Non-financial Factors Impact on Profitability and Riskiness of Slovak Agriculture Sector

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1 Non-financial Factors Impact on Profitability and Riskiness of Slovak Agriculture Sector Andrea Piterková 1, Peter Serenčéš 2, Marián Tóth 3 1, 2,3 Slovak University of Agriculture in Nitra Faculty of Economics and Management, Department of Finance Tr. A. Hlinku Nitra, Slovak republic 1, 2,3 : xpiterkovaa@uniag.sk, peter.serences@uniag.sk, marian.toth@uniag.sk Abstract Riskiness of the agriculture sector is composed by many different individual sources of risk resulting from the business activities, including the production, market, institutional or personal risks, as well as the different methods of financing activities. These risks are very rarely completely independent from each other, particularly when measured in terms of their impact on the income variability. Many of the risk factors may not be eliminated by the producers themselves, however number of decisions made by farmers can lead to more effective production, profitability and risk elimination. This paper examines the profitability and riskiness of Slovak primary agriculture sector, with the focus on certain non-financial factors causing significant differences in their development. The analysis emphasises those individual farmers decisions about agriculture production that are able to improve economic development of Slovak agribusinesses. Keywords: agriculture sector, non-financial factors, profitability, riskiness, significant differences JEL Classification: Q13, Q14, C12 1. Introduction The Slovak agriculture sector development has been in the recent years affected by the number of substantial changes and dynamics, caused by the Common Agriculture Policy assessment in 2004, new political regulations, unstable market and climate conditions, or crisis influence in These events have been ultimately impacting economic development in this sector, as well as the profitability and riskiness of individual businesses. The average economic results of businesses in agriculture sector show very high level of volatility of financial indicators such as ROE, 4.39% in 2007, 0.4% in 2009, 2.84% in 2011 or ROA, 1.76% in 2007, 0.04% in 2009, 1.11% in 2011, (Serenčéš et al. 2014). This low profitable, unstable and risky development of Slovak agriculture can be subjected to strong variability due to several reasons and factors affecting the production, income, and welfare of businesses. Profitability and riskiness of agriculture companies are resulted from many different individual sources of impact, which can be commonly divided into financial and non-financial factors. Huirne et al. (2000) and Hardaker et al. (2004) distinguished two main types of factors influencing agriculture. Firstly, the business factors, including the production, market, institutional and personal risks, and secondly, the financial factors resulting from different methods of financing the business activities, fluctuation of interest rate or loans availability. These risks are very rarely completely independent from each other, particularly when measured in terms of their impact on the profit or income variability. The financial factors impact on the financial distress of Slovak agriculture companies has been analysed in the number of scientific papers of Chrastinová (1998), Gurčík (2002), Bieliková et al. (2014) and others. However, only a little evidence of non-financial factors impact on agriculture companies prosperity can be found. In the previous studies was investigated mainly the impact of legal, organisational and size structure on the performance of farms (Lančárič et al., 2013; Ciaian et al., 2009; Kopta, 2013). Generally, the non-financial 292

2 factors impact on different businesses was emphasised in the works of Cumby and Condor (2001), Khizer et al. (2011) and others. Therefore, we decided to extend the previous studies and investigate whether exist significant differences in risk and return development dividing the companies according to their legal form, production orientation and size of utilized agriculture area UAA (LPIS). Furthermore, using the linear discriminant analysis we tried to find the non-financial factors, which determine the successful performance of farms in agriculture primary sector measured by ROE. The main objective of the paper is to examine and evaluate the non-financial factors impact on prosperity of Slovak agriculture companies. 2. Data and Methods The following part provides overview of data and methods applied, in order to meet the objective of the paper and examine the existence of non-financial factors significant impact on performance of Slovak agriculture firms. 2.1 Data The data used for the analysis was obtained from the Ministry of Agriculture and Rural Development of the Slovak Republic, processed in the internal dataset of the of the Slovak Agricultural University in Nitra. The dataset consists of financial statements of all agricultural farms operating in the Slovak Republic during the period However, for the analysis were selected only information from balance sheets and profit and loss statement of farms operating during each year of the period , with legal form of cooperatives or capital companies. After outliers detection 842 farms remained and created a sample for our analysis. 2.1 Methods The first part of our analysis was focused on proving the statistically significant differences in development of individual risk and individual return (ROE) dividing the farms according to determined criteria. In testing the Z-test was used, which is preferable when the number of observations n is greater than 30. The assumptions of Z-test include the equal known variances and the normal distribution criterion, unless the sample size is large enough. For this reason, we expect that the particular samples are large enough and the normal distribution condition might be excluded. The equal variances were examined using F-test. Table 1: Companies division for Z-testing Number Criterion All companies 842 Legal form Capital companies 434 Ltd., JSC. Cooperatives 408 Cooperative Production orientation Crop production Animal production Over 50 % Crop revenues Over 50 % Animal revenues Other production 185 Over 50 % Other revenues Utilized agriculture area (LPIS) Source: Authors < 500 ha ha ha > 2000 ha 293

3 The fundamental tool for examination the financial prosperity of enterprise, regarding different factors, represents the discriminant analysis. Discriminant analysis methods are divided into one-dimensional model, that predicts financial distress of company by using a single indicator (Beaver model, Zmijevsky model, and others) and multivariate discriminant analysis using a set of several weighted indicators (Bonity index, Altman Z score, Fulmer model, Taffler model and others). For constructing the classification model, using the discriminant analysis, is required to define relevant criteria of prosperous and unprosperous company. The unprosperous farms are considered to be those, generating loss (negative ROE) in each of years Oppositely, the prosperous farms were considered to be all generating profit during observed period. Because very large sample of prosperous farms remained for the analysis we added the prosperous criterion with ROE greater than 5 %. We did not use the balance sample approach, to select the same number of prosperous and unprosperous farms, in order not to influence and deteriorate the results and include all firms meeting our conditions of prosperity. The particular samples consisted of 82 unprosperous and 158 prosperous farms. The model was developed using the stepwise discriminant analysis. According to Stankovičová, Vojtková (2007), in the stepwise approach the examined variables are evaluated separately, and those with the best discriminant ability are chosen to become variables in the final equation. The results of stepwise selection process are determined by considering the statistically significant correlation between ratios. The condition for excluding some variable from analysis depends on the discriminant ability, described by the partial determination coefficient. To construct the equations the descriptives such as Univariate Anova s, Fisher s, Box s M and unstandardized function coefficients are requested. More detailed characteristic of the method can be found in Stankovičová, Vojtková (2007), or Kráľ et al. (2009). Table 2: Input variables of discriminant analysis Variable Calculation Variable Calculation Y Prosperity X 5 Crop production revenues x100 Total revenues X 1 Legal form X 6 Animal production revenues x100 Total revenues X 2 UAA size (LPIS) X 7 Other revenues x100 Total revenues X 3 Employees X 8 Crop production revenues Land size (ha) Land size (ha) X 4 Owners X 9 Animal production revenues Land size (ha) Land size (ha) Source: Authors The farms in prosperous sample were signed by number 1 and unprosperous by number 0. All the input variables are the quantitative character except for variable X 1 Legal form. For this reason the legal form Cooperatives was signed by number 0 and capital companies by number 1. All the calculations and methods were applied using the Microsoft Excel and statistical software IBM SPSS Statistics

4 3. Results and Discussion In the following part the summary of results is provided with the objective to prove and evaluate the non-financial factors impact on the development of risk and return of Slovak agriculture entities. 3.1 Z-tests In the first part of analysis were used all 842 agriculture companies operating in the period , which were divided according to their legal form, production orientation and size of the land. After division the statistically significant differences between their individual average return measured by ROE and individual risk (standard deviation), were examined with the Z-tests. Table 3: Z-tests results Z-test Average return p-value 295 Statistically significant differences Individual risk p-value Statistically significant differences Cooperatives and Capital companies Crop and Animal production E-10 Crop and Other production Animal and Other production UAA size < 500ha and ha UAA size < 500ha and ha UAA size < 500ha and over 2000 ha No No No E No Source: Authors The results of Z-test show statistically significant differences in individual returns and risks of capital companies and cooperatives, crop and animal production orientation and animal and other production orientation. When we divided the farms according their land size LPIS in hectors, we found the significant differences between each group considering companies individual risk. However, there does not exist significant difference in their individual return on equity except the comparison of farms with UAA size lower than 500 ha and over 2000 ha. We examined also the differences of individual risk and return between utilized agriculture area size ha, ha and over 2000 ha, but none of the comparisons proved statistically significant difference. For this reason it is not necessary to present it in Table Discriminant analysis One of the fundamental assumptions of discriminant analysis is the homogeneity of intragroup covariance matrixes within individual groups. The results of our Box s M test Sig. are 0.00, which means we cannot consider the covariance matrixes to be equal. For this reason the quadratic discriminant analysis should be used, however, it is more sensitive to the failure of meeting the assumption of multivariate normality. Because, the analysed data do not have the character of a normal distribution, and we assume that the linear discriminant analysis is resistant to not meeting the normality distribution condition. When the sufficient number of observations is used, and the differences between covariance matrixes are not so big, we decided to apply the linear discriminant analysis. An eigenvalue in Fig. 1 indicates the proportion of variance explained, between-groups sums of squares divided by within-groups sums of squares. A large eigenvalue is associated with a strong function. The canonical relation is a correlation between the discriminant scores and the levels of the dependent variable. A high correlation indicates a function that discriminates well. In our case the results are more than satisfying with the value of Canonical correlation which is extremely

5 high, very close to 1. Wilks Lambda is the ratio of within-groups sums of squares to the total sums of squares. This is the proportion of the total variance in the discriminant scores not explained by differences among groups that is in our analysis only 0,187. Figure 1: Eigenvalues and Wilks Lambda results Eigenvalues Function Eigenvalue % of Variance Cumulative % Canonical Correlation 1 4,345 a 100,0 100,0,902 a. First 1 canonical discriminant functions were used in the analysis. Wilks' Lambda Test of Function(s) Wilks' Lambda Chi-square Df Sig. 1, ,563 4,000 Source: Authors, Output of SPSS The 9 input variables were in the discriminant analysis independently evaluated using the stepwise selection, with the result that only 4 variables to have significant impact in prosperity classification: Legal form, Animal production (%), Employees per ha and Owners per ha. The rest of variables are not considered to have significant discriminant ability. The Standardized Canonical Discriminant Function Coefficients evaluates the impact of each factor, as well as its ability to discriminate farms into prosperous and unprosperous group. The Unstandardized Canonical Discriminant Function Coefficients indicate the unstandardized scores concerning the independent variables. It is the list of coefficients of the unstandardized discriminant equation. Each subject s discriminant score would be computed by entering variable values for each of the variables in the equation. The critical values for discriminant score from the final equation are stated by the results of Group Centroids, which give us the boundaries of scores for each farm, in order to decide about its classification into particular group. Table 4: Standardized and Unstandardized Canonical Discriminant Function Coefficients Standardized Canonical Discriminant Function Coefficients Unstandardized Canonical Discriminant Function Coefficients Function 1 Function 1 Legal form -0,693-2,455 Animal production % 0,578 2,777 Employees per ha 0,137 2,395 Owners per ha 0,465 6,400 (Constant) - 0,120 Source: Authors, Output of SPSS Y = 0,120 2,455X 1 + 2,395X 3 + 6,400X 4 + 2, 777X 6 (1) Y unprosperous Y (2.881,-1.495) average/indifferent Y prosperous 296

6 The equation (1) is constructed in the way that higher score than 2,881 reflects the unprosperous farm, the range between from 2,881 to -1,495 is the indifferent zone, and lower score than -1,495 classifies the farm as prosperous. The variable X 1 - Legal form is the only one entering the equation with mines sign, it means with the indirect impact. According to Standardised canonical coefficient is it the variable with the best discriminant ability. Previously, we assigned number 1 to Capital companies and 0 to Cooperatives. In this case the interpretation means to have legal form of Capital company decreases the score from equation and so decreases the possibility to be classified as unprosperous. Therefore, in the decision making of farmers in primary sector the legal form of Joint-Stock company, or Limited Liability company should be preferable. The variable X 3 Employees per ha reached the lowest direct impact from the point of classification. The higher the ratio, the higher the score from equation what leads to the classification of farm into unprosperous group. It can be related to the efficiency of businesses and theory of economy of scale, when the lower portion of employees to the size of land could represent more efficiently used human capital in the company. The variable X 4 Owners per ha achieved the highest unstandardized coefficient in the classification equation. The high portion of ratio Number of owners/land size (ha) leads to the identification of farm as unprosperous. This result is corresponding with the fact that the agricultural firms with more owners are usually cooperatives. The variable X 6 (Animal production revenues / Total revenues)*100 refers to the percentage of revenues from animal production to the total revenues of farm. The results of discriminant analysis show, the higher the share of animal production in the farm, the higher score from equation, it means higher possibility to be classify as unprosperous. The direct impact of variable in equation resulted from the fact that the companies oriented on animal production have more difficulties to become profitable than the crop oriented farm. Table 5: Classification results Original Count 0 1 % 0 1 Source: Authors, Output of SPSS Prosperity Predicted group membership Cross-validated The classification results table 5 is a simple summary of number and percent of subjects classified correctly and incorrectly. Based on the results is obvious that the constructed model has very high discriminant ability. However, it is important to realise that this result is overvalued, because the basic disadvantage of this method is that the model is tested on the same dataset from which was constructed (Kráľ et al., 2009). 4. Conclusion The identification of factors that would enable to provide necessary steps to improve the economic performance of the company, belong to the crucial point for each company management. Moreover, the importance of distress identification increases in such a low profitable sector as agriculture is. The number of studies emphasised the certain financial factors impact of profitability and riskiness of agriculture firms, however the sufficient evidence of non-financial factors is missing. The structure of farms in Slovakia is different 297

7 compared to EU average. The majority of UAA is cultivated by large farms with over 500 hectares. This results from the historical development of agriculture in former Czechoslovakia before In EU the UAA per farm is much lower. Therefore also measures implemented through CAP result different in Slovakia. The first part of the analysis is focused on proving the statistical significant differences in development of individual risk and return between legal forms, production orientation and size of land (LPIS). We found that the individual return and risk differ taking into consideration separately the Cooperatives and Capital companies, crop production oriented and animal production oriented companies, as well as other production oriented companies. The significant differences were observed also between individual risks of companies in tested categories of UAA size however we did not prove the same result when considering their returns. From the results can be assumed that determinants of company s riskiness might be the examined non-financial factors, however this topic will be the objective of our later analysis. One of the goals of CAP is the income stabilisation of farmers. In Slovakia based on the farm structure this includes also hired workforce (employees) not only farmer himself. The differences in income (ROE) and its stability do not cover the income stabilisation of the whole workforce linked to agriculture. We only observed the differences on the farm level. Factors influencing the return and risk are the legal form of the farm, production focus and the UAA in hectares. Further research should be focused on return and risk including the hired workforce. The second part of our analysis more detailed focused only on profitability of agriculture companies. With the use of linear discriminant analysis were from 9 input variables selected by the stepwise method 4 variables having significant impact on prosperity. We conclude that the profitability of Slovak farms can be anticipated by non financial variables. These variables have the highest discriminant ability for classification of farms into prosperous and unprosperous legal form, share of animal production (%), employees per ha and owners per ha. Simply explained, classification of farm into the group of not prosperous farms in Slovakia is the case if the farm has the legal form of cooperative, is oriented on animal production, has high number of employees per ha and owners per ha. These results are in line with the general opinion that cooperatives are ineffectively managed, because of their higher number of owners and incorrect use of excessive human capital. The results show that the increase of animal production share (%) leads to increased possibility for company to be identified as unprosperous is also corresponding the nowadays situation. It supports the recent development, when the animal producers rather change their business orientation into crop production, because they are not able to cover the cost by the revenues. The other reason might be the high of subsidies depending on the hectares of farms, which is generally higher in the case of crop producers. From this point of view is surprising that the variable utilized agriculture area size LPIS have not been considered to have significant impact and haven t been selected in the final equation. With respect to the legal form we can expect, that the number of cooperatives will decrease in future in favour of the more profitable cooperatives. This is the fact since 1989 and will continue. The animal production in Slovakia is decreasing because of the low profitability and therefore policy measures in the future should be more focused on supporting animal production. The market revenues from animal production do not cover the cost and therefore it is not profitable. The negative aspect in Slovak agriculture is the sharply decreasing number of workforce in agriculture. This is due to economy of scale and because of the farm structure with large farms in Slovakia. The less employees, the lower the cost and the higher the profit. But support rural development means also that public funds in form of subsidies should not be 298

8 concentrated in a small group farm owners only which is the case in Slovakia. Therefore further public support should also be linked to the ability of farms to generate rural development through higher employability. One of the possibilities to extend the study is the of more non-financial input variables or different techniques of classification and prediction models such as logistic regression, decision trees or neural networks. In our further analysis we are going to pay more attention to the detailed analysis of riskiness from the point of non-financial factors impact. References [1] Bieliková, T., Bányiová, T., & Piterková, A. (2014). Techniques of prediction of agriculture enterprises failure. Enterprise and the Competitive Environment 2014 conference, Paper in proceeding. [2] Chrastinová, Z. (1998). Metódy hodnotenia ekonomickej bonity a predikcie finančnej situácie poľnohospodárskych podnikov. Bratislava, VÚEPP, pp. 34. [3] Ciaian, P., Pokrivčák, J., & Drábik, D. (2009). Transaction costs, product specialisation and farm structure in Central and Eastern Europe. Post-Communist Economies, 21(2), doi:/ / [4] Cumby, J., & Conrod, J. (2001). Non-financial performance measures in the Canadian biotechnology industry. Journal of Intellectual Capital, 2(3), doi: / [5] Gurčík, Ľ. (2002). G-index- metóda predikcie finančného stavu poľnohospodárskych podnikov. Agricultural Economics, 48(8), [6] Hardaker, J., Huirne, R., Anderson, J. & Lien, G. (2004). Coping with risk in agriculture. CABI Publishing doi: / [7] Huirne, R., Meuwissen, M., Hardaker, B., & Anderson, J. (2004). Risk and risk management in agriculture: an overview and empirical results. International Journal of Risk Assessment and Management, ISSN [8] Kráľ, P. et al. (2009). Viacrozmerné štatistické metódy so zameraním na riešenie problémov ekonomickej praxe (pp ). Banská Bystrica: Univerzita Mateja Bela. ISBN: [9] Kopta, D. (2013). Impact of the structure of agricultural production to the financial health of farms. Acta universitatis agriculturae et silviculturae mendelianae brunensis, 61(7), doi: /actaun [10] Lančárič, D., Tóth M., & Savov, R. (2013). Which legal form of agricultural fi rm based on return on equity should be preferred? A panel data analysis of Slovak agricultural firms. Studies in Agricultural Economics, 115, doi: /j.1323 [11] Serenčéš, P., Tóth, M., Čierna, Z., Rábek, T., & Prevužňáková, J. (2014). Benchmarking pomerových ukazovateľov finančnej analýzy v slovenskom poľnohospodárstve. SPU v Nitre. ISBN: [12] Stankovičová, I., & Vojtková, M. (2007). Viacrozmerné štatistické metódy s aplikáciami (pp ). Bratislava: Iura Edition 299