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ние себестоимости, бухгалтерский учет, налоги, отчетность - Сер. Полное руководство бухгалтера. - М.: Рид Групп, 2011.
8. Петров A.M., Мельникова Л.А. Формирование отчетности е соответствии с требованиями МСФО как объективная необходимость на современном этапе развития экономики РФ // Проблемы современной экономики. - 2017. - № 2 (62). - С 105-107.
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15.Петрова O.A. Цифровизация консолидирован ной финансовой отчетности в страховом бизне се // Экономические науки. - 2021. - № 203. -С 268 270.
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ПАНДЕМИЧЕСКАЯ ВСТРЯСКА МИРОВОЙ ЭКОНОМИКИ: СТА ТИСТИЧЕСКИЕ ХАРАКТЕРИСТИКИ ДИНАМИКИ ЭКОНОМИЧЕСКИХ ИНДЕКСОВ В 2020 ГОДУ
Сейдаметова Зарема Сейдалиевна, доктор педагогических наук, профес сор Темненко Валерий Анатольевич, кандидат физико-математических наук, доцент
Крымский инженерно-педагогический университет имени Февзи Якубова, Симферополь, Республика Крым
Цель исследования - детальное описание изменений, произошедших в течение "пандемического"2020 года в распределениях стран мира по основным экономическим индексам и сопоставление этих изменений с годичной динамикой индексов "допандемичес.кого" 2018 года, При описании этих изменений используются статистические данные Мирового Банка, Международного Валютного Фонда и the Transparency International. Научная новизна заключается в явном графическом и вербальном описании динамики экономических индексов в 2020 г. с помощью построения гистограмм и двумерных графиков. Сопоставление этих распределений 2.020 года с аналогичными распределениями 2018 года позволяет выявить в результате различия между пандемической встряской и годичной динамикой экономических коэффициентов "спокойного", "равновесного" года мировой экономики.
Ключевые слова: валовый внутренний продукт (GDP'); валовый внутренний продукт на душу населения (GDP/ PC); бюджетная нагрузка (ВЦ); индекс восприятия коррупции (CPI); правительственный долг (GD).
D0I 10.24923/2222-243Х.2022-43,12
УДК 339.97:330.43 ВАК РФ 5.2.5/08,00.14
& Seidametova Z.S., 2022 0 Temnenko V.A., 2022
SEIDAMETOVA la re ma Seidattevna, DSc of Pedagogical sciences, Professor
TEMNENKO Valerfi AnatoHevich, PhD of Physics and Mathematical sciences, Associate Professor
Fevzi Yakubov Crimean Engineering-Pedagogical University, Simferopol, Republic of Crimea
THE PANDEMIC SHAKE-UP OF THE WORLD ECONOMY: STATISTICAL CHARA CTERSSTfCS OF THE ECONOMIC INDICES'DYNAMICS IN2020
The purpose of the study is a detailed description of the changes that took place during the "pandemic" 2020 in the distribution of the countries of the world according to the main economic indices and a comparison of these changes with the annual dynamics of the indices of the "pre-pandemic" 2018. These changes are described using statistics from the World Bank, the International Monetary Fund, and Transparency International. The scientific novelty of the research lies in the explicit graphical and verba! description of the dynamics of economic indices in 2020 using the construction of histograms and two-dimensional graphs. Comparison of these distributions in 2020 with similar distributions in 2018 makes it possible to identify, as a result, the difference between the pandemic shake-up and the annual dynamics of the economic coefficients in the "calm" "equilibrium" year for the world economy.
Keywords: gross domestic product (GDP); gross domestic product per capita (GDP/ PC); budget loading (BLi); corruption perception index (CPi); government debt (GD).
Introduction
In a series of previous papers ([1J - LU ]), we presented a detailed description of the state of the global economy in 2019 in the three-dimensional space of economic indices EPi, BLI, CPi.TheEPI index measures the relative economic productivity of a country as a percentage of the most economically productive country in the same year:
EPI -
GDP/PC ma;t(GDP/PC)
100,
m
where GDP/PC is the gross domestic product per capita of a given country, and max (GDP/PC) is the maximum value of GDP/PC achieved in some country in the wo rid in the same year. We borrow data on the value of GDP/PC from the World Bank database [12]. We use GDP and GDP/PC data in current US dollars. For the first time, the economic index EPI was introduced by us in the paper [13] as a tool for describing the state of the global economy in 2016.
The second economic index used by us is called in the paper [13] the"budget loading index" BIJ. This index is determined by the formula:
BR
BLI ---100,
GDP
m
where BR is the country's government budget revenues and GDP is the country's gross domestic product in the same year.
Index (2) is used by many researchers. Some of them call it "State Strength" (see, for example, [141 and f15]}. We believe the term "State Strength" is not very appropriate in an economic context.
We borrow data on BU from the database of the international Monetary Fund [16].
In conditions of economic equilibrium (or more precisely, "in the absence of a country or global economic crisis"), government spending does not differ significantfy from government revenue. But in the context of a global economic crisis or pandemic, government spending and revenues can vary significantly. In such a situation, it is necessary to analyze, in addition to BLI, indicators of government debt growth.
The third economic index we use is the "Corruption Perceptions Index" CPI. it is an expert assessment on a 100-point scale. The CPS index characterizes the level of corruption in the country. A very sophisticated and complex method for determining this index is given in [17]. We borrow data on the CP! index from the database of the international organization Transparency International [18].
The purpose of this paper is to give a detailed description of the changes that took placeduring the "pandemic" 2020 in the distribution of countries of the world according to the main economic indices - it structures the following tasks: 1) building and describing a histogram of the relative change in GOP/PC in countries of the worid in 2020 and comparing with the annua! dynamics of this indicator in the "equilibrium" 2018; 2) construction and description of the histogram of BLI changes in 2020 and comparison with the annual dynamics of the BLI index in the "equilibrium" 2018; 3} description of changes in the distribution of CPS countries of the world in 2020 and comparison with similar changes in 2018; 4) description and analysis of the dynamics of government debt in 2020.
The theoretical basis of the study were publications CflH11]). in particular, the paper [1] described in detail the method of dividing the global economy into EPI-groups. Papers [2] and [3] provide a detailed description of the 2019 EPi-groups. in papers [4] and [5] the distribution of the countries of the world according to the parameter of socio-economic equilibrium in 2019 was studied. The paper [6] constructed an axial line of the global economy swarm in the three-dimensional space of economic indices EPI, BLI, CPI. Papers [7] and [8] studied the distribution of countries' budget deviations from some "ideal" budget, determined by the parameters of the axial line of the global economy swarm. The paper [9] introduces the "indicator of relative productivity" of the countries of the worid, which characterizes the deviation of the EPI index from some "ideal" value, determined by the parameters of the axial line of the global economy swarm, in the papers [10] and [11] "normality nucleus" of EPI-groups were studied. These nuclei contain countries with iittie deviation from the axial line of the global economy swarm.
There is no doubt that after the pandemic "shake-up" of 2020-2021 the global economy will gradually come toa certain state that will deserve the same detailed description as the description of the state of the global economy in 2019 presented in papers [1] - [11], The global economy in 2020 was in an extremely disequilibrium state, and our task is to see and describe this disequilibrium dynamics.
The practical significance of this study is to create a basis for identifying the mechanisms of adaptation of different countries to the conditions of the global "shock" of the economy.
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Main part
Global distribution of GDP/PC growth rate. In the papers cited above [1 ]-[11 ], [13] we considered the "swarm of the global economy" in the three-dimensional space of economic indices (EP!, 8LI, CPf). When studying the 2020 pandemic shake-up, it is convenient to study the dynamics of three univariate distributions - the distribution by GDP/PC change, by BLi change, and by CPI 3 change. Unfortunately, the three univariate lists SC of countries that show the corresponding ^ economic characteristics for two consecutive J2 years contain a different number of countries, if we leave as an object of study only those countries U-l that are on all three lists, then we get a depleted, ^ incomplete picture of the pandemic "shake-up" q Let's introduce the value v, which i j i characterizes relatively the change in GDP/PC for 3" each country:
s
T GDP/PC2Q - GDP/PC19 o 17 =-gdpTPC^—100' ^
Q where GDP/PCi0 is the value of GDP/PC in 2020 ij; and GDP/PC,9 is the value of GDP/PC in 2019. The CO value /shows the increase in GDP/PC during 2020 ,8 expressed as a percentage to GDP/PC 2019.
The World Bank database includes 193 countries for which v can be calculated. For 41 countries f>0. For 152 countries e<0. The pandemic "shake-up" has led to the fact that the number of countries showing a decline in economic productivity (i*;0) significantly exceeds the number of countries showing an increase in productivity (oO). On fig. Figure 1 shows a histogram of the distribution of countries in the world by fin 2020. To build a histogram, the entire range of e from the minimum value v . =-54.29% J
mm
{Macao SAR, China) to the maximum value vmaj(=+18.22% ... {Egypt, Arab Republic) was divided into 35 equal half-open intervals (the left edge of the interval belongs to it, the right one does not, except for the last i closed interval). The heights of the bars in this histogram show the number of countries that have values /within the given interval.
Neglecting the discrete "ruggedness" of the histogram in fig. 1, it can be qualitatively described as follows, Most of the countries are in some asymmetricdomebetween s« -25 and j» r; 10 with
the top of the dome in the region of small negative values ess-0.5+ -5,0, There are several countries with a sharp drop in economic productivity { k<-25%). In addition to the previously mentioned Macao, these are Libya (1^-51.87), Lebanon ((¿=-38.65), Angola {(¿=-36.78), Seychelles (<¿=-33.61), Maldives {(¿=-34.44), Iraq {1^-30.68), Suriname {(¿=-28.26), Bahamas ((¿=-25.46).This list of "outsiders" includes several states with protracted internal conflicts {Libya, Lebanon, Iraq). This list also includes states and territories whose economy is heavily dependent on international tourism {Macao, Seychelles, Maldives, the Bahamas). In 2020, there were three states with very significant GDP/PC growth. These are Egypt {(¿=+18.22), Myanmar {(¿=+15.46) « Guinea (i*=+12.S4J:
Previously, we studied the dynamics of GDP/ PC on an annual scale for 2018 [19]. Perhaps this year can be considered a year of relative prosperity for the global economy: a decade has passed since the global financial crisis. According to [19], in 2018 there were only 19 countries with v<0. With the "shake-up" of 2020, there are many more such countries, in one of these countries, an economic disaster occurred in 2018: GDP/PC fell three times (Sudan). Significant economic slowdown occurred at the same time in Somali {(¿=-34.2). Armed conflicts are taking place on the territory of these two countries. In 2018, there was also a group of countries with a noticeable drop in productivity (from i^-20to (¿=-10). Among them are Argentina (v=-18.8), Angola (t^-16.3), Yemen {(¿=-14.7), Turkey {(¿=-11.2), Timor-Leste (1^-19.7).
Fig. 1. Histogram of country distribution by relative GDP/PC growth in 2020
In 2018, the distribution of one hundred and fifty countries by (/{for ic-0) could be qualitatively
1
|
■4 t -n, rr-n m- mn
described as foliows [19]. ff we ignore the "jaggedness" of the discrete histogram, then it is a smooth unimodular function with a wide maximum at vx 5 + 10%,
Consequently, the distribution of countries by v in the context of a global shake-up is radically different from the distribution by v'm a "prosperous" or "equilibrium" year.
World distribution of BL1 growth in 2020. Let us define the growth of the budget index ABU as follows:
assertion: in a "favorable" year, the budget of the next year does not differfrom the budget of the previous year by more than ±2 percentage points with a probability of about 40%; there is a -30% chance that the budget will grow by +2 - +8% according to the BLI index [19]. The pandemic "shake-up" has made solutions with ABLi<0 more acceptable for the governments of the world.
ABLI-BLL,,- BLI1;;,
(4)
where BLI^ is the BLI value in 2020 and 811K is the BLI value in 2019.
In 2020 we have 195 countries with a known ABU, Fig. 2 shows a histogram of the distribution of countries in the world in terms of ABLI. This histogram is constructed by dividing the entire range of change from the minimum value ABU . =-19.10 (Libya) to
mm '
the maximum value ABLIrns!(=+2tJ (Nauru) into 40 haif-open intervals. In 2020, 121 countries showed a decrease in BLf (ABLi<0) and 74 countries showed an increase in BLI (ABL!>0). However, this difference is achieved mainly due to a significant number of countries with small negative ABLI, from ABLI= -1% to ABLI=0. According to figure 2, there are almost fifty such countries, ignoring the discreteness of the histogram in fig. 2, its main part from ABLi=-7% to ABU=+8% can be described as an almost symmetrical dome with a fairly sharp maximum, slightly shifted towards negative v.
This picture of the annua! dynamics of the budget loading index BLI in the context of the pandemic "shake-up" in 2020 is radically different from the similar picture in the conditions of the "prosperous" 2018 [19]. The corresponding histogram of the distribution of ABU in 2018 showed the strongest "+■/-" - asymmetry: decisions to increase the budget loading were made more often than decisions to reduce the budget loading. The ratio of countries with a positive ABU to the number of countries with a negative ABLI was about 2:1. Only in a narrow range of j ABLIj < 2% was this ratio approximately 1:1 in 2018. In other words, in a "good" year, decisions to increase BLI slightly and decisions to decrease BLI sfightly are almost equally likely. The 2018 data allowed us to make the following
3 t s 3 i : s s « ^ « ! i S s s f ' M H § ^ Î s's * 5 ^ i " E" s
Fig. 2. Histogram of ABLI distribution in 2020
Worldwide distribution of changes in the Corruption Perceptions Index CPI.I n 2020, for 174 countries, the CPI value for 2020 (CPLÇ) and the CPI value for 2019 [CPI J were known. Let ACPI
be the annual change in the CPI:
(5)
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In 2020 three countries had abnormally high ACPI values: Maldives (ACPI=+14) Suriname (ACPI=-6) and Armenia (ACPI =+7). We tend to consider the CPI index to be a very conservative, slowly changing value that carries important information about the state of society. We do not know the reasons (other than a change in the calculation method or a change in the composition of experts) that could lead to such significant annual changes in CPL Of course, in the event of a sharp increase in social unrest or the outbreak of armed conflict, the CPI index can quickly fall.
All other ACPI values had a rather symmetrical distribution in 2020 from ACPI= - 4 to ACPi=+4(see fig. 3).
The distribution shown in fig. 3, in our opinion, does not contain a dear response to the 2020 pandemic shake-up. It expresses some natural instability of a discrete expert assessment. From this distribution, we can conclude that according to the 2020 data, the probability pQ that the CPI index will not change during the year is 38%. The
70
probability p., that the CP! index will change by no more than 1 point (i.e., ACPi= 0, +1 or -1 ) is 79%. The probability p1 that the CPI index will change by no more than 2 points {i.e., ACPi= 0, ±1, ±2) is91%.The probability p} that the CPI index will change by no more than 3 points is 97%.
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How can you describe the dynamics of government debt?
Currently, the IMF database contains information on the amount of government debt of 86 countries, expressed as a percentage of GDP [20]. Let's denote this value as £?and call it the "government debt index". On fig. 4 shows the dependence of the 2020 £?index (£?J on the 2019 O index (D ). This dependence is well approximated by a straight line:
D^-l. 1299D;9 ' 3.2688.
<6)
Fig. 3. Distribution of countries by ACPI in 2020
It can be compared this probability distribution with the similar distribution of the "prosperous" year 2018 [19]. In 2018 p^as29%, p. ss 68%, p2 % 90%, p^ ss 95%, We do not know whether the differences in these two sets of probabilities in 2018 and 2020 should be considered statistically significant.
Of course, there are countries in the world economy that show a monotonous change in the CPI index over a number of years. The most striking example is the USA, for which the CPI index decreased by 8 points from 2017 to 2020 ({A CPi=-4 B 2018 [18], ACPi-2 8 2019 and ACPI=-2 in 2020}. Apparently, this systematic change exceeds the natural error of a discrete expert assessment by 1-2 points. It reflects, perhaps, the real processes in the society of the USA during the presidency of D. Trump.
Growth of the General Government Debt.Q ne of the most significant phenomena in the economy of the "pandemic" 2020: the excess of government spending over government revenue for many countries, in some cases, this excess is offset by loans from international banks. In many cases, this excess is compensated with the help of "printed money", i.e. introduction into circulation of national banknotes that do not have material support. This phenomenon is not explicitly reflected in the above descriptions of changes in the economic indices of countries in 2020. Hypothetically, this phenomenon may have a hidden image in the histogram c(fig. 1): if there were no "printed money", the fall in economic productivity for many could be significantly larger.
On fig. 4, there are "super debtors" whose D20 exceeds 200%: Japan (£>]S=235,£> ,-254), Venezuela (£><=233, £>s=304), Greece (¿>,<-185, 211). "Heavyweights" are also noticeable, in which both and exceed 100%: (D„<=134, DK=155}, Portugal (£=116, 135), US (£> =108, ^=134).
From formula (6} it follows that the difference Agrows with the growth of £>]SJ. However, this difference does not give a clear idea of the spread of the "printed money" phenomenon: the government debt index is assigned in D;3 and D!9 to different bases (GDP,,, and GDP, J. it is convenient to introduce another index R, which characterizes the percentage of the annual increase in government debt for 2020 to GDP in 2019,
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A
Mm* t J,j* R* a 0,9m № / /
/ /
/ • » y
J r
/
Fig. 4. Government debt index D2B as a function of government debt P,q
Since the absolute value of government debt is P^-GDP^ in 2020 and P,!9-GDP?S in 2019, the increase in debt is the difference between these numbers. And the R index is equal to the ratio of this difference to GDP10:
R = A- Dzo - Dl9,
(7)
where
л =
GDP,
20
GDP,
(8)
19
Figure 5 shows a histogram of the distribution of the index /?in 2020. Tl iis histogram shows two small iocai maxima in the intervals from Rta 3,5 to /fss 5.5 (16 countries) and from Ra 11.5 to /?ss13.5 (12 countries) and a relatively even distribution between these two local maxima, from /?si5.5 to /fssll.S (31 countries). Four countries have large lvalues: Canada (/£=24.19), Egypt (/£=24.05), US (/£=22.46), Cyprus (/£=1 9.80). Eight countries have R<0 (government debt decreased in 2020): Brazil (/£=-11.44), Marshall Island (/£=-5.43), Nauru (£=-4.70), Norway (£=-3.85), Saint Kitts and Nevis (/£=-3.57), Micronesia (/£=-2.58), Congo, Dem. Rep. (/£=-0.35), Mauritius (/£=-0.03).
Figure 6 shows the mapping of the world economy onto the plane of economic indices [R,V\- Here R is the government debt index introduced above, and Vis the 2020 GDP growth rate, divided by 2019 GDP and expressed as a percentage:
2,The influence of Ron ^appears with a time lag, perhaps more than one year and possibly different for different countries,
3, There is no connection between /?and V. "Printed money" affects voter behavior, but does not affect the economy.
V
■
* • •>
f- .1 1 1 1 • * a * I/* s 1 * « :
I • ■ * * ♦ •
• * • • •
• *
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V = 100- (A- 1). The value of A is defined by formula (8).
(9)
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» t t ..-ч..-*....
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* í i
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Fig. 5. Histogram of government debt distribution in 2020
Fig. 6 do not demonstrate the existence of a statistically significant relationship between "printed money" (R) and relative changes in GDP (VAhis discouraging fact can have three different explanations:
1. The data is incomplete: fig. 6 presents 86 countries out of almost two hundred existing now.
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Fig. 6. The world economy on the plane of economic indices ;'/?,!/}
This third explanation seems overly radical and perhaps unacceptable to both economists and politicians.
Conclusions The article presents an analysis of the annua! dynamics of economic indices during the pandemic "shake-up" 2020. A comparison of this pandemic dynamics with the annual
_ dynamics of these indices in a
relatively prosperous 2018 is also presented. We believe that the global economy in 2020 is in a significantly disequilibrium state, significantly different from the state of the swarm of theglobal economy in 2019.The article also presents an analysis of the dynamics of government debt in 2020 for a large group of countries. The existence of a relationship between the growth of government debt and the annual dynamics of GDP has not been revealed.
References:
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s
71
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EFFICIENCY OF NATIONAL PROJECTS IMPLEMENTATION: PROBLEMS AND SOLUTIONS Sobgafda Elizaveta Aiexandrovna, Undergraduate student, Russian Customs Academy, Lyubertsy
The article deals with the activities of the state in the framework of achieving national goats through the implementation of national projects, which is a priority for the Russian Federation. The purpose of the study is to identify the problems affecting the effectiveness of the implementation of the National Projects in the Russian Federation and the search for directions to improve the effectiveness of the implementation of the National Projects. The article substantiates the idea that under the conditions of systemic changes in the socio-economic situation and the emergence of additional risks, the problem of adjusting the national programs becomes acute. Particular attention is paid to the fact that the developed methodological recommendations for the implementation of the National Projects are only partially focused on increasing the role of local governments in the implementation of national programs. On the basis of the study, it has been established that a clearer reorientation is needed from the purely executive role currently assigned to local governments in the direct implementation of specific activities, transforming them into real full actors directly involved in the preparation, decision-making and implementation of the National Projects. At the same time, an important basis for decision making in assessing the development and effectiveness of the national projects is the assessment of the socio-economic situation and the effectiveness of
