Corresponding author: Ivan V. Rozmainsky ( irozmain@yandex.ru ) © 2021 Nonprofit partnership “Voprosy Ekonomiki”.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BYNCND 4.0), which permits to copy and distribute the article for noncommercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Tretyakov DS, Rozmainsky IV (2021) An empirical analysis of the influence of financialization on investment in Russia. Russian Journal of Economics 7(3): 233249. https://doi.org/10.32609/j.ruje.7.58419

This paper tries to estimate the impact of financialization on fixed investment in Russia. The work is carried out by using panel data based on reports of nonfinancial publicly listed companies for 1999–2019. The study finds that financial expenses aimed at paying interest on external financing and paying dividends — that is, focusing on shareholder value, and hence decreasing the internal funds of companies, reduce real investments. Financial incomes have shown the crowdingout effect for large companies. Financial incomes as additional “free” funds in large companies are not perceived as an opportunity to accumulate fixed assets. Managers prefer to increase financial investments instead of real ones. In small and mediumsized companies, financial incomes, however, drive the growth of physical investment. This is because small firms, at a particular stage in their lives, find it more profitable to invest in their own growth. The results from the general sample, without dividing by size, indicate that financialization in Russia clearly reduces real investment.
financialization, investment, panel data, nonfinancial companies, Russian economy.
Over the past few decades, there has been a significant strengthening of financial markets and institutions in the global economy. The number of financial transactions and the amount of funds invested in financial assets are constantly growing. As a result, economic policy, the behavior of individual firms, and the structure of financial markets are changing. The functioning of economic systems at both the macro and micro levels is being transformed. This process is called financialization. We use a a definition of financialization formulated by
The mainstream literature — more specifically, the neoclassical one — on financialization and economic growth states that financial markets contribute to financing and to the efficient allocation of investments. However, in recent years, this thesis has been repeatedly questioned. It turns out that the impact of financialization on investment depends on institutional features, as well as on the time period in which the financial sector is strengthening.
In this paper, the effects of financialization on physical investment in Russia will be analyzed using the PostKeynesian approach. This study seeks to find out how such parameters of financialization as interest and dividend payments, and financial incomes, affect investments in the real sector. This inquiry will be conducted for Russian publicly listed companies in the nonfinancial sector only.
Recent research shows that an unambiguous approach to the study of financialization is not correct. In certain circumstances, this process may increase investment, while in others it may significantly reduce it. For example,
Nowadays, there are quite a lot of macrostudies of financialization, but few economists — like
Financialization is the process of strengthening the role of the financial sector and making it dominate the real one.
The effects of financialization can be observed in three ways (
(1) changing the structure and functioning of financial markets;
(2) changing the behavior of real sector companies;
(3) changing economic policy.
According to
Mainstream economic theory played an important role in legitimizing financialization. First, in the sphere of relations between companies and financial markets, mainstream theory assumed that the main aim of corporate governance was to coordinate the interests of managers and financial market participants. Moreover, the theory suggests that the company’s only goal is to maximize shareholder returns. Other corporate goals and the interests of other stakeholders were not taken into account (Palley, 2013).
Furthermore, neoclassical economics states that strengthening the positions of financial markets increases the efficiency of the economy due to the efficient allocation of investment. As
The increase in the percentage of managers’ compensation based on stock options has increased the motivation of company managers to maintain high stock prices in the short term by paying high dividends and making large share buybacks. The growing number of institutional investors who demand everrising stock prices has forced managers to raise the payout ratio (
Several PostKeynesian papers — mentioned both above and below — describe the negative impact of financialization on investment, income distribution and aggregate demand. In the process of financialization, interest and dividend payments and share repurchase of nonfinancial companies are increasing. As a result, companies have less funds for physical investment in the real sector.
Now we move to a consideration of those papers and lay the foundations for our own empirical analysis.
This study will be based on the assumptions of the Keynesian investment model. The PostKeynesian model developed by
The empirical model looks like this:
INVST = a_{0} + a_{1} L (SALES) + a_{2} L (IFIN) + a_{3} L (INTEXP) + a_{4} (GPLANT), (1)
where: INVEST — annual investment; SALES — net annual sales; IFIN — internal finance defined as profit after tax plus depreciation and amortization minus ordinary and preferred dividends; INTEXP — net annual interest expense; GPLANT — book value of the company’s gross output; L refers to lags.
Empirical data show that cash flows are important constraints on investment. In particular, the fundamental contribution of
However, the main weakness of the authors’ research is the lack of indicators that reflect the financial incomes of the firm, which is an important aspect of financialization. Moreover, the researchers include interest payments, but ignore dividend payments.
${\left(\frac{I}{K}\right)}_{it}={\beta}_{0}+{\beta}_{1}\sum _{j=1}^{2}{\left(\frac{I}{K}\right)}_{itj}+{\beta}_{2}\sum _{j=1}^{2}{\left(\frac{\pi CD}{K}\right)}_{itj}+{\beta}_{3}\sum _{j=1}^{2}{\left(\frac{S}{K}\right)}_{itj}+\phantom{\rule{0ex}{0ex}}+{\beta}_{4}\sum _{j=1}^{2}{\left(\frac{F}{K}\right)}_{itj}+{\beta}_{3}\sum _{j=1}^{2}{\left(\frac{{\pi}_{F}}{K}\right)}_{itj}+{\beta}_{t}+{\epsilon}_{it}$, (2)
where: I — gross addition to fixed assets; π — operating income; CD — cash dividends; S — net sales; F — interest and dividend payments; π_{F} — financial incomes; K — capital.
As a result, it turned out that in addition to financial expenses caused by external financing, the total financial incomes represented by interest and dividends have a significant and, most importantly negative, impact on physical investments. This conclusion means that financial investments crowd out physical ones. Moreover, the authors evaluated the model for small and large companies and found that financial incomes have a positive impact on investment in small companies, but in large ones it remained negative.
According to the results obtained by the authors, the growing focus on external financing and internal substitution of fixed accumulation by financial activities played a fundamental role in suppressing investment. On the one hand, an increase in financial payments reduces the internal funds of nonfinancial companies. On the other hand, the negative crowdingout effect of financial investments on accumulation more than offsets the benefits of easing restrictions on cash flows. Financial incomes had a positive effect on investment only for small British companies (
Another revealing study on this topic is the work of
The results obtained in the study indicate a negative relationship between financialization and capital accumulation, especially for large firms. The results confirm the point that financialization has negative consequences for firms’ investment behavior.
As we already said,
There are very few studies of financialization in Russia. So, for example,
In Russia, as well as throughout the world, the role of the financial sector is increasing. Nonfinancial companies are increasing their presence in the financial markets by increasing the number and volume of financial transactions. However, some researchers are convinced that the potential for financialization, both internal and external, is not fully used in Russia. Despite growth, corporate borrowing by issuing bonds still remains at a rather low level. Researchers say that the lack of resilience of the Russian financial system is severely hampering financial market growth and investment in the country (
We should remember that until 1991 the Russian economy was part of the Soviet socialist economy and capitalist finances were therefore nonexistent. Both financial markets and the system of commercial/investment banks were absent in the Soviet economy. On the other hand, the expansion of financial markets in Russia after 1991 was unstable because of the lack of relevant skills of participants and the “invasion” of insolvent and fraudulent companies into such markets (
Thus, there is reason to believe that the level of development of financial markets in Russia is not high enough, that is, the process of financialization may contribute to the growth of investments in fixed assets.
To analyze the effects of financialization, we take the model proposed by
$\mathrm{ln}{\left(\frac{I}{K}\right)}_{it}={\beta}_{0}+{\beta}_{1}\mathrm{ln}{\left(\frac{I}{K}\right)}_{it1}+{\beta}_{2}\mathrm{ln}{\left(\frac{\pi div}{K}\right)}_{it1}+{\beta}_{3}\mathrm{ln}{\left(\frac{R}{K}\right)}_{it1}+\phantom{\rule{0ex}{0ex}}+{\beta}_{4}\mathrm{ln}{\left(\frac{IE}{K}\right)}_{it1}+{\beta}_{t}+{\epsilon}_{t}$, (3)
where I represent investments, expressed in capital investments in fixed assets; K is the capital of the company used in the model to normalize the company’s size; π — operating profit; div — paid dividends; (π – div) — retained earnings; R — revenue; IE — interest expense; β_{t} — annual dummy used for control. It is expected to obtain positive coefficients with lags of investments, retained earnings, and revenue. The coefficient on interest expense is expected to be negative, but it may well turn out to be positive for the reasons described earlier.
In the first model (3), dividends are included only as an indicator that is introduced into the model only for calculating retained earnings. In other words, it acts as a parameter that reduces the company’s “free” funds. However, dividends themselves can reflect financialization. The company may decide to pay large dividends in order to increase its shareholder value, instead of launching any investment projects. In other words, through its payment of dividends a company can demonstrate its focus on the financial sector, which is a manifestation of financialization. In this regard, we add dividends to the model:
$\mathrm{ln}{\left(\frac{I}{K}\right)}_{it}={\beta}_{0}+{\beta}_{1}\mathrm{ln}{\left(\frac{I}{K}\right)}_{it1}+{\beta}_{2}\mathrm{ln}{\left(\frac{\pi div}{K}\right)}_{it1}+{\beta}_{3}\mathrm{ln}{\left(\frac{R}{K}\right)}_{it1}+\phantom{\rule{0ex}{0ex}}+{\beta}_{4}\mathrm{ln}{\left(\frac{IE}{K}\right)}_{it1}+{\beta}_{5}\mathrm{ln}{\left(\frac{div}{K}\right)}_{it1}+{\beta}_{t}+{\epsilon}_{t}$. (4)
In this case, the same coefficients are assumed as in the previous model. The coefficient of dividend is expected to be negative.
The interaction of nonfinancial companies with the financial sector is not limited to interest and dividend payments. They can also make nonoperating investments for financial gains. Therefore, we expand the model to include financial incomes (π_{F}), which contain all incomes from the company’s financial activities. The impact of financial incomes on physical investments is difficult to define unequivocally. On the one hand, financial incomes can increase the amount of available funds for sale, which can allow companies to increase their investments in fixed assets. On the other hand, a focus on financial investment can affect agents negatively. Financial investments are often much less risky and reversible. Moreover, they are no less profitable than physical ones. In this regard, the increase in financial incomes by nonfinancial companies may generate a decrease in real investments. Here is the model with the effect of financial incomes:
$\mathrm{ln}{\left(\frac{I}{K}\right)}_{it}={\beta}_{0}+{\beta}_{1}\mathrm{ln}{\left(\frac{I}{K}\right)}_{it1}+{\beta}_{2}\mathrm{ln}{\left(\frac{\pi div}{K}\right)}_{it1}+{\beta}_{3}\mathrm{ln}{\left(\frac{R}{K}\right)}_{it1}+{\beta}_{4}\mathrm{ln}{\left(\frac{IE}{K}\right)}_{it1}+\phantom{\rule{0ex}{0ex}}+{\beta}_{5}\mathrm{ln}{\left(\frac{div}{K}\right)}_{it1}+{\beta}_{6}\mathrm{ln}{\left(\frac{{\pi}_{F}}{K}\right)}_{it1}+{\beta}_{t}+{\epsilon}_{t}.$. (5)
In theory, the coefficient of financial incomes could be either positive or negative, but we expect to see a negative impact.
Next, we combine the dividend and interest payments into financial expenses (FE). Thus, we get a model in which financialization is characterized by two parameters: financial expenses (interest and dividend payments) and financial incomes. The model is presented below:
$\mathrm{ln}{\left(\frac{I}{K}\right)}_{it}={\beta}_{0}+{\beta}_{1}\mathrm{ln}{\left(\frac{I}{K}\right)}_{it1}+{\beta}_{2}\mathrm{ln}{\left(\frac{\pi div}{K}\right)}_{it1}+{\beta}_{3}\mathrm{ln}{\left(\frac{R}{K}\right)}_{it1}+{\beta}_{4}\mathrm{ln}{\left(\frac{FE}{K}\right)}_{it1}+\phantom{\rule{0ex}{0ex}}+{\beta}_{5}\mathrm{ln}{\left(\frac{div}{K}\right)}_{it1}+{\beta}_{6}\mathrm{ln}{\left(\frac{{\pi}_{F}}{K}\right)}_{it1}+{\beta}_{t}+{\epsilon}_{t}$. (6)
The coefficient for financial expenses in general, as well as individually, is expected to be negative.
We assume that financialization, and specifically the effect of financial incomes, can have different effects on large and medium and small companies. Large companies are usually more focused on the financial market and make large financial investments. In turn, medium and small companies are more focused on their own expansion and buildup of fixed assets. In this regard, we include a dummy variable (D_{big25}) to highlight large firms. It takes a value of 0, the average total assets of the company are less than the 75^{th} percentile, and a value of 1 if greater. We get the following specification:
$\mathrm{ln}{\left(\frac{I}{K}\right)}_{it}={\beta}_{0}+{\beta}_{1}\mathrm{ln}{\left(\frac{I}{K}\right)}_{it1}+{\beta}_{2}\mathrm{ln}{\left(\frac{\pi div}{K}\right)}_{it1}+{\beta}_{3}\mathrm{ln}{\left(\frac{R}{K}\right)}_{it1}+{\beta}_{4}\mathrm{ln}{\left(\frac{FE}{K}\right)}_{it=1}+\phantom{\rule{0ex}{0ex}}+{\beta}_{5}\mathrm{ln}{\left(\frac{{\pi}_{F}}{K}\right)}_{it1}+{\beta}_{6}\mathrm{ln}{\left(\frac{{\pi}_{F}}{K}{D}_{big25}\right)}_{it1}+{\beta}_{t}+{\epsilon}_{t}.$. (7)
The difference between this model and the previous one is that here the effect of financial incomes of small and mediumsized companies will be β_{5}, and for large ones, the sum is β_{5} and β_{6}. This way we can trace the differences between the impact of financialization on physical investment in large and small companies.
In this specification, we assume that the coefficient β_{6} will be negative; moreover, it will cancel out the positive effect of β_{5}.
The data was collected from the Thomson Reuters financial information database on the financial statements of publicly listed companies. The base contains information about both standardized indicators of the balance sheet and standardized information about financial incomes and payments. This allows the most effective study of financialization, since the problem of different approaches to the formation of reporting disappears. The study uses data on all functioning and nonfunctioning publicly registered nonfinancial companies in Russia. Data are taken for 1999–2019. Earlier period was ignored due to transformation recessioninspired collapse of fixed investment (1991–1998 crisis). Descriptive statistics are presented in Appendix Table
The sample looks like an unbalanced panel data, as Russian firms often do not provide complete financial information for each year. It is important to note that the number of data gaps is quite large, which greatly complicates the study. Using a balanced sample is not advisable, as estimates can be biased. This is because a large number of companies will have to be excluded just because they did not provide complete information for all the years of operation. Moreover, only the companies that have been operating for the entire period under study will remain. Thus, the sample may turn out to be unrepresentative and give inadequate estimates. Data at the company level often suffers from outliers, and hence the need to filter such data carefully. Several steps have been taken to combat anomalies. The first step was to exclude from the study companies that did not have data on any metric for more than ten years. Secondly, 1% of observations on each side of the distribution of variables were adjusted by the Windsor method. Using this method allows for not discarding the extreme members of the selection, but rather replacing them with ones closest to them from the remaining values. Furthermore, companies with persistent negative operating income were excluded.
To exclude the presence of multicollinearity, we construct the correlation and pair correlation matrices (Tables
Variables  L.capex_ln  div_ln  rev_ln  NIdiv  IEln  II_ln  IE+Div 
L.capex_ln  1.000  
div_ln  0.045  1.000  
rev_ln  0.109  0.298  1.000  
NIdiv  0.038  0.091  0.160  1.000  
IE_ ln  0.005  –0.113  0.112  –0.207  1.000  
II_ln  –0.075  0.295  0.170  0.195  –0.036  1.000  
IE+Div  0.091  0.664  0.486  –0.011  0.339  0.263  1.000 
Variables  L.capex_ln  div_ln  rev_ln  NIdiv  IE _ln  II_ln  IE+Div 
L.capex_ln  1.000  
div_ln  0.143  1.000  
rev_ln  0.169  0.105  1.000  
NIdiv  0.104  0.210  0.202  1.000  
IE_ ln  0.045  –0.144  0.380  –0.162  1.000  
II_ln  –0.095  0.300  0.157  0.166  0.052  1.000  
IE+Div  0.069  0.605  0.496  0.008  0.435  0.214  1.000 
As can be seen from the tables, the correlation between the variables that are simultaneously included in any model does not exceed 0.496; therefore there is no reason to believe that multicollinearity is present.
Below is some information about the sample. As can be seen in Fig.
Fig.
The ratio of financial incomes to total income in Russia, 1999–2019 (%).
Source: Compiled by the authors.
Fig.
The ratio of financial expenses to capital in Russia, 1999–2019 (%).
Source: Compiled by the authors.
In conclusion, we would like to note that starting from 2010 one can observe a noticeable strengthening of the process of financialization in Russian nonfinancial companies. However, with the exception of the last period, capital investments during these years were in stagnation.
The equations presented above will be estimated in 2 stages. In the first step, they will be assessed using a fixed effects model. Such models are suitable for cases where there is a certain set of companies, and the estimates of interest to the researcher are related to the behavior of these companies. To eliminate the consequences of heteroscedasticity, the models will be built with robust errors. Furthermore, Ftests and Hausman tests will be performed on all models to confirm that the estimates obtained with the fixed effects model are better than the OLS or random effects model. As can be seen from the equations, in addition to the set of explanatory variables, the lags of the dependent variable will be included in the model. The main disadvantage of this model is that it does not solve the potential problem of endogeneity of lags in the dependent variable. Moreover, this model may be sensitive to unobservable panel heterogeneity. In this regard, the equations will be estimated by another model, and the results of the two estimates are compared.
The second step will be to evaluate the presented equations using a twostep difference GMM (Generalized method of moments) model (
In each assessed specification, 2 and 3 lags of the dependent variable will be included as instruments, as well as the first lags of the remaining parameters as predefined indicators. For additional control over heteroscedasticity, robust errors will be used. The Arellano–Bond autocorrelation test will be used to test the endogeneity of the instruments. The validity of the instruments will be checked with the Hansen test.
In this part of the paper, the results of the equation estimates using the fixed effects model and the GMM model will be presented and analyzed. Moreover, we will compare the results of estimates obtained by different models. Table
Let us start the analysis by estimating the equations using the fixed effects model. Column (1) of the Table
Variables in estimates  Variables in equations  Description of variables 
L.capex_ln  ln ()_{it –}_{1}  Investment in fixed assets 
L.NIdev_ln  ln ()_{it –}_{1}  Retained earnings 
L.rev_ln  ln ()_{it –}_{1}  Revenue 
L.IE_ln  ln ()_{it –}_{1}  Interest payments 
L.CD_ln  ln ()_{it –}_{1}  Dividend payments 
L.II_ln  ln ()_{it –}_{1}  Financial incomes 
L.IE+Div_ln  ln ()_{it –}_{1}  Financial expenses 
L.II_K_big  ln (D_{big25})_{it –}_{1}  Financial incomes of 25% biggest companies 
(1)  (2)  (3)  (4)  (5)  
L.capex_ln  0.358*** (0.0491) 
0.388*** (0.0483) 
0.414*** (0.0498) 
0.373*** (0.0514) 
0.363*** (0.0512) 
L.NIdiv_ln  0.0142 (0.0256) 
0.0187 (0.0244) 
0.0439* (0.0239) 
0.0467* (0.0255) 
0.0466* (0.0253) 
L.rev_ln  0.161* (0.0911) 
0.0144 (0.0944) 
0.0641 (0.101) 
0.232** (0.0969) 
0.209** (0.0966) 
L.IE_ln  –0.0888*** (0.0322) 
–0.0517 (0.0337) 
–0.0706** (0.0322) 

L.CD_ln  0.0151 (0.0179) 
–0.0280 (0.0184) 

L.II_ln  –0.0300 (0.0259) 
–0.0196 (0.0264) 
0.168** (0.0830) 

L.IE+Div_ln  –0.114*** (0.0331) 
–0.111*** (0.0329) 

L.II_K_big  –0.209** (0.0876) 

F pvalue  0.000  0.000  0.000  0.000  0.000 
Hausman pvalue  0.000  0.000  0.000  0.000  0.000 
In Table
(1)  (2)  
L.capex_ln  0.458*** (0.0575) 
0.431*** (0.0585) 
L.NIdev  0.0769*** (0.0262) 
0.0762*** (0.0260) 
L.rev_ln  0.0128 (0.131) 
–0.00567 (0.130) 
L.IE+Div  –0.0138 (0.0424) 
–0.0125 (0.0421) 
L.II_ln  –0.0575* (0.0339) 
0.166 (0.114) 
L.II_K_big  –0.244** (0.119) 

F pvalue  0.000  0.000 
Hausman pvalue  0.000  0.000 
Fixed effects estimates were not comprehensive because too many factors were found to be insignificant.
Next, let us move on to the GMM model. As stated earlier, this model is most often applied to this kind of dynamic panel data. Column (1) of the Table
Further, by analogy with the estimates using the fixed effects model, we divide the sample by years and estimate models (6) and (7) starting from 2010, see Table
(1)  (2)  (3)  (4)  (5)  
L.capex_ln  0.365*** (0.0371) 
0.362*** (0.0462) 
0.340*** (0.0615) 
0.246*** (0.0378) 
0.262*** (0.0477) 
L.NIdev  0.137*** (0.0181) 
0.0771*** (0.0208) 
0.0638*** (0.0190) 
0.0968*** (0.0152) 
0.0901*** (0.0188) 
L.rev_ln  0.206*** (0.0622) 
0.297*** (0.0656) 
0.602*** (0.150) 
0.347*** (0.0604) 
0.340*** (0.0417) 
L.IE_ ln  –0.0583 (0.0373) 
–0.199*** (0.0438) 
–0.273*** (0.0366) 

L.CD_ln  –0.107*** (0.0241) 
–0.150*** (0.0228) 

L.II_ln  –0.00480 (0.0266) 
–0.0188* (0.0126) 
0.127* (0.090) 

L.IE+Div  –0.253*** (0.0268) 
–0.236*** (0.0294) 

L.II_K_big  –0.148* (0.091) 

ArellanoBond test AR(2) pvalue  0.008  0.023  0.066  0.032  0.016 
Hansen test pvalue  0.400  0.190  0.381  0.408  0.381 
(1)  (2)  
L.capex_ln  0.284** (0.129) 
0.274** (0.132) 
L.NIdev  0.163** (0.0687) 
0.178** (0.0680) 
L.rev_ln  0.0654 (0.383) 
0.340* (0.318) 
L.IE+Div  –0.292*** (0.0923) 
–0.257*** (0.0895) 
L.II_ln  0.00174 (0.0697) 
0.525** (0.235) 
L.II_K_big  –0.548** (0.250) 

ArellanoBond test AR(2) pvalue  0.093  0.039 
Hansen test pvalue  0.422  0.340 
Having estimated all our equations using two approaches, we are inclined to believe that the GMM model gives more efficient and meaningful estimates, so further analysis will be based on it. While analyzing the models, we expectedly received a positive effect of investment lags, retained earnings and revenues. These results are consistent with those obtained by
It is worth noting that revenue, which approximates capacity utilization in the post‑Keynesian investment model, has the greatest positive impact on physical investment. In turn, retained earnings do not have a significant impact. Financialization, expressed by financial outflows, has a significant impact on investment decline. The accumulation of fixed assets would have been 23.6% higher if it were not for the growth of financial payments. The growth of financial incomes in small and mediumsized companies increases investment by 12.7%, and in large companies it decreases investment by 2.1%. In the postcrisis period, the effect of financialization for large companies remained at about the same level, while for medium and small companies the effect of financial incomes doubled. Given the elasticity, it is financial payments that have the strongest negative impact on physical investments.
This paper presents an empirical study of the impact of financialization on fixed investment by nonfinancial companies in Russia, based on dynamic panel data. It was found that financial expenses aimed at paying interest on external financing and paying dividends — that is, focusing on shareholder value, and hence decreasing the internal funds of companies — reduce real investments. Financial incomes have shown the crowdingout effect for large companies. Financial incomes as additional “free” funds in large companies are not perceived as an opportunity to accumulate fixed assets. Managers prefer to increase financial investments instead of real ones. In small and mediumsized companies, financial incomes, however, drive growth. This can be explained because small firms, at a particular stage in their lives, find it more profitable to invest in their own growth. Results from the general sample, without dividing by size, indicate that financialization in Russia clearly reduces real investment. It is important to note that our results were obtained on a specific sample of publicly listed nonfinancial companies in Russia and may differ from other similar studies.
Our results are consistent with those of other authors obtained in other countries. So, for example, our results are comparable (but not completely so) to the results obtained by
It is worth noting that our results confirm the criticism of mainstream literature (first of all, Neoclassical economics), which asserts the extremely beneficial effects of financialization on economic growth. The assumptions made in the post‑Keynesian literature have been confirmed in our study.
This research is not exhaustive. It is necessary to continue research on this issue, to consider approaches to investment and financialization from a different point of view. In addition, other model specifications need to be checked. Furthermore, we did not consider in this paper the next important issues: what is the role of oil revenues in financialization? How is the Russian economy placed in the global economy? How does it impact the mode of financialization of Russian economy? These issues — as well as a detailed descriptive comparative analysis — are beyond the scope of this paper and can be relevant subjects in future investigations.
The authors are grateful to the anonymous referees for valuable comments and suggestions on the earlier version of this paper.
Variable  Mean  Std. Dev.  Min  Max  Observations  
compan~e  overall  236  135.9726  1  471  N  =  9891 
between  136.1102  1  471  n  =  471  
within  0  236  236  Tbar  =  21  
capex_ln  overall  –2.978716  1.466007  –12.93637  2.600736  N  =  5215 
between  1.068768  –10.55236  –0.309454  n  =  413  
within  1.159565  –12.53996  2.292087  Tbar  =  12.6271  
NI_ln  overall  –2.724152  1.549003  –13.12239  1.653318  N  =  5208 
between  1.187168  –11.94350  0.099307  n  =  423  
within  1.192258  –10.00700  1.681814  Tbar  =  12.3121  
div_ln  overall  –5.095919  2.886009  –17.47551  2.225593  N  =  2460 
between  2.279001  –13.76912  –0.258572  n  =  334  
within  2.070485  –14.83358  2.581405  Tbar  =  7.36527  
rev_ln  overall  0.5093375  1.355633  –11.741300  9.020822  N  =  5556 
between  1.475678  –9.234753  4.385908  n  =  424  
within  0.626564  –6.310561  6.995876  Tbar  =  14.0472  
NI_dev  overall  –2.817464  1.268519  –9.674927  –0.343412  N  =  2128 
between  0.968546  –7.505400  –1.252509  n  =  325  
within  0.957019  –9.517336  0.163441  Tbar  =  6.46805  
IE_abs~n  overall  –3.867194  1.561506  –13.106740  2.597495  N  =  1005 
between  1.663068  –9.987738  0.797694  n  =  118  
within  0.866851  –9.705004  0.520244  Tbar  =  8.51655  
II_ln  overall  –5.289112  1.430846  –13.516010  2.556244  N  =  696 
between  1.180628  –8.793372  –2.366650  n  =  88  
within  0.962155  –12.652750  –0.252544  Tbar  =  7.90505  
IEpDiv  overall  –3.010854  1.132211  –11.103380  1.553662  N  =  667 
between  1.106447  –5.966106  0.797875  n  =  87  
within  0.782303  –10.217220  –0.367189  Tbar  =  7.66667 