M.Melnichuk, Dr., Prof.

A.Karaev, Dr., Prof.

Financial University under the Government

of the Russian Federation,

Moscow

Approaches to Regional Economic Growth Strategies

(investment and fiscal aspects)

To find out the necessary conditions for sustainable social and economic regions development the authors have carried out system research and factors performance analysis in the framework of neoclassical economic growth theory. Investment aspect of social and economic regional differentiation have been studied with the use of production functions.

The articles also reviews a model analysis based on the production-institutional functions of the tax burden impacting economic growth in the regions of Russia (Moscow and Khanty-Mansi Autonomous Area - Yugra) as well as throughout Russia from 2000 to 2011. It also reviews tax system effectiveness evaluations. To determine the optimal settings of the tax system, we analyzed Laffer’s points with respect to a combined tax burden index.

Key words: economic growth; tax burden; Laffer’s 1st and 2nd type points; production-institutional functions; fiscal system efficiency.

The Russian economic space is characterized by extraordinary non-

uniformity and unevenness of development caused to a considerable extent by

nature differences, the geographic evolution of the Russian state, phases of the

country’s current territory development.

In the framework of this spatial non-uniformity, the principal state

economic policies tend to efficiently combine regional diversity, preservation

of the national space integrity and its effective integration into the globalizing

world. Therefore, “Russia’s way in the 21st century is to reject the regional

uniformism in the social-economic policy and focus on making use of

advantages of every region and interregional cooperation, harmony of regional

society interests, implementation of the equal opportunities principle for all

citizens irrespective of their residence” [1].

The historically developed unevenness of the economic space of Russia

has a significant impact on the structure and effectiveness of the economy, the

strategy and tactics of institutional reforms and the social-economic policy. The

differentiation of regions increased dramatically in the 1990ies. It was due to a

number of reasons: development of the market competition mechanism,

disruption of national economic links, different market adaptability of regions

with different structures of the economy and different mentalities of the

population and authorities, reduction of government investments into regional

development, etc. A positive feature of the economic dynamics in 2000-2011 is

that the economic growth enveloped the major part of the Russian space leading

to higher real incomes and consumer spendings of the population in every and

all subjects of the Russian Federation. However, even the wide-spread and

sustainable economic growth is so far unable to overcome the tendency to the

increasing differentiation (divergence) of regions by their economic

development levels.

The non-uniformity general to the structure of the Russian economic space

may increase through emergence of new points of growth, development poles,

effective regional clusters leading to further aggravation of negative non-

uniformity effects such as appearance of depressed and non-competitive areas

lagging more and more behind regional leaders and falling out of the common

and humanitarian space, which impedes thereby a uniform and successful state

social-economic policy. Though lagging regions receive significant government

support, financial mechanisms applied solve, for the most part, just current

social tasks (fiscal capacity equalization) rather than provide incentives for

accelerated economic development of regions as the basis for social task

solution on the regional level.

To smooth the spatial economic differentiation substantially, more

effective instruments of the economic policy are needed, primarily

enhancement of the investment and innovation activities. Running a regional

economic policy in a situation of economic restructuring traditionally leads to

concentration of investments in one or several regions, with loss of the

economic potential and investment attractiveness on the rest territory of the

country. In this regard, one of the most important measures of the state

influence on the spatial distribution of production factors is an active

investment policy based on the qualitative evaluation of the investment

efficiency of regions where the contribution of investments into the gross

regional product is determined.

The starting point in investigation of investment processes in the economy

of Russia is to analyze the dynamics of social-economic indicators of the Russian

Federation and individual regions for the recent decade. Changes in

macroeconomic proportions of the Russian economy make it possible to expose

a number of principal factors that have had a substantial effect on the nature and

dynamics of transformation shifts at all levels of the economy, which, in turn,

allows a better insight into the role and contribution of separate areas and

subjects of the Russian Federation into the country’s GRP and helps to reveal

specifics of the investment policy run in given regions.

The most popular instrument in the study of the production-factors-to-

GRP relationship, including the regional frame of reference, also needed for

forecasting GRP dynamics of regions is the production function apparatus and,

above all, the standard multiplicative Cobb-Douglas function: 1Y AK L ,

where Y – gross regional product (GRP); А – residual or technological

parameter; K – fixed assets input; L – annual average labor input; α – GRP

fixed assets elasticity.

However, certain complications arise in building up production functions

of the Russian region economy. First, time series are so far quite short since the

transition to the market economy has begun comparatively recently. Secondly,

the available data are not sufficiently accurate due to the transient nature of

processes going on in the country. One of the reasons for data inaccuracy in

evaluation of fixed assets and the GRP may be inaccuracy in price

measurements resulting from considerable price volatility: price leaps in the

Russian economy exceed by far slow changes occurring in developed countries

of the West. The third, and maybe the main reason that impedes formulation of

the production function, is extreme inaccuracy in measuring the capital used in

production. There are several factors contributing to this:

- with the beginning of the transformation downturn, fixed assets ceased

to be used in the full extent, therefore fixed assets data do not correspond to

their actually used portion;

- in transition from resource limitations to demand limitations fixed

assets have become redundant, which, on the one hand, diminishes their

significance as a factor capable of determining the GRP performance dynamics,

and, on the other hand, makes impossible their market-based assessment.

One of the solutions to the problem of missing or inadequate fixed assets

data is to use fixed capital investment data rather than fixed assets data. The

advantages of this approach are determined by high efficiency of investments

assigned both for involvement of idle assets into circulation and acquisition of

new assets; thereby the share of the efficiently used capital increases. A fact of

no small importance is that there are statistical data reflecting the dynamics of

investments into fixed assets and the dynamics of paid labor; therefore

production functions of the type Y=F(I,W) were used in the work, where I is

investments into fixed assets, and W is investments into labor or paid labor.

As a result of the author’s investigation based on the linear multivariate

regression analysis using macroeconomic indicators of regions as the model

inputs (observed variables), production functions of Russian regions were built.

By way of illustration, data on Central, Northwestern, Volga and Urals federal

districts are provided (see the Table 1 below). The analysis of the Table makes

it possible to conclude that the production functions obtained for RF region

economies meet principal statistical criteria (R2 – determination factor and DW

– Durbin-Watson factor) and may be regarded quite operable and fit for

practical use.

Table 1

Parameter values of production functions r tY A I W e

for the RF region economy (2000-2011)

Region A α β α + β r R2 DW

1 2 3 4 5 6 7 8

Central Federal District

Belgorod Area 23.4637 0.4338 0.5662 1 0 0.978 2.235

Bryansk Area 28.5708 0.5006 0.3994 0.9 0 0.979 2.044

Vladimir Area 69.5877 0.3427 0.3583 0.7 0 0.96 3.182

Voronezh Area 86.6149 0.3417 0.546 0.90 0 0.935 2.403

Ivanovo Area 222.4745 0.2818 0.5532 0.83 0 0.954 1.707

Kaluga Area 49.6854 0.3569 0.5913 0.95 0 0.990 2.773

Kostroma Area 12.5826 0.3244 0.5756 0.9 0 0.973 1.965

Kursk Area 47.3473 0.349 0.5013 0.85 0 0.979 1.763

Lipetsk Area 24.3307 0.3507 0.6493 1 0 0.923 1.851

Moscow Area 14.9924 0.4071 0.5929 1 0 0.985 2.885

Orel Area 168.3605 0.239 0.7216 0.96 0 0.978 2.136

Ryazan Area 88.2451 0.1238 0.6968 0.82 0 0.958 2.300

Smolensk Area 82.1269 0.2688 0.5155 0.78 0 0.980 2.476

Tambov Area 213.2946 0.1394 0.5604 0.7 0 0.989 2.955

Tver Area 14.0426 0.3365 0.6635 1 0 0.926 2.139

Tula Area 50.4406 0.3452 0.623 0.95 0 0.940 1.994

Yaroslavl Area 19.5029 0.1907 0.8093 1 0 0.982 2.181

Moscow City 4.5113 0.9155 0.0845 >1 0 0.962 2.545

Moscow City 9.3744 0.8805 0.1195 1 0.01

8

0.937 2.516

Northwestern Federal District

Republic of Karelia 48.7695 0.2323 0.6291 0.86 0 0.938 2.414

Komi Republic 20.3274 0.3935 0.5559 0.95 0 0.968 1.732

Arkhangelsk Area 97.391 0.261 0.5302 0.79 0 0.951 1.494

Vologda Area 121.7085 0.3658 0.3906 0.76 0 0.902 1.445

Kaliningrad Area 99.2605 0.254 0.5068 0.76 0 0.962 2.521

Leningrad Area 22.9929 0.3391 0.5988 0.94 0 0.996 2.069

Murmansk Area 73.4754 0.137 0.7093 0.85 0 0.960 1.974

Novgorod Area 65.0786 0.189 0.6543 0.84 0 0.966 2.276

Pskov Area 15.2595 0.326 0.6731 1 0 0.951 2.356

Saint-Petersburg City 56.9446 0.6502 0.3498 1 0 0.959 2.386

Volga Federal District

Bashkortostan Republic 9.773 0.5775 0.4225 1 0 0.957 1.813

Mari El Republic 82.6947 0.2173 0.5543 0.77 0 0.968 2.935

Republic of Mordovia 69.777 0.1799 0.5269 0.71 0 0.891 1.300

Republic of Tatarstan 6.277 0.7917 0.2083 1 0 0.929 2.449

Udmurt Republic 13.8069 0.4425 0.5575 1 0 0.992 2.235

Chuvash Republic 29.3765 0.6737 0.1452 0.82 0 0.963 2.042

Perm Krai 16.6735 0.3394 0.6606 1 0 0.943 2.607

Kirov Area 241.9934 0.2081 0.4618 0.67 0 0.895 1.457

Nizhny Novgorod Area 70.1515 0.5049 0.4951 1 0 0.976 2.645

Orenburg Area 13.6921 0.4809 0.5191 1 0 0.953 2.222

Penza Area 116.2327 0.2409 0.5526 0.80 0 0.969 2.411

Samara Area 11.1188 0.6005 0.3995 1 0 0.944 2.791

Saratov Area 8.9634 0.4638 0.4862 0.95 0 0.861 2.398

Ulyanovsk Area 131.7153 0.3413 0.5566 0.9 0 0.934 1.647

Urals Federal District

Kurgan Area 86.5498 0.3415 0.5221 0.86 0 0.994 3.133

Sverdlovsk Area 45.6314 0.6799 0.2478 0.93 0 0.919 1.435

Tyumen Area 0.0614 0.8758 0.1439 >1 0 0.928 1.439

Tyumen Area 2.324 0.8623 0.1377 1 0

.015

0.981 2.486

Chelyabinsk Area 48.2681 0.3747 0.5932 0.89 0 0.978 1.811

Source: The author’s calculations based on data of the statistical yearbook “Regions of Russia. Social-Economic Indicators”. Moscow, Rosstat Publishers, 2012.

It should be noted that the factors of investment into fixed assets and paid

labor predetermine over 90% of all GRP changes. Moreover, for the majority of

regions the value of the GRP investment elasticity coefficient for the entire time

interval considered is significantly less than 1, which means the future need for

the savings rate growth and, respectively, the consumption rate reduction in

order to increase the production efficiency or the labor productivity.

To substantiate conditions required for Russian regions to enter the path of

balanced economic growth, the author successfully established the

interrelationship between production dependency parameters and Keynesian

Multipliers (static and dynamic). The expression II

cc

YY

*11

connecting

the GRP growth rates with the investment growth rates was taken as the basis,

where c and c* are the average and the maximum consumption rates,

respectively, the expression in parentheses is the GRP investment elasticity (let

it be E) representing a combination of the average and the maximum

propensities to consume.

Assuming c and c* as values of the same magnitude (*, [0,1]c c ), the

elasticity coefficient value, E, ranges from zero to one. Two maximum values

of the GRP investment elasticity coefficient *

(1 )(1 )

dY I cEY dI c

are consistent

with two asymptotic trajectories: the stable balanced economic growth path

* *1, ( ) 0 , ,C dC dY dCE c c c cY dY Y C

and the negative economic

growth path

* *0, ( ) 1 1, 0, 1, 0, , .C dCE c c c c C Y C constY dY

For the GRP investment elasticity coefficient values close to 1 ( 1E ), a

situation is observed when relative changes of the consumption volume, the

savings volume, investments and the GRP are equal: dY dC dS dIY C S I

. In this

case we may talk about the balanced model of endogenous stable economic

growth since we have an optimized breakdown of the GRP to the current

consumption and savings as potential investments for the subsequent GRP

growth, that is, the availability of savings ensures the availability of

investments as well as the availability of a positive feedback for a cumulative

economic cycle.

If the GRP investment elasticity coefficient is close to 0 ( 0E ), a

situation occurs when the whole GRP volume of the previous stage is spent on

the current consumption and in this case the value of the consumption volume

does not depend on the GRP value since the scale of the GRP value is by far

less than the potential consumption volume scale, that is, there are no savings

and hence no investments required for a stable economic growth.

It should be noted that the availability of two attractors (phases) makes it

possible to group regions based on their economic development trajectory

attraction to one of them, hence any qualitative change of a trajectory is made

possible only as a result of a phase transfer. From the analysis of the author’s

computations it follows that the majority of Russian regions (90%) tend to drift

to the negative growth attractor, and only an insignificant number of regions

gravitate to the stable economic growth attractor.

For estimation of the production scale effect in regional economies, cost

functions were built apart from production functions, thereby allowing

development of the author’s concept of type classification of Russian regions in

terms of investment efficiency.

According to the author’s concept of type classification of regions, the

principal indicators characterizing their economic efficiency include, on the one

hand, the investment efficiency of a region (GRP investment elasticity, or the α

factor – the third column of Table 1) and, on the other hand, the indicator of the

scale effect in a regional economy (the sum of the {α + β} production function

indices - the fifth column of Table 1). The author’s criterion for breaking the

regional economy into low-efficiency and high-efficiency economies is based

on the following assumptions: at α < 0.5, {α + β} < 1, a region has a low-

efficiency economy; at α > 0.5, {α + β} ≥ 1, a regional economy is a high-

efficiency one.

As seen from Tables 1 and 2, for the overwhelming majority of the

country’s regions (over 90%) the low-efficiency economy status is observed,

and only few regions demonstrate the high-efficiency economy status, which

fact characterizes phase separation of regions with a basically different

mechanism of economic behavior.

Speaking of economic growth we can’t help mentioning the problem of

optimizing a combined tax burden for a regional economy of Russia. Effective

tax system is one of the critical factors for dynamic development of the national

economy. As known [3-6], fiscal and regulating tax functions are of antagonist

nature to each other clashing the growing state financial needs and

entrepreneurs’ interests, especially during the crisis. Economic growth and

budget balance are the optimal mode for economical functioning from the

perspective of state regulation effectiveness.

Governments of most modern mature economies have to balance. If the

priority is the wellbeing of the budget, then due to increased tax burden the

economic growth slows down exerting a negative impact on re-production

capacities of enterprises winding up the business activity. Thus, short-term

gains may result in serious problems in the future.

If the state policy aims to achieve an economic rise by means of

lessening the tax load, the budget starts losing some income which will

negatively affect the social policy of the democratic state. However, in the

future the growing production may expand the tax base and the lost income will

be compensated in a while. Moreover, the total arrival of funds may rise.

Therefore, short-term budget interests contradict the long-term production

purposes of the entrepreneurs.

The problem of optimizing the settings of the tax system may as a rule be

resolved by identifying so-called Laffer’s points with regard to a combined tax

burden index. Moreover, the disagreement value of two Laffer’s points is the

main criteria and indicator of national fiscal system’s effectiveness.

Table 2Region Clusters of Russia in Terms of Economic Efficiency

(Investment Matrix)

Scale effectInvestment activity

Low(0<α<0.5)

Medium(0.5≤ α<0.7)

High(0.7≤ α<1.0)

Low0<(α+β)<0.85

Vladimir Area, Ivanovo Ar., Ryazan Ar., Smolensk Ar., Tambov Ar., Arkhangelsk Ar., Vologda Ar., Kaliningrad Ar., Novgorod Ar., Daghestan

Rep., Ingush Rep., Kalmykia Rep., Karachay-Cherkessia Rep., Mari-El Rep., Rep. of Mordovia, Kirov Ar., Penza Ar., Altai Rep., Khakasia Rep., Irkutsk Ar., Primorsky Krai (Territory), Khabarovsk Krai, Amur Ar., Sakhalin Ar.,

Jewish Auton. Area., Chukotka Auton. Dist.

Chuvash Republic

Medium0.85≤( α+β)<1.0

Voronezh Area, Kaluga Ar., Kostroma Ar., Kursk Ar., Orel Ar., Tula Ar., Rep. of Karelia, Komi Rep., Leningrad Ar., Murmansk Ar., Adyg Rep., Kabardin-

Balkar Rep., Rep. of North Ossetia-Alania, Saratov Ar., Ulyanovsk Ar., Kurgan Ar., Chelyabinsk Ar., Rep. of Buryatia, Altai Krai, Kemerovo Ar.,

Novosibirsk Ar., Omsk Ar., Chita Ar., Kamchatka Ar.

Sverdlovsk Area, Bryansk Ar.

High(α+β)≥1.0

Belgorod Area, Lipetsk Ar., Moscow Ar., Tver Ar., Yaroslavl Ar., Pskov Ar., Krasnodar Krai, Stavropol Krai, Astrakhan Ar., Udmurt Rep., Perm Krai.,

Orenburg Ar., Tyva Rep., Tomsk Ar., Magadan Ar.

City of Saint-Petersburg, Volgograd Ar., Rostov Ar., Bashkortostan Rep., Nizhny Novgorod Ar., Samara Ar.,

Krasnoyarsk Krai, Sakha Rep.

City of Moscow,

Tatarstan Rep., Tyumen Ar.

Fiscal regulation logics may represent three goals or guiding principles.

First goal – ensure absence of contradictions between manufacturer’s interests

and the budget which may be proved by a coincidence of Laffer’s 1st and 2nd type

points: q*q**. Second goal – balance nominal fiscal load on the left arch of the

Laffer’s production curve so that the nominal fiscal load value is not more than

Laffer’s 1st type point: qN<q* . Third goal – establish taxation discipline to mitigate

the tax debts.

Detailed principles for developing a fiscal policy enable a wide application of

fiscal indicators. Considering simplicity of the proposed tools, all these indicators

may be of realistic assistance in carrying out applied forecast and analytical

calculations.

Balatsky and others’ works contain a general methodology and specific tools

for forecast and analytical calculations to identify the tax influence on the economic

growth and budget of the country as well as an empiric analysis of effectiveness of

the country’s fiscal policy. Among unquestionable virtues of these works is the fact

that the role of Laffer’s 1st and 2nd type points as leading fiscal macroindicators has

become clearer and dialectics of the stimulating (regulating) and fiscal (budget)

functions of tax tools have shown themselves in a new light.

Currently, methodology of modeling production-fiscal effects has seen a more

complete reflection in the conception related to “splitting” of tax influence into two

constituent parts [3]. The first one is connected with the production curve study

Y=Y(q) in the coordinate system “tax burden (q) – production volume (Y)”. This

curve reaches a local maximum in the point q* which is called Laffer’s 1 st type point

and for which the following conditions are fair: dY(q*)/dq=0; d2Y(q*)/dq2<0. The

second constituent is connected with the fiscal curve study T=T(q) in the coordinate

plane “tax burden (q) – tax payment volume (T)”. This curve reaches a local

maximum in the point q**, which is called Laffer’s 2nd type point: dT(q**)/dq=0;

d2T(q**)/dq2<0.

Economically Laffer’s 1st type point means the tax burden limit when the

production system has not shifted to a recession mode yet. Laffer’s 2nd type point

12

shows the tax burden value outside of which the increase of tax payments becomes

impossible. Identifying Laffer’s 1st and 2nd type points and their comparison with

actual and nominal tax burden allows evaluating the quantity settings of the tax

system and establishing the areas to be optimized. This is the main idea of using the

expanded conception of Laffer’s curve.

The basis for model analysis of fiscal climate is production-institutional

functions (PIF) [3-8] which are the generalization of a traditional apparatus of

production functions (PF) applicable to macro-level. The only difference is that

ordinary PFs use an output volume (as a rule GDP) as an endogenous indicator and

labour (number of the employed) and capital (basic assets) as micro-factors whereas

PIF macro-factors are supplemented by a variable characterizing the institutional

environment – medium tax burden (taxes imposed by the state in the volume of

GDP). Given that apart from technological (resource) aspect of the economic growth

(volumes and effectiveness of labor and capital) the model also allows for

institutional climate (tax burden), traditional PF transforms into PIF accordingly.

Introducing PIF for review seems reasonable and grounded. In fact, the

connection between output and macro-factors are mainly determined by the

institutional climate in the economy. It is quite logical to assume that all other

technological conditions being equal, (volume of labour and capital) a different level

of tax burden will produce a different GDP. Taxes participating in the formation of

the system of stimuli of economic agents directly impact the levels of business and

therefore production activity of the system.

Methodology of fiscal analysis using production-institutional functions is the

following. Specifying the above in relation to specific functional dependencies, the

following type of PIF can be used:

(1)

where: Y — output (country’s GDP volume); K — capital (basic assets); L — labour

(number of the employed in the economy); q — tax burden (relative tax burden

calculated as a share of tax payments T in GDP, q=T/Y); D — trend operator

(function depending on time t); , a, b, c и d — parameters evaluated statistically on

13

the basis of retrospective dynamic rows. Variables Y, K, L and T are taken from the

relevant year t.

The peculiarity of function (1) is that macro-product of the country (region)

depends on the labor, capital and tax burden. Moreover, labour and capital influence

on economic growth depends itself on fiscal climate. Furthermore, elasticities of

labour and capital are quadratic functions of tax burden which automatically pre-

determines the non-triviality of the entire analysis.

Function (1) sets a production curve that is dependence between output and tax

burden. Then fiscal curve that is dependence between mass of collected taxes and

relative tax burden is described by the following function:

(2)

The key idea of fiscal analysis on the basis of PIFs (1) and (2) is to determine

the mutual location of Laffer’s 1st and 2nd type points and actual value of tax burden.

Reviewing these three fiscal indicators allows to create quite a complete picture of

the tax climate and to determine its role in establishing the dynamics of the economic

growth.

Let us remind you that according to the classification provided in [1], the fiscal

Laffer’s 1st type point is called the apex (that is the point of maximum) of the

production curve (1), when dY/dq=0. After making simple transformations, we may

expressly write the expression for Laffer’s 1st type point of function (1):

(3)

Analogically we determine the fiscal Laffer’s 2nd type point q** which is an

apex (that is a point of maximum) of fiscal curve (2), when dT/dq=0. The simplest

calculations will allow to receive the following formula for Laffer’s 2nd type point of

function (2):

(4)

Econometric model (1) assumes another very important perspective of the

macroeconomic analysis which should be reviewed separately. The thing is that such

14

a connection form assumes interweaving of technological and purely fiscal factors of

economic growth. This is, in particular, shown in such way that the nature of labour

and capital influencing the output (derivatives Y/K and Y/L) non-linearly

depends on the value of the tax burden. This fact assumes review of two more fiscal

indicators in the form of switch points qF and qL corresponding to stationary

conditions Y/K=0 и Y/L=0:

(5)

(6)

If parabola is convex upwards, then with a tax load less than level

(5), the ultimate capital output is positive and any increase in basic assets will lead to

a production growth. If the tax load will appear more than a point, the ultimate capital

output will become negative and extensive increase of this factor will only provoke a

production recession. If parabola is convex downwards, the situation

becomes diametrically opposite. Similar assumptions are applicable to a switch point

(6). Thus, technological and fiscal analyses appear associated: such technological

characteristics as ultimate labour and capital production directly depend on the value

of the tax burden.

When investigating the interconnections of fiscal and technological factors,

such an indicator as elasticity of replacement of capital for labour acquires its own

independent value E=(L/K)(dK/dL):

(7)

Thus, all methodology of the analysis being conducted is based on six

indicators: actual tax burden q and indicators (3)-(7). These characteristics with

regard to geometric properties of the curves allow to carry out quite a precise

diagnostics of fiscal climate and its role in setting the specific trajectory of economic

growth.

However, there is a problem of violating the invariance of Laffer’s points when

making econometric calculations. Applied calculations made in works [2-6] using

formulas (1)-(4) for Russia, USA, Sweden and UK gave reliable results from the

15

viewpoint of statistical significance of all dependencies and from the viewpoint of

their contents. In terms of contents the model (1) assumes a few ways of considering

macroeconomic factors. Thus, for instance, labour in the basic version is allowed for

in physical expression as the number of the employed in the national economy (L).

Meanwhile, a labour factor may be considered in value terms (W) as expenses for

labor payments. As regards the other macrofactor (capital), the following variations

are possible: basic production assets (K) in value terms and investments in basic

production assets (I) in value terms.

E.V.Balatsky and others’ works [2-8] ground the use of two pairs {L,K} and

{W,I} as a calculation priority to narrow possible calculations.

Besides, the model analysis has shown that the role and value of Laffer’s 1

type point as a macroeconomic indicator is higher than Laffer’s 2 type point. This

conclusion seems quite important as in traditional concept of Laffer’s curve the

interest is focused on 2 type point. This theoretical clarification is absolutely natural,

if we consider that in practice as a rule the “competition” occurs between Laffer’s 1

type points and actual tax burden (that is to determine what is higher); Laffer’s 2 type

point is mainly displaced upwards in the area of low-realistic fiscal values (as for

example in Russia). Exceptions to this are only special cases like in the US when

Laffer’s 1 and 2 type points practically coincided (the gap between them was just

1%) and actual tax burden fluctuated between them and in their areas. However,

such examples are very rare.

To try out the functionality of PIF (1) and determine the results of econometric

evaluation of three-factor PIF we used statistics from 2000 to 2011: Russia and

Moscow and Khanty-Mansi AA – Yugra. This choice was determined by the desire

to use the maximum ultimate regions with a different set of factors stimulating

economic growth Y: for Moscow – this is a set of {I,W} and for Khanty-Mansi AA-

Yugra – the set is {I,Rm}, where: I – investments into basic assets in value terms; W

– salary fund; Rm – expenses for natural resources in value terms. These two regions

are the main basis for economic growth of the country.

16

When forming retrospective statistic rows of indicators: Y; I; W; Rm

Goskomstat (State Statistics Committee) data published in the book “Regions of

Russia. Main characteristics of the RF subjects. 2012” were used. Evaluating the tax

burden value, use was made of the following data on collection of taxes, charges and

other mandatory payments to the RF budget published in the section “Finances”.

Generally, the calculations showed that PIF (1) suits very well to describe the

economic growth in all cases selected by us. Identifying PIF (1) allows moving to the

main problem that is the analysis of a tax factor role when forming the trajectory of

economic growth. Definitely, we cannot but mention the specifics of the economic

growth in Russia. Results of calculations using formulas (3), (4) and (7) are stated in

Table.3.

Table 3.

Fiscal and technological indicators of Russia’s economy, %.

Production function:

earLaffer’s 1 type

point (q*)

Laffer’s 2 type

point (q**)Actual tax burden (q)

Elasticity of replacement

of investments for salary

fund (E)

2000 34.45 36.68 28.72 -8.323

2001 34.45 36.57 30.01 -8.345

2002 34.45 36.50 32.49 -8.354

2003 34.46 36.44 31.25 -8.356

2004 34.45 36.40 31.85 -8.386

2005 34.46 36.34 39.67 -8.379

2006 34.46 36.40 36,30 -8.375

2007 34.45 36.40 36.10 -8.354

2008 34.45 36.42 35.72 -8.320

2009 34.45 36.41 35.81 -8.319

2010 34.45 36.41 35.93 -8.348

2011 34.45 36.42 35.76 -8.323

What peculiarities were natural for Russia’s economy in the period of 2000-

2011? First of all, we can see a high stability of Laffer’s 1 type point – during 12

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years its value ranged from 34,45 to 34,46%. Thus, fiscal variation during these years

amounted to just 0,01% and we can evaluate the value of Laffer’s 1 type point in

Russia with very high accuracy at 34,45%. The specified high stability of q*, first of

all, demonstrates the stability of the Russian producer’s psychology as to maximum

permissible costs. In this case, this value exceeds 1/3 of the produced added value to

some extent and is close to American empiric standard of 35%.

Secondly, Table 3 also demonstrates a very high stability of Laffer’s 2 type

point which fluctuated in the range of 36,34-36,68%. Thus, the range was only 0,3%

which considering the value of q** seems quite narrow for such a fiscal indicator.

Given that the average value of q** made up about 36,50%, we can ensure that in the

short term (from 2000 to 2005) increasing taxes for the producer exerted only

positive impact on the Russian budget. Besides, the change of Laffer’s 2 type point

had a very weak tendency for lessening which means that the reliability of the tax

constituent of the country’s budget grew slowly but confidently.

Thirdly, the state policy’s effectiveness was not the same in different sections

of the period analyzed. Thus, for example, actual tax burden for the period from

2000 to 2004 was lower than Laffer’s 1 type point, let alone Laffer’s 2 type point. In

fact, the tax burden during this period in Russia was moderate.

During 2005 the actual tax burden exceeded the values of both q* and q**.

Geometrically, the Russian economy moved to a descending branch of production

and budget curves. That means that year the state did harm to itself and, therefore,

the fiscal policy of that stage may be regarded as ineffective if not absolutely

erroneous at all. Though the following 2006-2011 years, the actual tax burden moved

in the range between values of q* and q**.

It is extremely interesting that the fiscal gap between Laffer’s 1 and 2 type

points is very narrow and made up just about 2,50%, which allows to state that the

country’s budget reaction is not much different to a consumer’s reaction. In other

words, as soon as the tax burden starts exerting a de-stimulating impact on the

producer, the state’s fiscal income immediately begins to fall. This means that when

manipulating tax rates, fiscal bodies’ attention should be aimed at the producer as its

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reaction will be automatically reproduced by the budget. Therefore, enhancing

financial pressure on the producer will automatically worsen the country’s budget,

which confirms a high sensitivity of Russia’s fiscal system to production dynamics in

this period.

Earlier E.V.Balatsky’s works established that from 1991 to 2000

(transformation recession ) the weak point of Russia’s economy had been basic assets

the extensive increase of which promoted a reduction of production and

transformation recession in Russia had had a resource-technological nature. That is

Russia’s economy functioned under conditions of ineffectiveness of one of the

macrofactors and tax tools could not normalize the factor disbalance.

It should be noted that currently Russia (2000-2011) has started implementing

a rational investment policy which boils down to depriving of old production

capacities with their parallel replacement for modern equipment. During this period

as compared to previous years we noted a dramatic increase of investments into basic

assets. Annual growth of investments into basic assets made up 12%.

As an addition to the above-said is the fact that during 12 years the elasticity of

replacement of investments for paid labor, above all, had been negative which

indicates a direct interconnection of key macrofactors, secondly, invariable in terms

of value (Table 3). The second aspect shows that the country has a labor-saving

tendency of scientific-technical progress.

The main driving force of the Russian economy is quite an effective capital

formed by investments into capital – deficit production factor. Labour is an auxiliary,

just a necessary appendage to it and economic growth is ensured not only through

fiscal encouragement of the producer but more through extensive growth of main

capital [9].

Now let us start analyzing the economy of Khanty-Mansi AA-Yugra the

indicators of which are stated in table 4. The most interesting here are the following

conclusions.

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Table 4.

Fiscal and technological indicators of economy

Khanty-Mansi Autonomous Area - Yugra, Tyumen Region, %.

Production function:

YearLaffer’s 1 type

point (q*)

Laffer’s 2 type

point (q**)Actual tax burden (q)

Elasticity of replacement

of investments for natural

resources expenditure (E)

2000 51.01 52.93 32.46 -0.22

2001 51.04 52.94 35.23 -0.22

2002 51.06 52.93 45.43 -0.22

2003 51.07 52.92 41.40 -0.22

2004 51.09 52.91 55.83 -0.22

2005 51.03 52.93 73.98 -0.22

2006 51.01 52.91 57.89 -0.22

2007 51.02 52.92 53.66 -0.22

2008 51.03 52.93 53.48 -0.21

2009 51.04 52.91 52.88 -0.22

2010 51.06 52.92 52.76 -0.22

2011 51.03 52.93 52.86 -0.22

First of all, the economy of this region is focused on recovery and sale of

hydrocarbon material, mostly oil. Considering favourable oil prices within this period

(oil prices skyrocketed almost 8 times from 2000 to 2011), GRP growth was coupled

with oil prices growth as annual raw material recovery did not grow a lot physically

within the period analyzed.

Secondly, Laffer’s 1 and 2 type points and actual tax burden starting 2001 are

far beyond the empiric standard of 35% and on the average make up 51% and 53%

accordingly. Therefore, Khanty-Mansi AA-Yugra demonstrates the uniqueness of its

ineffective tax regime from this point too.

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Thirdly, we can observe the record instability of a tax burden. Fluctuations of

actual tax rates occurred in a very broad range  — 32,46-73,98% (this corresponds to

a variation of more than 100%). That is the state is adjusting its fiscal policy not to

allow for the producer’s behaviour but the existing prices for energy carriers.

Fourthly, from 2004 to 2008 the actual tax burden was above Laffer’s 1 and 2

type points. The problem of excessive tax burden is resolved not at the expense of its

decrease but by means of massive boosting of one of the macrofactors (recovered oil

cost). However, this approach cannot be long-term as the hydrocarbon material cost

cannot grow permanently. As a whole, Russia’s fiscal system within Khanty-Mansi

AA-Yugra can be characterized as destructive in that period.

Moreover, high taxes hold back scientific-technical progress (STP). It may

seem that if the actual tax burden is more than Laffer’s 1 type point and, moreover,

more than Laffer’s 2 type point, the economy should collapse. However, you may not

encounter this in practice as the economy may develop extensionally. Thus,

according to model (1) the output depends not only on tax load but also on volume of

macrofactors but they may increase regardless of tax rates. This is what happens in

Khanty-Mansi AA-Yugra where economic growth was ensured not only at the cost of

producer’s fiscal encouragement but also at the cost of extensive increase in natural

resources recovery.

The most interesting element of model analysis is Moscow economy. Here we

can also highlight a few moments (Table 5).

Table 5.

Fiscal and technological indicators of Moscow economy, %.

Production function (with a STP factor):

YearLaffer’s 1 type

point (q*)

Laffer’s 2 type

point (q**)Actual tax burden (q)

Elasticity of replacement

of investments for salary

fund (E)

2000 32.67 33.82 31.03 -6.502

2001 32.71 32.83 34.55 -6.501

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YearLaffer’s 1 type

point (q*)

Laffer’s 2 type

point (q**)Actual tax burden (q)

Elasticity of replacement

of investments for salary

fund (E)

2002 32.71 33.80 29.05 -6.503

2003 32.72 33.79 27.27 -6.502

2004 32.71 33.76 21.57 -6.504

2005 32.72 33.75 20.01 -6.505

2006 32.73 33.76 20.81 -6.503

2007 32.72 33.75 20.90 -6.502

2008 32.71 33.76 21.23 -6.508

2009 32.72 33.76 24.76 -6.504

2010 32.74 33.76 25.00 -6.503

2011 32.72 33.76 24.96 -6.506

Firstly, fiscal gap between Laffer’s 1 and 2 type points is incredibly small and

is about 1% (Table 5). Such a difference is within the limits of ordinary statistic error.

That means the budget reaction is almost equivalent to producer’s reaction.

Secondly, given the tendency for coincidence of Laffer’s 1 and 2 type points ,

the selection of effective tax burden rate is significantly simplified. By 2011

reasonable tax burden rate was limited to 25%.

Thirdly, Moscow had a slow but reliable formation of fiscal oasis with a low

characteristic tax pressure. Thus, from 2000 to 2011 except 2001, the actual tax

burden was below Laffer’s 1 type point.

In analyzing Moscow economy, interweaving of fiscal and technological

factors is of special interest. Thus, the calculations have shown that the actual tax

burden should be within the range: TW<T<TI (20%<T<32,7%). Table 5 shows that

within all 12 years except 2001 the actual tax burden was strictly within this range,

which indicates that the fiscal policy in Moscow is ideally set up for achieving a

maximum technological effect.

However, apart from high effect from macrofactor I (investments to basic

assets) Moscow economy also experiences a vigorous labour-saving STP. This is

testified by both a total factor production indicator (directly linked to STP results)

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and an indicator of elasticity of replacement of investments for labor with a value of –

6,5. We did not see anything similar in other cases. Thus, Moscow economy shows a

high social focus of production and STP.

After carrying out calculations, we may draw the following conclusions:

– we can see a field differentiation of tax burden in Russia that is different fields

and productions are not in the same conditions in terms of fiscal pressure. It goes

without saying that the specified bias in Russia’s fiscal policy has nothing to do with

the size of tax rates. Russia’s taxation system and, above all, the tax base are

designed so that some fields are put in more favourable conditions than the others.

This is one of the most important faults of the modern fiscal system of Russia;

– it is very dangerous to keep the current situation when various fields are not in

the same conditions in terms of fiscal pressure. Almost all fields of extractive

industry function under very heavy tax burden. Though the burden is not provoking

a production recession yet, it forms a natural system of producer’s stimuli and makes

it adopt complicated, sometimes quite exotic production decisions. It is advisable to

keep to more or less equal tax share in produced added value of the field and it is

worth starting to diminish the tax burden on a real sector of economy. Definitely, it

is possible to increase the tax pressure, if we have a “devastating” growth and

economic overheating and, on the contrary, to reduce taxes, if we have a pre-crisis

situation. However, big differences in tax burden cannot exist as a norm of economic

life;

– we can observe a set-up of market mechanisms ensuring timely adjusting of

investment flows; an upgrading of equipment; capital movement between fields and

profit norm movement. It is advisable to develop and actively implement the policy

of interfiled and intertemporal equalization of field re-producing conditions using all

regulating instruments available to the authorities including taxes and investments

from federal, regional and local budgets. Otherwise, re-producing differences will

initiate spurts in development of fields which are always undesirable;

– it is technological differences which are the basis for setting up

incommensurable re-producing regimes in Russia. Therefore, the main efforts of

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Russia’s management should be aimed at ensuring a technological breakthrough

which will allow further to optimize the country’s institutional system including the

fiscal and investment climate.

Balance Sheet Recession. One of the main effects of the crisis on the global

economy is deleveraging. A good description of how it happens is given in a research

work of Japanese economist Richard Koo [10]. Analyzing the “lost decade” in Japan

he refers to that period as a balance sheet recession. Before the recession in the early

90ies there was a sharp increase in prices of shares, financial assets, real estate. Many

companies made extensive use of real estate and shares as collateral for borrowings.

As a result, when the markets collapsed the balance sheets of companies plunged into

red. In the decade to follow Japanese enterprises were making efforts to neutralize

accumulated losses out of current profits. At that time companies preferred not to take

loans because their balance sheets would fail the bank test for stability.

Fig. Sectorial Balances (% GDP): Russia. (PFB = Private financial balance = S – I, GFB = Government financial balance = T – G – NTR, FFB = Financial balance of the foreign sector = M – X – NIA. S = Private saving, I = Private investment, T = Tax receipts including social security contributions, G = Final government expenditures in final goods, NTR = Net transfers from the government to the private and foreign sectors - interest payments on public debt, social security benefits and subsidies, foreign aid, etc., NIA = Net income received from abroad (including government and private transfers).

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According to Richard Koo, in these circumstances the Central Bank is losing tools

of impact on the situation, the monetary policy is ineffective, and the only tool

available to the government is regulation of the budget deficit and expenditures.

Fiscal stimuli should be put in and maintained until the private sector recovers its

financial health to the extent when it is able to borrow money and spend it. A similar

effect with a high degree of probability may be also expected on recovery from the

current crisis on both sides of the Atlantic including the Russian economy. As seen

from the graph of the sectorial balances of Russia (1995-2012), the budget deficit is

much less in its size than the private sector’s net savings and the foreign sector

deficit, therefore the balance sheets of the private and foreign sectors mirror

(replicate) each other. But if the net export shrinks and the current account of the

country’s balance of payments deteriorates, it is the budget expenditures and deficit

that will be the main factor ensuring preservation of the private sector’s net saving

and hence the economic growth of the country. Of course, in case of a drastic fall of

the demand and prices for the export product namely hydrocarbons, the main product

of resource-extraction regions (Tyumen Region, Khanty-Mansi Autonomous Area,

Yamal-Nenets Autonomous Area), a certain reserve allowing increase of the tax

burden on economies of a number of regions (Moscow, Saint Petersburg, Nizhny

Novgorod) is available, as evidenced by Table 5 – the actual tax burden for them is

below Laffer point of the 1-st type. This additional burden could play an important

part in times of crisis by increasing the budget revenues.

References

1. Granberg A.. Modeling Spatial Development of National and World Economy: Evolution of Approaches, “Region: Economics and Sociology” Journal, No.1, 2007, pp.87-106.

2. Bessonov V.. Problems of Building Production Functions in the Russian Transitional Economy. – Moscow: The Institute of Transition Period Economy, 2002, p.46.

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3. Balatsky E.V. Evaluation of fiscal instruments impact on economic growth “Forecasting Issues”, №4, 2004. P.124-135

4. Balatsky E.V. Analysis of tax burden influence on economic growth by means of production-institutional functions // “Forecasting Issues”, №2, 2003.

5. Balatsky E.V. Invariance of Laffer’s fiscal points // “World economy and international relations”, №6, 2003.

6. Gusev A.B. Taxes and economic growth: theories and empiric evaluations. M.: Economics and Law. 2003.

7. Balatsky E.V. Re-production cycle and tax burden // “Economics and mathematical methods”, №1, 2000.

8. Balatsky E.V. Effectiveness of the state’s fiscal policy // “Forecasting Issues”, №5, 2000.

9. M. Melnichuk. Determination of the optimal settings for the tax system in Russian regions // “Perspektywiczne opracowania nauki I Techniki-2008”. -Przemysl: Nauka i studia. - 2008. Volume 6. Economiczne nauki.

10.Richard C. Koo Central Banks in Balance Sheet Recessions: A Search for Correct Response / Nomura Research Institute. March 31, 2013. The Holy Grail of Macroeconomics

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