IntroductionTop

The sector of the raising of swine belongs to the traditional and significant segment of agriculture in the Czech Republic. Pork is extremely popular among Czechs. According to Czech Statistical Office data, pork consumption of is about 43 kg per capita which represented approximately 53% of the total consumption of meat in the country in 2018. The growing trend in consumption, is evident wherein consumption was 42.3 kg per capita in 2017 and 43.2 kg in 2018. The Czech Republic is not self-sufficient in pork production in the long term and the self-sufficiency rate has lessened. It reached about 51% in 2019 and compared to 2010, the self-sufficiency rate decreased by 13 percentage points (CSO, 2020; Ministry of the Agriculture of the Czech Republic, 2020). The external trade with live pigs and pork is in deficit. The Czech Republic is net importer of pork and on the other hand, the external trade with live pigs reached a surplus. As a result, the nation is a net exporter of live pigs (CSO, 2020; Ministry of the Agriculture of the Czech Republic, 2020).

In terms of the overall efficiency of this sector, the industry suffers from the highest production costs within the monitored countries of the European Union (EU). In the Czech Republic, 1.64 €/kg hot carcass weight was spent in 2018, which is approximately 0.18 €/kg hot carcass weight more compared to the average costs of the monitored EU members. (Hoste, 2020).

The swine sector depends heavily on subsidies from common agricultural policy of the EU. This subsector is characterised by relatively low profitability compared to other types of farming (Boudný & Špička, 2012). Since the year 2012, the profitability of the Czech pig farms has grown due to favourable prices and better efficiency. Still there are significant differences among individual firms in this sector with a gap between top and bottom pig farms (Špička, 2014).

The evaluation of the firm performance and firm efficiency is part of the central themes of business economics, and efficiency is the central theme of theoretical economics. Generally, efficiency signifies the use of resources in the best way (Samuelson & Nordhaus, 1998). In business, production or economic efficiency is most often examined, where these terms require minimizing inputs at a given level of outputs or maximizing outputs at a given level of inputs (Farrell, 1957; Ali & Byerlee, 1991).

The issue of evaluation of firm efficiency is not important only for academics but especially for the companies and for managers at all levels. Assessing firm efficiency in the context of competitors offers assumptions for successful business management and supports creating and implementing a successful strategy. Economic efficiency also plays an important role in swine sector; it is an essential condition for sustainable business and the maintenance of this traditional agricultural sector.

This study analyses the link between firm efficiency and its profitability. Higher business efficiency is often associated with higher profitability. Extremely profitable companies are thus considered to be efficient. However, this simplification has its limits, and this link may not be unambiguous. Business economics describes the relationship between profitability and efficiency using the efficiency-profitability matrix. In this context, the firms are divided among four groups based on their profitability and efficiency. The first group consists of firms who enjoy a high level of efficiency and profitability (star). The second group covers firms with low profitability and efficiency (underdog). The third group includes firms with low efficiency and high profitability (lucky), and finally, the fourth group comprises firms with high efficiency and low profitability (unlucky) (Kumar, 2008). The market imperfections are the main argument used to describe the existing unclear relationship between profitability and efficiency where especially the level of competition and the type of product play an especially important role (Kumar, 2008; Keramidou et al., 2013).

The study aims to examine the link between economic efficiency and profitability of firms belonging to the swine sector in the Czech Republic. To our knowledge, this is the first study that tests the relationship between economic efficiency and profitability in the industry. It extends the knowledge in the field of the relationship between f irm efficiency and profitability and proposes new insights into this issue in the Czech Republic.

Material and methodsTop

Literature review

The efficiency and efficient use of scarce resources are central to theoretical economics. The efficiency and efficient use of scarce resources are also important from other perspectives. They have strong environmental and social overlaps where the efficient use of resources is very important with regard to sustainable development.

From the perspectives of business economics, production efficiency is usually examined. Farrell (1957) distinguishes two components of production efficiency: technical efficiency (TE) and price efficiency. Technical efficiency indicates minimizing inputs at a given level of outputs or maximizing outputs at a given level of inputs. Price efficiency implies the optimal combination of inputs and outputs according to their price. Production efficiency, which is often referred to as economic efficiency, suggests choosing the volume and structure of inputs and outputs that minimize cost or maximize revenue (Farrell, 1957; Ali & Byerlee, 1991).

There are many empirical studies that assess firm efficiency in agriculture and the field of pig farming (e.g. Lansink & Reinhard, 2004; Galanopoulos et al., 2006; Latruffe et al., 2013; Manevska-Tasevska et al., 2017; Calafat-Marzal et al., 2018; Yan et al., 2020). The stochastic frontier approach (SFA), developed by Aigner et al. (1977), and the data envelopment analysis (DEA), created by Charnes et al. (1978) belong among the most frequently used methods to measure firm efficiency in these studies.

A number of studies are devoted to the evaluation of the effectiveness of Czech agriculture. These studies examine various aspects of efficiency in selected agricultural sectors; however, only a small part of them concentrates on pig farming. Čechura (2010) examined the TE of Czech agriculture and concluded that the average level of TE is approximately 90% for agricultural companies. Žáková Kroupová (2016) identified similar TE of dairy farms, where the average TE was approximately 94%. She also examined the system of subsidies and identified the strong negative relationship between operational subsidies and TE change using the sample of dairy farms. Matulová & Čechura (2016) focused on the TE of individual sectors of Czech agriculture. The results suggested that TE varies in individual sectors (combined, plant, animal and other production), the lowest being animal production (average 83%), and the highest in other production (average 92%). They also examined the impact of subsidies on TE and identified a positive relationship between single area payments and TE. On the other hand, the effect of national additional payments was not statistically significant. Kostlivý & Fuksová (2019) addressed the TE of Czech livestock farms and compared conventional and organic farming. They confirmed that the localisation, economic size, type of farming and organic agriculture are the important determinants of TE of livestock types of farms. Rudinskaya et al. (2019) focused on the location of farming and examined the technical effectiveness of farms operating in less favoured areas. They identified the signif icant impact of location on TE. They concluded that the farms located in areas outside the less favourable areas achieve better TE. The results also showed the higher TE of animal farms compared with plant production and bigger farms compared with small ones.

Friebel & Friebelová (2006) used the DEA model to examine the efficiency of 20 Czech pig farms. The effectiveness of firms was assessed separately in three areas: piglet production, start period of fattening and fattening. The average TE was approximately 90% in each of the examined categories. Malý et al. (2011) examined the effectiveness of Czech pig farming with a sample of 32 f irms and focused on pig fattening. They estimated the production function and examined the determinants of effectiveness of pig farms. They concluded that there were significant differences among the surveyed farms. The feed compound and new stock weight is among the most important factor of final production. Pechrová & Medonos (2016) reviewed the development of productivity of Czech pig farms in the period 2006-2013. Using a sample of 41 firms while applying the Malmquist productivity index, they found that the total factor productivity declined in the examined period and this explains the low competitiveness of Czech pig farms on the EU market.

The part of the empirical literature is focused on examining the link between efficiency and profitability. This relationship was examined for different industries and the conclusions of individual studies varied. Some studies identified the positive impact of efficiency on profitability (e.g. Greene & Segal, 2004; Mostafa, 2010; Guillén et al.; 2014; Aissa & Goaied, 2016). In contrast, others found only a small or no relationship between efficiency and profitability (Sellers-Rubio & Nicolau-Gonzálbez, 2009; Olson & Zoubi, 2011; Shieh, 2012; Keramidou et al., 2013; Palečková, 2015; Hedija et al., 2017). According to these studies, the size of the companies and economies of scale and scope, group ownership and vertical integration suggest that the firms producing their products with the best practices are not capable to generate the maximum profits.

The differences in results might be closely related to the industry under examination, its specifics, and the specifics of the country in which the relationship is examined. Despite the existence of many studies that focus on the link between efficiency and profitability, little attention has been paid to this relationship in agriculture.

 

Methods

To assess the economic efficiency of firms, we used data from the Albertina CZ Gold Edition database that is provided by Bisnode company (Bisnode, 2019). It contains information on all profit and non-profit entities in the Czech Republic that have been assigned a personal identification number (IČ). At present, this database contains data for more than 2.7 million subjects. We chose the data for swine sector (group 01.460 Raising of swine) for the period 2008-2017 using the statistical classification of economic activities in the European Community Rev.2 (NACE Rev.2).

First, we narrowed the sample to analyse only active firms. We selected only firms with sales higher than 100,000 CZK (~ €3,700) per year in all examined years and those which provided all the information necessary to assess economic efficiency and profitability. The final sample comprises data of 68 pig farms. The number of f irms in individual years is shown in Table 1. According to the Czech Statistical Office data, the sample represents, on average, approximately 18% of pig farms in individual years.

Pig breeding is concentrated in large farms, where farms with more than 2,000 pigs account for almost 90% of the total number of pigs (Ministry of the Agriculture of the Czech Republic, 2020). Our sample consists mainly of large farms, where the average sales reached the value of 225 million CZK (~ € 9 million) and thus ref lects appropriately the situation on the Czech market of pig production.

We used DEA models to evaluate the economic efficiency of the firms. The advantage of DEA is that it does not require a specification of the production function and is suitable also for small sample of firms in comparison to SFA, which can be also used to assess the economic efficiency. The specification of production function is critical in productivity and efficiency analysis (Tian et al., 2015). SFA has the advantage of using various types of functions but it is data intensive requiring a lot of DMUs (decision making units). On the other hand, DEA is suitable for analyses using a relatively small sample of firms with a homogenous product, which is the case of our study (Kumar, 2008; Porcelli, 2009). Following the studies of Asmild & Hougaard (2006), we applied the output-oriented DEA models to assess economic effi-ciency. Output oriented models are frequently used in the studies to examine the economic efficiency of firms. The reason to select this type of model is that the behaviour of firms is output-oriented. Firms aim to maximize their outputs, although inputs are also under control (Barros & Alves, 2003). The same is true when analyzing swine sec-tor since the farmers aim to maximize the production of pigs where inputs can be seen as fixed in terms of short-run (Asmild & Hougaard, 2006).

Two variants of output-oriented models are used for comparison: a model with constant returns to sca-le (CCR-O) and a model with variable returns to scale (BCC-O). However, a BCC-O model is more suitable for swine sector where the non linear relationship between inputs and output is expected. Economies of scale may occur with growing breeding in this industry. Thus, the as-sumption about linearity of outputs (revenues) and inputs (costs) is not appropriate.

The basic principle of DEA models is to estimate an efficient frontier that is not pre-set, but it is based on the “best ratio” of the compared DMUs outputs and inputs. A DMU which is on the frontier is referred to as an efficient unit. This relative full efficiency means that performances of other DMUs do not demonstrate that some inputs or outputs are improved without worsening other inputs or outputs (Cooper et al., 2011). One of the results of the DEA models is the efficiency score for each DMU – when the score is equal to 1, the DMU is efficient, otherwise inefficient. Virtual inputs and outputs for inefficient units are also provided by the model. They show the necessary change of input or output for the DMU to be efficient. As it was mentioned previously, DEA models are either input or output-oriented. The output-oriented model assumes a fixed level of inputs, the input-oriented model assumes a fixed level of outputs. The basic DEA model called CCR by authors Charnes, Cooper and Rhodes (Charnes et al., 1978) is used for constant returns to scale. In case of va-riable returns to scale, BCC model (Banker, Charnes and Cooper) is used (Banker et al., 1984). An overview and detailed information on DEA models is found in Cooper et al. (2006). The main idea of the efficiency calculation is based on the linear optimization model with the objec-tive function maximizing the rate of the weighted sum of outputs divided by the weighted sum of inputs. The CCR output-oriented model transformed (Charnes-Cooper transformation) into linear programming form is defined as follows (CCR-O) (Cooper et al., 2006):

 

where q is the evaluated DMU, y_ik are known r outputs, x_jk are known m inputs of the k-th DMU out of n DMUs, ui and vj represents the variable weights to be determined by the solution of this problem and ɛ is the infinitesimal constant, which is usually set as 10^-8.

The BCC output-oriented model is slightly different, as it uses additional parameter v relating to the convex effi-ciency frontier. The model is defined as follows (BCC-O) (Cooper et al., 2006):

 

The unit U_q is efficient when the optimal value of the objective function (calculated by the model) z is equal to 1. All inefficient units have z higher or lower than 1 depending on the DEA type of the model used. The effi-ciency score describes the relative distance from the efficient frontier (Cooper et al., 2006). The number of DMUs should be high enough because if many inputs and outputs are included in a model, all or nearly all units are considered to be efficient. When both BCC and CCR mo-dels are applied to the same data, the BCC model usually finds more efficient DMUs than CCR model. It is because of the type of the efficiency frontier - convex in BCC and conical hull in CCR (Cooper et al., 2006).

Models (1) and (2) calculate the efficiency score only for 1 DMU. That is why the calculation must be run n times when n is the number of DMUs to find all results. In that case, usage of the specialized software for the solution of DEA models is suitable. In our case, STATA software was used for the DEA models solution.

To estimate the economic efficiency of firms, we used the DEA model with one output and three inputs. This number of inputs and outputs assures the distinction be-tween efficient and inefficient units achieved in DEA re-sults, as the number of DMUs for all years is more than 3 times higher than the number of criteria (inputs and ou-tputs) selected. It is in line with the recommendations of Cooper et al. (2006).

As the output, we used sales and subsidies that repre-sents the value of final production. Sales are calculated as the sum of revenues from sold own products and services and merchandise since they reflect the revenues from pro-duction without subsidies. However, subsidies represent a significant income of pig farmers and it is appropriate to take it into account when assessing the effectiveness of pig farms. Hence we used indicator “sales and subsidies” which also covers subsidies and it is calculated as the sum of sales and other operating revenues, which represent just subsidies.

As the inputs, we employed (i) personnel costs, (ii) tangible and intangible assets, and (iii) consumption for products which consists of costs of goods sold, material, energy and services consumption. The data about these categories are available from financial statements of fir-ms and represent suitably the main costs of pigmeat pro-duction which include in particular feed cost, labour cost, building and capital cost and other variable costs (artifi-cial insemination and sow replacement costs, health costs, energy, maintenance of buildings, levies, manure disposal costs and miscellaneous variable costs) (Hoste, 2020).

All inputs and outputs of the DEA models reached po-sitive values. A DEA model was applied for each of the selected years (2008-2017) separately to observe changes in the sector. The output and inputs of the DEA models and descriptive statistics are shown in Table 1.

The aim of the study is to examine the link or corre-lation between efficiency (obtained by the DEA model) and profitability. We used three frequently employed indicators of profitability: return on equity (ROE), return on assets (ROA) and return on sales (ROS) (Hult et al., 2008). ROE is calculated by dividing the earnings after tax (EAT) by equity; ROA is calculated as a ratio of ear-nings before interest and taxes (EBIT) and total assets; and ROS is calculated by dividing earnings before interest and taxes (EBIT) by sales. The use of ROE as a profi-tability indicator is associated with the problem of false positivity if both equity and profit reach negative values. Another problematic situation can occur when a com-pany makes a profit, but due to its negative equity, ROE is negative. In order to eliminate these problems, we used the absolute value of equity computing the ROE. The means of profitability ratios in individual years are shown in Table 1.

To assess whether there is a direct link between profi-tability and efficiency, the Pearson correlation coefficient was used (Rose et al., 2015). We used STATA software for the calculations.

 

Table 1. Mean of inputs, outputs of DEA models (in thousands of CZK) and profitability ratios

DEA: data envelopment analysis. Standard deviations in brackets. Source: Albertina CZ Gold Edition; authors’ computations