1DETERMINATIONS OF PROFITABILITY IN THE AGRICULUTRAL
SECTOR IN SERBIA
Ivana Milosev 1 *Corresponding author E-mail: ivana79.milosev@gmail.com
A R T I C L E I N F O Original Article Received: 15 June 2023 Accepted: 20 August 2023 doi:10.59267/ekoPolj2304953M UDC 338.486.4:338.43(497.11) Keywords:
profitability, agricultural industry, Serbia, panel data analysis
JEL: C36, O13, Q14
A B S T R A C T
This research paper considers performance determinants at firm and industry level from the perspective of agricultural industry since this industry is recognized as one of key drivers of Serbian national economy. Sample is consisted of 115 large and medium-sized firms as they participate around 50% in total sector turnover, during years 2017 - 2021. The Generalized Method of Moments was used to analyze how firm factors (lagged profitability, leverage, labor cost, size, liquidity, sales growth) and industry factors (market share and capital intensity) affect profitability. Research results showed that profitability is strongly positively affected by its past values and stronlgy negatively affected by leverage, labours costs and industry factor capital intensity. The results suggest that internal factors are key determinants of the performance of agricultural firms rather than external factors and that leverage is the most significant determinant of profitability therefore managers should pay more attention to debt policy.
Introduction
The agricultural sector is a specific sector as it highly depends on natural factors from one side and state subsidies and agricultural policies from another. Agriculture is one of the most important sectors in Serbian national economy. Production of food for domestic market reduces demand for import and benefits to national economy by developing rural areas and employment (Dasic et al, 2022).
According to the national accounts published on Statistical Office of the Republic of Serbia [hereinafter SORS] in 2022 agricultural sector in Serbia contributes with 6.3% in total Gross Domestic Product (GDP), while agricultural sector in EU contributes with 1.3% (Eurostat, 2022) in total EU GDP. This sector plays essential role in Serbian economy as total output value was 394,576 million RSD in 2021 which places the agricultural on fourth place, behind manufacture sector, trade, and real estate sector. Agriculture employs 15.2% of total employees in 2021 in Serbia and is on third place
1 Ivana Milosev, PhD student, University of Novi Sad, Faculty of Economics, 9-11 Segedinski put, 24000 Subotica, Serbia; Phone: +381 62654603, E-mail: ivana79.milosev@gmail.com, ORCID ID (https://orcid.org/0009-0001-0098-6354/)
behind service and manufacture industries. Participation of agricultural sector in GDP and employment is decreasing (SORS, 2023) but still has a very important role in domestic economy. Agriculture dominates rural areas and secures minimum levels of food and social and economic stability (Volk et al, 2014).
Determination of factors which influence the performance of agricultural firms can be crucial for management and policy makers. There are two opposites theories on what factors are decisive for firm performance. The theory of the Industrial organization was developed in 1950s. It studies the economic process, how markets and companies are organized and what strategies are applied (Uzunidis, 2016). Structure - conduct -performance model (S-C-P) represents the school of point that industry characteristics like barriers to entry, product differentiation, industrial concentration are determinants of firm performance (Bain, 1956). This model accepts industry characteristics as decisive elements of firm performance.
Resource- advantage theory was developed during the 1990s as a reaction to industry structure theory. Firm strategic resources are not homogeneous and can create competitive advantages by using and developing superior resources (Barney, 1991). This theory focuses on firm specific, internal resources and factors. Barney (1991) suggested VRIN criteria (valuable, rare, imperfectly imitable, and not replaceable) for creating competitive advantage which can lead to high performance. The main company advantage is management's ability to combine individual technologies and skills and build competence to create opportunities (Prahalad & Hamel, 1990).
Most studies confirm the influence of both firm specific and industry factors as determinants of profitability; however, results are not consistent. The main goal of this research was to determine driving factors of profitability in Serbian large and midsized agriculture firms. The results were studied in relation to Theory of the Industrial organization (Bain, 1956) and Resource- advantage theory (Barney, 1991; Prahalad & Hamel, 1990) though five-year period (2017-2021).
Many papers and scholars are focused on investigating determinants of firm profitability as profitability is the ultimate measure of every firm's success. The focus ofthis research are internal and external determinants of financial performance of firms operating in agricultural industry since this industry is recognizes as one of key drivers of national economy (share in GDP, share in total employment, etc.).
Literature review
When analyzing factors which determine a firm's profitability, empirical studies recognize two different types of factors: internal and external (industry and macroeconomic level indicators). Some research papers are only focused on external determinants of firm's profitability like Callado & Soares (2014) or McGahan & Porter (1997, p29) showing that industry have impact on profitability and are more persistent over time and "...
Some papers are focused only on internal determinants. Mijic & Jaksic (2017) investigated internal factors that influence profitability of agricultural companies in SEE region. Lagged profitability, leverage, liquidity, and growth showed positive, and size and capitalization showed negative impact on profitability of examined companies in Hungary and Romania. Lagged profitability, growth and liquidity showed positive impact on profitability of examined companies in Serbia and Bosnia and Herzegovina. Nguyen & Nguyen (2020) showed that firm size, financial leverage, and liquidity have positive impact on ROA in study from Vietnamese listed enterprises. Kryszak et al. (2021) examined if farm size has an impact on farm profitability in EU region. In the study it is highlighted that high level of equity turnover is important and that leverage is a negative determinant in almost all group sizes. Dahmash et al. (2021) also analyzed only firm specific factors for listed companies in Jordan, showing that size and assets growth have positive and tangibility negative effect on its profitability, while leverage doe does not have significant effect. Martinho (2022) showed that current assets and current liabilities have positive and leverage negative impact on financial performance of farms from the former 28 EU countries.
Most papers focus on both types of factors. Analyzing listed companies in China from agricultural sector, Liu et al. (2021) showed that size, long term leverage and growth are positively while leverage, capital and export intensity are negatively related to profitability. Authors argued that internal factors have more significant impact on firm performance, same as Yazdanfar (2013). On the other hand, Korneta (2019) stated that although external determinants are not controllable should also be considered during the business process. Pervan at al. (2019) designed three categories to determine what influence on profitability in the manufacturing industry in Croatia. Results showed firm-specific determinants: lagged profitability and firms age have positive and labor cost negative impact, sector-specific: HHI index has negative impact and macroeconomic determinants inflation and growth of GDP have positive impact. Stierwald (2010, p2) showed that profitability is mostly determined by firm characteristics on example of large firm in Australia. And that industry effects are significant but to considerably lower extent. Blazková & Dvoulety (2018) studied food processing companies in Czech. The paper determined that both industry determinants and firm level determinants have significant influence on profitability. Market concentration (CR4) and market share (MS) have a positive impact implying that higher profitability can be achieved in a more concentrated sector. And age, leverage and short risk have negative impact as young firms can quickly react to any change in examined food processing sector (Blazková & Dvoulety, 2018, p40). Fernández et al. (2019) showed that performance of large and small Spanish firms is predominately explained by firm factors, but medium firm's performance is predominately explained with industry effects.
Materials and methods
For this research paper data were obtained from Serbian Business Registers Agency [hereinafter SBRA]. Selection of sample was based on following criteria: active
firms classified in Sector A (Agriculture, Forestry and Fishing) according to Serbian Regulation of Classification of Economic Activities (Official gazette of the Republic of Serbia, 2010) with submitted financial statements for observed period. Having in mind that there are almost 4,000 agricultural companies in Serbia, and large and medium-sized agricultural companies have market share of 52% in total sector (SBRA, 2021) large and medium companies are selected. Criteria for determine large and medium companies was based on Accounting Law in Serbia, article 6 (Official gazette of the Republic of Serbia, 2021). The final sample is consisted of 115 companies (N=115) which are observed during period 2017-2021 (T=5). The model has 575 observations, there are no missing data, so we are dealing with a strongly balanced panel.
Selection of variables
Based on data availability and literature review Liu et al. (2021), Pervan at al. (2019), Stierwald (2010), Blazková & Dvoulety (2018) Fernández et al. (2019) selected are two types of variables. Variables specific for each large and middle-sized agricultural firm in Serbia - internal and specific for the whole agricultural sector - external. The dependent variable is an indicator of performance calculated as return on total asset - ROA.
Table 1. Calculation of selected variables
Determinants of profitability -Variables Calculation References
Dependent variable Profitability: Return on Asset (ROA) Ration of firm's net profit and total asset Blazkova & Dvoulety (2017), Nguyen & Nguyen (2020), Pervan et al. (2019), Liu et al. (2021), Mijic & Jaksic (2017)
Firm specific variable Lagged Profitability: Return on Asset (ROAl) Lagged ROA Yazdanfar (2013), Pervan & Mlikota (2013), Stojcic & Vojvodic (2012), Mijic & Jaksic (2017), Pervan et al. (2019)
Debt Indicator (DR) Ratio of firm's total long- term and short-term debt and total assets Blazkova & Dvoulety (2017), Nguyen & Nguyen (2020), Liu et al. (2021), Mijic & Jaksic (2017), Dahmash et al. (2021)
Current ratio (Liq) Ratio of firm's current assets and total assets Andrasic et al (2018), Nguyen & Nguyen (2020), Pervan et al. (2019), Liu et al. (2021), Milosev (2021)
Labor cost (Labcost) Ratio of firm's labor costs and total sales Williams et al. (1989) Pervan et al. (2019), Korneta (2019)
Size (Size) Natural logarithm of firm's assets book value Yazdanfar (2013), Andrasic et al. (2018), Nguyen & Nguyen (2020), Pervan et al. (2019), Liu et al. (2021), Dahmash et al. (2021), Singh & Bagga (2019), Milosev (2021)
Firm specific variable Sales growth (Salesgr) (Current year firm's sales -previous year firm's sales) / previous year firm's sales Andrasic et al (2018), Mijic & Jaksic (2017) Liu et al. (2021), Singh & Bagga (2019)
Industry specific variable Market Share (CRn) Ratio of firm's sales and total industry sales Pervan & Mlikota (2013), Feeny & Rogers (2000), Blazková & Dvoulety (2017). Andrasic et al. (2018), Stojcic & Vojvodic (2012)
Capital intensity (Cap) Ratio of firm's fixed assets and total sales Liu et al. (2021), Mijic & Jaksic (2017), Singh & Bagga (2019)
Source: Author illustration based on Pervan et al., 2019
Lagged profitability, leverage, liquidity, size, cost of labor and potential of sales growth are used as firm specific variables. Bearing in mind that firm performance depends on its past values, a lagged profitability variable is used in this model as an independent variable. It is proved that profitability from previous year has positive impact on current profitability (Mijic & Jaksic, 2017; Yazdanfar, 2013).
Debt indicator is widely used variable in research papers which are determining profitability of companies. Findings are consistent in some papers, authors (Yazdanfar, 2013; Kryszak et al., 2021; Korneta, 2019, Liu et al., 2020; Andrasic et al.; 2018, Marinho, 2022; Milosev, 2021) find negative relationship. Some papers claim (Mijic & Jaksic, 2017) that performance of agriculture companies in Romania, Hungary and Bosnia and Herzegovina is positively affected by leverage or is statistically insignificant (agricultural companies in Serbia). Nguyen & Nguyen (2020) confirmed negative impact on debt indicator to ROE and ROS, but positively to ROA indicator.
Results in many studies find that relationship between firm size and profitability (Liu et al., 2020; Nguyen & Nguyen, 2019; Dahmash et al., 2021), and relationship between ratio of current to total assets and profitability (Andrasic et al., 2018; Nguyen & Nguyen, 2019; Mijic & Jaksic, 2017; Martinho, 2022) is positive, however some papers show negative relationship regarding size (Andrasic et al., 2018; Stojcic & Vojvodic, 2012, Milosev, 2021).
Williams et al. (1989, p281) showed that labor costs in British manufacture industry determine profitability when profit is relatively small. According to Pervan et al. (2018, p.977) labor cost has significant impact on determining profitability of manufacturing companies in Croatia. Higher costs decrease profitability, so cost strategy is very important in the traditional manufacturing industry. Korneta (2019) show significant and negative influence of salaries to profitability of Polish agricultural distributors.
Sales growth potential is used to represent the size of agricultural market in Serbia and if demand is growing. Andrasic et al. (2018), Mijic & Jaksic (2017), Liu et al. (2021), Blazková & Dvoulety (2017) show that performance is positively affected by growth.
Industry specific variables market share and capital intensity are included in the model to
analyze if industry specific effects contribute to firm profitability. Relationship between industry and specific effects are complex (McGahan & Porter,1997, p15) as they are not consistent in each industry sector. Market share (CRn) is one of the most relevant ratios to measure market concentration beside the Herfindahl-Hirschman Index - HHI. In this model is used CRn and not HHI as market share data for all agricultural company in Serbia were not available. Market share represents company share in relevant industry. Low value of CR represents high level of competition in particular industry (Naldi & Flamini, 2014, p5). Positive effect is confirmed in papers Pervan & Mlikota (2013), Hirsch et al. (2014), Blazkova & Dvoulety (2017), and Andrasic et al. (2018). On the example of large firms in Australia Feeny & Rogers (2000) showed U-shaped relationship of profitability and market share, highlighting that profitability is declining at the beginning and rising when market share is above 30%. In agricultural companies in China (Liu et al., (2021), agro cooperatives in U.S. (Singh & Bagga, 2019) agricultural companies in Hungary and Romania (Mijic & Jaksic, 2017) profitability is negatively affected by capital intensity. In agricultural companies in Bosnia and Herzegovina and Serbia (Mijic & Jaksic, 2017) impact is not significant.
Estimation method
Examination of profitability determinants in the agricultural sector in Serbia is research goal and next hypothesis is defined:
H1: Firm specific variables (lagged profitability, leverage. liquidity, labor costs, size, and sales growth potential) and industry specific variables (market share and capital intensity) determine profitability of large and medium agricultural companies in Serbia.
Many relationships in the economy have dynamic aspects. It is already mentioned in this paper that financial performance of agricultural companies in Serbia depends on its past values. Therefore, dynamic element as a lagged profitability variable is used in this model:
ROA i t = p0 + p1 ROAi t1 + p2 DRi t + P3Liqi t + P4Labcost t + P5Sizei t+
f^Salesgr,t + P7&p. t + P8M Su Pn dum tn + n + e. t P6 ' (1)
Where: ROA represents profitability as dependent variable, i stands for number of observed agricultural companies (i=1, 2, ...115), t represents observed period offive years, firm specific variables are leverage, liquidity, labor cost, growth potential and size, capital intensity and market concentration presents industry specific variables. Dependent variable from previous years, lagged profitability, is included in model as independent variable to control of bias and inconsistency. To consider specific year effect time dummies variable is also included in the model (y*). The regression coefficients of independent variables are P, e represents a random error.
Results and discussion
Descriptive statistics of Serbian agricultural sector for 5-year period (2017-2021) is presented in the Table 2, the model had 575 observations. The agricultural sector had low profitability as an average profitability was 3.03% and most theories believe reference value should be above 10% (Mijic & Jaksic, 2017, p162). The mean ROA was partly reduced by the effect of limiting the minimum ROA value at -0.259108 and maximum ROA value at 0.218391 after adjusting the outliers in the 99th and 1st percentile. The debt ratio had a mean of 45,36% implying that agricultural companies relied on debt as a way of financing business. The average liquidity of the observed companies was 2.29, and size was 14.39. Labor cost participated in average 10,94% in total sales. Market share in average was 0.4%, with minimum values of 0.01% and maximum 0.3% showing that agricultural market in Serbia was very competitive. Variables profitability and lagged profitability were adjusted for outliers using Winsorize method in STATA. In Appendix 1 are presented values before adjustment.
Table 2. Descriptive statistics of Serbian agricultural sector
Variable Obs Mean Std. Dev. Min Max
roa_w 575 .0303965 .058048 -.259108 .218391
roal_w 460 0.797795 .0898612 -.159218 .218391
dr 575 .4536188 .3101597 .011839 2.10453
liq 575 2.299656 3.711358 .077906 60.2663
size 575 14.39828 .9444567 12.3576 18.7377
labcost 575 .1094107 .1102613 .000214 .773606
cr 575 .0041159 .0042187 .0001034 .0302241
cap 575 1.243425 1.465071 0 9.74136
salesgr 575 .4750551 1.51065 -.98526 19.3812
Source: STATA, Authors' elaboration
To test if there was multicollinearity problem in the model the correlation among the variables was analyzed through Pearson correlation matrix (Table 3). Performance had low, positive, and significant correlation with its lagged variable, liquidity, and market share; low, negative, and significant correlation with leverage and capitalization and low, negative but not significant with company size and sales growth. Moderate, negative, and significant correlation was shown between performance and labor cost and moderate, positive, and significant correlation between capitalization and size, capitalization and labor cost and market share and size.
Table 3. Pearson correlation analysis of profitability and its determinants
roa_w roal_w dr liq size labcost cr cap salesgr
roa_w 1.0000
roal_w 0.2412* 0.0000 1.0000
dr -0.2455* 0.0000 0.0242 0.6040 1.0000
liq 0.1376* 0.0009 0.0414 0.3759 -0.3598* 0.0000 1.0000
size -0.0736 0.0777 -0.1042* 0.0255 -0.3287* 0.0000 0.0400 0.3380 1.0000
labcost -0.4469* 0.000 -0.1565* 0.0008 -0.1353* 0.0011 0.0884* 0.0340 0.2142* 0.0000 1.0000
cr 0.1179* 0.0046 0.0487 0.2969 -0.0118 0.7778 -0.0561 0.1791 0.5375* 0.0000 -0.1646* 0.0001 1.0000
cap -0.2992* 0.0000 -0.1411* 0.0024 -0.2627* 0.0000 0.0349 0.4041 0.4005* 0.0000 0.4954* 0.0000 -0.2092* 0.0000 1.0000
salesgr -0.0687 0.1001 -0.0984* 0.0349 0.1072* 0.0101 -0.087* 0.0362 0.0062 0.8818 0.0384 0.3577 -0.0545 0.1915 0.0327 0.4335 1.0000
* Statistical significance at 5% level. Source: STATA, Authors' elaboration
Variance Inflation Factor (VIF) was used as superior test. There was no multicollinearity problem (Table 4) in the model as results are below 10.
Table 4. VIF multicollinearity test for profitability and its determinants
Variable VIF 1 / VIF
size 2.71 0.368353
cr 2.25 0.443816
cap 2.09 0.477867
labcost 1.38 0.724535
dr 1.36 0.732730
liq 1.16 0859089
roal_w 1.05 0.951670
salesgr 1.01 0.966651
Mean VIF 1.63
Source: STATA, Authors' elaboration
Model was facing heteroskedasticity (BP (8, 451) = 4.81 Prob > F= 0.0000) and the endogeneity problems (DW (9,460) = 0.9348137). Generalized Method of Moments was used to work around autocorrelation within panels of groups, heteroskedasticity and endogeneity. Roodman (2009, p.102) introduced G.M.M. in Stata and suggested to use lagged dependent variable as independent i.e., to instrument variable to work around endogeneity problem. The model, internal and external determinants of profitability,
(Table J) showed that instruments are valid. The model had 5 instruments for one endogenous variable. The null hypothesis that instruments are valid cannot be rejected as the Hansen (1982) J statistic was not significant and model was correctly specified. The second order of autocorrelation was excluded as Arellano-Bond test AR (2) was not statistically significant. In our model a time dummy variable (Sarafidis, et al., 2009) was added to prevent cross section dependence and to consider the specific year effect.
Table 5. Internal and external determinants of profitability
Dedendent variable: Profitability - roa_w Model two step system GMM
Coefficient Corrected Std. Err. P> I t |
roal_w .4942642 .1362104 0.000***
dr -.657133 .01884623 0.001**
liq -.0000272 .0004381 0.951
labcost -.1239455 .0503485 0.015*
size -.002203 .00458 0.631
cap -.0065121 .0014385 0.000***
cr .3773895 .9423669 0.690
salesgr .0031164 .0036197 0.391
year .0250224 .0092353 0.008**
yr3 .0607405 .0143853 0.000***
yr4 .0399579 .0081282 0.000***
_cons -50.48934 18.65055 0.008***
No of observations 460 F(11, 114) 30.02
No of groups 115 Prob > F 0.000
No of instruments 17 AR (2) 0.051
Year Dummies Yes Hansen test chi2(5) = 7.04 0.218
Obs per group 4
Note * significance at the level of 5%, ** at the level of 1% and *** at the level of 0.1%. Source: Authors' calculation, Software used: STATA
The gained results demonstrate that regarding firm specific factors, the previous year's performance has a significant and positive effect on performance. Labor cost and leverage have significantly and negative effect on performance of large and medium agricultural firms in Serbia. Gained results demonstrate that firms achieve higher performance if their liquidity is higher, but the relation is not statistically significant. Positive but not significant result is obtained between size and performance.
The capital intensity indicator, as industry factor, is confirmed to be determinant of performance for agricultural firms in Serbia. This variable also has a significant negative effect but with lower coefficient than firm specific factors. Capital intensity may affect performance as an entry barrier (Pervan et al., 2019, p977). Market share has a negative but not significant impact on the performance of observed companies.
Conclusions
The results suggest that internal factors are key determinants of the performance of agricultural firms rather than external factors. The results show that profitability is strongly positively affected by its past values and strongly negatively affected by leverage, labor costs and industry factor capital intensity. The empirical results suggest that leverage is the most significant determinant of profitability and that managers should pay more attention to debt policy. When level of leverage and the level of labor costs to sales are increasing profitability (measured as Return on Asset - ROA) of agricultural firms in Serbia is decreasing. Liquidity, size, growth potential, and market share do not have impact on profitability according to the results of this study.
Selected sample can be the limitation of this study. Further research might take into consideration small and micro sized firms from the agricultural sector or other sectors which have more impact on GPD - for example manufacture industry or longer time periods.
Conflict of interests
The authors declare no conflict of interest.
References
1. Andrasic, J., Mijic, K., Mirovic. V., & Kalas. B., (2018). The modeling factors of agricultural companies performances. Custos e Agronegocio, 14(4), 154-173. ISSN 1808-2882 (Available at: http://www.custoseagronegocioonline .com.br/ numero4v14/OK%2010%20performance.pdf date of access 18.02.2023.)
2. Bain, J. (1956). Barriers to New Competition: Their Character and Consequences in Manufacturing Industries. Cambridge, MA and London, England: Harvard University Press. https://doi.org/10.4159/harvard.9780674188037
3. Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
4. Blazková I. & Dvoulety, O. (2017). Drivers of ROE and ROA in the Czech Food Processing Industry in the Context of Market Concentration. Agris on-line Papers in Economics and Informatics, 9(3), 3-14. doi: 10.7160/aol.2017.090301
5. Blazková I. & Dvoulety, O. (2018). Sectorial and firm-level determinations of profitability: A multilevel approach. International Jornal of Enterpreneurial Knowledge, 6(2), 32-44. doi: 10.2478/IJEK-2018-0012
6. Callado, A. L. C., & Soares, K. R. (2014). Analysis of the use of performance indicators in the context of agribusiness, Custos e Agronegocio, 10(2), 272-284
7. Dahmash, F., Salamat, W. A., Masadeh, W. M. & Alshurafat, H. (2021). The effect of a firm's internal factors on its profitability: Evidence from Jordan. Investment Management and Financial Innovations, 18(2), 130-143. doi:10.21511/ imfi.18(2).2021.11
8. Dasic, D., Stanic, T., & Zivkovic, D. (2022). Market of agricultiral and food prodicts in the Republic of Serbia: Possibilities and implications. Economics of Agriculture, 69(1), 57-74. https://doi.org/10.5937/ekoPolj2201057D.
9. Eurostat (2022). Performance of agricultural sector. Eurostat. (Available at: https:// ec.europa.eu/eurostat/statistics-explained/index.php?title= Performance_of_the_ agricultural_sector date of access 10.03.2023.)
10. Feeny, S. & Rogers, M (2000). The Role of Market Share and Concentration in Firm Profitability: Implications for Competition. Economic Analysis and Policy, 30 (2), 115-132, https://doi.org/10.1016/S0313-5926(00)50015-0.
11. Fernández, E., Iglesias-Antelo, S., López-López, V., Rodríguez-Rey, M., Fernandez-Jardon, C. M. (2019). Firm and industry Effects on Small, Medium-Sized and Large Firms' Performance. BRQ Business Research Quarterly, 22 (1), 25-35, https://doi.org/10.1016Zj.brq.2018.06.005.
12. Hansen, L. P. (1982). Large Sample Properties of Generalized Method of Moments Estimators. Econometrica, 50(4), 1029-1054. https://doi.org/10.2307/1912775
13. Hirsch, S., Schiefer, J., Gschwandtner, A. Hartmann, M. (2014). The Determinants of Firm Profitability Differences in EU Food Processing. Journal of Agricultural Economics. 65(3), 703-721. https://doi.org/10.1111/1477-9552.12061
14. Korneta, P. (2019). Determinants of sales profitability for Polish agricultural distributors, International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, 55(1), 40-51.
15. Kryszak L., Guth M., Czyzewski B. (2021): Determinants of farm profitability in the EU regions. Does farm size matter? Agricultural Economics, 67(3),90-100. doi:10.17221/415/2020-AGRICECON
16. Liu. L., Xu. J., Shang.Y. (2020). Determining factors of financial performance of agricultural listed companies in China. Custoseagronegocioonline. 16(4). ISSN 1808-2882 (Available at: https://www.researchgate.net/publication/349408981 _Determining_factors_of_financial_performance_of_agricultural_listed_ companies_in_China date of access 15.02.2023.)
17. Martinho, Vítor. J. P. D. (2022). Profitability and financial performance of European Union farms: An analysis at both regional and national levels, Open Agriculture, 7(1), 529-540. https://doi.org/10.1515/opag-2022-0108
18. McGahan, A. M., & M. E. Porter (1997). "How Much Does Industry Matter, Really?" Special Issue on Organizational and Competitive Influences on Strategy and Performance. Strategic Management Journal, 18 (1), 15-30.
19. Mijic, K. & Jaksic, D. (2017). The determinants of agricultural industry profitability: evidence from southeast Europe. Custos e Agronegocio, 13(1), 154-173. ISSN 1808-2882 (Available at: https://www.researchgate.net/publication/316957667_ The_determinants_of_agricultural_industry_profitability_Evidence_from_ southeast_Europe date of access 18.02.2023.)
20. Milosev, I. (2021). Impact of Debt Management on Profitability of Large Non-Financial Firms in Serbia. Economic Themes. 59(4). 461-477. DOI 10.2478/ ethemes-2021-0026
21. Naldi, M. & Flamini, M. (2014), The CR4 Index and the Interval Estimation of the Herfindahl-Hirschman Index: an empirical comparison. (hal-01008144) (Available at: https://hal.science/hal-01008144/file/NF-SSRN-2014.pdf date of access 18.02.2023.)
22. Nguyen, T.N.L. & Nguyen, V.C. (2020). The Determinants of Profitability in Listed Enterprises: A Study from Vietnamese Stock Exchange. Journal of Asian Finance, Economics and Business. 7 (1), 47-58. https://doi.org:10.13106/jafeb.2020.vol7.no1.47
23. Official gazette of the Republic of Serbia, Classification of Activities "Official Gazette of RS", no. 54/2010. (Available at: https://www.paragraf.rs/propisi/ uredba_o_klasifikaciji_delatnosti.html date of access 15.3.2023) date of access 18.02.2023.)
24. Official gazette of the Republic of Serbia, Law on Accounting "Official Gazette of RS", no. 73/2019 and 44/2021. (Available at: https://www.paragraf. rs/propisi/ zakon-o-racunovodstvu-2020.html date of access 18.02.2023.)
25. Pervan, M., & Mlikota, M. (2013). What determines the profitability of companies: case of Croatian food and beverage industry. Economic research-Ekonomska istrazivanja, 26(1), 277-286. D0I:10.1080/1331677X.2013.11517602
26. Pervan, M., Pervan, I. & Curak, M. (2019). Determinants of firm profitability in the Croatian manufacturing industry: evidence from dynamic panel analysis. Economic Research-Ekonomska Istrazivanja, 32:1, 968-981, DOI: 10.1080/1331677X.2019.1583587
27. Prahalad, C.K., & Hamel, G. (1990). The Core Competence of the Corporation. Harvard Business Review, 68, 275-292.
28. Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata, The Stata Journal, 9(1), 86-136.
29. Sajwan, R. & Choudhury, S.B.R. (2018). How to test time series multicollinearity in STATA? [online] Project Guru. (Available at: https://www.projectguru.in/time-series-multicollinearity-stata/ access 27.3.2023)
30. Sarafidis, V., Yamagata, T., & Robertson, D. (2009). A test of cross section dependence for a linear dynamic panel model with regressors. Journal of Econometrics, 148(2), 149 - 161, https://doi.org/10.1016/jjeconom.2008.10. 006.
31. Serbian Business Registers Agency, Financial Statements Annual Bulletins, Belgrade, (Available at https://www.apr.gov.rs/registri/finansijski-izvestaji/publikacije/ godisnji--bilten-finansijskih-izvestaja.2127.html date of access 25.3.2023).
32. Singh, N. P., & Bagga, M. (2019). The Effect of Capital Structure on Profitability: An Empirical Panel Data Study. Jindal Journal of Business Research, 8(4), 65-77 https://doi.org/10.1177/2278682118823312
33. Stancic, K. (ed.) (2023). Trends Q4 2022. Statistical Office of the Republic of Serbia, ISSN1820-1148. [in Serbian: Stancic, K. (urednik) (2023). Trendovi IV kvartal 2022, Republicki zavod za statistiku, ISSN1820-1148]. (Available at: https://publikacije.stat.gov.rs/G2023/pdf/G20238001.pdf access 27.3.2023)
34. Statistical Office of the Republic of Serbia (2022). National Accounts - Yearly national accounts. [in Serbian: Republicki zavod za statistiku (2022). Nacionalni racuni - Godisnji nacionalni racuni] (Available at: https://publikacije.stat.gov.rs/ G2022/HtmlL/G20221275.html date of access 27.3.2023)
35. Statistical Office of the Republic of Serbia (2022), Economic Accounts for Agriculture, 2021. [in Serbian: Republicki zavod za statistiku (2022). Ekonomski racuni poljoprivrede, 2021] (Available at: https://publikacije.stat.gov.rs /G2022/ Pdf/G 202210122.pdf date of access 18.01.2023.)
36. Stierwald, A. (2010). Determinants of Profitability: An Analysis of Large Australian Firms. Melbourne Institute Working Paper, 3(10), 2-35. http://dx.doi. org/10.2139/ssrn.1632749
37. Stojcic, N. & Vojvodic, K. (2012). Determinants of profitability of firms in the retail sector: The case of Croatia. MPRA Paper 109130, University Library of Munich, Germany.
38. Uzunidis, D. (2016). Propaedeutics in the theory of the industrial organisation: the SCP (structure, conduct, performance) model. Journal of Innovations Economics & Management 20 (2). DOI:10.3917/jie.020.0197
39. Volk, T., Rednak, M., & Eijavec, E. (2014). Cross Country Analysis of Agriculture and Agricultural Policy of Southeastern European Countries in Comparison with the European Union. In: Volk. T, Erjavec. E, & Mortensen. K. (ed.) Agricultural policy and European integration in Southeastern Europe. Food and organization of the United Nations. Budapest, 2014. 9-39.
40. Williams, K., Williams, J., & Haslam, C. (1989). Do Labour costs really matter? Work, Employment & Society, 3(3), 281-305. http://www.jstor.org/ stable/23746499
41. Yazdanfar, D. (2013). Profitability determinants among micro firms: Evidence from Swedish data. International Journal of Managerial Finance. 9(2), 151-160. https://doi.org/10.1108/17439131311307565
Appendix no 1. Minimum and maximum values before Winsorized adjustment of Agricultural
industry in Serbia
Variable Obs Mean Std. Dev. Min Max
roa 575 .0328676 .1077702 -.716254 1.99956
roal 460 0.0354547 .1123166 -.716254 1.99956
dr 575 .4536188 .3101597 .011839 2.10453
liq 575 2.299656 3.711358 .077906 60.2663
size 575 14.39828 .9444567 12.3576 18.7377
labcost 575 .1094107 .1102613 .000214 .773606
cr 575 .0041159 .0042187 .0001034 .0302241
cap 575 1.243425 1.465071 0 9.74136
salesgr 575 .4750551 1.51065 -.98526 19.3812
Source: STATA, Authors' elaboration
