Dataset Modelling of the Financial Risk Management of Social Entrepreneurship in Emerging Economies
1
Chair “Economic Policy and Public-Private Partnership”, Moscow State Institute of International Relations (MGIMO University), 119454 Moscow, Russia
2
Center for International Development, Harvard University, Cambridge, MA 02138, USA
3
Department of Economics, University of Messina, 98122 Messina, Italy
*
Author to whom correspondence should be addressed.
Risks 2021, 9(12), 211; https://doi.org/10.3390/risks9120211
Submission received: 3 October 2021
/
Revised: 12 November 2021
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Accepted: 17 November 2021
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Published: 26 November 2021
(This article belongs to the Special Issue Managing Financial Risks Based on Corporate Social Responsibility for Sustainable Development)
Abstract
:The relevance of this study lies in the fact that financial risk is a serious obstacle to the development of social entrepreneurship, preventing the implementation of potential support for sustainable development goals in business. The purpose of this article is to clarify specific aspects of financing factors and financial risk related to social entrepreneurship in developing countries (in comparison with the standard financial risk related to commercial entrepreneurship) in order to analyze the influence of the financing factors of social entrepreneurship on sustainable development, as well as to determine the potential for the development of social entrepreneurship through financial risk management. To achieve this goal, this article uses the methodology of econometrics—dataset modelling of financial risk management in social entrepreneurship to achieve sustainable development in emerging economies. On the basis of the results of this study, firstly, it is substantiated that the financial risks entailed by social entrepreneurship differ from the standard financial risk present in commercial entrepreneurship. Specific factors of the financing of sustainable development in emerging economies are determined and, on the basis of this, financial risks specific to social entrepreneurship in emerging economies are identified as follows: (1) reduced stimulus to use financial resources in long-term investments, which disrupts the stability and decreases inclusion; (2) joint public–private investments and decreased investment in R&D; and (3) expanded investment in the skills required for jobs and “markets of tomorrow”. Secondly, a contradictory influence of financing factors on sustainable development is demonstrated. Thirdly, a large potential for the development of social entrepreneurship by means of financial risk management (maximum reduction) was identified. With the minimization of financial risk, social entrepreneurship would demonstrate substantial progress, with an increase of 99.61% (more than 50%) from 45.18 points to 90.18 points. A novel contribution of this paper to the extant literature consists of the specification of the essence and specifics of social entrepreneurship in emerging economies through the identification of financial risks and the provision of recommendations for their management.
1. Introduction
Social entrepreneurship is a special type of business that incorporates the individual or simultaneous implementation of the following directions of activity: (1) corporate social responsibility; (2) corporate ecological responsibility; (3) non-commercial activities (including charity) towards the provision of public and socially important benefits. Financial risks are a serious obstacle to the development of social entrepreneurship, preventing the implementation of potential support for sustainable development goals in business. This is the problem addressed by this research. The following issues hinder the development of solutions to this problem.
The first issue is that the way in which social entrepreneurship is financed is quite different from the manner in which commercial business is. The investment climate is heterogeneous, and there could be a situation in practice in which, in case of a favourable—on the whole—climate, there could be high investment attractiveness for commercial projects but low investment attractiveness and investment defecits for sustainable development. There are no special statistics on the investment attractiveness of social entrepreneurship, resulting in uncertainty with respect to specific financial risks related to it. The orientation of the general investment climate in an investment system can lead to imprecise and distorted evaluations of the financial risks entailed by social entrepreneurship.
The second issue is that, unlike commercial entrepreneurship, where financial risk management constitutes one of its main activities, financial risk management takes a background role in social entrepreneurship activities. Social entrepreneurship has limited capabilities in the sphere of financial risk management, requiring the implementation of risk management at the level of state regulators. However, despite the active development of social entrepreneurship around the world and the adoption of the SDGs in national strategies, not enough attention has been paid to financial risk management in social entrepreneurship at the national level due to the inflexibility of institutions.
The third issue is global inequality, due to which the financial risks entailed by social entrepreneurship in emerging economies are large and cannot be easily overcome. This is because emerging economies are peculiar in that they posses the largest and most chronic financial resource deficits and less favourable—on the whole—investment climates (as compared to advanced economies). In addition to this, the effectiveness of institutions in emerging economies is also low, making them less flexible and hindering government support in managing the financial risks entailed by social entrepreneurship (Kliestik et al. 2018; Kovacova et al. 2019).
The purpose of this article is to clarify specific financing factors and financial risks related to social entrepreneurship in developing countries (in comparison with the standard financial risks entailed by commercial entrepreneurship), to analyze the influence of the financing factors related to social entrepreneurship on sustainable development, and to determine the potential for social entrepreneurship development through financial risk management.
To achieve this goal, the article performs dataset modelling of financial risk management in social entrepreneurship for sustainable development in emerging economies. The subject of this research is the social entrepreneurship index, standard financial factors related to commercial entrepreneurship, and those specific factors of financing sustainable development in developing countries, for which statistics of specific factors of financing sustainable development are available. The study is based on data for 2021 (at the end of 2020).
This paper’s originality consists of implementing not traditional but dataset modelling of financial risk management in social entrepreneurship. This allows (not due to the use of the report but the use of the dataset on social entrepreneurship) for the systemic (comprehensive and complex) consideration of all manifestations of social entrepreneurship—corporate responsibility (social and ecological) and non-commercial activities. This allows us to fill the gap in the statistical data, which are given fragmentarily (only in one of three directions) in the existing reports.
The novelty of this paper consists of considering the specifics of social entrepreneurship during the identification of its special financial risks. This allows us to avoid the association of social entrepreneurship with commercial entrepreneurship during the study of financial risks and incorrect results. Instead, we obtain precise results that are correct for social entrepreneurship. This paper’s uniqueness consists of the consideration of the emerging economies’ experience and the specifics of the manifestation of financial risks of social entrepreneurship in them.
This introduction is followed by the materials (literature review and gap analysis) and methods (methodology and logic of testing the offered hypotheses, the empirical basis of the research). Then, the research results are given, which are followed by the conclusions of our research.
2. Materials and Methods
2.1. Theoretical Basis, Literature Review and Gap Analysis
The theoretical basis of this research is the concept of financial risk and risk management, sustainable development, and social entrepreneurship (in which it is opposed to commercial entrepreneurship). The concept of categorising countries by the criterion of the level of income and level of markets’ development, according to which emerging and advanced economies are distinguished.
This research is based on the following work in the sphere of financial risk management in entrepreneurship: Bakos and Dumitrașcu (2021), Bouri et al. (2021), Dalwai and Salehi (2021), Duygun et al. (2020), Elkhal (2019), Lasloom (2021), Locurcio et al. (2021), Sabău et al. (2021), and Syed and Bawazir (2021).
We also use the materials of the following works on the topic of the contribution of social entrepreneurship to sustainable development: Al-Omoush et al. (2021), Cardella et al. (2021), Chandra et al. (2021), Fhiri et al. (2021), Fridhi (2021), Méndez-Picazo et al. (2021), Popkova et al. (2020), Sahrakorpi and Bandi (2021), Setiawan et al. (2021), Suseno and Abbott (2021), and Thörnqvist and Kilstam (2021).
We also use the published materials on the topic of sustainable development and implementation of the SDGs in emerging economies of such researchers as Alam et al. (2021), Galindo-Martín et al. (2021), Hassani et al. (2021), Sebestyén and Abonyi (2021), Tabares (2021), Tang et al. (2021), and Ullah et al. (2021).
The literature review on this research problem has shown a high level of elaboration and a lack of solutions due to specific research gaps. The first gap is the incompleteness of the current statistics in social entrepreneurship, which does not allow for its precise and correct measurement. The second gap is the uncertainty surrounding the specifics of social entrepreneurship’s financial risks. The third gap is the poor elaboration of the experience and insufficient information on the specifics of social entrepreneurship and its financial risks in emerging economies. These gaps predetermine the three following research questions (RQ).
RQ1: What (exactly) are the financial risks of social entrepreneurship?
Based on the specifics of investments in sustainable development, which is noted and emphasised in the works (Azmat et al. 2021; Chen 2021; Staszkiewicz and Werner 2021), we offer Hypothesis 1:
Hypothesis 1 (H1).
The financial risks of social entrepreneurship differ from the standard financial risks of commercial entrepreneurship (receipt of credits, protection of minority investors, taxation, and solution to non-solvency).
RQ2: What is the correct way of managing the financial risks of social entrepreneurship?
Based on the existing publications (He and Yan 2020; Lee 2020; Zhang and Wang 2021), which note the contradictory impact of the factors of financing on sustainable development, we offer Hypothesis 2:
Hypothesis 2 (H2).
The financial risk management of social entrepreneurship should be flexible and take into account the multidirectional impact of the factors of financing on it (stimulating some factors and restraining other factors).
RQ3: What is the potential of social entrepreneurship’s development by means of management (maximum reduction) of financial risks?
According to the accumulated scientific knowledge in the sphere of market economies, given in the works (Graafland and Wells 2021; Shao et al. 2021; Wut et al. 2021), we also offer Hypothesis 3:
Hypothesis 3 (H3).
Despite its non-commercial nature, social entrepreneurship faces large financial risks and largely depends on their overcoming—that’s why social entrepreneurship would demonstrate significant progress with minimal financial risks.
This paper aims to perform the dataset modelling of the financial risk management in social entrepreneurship for sustainable development in emerging economies.
2.2. Methodology and Empirical Basis of the Research
To check Hypothesis 1, we use the method of regression analysis. We determine the regression dependence of the social entrepreneurship index on the standard financial factors of commercial entrepreneurship (receipt of credits, protection of minority investors, taxation, and solution to non-solvency—given in Table 1) and on the specific factors of financing sustainable development (given in Table 2).
To check the reliability of the regression models, we perform an F test (by F criterion). The research sample includes all (11) emerging economies for which the statistics on the specific factors of financing sustainable development are available in the materials of the World Economic Forum (2021). To form a sufficient volume of panel data, statistical data on 26 developed countries included in the World Economic Forum (2021) rating are also collected and used in this article.
The logic of testing the offered hypothesis is as follows: the regression dependence of social entrepreneurship on the specific factors of financing sustainable development alone should be observed, and for the standard financial factors of commercial entrepreneurship, the F test must not be passed (the model has to be insufficiently reliable).
The source of the data on social entrepreneurship is the dataset “Social entrepreneurship in the global economy: from virtual scores to big data”. We perform dataset modelling, the advantage of which is the fullest consideration of the manifestations of social entrepreneurship. The relevant statistics are given in the dataset and the calculated integral index is given (Figure 1 and Figure 2).
As shown in Figure 1, the highest level of development of social entrepreneurship in emerging economies of the sample in 2021 (as a result of 2020) is observed in Russia (61.15 points), and the lowest in Slovakia (34.70 points) and Argentina (34.61 points).
As shown in Figure 2, the highest level of development of social entrepreneurship in developed economies of the sample in 2021 (as a result of 2020) is observed in New Zealand (84.173 points), and the lowest in Hungary (34.302 points).
To test Hypothesis 2, we use comparative and logical methods; we determine and compare the impact of financing factors on social entrepreneurship. The economic and mathematical sense of the offered hypothesis is as follows: the regression coefficients in the model of social entrepreneurship’s dependence on the specific factors of sustainable development financing must have positive and negative signs.
To test Hypothesis 3, we use the substitution method. We put the optimal values of the financial factors in the obtained regression models (the reliability of which has been confirmed). We also use the trend analysis method to determine the social entrepreneurship index’s growth (dynamics of change) compared to the current level (2021). The hypothesis is deemed proven if financial risk management leads to an increase in the social entrepreneurship index of more than 50%.
Additionally, this article uses the dynamic research method to get more accurate results. Dynamic modelling of the impact of financial risks on social entrepreneurship is carried out using the example of Russia as a vivid example of a developing country (part of BRICS). The Sustainable Development Vector Index (MRSV) calculated by the Moscow Exchange (2021a) is used as an indicator of social entrepreneurship. The Index of the Moscow Exchange (IMOEX), also calculated by the Moscow Exchange (2021b), serves as an indicator of financial risk.
The Sustainable Development Vector Index (MRSV) is relatively new—it has been calculated since 21 September 2020. Therefore, to obtain a large enough sample, this article uses monthly data from both indices. The dynamics of the values of these indices from 21 September 2020 to 11 November 2021 is given in the Supplementary Materials to this article (since the data table contains 292 observations—it is too large to be included in the text of the article). If a negative dependence is revealed in the function IMOEX = F (MRSV), indicating the negative impact of financial risk on social entrepreneurship in Russia, this will provide additional confirmation of the hypothesis put forward.
3. Results
For dataset modelling of the impact of the financial factors on social entrepreneurship in emerging economies, let us consider regression analysis results. Before modelling, we will analyze the multicollinearity of the variables selected for the study. For this, their cross-correlation is calculated in Table 3.
The results of the correlation analysis from Table 3 indicate the absence of multicollinearity. First, let us consider the dependence of social entrepreneurship on the standard financial factors of commercial entrepreneurship (Table 4).
The correlation of the indicators (60.9%) is moderate. Estimate F equals 4.5696. Table F at 11 observations and 4 factor variables (k1 = m = 4, k2 = n − m − 1 = 37 − 4 − 1 = 32) at the significance level α = 0.05 equals 2.14. Since table F exceeds estimate F (4.5696 > 2.14), the F test is not passed, and the regression equation is insufficiently reliable at the set α. The influence of all considered factors on social entrepreneurship turns out to be negative—this is evidenced by the negative values of the coefficients. Let us carry out a correspondence analysis—for this, we estimate the linearity (Figure 3) and diagnose the model assumptions (Figure 4).
The graphs in Figure 3 indicate that the relationship of the resulting variable with all factorial variables is quite reliably described by linear regression.
The graphs in Figure 4 indicate the homogeneity of the dispersion of the residues. Now let us consider the dependence of social entrepreneurship on the specific factors of sustainable development financing (Table 5).
The correlation of the indicators (63.10%) is moderate. Estimate F equals 5.2921. Table F at 11 observations and 4 factor variables (k1 = m = 4, k2 = n − m − 1 = 11 − 4 − 1 = 6) at the significance level α = 0.05 equals 0.14. Since estimate F exceeds the table (5.2921 > 2.14), the F test is passed, and the regression equation is reliable at the set α. Let us carry out a correspondence analysis. For this, we estimate the linearity (Figure 5) and diagnose the model assumptions (Figure 6).
The graphs in Figure 5 indicate that the relationship of the resulting variable with all factorial variables is quite reliably described by linear regression.
The graphs in Figure 6 indicate the homogeneity of the dispersion of the residues. This allows for the compilation of a regression model of social entrepreneurship’s dependence on the specific factors of sustainable development financing in emerging economies:
SEPR = 1,602,665 + 0.1693 msr1 − 0.3647 msr2 + 0.3685 msr3 − 0.4548 msr1
According to this regression model, social entrepreneurship in emerging economies is peculiar for the following financial risks:
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- reduction in stimuli for using financial resources in long-term investments, which disrupts stability and decreases inclusion: an increase in msr1 of 1 point leads to an increase in the social entrepreneurship index of 0.1693 points;
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- joint public–private investments; reduction in investments in R&D: an increase in msr2 of 1 point leads to a decrease in the social entrepreneurship index of 0.3647 points;
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- decrease in investment in R&D: increase in msr3 of 1 point leads to an increase in the social entrepreneurship index of 0.3685 points;
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- expand investment in the skills needed for jobs and “markets of tomorrow”: increase in msr1 of 1 point leads to an increase in the social entrepreneurship index of 0.4548 points.
Let us insert the optimal values of the financial factors into the obtained regression model (1). The method of trend analysis is used to determine the growth (dynamics of change) of the indicators as compared to the current level (2021) (Figure 7).
Figure 3 shows that managing the financial factors that negatively influence social entrepreneurship (facilitate the creation of “markets of tomorrow”, especially in areas that require public–private collaboration; expand investment in the skills needed for jobs and “markets of tomorrow”) prevented the growth of the impact (to prevent the risk increase). This fact explains that support for social entrepreneurship in sustainable development is not the only priority of emerging economies. Thus, they cannot adapt their financial systems only to this priority at the expense of other preferences.
Increase the stimuli for using financial resources in long-term investments, which disrupts the stability and decreases inclusion by 84.87% (up to the maximum 100 points). An increase in investments in R&D of 187.5% (also to 100 points) leads to a rise in the social entrepreneurship index of 99.61% (up to 90.18 points).
Additionally, we constructed a dynamic regression model of the impact of financial risks on social entrepreneurship in Russia. The dependence of the Sustainable Development Vector Index (MRSV) on the Index of the Moscow Exchange (IMOEX) is given in Table 6.
The correlation of the indicators (99.44%) is very high; however, the variables do not duplicate each other (multicollinearity is absent). Estimate F equals 25,506.1466. Table F at 292 observations and 1 factor variable (k1 = m = 1, k2 = n – m − 1 = 292 – 1 − 1=290) at the significance level α = 0.05 equals 3.92. Since estimate F exceeds the table (25,506.1466 > 3.92), the F test is passed, and the regression equation is reliable at the set α. Let us carry out a correspondence analysis—for this, we estimate the linearity and diagnose the model assumptions (Figure 8).
The graphs in Figure 8 indicate that the relationship of the resulting variable with all factorial variables is quite reliably described by linear regression. The graphs also indicate the homogeneity of the dispersion of the residues. This allows us to compile a regression model of social entrepreneurship’s dependence on the specific factors of sustainable development financing in emerging economies:
MRSV = −960,277,188,684.289 IMOEX + 2.37
According to this regression model (2), with an increase in the Index of the Moscow Exchange (IMOEX) of 1 RUB, the Sustainable Development Vector Index (MRSV) is down 960.28 billion rubles. The considered example of Russia is indicative—it demonstrates that social entrepreneurship is indeed largely determined by financial risks. The Russian experience can be extended to other developing countries.
4. Discussion
Thus, the performed dataset modelling of financial risks management in social entrepreneurship for sustainable development in emerging economies has shown that the financial risks of social entrepreneurship differ from the standard financial risks of commercial entrepreneurship. The standard financial factors of commercial entrepreneurship (receipt of credits, protection of minority investors, taxation, and solution to non-solvency) have not demonstrated either high (correlation equals 43%, which is low) or statistically significant connection with social entrepreneurship in emerging economies (Hypothesis 1 has been proven).
We determined specific factors of sustainable development financing in emerging economies, which have shown a stronger impact (correlation—83%) on social entrepreneurship, and the regression model of their dependence is reliable. This allows us to identify the special financial risks of social entrepreneurship in emerging economies:
- −
- reduction of stimuli for using financial resources in long-term investments, which disrupts the stability and decreases inclusion;
- −
- joint public–private investments; decrease in investments in R&D;
- −
- expand investment in the skills needed for jobs and the “markets of tomorrow”.
A contradictory influence of the factors of financing on sustainable development is substantiated. Increased incentives to direct financial resources towards long-term investments strengthen stability, expand inclusion (factor 1), and incentivize and expand patient investments in research, innovation, and invention (factor 3), which have positively impacted social entrepreneurship in emerging economies.
The influence of support for the creation of the “markets of tomorrow”, especially in areas that require public–private collaboration (factor 2) and expansion of investments in skills for the “markets of tomorrow” (factor 4), on social entrepreneurship in emerging economies was negative (Hypothesis 2 has been proven). Therefore, the financial risk management of social entrepreneurship should be flexible in considering the multidirectional financing factors (stimulating some factors and restraining other factors).
Additionally, the large potential of social entrepreneurship’s development by means of management (maximum reduction) of financial risks has been determined. Despite its non-commercial nature, social entrepreneurship in emerging economies faces large financial risks and largely depends on their overcoming. That is why with minimum financial risk—in the case of an increase in risks for using financial resources in long-term investments, which disrupts the stability and decreases inclusion by 84.87% and increase of investments in research, innovation, and investments that could create new “markets of tomorrow” by 187.5%—social entrepreneurship will demonstrate large substantial progress: an increase of 99.61% (more than 50%), from 45.18 points to 90.18 points (Hypothesis 3 has been proven).
The results obtained, firstly, clarified the specifics of investments in sustainable development noted in the works (Azmat et al. 2021; Chen 2021; Staszkiewicz and Werner 2021). Our results also identified unique factors in financing social entrepreneurship for (1) increasing incentives for directing the financial resources into long-term investments, strengthening stability and increasing inclusiveness; (2) promoting the creation of “markets of tomorrow”, especially in areas where public–private cooperation is required; (3) encouraging and expanding investment in research, innovation and inventions that can create new “markets of tomorrow”; (4) updating curricula and increasing investment in skills for work and the “markets of tomorrow”. This showed for the first time that investment in sustainable development needs to take into account both the specifics of social entrepreneurship and developing countries. Unlike in past studies, it has been proven that the standard financial risks of commercial entrepreneurship (obtaining loans, protecting minority investors, taxation, and resolving insolvency) are not universal—they do not apply to social entrepreneurship in developing countries.
Secondly, the article expands on the scientific background of existing work (He and Yan 2020; Lee 2020; Zhang and Wang 2021) on the conflicting influence of financing factors on sustainable development. For the first time, funding factors were shown to have both an enabling and a disincentive effect on social entrepreneurship in developing countries. Based on this, the article proposes flexible management of financial risks of social entrepreneurship (to stimulate some factors and restrain others).
Thirdly, in contrast to existing publications in the field of market economics (Graafland and Wells 2021; Shao et al. 2021; Wut et al. 2021), this article argues that despite the difference in goals (commercial/non-commercial) and funding factors, both commercial and social entrepreneurship face high financial risks and need financial risk management. This conclusion requires a revision of the existing approach to social entrepreneurship management in favour of greater attention to financial risk management.
5. Conclusions
This paper’s contribution to the literature consists of specifying the essence and features of social entrepreneurship in emerging economies by determining unique financial risks and developing recommendations for their management. The scientific significance of this paper consists of the substantiation of the fact that commercial factors (in the form of financial risks) have a more critical role in social entrepreneurship’s development than was previously (in the existing literature) believed.
The potential of social entrepreneurship’s development in emerging economies is implemented (in 2021) only by 50% because of the restraining influence of financial risks. In case of the optimal management of financial risks (their minimisation), the level of social entrepreneurship’s development in emerging economies will approach a maximum, which will reduce their underrun from developed countries in the sphere of implementing SDGs and support global sustainable development.
It should be concluded that a specific limitation of this work’s results is the study of the experience of only emerging economies. The absence of standard financial risks of commercial entrepreneurship with social entrepreneurship could be predetermined by the specifics of emerging economies—underdevelopment of market relations and reduced effectiveness of institutions. Future studies should deal with this limitation and pay attention to the experience of advanced economies and compare it to the experience of emerging economies.
It must also be recognized that the sample of developing countries is limited (due to the unavailability of data for many developing countries) and the variables are measured by indices (due to the lack of more accurate statistics on the topic of social entrepreneurship), which can cause bias in empirical results. The inclusion of developed countries in the sample did not allow for the full consideration of the characteristics of developing countries.
To overcome this limitation, further research is recommended to support the development of global statistics on social entrepreneurship with high empirical specifications and full coverage of developing countries. After more accurate statistics become available, it is advisable to continue the dataset modelling and refine the results obtained in this article.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/risks9120211/s1, Supplementary Materials: data table.
Author Contributions
Conceptualization, E.G.P.; methodology, E.G.P.; investigation, E.G.P.; writing—original draft preparation, E.G.P.; writing—review and editing, B.S.S.; supervision, B.S.S.; project administration, B.S.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Social entrepreneurship index (SEPR) in emerging economies of the sample in 2021 (as a result of 2020), points 1–100. Source: Compiled by the authors based on data from the Institute of Scientific Communications (2021).
Figure 1.
Social entrepreneurship index (SEPR) in emerging economies of the sample in 2021 (as a result of 2020), points 1–100. Source: Compiled by the authors based on data from the Institute of Scientific Communications (2021).
Figure 2.
Social entrepreneurship index (SEPR) in developed economies of the sample in 2021 (as a result of 2020), points 1–100. Source: Compiled by the authors based on data from the Institute of Scientific Communications (2021).
Figure 2.
Social entrepreneurship index (SEPR) in developed economies of the sample in 2021 (as a result of 2020), points 1–100. Source: Compiled by the authors based on data from the Institute of Scientific Communications (2021).
Figure 3.
Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Figure 3.
Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Figure 4.
Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Figure 4.
Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Figure 5.
Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Figure 5.
Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Figure 6.
Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Figure 6.
Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Figure 7.
The perspective of social entrepreneurship’s development in emerging economies through the reduction of financial risk. Source: Authors.
Figure 7.
The perspective of social entrepreneurship’s development in emerging economies through the reduction of financial risk. Source: Authors.
Figure 8.
Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Figure 8.
Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Table 1.
Standard financial factors of commercial entrepreneurship in emerging economies of the sample in 2021 (as a result of 2020), position (the higher, the better). Source: Compiled by the authors based on the World Bank (2021).
Table 1.
Standard financial factors of commercial entrepreneurship in emerging economies of the sample in 2021 (as a result of 2020), position (the higher, the better). Source: Compiled by the authors based on the World Bank (2021).
| Country Category | Country | Receipt of Credits | Protection of Minority Investors | Taxation | Solution to Non-Solvency |
|---|---|---|---|---|---|
| Fr1 | Fr2 | Fr3 | Fr4 | ||
| Developing countries | Argentina | 104 | 61 | 170 | 111 |
| Brazil | 104 | 61 | 184 | 77 | |
| Chile | 94 | 51 | 86 | 53 | |
| China | 80 | 28 | 105 | 51 | |
| India | 25 | 13 | 115 | 52 | |
| Indonesia | 48 | 37 | 81 | 38 | |
| Mexico | 11 | 61 | 120 | 33 | |
| Russia | 25 | 72 | 58 | 57 | |
| Slovakia | 48 | 88 | 55 | 46 | |
| South Africa | 80 | 13 | 54 | 68 | |
| Turkey | 37 | 21 | 26 | 120 | |
| Developed countries | Finland | 132 | 72 | 95 | 65 |
| New Zealand | 1 | 3 | 9 | 36 | |
| Sweden | 80 | 28 | 31 | 17 | |
| Austria | 94 | 37 | 44 | 22 | |
| Japan | 94 | 57 | 51 | 3 | |
| Denmark | 48 | 28 | 8 | 6 | |
| France | 104 | 45 | 61 | 26 | |
| Ireland | 48 | 13 | 4 | 19 | |
| Israel | 48 | 18 | 13 | 29 | |
| Belgium | 67 | 45 | 63 | 9 | |
| Australia | 4 | 57 | 28 | 20 | |
| Estonia | 48 | 79 | 12 | 54 | |
| Netherlands | 119 | 79 | 22 | 7 | |
| Italy | 119 | 51 | 128 | 21 | |
| Germany | 48 | 61 | 46 | 4 | |
| Republic of Korea | 67 | 25 | 21 | 11 | |
| Canada | 15 | 7 | 19 | 13 | |
| UK | 37 | 7 | 27 | 14 | |
| Greece | 119 | 37 | 72 | 72 | |
| Portugal | 119 | 61 | 43 | 15 | |
| Poland | 37 | 51 | 77 | 25 | |
| Spain | 80 | 28 | 35 | 18 | |
| Switzerland | 67 | 105 | 20 | 49 | |
| Czech Republic | 48 | 61 | 53 | 16 | |
| Hungary | 37 | 97 | 56 | 66 | |
| USA | 4 | 36 | 25 | 2 |
Table 2.
Specific factors of financing of sustainable development in emerging economies of the sample in 2021 (as a result of 2020), points 1–100.
Table 2.
Specific factors of financing of sustainable development in emerging economies of the sample in 2021 (as a result of 2020), points 1–100.
| Country Category | Country | Increase Incentives to Direct Financial Resources towards Long-Term Investments, Strengthen Stability and Expand Inclusion | Facilitate the Creation of “Markets of Tomorrow”, Especially in Areas that Require Public-Private Collaboration | Incentivize and Expand Patient Investments in Research, Innovation and Invention That Can Create the New “Markets of Tomorrow” | Update Education Curricula and Expand Investment in the Skills Needed for Jobs and the “Markets of Tomorrow” |
|---|---|---|---|---|---|
| msr1 | msr2 | msr3 | msr4 | ||
| Developing countries | Argentina | 32.8 | 34.3 | 31.9 | 46.9 |
| Brazil | 60.3 | 38.0 | 36.2 | 39.5 | |
| Chile | 57.5 | 39.7 | 31.7 | 52.1 | |
| China | 72.8 | 49.7 | 50.0 | 67.0 | |
| India | 54.5 | 40.2 | 32.5 | 43.5 | |
| Indonesia | 59.7 | 45.0 | 45.6 | 49.0 | |
| Mexico | 49.0 | 35.7 | 27.2 | 43.3 | |
| Russia | 55.3 | - | 35.6 | 44.9 | |
| Slovakia | 54.7 | 39.3 | 31.3 | 46.5 | |
| South Africa | 48.6 | 35.6 | 31.7 | 42.6 | |
| Turkey | 49.8 | 38.5 | 28.9 | 39.8 | |
| Developed countries | Finland | 95.4 | 59.5 | 53.4 | 75.3 |
| New Zealand | 93.2 | 45.0 | 45.2 | 63.4 | |
| Sweden | 89.0 | 52.2 | 50.8 | 69.4 | |
| Austria | 88.3 | 47.3 | 38.8 | 60.6 | |
| Japan | 84.7 | 53.5 | 54.7 | 51.3 | |
| Denmark | 84.6 | 46.7 | 41.7 | 71.5 | |
| France | 83.0 | 50.1 | 50.8 | 56.8 | |
| Ireland | 81.9 | 46.6 | 36.1 | 59.5 | |
| Israel | 81.77 | 51.2 | 53.1 | 66.6 | |
| Belgium | 81.2 | 49.3 | 47.8 | 65.8 | |
| Australia | 81.2 | 44.0 | 42.9 | 63.5 | |
| Estonia | 81.1 | 44.9 | 43.4 | 56.8 | |
| Netherlands | 79.9 | 50.4 | 48.3 | 71.8 | |
| Italy | 79.8 | 43.0 | 36.9 | 40.7 | |
| Germany | 79.3 | 48.1 | 49.2 | 61.4 | |
| Republic of Korea | 78.3 | 46.7 | 53.4 | 60.0 | |
| Canada | 75.1 | 49.5 | 42.8 | 65.3 | |
| UK | 72.4 | 46.1 | 40.9 | 59.7 | |
| Greece | 68.3 | 36.0 | 25.2 | 38.7 | |
| Portugal | 67.1 | 44.6 | 42.2 | 49.8 | |
| Poland | 62.7 | 37.5 | 32.1 | 41.9 | |
| Spain | 59.7 | 44.4 | 40.4 | 51.4 | |
| Switzerland | 59.2 | 50.8 | 51.6 | 70.8 | |
| Czech Republic | 58.2 | 41.9 | 40.2 | 48.5 | |
| Hungary | 52.0 | 39.4 | 36.7 | 40.8 | |
| USA | 47.8 | 57.7 | 57.3 | 68.2 |
Source: Compiled by the authors based on data from the World Economic Forum (2021).
Table 3.
Dataset modelling of the impact of the standard financial factors of commercial entrepreneurship on social entrepreneurship.
Table 3.
Dataset modelling of the impact of the standard financial factors of commercial entrepreneurship on social entrepreneurship.
| SEPR | Fr1 | Fr2 | Fr3 | Fr4 | msr1 | msr2 | msr3 | msr4 | |
|---|---|---|---|---|---|---|---|---|---|
| SEPR | 1 | - | - | - | - | - | - | - | - |
| Fr1 | −0.30 | 1 | - | - | - | - | - | - | - |
| Fr2 | −0.34 | 0.24 | 1 | - | - | - | - | - | - |
| Fr3 | −0.42 | 0.37 | 0.21 | 1 | - | - | - | - | - |
| Fr4 | −0.49 | 0.15 | 0.15 | 0.47 | 1 | - | - | - | - |
| msr1 | 0.47 | 0.18 | −0.20 | −0.41 | −0.53 | 1 | - | - | - |
| msr2 | 0.27 | 0.18 | −0.15 | −0.28 | −0.43 | 0.51 | 1 | - | - |
| msr3 | 0.51 | 0.05 | 0.00 | −0.39 | −0.51 | 0.54 | 0.67 | 1 | - |
| msr4 | 0.57 | −0.03 | −0.10 | −0.51 | −0.49 | 0.62 | 0.65 | 0.76 | 1 |
Source: Obtained by the authors automatically with the help of the “Correlation” function in Microsoft Excel.
Table 4.
Dataset modelling of the impact of the standard financial factors of commercial entrepreneurship on social entrepreneurship.
Table 4.
Dataset modelling of the impact of the standard financial factors of commercial entrepreneurship on social entrepreneurship.
| Regression Statistics | ||||||
| Multiple R | 0.6029 | |||||
| R-square | 0.3635 | |||||
| Adjusted R-square | 0.2840 | |||||
| Standard error | 10.1788 | |||||
| Observations | 37 | |||||
| Dispersion analysis | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 4 | 1893.7802 | 473.4450 | 4.5696 | 0.0049 | |
| Residue | 32 | 3315.4357 | 103.6074 | |||
| Total | 36 | 5209.2159 | ||||
| Coefficients | Standard Error | t-Statistics | p-Value | Lower 95% | Upper 95% | |
| Constant | 67.9504 | 4.3443 | 15.6412 | 0.0000 | 59.1013 | 76.7996 |
| Coefficient at Fr1 | −0.0456 | 0.0499 | −0.9137 | 0.3677 | −0.1472 | 0.0560 |
| Coefficient at Fr2 | −0.1029 | 0.0675 | −1.5254 | 0.1370 | −0.2403 | 0.0345 |
| Coefficient at Fr3 | −0.0417 | 0.0467 | −0.8921 | 0.3790 | −0.1368 | 0.0535 |
| Coefficient at Fr4 | −0.1515 | 0.0663 | −2.2859 | 0.0290 | −0.2865 | −0.0165 |
Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Table 5.
Dataset modelling the impact of the specific factors of sustainable development financing on social entrepreneurship.
Table 5.
Dataset modelling the impact of the specific factors of sustainable development financing on social entrepreneurship.
| Regression Statistics | ||||||
| Multiple R | 0.6310 | |||||
| R-square | 0.3981 | |||||
| Adjusted R-square | 0.3229 | |||||
| Standard error | 9.8983 | |||||
| Observations | 37 | |||||
| Dispersion analysis | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 4 | 2073.9920 | 518.4980 | 5.2921 | 0.0022 | |
| Residue | 32 | 3135.2239 | 97.9757 | |||
| Total | 36 | 5209.2159 | ||||
| Coefficients | Standard Error | t-Statistics | p-Value | Lower 95% | Upper 95% | |
| Constant | 16.2665 | 9.3239 | 1.7446 | 0.0907 | −2.7258 | 35.2587 |
| Coefficient at msr1 | 0.1693 | 0.1398 | 1.2112 | 0.2347 | −0.1154 | 0.4540 |
| Coefficient at msr2 | −0.3647 | 0.2427 | −1.5031 | 0.1426 | −0.8590 | 0.1295 |
| Coefficient at msr3 | 0.3685 | 0.3192 | 1.1544 | 0.2569 | −0.2817 | 1.0187 |
| Coefficient at msr4 | 0.4548 | 0.2528 | 1.7990 | 0.0815 | −0.0602 | 0.9698 |
Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
Table 6.
Dataset modelling of the impact of the Index of the Moscow Exchange (IMOEX) on the Sustainable Development Vector Index (MRSV) in Russia from 21 September 2020 to 11 November 2021.
Table 6.
Dataset modelling of the impact of the Index of the Moscow Exchange (IMOEX) on the Sustainable Development Vector Index (MRSV) in Russia from 21 September 2020 to 11 November 2021.
| Regression Statistics | ||||||
| Multiple R | 0.9944 | |||||
| R-square | 0.9888 | |||||
| Adjusted R-square | 0.9887 | |||||
| Standard error | 5.82 × 1011 | |||||
| Observations | 292 | |||||
| Dispersion analysis | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 1 | 8.64 × 1027 | 8.64 × 1027 | 25,506.1466 | 1.1 × 10−284 | |
| Residue | 290 | 9.83 × 1025 | 3.39 × 1023 | |||
| Total | 291 | 8.74 × 1027 | ||||
| Coefficients | Standard Error | t-Statistics | P-Value | Lower 95% | Upper 95% | |
| Constant | −9.6 × 1011 | 2.68 × 1011 | −3.58439 | 0.0004 | −1.5 × 1012 | −4.3 × 1011 |
| Coefficient at IMOEX | 2.3668 | 0.0148 | 159.7064 | 1.11 × 10−284 | 2.3377 | 2.3960 |
Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel.
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Popkova, E.G.; Sergi, B.S. Dataset Modelling of the Financial Risk Management of Social Entrepreneurship in Emerging Economies. Risks 2021, 9, 211. https://doi.org/10.3390/risks9120211
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Popkova EG, Sergi BS. Dataset Modelling of the Financial Risk Management of Social Entrepreneurship in Emerging Economies. Risks. 2021; 9(12):211. https://doi.org/10.3390/risks9120211
Chicago/Turabian StylePopkova, Elena G., and Bruno S. Sergi. 2021. "Dataset Modelling of the Financial Risk Management of Social Entrepreneurship in Emerging Economies" Risks 9, no. 12: 211. https://doi.org/10.3390/risks9120211
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