Content uploaded by Ivan Rozmainsky
Author content
All content in this area was uploaded by Ivan Rozmainsky on Dec 31, 2022
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=mpke20
Journal of Post Keynesian Economics
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/mpke20
Empirical analysis of the financial fragility of
Russian enterprises using the financial instability
hypothesis
Elena Perepelkina & Ivan Rozmainsky
To cite this article: Elena Perepelkina & Ivan Rozmainsky (2022): Empirical analysis of the
financial fragility of Russian enterprises using the financial instability hypothesis, Journal of Post
Keynesian Economics, DOI: 10.1080/01603477.2022.2134036
To link to this article: https://doi.org/10.1080/01603477.2022.2134036
Published online: 31 Oct 2022.
Submit your article to this journal
View related articles
View Crossmark data
Empirical analysis of the financial fragility of Russian
enterprises using the financial instability hypothesis
Elena Perepelkina and Ivan Rozmainsky
ABSTRACT
This paper conducts an empirical analysis of the financial
instability hypothesis on Russian data. The main literature on
this topic has been reviewed, and two financial fragility
indexes—developed by Mulligan and by Torres Filho with
coauthors—to determine whether Russian firms are in a hedge,
speculative or Ponzi regime are used. To do empirical analysis,
371 Russian firms from nine industries—Agriculture,
Construction, Investment, Light Industry, Power Industry,
Machinery, Steel Industry, Trade, and also Oil, Gas, and
Chemicals Industry—were selected, and these panel data
include observations from 2005 to 2020. This period includes
three cases of falling GDP in Russia: 2008–2009, 2014–2015,
and 2020. After identifying the regime of firms according to the
two above-mentioned criteria, we make a logistic regression on
the base of the Nishi approach to determine what affects a
firm’s probability of becoming a Ponzi unit. According to our
analysis, the increase in GDP and Profitability leads to declining
in the Russian firms’probability to become Ponzi, whereas the
rise in Short-term Debt results in the growing probability to
have a fragile financing regime. In general, speculative financ-
ing dominated, and Construction, Investment, Power Industry,
and Machinery were the most fragile sectors.
KEYWORDS
Financial fragility
hypothesis; financial
instability hypothesis;
Minsky; Ponzi firms; Russia
JEL CLASSIFICATION
CODES
E12; E32; E44; E52
Introduction
As it is known, the financial instability hypothesis (aka financial fragility
hypothesis) was developed by Hyman Philip Minsky (1919–1996) in the
late 1970s and early 1980s. According to that theory, the capitalist eco-
nomic system, during the economic recovery, endogenously creates fragile
financial relationships between economic agents that make it susceptible to
crises. In the upswing of the business cycle, the financial system is grad-
ually becoming fragile, and the risk of financial shocks is increasing. This is
related to the changing of financing regimes during which firms move
from robust regimes to fragile ones, which threatens a chain of massive
bankruptcies of such firms and entire sectors of the economy. According to
the hypothesis, firms are less likely to attract external financing in the
Elena Perepelkina is at National Research University Higher School of Economics, Saint Petersburg, Russia.
Ivan Rozmainsky is at National Research University Higher School of Economics, Saint Petersburg, Russia.
ß2022 Taylor & Francis Group, LLC
JOURNAL OF POST KEYNESIAN ECONOMICS
https://doi.org/10.1080/01603477.2022.2134036
post-crisis period, as key decision-makers remember the recent crisis and
try to be cautious. Interest in the financial instability hypothesis, especially
in its empirical applications, has grown strongly since the Great
Recession—the major macroeconomic shock of the last 75 years, which
started with the US mortgage crisis in 2007 and spread into the 2008–2009
worldwide crisis.
In this paper, the financial instability hypothesis will be used to explore
the recessions in Russia, which experienced several economic crises lately:
firstly, in 2008–2009, like the USA and most Eurozone countries; secondly,
the currency crisis of 2014–2015; thirdly, the 2020 pandemic crisis. We will
consider enterprises from industries where the most significant changes
(both positive and negative) were observed: trade, machinery, power indus-
try, investment, light industry, agriculture, construction, steel industry, and
also oil, gas, and chemicals industry.
The aim of the paper is to make a connection between the dynamics of
GDP and the financial fragility of firms in selected industries. The novelty
and relevance of this work are due to the fact that there is only one
research where a crisis in Russia (2008–2009) has been considered on the
basis of the financial instability hypothesis (Stolbov 2010), and the men-
tioned paper did not include both firms’panel data-based empirical ana-
lysis and econometric modeling.
Based on the financial fragility hypothesis, it is expected that the propor-
tion of firms with fragile finances, or Ponzi firms, before and during the
first years of crises reaches its maximum. In the case of Russia, during the
2005–2020 period—as we mentioned above—this country experienced three
crises: 2008–2009, 2014–2015, and 2020, so we expect that the highest
shares of Ponzi companies should occur in these years.
Development of the financial instability hypothesis and its application:
a review of relevant literature
The first mention of the financial instability hypothesis (FIH) was, perhaps,
made in Nebraska Journal of Economics in 1977 (Minsky 1977). Hyman
Minsky suggested the FIH as an alternative to the standard neoclassical the-
ory based on the interpretation of Keynes’General Theory of Employment,
Interest, and Money. According to the standard theory (neoclassical synthe-
sis), both financial crises and serious fluctuations in production and
employment are anomalies. In General Theory, Keynes developed the the-
ory of the capitalist process, which explained it as a result of market behav-
ior in conditions of uncertainty, as well as non-equilibrium forces acting
on financial markets. According to Keynes, money is a very significant and
2 E. PEREPELKINA AND I. ROZMAINSKY
essential part of the economic scheme, while, according to the neoclassical
approach, money does not affect the essential economic behavior.
The financial instability hypothesis started to attract a lot of attention
only after the Great Recession. One of the first works with the empirical
application of the financial instability hypothesis were Mulligan (2013) and
Mulligan, Lirely, and Coffee (2014). He analyzed in the context of the FIH
and reinterpreted it in terms of Austrian Business Cycle (ABC) theory 8707
firms traded on North American exchanges between 2002 and 2009. The
main and only difference between ABC theory and the FIH is that Minsky
sees business cycles as the endogenous consequence of prosperity, while the
Austrians consider business cycles are the result of exogenous credit expan-
sion caused by economic policy. The criterion for the classification of firms
is based on the interest coverage ratio: the sum of net income and interest
expense divided by interest expense. Mulligan (2013, p. 452) classified firms
by regimes as follows: the interest coverage ratio, 4 units, is a characteris-
tic for hedge firms, from 4 to 0—for speculative firms, <0—Ponzi firms. It
was noticed that the number of speculative and Ponzi firms was particu-
larly high (73%) at the trough of the recession in the first quarter of 2002,
while this proportion declined as the economy recovered and returned to
relative prosperity. Similarly, when the financial crisis occurred in the
fourth quarter of 2008, numerous hedge firms became either speculative or
Ponzi, and speculative firms transformed into Ponzi.
Almost at the same time, Beshenov and Rozmainsky (2015) analyzed
data both on the macroeconomic indicators of the Greek economy and on
36 private borrowing companies in the 2001–2014 period and interpreted
them in the context of the financial instability hypothesis. The criterion for
classification is based on the adjusted interest coverage ratio: EBIT divided
by interest expense. This index is higher than that computed using the
Mulligan method, therefore, the thresholds for classifying firms are also dif-
ferent. For the adjusted interest coverage ratio, it amounts to 3 while for
Mulligan’s criterion it is 4 (Rozmainsky, Kovezina, and Klimenko 2022). A
significant part of the companies in the 2000s switched to speculative and
Ponzi financing regimes, becoming financially fragile and vulnerable to
bankruptcy. This study revealed the adverse consequences of austerity
measures by the Greek government.
Later, a study on non-financial sectors in Japan was carried out (Nishi
2019). The author conducted an empirical analysis using a panel logistic
regression to identify financial fragility in Japan throughout the 1975–2015
period. Nishi employed two approaches to measure the financial fragility: a
cash-flow accounting framework (Schroeder 2009) and the margins of
safety (Minsky 1986). To calculate the first index Nishi used the difference
between the sources and uses of funds at the level of a firm, the former
JOURNAL OF POST KEYNESIAN ECONOMICS 3
composed of the sum of profits and borrowing and the latter composed of
new investments, debt service payments, and dividend payments (and all
these parameters were normalized by capital stock). To observe the evolu-
tion of financial instability in Japan, the author considered manufacturing
and non-manufacturing sectors as well as small, medium, and large compa-
nies separately. The results showed that speculative financing dominated in
the reviewed period, and small firms and non-manufacturing sector were
more financially fragile.
Another paper that contributes to the application of the financial
instability hypothesis on both the firm and sectoral levels is by Torres
Filho, Martins, and Miaguti (2019). This paper considers Brazilian compa-
nies from the electricity distribution sector for the years from 2007 to 2015.
The authors want to discover how the financial fragility of both relevant
companies and this sector has changed over the research period. To make
it Torres Filho with coauthors develop their own financial fragility index
which is the sum of interest expense and short-term debt divided by
EBITDA. If this index is <1or¼1 that a company is a hedge firm; if it is
higher than 1 that a company is a speculative firm (when interest payments
are less than EBITDA) or Ponzi firm (when interest payments are higher
than EBITDA). This method—as well as Nishi (2019) approach—fits more
deeply into the Minsky’s theoretical approach and does not suppose any
arbitrary limits for classification between different types of firms
(Rozmainsky, Kovezina, and Klimenko 2022).
Davis, De Souza, and Hernandez (2019) applied the FIH to non-financial
companies in the US for the years from 1970 to 2014. The criterion for the
classification of the firms is based on the difference between sources of
cash inflows and debt payments. If this difference is positive that a firm is
a hedge unit. If sources of cash are less than interest payments that a firm
is Ponzi unit. If sources of cash are higher than interest payments and less
than principal payments that a firm is a speculative unit. The authors did
not find links between the dynamics of financial fragility and business
cycles; but revealed a long wave of growing financial fragility as a response
to changes in financial norms and conventions, as evidenced by the 2008
financial crisis (Davis, De Souza, and Hernandez 2019; Rozmainsky,
Kovezina, and Klimenko 2022).
To analyze the Chile’s performance in the period from 1980 to 2019
(Budnevich Portales, Favreau Negront, and Caldentey 2020), two methodol-
ogies were used to measure the financial fragility: the Mulligan’s approach
(Mulligan 2013) and the Torres Filho with coauthors’one (Torres Filho,
Martins, and Miaguti 2019). The authors found that there was an accumu-
lation of financial fragility: “between 2011 and 2019 the percentage of firms
considered to be fragile (belonging to a speculative or Ponzi financing
4 E. PEREPELKINA AND I. ROZMAINSKY
regime) increased according to the two criteria considered (Budnevich
Portales, Favreau Negront, and Caldentey 2020, p. 94). Besides, Portugal’s
non-financial private firms were examined in terms of the financial
instability hypothesis throughout the 2001–2017 period (Novikova and
Rozmainsky 2020). Similarly, the authors calculated indices according to
the Mulligan’s criterion (Mulligan 2013) and the Torres Filho’s et al.
approach (Torres Filho, Martins, and Miaguti 2019). Additionally, they
used the inflow and outflow method—developed by Davis, De Souza, and
Hernandez (2019) as well as the adjusted interest coverage ratio (Beshenov
and Rozmainsky 2015). It was found that the number of speculative and
Ponzi firms was growing before and during the Great Recession which sup-
ports the financial instability hypothesis. After the crisis, the number of
companies with fragile financing started to decline due to the more cau-
tious firms’behavior and refusal from austerity policy made by the
Portuguese Government.
Finally, some researchers analyzed 340 non-financial private companies
from various industries in the Netherlands during the 2005–2019 period
(Rozmainsky, Kovezina, and Klimenko 2022). Financial fragility indices
developed by Mulligan (2013), Torres Filho, Martins, and Miaguti (2019),
and Nishi (2019) were applied. All methods validated the financial instabil-
ity hypothesis, however, every approach led to a rather different division of
firms into Ponzi, speculative, and hedge. Both “destabilizing stability”and
austerity measures contributed to an accumulation of financial fragility
among private Dutch firms; and non-manufacturing firms are turned to be
more financially fragile like Japanese companies according to Nishi (2019).
Then, a logistic regression based on Nishi’s criteria (Nishi 2019) was made.
Results of the regression confirmed the FIH: the probability of being Ponzi
rises with an increase in the output of industry and in the interest rate,
and decreases as the profitability rises.
Reasons for crises in Russia
Russia recovered from the consequences of bankruptcy and the collapse of
the Soviet economic system only by the end of the 1990s. During the
period 1999–2008, there was an economic and social upswing: Russia’s
GDP grew by more than 90% at an average annual rate slightly below 7%
(by half due to higher prices for oil, gas, and metals); industrial production
also grew, but slightly less rapidly; investments in fixed assets and agricul-
ture had the highest growth rates; a currency inflow to Russia occurred
thanks to the rise in export prices for oil, gas, metals and other goods.
However, the period of rapid economic growth was replaced by a signifi-
cant economic recession.
JOURNAL OF POST KEYNESIAN ECONOMICS 5
The 2008–2009 financial crisis started in the USA and spread worldwide,
and Russia was not an exception. However, despite the importance of the
impact of the global crisis, the crisis in Russia was caused by both external and
internal reasons (Potemkin 2019). The external factors included the fall in pri-
ces of raw materials (given that the export of raw materials was about 80% out
of all Russian exports) as well as a fall in world financial markets caused the
increase in interest rates and the absence of opportunity to attract new capital
(Potemkin 2019). Internal factors implied: a very small number of long-term
credits and stable projects; a conflict in South Ossetia in August 2008 that
caused tension between Russia and Western countries, so there was a huge cap-
ital outflow from Russia (Potemkin 2019); a decrease in demand for the most
important raw materials that form the basis of Russia’s export potential, and
decreasing prices for them; a chronic lack of investment, which led to Russia’s
dependence on imports of goods, machinery, and equipment; inefficiency of
the sectoral structure of the economy and exports based on raw materials.
As for the crisis in 2014–2015, it is considered one of the major eco-
nomic shocks in Russian history, and compared to the 2008 crisis, it is a
more local (Russian) shock during which the Russian currency was the
most affected. The first reason was that Russia depended much on oil pri-
ces (45% of the Russian budget was formed from oil and gas revenues) and
due to increased supply exceeding demand a fall in oil prices occurred in
2014. The second factor is that the USA, the European Union, Switzerland,
and some other countries imposed economic sanctions on Russia because
of the 2014 union of Crimea with Russia as well as accused Russia of start-
ing an armed conflict in Ukraine. Therefore, Russian companies were cut
off from Western debt markets (Potemkin 2019). As a consequence, GDP
growth fell to almost zero, fixed investment turned negative, and invest-
ment in the economy declined, which led to higher inflation and
unemployment, stagnation, a decline in consumer demand, and a decrease
in real income. It was very difficult to find substitutes for imports, as most
producers relied on foreign raw materials and mining. Foreigners have
taken a long-standing interest in investing in the banking, engineering, and
steel sectors. Production decreased by 30% in 2014 (Osipov and
Orlov 2021).
Finally, the 2020 crisis in Russia was caused by the COVID-19 pandemic
and the introduction of the overall quarantine. During this period, social
and economic life was suspended. The Russian economy faced the follow-
ing problems: a sharp decline in demand and supply for a large number of
goods and services; a fall in energy prices caused by a decrease in global
demand for hydrocarbons due to the adoption of quarantine measures and
the break-up of the OPECþ; reducing global demand for other Russian
commodities (Osipov and Orlov 2021).
6 E. PEREPELKINA AND I. ROZMAINSKY
The graph in Figure 1 illustrates the Russian real GDP growth: the most
significant decrease in 2008–2009, a stagnation in 2014–2016, and a sharp
drop in 2020.
Description of data
The panel data on Russian companies over the period from 2005 to 2020
were collected using GffR(https://spark-interfax.ru/) source. After
removing missing data from a sample of companies, 371 firms remained
for the financial fragility indices’calculation and further analysis. Based on
the sectors most affected in the crises years (Balandina 2016), the following
industries were considered:
1. Trade
Wholesale and Retail Trade
Transport, Machinery, and Equipment
Oil, Gas, and Chemicals
Metals and Ores
Power and Coal
2. Production
Machinery
Power Industry
Investment
Light Industry
Oil and Gas Extraction and Chemicals Production
Agriculture
Construction
Steel industry
It is worth mentioning that this is not a full list of sectors being affected
by crises. For example, recessions had a negative impact on banks and
0.00
20.00
40.00
60.00
80.00
100.00
120.00
Trillion rubles
Figure 1. The real GDP growth in Russia from 2005 to 2020. Source: http://global-finances.ru/
vvp-rossii-po-godam/.
JOURNAL OF POST KEYNESIAN ECONOMICS 7
companies in the field of real estate, but not enough data were found on
them (only industries with full information about a minimum of 20 firms
were considered).
The companies from the listed industries were analyzed for belonging to
the Ponzi, speculative, and hedge regime in the period from 2005 to 2020
both in each of the 16 years and during the whole period. Furthermore, an
industry-by-industry analysis was held to compare the shares of regimes,
identify the prevailing one in each industry and find out the differences
between the results of the two indices considered. Besides, a detailed ana-
lysis of each sector was conducted.
As for the structure of the sample, in the majority of the sectors about
40 companies were considered (Table 1).
Statistical analysis
To analyze Russian companies’financial fragility, indices developed by
Mulligan (2013) and Torres Filho, Martins, and Miaguti (2019) were used.
These indices were chosen as one of the most popular and due to the avail-
ability of the data necessary for their calculation. Unfortunately, the avail-
able database did not contain indicators that are relevant for an application
of Nishi’s index (Nishi 2019), perhaps, the most appropriate one, if we try
to be closer to the “spirit”of the Minskian approach (Minsky 1986;
Rozmainsky, Kovezina, and Klimenko 2022).
The EBITDA indicator in the second index—from Torres Filho, Martins,
and Miaguti (2019)—was replaced by EBIT as the data on depreciation
were not found. For the companies for which EBIT information was not
available, it was calculated as:
EBIT ¼NetProfit þInterestExpense þCurrentIncomeTax
The formulas and thresholds for considering a firm as hedge, speculative,
or Ponzi are represented in Table 2.
Econometric analysis
To determine the factors that influence the probability of firms to have
Ponzi financing regime, panel logistic regression models were constructed
using Stata. Logistic regression was used rather than linear one as the
Table 1. The number of firms observed in each industry.
Agriculture Construction Investment Light industry Machinery
39 39 39 40 40
Oil, gas, and chemicals Power industry Steel industry Trade Total
40 40 20 74 371
8 E. PEREPELKINA AND I. ROZMAINSKY
dependent variable was supposed to be categorical, and logistic model
known as probabilistic was most appropriate.
To separate the Ponzi companies from the hedge and speculative ones,
two categories for a binary response model were defined (so that 1—Ponzi,
0—hedge, and speculative). In the first case, a binary dependent variable
was calculated according to the methodology of Torres Filho, Martins, and
Miaguti (FFI-Torres Filho), in the second case—according to the Mulligan’s
one (FFI-Mulligan). They represent the probability of Russian companies
to be Ponzi. For each case, both the fixed-effects and the random-effects
regression models were used and compared during the analysis. In the
Fixed regression, it is assumed that individual effects correlate with regres-
sors, in the Random model there is no such a correlation.
As independent variables, the following indicators were taken:
1. GDP
It reflects the impact of economic expansions and recessions on financial
fragility. A similar indicator, the output of the company’s industry, was
used in the research papers of other authors—Nishi (2019), and Davis, De
Souza, and Hernandez (2019). As it was not possible to find the informa-
tion on each sector, the Russia’s GDP was taken from the open source.
2. Crisis
A dummy variable, 0—there was no crisis, 1—there was a crisis. The year of
the decline in the country’s GDP was taken as the year of crisis during the
2005–2020 period. According to the hypothesis, before and in the years of
crises the number of Ponzi firms should be the highest.
3. Interest Expense
Interest expense is an interest due—interest accrued but not paid yet.
The higher value means the higher probability of companies to
become Ponzi.
Table 2. The formulas used for the financial fragility indexes calculation.
Index Formula Thresholds
Interest coverage ratio (Mulligan’s) NetProfit þInterestExpense
InterestExpense
Hedge: ICR >4
Speculative: 0 ICR 4
Ponzi: ICR <0
Financial fragility index (Torres
Filho et al.’s)
InterestExpense þShortTermDebt
EBIT
or
InterestExpense þShortTermDebt
NetProfit þInterestExpense þTax
Hedge: FFI 1
Speculative: FFI >1 and
Interest rate <EBIT
Ponzi: FFI >1
Interest rate >EBIT or EBIT
<0
JOURNAL OF POST KEYNESIAN ECONOMICS 9
4. Profitability
Profitability ¼EBIT
Revenue
It is expected that the more profitable the company is, the less probabil-
ity of being Ponzi should be.
5. Short-term Debt
Similarly, the higher short-term debt, the higher the probability of relat-
ing to a fragile financing regime.
As the distributions of GDP, Profitability, Interest Expense, and Short-
term Debt indicators turned out to be far from normal, in the regression
model their logarithms are used.
Thus, a logistic regression model—inspired by and not dissimilar to
Nishi (2019) and Rozmainsky, Kovezina, and Klimenko (2022)—is as fol-
lows:
Probability of Ponzi ¼
b
0þ
b
1logGDPÞþ
b
2Crisis þ
b
3
log InterestExpense
ðÞ
þ
b
4log Profitability
ðÞ
þ
b
5
log ShortTermDebt
ðÞ
Descriptive statistics of all variables are shown in Table 3.
The total sample includes 5,936 observations.
Indicators of Ponzi firms—FFI-Mulligan and FFI-Torres Filho—are
dependent dummy variables as mentioned above (Min ¼0, Max ¼1).
Crisis is also a dummy variable with 0 and 1 possible values.
GDP ranges from 21.6 to 110 trillion rubles.
Profitability index starts from 9.525 and reaches 57.561.
Table 3. Descriptive statistics (2005–2020).
Variables Sample Mean SD Min Max
FFI-Mulligan 5936 0.035 0.183 0 1
FFI-Torres Filho 5936 0.091 0.289 0 1
GDP 5936 6.69e þ13 2.84e þ13 2.16e þ13 1.10e þ14
log(GDP) 5936 31.723 0.498 30.704 32.32793
Crisis 5936 0.125 0.331 0 1
Profitability 5936 0.123 0.802 9.525 57.561
log(Profitability) 5936 2.704 1.149 10.181 4.053
Interest expense 5936 9.02e þ08 1.28e þ10 1000 4.52e þ11
log(Interest expense) 5936 17.538 2.299 6.908 26.837
Short-term debt 5936 1.18eþ10 1.06e þ11 953000 3.68e þ12
log(Short-term debt) 5936 20.684 1.999 13.767 28.934
10 E. PEREPELKINA AND I. ROZMAINSKY
The minimal Interest Expense is 1,000 rubles, while the maximum is 452
billion rubles.
Short-term Debt of firms observed is between 0.953 million rubles and
3,680 billion rubles.
Results of statistical analysis
According to the calculated Mulligan’s indices, there was indeed an
increase in the number of Ponzi companies before and during the
2008–2009 and 2014–2015 crises, which confirms the financial instability
hypothesis (see Table 4). It can be seen both in the dynamics of Ponzi
companies and in the dynamics of firms with hedge and speculative
regimes. As for the 2020 crisis, the shares of speculative firms started to
decline sharply from 2018, whereas the proportion of Ponzi firms as well
as hedge companies started to increase steadily (see Figure 2).
Considering the shares (average values for the entire period) of each of
the financing regimes in different industries, it can be observed that Power
Industry and Construction had the highest shares of Ponzi firms during
Table 4. The number of firms by regimes from 2005 to 2020: Mulligan’s methodology.
Regime 2005 2006 2007 2008 2009 2010 2011 2012
Hedge 178 185 189 150 131 185 191 192
Speculative 176 180 177 212 231 183 174 175
Ponzi 17 6 5 9 9 3 6 4
Regime 2013 2014 2015 2016 2017 2018 2019 2020
Hedge 154 160 141 151 145 164 160 196
Speculative 205 192 215 211 214 185 185 142
Ponzi 12 19 15 9 12 22 26 33
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e Speculative Ponzi
Figure 2. The shares of firms by regimes from 2005 to 2020: Mulligan’s methodology.
JOURNAL OF POST KEYNESIAN ECONOMICS 11
2005–2020, while Oil, Gas, and Chemicals Industry and Steel Industry
proved to be the most financially stable with the lowest shares of Ponzi
firms and the highest percentages of hedge ones (see Figure 3). Thus, the
Mulligan’s Interest Coverage Ratio suggests that the share of Ponzi firms
was lower than 10% over the observed period and on average in all sectors.
Based on the indices calculated by Torres Filho’s et al. methodology,
there were rises in the number of Ponzi companies before and in the
period of all crises: 2007–2009, 2013–2015, and 2020 (Table 5). It also sup-
ports the idea of the financial instability hypothesis. The shares of compa-
nies with speculative regimes changed directly opposite to Ponzi firms,
while the amount of hedge firms fluctuated slightly throughout the period
and stayed approximately the same (Figure 4).
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PERCENTAGE
Hed
g
e Speculative Ponzi
Figure 3. The share of firms by regimes in different industries from 2005 to 2020: Mulligan’s
methodology.
Table 5. The number of firms by regimes from 2005 to 2020: Torres Filho et al.’s
methodology.
Regime 2005 2006 2007 2008 2009 2010 2011 2012
Hedge 44 56 51 44 45 38 40 51
Speculative 308 305 308 309 300 322 309 299
Ponzi 19 10 12 18 26 11 22 21
Regime 2013 2014 2015 2016 2017 2018 2019 2020
Hedge 38 28 36 29 39 43 34 47
Speculative 296 296 289 301 272 276 279 261
Ponzi 37 47 46 41 60 52 58 63
12 E. PEREPELKINA AND I. ROZMAINSKY
As for the proportions of the financial regimes in different industries, it
can be noticed that there were the highest shares of Ponzi firms in
Construction, Power Industry, and Investment, whereas, similarly to
Mulligan’s Index, in Oil, Gas, and Chemicals Industry and Steel Industry
the number of Ponzi firms was the least and the share of hedge ones was
the largest (Figure 5).
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hedge Speculative Ponzi
Figure 4. The shares of Russian firms by regimes from 2005 to 2020: Torres Filho et al.’s
methodology.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PERCENTAGE
Hedge Speculative Ponzi
Figure 5. The share of firms by regimes in different industries from 2005 to 2020: Torres Filho
et al.’s methodology.
JOURNAL OF POST KEYNESIAN ECONOMICS 13
Hence, we can highlight several similar and different features in the
structure and dynamics of Mulligan’s and Torres Filho’s et al. indices:
according to both methodologies, the financial instability hypothesis
was confirmed: before and during the crises of 2008–2009, 2014–2015,
and 2020 the percentage of Ponzi companies reached maximum;
in both cases in Power Industry and Construction there were the high-
est proportions of Ponzi firms, while the Oil, Gas, and Chemicals
Industry and Steel Industry turned out to be the most financially stable
and robust sectors throughout the entire period;
calculations on the Torres Filho et al.’s methodology showed much
higher shares of speculative and Ponzi companies and the relative
stability of hedge firms’dynamics compared to Mulligan’s
methodology.
Besides, annual total indices of firms in reviewed industries during the
2005–2020 period are presented in Figures 6 and 7. Considering both the
Mulligan’s criterion and the Torres Filho et al.’s one, the following features
can be emphasized:
Based on the Mulligan’s approach, Investment had the highest average
FFIs and the most significant fluctuations (within the hedge financing
limits): there were four huge drops in the years of crises. However,
according to the Torres Filho et al.’s methodology, the trend in
Investment was quite stable and the values of the index-matched fra-
gile regime.
0
5
10
15
20
25
30
35
Agriculture
Construction
Investment companies
Light Industry
Machinery
Oil, Gas and Chemicals
Power Industry
Steel Industry
Trade
Figure 6. Annual total indices of firms in different industries from 2005 to 2020: Mulligan’s
methodology.
14 E. PEREPELKINA AND I. ROZMAINSKY
Similarly, there was a greatly changing dynamics in Construction.
Following the Mulligan’s methodology, in each crisis, the index experienced
significant drops within the range from 2.5 in 2009 (speculative regime) to
22 (hedge). Applying the Torres Filho et al.’s criterion, the tendency was
steadily fragile except in 2012, when the value was negative.
As for Power Industry, consistent with the Mulligan’s approach, the
index indicated a hedge regime in 2005–2012 and 2017–2020, while in
-170
-160
-150
-140
-130
-120
-110
-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
70
Agriculture
Construction
Investment companies
Light Industry
Machinery
Oil, Gas and Chemicals
Power Industry
Steel Industry
Trade
Figure 7. Annual total indices of firms in different industries from 2005 to 2020: Torres Filho
et al.’s methodology.
JOURNAL OF POST KEYNESIAN ECONOMICS 15
2013–2016 speculative financing dominated. Based on the Torres Filho
et al.’s methodology, the values pointed to speculative and Ponzi
regimes in all years except 2014, when it was very negative.
According to the Mulligan’s criterion, in Oil, Gas, and Chemicals
Industry before 2015 there was mainly hedge financing, but later the
regime turned to speculative. Following the Torres Filho et al.’s method-
ology, the index values were positive referring to fragile regimes (espe-
cially 2015 and 2016) except 2005–2007.
In Steel Industry, applying the Mulligan’s methodology, hedge regime
was observed during the entire period except 2009 and 2013–2015 years.
Consistent with the Torres Filho et al.’s. criterion, similarly, the indices
indicated steadily fragile financing except for 2005 and 2007 years.
Regarding Machinery, based on the Mulligan’s approach, the regime
was speculative or Ponzi in all years except 2007 and 2011–2014.
According to the Torres Filho et al.’s methodology, the values pointed
to fragile regimes throughout the entire period.
Following the Mulligan’s criterion, the index in Trade defined hedge
regime during the whole period except 2008–2009 and 2016–2019.
Applying the Torres Filho et al.’s approach, the values were positive
indicating fragile financing.
Based on the Mulligan’s criterion, the regime in Light Industry was stead-
ily speculative except for 2016 and 2020. Following the Torres Filho
et al.’s methodology, in all years the indices indicated fragile financing.
Thus, based on the Torres Filho et al.’s criterion, the situation in nine
considered sectors proved to be rather dangerous in the 2005–2020 period.
There was a hedge regime only in several cases: Construction (2012),
Power Industry (2014), Steel Industry (2005–2007), Oil, Gas, and
Chemicals (2005–2007). The Mulligan’s index turned out to be more sensi-
tive to crises: almost all industries experienced decreases before 2009, 2015,
and 2017–2018. Construction, Investment, and Oil, Gas, and Chemicals
were the most fragile sectors.
Figure 8 illustrates the total indices of firms in different industries for
the 2005–2020 period. It can be noticed that Investment, Construction, and
Steel Industry have the highest interest coverage ratio, so, according to the
Mulligan’s approach, these sectors can be called the most robust. Similarly,
consistent with the Torres Filho et al.’s methodology, the most robust
industries are Investment and Steel Industry. Following the Mulligan’s cri-
terion, Agriculture, Oil, Gas, and Chemicals Industry, Light Industry,
Machinery, Trade, and Power Industry are related to the most fragile sec-
tors. Applying the Torres Filho et al.’s approach, the most fragile industries
16 E. PEREPELKINA AND I. ROZMAINSKY
are Machinery, Trade, Oil, Gas, and Chemicals Industry and
Power Industry.
Detailed sectoral analysis
To determine how financial fragility varies depending on the particular
methodology and sector, both indices—Mulligan and Torres Filho et al.—
were calculated for each sector and their dynamics was analyzed and com-
pared. Graphs for each industry and methodology are presented
in Appendix.
0
2
4
6
8
10
12
14
16
18
Index
Figure 8. Total indices of firms in different industries for the entire period (Green for Mulligan’s
index, Blue for Torres Filho et al.’s index).
Table 6. Detailed sectoral analysis.
Sector
Mulligan Torres Filho et al.
The increase in the
share of Ponzi
before and during
the crises
The maximum share
of Ponzi firms (with
a year)
The increase in the
share of Ponzi
before and during
the crises
The maximum share
of Ponzi firms (with
a year)
Agriculture 1, 2, 3 10% (2018) 1, 2 20% (2014)
Construction 1 13% (2018) 1, 2, 3 31% (2017, 2019)
Investment 1, 2, 3 13% (2014) 2, 3 36% (2020)
Light industry 2, 3 10% (2020) 1, 2, 3 18% (2014)
Machinery 3 13% (2019, 2020) 1, 2, 3 23% (2020)
Oil, gas,
and chemicals
1, 3 15% (2005) 1, 3 15% (2005)
Power industry 2, 3 18% (2020) 2, 3 23% (2019, 2020)
Steel industry 3 5%
(2012–2013,
2017–2019)
1, 2, 3 20% (2018)
Trade 3 7% (2019) 2, 3 16% (2017, 2019)
JOURNAL OF POST KEYNESIAN ECONOMICS 17
Table 6 reflects crises in which the increase in the share of Ponzi
occurred in a particular sector (1—the 2008–2009 crisis, 2—the 2014–2015
crisis, 3—the 2020 crisis) as well as the maximum share of Ponzi firms
(with a year it was). The data are provided for both approaches considered.
According to the Mulligan’s methodology, all sectors except
Construction experienced an increase in the proportion of Ponzi firms dur-
ing the 2020 crisis. The majority of industries had the highest share of
Ponzi companies in that period. The 2008–2009 crisis affected Agriculture,
Construction, Investment, Oil, Gas, and Chemicals Industry. Companies in
Agriculture, Investment, Light Industry, and Power Industry had negative
dynamics during the 2014–2015 crisis. It can be noticed that firms in
Agriculture and Investment were influenced by all crises reviewed.
Based on the Torres Filho et al.’s approach, the consequences of the
2020 affected all sectors except Agriculture. Similarly, the highest percent-
age of Ponzi firms almost in all sectors occurred during the pandemic
period. The 2014–2015 crisis negatively influenced all industries except Oil,
Gas, and Chemicals Industry, while Agriculture, Construction, Light
Industry, Machinery, Oil, Gas, and Chemicals, Steel Industry were influ-
enced by the 2008–2009 financial crisis. Such sectors as Construction, Light
Industry, Machinery, and Steel Industry experienced the impact of all
three crises.
Summarizing all obtained results and features, according to the financial
instability hypothesis, in Russia Steel Industry, Trade, Agriculture, and Oil,
Gas, and Chemicals Industry proved to be the most resilient to crises, while
Construction, Investment, Power Industry, and Machinery were the most
fragile sectors. Such a result can be associated with the fact that the most
fragile industries are those which depend much on imports and interaction
with other countries. As Russia has many deposits of ores, metals, oil, gas
as well as the variety of trade, crises years do not influence much
their production.
As it is known, in the research of Nishi non-financial sectors in Japan
were investigated throughout the 1975–2015 period (Nishi 2019). It was
found out that the non-manufacturing sector is more fragile than the man-
ufacturing sector in dynamic terms. A similar conclusion was received for
the Dutch non-financial firms for the 2005–2019 period (Rozmainsky,
Kovezina, and Klimenko 2022). Considering that Trade and Investment
sectors are non-manufacturing, whereas Steel Industry, Agriculture,
Construction, Power Industry, Machinery, and Oil, Gas, and Chemicals
Industry are manufacturing, the results are only partly similar to Nishi’s
conclusions. Investment related to the non-manufacturing industry is
indeed more fragile than several manufacturing sectors, such as Steel
Industry, Agriculture, and Oil, Gas, and Chemicals Industry. However,
18 E. PEREPELKINA AND I. ROZMAINSKY
neither for Trade, Steel Industry, Agriculture and Oil, Gas, and Chemicals
Industry nor for Construction, Power Industry, and Machinery the Nishi’s
result is appropriate.
Results of econometric analysis
The results of panel logistic regression models are presented in Table 7.
There are four models in total:
1. Fixed-effects model with a dependent variable calculated by Mulligan’s
methodology (F-1)
2. Random-effects model with a dependent variable calculated by
Mulligan’s methodology (R-1)
3. Fixed-effects model with a dependent variable calculated by Torres
Filho et al.’s methodology (F-2)
4. Random-effects model with a dependent variable calculated by Torres
Filho et al.’s methodology (R-2)
In the table, the average marginal effects (AMEs) are reported rather than
the estimated coefficients because the latter does not directly represent their
influence on the probability of companies to have Ponzi financial regime
(they ignore data shifts and scales), whereas the marginal effects of the regres-
sors handle this. Probability >v2reflects the likelihood ratios, where the null
hypothesis that all coefficients are zero is tested. For all models, this hypoth-
esis can be rejected, so they are significant at the 1% significance level.
Table 7. Average marginal effects: panel logit regression models (2005–2020).
Model F-1 R-1 F-2 R-2
Dependent variable Probability of
Ponzi (Mulligan’s)
Probability of
Ponzi (Mulligan’s)
Probability of Ponzi
(Torres Filho’s)
Probability of Ponzi
(Torres Filho’s)
Method Fixed Random Fixed Random
log(GDP) 0.000045
(0.000026)
0.005 (0.0022) 5.99e-
06 (0.000029)
0.021 (0.0069)
Crisis 5.80e-
06 (0.000028)
0.002 (0.0027) .0000318 (0.000067) 0.018 (0.0069)
log(Profitability) 0.000039
(8.20e-06)
0.005 (0.0014) 0.00019 (0.00041) 0.082 (0.0061)
log(Interest expense) 7.48e-06
(7.39e-06)
0.001(0.00059) 0.00017 (0.00035) 0.068 (0.0053)
log(Short-term debt) 0.000026
(0.000016)
0.002(0.00093) 0.000056
(0.00012)
0.043 (0.0042)
Sample size 276 5673 2144 5673
Log likelihood 33.182591 111.74762 230.72977 667.50703
Probability >v20.0000 0.0000 0.0000 0.0000
AIC 76.3652 237.4952 471.4595 1349.014
Hausman test NA v2ð5Þ¼35.97
Note: suggests on the significance at the 10% level, suggests on the significance at the 5% level,
suggests on the significance at the 1% level.
JOURNAL OF POST KEYNESIAN ECONOMICS 19
To choose between Fixed and Random model the best one, it was decided
to rely on the results of the Hausman test intended for this purpose. The v
2
statistics is presented to test the null hypothesis. If it is rejected (v
2
>0.05),
the Fixed-effects model is preferred. In the case of the Torres Filho et al.’s
approach, v
2
¼35.97 results in the Fixed model preference. In the case of the
Mulligan’s approach, the model failed to meet the asymptotic assumptions of
the Hausman test, and the v
2
statistics could not be interpreted properly.
Therefore, Log-Likelihood and AIC were considered to choose between F-1
and R-1. As for Log-Likelihood, the higher its value, the better a regression
model fits the data (Sharashova et al., 2017). The opposite situation with AIC,
which estimates the relative amount of information lost by this model: the
less its value, the higher the quality of a model. According to both indicators,
the F-1 model is better, than R-1 (in spite of the fact that GDP - statistically
significant at the 5% level).
Thus, F-1 and F-2 are the most appropriate models. However, the results
of all AMEs in F-2 are not statistically significant. Therefore, to draw con-
clusions about the impact of independent variables on the firms’probability
to become Ponzi, the outputs of the panel logistic regression model F-1
will be analyzed.
According to F-1, the sample size is 276. The AMEs are the following:
1. GDP—statistically significant at the 10% level. If GDP increases by 1%,
the probability of being Ponzi decreases by 0.000045.
2. Crisis—statistically insignificant. When it equals 1, the probability of a
company to become Ponzi declines by 5.80e-06.
3. Profitability—statistically significant at the 1% level. If Profitability
grows by 1%, the firms’probability to be Ponzi drops by 0.000039.
4. Interest Expense—statistically insignificant. The increase by 1% in Interest
Expense leads to 7.48e-06 decrease in the probability of becoming Ponzi.
5. Short-term Debt—statistically significant at the 10% level. If Short-term
Debt grows by 1%, the probability for a company to be Ponzi increases
by 0.000026.
Summarizing, according to the AMEs of Fixed-effects logistic regression
model based on the Mulligan’s methodology:
Increasing GDP reasonably leads to the lower probability of companies
to have Ponzi financing, which is quite logical.
The higher the profitability of the company, the less its possibility to
become Ponzi. It is evident, as the higher the firm’s income, the more it
can repay without borrowing.
A rising debt results in the growing firms’probability of being Ponzi.
20 E. PEREPELKINA AND I. ROZMAINSKY
The higher interest expense, the lower possibility to become Ponzi.
Surprisingly, a crisis reduces the probability of a company to have
Ponzi regime. It is the only result that is opposite to both common
sense and the logic of the FIH; but this effect is insignificant, and we
can ignore it.
Conclusion
Although the empirical analysis of the financial instability hypothesis has been
applied to many countries, Russia has not been considered in terms of the FIH
until now. Russian firms in such sectors as Agriculture, Construction,
Investment, Steel Industry, Machinery, Light Industry, Power Industry, Trade,
and Oil, Gas, and Chemicals Industry were analyzed during 2005–2020. In
that period, Russia experienced three crises: 2008–2009, 2014–2015, and 2020.
According to the FIH, before and throughout the crisis time the number of
firms using Ponzi-regime should reach the maximum. It happens due to the
fact that “stability is destabilizing”: agents’vigilance and caution decrease in
stable times, as optimism increases. The number of borrowings and debts
grows, which makes companies unable to pay not only the amount of debt but
even interest. Thus, the probability of being Ponzi increases.
On the results of the statistical analysis, it was found out that the finan-
cial instability was indeed observed for Russian companies in the period
reviewed: before and during the crises the shares of Ponzi firms were the
highest. Moreover, the proportions of companies with speculative regimes
usually increased in crises years too, which reflects that the number of
firms with fragile financing regimes grew in those years. Considering the
dynamics of the regimes, speculative financing dominated during
2005–2020, whereas the share of hedge companies increased in non-crisis
years. As for sectors, Construction, Investment, Power Industry, and
Machinery were prone to instability in crises years more than other sectors,
while Steel Industry, Trade, Agriculture, and Oil, Gas, and Chemicals
Industry proved to be the most stable and robust.
Finally, the panel logistic regression revealed that the increase in GDP
and Profitability leads to declining in the firms’probability to become
Ponzi, whereas the rise in Short-term Debt results in the growing probabil-
ity to have a fragile financing regime. These results could be other if we
use both other criteria for classifications of firms into three financing
regimes and other econometric modeling.
References
Balandina, O. 2016.“The Consequences of the Current Economic Crisis for Russia.”Young
Scientist 11 (115):598–601.
JOURNAL OF POST KEYNESIAN ECONOMICS 21
Beshenov, S., and I. Rozmainsky. 2015.“Hyman Minsky’s Financial Instability Hypothesis
and the Greek Debt Crisis.”Russian Journal of Economics 1 (4):419–438. doi:10.1016/j.
ruje.2016.02.005.
Budnevich Portales, C., N. Favreau Negront, and E. P. Caldentey. 2020.“Chile’s Thrust
towards Financial Fragility.”Investigaci
on Econ
omica 80 (315):81–106. doi:10.22201/fe.
01851667p.2021.315.77041.
Davis, L. E., J. P. A. De Souza, and G. Hernandez. 2019.“An Empirical Analysis of Minsky
Regimes in the US Economy.”Cambridge Journal of Economics 43 (3):541–583. doi:10.
1093/cje/bey061.
Minsky, H. P. 1977.“The Financial Instability Hypothesis: An Interpretation of Keynes and
an Alternative to “Standard”Theory.”Nebraska Journal of Economics 16 (1):5–16.
Minsky, H. P. 1986.Stabilizing an Unstable Economy. New Haven, CT: Yale University
Press.
Mulligan, R. F. 2013.“A Sectoral Analysis of the Financial Instability Hypothesis.”The
Quarterly Review of Economics and Finance 53 (4):450–459. doi:10.1016/j.qref.2013.05.
010.
Mulligan, R. F., R. Lirely, and D. Coffee. 2014.“An Empirical Examination of Minsky’s
Financial Instability Hypothesis: From Market Process to Austrian Business Cycle.”
Journal Des
Economistes et Des
Etudes Humaines 20 (1):1–17. doi:10.1515/jeeh-2013-
0005.
Nishi, H. 2019.“An Empirical Contribution to Minsky’s Financial Fragility: Evidence from
Non-Financial Sectors in Japan.”Cambridge Journal of Economics 43 (3):585–622. doi:10.
1093/cje/bey031.
Novikova, A. Yu., and I. V. Rozmainsky. 2020.“The Non-Financial Private Firms’Sector of
Portugal in 2001–2017: The Financial Fragility Hypothesis-Based Analysis.”Voprosy
Ekonomiki 3 (3):115–128. doi:10.32609/0042-8736-2020-3-115-128.
Osipov, A., and I. Orlov. 2021.“The Difference between the Coronavirus Crisis and the
Crises of 1998, 2008–2009 and 2014–2016 in Russia.”Student Issues 5 (57):291–298.
Potemkin, A. 2019.“Determination and Comparison of the Reasons for 2008 and 2015
Crisis in Russia.”Colloquium-Journal 13–10 (37):226–233.
Rozmainsky, Ivan, Yuliana Kovezina, and Anna Klimenko. 2022.“An Empirical
Application of the Financial Instability Hypothesis Based on Data from the Dutch Non-
Financial Private Sector.”Journal of Post Keynesian Economics 45 (2):281–300. doi:10.
1080/01603477.2021.1993072.
Schroeder, S. 2009.“Defining and Detecting Financial Fragility: New Zealand’s Experience.”
International Journal of Social Economics 36 (3):287–307. doi:10.1108/
03068290910932765.
Sharashova, E., K. Holmatova, M. Gorbatova, and A. Grzhibovsky. 2017.Application of
Multiple Logistic Regression Analysis in Healthcare Using SPSS. Moscow: Science and
Healthcare.
Stolbov, M. 2010.“The Hyman Minsky’s Financial Instability Hypothesis and the Economic
Crisis in Russia.”World Economy and International Relations (3):56–64. doi:10.20542/
0131-2227-2010-3-56-64.
Torres Filho, E. T., N. M. Martins, and C. Y. Miaguti. 2019.“Minsky’s Financial Fragility:
An Empirical Analysis of Electricity Distribution Firms in Brazil (2007–2015).”Journal
of Post Keynesian Economics 42 (1):144–168. doi:10.1080/01603477.2018.1503057.
22 E. PEREPELKINA AND I. ROZMAINSKY
Appendix
The dynamics of the financing regimes in different sectors
Agriculture
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hedge Speculative Ponzi
Figure A1. The share of firms in agriculture from 2005 to 2020: Mulligan’s methodology.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e Speculative Ponzi
Figure A2. The share of firms in agriculture from 2005 to 2020: Torres Filho et al.’s
methodology.
JOURNAL OF POST KEYNESIAN ECONOMICS 23
Construction
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e S
p
eculative Ponzi
Figure A3. The share of firms in construction from 2005 to 2020: Mulligan’s methodology.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e Speculative Ponzi
Figure A4. The share of firms in construction from 2005 to 2020: Torres Filho et al.’s
methodology.
24 E. PEREPELKINA AND I. ROZMAINSKY
Investment
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e S
p
eculative Ponzi
Figure A5. The share of investment firms from 2005 to 2020: Mulligan’s methodology.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e Speculative Ponzi
Figure A6. The share of investment firms from 2005 to 2020: Torres Filho et al.’s methodology.
JOURNAL OF POST KEYNESIAN ECONOMICS 25
Light industry
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e S
p
eculative Ponzi
Figure A7. The share of firms in light industry from 2005 to 2020: Mulligan’s methodology.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e Speculative Ponzi
Figure A8. The share of firms in light industry from 2005 to 2020: Torres Filho et al.’s
methodology.
26 E. PEREPELKINA AND I. ROZMAINSKY
Machinery
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
eSpeculative Ponzi
Figure A9. The share of firms in machinery from 2005 to 2020: Mulligan’s methodology.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e S
p
eculative Ponzi
Figure A10. The share of firms in machinery from 2005 to 2020: Torres Filho et al.’s
methodology.
JOURNAL OF POST KEYNESIAN ECONOMICS 27
Oil, gas, and chemicals industry
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hedge Speculative Ponzi
Figure A11. The share of firms in oil, gas, and chemicals industry from 2005 to 2020:
Mulligan’s methodology.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e Speculative Ponzi
Figure A12. The share of firms in oil, gas, and chemicals industry from 2005 to 2020: Torres
Filho et al.’s methodology.
28 E. PEREPELKINA AND I. ROZMAINSKY
Power industry
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e S
p
eculative Ponzi
Figure A13. The share of firms in power industry from 2005 to 2020: Mulligan’s methodology.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e S
p
eculative Ponzi
Figure A14. The share of firms in power industry from 2005 to 2020: Torres Filho et al.’s
methodology.
JOURNAL OF POST KEYNESIAN ECONOMICS 29
Steel industry
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e S
p
eculative Ponzi
Figure A15. The share of firms in steel industry from 2005 to 2020: Mulligan’s methodology.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e S
p
eculative Ponzi
Figure A16. The share of firms in steel industry from 2005 to 2020: Torres Filho et al.’s
methodology.
30 E. PEREPELKINA AND I. ROZMAINSKY
Trade
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e S
p
eculative Ponzi
Figure A17. The share of firms in trade from 2005 to 2020: Mulligan’s methodology.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Percentage
Hed
g
e Speculative Ponzi
Figure A18. The share of firms in trade from 2005 to 2020: Torres Filho et al.’s methodology.
JOURNAL OF POST KEYNESIAN ECONOMICS 31
