3.1. Common National Trends in Business Cost Analysis
The most significant expenses of the enterprise are incurred by the power and heating supply and transaction services of monopolists [
23]. The smaller the enterprise, the greater the share of power cost, and the stronger the impact is on business sustainability. In a large enterprise, it is five times lower than in a micro-enterprise. Therefore, for small enterprises, power cost constitutes a survival threshold.
Small forms of business shape out the basis of the entrepreneurial ecosystem of the economy. Thus, it sets the foundation for the growth of new forms of business and it provides an environment for the formation and implementation of entrepreneurial initiative. The more favorable the conditions for the manifestation of the first forms of entrepreneurship, the greater the chances to develop businesses in the regions, creating new forms of products and services [
25]. Small forms of business need state support not so much in the form of direct investment, but in the creation of conditions that do not hinder the manifestation of private initiative [
26]. In the conditions of high energy consumption in Kazakhstan, the environment for the development of risky private entrepreneurship is impoverished, especially in industries that need stable conditions in the first years of existence.
The total cost of heating and electric power in the micro-enterprise (sole proprietorship) constitutes up to 20% of production costs, and in small enterprises, 16%. The GoK declares a policy of active support for small businesses, trying not only to increase GDP growth in non-resource-based industries, but also to create a middle class based on small business, as a democratic and financially sustainable fundament of society. However, having decided to create a small business to implement the innovative idea, the Kazakhstani entrepreneur will have to give a fifth of the expenses to pay for heating and electric power generated and supplied by the state and quasi-state monopolists. It is clear, therefore, that the energy policy of the state determines the real conditions for development of private entrepreneurial initiatives.
At the same time, power costs include not only direct costs for the consumed energy, but also from additional transaction fees, to be paid services to quasi-governmental monopoly organizations. These costs include not only services related to direct connection or disconnection of power and heating network to the main lines, but also agency services for obtaining permits and information on the free capacity of power and heating transmission (
Table 3). Please note that tariffs for these services differ not only by region in Kazakhstan, but also depending on whether the power transmission and heat conducting organization are natural monopoly holders (NMH).
If direct production costs for electric power and heat are in a determined dependence on the volume of output, the associated transaction costs have no economic justification. The presented tables show that the formation of costs for energy infrastructure services has a major impact on the amount of fees, which cannot be justified by objective reasons.
Energy costs thus constitute a bottleneck that generates associated costs, exceeding the direct costs and triggering the total cost increase elsewhere in the value chain.
3.2. Regional Patterns of Costs for Electric Power and Heat Supply
Territorial analysis by region shows that there is no direct correlation between the climatic conditions and the duration of the heating season. Also, the analysis demonstrates that there is no direct correlation between the tariff level and the total fee. The transaction costs of energy services differ by region by a factor of six or more (
Table 4).
The Pearson correlation coefficient was used to determine the relationship between the two rows of variables. The correlation coefficient is calculated with the formula:
where
- are sample averages, defined as follows:
The coefficient of correlation of average temperature of the winter period in regions with indicators of expenses is −0.11 for heat supply. The correlation of average annual temperature in regions is −0.15 for electric power supply, and −0.18 for heat supply, which is not statistically significant, but indicates a feedback: the higher the average annual temperature in the region, the lower the share of energy in the structure of company expenses.
Figure 3 shows that the lower the average annual temperature and winter temperature, the higher the share of electric power and heat costs in almost all regions across Kazakhstan is.
Figure 3 presents exceptions, e.g., Kyzylorda, Mangistau, and South Kazakhstan regions where, at rather high annual average temperatures, the share of costs for electric power and heat supply is quite large. This fact can be explained by two reasons:
The desert climate in summer requires intensive air conditioning and cooling in offices and in public facilities.
These regions have the highest administrative barriers, with widespread bribery on the part of NMHs.
Thus, if the cost of energy consumption is independent of climatic conditions, then it is dependent on other, less obvious and biased factors.
One of the hypotheses in the paper is that there is a correlation between the number of natural monopolies, the number of services they provide (
Table 5), and the share of power costs of enterprises.
The calculated correlation coefficient between the number of NMH subsidiaries and the share of costs for electric power supply is 0.5, which points to a stable relationship. The more subsidiaries there are in a region, the greater the share of costs spent on the services of these monopolies. This situation leads to the conclusion that free-market business contains the natural monopoly. This conclusion is also confirmed by the survey of entrepreneurs, which shows that there is a significant corruption component in power costs [
23].
Improving energy efficiency is directly related to reducing energy consumption. But in the case of a cold climate and a large territory, it is not possible to reduce the energy consumption of heating. At the same time, finding a balance between the optimal proportions of energy consumption and the development of the region’s economy is recommended. Basic social standards are met, first of all, by introducing low-energy production facilities, mastering new energy-efficient equipment, reducing losses and unproductive expenses of fuel and energy resources. At the same time, it is necessary to set goals for reducing the costs of enterprises for heating and electric power in the overall cost structure of the enterprise.
The authors examined the following comparisons to search for these optimal values regarding the energy intensity of the regions with these specific electric power consumption levels and the share of electric power costs in the total costs of enterprises (
Figure 4 and
Figure 5)
The energy intensity of the regions for the year 2018 is presented by Kazakhstan’s national statistics providers, which were calculated as the ratio of the gross consumption of fuel and energy resources for all production and non-production needs in tons of oil equivalent to the value of the gross regional product (GRP) (USD 1000 as per the year 2000).
The horizontal axis of
Figure 4 shows the regions of Kazakhstan in the order previously shown in
Table 5. The vertical axis shows the value of electric power consumption, attributed to an average of 1 enterprise in the region (specific energy consumption). As observed in
Figure 3, the largest specific energy consumption in region 12, i.e., Pavlodar, is 2.65 million kWh for one enterprise and significantly differs from other regions. The average value of specific energy consumption for Kazakhstan is equal to 0.42 million kWh. Also, Pavlodar has the highest energy intensity (as observed by the size of the sphere corresponding to this region). In addition to the Pavlodar region, no. 6 (West Kazakhstan region), no. 9 (Kyzylorda region), and no. 10 (Mangistau region) are characterized by high energy intensity, the value of which is 2–3 times higher than the average value in Kazakhstan.
The high energy consumption and energy intensity of the Pavlodar region can be explained by both climate (the temperature in winter can drop to −40 °C) and industrialization of the region (it is a location for a number of energy-intensive non-ferrous metal works).
As regions with low energy consumption, West Kazakhstan, Kyzylorda, and Mangistau are highly energy intensive. Considering that the main industrial profile of these regions is oil production (without oil refining), a high energy intensity indicator may indicate insufficient energy efficiency of the technology and equipment used, including losses due to the inefficient operation of electric power suppliers.
The upper third quarter (horizontal) of
Figure 4 presents regions with high energy intensity: West Kazakhstan, Kyzylorda, Mangystau and Pavlodar. As observed in
Figure 5, the size of the sphere corresponding to the Pavlodar region, which presents the share of electric power costs in the total costs of the region’s enterprises, is the largest among all of the regions and 2.2 times higher than the average value in Kazakhstan. These areas represent the group of the least energy efficient regions of Kazakhstan.
The central part (horizontal) of
Figure 5 presents regions with an average energy intensity, namely Aktobe and Karaganda. The share of electric power costs in Aktyubinsk is equal to the average value, and in the Karaganda region, it is 1.5 times higher than the average value in Kazakhstan. Both areas are industrialized.
The lower third (horizontal) of
Figure 5 presents the most efficient regions, in terms of both energy intensity and power costs.
Based on the diagrams considered, the authors propose that the low electric power efficiency of the regions is not so much due to energy-intensive products manufactured in the region, but by inefficient (outdated) technologies and equipment located in such regions.
To test this hypothesis, this analysis applies a mathematical clustering of regions according to three previously applied variables: electric power consumption, energy intensity, and the share of electric power cost in the total costs of enterprises.
Using the Ward method and the principle of Euclidean distances, the authors developed a dendrogram of the distribution of regions (
Figure 6).
In
Table 6, at a confidence level of 10, four clusters of regions can be visually identified. Furthermore, the authors used the k-means method for clustering 16 regions of Kazakhstan, and developed a graph of the distribution of average values shown in
Figure 7.
Table 6 presents details of the values of indicators per cluster.
Cluster 1 includes Atyrau, Karaganda, Kostanai, and South Kazakhstan regions and it is characterized by small values of specific electric power consumption and energy intensity. Its average power cost index is also slightly higher.
Cluster 2 is characterized by maximum energy efficiency. This is the biggest cluster in Kazakhstan, and it includes Aktobe, Almaty, Zhambyl, North Kazakhstan, and East Kazakhstan regions, as well as the cities of Astana and Almaty.
Cluster 3 represents the Pavlodar region and is characterized by minimal energy efficiency. The indicators under consideration are the highest, as observed above in
Figure 4 and
Figure 5.
Cluster 4 is characterized by a low specific energy consumption, but a high energy intensity (nearly maximum) and an average indicator of power costs. The cluster is represented by four regions: Aktobe, Kyzylorda, Mangistau, and West Kazakhstan.
Thus, almost half of the regions of Kazakhstan (Cluster 2) are represented by energy-efficient enterprises, which are characterized by a minimal share of power costs in their total costs, which confirms the presented hypothesis.
The energy efficiency policy of Cluster 3, represented by Pavlodar region, needs to be reviewed in terms of all aspects that comprise the process of rational use of electric power.
In addition, the regions that comprise Cluster 4 need to consider ways to reduce their energy intensity, which brings this cluster closer to the level of inefficiency.
3.3. The Price of Energy Barrier for Business
As mentioned above, apart from direct costs for electric power and heating, business also bears the related transaction costs.
Table 7 demonstrates that a quarter of the surveyed entrepreneurs face transaction costs for obtaining permits (including those from the NMH subsidiaries) and practically every tenth of them bears the costs of bribes for the representative of the NMH subsidiary. These high transaction costs of business indicate that the GoK lacks a consistent support policy for the energy sector and small business.
The above structure of business and industry costs allow for the determination of the amount of financial resources that can be released into the economy by reducing the non-productive administrative barriers (
Table 8).
Such proposals were made by the working group based on the results of the analysis of frontal costs in 201. It was assumed that, by 2020, the level of total costs hampering business development will gradually decrease. The total amount of potential cost savings for the business could be KZT 254,973.9 million or USD 814.6 million at the current exchange rate during the study period. These funds could be used both to support business operations and to invest in the development of new production facilities. Back in 2017, the GoK was concerned about the high level of business costs incurred by the domestic enterprises and the proposed changes could make Kazakh business more resistant to adverse external influences. However, by 2020, the proposed measures have not been implemented as planned.
Previous studies have shown that a significant cost share of accounts for energy use costs regulated by quasi-state structures [
24]. At that time, reducing even some of the administrative barriers could save business considerable funds and lead the country’s energy policy into a new model of efficient consumption.
The impact of increasing power costs and the associated service and trade costs is particularly critical. With the first deterioration of external conditions, the services sector in general and trade in particular are unable to cross the critical threshold of costs of rent, utilities, and contributions to monopolists for the use of resources.
In the structure of expenses of trading enterprises, total expenses for heating and the electric energy constitute 10.5% and is the largest cluster of operational costs related to location (see
Figure 8).
Trading in Kazakhstan has industry-specific and region-specific patterns that affect the cost structure the enterprise. In trade, typical factors depend on the size of the rented space and a high share of overhead costs, the country’s characteristics, i.e., large open areas, low population density, and the total small market capacity, as well as significantly varying climatic conditions. Those factors (rent, waybills), which are part of the overall management process, became a prerequisite for business survival in Kazakhstan. The increase of rent and overhead costs in global trade is usually regulated by changes in the pricing policy and the optimization of the entire production process, but in Kazakhstan, low market capacity does not allow for enough turnover and the compensation of costs. In addition, indirect fees for monopolists for services related to energy transmission, as well as bribes, account for a significant share of trade business costs. Practice shows that, as the economic situation deteriorates, the size of these costs doesn’t decrease and their share in total costs is even higher. According to the survey results, 40% of entrepreneurs in the trade sector had faced this problem.
Thus, the associated costs are clustered around the increasing electric power and heating consumption in trade, resulting in up to one-third of total costs and exceeding other cost groups. These costs can be called “knots” not only because they combine 6–10 types of costs related to their localization, but also because the increase of these costs creates risks for the entire organization. It is the increase in rent and utility charges that is a critical threshold, which trade enterprises cannot overcome in the case of unfavorable conditions, even in a short period of time.
By early 2020, the share of small and medium-sized enterprises (SMEs) in Kazakhstan’s GDP increased to 29.5% and the number of its employees reached 3.3 million. The structure of SMEs in Kazakhstan is still dominated by service companies, especially traders. The growth in the number of trading companies over the last seven years has had almost no impact on the stability of business, and the service sector remains volatile. Given the favorable market conditions in the past years, the number of new arrivals and shutdowns in trade has almost equaled.
Trade is attractive for SMEs, and is the leading sector in terms of new enterprises, with approx. 16,000 enterprises registered annually. However, trade enterprises are fragile, as they can go bankrupt just as quickly (see
Figure 9). Over 60% of small enterprises in liquidation are wholesalers and retailers (2700) [
23]. The largest number of SMEs that have suspended their activities is also in the trade sector, i.e., 29,800.
Figure 10 shows the factors that have put additional strain on the business and created bankruptcy risk. The inability to pay for rent and utilities is the main threat for half of the SMEs and almost all forms of off-line trade.
Based on the observation of the market situation in Kazakhstan, it can be concluded that service companies (see
Figure 11), especially in the trade sector, incur huge losses and may go into bankruptcy soon. Entrepreneurship in Kazakhstan has not yet become a stable part of the economy, and with the loss of income from domestic businesses, small entrepreneurs will join the ranks of the unemployed if the income from their businesses is lost.
Research shows that the enterprises of the service sphere, especially trade, bear huge losses and will go bankrupt in the near future.