idea that ROEc comes from a normal distribution with 95% confidence.
ce for RO P-Value Appendix 5: Normality tests of the datasets for ROA, extraction from statgraphics Uncensored Data - ROA by time Data variable: ROA Too much data for the Shapiro test. We do it visually, and we can conclude that it is normally distributed! 3046 values ranging from -4,37674 to 3,08145 Fitted Distributions Normal mean = 0,0569658 standard deviation = 0,485766 The StatAdvisor This analysis shows the results of fitting a normal distribution to the data on ROA. The estimated parameters of the fitted distribution are shown above. You can test whether the normal distribution fits the data adequately by selecting Goodness-ofFit Tests from the list of Tabular Options. You can also assess visually how well the normal distribution fits by selecting Frequency Histogram from the list of Graphical Options. Other options within the procedure allow you to compute and display tail areas and critical values for the distribution. To select a different distribution, press the alternate mouse button and select Analysis Options.
Tests for Normality for ROA Test Shapiro-Wilk W Statistic D Too much data The StatAdvisor This pane shows the results of several tests run to determine
whether ROA can be adequately modeled by a normal 0,3 the data. The Shapiro-Wilk test was not performed because the sample size was greater than 2000.
The StatAdvisor This pane shows the results of tests run to determine whether ROA can be adequately modeled by a normal distribution. Since the smallest P-value amongst the tests performed is less than 0,05, we can reject the idea that ROA comes from a normal distribution with 95% confidence.
Appendix 6: ROE by year One-Way ANOVA - ROE by ANNEE Dependent variable: ROE Factor: ANNEE Number of observations: 2840 Number of levels: 5 The StatAdvisor This procedure performs a one-way analysis of variance for ROE. It constructs various tests and graphs to compare the mean values of ROE for the 5 different levels of ANNEE. The F-test in the ANOVA table will test whether there are any significant differences amongst the means. If there are, the Multiple Range Tests will tell you which means are significantly different from which others. If you are worried about the presence of outliers, choose the Kruskal-Wallis Test which compares medians instead of means. The various plots will help you judge the practical significance of the results, as well as allow you to look for possible violations of the assumptions underlying the analysis of variance.
Summary Statistics for ROE
The StatAdvisor 4 This table shows various statistics for ROE for each of the 5 levels of ANNEE. The one-way analysis of variance is 2006 2007 2008 2009 2010 primarily intended to compare the means of the different levels, listed here under the Average column. Select Means Plot ANNEE from the list of Graphical Options to display the means graphically. WARNING: The standardized skewness and/or kurtosis is outside the range of -2 to +2 for 5 levels of ANNEE. This indicates some significant nonnormality in the data, which violates the assumption that the data come from normal distributions. You may wish to transform the data or use the Kruskal-Wallis test to compare the medians instead of the means.
ANOVA Table for ROE by ANNEE
The StatAdvisor The ANOVA table decomposes the variance of ROE into two components: a between-group component and a within-group component. The F-ratio, which in this case equals 4,23794, is a ratio of the between-group estimate to the within-group estimate. Since the P-value of the F-test is less than 0,05, there is a statistically significant difference between the mean ROE from one level of ANNEE to another at the 95,0% confidence level. To determine which means are significantly different from which others, select Multiple Range Tests from the list of Tabular Options. ANOVA for ROE
2 Table of Means for ROE by ANNEE with 90,0 percent LSD intervals 0,04
The StatAdvisor This table shows the mean ROE for each level of ANNEE. It also shows the standard error of each mean, which is a measure of its sampling variability. The standard error is formed by dividing the pooled standard deviation by the square root of the number of observations at each level. The table also displays an interval around each mean. The intervals currently displayed are based on Fisher's least significant difference (LSD) procedure. They are constructed in such a way that if two means are the same, their intervals will overlap 95,0% of the time. You can display the intervals graphically by selecting Means Plot from the list of Graphical Options. In the Multiple Range Tests, these intervals are used to determine which means are significantly different from which others.
Multiple Range Tests for ROE by ANNEE Method: 90,0 percent LSD
* denotes a statistically significant difference. The StatAdvisor This table applies a multiple comparison procedure to determine which means are significantly different from which others. The bottom half of the output shows the estimated difference between each pair of means. An asterisk has been placed next to 6 pairs, indicating that these pairs show statistically significant differences at the 95,0% confidence level. At the top of the page, 2 homogenous groups are identified using columns of X's. Within each column, the levels containing X's form a group of means within which there are no statistically significant differences. The method currently being used to discriminate among the means is Fisher's least significant difference (LSD) procedure. With this method, there is a 5,0% risk of calling each pair of means significantly different when the actual difference equals 0.
Variance Check
The StatAdvisor The statistic displayed in this table tests the null hypothesis that the standard deviations of ROE within each of the 5 levels of 4 ANNEE is the same. Of particular interest is the P-value. Since the P-value is greater than or equal to 0,05, there is not a statistically significant difference amongst the standard deviations at the 95,0% confidence level. The table also shows a comparison of the standard deviations for each pair of samples. P-Values below 0.05, of which there are 7, indicate a statistically significant difference between the two sigmas at the 5% significance level.
Kruskal-Wallis Test for ROE by ANNEE
Test statistic = 57,0073 P-Value = 1,2328E-11 The StatAdvisor The Kruskal-Wallis test tests the null hypothesis that the medians of ROE within each of the 5 levels of ANNEE are the same. The data from all the levels is first combined and ranked from smallest to largest. The average rank is then computed for the data at each level. Since the P-value is less than 0,05, there is a statistically significant difference amongst the medians at the 95,0% confidence level. To determine which medians are significantly different from which others, select Box-and-Whisker Plot from the list of Graphical Options and select the median notch option. Mood's Median Test for ROE by ANNEE Total n = 2840 Grand median = 0,0583012 0,12
Test statistic = 46,0986 P-Value = 2,3492E-9 The StatAdvisor Mood's median test tests the hypothesis that the medians of all 5 samples are equal. It does so by counting the number of 0 observations in each sample on either side of the grand median, which equals 0,0583012. Since the P-value for the chi- 2006 2007 2008 2009 2010 square test is less than 0,1, the medians of the samples are significantly different at the 90,0% confidence level. Also ANNEE included (if available) are 90,0% confidence intervals for each median based on the order statistics of each sample.
Appendix 7:ROA by year One-Way ANOVA - ROA by ANNEE Dependent variable: ROA Factor: ANNEE Number of observations: 2840 Number of levels: 5 The StatAdvisor This procedure performs a one-way analysis of variance for ROA. It constructs various tests and graphs to compare the mean values of ROA for the 5 different levels of ANNEE. The F-test in the ANOVA table will test whether there are any significant differences amongst the means. If there are, the Multiple Range Tests will tell you which means are significantly different from which others. If you are worried about the presence of outliers, choose the Kruskal-Wallis Test which compares medians instead of means. The various plots will help you judge the practical significance of the results, as well as allow you to look for possible violations of the assumptions underlying the analysis of variance. Summary Statistics for ROA
The StatAdvisor 21 This table shows various statistics for ROA for each of the 5 levels of ANNEE. The one-way analysis of variance is 2006 2007 2008 2009 2010 primarily intended to compare the means of the different levels, listed here under the Average column. Select Means Plot ANNEE from the list of Graphical Options to display the means graphically. WARNING: The standardized skewness and/or kurtosis is outside the range of -2 to +2 for 5 levels of ANNEE. This indicates some significant nonnormality in the data, which violates the assumption that the data come from normal distributions. You may wish to transform the data or use the Kruskal-Wallis test to compare the medians instead of the means.
ANOVA Table for ROA by ANNEE
The StatAdvisor The ANOVA table decomposes the variance of ROA into two components: a between-group component and a within-group component. The F-ratio, which in this case equals 11,9622, is a ratio of the between-group estimate to the within-group estimate. Since the P-value of the F-test is less than 0,05, there is a statistically significant difference between the mean ROA from one level of ANNEE to another at the 95,0% confidence level. To determine which means are significantly different from which others, select Multiple Range Tests from the list of Tabular Options. ANOVA for ROA
0,2 0,8 1, Table of Means for ROA by ANNEE with 90,0 percent LSD intervals 0,03
The StatAdvisor This table shows the mean ROA for each level of ANNEE. It also shows the standard error of each mean, which is a measure of its sampling variability. The standard error is formed by dividing the pooled standard deviation by the square root of the number of observations at each level. The table also displays an interval around each mean. The intervals currently displayed are based on Fisher's least significant difference (LSD) procedure. They are constructed in such a way that if two means are the same, their intervals will overlap 95,0% of the time. You can display the intervals graphically by selecting Means Plot from the list of Graphical Options. In the Multiple Range Tests, these intervals are used to determine which means are significantly different from which others.
Multiple Range Tests for ROA by ANNEE Method: 90,0 percent LSD
* denotes a statistically significant difference. The StatAdvisor This table applies a multiple comparison procedure to determine which means are significantly different from which others. The bottom half of the output shows the estimated difference between each pair of means. An asterisk has been placed next to 6 pairs, indicating that these pairs show statistically significant differences at the 95,0% confidence level. At the top of the page, 2 homogenous groups are identified using columns of X's. Within each column, the levels containing X's form a group of means within which there are no statistically significant differences. The method currently being used to discriminate among the means is Fisher's least significant difference (LSD) procedure. With this method, there is a 5,0% risk of calling each pair of means significantly different when the actual difference equals 0.
Variance Check
The StatAdvisor The statistic displayed in this table tests the null hypothesis that the standard deviations of ROA within each of the 5 levels of 3 ANNEE is the same. Of particular interest is the P-value. Since the the P-value is less than 0,05, there is a statistically significant difference amongst the standard deviations at the 95,0% confidence level. This violates one of the important 2 assumptions underlying the analysis of variance and will invalidate most of the standard statistical tests. The table also shows a comparison of the standard deviations for each pair of samples. P-Values below 0.05, of which there are 9, indicate a statistically significant difference between the two sigmas at the 5% significance level. 0 Kruskal-Wallis Test for ROA by ANNEE
Test statistic = 73,6591 P-Value = 0 The StatAdvisor The Kruskal-Wallis test tests the null hypothesis that the medians of ROA within each of the 5 levels of ANNEE are the same. The data from all the levels is first combined and ranked from smallest to largest. The average rank is then computed for the data at each level. Since the P-value is less than 0,05, there is a statistically significant difference amongst the medians at the 95,0% confidence level. To determine which medians are significantly different from which others, select Box-and-Whisker Plot from the list of Graphical Options and select the median notch option. Mood's Median Test for ROA by ANNEE Total n = 2840 0001 Grand median = 0,034619 79
Test statistic = 58,6901 P-Value = 5,46618E-12 19 The StatAdvisor Mood's median test tests the hypothesis that the medians of all 5 samples are equal. It does so by counting the number of 1 observations in each sample on either side of the grand median, which equals 0,034619. Since the P-value for the chi-square 2006 2007 2008 2009 2010 test is less than 0,1, the medians of the samples are significantly different at the 90,0% confidence level. Also included (if ANNEE available) are 90,0% confidence intervals for each median based on the order statistics of each sample. Appendix 8: ROE by groups for dairy production One-Way ANOVA - ROE by Groups (ANNEE=2010&SPECIALISATION="Bovins lait") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2010&SPECIALISATION="Bovins lait" Number of observations: 141 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 13,0166 P-Value = 0,0111951 Mood's Median Test for ROE by Groups Total n = 141 6 04 Grand median = 0,104137
Test statistic = 11,2575 P-Value = 0,0238171 One-Way ANOVA - ROE by Groups (ANNEE=2009&SPECIALISATION="Bovins lait") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2009&SPECIALISATION="Bovins lait" Number of observations: 141 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 13,3289 P-Value = 0,00977558 Mood's Median Test for ROE by Groups ROE Total n = 141 Grand median = 0,0435052
Test statistic = 19,0281 P-Value = 0,000776032 One-Way ANOVA - ROE by Groups (ANNEE=2008&SPECIALISATION="Bovins lait") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2008&SPECIALISATION="Bovins lait" Number of observations: 141 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups 3
Test statistic = 3,68552 P-Value = 0,450235 ROE Mood's Median Test for ROE by Groups Total n = 141 Grand median = 0,0900887
Test statistic = 6,71467 P-Value = 0,151757 One-Way ANOVA - ROE by Groups (ANNEE=2007&SPECIALISATION="Bovins lait") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2007&SPECIALISATION="Bovins lait" Number of observations: 141 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups 4
Test statistic = 16,8905 P-Value = 0,00202995 Mood's Median Test for ROE by Groups Total n = 141 Grand median = 0,0919618
Test statistic = 19,1672 P-Value = 0,000728654 One-Way ANOVA - ROE by Groups (ANNEE=2006&SPECIALISATION="Bovins lait") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2006&SPECIALISATION="Bovins lait" Number of observations: 141 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 3,48819 P-Value = 0,479676 Mood's Median Test for ROE by Groups Total n = 141 Grand median = 0,0333134
Test statistic = 9,40599 P-Value = 0,0517153 Appendix 9: ROE by groups for cattle specialization One-Way ANOVA - ROE by Groups (ANNEE=2006&SPECIALISATION="Bovins viande") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2006&SPECIALISATION="Bovins viande" Number of observations: 45 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 6,23275 P-Value = 0,182427 Mood's Median Test for ROE by Groups Total n = 45 Grand median = -0,0364259
Test statistic = 4,80555 P-Value = 0,307838 One-Way ANOVA - ROE by Groups (ANNEE=2007&SPECIALISATION="Bovins viande") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2007&SPECIALISATION="Bovins viande" Number of observations: 45 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 6,0404 P-Value = 0,196151 Mood's Median Test for ROE by Groups Total n = 45 Grand median = 0,0260234
Test statistic = 5,68535 P-Value = 0,223911 One-Way ANOVA - ROE by Groups (ANNEE=2008&SPECIALISATION="Bovins viande") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2008&SPECIALISATION="Bovins viande" Number of observations: 45 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 15,5023 P-Value = 0,0037652 Mood's Median Test for ROE by Groups Total n = 45 Grand median = -0,00954206
Test statistic = 9,62062 P-Value = 0,0473268 One-Way ANOVA - ROE by Groups (ANNEE=2009&SPECIALISATION="Bovins viande") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2009&SPECIALISATION="Bovins viande" Number of observations: 45 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 4,4266 P-Value = 0,35134 Mood's Median Test for ROE by Groups Total n = 45 Grand median = -0,030158
Test statistic = 4,72004 P-Value = 0,317248 One-Way ANOVA - ROE by Groups (ANNEE=2010&SPECIALISATION="Bovins viande") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2010&SPECIALISATION="Bovins viande" Number of observations: 45 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 1,85464 P-Value = 0,762472 Mood's Median Test for ROE by Groups Total n = 45 Grand median = 0,0147703
Test statistic = 1,82153 P-Value = 0,76854 Appendix 10: ROE by groups for grain specialization One-Way ANOVA - ROE by Groups (ANNEE=2006&SPECIALISATION="Grandes cultures") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2006&SPECIALISATION="Grandes cultures" Number of observations: 126 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 4,57832 P-Value = 0,333361 Mood's Median Test for ROE by Groups Total n = 126 Grand median = -0,00516469
Test statistic = 4,73131 P-Value = 0,315994 One-Way ANOVA - ROE by Groups (ANNEE=2007&SPECIALISATION="Grandes cultures") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2007&SPECIALISATION="Grandes cultures" Number of observations: 126 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 8,90878 P-Value = 0,0634204 Mood's Median Test for ROE by Groups Total n = 126 Grand median = 0,217189
Test statistic = 13,6478 P-Value = 0,0085084 One-Way ANOVA - ROE by Groups (ANNEE=2008&SPECIALISATION="Grandes cultures") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2008&SPECIALISATION="Grandes cultures" Number of observations: 126 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 0,224908 P-Value = 0,994132 Mood's Median Test for ROE by Groups Total n = 126 Grand median = 0,158119
Test statistic = 1,81206 P-Value = 0,770275 One-Way ANOVA - ROE by Groups (ANNEE=2009&SPECIALISATION="Grandes cultures") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2009&SPECIALISATION="Grandes cultures" Number of observations: 126 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 2,47127 P-Value = 0,649788 Mood's Median Test for ROE by Groups Total n = 126 Grand median = -0,045463
Test statistic = 4,19911 P-Value = 0,37973 One-Way ANOVA - ROE by Groups (ANNEE=2010&SPECIALISATION="Grandes cultures") Dependent variable: ROE Factor: Groups Selection variable: ANNEE=2010&SPECIALISATION="Grandes cultures" Number of observations: 126 Number of levels: 5 Summary Statistics for ROE
Kruskal-Wallis Test for ROE by Groups
Test statistic = 9,08773 P-Value = 0,0589437 Mood's Median Test for ROE by Groups Total n = 126 Grand median = 0,0469154
Test statistic = 9,79713 P-Value = 0,0439872 Appendix 11: ROA by groups for dairy specialization One-Way ANOVA - ROA by Groups (ANNEE=2006&SPECIALISATION="Bovins lait") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2006&SPECIALISATION="Bovins lait" Number of observations: 141 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 4,98542 P-Value = 0,288797 Mood's Median Test for ROA by Groups Total n = 141 Grand median = 0,0220625
Test statistic = 7,73279 P-Value = 0,101872 One-Way ANOVA - ROA by Groups (ANNEE=2007&SPECIALISATION="Bovins lait") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2007&SPECIALISATION="Bovins lait" Number of observations: 141 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 12,1811 P-Value = 0,0160539 Mood's Median Test for ROA by Groups Total n = 141 Grand median = 0,0550632
Test statistic = 13,7443 P-Value = 0,00815748 One-Way ANOVA - ROA by Groups (ANNEE=2008&SPECIALISATION="Bovins lait") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2008&SPECIALISATION="Bovins lait" Number of observations: 141 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 6,29522 P-Value = 0,178159 Mood's Median Test for ROA by Groups Total n = 141 Grand median = 0,0501904
Test statistic = 3,70305 P-Value = 0,447682 One-Way ANOVA - ROA by Groups (ANNEE=2009&SPECIALISATION="Bovins lait") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2009&SPECIALISATION="Bovins lait" Number of observations: 141 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 14,9349 P-Value = 0,00483818 Mood's Median Test for ROA by Groups Total n = 141 Grand median = 0,0264797
Test statistic = 14,5707 P-Value = 0,00567971 One-Way ANOVA - ROA by Groups (ANNEE=2010&SPECIALISATION="Bovins lait") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2010&SPECIALISATION="Bovins lait" Number of observations: 141 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 8,97242 P-Value = 0,0617925 Mood's Median Test for ROA by Groups Total n = 141 Grand median = 0,0608248
Test statistic = 5,71883 P-Value = 0,221153 Appendix 12: ROA by groups for cattle specialization One-Way ANOVA - ROA by Groups (ANNEE=2006&SPECIALISATION="Bovins viande") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2006&SPECIALISATION="Bovins viande" Number of observations: 45 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 4,10541 P-Value = 0,391929 Mood's Median Test for ROA by Groups Total n = 45 Grand median = -0,0284627
Test statistic = 5,69488 P-Value = 0,223123 One-Way ANOVA - ROA by Groups (ANNEE=2007&SPECIALISATION="Bovins viande") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2007&SPECIALISATION="Bovins viande" Number of observations: 45 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 5,00392 P-Value = 0,286896 Mood's Median Test for ROA by Groups Total n = 45 Grand median = 0,0218779
Test statistic = 5,68535 P-Value = 0,223911 One-Way ANOVA - ROA by Groups (ANNEE=2008&SPECIALISATION="Bovins viande") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2008&SPECIALISATION="Bovins viande" Number of observations: 45 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 13,5611 P-Value = 0,00883607 Mood's Median Test for ROA by Groups Total n = 45 Grand median = -0,00847679
Test statistic = 9,62062 P-Value = 0,0473268 One-Way ANOVA - ROA by Groups (ANNEE=2009&SPECIALISATION="Bovins viande") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2009&SPECIALISATION="Bovins viande" Number of observations: 45 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 4,99582 P-Value = 0,287726 Mood's Median Test for ROA by Groups Total n = 45 Grand median = -0,0214517
Test statistic = 6,38753 P-Value = 0,172017 One-Way ANOVA - ROA by Groups (ANNEE=2010&SPECIALISATION="Bovins viande") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2010&SPECIALISATION="Bovins viande" Number of observations: 45 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 2,31184 P-Value = 0,678615 Mood's Median Test for ROA by Groups Total n = 45 Grand median = 0,0134522
Test statistic = 1,82153 P-Value = 0,76854 Appendix 13: ROA by groups for grain specialization One-Way ANOVA - ROA by Groups (ANNEE=2006&SPECIALISATION="Grandes cultures") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2006&SPECIALISATION="Grandes cultures" Number of observations: 126 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 2,3065 P-Value = 0,679586 Mood's Median Test for ROA by Groups Total n = 126 Grand median = -0,0106826
Test statistic = 3,30621 P-Value = 0,507949 One-Way ANOVA - ROA by Groups (ANNEE=2007&SPECIALISATION="Grandes cultures") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2007&SPECIALISATION="Grandes cultures" Number of observations: 126 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 3,648 P-Value = 0,455734 Mood's Median Test for ROA by Groups Total n = 126 Grand median = 0,119947
Test statistic = 4,7245 P-Value = 0,316751 One-Way ANOVA - ROA by Groups (ANNEE=2008&SPECIALISATION="Grandes cultures") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2008&SPECIALISATION="Grandes cultures" Number of observations: 126 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 2,51113 P-Value = 0,642644 Mood's Median Test for ROA by Groups Total n = 126 Grand median = 0,112204
Test statistic = 1,86041 P-Value = 0,761413 One-Way ANOVA - ROA by Groups (ANNEE=2009&SPECIALISATION="Grandes cultures") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2009&SPECIALISATION="Grandes cultures" Number of observations: 126 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 2,3342 P-Value = 0,674549 Mood's Median Test for ROA by Groups Total n = 126 Grand median = -0,0253879
Test statistic = 6,20485 P-Value = 0,184363 One-Way ANOVA - ROA by Groups (ANNEE=2010&SPECIALISATION="Grandes cultures") Dependent variable: ROA Factor: Groups Selection variable: ANNEE=2010&SPECIALISATION="Grandes cultures" Number of observations: 126 Number of levels: 5 Summary Statistics for ROA
Kruskal-Wallis Test for ROA by Groups
Test statistic = 7,96551 P-Value = 0,0928499 Mood's Median Test for ROA by Groups Total n = 126 Grand median = 0,0352505
Test statistic = 9,6921 P-Value = 0,0459461 Appendix 14: Cost of debt for farms of Isère Specialization number of observation cost of debt (market value) 15 3,8% 8 3,6% 15 3,5% 8 3,5% Dairy Cattle Grain Diversified 46 3,5% Mean The cost of capital has been estimated using the market value for the cost of debt. For some specialization like cattle farming, we do not dispose of enough farms to collect 15 recent interest rates. |
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