Investigating a comprehensive model for evaluating occupational and environmental risks of dimensional stone mining

One of the most hazardous fields of engineering is mining operations and accordingly, it requires extra consideration on risk evaluation. Decorative stone quarrying is one of the main branches of the mining industry in Iran. Currently, Iran, China and Italy are known as the three main producers of decorative stones in the world. Subsequently, risk assessment is one of the main steps of quarrying organization. One of the main steps of risk assessment is Risk Breakdown Structure (RBS). In order to achieve this goal, a comprehensive structure of dimensional stone quarrying is modelled in this research and divided into 17 main levels. One of the most useful procedures of risk ranking is Multi attribute decisionmaking methods which state that they have numerous affecting parameters. In the next step of this study, the main hazards of main decorative stone quarrying are assessed by implementing the ‘Preference Ranking Organization Method for Enrichment Evaluation’ (PROMETHEE) technique. As a final point, Economical, Management, and Schedule risks are determined as the most intimidating hazards in this field.

Introduction
Risk is an uncertainty, occurrence or condition and its existence has different effects on the aims of the operation, e.g. including threats and also opportunities for the project goals (Pinto, 1998). Currently, research in mining operations are dramatically influenced by dangers and any decision needs risk aspects. Risk is defined, according to the Project Risk Management standard PM-BOK or Project Management Body of Knowledge as the uncertainty that may or may not have a negative impact which could cause injury or loss (Guide, 2001). Categorizing, processing and proper responding to all hazards of operation for improving impacts of wanted phenomena and decreasing unwanted events, developed a comprehensive risk management method. As mentioned in PMBOK, risk controlling projects contain six main levels: Programing, Risk management, Determining risks, Qualitative and Quantitative risk processing, Preparing proper respects to risk and Monitoring (Yari et al., 2016).
Since quarrying operations are one of the most dangerous operations (in the designing and application phases), they require extra attention for their hazardous aspects. Determining all full parameters of risk, caution regarding the impacts of hazards and ranking are basic activities in risk management. Having a comprehensive risk management system leads to making reliable decisions in a limited time. It also helps in making the proper response to applied risks and in determining the negative aspects of hazards ( All risks of open-cast mining were determined by Steffen in 1997. In this investigation, risks of open-cast operations contain two fundamental branches: technical risks and management risks (Steffen, 1997). The determination and designation of mining equipments' risk factors was carried out by Joy in 2004. For achieving this goal, the possibility of risk was examined and the impact of risks on the production rate was discussed. As a result, all influencing risks are ranked by implementing the probability-consequence matrix (Joy, 2004). In 2004, a novel model was developed by Arends et al., for prognosticating the costs of risk evaluating in underground spaces. In this research, qualitative methods are used for risk assessment (Arends et al., 2004). Duzgun and Einstein (2004) presented a model for evaluating risks in the underground coal mines of Turkey (Duzgun and Einstein, 2004).
In 2005 "@Risk" software was applied by Heuberger for assessing the risks of gold mines using the DCF method (Heuberger, 2005). In order to complete fire risk assessment in underground excavations, Soons et al. presented a novel qualitative model in 2006(Soons et al., 2006. Reilly et al. in 2007 considered the time of occurrence and the applied cost of risks. For risk assessment in underground projects (tunnels and excavations) in this research, a regression model and probability-consequence matrix are used for processing all risks (Reilly and Brown, 2004; Reilly and Parker, 2007). Evans and Brereton in 2007 indicated that both positive and negative impacts of risk should be regarded as a system. They indicated that sustainable development models should be used for the risk assessment process (Evans et al., 2007). In other research, all influencing factors of flooding in coal mines are determined in eight dominant categories in four levels of danger (Wang et al., 2008) . The Monte Carlo model is used by Fuentes for risk assessment in Chilean copper mines (Fuentes et al., 2009). All technical and economic uncertainties are explored by Dehghani and Ataee-pour for resource exploration projects. In this research, all appropriate responses to monitored risks are presented clearly (Dehghani and Ataee-pour, 2012). The previously mentioned studies proved that there is no comprehensive model for risk assessment in decorative stone quarries. In addition, the majority of previous research used the probability-consequence matrix for evaluating risks. It is proven in this investigation that these classic methods are not sufficiently reliable (Chapman and Ward, 2003). One of the main disadvantages of this process is that low possibility risks with high consequence and high probability risks with low consequence are considered equal (Pipattanapiwong, 2004).
Multi Attribute Design Making (abbr. MADM) models helps researchers to use experts' recommendations for decision-making Safety factors for decorative stone quarries were examined for the first time through an analysis of different incidents and injuries which occurred. For this purpose, standard guides were implemented for safety, quality measurement and risk analysis . A classification of quarries considering safety and environmental attributes help mining companies in all aspects of health, safety and environmental management. This process helps them to convert their qualitative assessment to quantitative evaluation and reliable judgment (Yari et al., 2016).
In the first step of this study, all hazards with an impact on the production rate of decorative stones quarries were analysed by using 21 experts' opinion as a Risk Breakdown Structure (RBS) in dimensional stone quarries in 17 main levels and 128 sublevels. In the next step, the weights of assessment factors were evaluated using Fuzzy-Analytical Hierarchy Processes (Fuzzy-AHP) based on experts' opinions. Finally, using the PRO-METHEE method, all the defined hazards concerning the imposing aspects were evaluated and the most threatening risks were determined for the Ghasre dasht mine (Yari et al., 2014).

Risk breakdown structure
Risk Breakdown Structure (RBS) is implemented as a reliable method for hazard administration in prominent guides such as PMBOK. The application of RBS is comparable to Work Breakdown Structure (WBS). RBS is a pyramidal model of possible hazards which could be useful for administration to recognize all the risks of an operation. More detailed risk factors are present in the sub levels of the RBS for an exact definition of them (Iranmanesh et al., 2007). Full-detailed RBS is valuable for documentation of the hazards of engineering operations, but it is not essentially containing all the risks of the operation. Consequently, a proper RBS should be organized for each project considering its special features. Quarrying operations inherently include two main branches of risks: internal and external risks. Internal conditions of quarrying operations applies internal risks to a project and external restrictions such as economic policies which lead to external risks (Hillson, 2003). The main hazards of the mining operation in Mongolia have been classified by Chinbat and Takakuwa as: Owners' economic uncertainty, weak management, technical risks, government bureaucracies, incorrect assessment of the resource, workers' carelessness, transportation in need of improvement, a lack of skilled workers, delay of equipment, government inspectors' restrictions, variations in regulations, fuel deficiency, accidents during the production process, unpredicted environmental events, inadequate investment, organization/human confrontation (Chinbat and Takakuwa, 2009). The previous studies show that there are no comprehensive categorizations of risk parameters in quarrying operations and only some of the hazard factors are regarded by previous investigations. Numerous and different types of risks that occur in quarrying operations makes this process as one of most hazardous fields of engineering. RBS is a reliable structure that includes all risks of a particular project and is applicable for all similar projects (Mac-

Dermott et al. 1996).
This investigation presented a complete RBS for dimensional stone quarries which contains two dominant branches of hazards: internal risks (including: 11 main categories and 79 sublevels (see Figure 1)) and external risks (including: 6 main groups and 49 sub-groups (see Figure 2)).

Evaluating attributes for risk assessment
As mentioned in the introduction, all conversional methods which are implemented for risk evaluation (such as the probability-consequence matrix) are complex models and are not able to present a comprehensive risk management model (Yari et al., 2015). For providing a comprehensive model, all impactful factors and criteria on risk assessment are determined (as shown in Table 1).

Multi attribute decision-making methods
Multi Attribute Decision Making (MADM) is a systematic model for assessing, sorting and determining the most proper alternative among several conditions. In all MADM problems, there are two main indexes: attributes and alternatives. A decision matrix is a structure which determines the value of each alternative in the perspective of attributes (Yari et al., 2016; Yoon and Hwang, 1995). All columns of the decision matrix have a scale

Fuzzy-AHP method and steps
In a decision-making problem, there are various attributes (criteria) with different important values. These values are specified by a number between 0 and Y and indicate the preference of each attribute in comparison with others. These indexes are called weights in decision-making problems. In this study, the Fuzzy-AHP method is implemented for calculating the relative preference of attributes under the condition of uncertainty (Ooriad et al., 2018). For applying this method, the relative preference of each attribute in comparison with other attributes is determined by using a couple comparison matrix. After carrying out the systematic stages, the weights of the attributes are determined.
The decision-making is a problem which has a number of attributes with various degrees of importance. Consequently, each attribute is assumed a weight and the preference of each index over the other attributes is determined using these weights. There are different methods for measuring the weights of the attributes. In this study, the Fuzzy-AHP method was used due to its comprehensive application (Saaty and Vargas, 2000).
Fuzzy-AHP follows all steps of the AHP method in a Fuzzy environment. In other words, this model is presented for solving decision-making problems in conditions where there is no capability of using crisp numbers. In this condition, the fuzzy environment converts all linguistic factors to fuzzy numbers. The Fuzzy-AHP model consists of the following steps (Özdağoğlu and Özdağoğlu, 2007):

Break down the complex problem
Break down the complex problem into a graded structure form.

Forming a couple comparison matrix
Forming a pair comparison matrix with n rows and m columns (shown in Equation 1).

Calculating fuzzy synthetic amount
(2) Calculating the fuzzy synthetic amount value s i four rows of pairwise comparison matrix as shown in Equation 2. Where: -triangular fuzzy numbers, s i -fuzzy synthetic amount value, i -each of the attributes, j -each of the criteria, m -number of criteria, n -number of attributes.
To get perform "fuzzy addition operation" of m extent analysis values for a particular matrix given in Equation 3. ( Where: l -the lower limit value, m -the most promising value, u -the upper limit value. To obtain perform the "fuzzy addition operation" of values are given as   W -normalized weight vector, -n attributes. To evaluate the risks, experts only choose the related linguistic variable, and for calculations, they are converted into the following scale, with triangular fuzzy numbers advanced and are quantified as shown in Table  2 (Chang, 1996). The degree possibility for a curved fuzzy number to be greater than k convex fuzzy numbers can be defined in Equation 7. and , Where: M i -convex fuzzy numbers.

Calculating the weights of attributes
Calculating the weights of attributes in pair wise comparisons matrix (see Equations 8 and 9).

PROMETHEE method
The PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) is one of the most reliable methods of MADM for the ranking of alternatives in decision-making problems. This model was presented by Brans et al. in 1984 and1986  The advantages of this method are mentioned as follows: 1. The simplicity of this method for decision-making. 2. This model is applicable for real problems. 3. In partial and total decision problems PROMETH-EE makes adequate, simple and reliable decisions. Two substantial types of data are necessary for decision-making using PROMETHEE (Macharis et al., 2004): 4. The weight of criteria which indicates the relative preference of them. 5. The standard guides which experts use for determining the relative preference of alternatives for each criterion. Step 1: defining a preference function pj(d) for criterion j.
Step 2: determining the weights vector for the criteria.
Step 3: equation π should be determined for all criteria (see Equation 11).
(11) π (a t , a t' ) as a preference indicator presents the preference degree of experts in comparison of a t and alternatives a t' .
Step 4: Ø + (a t ) leaving flow should be calculated as an index for determining power of alternatives (see Equation 12). Where: Ø + (a t ) -leaving flow. (13) Step 5: Ø -(a t ) (entering flow) should be determined as an index for weakness of alternatives (see Equation 13). Where: Ø -(a t ) -entering flow.
Step 6: a graphical assessment of the sorting equation is derived. Basically, the higher the leaving flow and the lower of the entering flow, the more proper the action. These outcomes are indicated graphically using a rela-tive preorder (PROMETHEE I) or an entire preorder (PROMETHEE II).

Case study
The case study which was used for verifying the presented model is a high production rate marble quarry in the north-east of the Fars Province, Iran (see Figure 4). This area contains a satisfying marble resource in the North West-South East trend. This region also includes Bangestan marls.

Results and discussion
For accumulating experts' assessments, two types of questionnaires were composed: • The first questionnaire: defining the weights of criteria by implementing the Fuzzy-AHP method. • The second questionnaire: for creating an initial decision matrix for assessing and sorting the determined risk by using PROMETHEE. All questionnaires were designed and filled in accordance with the standards of the AHP and PROMETHEE methods.
The initial decision matrix contains 11 columns and 18 rows which includes criteria and alternatives respectively. The opinions of 21 decorative stone experts were gathered for filling the initial decision matrix accurately. By using Fuzzy-AHP, the weights (relative importance)
In the next stage, by forming an initial decision matrix and determining the weights of criteria, graphic PRO-METHEE software was applied for the sorting and evaluating of risks. All stages of PROMETHEE were consequently followed and the results are presented in Table  3. Later assessing the weight of attributes and applying all the mentioned steps of the PROMETHEE method, the ranking process of risks is shown by using the Graphic PROMETHEE Software. Figure 6 and Table 3 show the results of PROMETHEE for sorting threatening risks of the Ghasre dasht marble quarry.
The mining process is one of the unrepeatable processes that affect different factors and conditions of op-erations. Therefore, the risks in different mines are different. The results obtained in this study are only related to dimensional stone quarries.

Conclusion
Quarrying and extracting operations are the riskiest and most hazardous operations in mining. The assessment, controlling and management of risks in quarrying regions are significant issues which could lead to sustainable production. Categorizing, processing and proper responding to all hazards of the operation for improving the impacts of wanted phenomena and decreasing unwanted events, lead to the development of a comprehensive risk management method. The Risk Breakdown Structure process is an applicable and helpful means of risk evaluation. In this research, decorative stone quarrying risks are explored for the first time and divided into 17 main branches and 127 subcategories. These risks are political risks, social risks, environmental risks, economical risks, act of God risks, legal risks as external sources and financial risks, management risks, technical risks, material and equipment risks, contracting risks, loss or damage, personnel risks, schedule risks, employer involvement risks and planning risks as internal risks. After determining the main and sublevel risks, these hazards are ranked by using PROMETHEE by regarding all the influencing criteria of risk assessment. The opinions of 21 decorative stone experts were gathered for filling the initial decision matrix accurately. By using Fuzzy-AHP, the weights of criteria are determined. The results indicate that the employer risk is in the first rank


Banking Sector Intermediation Development and Growth of a Developing Economy: An Empirical Investigation of Nigeria

In developing economies, banks act as a conduit for the ecient mobilization of nancial resources from the surplus sectors for effective allocation to the decit sectors for productive investment that will in turn lead to economic growth. Thus, the study is aimed at evaluating whether development in the banking sector intermediation process in the form of increase in the number of branches, credit to private sectors, intermediation eciency and total assets stimulates economic growth in Nigeria during the period of 1987 to 2018. The study employed the Johansen cointegration test, dynamic ordinary least square (DOLS) regression and error correction model in determining the relationship between the variables. The results of the cointegration test conrmed the existence of long-run relationship between banking sector development indicators and economic growth in Nigeria. Whereas, in the short run, only number of bank branches and bank’s total asset have a positive and signicant impact on economic growth signifying that much of Nigeria’s superior growth performance is attributed to increase in the number of bank branches and growth in bank’s assets. Credit to private sector has negative and insignicant relationship with economic growth while bank’s intermediation eciency has positive and insignicant relationship with economic growth.

Also, most banks in Nigeria have concentrated in opening up branches in the urban areas at the expense of the rural areas. Hence, huge amount of cash are lying idle in the rural areas and thus, being left out of the banking stream (Azolibe, 2019). These idle funds are supposed to have been mobilized by banks and channeled to the de cit sector of the economy for productive investment that will in turn drive economic growth.
Empirically, the relationship between banking and nancial sector intermediation development has been widely investigated in most developing and developed countries of the world. Limited evidence however exist in Nigeria and also, the few empirical studies conducted in Nigeria and other developing countries such as Ayunku and Etale (2014), Nwaeze, Michael and Nwabekee (2014), Iwedi, Okey-Nwala, Kenn-Ndubuisi and Adamgbo (2016), Murari (2017), Petkouski and Kjosevski (2014), Awdeh (2012) have concentrated mainly on using bank deposit, credit to private sector, interest rate and money supply as a measure of banking sector intermediation development. However, this study will improve on the variables employed by previous studies by using growth in the number of bank branches, intermediation e ciency and bank's total assets as a measure of banking sector intermediation development. Thus, this study is aimed at determining how banking sector intermediation development measured by the growth in the number of bank branches, growth in credit to private sector, growth in intermediation e ciency and growth in bank's total asset contributes to economic growth in Nigeria.
The rest of the article is structured as follows. The second section covers the Banking sector intermediation development indicators. The third section covers the theoretical and empirical framework. The fourth section covers analytical model, estimation strategy and analysis and discussion of results and nally, the Conclusion and recommendations are drawn in the fth section.

Banking Sector Intermediation Development Indicators
The banking sector intermediation development is measured by growth in the number of bank branches, growth in credit to private sector, growth in intermediation e ciency and growth in total asset in this study. They are discussed as follows;

Growth in the Number of Bank Branches
The banking sector in Nigeria has witnessed growth in terms of spread in the number of branches. According to CBN (2018), the number of bank branches, increased to 5,714 in 2017 from 5,570 in 2016. Before the introduction of the rural banking scheme in 1977, most banks in Nigeria concentrated mainly in opening up branches in the urban areas. However, Government through the rural banking scheme had made it compulsory for banks to open up branches in the rural areas. Although, most Deposit Money Banks complied with these government directives, they did so reluctantly and are still doing so in recent time. Generally, rural branches were considered unpro table by Deposit Money Banks for reasons ranging from heavy capital outlay, due to lack of infrastructure in these rural areas, to inadequate manpower to meet the needs of these rural branches because of mass expansion.
In Nigeria, the banking system credit to private sectors has increased tremendously right from the period of the post structural adjustment programme (SAP) which led to an increase in the number of banks. Credit to private sector increased from N21.08 billion in 1987 to N22,521.93 billion in 2018. However, the capacity of the banking system to nance the economy declined as the ratio of credit to private sector to GDP fell from 8.53 per cent in 1988, to 6.71 per cent in 1990 and from 10.10 per cent in 1993, to 6.22 per cent in 1995 and then in recent time, the ratio of credit to private sector to GDP fell from 20.77 per cent in 2016, to 17.63 per cent in 2018 (CBN, 2018) as shown in table 3 below. Banking sector intermediation e ciency is measured by the ratio of currency outside banks to broad money supply. Currency outside banks refers to those currencies that are physically used to conduct transactions between consumers and businesses and which are not stored in the bank, nancial institution or central bank. Broad money supply includes bank money and any cash held in easily accessible accounts. The current measure of broad money supply in Nigeria is M3. However, banking sector intermediation e ciency is the ease at which funds are transferred from the surplus sectors of the economy to the de cit sectors of the economy for productive investment that will spur economic growth.  (2018) observes that there was an improvement in intermediation e ciency indicator, measured by the ratio of currency outside banks to broad money supply, which stood at 7.63 per cent in 2018, from 7.97 per cent at end-December 2017.

Growth in Total Assets
A typical bank's asset consists of all forms of personal and commercial loans, mortgages and securities. Central Bank of Nigeria grouped Deposit Money Bank's assets into the following categories: reserves, claims on Central Bank, foreign assets, claims on Central Government, claims on state and local Government, claims on private sector, nancial derivatives and unclassi ed assets.
However, bank's total assets in Nigeria increased from N49.83 billion in 1987 to N17,522.86 billion in 2009. It dropped slightly to N17,331.56 billion in 2010 and then rose sharply to N37,206.99 billion in 2018.

Banking Sector Intermediation Development and Economic Growth: Theoretical and Empirical Framework
A well-structured banking sector mostly decreases costs of transactions and constraints of credits, conditions that may delay the growth of economy in a country. A banking sector that is not effective can by its ineffectiveness result in little activity and economic growth. The banking sector does propel economic growth through its investment function. Banks invest its excess deposit on real investment such as real estate, partnering with private individuals in real production of goods, procuring and leasing of equipment etc.
The theory nancial intermediation that was rst put forth by Schumpeter (1911) and later supported by the works of Shaw (1973), McKinnon (1973), Gupta (1984), Fry (1988), Greenwood and Jovanovic (1990), Bencivenga and Smith (1991) among others, postulates that nancial expansion causes the economy to grow. The theory posits that a strong developed sector of nance facilitates vital services that reduce transaction, information and monitoring costs and enhance the effectiveness of intermediation. As such, it identi es and funds good business projects, mobilizes savings, enables trading and risks diversi cation, promotes exchange of services and goods, monitors the performance of managers. All these services result in effective allotment of resources; lead to a quick increase of human and physical capital; and enables faster technological innovation. This eventually brings the outcome into faster and long-term economic growth (Schumpeter, 1911). Previous empirical studies in Nigeria and in other countries of the world have found a positive relationship between nancial and banking sector development and economic growth. Some found a negative relationship. Ayunku and Etale (2014) examined the relationship between banking sector development and economic growth in Nigeria. The result of the study revealed that trade openness, domestic credit and interest rate have positive relationship with real GDP while credit to private sector and deposit liabilities have negative relationship with real GDP. The result of their study lends very strong support to the existence of a short and long-run relationship between banking sector development and economic growth in Nigeria. Similarly, Tripathy and Pradhan (2014) extended the study to the Indian economy by investigating the short run as well as long-run relationships and also the causality relationships between banking sector development and the economic growth between the period of 1960 and 2011. They found strong evidence that banking sector development caused economic growth in the Indian economy. In another related study, Aigbovo and Uwubamwen (2014) included the stock market segment in their study to examine the short-run and long-run relationships between nancial system development and economic growth in Nigeria. The Granger causality test was used to determine the direction of causality among the variables. The ndings of the study were that nancial development (measured by banking system and stock market development) positively in uenced economic growth in Nigeria; that causality runs from nance to growth in the nance-growth nexus.
In a cross country study, Petkouski and Kjosevski (2014) examined the relation between banking sector development and economic growth in 16 transitional economies from Central and South Eastern Europe and they showed that credit to the private sector and interest margin were negatively related to the economic growth while ratio of quasi money was positively related to economic growth. In the same vein, Murari (2017) explored the relationship between nancial development and economic growth, using a panel data of South Asian middleincome countries for the years 1980-2013. The results indicate that the domestic credit provided by the banking sector has a signi cant association with economic growth in both directions but domestic credit to the private sector is associated with the economic growth in forward direction only, which con rms dearth in credit allocation in the region and suggests pathetic nancial regulation and supervision.
In a more recent study, Constantinos, Sofoklis and Joseph (2018) assessed the relationship between the nancial sector and economic growth in 34 European and commonwealth of independent states economies. Their results suggested that there has been a link between nancial sector and the real economy.
Dritsakis and Adamopoulos (2001) empirically examined the causal relationship between the degree of openness of an economy, nancial development and economic growth by using a multivariate autoregressive VAR model in Greece for the period of 1960 quarter one to 2000 quarter four. The granger causality tests based on error correction models show that there is a causal relationship between nancial development and economic growth and also between the degree of openness of the economy and economic growth. Keho (2010) interacted in ation rate in their model to analyze whether the strength of the relationship between nance and growth depends on in ation rate using time series data of seven African countries. The empirical ndings did not provide signi cant evidence of nonlinearity in the nance-growth relationship. Financial development has no signi cant effect on economic growth regardless of the level of in ation. Also, Kyophilavong, Uddin, and Shahbaz (2016) examined the nexus between nancial development and economic growth by testing the supplyleading hypothesis and demand-following hypothesis using time series data from Lao PDR. Using the autoregressive distributed lag (ARDL) bound testing approach to cointegration, the result con rmed the feedback effect between both of the variables. Financial development promotes economic growth and as a result, economic growth leads to nancial development. Panicos and Siong (2006) evaluated the relationship between nance, institutions and economic development using data from 72 countries for the period 1978-2000. They found that nancial development has larger effects on GDP per capita when the nancial system is embedded within a sound institutional framework. Moreover, they found that nancial development is most potent in middleincome countries, where its effects are particularly large when institutional quality is high. Importantly, they also found that in low-income countries, the in uence of nancial development is at its weakest; in these countries, more nance without sound institutions may not succeed in delivering long-run economic bene ts. Moreover, the study conducted by Malarvizhi, Zeynali, Mamun and Ahmad (2018) on the relationship between nancial sector development and economic growth of ASEAN-5 countries (Malaysia, Indonesia, Singapore, Thailand and Philippines) from 1980 to 2011 also revealed that nancial development has a signi cant positive effect on economic growth. Awdeh (2012) studied the causality direction between banking sector development and economic growth in Lebanon over the period 1992-2011 and found a one way causality running from economic growth to banking sector measures such as deposit growth and credit to local private sector. In another causality approach, Helmi, Rashid and Bedri (2014) investigated the causal relationship between nancial development and economic growth in Gulf Cooperation Council (GCC) countries that is, Bahrain, Oman, Kuwait, Qatar, United Arab Emirates and Saudi Arabia over the period of 1980 to 2012. Using error correction model and cointegration techniques to detect the long-run and short-run causalities between the variables, their overall empirical results revealed that nancial sector development contributes signi cantly to economic growth in GCC countries. King and Levine (1993a) studied a sample of 80 countries and concluded a strong positive relation between nancial development and economic growth. Also, King and Levine (1993b) studied a sample of 70 countries and examined the impact of nancial development on economic growth, capital accumulation and economic factor productivity and found a strong link between nancial development and growth. Levine, Loayza and Beck (2000) evaluated the role of nancial development in motivating economic growth and found that higher banking sector development implies higher economic growth and total factor productivity growth. Levine (2005) studied the in uence of bank system development and stock markets on economic growth by sampling eleven Arab countries, he concluded that countries with undeveloped nancial systems affects negatively the economic growth of those countries. He stressed that a sound nancial system is vital. Hence Levine studied a negative link between banking development and growth of economy due to an inadequately developed nancial banking system. However, from the above empirical literature, credit to private sector, interest rate and bank deposit has been commonly used by most scholars as a measure of banking sector development. Our study is unique and contributes to the empirical debates by introducing new variables such as number of bank branches, bank's total assets and bank's intermediation e ciency as a measure of banking sector development.

Analytical Model
The regression model that was used in this study comprised of one dependent variable, four independent variables and three control variables. Data for the variables were compiled from World Bank, World Development Indicators and Central Bank of Nigeria (CBN) statistical bulletin 2018. Dependent variable was economic growth proxied by real Gross domestic product (RGDP). While variables that were independent include: number of bank branches, ratio of credit to private sector to GDP (CPS/GDP), banking sector intermediation e ciency proxied by ratio of currency outside bank to broad money supply (COB/M2) and ratio of bank's total assets to GDP (BTA/GDP).
The control variables include lending interest rate, export and Government expenditure. The lending rate refers to the rate at which Commercial banks grant loans to the productive sector of the economy. Higher bank lending rate discourages people from borrowing for investment purposes and will in turn reduce the level of investment and economic growth and vice versa. Commercial bank lending rate to the productive sectors is usually in uenced by the Central Bank rate. Export represents goods or services that are sold abroad. Export trade is an instrument for growth as it increases foreign exchange earnings, improves balance of payment position, creates employment and development of export oriented industries in the manufacturing sector and improves government revenue through taxes, levies and tariffs. These bene ts will in turn enhance the process of growth and development in such economy. Finally, Government expenditure on infrastructure such as road, transport, defense power supply, health etc. will increase investment and economic growth.
However, the regression model is been shown as follows: Y=α+β1 X1+β2 X2+β3 X3+ β4 X4 + β5 X5 + β6 X6 + β7 X7+ Transforming the above equation into natural logarithm, we have LogY=α+β1 LogX1+β2 LogX2+β3 LogX3+ β4 LogX4 + β5 LogX5 + β6 LogX6 + β7 LogX7+ Where: LogY= Log of Economic growth which was measured by the real GDP, = Error term normally distributed about the mean of Zero and β1, 2, 3, 4, 5, 6, 7 = Regression coe cients of the variations to determine the volatility of each variable to economic growth in the regression model. The variables were logged because most time series data have an unequal variance (heteroskedastic in nature), thus the natural logarithm helps to stabilize the variance within the sample, which helps to improve our analysis.

Population and Sample Size
Population according to Onwumere (2005), represents a universe or elements with similar characteristics, hence it is a census of all relevant elements and may be nite or in nite while a sample is a group of variables or items derived from a relevant population for the purpose of examination or analysis. Based on this, the population of this study will comprise of all banks in Nigeria such as Deposit Money Banks, Micro nance banks, Merchant banks and Non-interest banks. These banks are involved in nancial intermediation in one form or the other. Sequel to the fact that there may be obvious di culties in studying the entire population due to the pattern and size of distribution, su cient knowledge of the entire population will be gotten from studying a sample of the population.
The sample of this study shall be the Deposit Money Banks in Nigeria. The choice of these banks is based on the fact that they are the dominant institution of nancial intermediation in Nigeria and hence, holds the largest proportion of household savings and also based on the availability of data on the variables. The sample size will also cover a period of 1987 to 2018. The choice of this period is based the fact that it was the period of the post structural adjustment programme (SAP) during which the nancial sector was deregulated and thus paved way for the entrance of new banks. There were more banks intermediating between the surplus sectors and the de cit sectors.

Estimation Procedure
In the determination of the relationship between banking sector intermediation development and economic growth in Nigeria, the study employed dynamic ordinary least square (DOLS) regression analysis. The DOLS model is a robust single equation approach which corrects for endogeneity and correlation by including lags and leads of rst-difference variables. Descriptive statistics of the variables were presented and unit root test was conducted to ensure that the time series analysis is free from stationarity defects. Also, Johansen cointegration test and error correction model were estimated. In the event that variables in the model exhibit a long-run harmonious relationship (cointegrated), it may be necessary to determine the speed of adjustment of economic towards a steady state in response to systemic change. From the table above, the time series residual variable data of all the variables are normally distributed as the probability of the Jarque-Bera statistic is absolutely greater than the critical value of 0.05 hence the null hypothesis (H0) is rejected in favour of the alternative (H1) that the residual of the distribution of the model is normally distributed.

Unit Root Test
In statistics, a unit root test tests whether a time series variable is non-stationary and possesses a unit root. The null hypothesis is de ned as the presence of a unit root and the alternative hypothesis is de ned as stationarity or variables have no unit root. The present study uses augmented Dickey Fuller (ADF) unit root test to examine the stationarity of the data series. It consists of running a regression of the rst difference of the series against the series lagged once, lagged difference terms and optionally, a constant and a time trend. This can be expressed as follows: The additional lagged terms are included to ensure that the errors are uncorrelated. In this ADF procedure, the test for a unit root is conducted on the coe cient of Y t-1 in the regression. If the coe cient is signi cantly different from zero, then the hypothesis that Y t contains a unit root is rejected. Rejection of the null hypothesis implies stationarity. Precisely, the null hypothesis is that the variable Y t is a non-stationary series (H 0 : α 2 = 0) and is rejected when α 2 is signi cantly negative (H a : α 2 < 0). If the calculated value of ADF statistic is higher than McKinnon's critical values, then the null hypothesis (H 0 ) is not rejected and the series is non-stationary or not integrated of order zero, I(0). Alternatively, rejection of the null hypothesis implies stationarity. Failure to reject the null hypothesis leads to conducting the test on the difference of the series, so further differencing is conducted until stationarity is reached and the null hypothesis is rejected.
If the time series (variables) are non-stationary in their levels, they can be integrated with I(1), when their rst differences are stationary. From the above unit root test result, all variables were found to be stationary at rst difference and as such we can go on and use the variables for further analysis. The optimal lag for ADF test is selected based on the Akaike info Criteria (AIC).

Cointegration Test
This study employs VAR based approach of Johasen and Juselius (1990) test which proposes the use of two likelihood ratio tests. The cointegration test was performed to ascertain the long run relationship between the variables that were captured in the model.
The Trace test: The trace statistic for the null hypothesis of co-integrating relations is computed as follows: Maximum eignvalue static tests the null hypothesis of r co-integrating relation against r + 1 co-integrating relations and is computed as follows: Г max (r, r + 1) = -log (1-λ r + 1 ) Page 13/18 The trace values and the maximum eigen values are then compared with the critical value at 5% level. If at most one trace value and the maximum eigen value exceeds the critical value at 5% level, then the null hypothesis of no co-integration is rejected, hence there is cointegration and vice versa. Cointegration of variables is an indication that they move together in harmony and are most likely to converge in the long run, which augurs well for policy making. Cointegration results are reported in Table 7 and indicate that maximum Eigen-statistic and trace-statistic reject the rst null hypothesis of no cointegrating relation among variables at the 5 per cent level of signi cance. The second hypothesis of at most one cointegrating relationship is also rejected. It follows that two or more cointegrating relationships exist among the variables; hence they are most likely to converge in the long run. The maximum Eigen-statistic and trace-statistic show similar results, con rming that the unit root coe cients of cointegrating vectors are signi cant at the 5 per cent level. This implies the variables are indeed cointegrated and results are not signi cantly sensitive to choice of test statistic. Since the variables are all found to be stationary in rst differences and also cointegrated, the model is estimated by employing DOLS and ECM estimation techniques. From the DOLS result, it can be observed that it is only number of Deposit Money Bank branches and bank's total asset that have positive and signi cant relationship with economic growth in Nigeria. An increase in number of Deposit money banks branches and ratio of bank total asset to GDP led to a 118% and 84% increase in economic growth of Nigeria respectively. This positive and signi cant relationship between number of bank branches and economic growth in Nigeria is due to the fact that branch network expansion remains one of the traditional and most effective methods used by Nigerian banks in mobilizing funds from the public for onward lending to the de cit economic units. As stated earlier, banks in developed countries such as USA, Canada, Australia, Germany, United Kingdom e.t.c, uses the electronic channels such as the Automated teller machine (ATM) to mobilize more deposit from the public as their ATM can perform both deposit and withdrawal operations. But in a developing country like Nigeria, the ATM is currently deemed as simply a cash dispensing machine as only withdrawals can be made. Hence, bank branches remain the only channel where cash deposit transactions can be made by the public. The branches are usually located in areas with high population growth and business activities for enhanced deposit mobilization.

Regression Analysis and Interpretations
Contrary to expectation, credit to private sector has negative and insigni cant relationship with economic growth in Nigeria. A one percent increase in credit to private sector led to 13% decrease in economic growth of Nigeria. These negative relationship between credit to private sector and economic growth could also be attributed to the fact that most individuals in Nigeria who are opportune to obtain credit from banks tend to divert the loan to unproductive ventures such as acquiring expensive cars, private apartment and other luxuries rather than investing it in productive projects that will likely spur economic growth. Also, it could be attributed to the ine ciency of the Nigerian banking sector and the poor quality of bank's risk selection policy. The negative relationship between ratio of credit to private sector to GDP and economic was in support of the ndings of Ayunku and Etale (2014) and Petkouski and Kjosevski (2014). While it disagreed with the ndings of Murari (2017) whose results indicate that the domestic credit provided by the banking sector has a signi cant association with economic growth.
Also, the ratio of currency outside bank to broad money supply which is a measure of bank's intermediation e ciency has positive and insigni cant relationship with economic growth in Nigeria. A one percent rise in bank's intermediation e ciency led to 27% increase in economic growth of Nigeria. Finally, the control variables export and Government expenditure have negative and signi cant relationship with economic growth at 10% level of signi cance while lending interest rate had negative and insigni cant relationship with economic growth in Nigeria.
The adjusted R2 indicates all variables together account for 92 per cent of systemic variation in economic growth of Nigeria. Note: Robust standard errors are in bracket. * and ** denotes signi cant at 1% and 5% level respectively.
The main focus in the estimation of error correction model is an adjustment of economic towards equilibrium when a systemic change occurs as indicated by the coe cients of ECM-1 in the table 9. The adjustment coe cient of −0.578 for indicates that economic growth adjusts by 58 per cent per year. This implies that full adjustment to a steady state takes about 2years and 2months.

Conclusion
The prime objective of this study is to determine the relationship between banking sector intermediation development and economic growth by adding lending interest rate, export and Government expenditure as a control variable from 1987 to 2018 with evidence from Nigeria. The stationary of data is checked by the Augmented Dickey Fuller (ADF) unit root test and this test con rms integrated order at rst difference. We have applied the Johansen cointegration to explore the cointegration between variables. The results con rm the existence of the long-run relationship between banking sector intermediation development and economic growth in Nigeria. However, in the short run, only number of bank branches and bank's total asset have a positive and signi cant impact on economic growth. Credit to private sector has negative and insigni cant relationship with economic growth while bank's intermediation e ciency has positive and insigni cant relationship with economic growth. The control variables export and Government expenditure have negative and signi cant relationship with economic growth while lending interest rate has negative and insigni cant relationship with economic growth in Nigeria.
The result of the analysis has proven that number of Deposit money Bank branches and bank's total asset is the major driver of economic growth in Nigeria. This shows that much of Nigeria's superior growth performance is attributed to increase in the number of bank branches and growth in bank's assets. However, growth in credit to private sector was found to be negative and insigni cant. This shows that the credit given to private sectors is not effectively utilized for productive investment that will spur economic growth. Most entrepreneurs who are opportune to obtain credit from banks in Nigeria tend to divert it to unproductive ventures and thus reducing the level of investment in the country. The calls for policy makers to develop good policy measures that will ensure that bank credit are utilized for productive investment that will spur economic growth. The study contributes to knowledge as it provides a good policy recommendation that will help address the negative relationship between credit to private sectors and economic growth in Nigeria and in other developing countries of the world. Extant literatures have not been able to address this challenge.


The Self-Employment Rate Index as a Measure of Economic Trends: Impact of Heterogeneity of the Self-Employed on the Quality of Indicators

Purpose: The aim of this part of the paper was to examine whether there are any observable trends between the number or percentage of self-employed in the labour market and business climate indicators, and can any conclusions be drawn whether the currently existing methods of monitoring labour are sufficient. Approach/Methodology/Design: In this paper, the hypothesis was tested that the selfemployed are not a cohesive group. That is why labour market monitoring structure based on indicators related to “self-employment rate” is not sufficient to observe the trends in changes occurring in the contemporary labour market. Correlations between the business climate and labour market indicators and the various self-employed subgroups tend to be not similar. The group of self-employed is diverse and includes different areas and forms of work, which demonstrate varied tendencies and should be considered separately. Moreover, it includes groups that can now be formally separated, for example individual farmers, traditional and hybrid self-employed, employers. Findings: Our analysis clearly shows that the commonly used indicator "number of selfemployed" or “self-employment rate” is inadequate for analysing trends in the labour market as it doesn’t showcase the complexity of problems related to changes in this market. This indicator should not be the basis for assessing economic trends, including those in the labour market. It is also insufficient for concluding about the very nature of labour and for monitoring Economy 4.0. Practical Implications: It is necessary to discuss the new classification of people who are active in the labour market. New indicators should be developed, to demonstrate changes in the labour market more accurately.

Introduction
Many economists believe the economy is currently undergoing quite fundamental changes (Leceta et al., 2017;Malik, Janowska, 2018;Rifkin, 2014;Rifkin, 2001;Scholz, 2013;Tapscott, 2014). The changes affect many spheres of human life. Technological revolution, along with its new ways of communication, impact realworld economy changing the way goods are produced, how services are provided, how business is organized and how work is provided and sold. The changes also impact culture, value systems, lifestyle, and leisure time, etc. The transformation is crucial enough for scientists to try to analyse its consequences on an ongoing basis, to find interdependencies and create adequate theories, considering new operating conditions and developing forecasts as to its directions and intensity. The changes in management are also reflected in the change in vocabulary. In articles, the current economy is described with various adjectives: economy 4.0., digital economy, knowledge-based economy, or on-demand economy (De Stefano 2016;Aloisi 2015). Schwab (2016) claims that we are now living in the times of the Fourth Industrial Revolution, which introduces not only changes in management and work, but also in the very nature of these activities. Schwab (2016) gives an example of a new ecosystem of workthe application economy that can be dated from 2008. In 2018, the value of this market was estimated at over USD 100 billion.
The change in the economy, related to the ways of running a business, is accompanied by the transformation of the labour market and its conditions. The first and fundamental condition is the projected decline in global demand for labour. As demand for labour becomes a global one, it will depend on global (and not local) labour costs. Global capital and new technologies, together with the increase of relative labour costs, will cause the tendency to substitute it with technology and capital to increase. The World Bank predicts that as much as 57% of jobs in OECD countries will undergo automation (Acemoglu and Restrepo, 2017). These are no longer Neo-Luddist forecasts, whose views are considered extreme and who anticipated the end of work (Reich 2000;Rifkin 2001). The inevitable decline in the global number of jobs is also recognized by economists and practitioners of economic life (Palacios-Huerta, 2016;Gates, 2017).
Another change in the nature of work is the increase in the importance of universally understood flexible work, or "on-demand" work. According to some economists (Popma, 2013;Smith and Leberstein, 2015;Dwyer, 2016;Katz and Krueger, 2019;Supiot, 2001;De Stefano, 2016) this will involve changing the type of relationship between buyers and sellers of work. In the traditional economy, it was common to establish long-term relationships between employers and employees. Both parties were bound by a contract of employment, which contract was subject to legal regulations (the Labour Code). Under the contract, the employee provided work for the employer, but at the same time they were part of the organization, i.e. the enterprise. The employer co-participated in the employee's professional development, co-financed or financed their training, set their goals, the path of promotion, etc. The relationship between the employee and the employer was often long-term, often lasting throughout the employee's working life. Currently, such relationships are in decline. What is increasingly important is "work on demand", i.e. obtaining work results in as necessary a scope as for the implementation of the current tasks of the organization.
At the same time, it is difficult to distinguish whether it is the sale of labour (provided by employees) or of services (provided by self-employed entrepreneurs). The fact is that industry 4.0 does not increase the demand for labour, i.e. for formal, full-time jobs, but the demand for the purchase of "projects", "services" or specific tasks. These tasks can be carried out by contractors who are not necessarily related to the client in terms of location, thanks to technological development. Schwab mentions the "human cloud", i.e. people who provide services as independent specialists, often using technology, such as internet platforms. This cloud creates a highly competitive environment, with higher productivity and lower operating costs.
One of the elements of cost reduction in such an economy is the possibility of reducing the social security costs of the service providers, as stated by the labour law. Those working in the "cloud" are not employees, but self-employed, and as such, they are not subject to these provisions. Transformations of management, including the ways of providing labour, raise many questions: 1. are the above-mentioned changes already noticeable, or are they future predictions (if at all successful)? 2. is the number of self-employed, and their share in total job force, increasing? 3. how does the number of self-employed changes along with the change in business climate indicators, especially the unemployment rate and the GDP growth rate?
In addition, there are general questions: are self-employed part of the labour market or part of the universally understood entrepreneurship? Among experts and researchers of self-employment there is no consensus on the nature of this phenomenon and its impact on the economic growth of individual countries. For some, self-employment is a form of entrepreneurship (Norena-Chavez, 2020). Sometimes the terms "self-employment" and "entrepreneurship" are used synonymously. The self-employed are identified as a group of micro-entrepreneurs who run independent business and bear the related risk. What differentiates them from entrepreneurs is only the scale of operation. However, there is a considerable group of researchers for whom self-employment is a flexible form of labour (Szaban and Skrzek-Lubasińska, 2018).
The questions are various. In order to answer them, one needs to constantly monitor the labour market. This need has been noticed by the International Labour Organization (ILO), which prepares guidelines on key indicators of the labour market. ILO indicators are considered by leading national and international statistical organizations such as Eurostat or OECD (ILO 2015;2016a;2016b).
This paper is in line with the trend of monitoring changes in the labour market, with particular emphasis on self-employment. First, the definitions and problems related to the monitoring of self-employment were presented, followed by the ILO guidelines on this statistics and existing methodological doubts. Secondly, a review of the literature and research was conducted on the correlation between business climate indicators (GDP growth rate and unemployment rate) and the number of self-employed. The aim of this part of the paper was to examine whether there are any observable trends between the number or percentage of self-employed in the labour market and business climate indicators, and can any conclusions be drawn whether the currently existing methods of monitoring labour are sufficient. The impact of selected business climate indicators on the number of self-employed persons in Poland was also presented. The data analysed were from the years 2009-2017 when Poland underwent major changes in the labour market. The case of Poland and the study period were selected for several reasons: 1. there was a large volatility of both business climate indicators and labour market indicators in the discussed period. 2. Poland is considered a country with rigid labour law regulations. Poland ranked 30th among 41 EU and OECD countries in the Employment Flexibility Index in terms of flexibility in employment. The following restrictions are mentioned: ➢ high minimum wage in relation to labour productivity, ➢ rigid regulations regarding working hours, ➢ high costs and rigid regulations regarding the dismissal of employees.
At the same time, Poland is a country with one of the lowest indexes of permanent employment per total workforce. This means that a significant percentage of workers is not included in labour law regulations. This allows a conclusion that the flexibility of the Polish labour market is one of the highest in the EU. For the above reasons, the case of Poland may prove interesting with regard to consideration of the impact of business climate indicators and the labour market on self-employment. In summary, the following hypothesis was tested in the paper: The self-employed are not a cohesive group. Labour market monitoring based on indicators related to selfemployment is not sufficient to observe the trends in changes occurring in the contemporary labour market. Correlations between the business climate and labour market indicators and the various self-employed subgroups tend to be not similar.
Finally, an analysis of labour market indicators was presented, whether the currently collected data are sufficient and suitable for monitoring economic changes on the labour market. Do they adequately reflect the scope of changes in the very nature of labour? Are they adequate for monitoring Economy 4.0? The results of the conducted analyses can constitute a starting point for creating institutional conditions supporting economic development, including institutional changes in labour relations in Europe.

Overview of Literature and Research
When discussing problems related to self-employment, authors use many terms that are considered synonymous (Leighton and Wynn 2011;Phillips and McKeown 2014). The following definitions of the self-employed have been found in various publications: • Freelancer ( Significant freedom of terminology, and the fact that the terms are not synonymous par excellence even in the interpretation of the researchers themselves, makes it difficult to conduct comprehensive analyses of the self-employment phenomenon, to compare the results and to refer to the conclusions of other researchers. The situation is further complicated by the fact that the synonyms used have their own definitions, different than the definition of self-employment. For example, some researchers believe that the concepts of self-employed and freelancer are synonymous, for others they are categories that overlap only partially, as do the terms "entrepreneur" and "self-employed". For some, a self-employed person is an entrepreneur, and a country's level of entrepreneurship can be estimated on the basis of the percentage of self-employed in its economy. Others, however, claim this approach is erroneous (Szaban and Skrzek-Lubasińska, 2019; Skrzek-Lubasińska and Gródek-Szostak, 2019).
According to Blanchflower (2000) self-employment is the simplest form of entrepreneurship. Researchers believe that self-employment can be treated as the The Self-Employment Rate Index as a Measure of Economic Trends: Impact of Heterogeneity of the Self-Employed on the Quality of Indicators 488 smallest but also the most vital part of entrepreneurship (Demirgüc-Kunt et al. 2007). But there are also contrary opinions. Henrekson and Sanandaji (2013) as well as Hurst and Pugsley (2010) even believe that assessing the level of entrepreneurship in a country by the level of self-employment is misleading as self-employment mostly does not refer to entrepreneurship in the Schumpeterian sense. Self-employed people are usually not a source of any innovations (even in the broad sense of the word), and often their goal is not to grow their business (in the market sense). In addition, there are problems in distinguishing between proper and dependent selfemployment (Bjuggren et al., 2010). On the other hand, Faggio and Silva (2012) confirmed a positive correlation between the size of the self-employment rate and the level of innovation in the region, based on research in the United Kingdom.
Therefore, it is important to consider the correlation between economic growth and the level of economic development, and the increase in the number of selfemployed, or the share of self-employment in the total workforce of a national economy. Blanchflower (2000; said that "evidence from a series of GDP growth equations presented in did not suggest that the self-employment rate increased the real growth rate of the economy, in fact there was even evidence of the opposite. I have seen no convincing evidence of any kind in the literature that either increasing the proportion of the workforce that is self-employed or having a high level of self-employment produces any positive macroeconomic benefits. Such evidence that does exist suggests quite the reverse. More is not better". According to Gindling and Newhouse (2012), as per capita GDP increases, workers transition out of self-employment. Other researchers noticed the difference between the impact of entrepreneurship (measured e.g. by the share of self-employment in the national economy) on economic growth at the macroeconomic level in countries with different levels of economic development. Stam and van Stel (2011) pointed that entrepreneurship does not have an effect on economic growth in low income countries, in contrast to transition and high-income countries where especially growth-oriented entrepreneurship seems to contribute strongly to macroeconomic growth.
In summary, self-employment appears as a domain of less-developed countries. Along with the economic growth, the share of self-employment in the national economy is decreasing. The type of entrepreneurship also changes from forced by the lack of options, to innovative. As in the case of economic growth, numerous studies were also related to the impact of the level of unemployment on the level of self-employment. Bögenhold and Staber (1991) demonstrated that the level of selfemployment increases at the macroeconomic level when the unemployment rate of a given country is high and the economic growth is low. It decreases, however, when the economic situation of the country improves, and the unemployment rate drops. Among others, Highfield and Smiley (1987) have reached similar conclusions. The effect of recession and increase in the unemployment rate, which fuels an increase in the number of self-employed, was called the refugee effect.
However, there are also contrary opinions in literature. According to Evans and Leighton (1990) and Thurik et al. (2008) the number of self-employed is growing when the situation on the labour market improves, pointing to the so-called entrepreneurial effect (Norena-Chavez and Guevara, 2020). Parker (2004) stated that both effects can be associated with motivation for self-employment. It can be negative motivation, i.e. forcing self-employment, or positive, attracting to selfemployment. In the above research, the impact of the economic situation on the level of self-employment was based on international labour market indexes, mainly OECD and Eurostat. The diversity of the group was not considered despite the fact that many researchers have pointed to its heterogeneity (OECD, 2017;van Stel and de Vries, 2015;Rapelli, 2012). In many studies, no answer was found to the question whether considering the heterogeneity of self-employment would change the results of this type of research.

Self-Employment Rate vs. Business and Labour Market Indicators
The number of self-employed in an economy is considered to be an important indicator describing the economy and the labour market. For example, the share of self-employed in the total national workforce is analysed. In some publications, it is interpreted as an indicator of entrepreneurship in a given country (OECD, 2014), based on data from the Labour Force Survey. According to these calculations, in 2017 Poland had one of the highest self-employment shares in total workforce, 20.4%, whereas the EU average was 15.5%.

Figure 1. Self-employment rate
Source: OECD (2019)  Based on these data, far-reaching conclusions are drawn. For example, in Selfemployment in Europe (Hatfield, 2014) while considering the rate of selfemployment in the UK, the author stated: "If the current growth in self-employment continues in the UK and the Netherlands then in 10 years' time they could have selfemployment rates approaching those of Poland, Spain and other South/Eastern European nations. It seems likely that the recent growth in self-employment has, at least to some extent, been a genuine compositional shift rather than merely a cyclical effect" Therefore, it is necessary to evaluate whether the quoted indicators reflect trends in the labour market accordingly. To this end, the case of Poland was analysed. It was investigated whether the indicator based on the number of self-employed obtained from the general Labour Force Survey, conducted in accordance with the ILO guidelines, is an indicator that can accurately reflect trends in the labour market.

The Case of Poland
The years 2009-2017 saw economic growth in Poland, although different in individual years. First, the Polish economy grew at a rate of around 3% annually until 2012, when the pace of development slowed significantly (to about 1.5% for the next two years). At the time, domestic demand dropped, and net exports was the main growth driver. In 2014, the economy started to accelerate again. The years 2017-2018 saw dynamic economic growth driven by internal demand. When analysing the labour market, authors most often present labour statistics (number of employees and employers, the unemployment rate, etc.) in different classifications and configurations. Information on the level of labour market flexibility is less frequently added. In Poland, the situation on the labour market has been subject to significant changes since 2009. First of all, after the financial crisis of 2008, the unemployment rate was growing until 2012, when it reached 10.1% 3 . Then the trend reversed, and the unemployment rate began to drop to reach 4.5% by late 2017, which some economists consider to be close to natural unemployment. The number of employees in the national economy increased in the years 2009-2017 by 6.71% (from 15.7 to 16.4 million people). At the time, the number of selfemployed changed only slightly, by 1%, while the number of employers increased by 11.09%. Based on the data, one could say that the improvement of the labour market situation triggered an increase in the number of entrepreneurs running businesses, which, in turn, led to increased demand for labour and, as a result, an increase in the number of employees and employees in the national economy. Apart from the above, the labour market remained quite stable. Detailed data on the labour market in Poland in the years 2009-2017 are presented in Table 2. All in all, the demand for labour increased along with the improvement of the situation on the labour market, but it is being satisfied in a different way. The number of non-agricultural entrepreneurs increased much more than the number of employees. However, this was not an increase in false self-employment, i.e. forcing self-employment on employees in order to reduce costs resulting from legal provisions (this type of self-employment constitutes approx. 10% of all selfemployed). This increase may therefore result from the change in the nature of the labour described above. The share of self-employed outside agricultural production has increased. In 2017, it amounted to 7.6% of all national workforce, while in 2009, only to 5.8%.
The relationship between the number of self-employed representing various groups was examined: traditional (X1) and hybrid (X2), employers (X3) and independent contractors (X4) and universally understood business conditions (Y). Two groups of business climate indicators were adopted: • reflecting the situation on the labour market, measured by the following: -number of people working in the national economy (total) -number of employees -number of unemployed • reflecting the general situation of the economy, measured by the following: -GDP growth rates -growth rates of value added in the economy, and -growth rates of internal (domestic) demand The relationship between the above indicators and the number of self-employed was determined using the r-Pearson correlation coefficient, according to the formula: r (x,y)= (1) wherein: r (x,y) is the r-Pearson correlation coefficient between variables x and y; C (x,y) -covariance between x and y variables; s -standard deviation for x and y.
Data from the Social Insurance Institution and the Central Statistical Office for the years 2009-2017 were used (in each case as per December 31st of a given year). Table 3 presents values of correlations between the number of self-employed from selected groups and indicators of the labour market situation and selected business climate indicators. A very strong, or strong (significant) dependence was found between the number of self-employed from various groups (traditional and hybrid) and employers, and the indicators of the labour market situation and business climate. On the other hand, the indicator created to analyse independent labour (i.e. the number of self-employed as a total) shows practically no such dependence. This is most likely due to this indicator's construction and assigning workers to this group in a number of different ways, including e.g. individual farmers. It should be emphasized that groups of selfemployed include micro-entrepreneurs who provide services only to a single client. They should be treated as employees, and they are referred to as dependent or false self-employed. On the other hand, this group does not include freelancers and independent contractors if they have signed a short-term contract or a contract of mandate (types of contracts covered by the provisions of the Labour Code). This means that the total self-employed group may be underestimated.
The above analysis clearly shows that the commonly used indicator "number of selfemployed" or "self-employment rate" is inadequate for analysing trends in the The Self-Employment Rate Index as a Measure of Economic Trends: Impact of Heterogeneity of the Self-Employed on the Quality of Indicators 494 labour market as it doesn't showcase the complexity of problems related to changes in this market and shouldn't be the basis for assessing economic trends, including those in the labour market. It is also insufficient for concluding about the very nature of labour, as is currently the case.

Monitoring Trends in the Labour Market
The need to monitor the labour market is a priority for many international organizations, such as the International Labour Organization (ILO), OECD and Eurostat. Many experts emphasize that currently, in the era of fundamental changes in labour relations, such monitoring is particularly important. ILO's official website states "as economies and societies become more interdependent, the need to enhance our understanding of the world of work becomes increasingly important. Timely and focused information on the world's labour markets is essential; information that can answer such questions as: • What types of economic activities are countries and people engaged in?
• What are the size and composition of the labour force?
• How is the level of economic development reflected in a national labour market? (…) Answering these questions requires detailed analysis of a large volume of statistics." 4 Guidelines on how to monitor the labour market result directly from the adopted institutional classification of the labour market. The 15 th International Conference on Labour Statistics 5 in 1993 (ICLS-93) developed definitions related to the labour market, distinguishing the following groups: 1. Employees 2. Employers 3. Own-account workers 4. Members of farmer cooperatives 5. Contributing family workers This classification had been used thus far in public statistics research. In 2018, during the 20th ICLS Conference, it was deemed insufficient. The General Report 6 states: "… these five categories do not provide sufficient information to adequately 4 ILO's official website https: //www.ilo.org/empelm/areas/employment-trends/langen/index.htm (accessed on 15. March 2019) 5 ILO (1993: Fifteenth International Conference of Labour Statisticians, Report of the Conference. ICLS / 15 / D.6 (Rev. 1). International Labour Office, Geneva 1993.http://Laboursta.ilo.org/applv8/data/icsee.html 6 ILO ICLS (2018b) Report General, 20th International Conference of Labour Statisticians, ICLS/20/2018 monitor the changes in employment arrangements that are taking place in many countries, and are not sufficiently detailed to monitor various forms of non-standard employment. A variety of new, or non-standard, types of employment arrangements that aim to increase flexibility in the labour market have generated a strong demand for statistical information to monitor their impact on workers, on employers and on the functioning of the labour market." The new classification is not yet fully developed. During the conference, there was much focus on the boundaries between different types of professional activity and different classification criteria were adopted. Among discussed issues were the level of independence and level of risk. These are rather subjective criteria, difficult to operationalize objectively. The following Table 4 shows different classification possibilities of the labour status, depending on the criterion adopted. • the degree of independence and risk taking (independence, controlling his/her own work without formal supervision, risk taking versus dependency, lack of autonomy in work-related matters, belaying) • the motivation to become self-employed: pull-in & push-out factors (voluntary self-employment, individual's own choice of such employment form, without external, pressure versus necessity self-employment due to the labour market situation (no other, satisfying jobs available) or the requirements of the main employer-contractor • professionalism (individual professionalism, individual talent, knowledge, experiences, skills, and competences; position, a job in which an individual cannot be easily replaced versus lack or low level of professionalism (work can be easily done by others) • self and social perception regarding one's financial outcome and autonomy (success: gain, esteem versus failure, loss).
All of the above subjective criteria require considerable discussion on how to translate them into the language of statistical surveys. Currently, OECD countries (thus also EU countries) use the Guidelines of the International Labour Organization in labour market research 7 . In the Guidelines, attention is paid to one more type of difficulty with the study of various forms of labour, e.g. due to varying regulations, the lack of precise government guidelines or classification criteria 8 . For this reason, labour providers organized in the same legal form can be considered as employees hired for pay and, at other times, depending on the circumstances, independent contractors working for profit. Legal forms can also be incomparable in separate countries.
As illustrated by the case of Poland, the structure of employees in the labour market is changing. At the same time, however, the ways to monitor these changes do not change, which leads to big misunderstandings and lack of precision in presenting facts. An example of this is the study of labour market flexibility in Poland. According to the OECD (2016), the labour market in Poland is extremely flexible. This is testified by one of the highest shares of people in flexible forms of employment (including temporary or fixed-term work) in OECD countries. In 2016, in Poland it amounted to over 27% compared to 12% of the average for OECD countries. On the other hand, the study "Employment Flexibility Index EU and OECD countries 2019" points to Poland as a country with one of the lowest employment flexibility ratios. Poland ranks 30th out of 41 countries surveyed. The index considers 35 indicators in four areas: regulations related to employment, working time flexibility, dismissal policy and costs related to redundancies. Such a research methodology, based on the guidelines of the World Bank, indicates that the 7 ILO (2013) Decent work indicators. Guidelines for producers and users of statistical and legal framework indicators, ILO Manual, Second Version, 20138 ILO (2013 labour market in Poland is very inflexible, which means that the economy is slower to adapt to technological progress and economic changes. According to the authors of the study, the reason for this low flexibility is the rigidity of the Polish Labour Code. The conclusions relate to the total flexibility of employment, but the study refers to permanent employees, protected by the Labour Code. Even these two examples of research conducted by international institutions indicate that the lack of precision in the definitions used and ill-adjustment of the monitoring method to the actual realities of the labour market means that one can't expect information about real directions of changes in the economy. Future research should aim to "make the invisible visible" (ILO 2018c). Research is needed into new ways to measure entrepreneurship and work, including self-employment heterogeneity.

Summary and Conclusions
Studies have shown that the commonly used indicator of "number of self-employed" is neither sufficient for analysing trends in the labour market, nor showcases the entire complexity of problems related to changes therein. Thus, the initial hypothesis was verified that the group of self-employed is diverse and includes very different areas and forms of work, which demonstrate varied tendencies and should be considered separately. The article includes groups that can now be formally separated: individual farmers, traditional and hybrid self-employed, employers. However, this classification does not seem sufficient. The group of traditional selfemployed includes, e.g. traditional service providers (hairdressers, tailors), freelancers, contractors using internet platforms, or small traders and manufacturers. As can be observed, this group is diverse in terms of skills, investment, productivity and development opportunities. It is necessary to discuss the new classification of people who are active in the labour market. New indicators should be developed, to demonstrate changes in the labour market more accurately.
Regardless of whether one considers changes related to self-employment as changes in the nature of labour (Palacios-Huerta, 2016), or believes this a historically natural phenomenon involving marginal activity on the verge of the mainstream of paid work (Arum, Muller 2004), monitoring this phenomenon (which, incidentally, is becoming less and less marginal) seems necessary, to understand any interdependencies existing on the labour market.


Coherence degree characteristic of production, industrial and food security of agro-industrial complex subsystems

The article substantiates the urgent need to assess the coherence degree of production, industrial and food security in the framework of the state agricultural development program implementation. The authors combine two approaches to assessing industrial and production security, and also the processes of ensuring industrial security are considered using the example of dairy production. The small-scale coherence degree of the industrial component in ensuring the food security of Russia is shown.

Introduction
It is well known that the successful development of the modern Russian agro-industrial complex is possible only taking into account the achievement of national independence, in particular, production and food security issues of the country [1;2;3]. In 2010, the Government approved the Food Security Doctrine, which prescribes the necessary ratio of domestic and imported products in the country, as well as market risk zones. In this regard, indicators for the production of domestic grain (99.4% at a rate of 95%), sugar (95.6% at a rate of 80%), vegetable oil (81.5% at a rate of 80%), as well as meat and meat products ( 92.8% at a rate of 85%) have been met.
The specialists of the Ministry of Agriculture determined that in recent years Russia has achieved food security values in almost all key areas: grain, vegetable oil, sugar, meat and meat products. The lag in milk production is observed, however, the implementation of state support measures allowed an increase in raw materials production in 2018 by 1.5% compared to 2017 -up to 30.6 million tons. The Government Agency expects that the positive trend will continue in 2019, and the increase in production will be 1.6% -up to 31.1 million tons.
The authors' trends analysis of the main AIC production subsystems functioning notes the need of import-substituting orientation of their development to ensure, first of all, production security (since the imported products intervention is caused not only by food products supply but also resourcetechnological and technical components for food production, as well as ancillary and supporting types of economic activity) [1 3; 14; 15].

Materials and methods
In 2018, the share of Russian milk in the domestic market was 84.2% with a threshold value not less than 90%. The milk-processing production AIC subsystem in Russia is a combination of organizational and economic relations that significantly reduce the food security of Russia. The low In our opinion, in the milk processing subsystem, it is advisable to create an agri-food formation which will ensure that interrelated interests of its participants are taken into account [4] and achieve the benefits of integration based on such properties as self-determination, self-regulation, selffinancing, self-development, etc., in accordance with the emergence law and, as a result, increased integration of economic entities.
In this regard, the authors combined two approaches to the production security assessment: 1) the potential of ensuring industrial and production security by agro-food formation is estimated; 2) after creation of agro-food formation, the results of its activities are evaluated -contribution of the cluster to food security.
To characterize the coherence degree of production, industrial and food security of the formations in the agro-industrial complex, we propose to determine the import intervention levels in tabular form. Table. 1. Coherence degree characteristic of production, industrial and food security

Stages of import intervention
Dairy and milk processing production Coherence degree of security 1-import of sub-sub-raw materials Purchase of zooveterinary drugs Production 2-import of sub-raw materials Purchase of breeding males Production

3-import of raw materials Supply of fresh (frozen) meat and milk powder
Industrial and production

4-import of similar finished products
Supply of meat and milk processing products Food The first stage of import intervention involves the supply of zooveterinary preparations components, which analogues are not produced by Russian chemical organizations, and currently, the import is the only possible option to comply with technological regulations (that is, an economic assessment of the agri-food formation potential at this level is not advisable). Since the results of the import intervention at this stage primarily affect the competitiveness of domestic cattle breeds, and as a result their use, it seems necessary to determine the domestic breeding potential on two main components: agricultural and production.
The sanctions imposed against Russia by a number of Western countries and the response of the Russian government had a stimulating effect on import substitution in the economy. Import substitution in 2019 is within 30%, which means that all goods, products and services produced in Russia make up only 30%, the rest has to be purchased abroad [5;6;8]. Import substitution is possible under the condition of good production capacities and the creation of competitive enterprises, able to produce quality products at the appropriate prices. Further import decline is possible due to development and stimulation of new technologies and additionally created production.
In response to foreign sanctions, the Russian government has banned some products shipped from the United States and the EU. The list of banned products includes beef and other cattle meat, poultry, pork, fish and some crustaceans, as well as sausages, cheese, vegetables and fruits.
The main directions of import substitution in agriculture are seed production, cows breeding (based on the method of embryo transplantation), fish processing, gardening, and vegetable growing. Small and medium-sized businesses have a chance to realize their potential. In the absence of foreign competition, entrepreneurs have unlimited opportunities [7; 9].

Discussion of results
Food security is considered one of the main indicators of socio-economic development of the state. At the World Food Summit, it was stated that food security exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life, therefore, at the top of the rating are those countries in which food is most accessible to the population. According to the Food Security Doctrine food security of the Russian Federation is the state of the country's economy that ensures food independence of the Russian Federation, physical and economic availability of foodstuffs to every citizen that meet the requirements of the laws of the Russian Federation on technical regulation, in quantities no less than the balanced food consumption rates required to support active and healthy lifestyle.
The cumulative value of the industrial and production security degree is determined by the lowest value of agricultural and production components: К -potential stage of production security, %; К С -potential stage of agricultural component of production security, %; К П -potential stage of production component of production security, %; The agricultural component is the aggregate ability of organizations to provide the necessary amount of domestic pedigree livestock in the country. The choice of these coefficients is due to the negative dynamics of changes in the number of breeding farms throughout the territory of the Russian Federation, including farms specializing dairy cattle breeding.
At the second stage, the main factors of import intervention are the supply of breeding males, as well as agricultural equipment and components: К -potential stage of production security, %.
At the third stage of the import intervention, the authors identified only one component -industrial, since the intervention is mainly characterized by the import of fresh (frozen) meat and milk powder. Therefore, there is a correlation: К = К ЗП . In this regard, the level of industrial component is determined as follows: К П -potential level of industrial component, % М ф -production capacity of the j-th raw material processing plant (actual), tpd; О с.необх -the amount of milk necessary to ensure the physiological needs of the population, tons; П сн -production season duration of plants (standard), days; В сн -finished products output of plants (normative). Due to expansion of the directions and volumes of state support for the dairy industry, the production of commercial milk is increasing and in 2018 the growth amounted to 0.5 million tons. An increase in demand for Russian dairy products in both domestic and export markets -76.1% in 2013 to 84.2% in 2018-is also a serious incentive to maintain investment activity [10;11;12].

Conclusion
In conclusion, we note that the potential level of ensuring production and, consequently, food security in Russia is characterized by the lowest value of agricultural or industrial components since the level of one determines the effectiveness of the other. Now the Russian authorities are reviewing the Food Security Doctrine. The new document will take into account economic restrictions, WTO accession, as well as closer interaction of countries within the EAEU. The country's food independence will be assessed not only in terms of selfsufficiency but also export potential of industries. Physical and economic accessibility of products for citizens will also be analyzed.