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 2ndtype 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:, 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 functionsfor the RF region economy (2000-2011)
(см.табл.1
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 / 0.018 / 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 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 (), the elasticity coefficient value, E, ranges from zero to one. Two maximum values of the GRP investment elasticity coefficient are consistent with two asymptotic trajectories: the stable balanced economic growth path and the negative economic growth path
For the GRP investment elasticity coefficient values close to 1 (), a situation is observed when relative changes of the consumption volume, the savings volume, investments and the GRP are equal: . 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 (), 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 αfactor – 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. Asknown[3-6],fiscalandregulatingtaxfunctions are of antagonist nature to each other clashing the growing state financial needs and entrepreneurs’ interests, especially during the crisis.Economicgrowthandbudgetbalance 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.
Ifthestatepolicyaimstoachieveaneconomicrisebymeansoflessening 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, thetotalarrivaloffundsmayrise.Therefore, short-termbudgetinterestscontradictthelong-termproductionpurposesoftheentrepreneurs.
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, thedisagreementvalueoftwo Laffer’s points is the main criteria and indicator of national fiscal system’s effectiveness.
Table 2
Region Clusters of Russia in Terms of Economic Efficiency
(Investment Matrix)
Scale effect / Investment activityLow
(0<α<0.5) / Medium
(0.5≤ α<0.7) / High
(0.7≤ α<1.0)
Low
0<(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., PrimorskyKrai (Territory), Khabarovsk Krai, Amur Ar., Sakhalin Ar., Jewish Auton. Area.,ChukotkaAuton. Dist. / Chuvash Republic
Medium
0.85≤( 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.
Firstgoal–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**. Secondgoal – balancenominalfiscalloadontheleftarchoftheLaffer’sproductioncurveso that the nominal fiscal load value is not more than Laffer’s 1st type point: qNq* . Third goal – establish taxation discipline to mitigate the tax debts.
Detailed principles for developing a fiscal policy enable a wide application of fiscal indicators. Consideringsimplicityof the proposed tools, all these indicators may be of realistic assistance in carrying out applied forecast and analytical calculations.
Balatskyandothers’ workscontainageneralmethodologyandspecifictoolsfor forecastandanalyticalcalculationstoidentifythetax influence on the economic growth and budget of the country as well as an empiric analysis of effectiveness of the country’s fiscal policy.AmongunquestionablevirtuesoftheseworksisthefactthattheroleofLaffer’s 1stand 2ndtypepoints as leading fiscal macroindicatorshasbecomeclearerand dialectics of the stimulating (regulating) and fiscal (budget) functions of tax tools have shown themselves in a new light.
Currently, methodologyofmodelingproduction-fiscaleffectshasseenamorecompletereflectioninthe conception related to “splitting”of tax influence into two constituent parts [3]. Thefirstoneisconnectedwiththeproductioncurvestudy Y=Y(q) in the coordinate system “tax burden (q)– production volume(Y)”. Thiscurvereachesalocalmaximuminthepointq* which is called Laffer’s 1sttypepoint and for which the following conditions are fair:dY(q*)/dq=0; d2Y(q*)/dq2<0. Thesecondconstituentisconnectedwiththefiscal curve study T=T(q) in the coordinate plane “tax burden(q)– tax payment volume(T)”. Thiscurvereachesalocalmaximuminthepointq**, which is called Laffer’s2nd typepoint: dT(q**)/dq=0; d2T(q**)/dq2<0.
EconomicallyLaffer’s 1sttypepointmeans the tax burden limit when the production system has not shifted to a recession mode yet. Laffer’s 2ndtypepointshowsthetaxburdenvalue outside of which the increase of tax payments becomes impossible. IdentifyingLaffer’s 1stand 2ndtypepointsandtheircomparisonwithactualandnominaltaxburdenallows 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.
Thebasisformodelanalysisoffiscalclimateisproduction-institutionalfunctions (PIF) [3-8] which are the generalization of a traditional apparatus of production functions (PF) applicable to macro-level. TheonlydifferenceisthatordinaryPFsusean outputvolume (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). Giventhatapartfromtechnological (resource) aspectoftheeconomicgrowth (volumesandeffectivenessoflaborandcapital) the model also allows for institutional climate (tax burden), traditional PF transforms into PIF accordingly.