Corresponding author: Alexey Balaev ( a.balaev@gmail.com ) © 2019 Nonprofit partnership “Voprosy Ekonomiki”.
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Citation:
Balaev A (2019) The structure of public spending and economic growth in Russia. Russian Journal of Economics 5(2): 154176. https://doi.org/10.32609/j.ruje.5.38705

This paper examines how Russia’s GDP growth responds to changes in the structure of general government spending. We consider models with expenditures as a percentage of total spending and expenditures as a percentage of GDP. Each model is constructed as a structural vector autoregression (SVAR). We show that redistribution in favor of productive expenditures (national economy, education, healthcare) increases the rate of economic growth, and an increase in the share of unproductive expenditures (national defense, social policy) reduces it. The maximum positive effect comes from expenditures on the national economy: their increase by 1% of GDP with constant total expenditures increases the growth rate of GDP by 1.1 p.p. An increase in expenditures on education by 1% of GDP with constant total expenses contributes +0.8 p.p. to the growth rate of GDP. The corresponding effect of healthcare expenditures is +0.1 p.p. Defense and social spending make negative contributions: –2.1 and –0.7 p.p. respectively. These results are consistent with existing estimates of fiscal multipliers for Russia and calculations based on data from other countries and crosscountry data.
public expenditures, expenditures structure, productive expenditures, economic growth, econometric model
The task of optimizing government spending allocation became the focus of attention in many countries around the world in the aftermath of the 2008 financial crisis. The unexpected deceleration of economic growth and limited opportunities for financing deficits triggered the need to carry out largescale budget consolidations in most developed and in some developing countries. The protracted stagnation forced governments to look for new sources of growth, and more active investment in infrastructure became one of the options. Thus, the international community (G20, IMF, World Bank, OECD) and leading world economies focused their attention on adjusting the allocation of government spending. Discussions and the respective empirical research on the general approaches to these adjustments have picked up momentum.
Optimizing the allocation of budget expenditures is an important task for Russia as well, since resolving it would make a considerable contribution. First, it would help to raise GDP growth rate to be at least on par with the world economy, and second, in restoring the longterm balance in the budget system which was disrupted by the crash in oil prices. The Strategy2020 plan developed per instruction by the Russian Government, proposed a “budget maneuver,” i.e. adjusting the budget system’s expenditure allocation by 2% to 4% of the GDP (Mau and Kuzminov, 2013). The economic program of the Center for Strategic Research also proposes a “budget maneuver” (
The optimal level and allocation of government spending have been discussed in literature for a long time. A consensus opinion can only be seen with respect to certain aspects. Empirical papers show that there is a certain level of government spending (as a percentage of GDP) below which an increase encourages economic growth, while exceeding that level has an adverse impact on growth (
This paper examines how the allocation of general government spending impacts Russia’s GDP growth rate. It uses models that show the correlations between GDP growth rate and the proportion of expenditures on defense, social policy, national economy, healthcare, and education. It also applies the wellproven empirical structural vector autoregression (SVAR) methodology.
Section 2 presents an overview of the empirical studies on the effect of government spending allocation on economic growth, using both foreign and Russian data. Sections 3 to 5 estimate the impact of budget expenditure allocation on Russia’s GDP growth rate. The conclusion contains our findings and suggests recommendations for optimizing the budget spending allocation given the Russian economy’s current development stage.
One of the key concepts in modern theories of economic growth is associated with classifying budget expenditures into productive and unproductive. Productive expenditures generally include investments in human capital (primarily education and healthcare) and in physical capital, including infrastructure expenditures (
Based on panel data from OECD countries,
There is a comparatively small number of studies on the effect of government spending allocations on economic growth rate in Russia.
Empirical estimates of the effect of defense spending on output for Russia are controversial. For example, in Ivanova and Kamenskikh (2011), the corresponding fiscal multiplier is 0.29,
Empirical works show that expenditures on the national economy have the greatest multiplying effect on Russia’s GDP growth: 0.55 with a lag of three to four quarters (Ivanova and Kamenskikh, 2011) or 0.32 with a oneyear lag (
It should especially be noted that
It should be noted that in all of the above studies based on Russian data, the effects of changes in the government spending allocation were calculated without fixing their total amount. The effect of growth in total spending is partly adjusted due to the fact that spending is measured as a percentage of the GDP. However, this is not a complete adjustment. In this paper, we calculate the effects of changes in the general government spending allocation, while fixing its total amount.
Our models use annual GDP data published by Rosstat and Federal Treasury reports on the execution of budgets for the Russian Federation^{2} in terms of expenditures during the period from 2000 (data are not available for all series from that year) to 2017.
The time series used include the following indicators:
Notably, this paper does not isolate expenditures on national security and law enforcement but combines them with national defense. Security and law enforcement expenditures, as well as defense expenditures, are classified as unproductive. In 2017 and 2018, both defense and security and law enforcement expenditures declined (as a percentage of GDP) in Russia. Judging by the budget plans, this trend will continue in the near future. Therefore, from the standpoint of the reality of Russia’s current fiscal policy, it is reasonable to combine defense, security, and law enforcement expenditures, rather than separate them.
Table
Dynamics of the main general government expenditure items (% of total spending and % of GDP).
Sources: Rosstat; Federal Treasury; author’s calculations.
Descriptive statistics.
DEF/EXT × 100  SOC/EXT × 100  ECO/EXT × 100  HEA/EXT × 100  EDU/EXT × 100  
Average  8.310  31.144  13.780  9.851  11.089 
Maximum  12.060  35.236  17.336  11.387  12.209 
Minimum  7.264  25.061  11.204  9.042  9.907 
Standard deviation  1.314  3.444  1.938  0.712  0.657 
Observations  17  12  13  12  12 
DEF/GDPN × 100  SOC/GDPN × 100  ECO/GDPN × 100  HEA/GDPN × 100  EDU/GDPN × 100  
Average  2.794  10.763  4.713  3.379  3.805 
Maximum  4.390  12.591  6.721  3.714  4.261 
Minimum  2.352  8.041  3.305  3.147  3.561 
Standard deviation  0.568  1.713  0.924  0.204  0.204 
Observations  17  12  13  12  12 
Below we analyze various expenditures as a percentage of total general government spending and as a percentage of GDP to identify correlations between them, GDP growth rate and oil price changes. The corresponding correlations are provided in Table
Correlations between expenditures as a percentage of total spending, GDP growth rate, and oil price changes.
DEF/EXT  SOC/EXT  ECO/EXT  HEA/EXT  EDU/EXT  DLOG(GDP)  
SOC/EXT  0.446  1  
ECO/EXT  –0.372  –0.323  1  
HEA/EXT  –0.110  –0.729  –0.273  1  
EDU/EXT  –0.723  –0.711  0.025  0.481  1  
DLOG(GDP)  –0.361  –0.366  –0.454  0.531  0.607  1 
DLOG(URALS)  –0.596  –0.332  –0.117  0.233  0.609  0.831 
Negative correlations between percentages are of no interest, as they are largely attributable to the fact that whenever an expenditure item is increased while total spending remains constant, some other expenditure item is bound to decrease. Positive correlations can be observed between the shares of social (SOC/EXT) and defense expenditures (DEF/EXT), as well as between education (EDU/EXT) and healthcare (HEA/EXT) expenditures. The GDP growth rate is negatively correlated with the percent shares of defense, social policy and national economy expenditures. Intuitively, the latter expenditure item should rather have a positive correlation with economic growth. As demonstrated by the models in the next section, correlation does not reflect causality in this case, and expenditures on the national economy have a positive impact on GDP growth. The table above also shows that GDP growth rates are positively correlated with the percent shares of healthcare and education expenditures. Correlations between the proportion of expenditures and oil price changes are generally similar to the respective correlations with the GDP, which is evidently attributable to the high correlation between the GDP and the oil price. For example, the share of defense expenditures demonstrates a clear negative correlation with oil price changes, more largely reflecting the trends in recent years, e.g. growing defense expenditures against a backdrop of falling oil prices. The distinctly positive correlation between the percent share of educational expenditures with oil price changes is largely attributable to the fact that, since 2013, it has been falling against a falling price.
Table
As shown in the table, expenditures as percentages of GDP are positively correlated with each other, except for education expenditures, which were found to be negatively, albeit insignificantly, correlated with defense expenditures. Notably, all expenditures as percentages of GDP are negatively correlated with GDP growth rate and oil price changes. This is also not quite consistent with the intuitive notion of correlation between expenditures and GDP growth, but the econometric models constructed in the next section help identify the causality, addressing the problem.
Correlations between expenditures as a percentage of the GDP, GDP growth rate, and oil price changes.
DEF/GDPN  SOC/GDPN  ECO/GDPN  HEA/GDPN  EDU/GDPN  DLOG(GDP)  
SOC/GDPN  0.633  1  
ECO/GDPN  0.067  0.281  1  
HEA/GDPN  0.379  0.128  0.365  1  
EDU/GDPN  0.168  0.280  0.765  0.267  1  
DLOG(GDP)  –0.575  –0.629  –0.722  –0.480  –0.572  1 
DLOG(URALS)  –0.709  –0.517  –0.372  –0.545  –0.240  0.831 
Our models use the empirical methodology for estimating the structural vector autoregression (SVAR) from (
To measure the effect of the general government spending allocation on GDP growth rate, we considered the following models:
where URALS is the annual average oil price in USD, EXP_{i} is expenditures in the ith expenditure category, GDP is the GDP in constant prices, and e is a shock variable. Model (1) describes the impact on GDP growth rate from changes in the percent share of the ith category in general government spending — EXP_{i} /EXT, while model (2) describes the impact of expenditures in the ith category as a percentage of GDP — EXP_{i} /GDP. At the same time, since we are constructing structural VAR models, shocks are determined by the following restriction:
where u shocks are independent and identically distributed, with average zero and dispersion one. Thus, to measure structural VAR models, we use a restriction with the following description:
Ae = Bu, where
and with b = C_{2} /C_{1} we estimate parameter b which reflects the response of GDP growth rate to an unexpected shock in the percent share of expenditures in model (1) or their level as a percentage of GDP in model (2). In the event of an unexpected increase in the share of expenditures by 1 p.p. in model (1) or by 1% of the GDP in model (2), GDP growth rate (roughly equal to its logarithm difference) will change by b p.p.
The magnitude of lag n in the models under review was chosen based on Akaike, Schwartz, and Hannan–Quinn information criteria. Out of the models in favor of which these criteria speak, we chose the one with the minimum lag. Table
Example of lag choice in the VAR model (for the share of defense expenditures).
Endogenous variables: DEF/EXT DLOG (GDP) Exogenous variables: C DLOG (URALS) Sample: 2001–2017 

Lags  LogL  LR  FPE  AIC  SC  HQ 
0  64.031  –  1.56e^{–07}  –10.005  –9.843  –10.065 
1  76.818  17.049*  3.77e^{–08}*  –11.469  –11.146  –11.589 
2  78.692  1.873  6.21e^{–08}  –11.115  –10.630*  –11.294 
3  87.165  5.649  4.20e^{–08}  –11.860  –11.214  –12.100 
4  91.509  1.447  9.87e^{–08}  –11.918*  –11.110  –12.217* 
It should be separately noted that in some cases, we use models in levels, i.e. equations (1) and (2) are considered without a transition to differences ∆. This is dictated by statistical (including information) criteria. At the same time, the interpretation of b remains the same.
We also note an important aspect of model (2). Since we are seeking the effect of changes in the spending allocation, their sum EXT should remain constant within the model (2). Therefore, in type (2) models, the series of expenditures in the particular categories EXP_{i} are scaled so that the sum of the expenditures EXT remains constant across the entire sample used to estimate the model.
This section contains estimates for the main equation of models (1) and (2) for various expenditure categories, as well as the corresponding estimates of matrix B, defined above.
In model (1), defense and social expenditures produce negative values for b. Increasing the share of defense expenditures in total general government spending by 1% reduces GDP growth rate by 0.8 p.p. (Tables
VAR model of the percent share of defense expenditures.
Sample: 2003–2017  
DEF/EXT  DLOG (GDP)  
DEF (–1)/EXT (–1)  1.338  –0.006 
(0.302)  (1.512)  
DEF (–2)/EXT (–2)  –0.221  2.637 
(0.475)  (2.378)  
DLOG (GDP (–1))  –0.002  0.069 
(0.038)  (0.190)  
DLOG (GDP(–2))  –0.049  0.051 
(0.038)  (0.188)  
C  –0.004  –0.185 
(0.024)  (0.119)  
DLOG (URALS)  –0.009  0.137 
(0.006)  (0.028)  
R ^{2}  0.910  0.795 
Fstatistic  16.272  6.205 
Loglikelihood  57.382  34.842 
Akaike criterion  –7.340  –4.120 
Schwarz criterion  –7.066  –3.846 
Loglikelihood  92.403  
Akaike information criterion  –11.486  
Schwarz information criterion  –10.938 
SVAR model of the percent share of defense expenditures.
Sample: 2003–2017  
Coefficient  Standard error  z–statistic  P–value  
C (1)  0.005  0.001  5.292  0.000 
C (2)  –0.004  0.007  –0.599  0.549 
C (3)  0.026  0.005  5.292  0.000 
Loglikelihood  84.568  
Estimated matrix B:  
0.005  0.000  
–0.004  0.026 
Increasing the percent share of social policy expenditures by 1% reduces GDP growth rate by 0.3 p.p. (Tables
VAR model of the percent share of social policy expenditures.
Sample: 2008–2017  
SOC/EXT  LOG (GDP)  
SOC (–1)/EXT (–1)  0.234  0.077 
(0.383)  (0.170)  
SOC (–2)/EXT (–2)  0.692  0.037 
(0.788)  (0.349)  
LOG (GDP (–1))  –0.639  0.654 
(0.481)  (0.213)  
LOG (GDP (–2))  0.386  –0.081 
(0.287)  (0.127)  
C  2.897  4.565 
(5.048)  (2.239)  
LOG (URALS)  –0.017  0.025 
(0.046)  (0.021)  
D09  0.031  –0.094 
(0.053)  (0.024)  
R ^{2}  0.758  0.983 
Fstatistic  1.045  19.721 
Loglikelihood  26.261  33.577 
Akaike criterion  –4.280  –5.906 
Schwarz criterion  –4.127  –5.753 
Loglikelihood  63.784  
Akaike information criterion  –11.063  
Schwarz information criterion  –10.756 
SVAR model of the percent share of social policy expenditures.
Sample: 2008–2017  
Coefficient  Standard error  zstatistic  Pvalue  
C (1)  0.028  0.007  4.243  0.000 
C (2)  –0.009  0.003  –2.725  0.006 
C (3)  0.008  0.002  4.243  0.000 
Loglikelihood  50.247  
Estimated matrix B:  
0.028  0.000  
–0.009  0.008 
For expenditures on the national economy, b was estimated at 0.2 p.p. (Tables
VAR model of the percent share of expenditures on the national economy.
Sample: 2006 – 2017  
ECO/EXT  LOG (GDP)  
ECO (–1)/EXT (–1)  –0.134  –0.277 
(0.266)  (0.180)  
LOG (GDP (–1))  0.063  0.667 
(0.058)  (0.039)  
C  –0.645  3.571 
(0.617)  (0.417)  
LOG (URALS)  0.026  0.034 
(0.014)  (0.009)  
D09  0.044  –0.089 
(0.016)  (0.011)  
R ^{2}  0.647  0.986 
Fstatistic  2.755  107.198 
Loglikelihood  34.561  38.872 
Akaike criterion  –5.375  –6.159 
Schwarz criterion  –5.194  –5.978 
Loglikelihood  74.071  
Akaike information criterion  –11.649  
Schwarz information criterion  –11.288 
SVAR model for the percent share of national economy expenditures.
Sample: 2006–2017  
Coefficient  Standard error  zstatistic  Pvalue  
C (1)  0.014  0.003  4.690  0.000 
C (2)  0.003  0.003  1.130  0.259 
C (3)  0.009  0.002  4.690  0.000 
Loglikelihood  67.404  
Estimated matrix B:  
0.014  0.000  
0.003  0.009 
Healthcare expenditures have the most notable positive impact on economic growth, as increasing their share in the total general government budget by 1 p.p. adds 2.7 p.p. to GDP growth rate (Tables
VAR model for the percent share of healthcare expenditures.
Sample: 2007– 2017  
HEA/EXT  LOG (GDP)  
HEA (–1)/EXT (–1)  0.326  –0.217 
(0.304)  (2.005)  
LOG (GDP (–1))  –0.044  0.560 
(0.035)  (0.234)  
C  0.579  4.647 
(0.411)  (2.714)  
LOG (URALS)  –0.006  0.051 
(0.006)  (0.037)  
R ^{2}  0.536  0.622 
Fstatistic  2.308  3.292 
Loglikelihood  40.160  21.280 
Akaike criterion  –7.232  –3.456 
Schwarz criterion  –7.111  –3.335 
Loglikelihood  62.359  
Akaike information criterion  –10.872  
Schwarz information criterion  –10.630 
SVAR model for the percent share of healthcare expenditures.
Sample: 2007–2017  
Coefficient  Standard error  zstatistic  Pvalue  
C (1)  0.006  0.001  4.472  0.000 
C (2)  0.015  0.011  1.353  0.176 
C (3)  0.034  0.008  4.472  0.000 
Loglikelihood  57.251  
Estimated matrix B:  
0.006  0.000  
0.015  0.034 
An increase in the proportion of education expenditures results in a 0.3 p.p. increase in GDP growth rate (Tables
VAR model for the percent share of education expenditures.
Sample: 2007–2017  
EDU/EXT  LOG (GDP)  
EDU (–1)/EXT (–1)  0.520  –2.240 
(0.162)  (2.484)  
LOG (GDP (–1))  –0.018  0.481 
(0.014)  (0.209)  
C  0.211  5.725 
(0.160)  (2.454)  
LOG (URALS)  0.009  0.057 
(0.002)  (0.035)  
R ^{2}  0.894  0.667 
Fstatistic  16.909  3.998 
Loglikelihood  49.219  21.906 
Akaike criterion  –9.044  –3.581 
Schwarz criterion  –8.923  –3.460 
Loglikelihood  71.127  
Akaike information criterion  –12.625  
Schwarz information criterion  –12.383 
SVAR model for the percent share of education expenditures.
Sample: 2007–2017  
Coefficient  Standard error  zstatistic  Pvalue  
C (1)  0.002  0.001  4.472  0.000 
C (2)  0.001  0.011  0.067  0.947 
C (3)  0.035  0.008  4.472  0.000 
Loglikelihood  66.019  
Estimated matrix B:  
0.002  0.000  
0.001  0.035 
Table
Sensitivity of GDP growth rate to shocks in shares of various categories of expenditures.
Category of expenditures  Estimated b 
National defense expenditures  –0.795 
Social policy expenditures  –0.339 
National economy expenditures  0.224 
Healthcare expenditures  2.706 
Education expenditures  0.323 
Thus, the results obtained are consistent with the hypothesis that increasing the share of productive expenditures (national economy, education, healthcare) has a positive effect on economic growth rate, whereas increasing the share of unproductive expenditures (national defense and social policy) has an adverse impact on GDP growth rate. At the same time, the highest positive effect among productive expenditures results from healthcare expenditures, while the highest negative effect among unproductive expenditures results from national defense expenditures.
Below are tables containing estimates for the main equation of model (2) for various expenditure categories, as well as the corresponding estimates for matrix B.
For defense and social expenditures, model (2) produces negative values for b: –2.1 p.p. and –0.7 p.p. respectively. Increasing defense expenditures by 1% of the GDP while keeping total spending constant reduces GDP growth rate by 2.1 p.p. (see Tables
VAR model for defense expenditures as a percentage of GDP.
Sample: 2002–2017  
DEF/GDPN  LOG (GDP)  
DEF (–1)/GDPN (–1)  0.692  6.786 
(0.147)  (2.615)  
LOG (GDP (–1))  0.029  0.519 
(0.005)  (0.088)  
C  –0.277  4.581 
(0.045)  (0.801)  
LOG (URALS)  –0.008  0.122 
(0.002)  (0.028)  
R ^{2}  0.959  0.981 
Fstatistic  85.593  192.147 
Loglikelihood  80.282  37.085 
Akaike criterion  –10.171  –4.411 
Schwarz criterion  –9.982  –4.223 
Loglikelihood  117.473  
Akaike information criterion  –14.596  
Schwarz information criterion  –14.219 
SVAR model for defense expenditures as a percentage of GDP.
Sample: 2002–2017  
Coefficient  Standard error  zstatistic  Pvalue  
C (1)  0.001  0.000  5.477  0.000 
C (2)  –0.003  0.006  –0.460  0.645 
C (3)  0.024  0.004  5.477  0.000 
Loglikelihood  112.820  
Estimated matrix B:  
0.001  0.000  
–0.003  0.024 
Increasing the share of social policy expenditures by 1% of the GDP while keeping total spending constant reduces GDP growth rate by 0.7 p.p. (see Tables
VAR model for social policy expenditures as a percentage of GDP.
Sample: 2008–2017  
SOC/GDPN  DLOG (GDP)  
SOC (–1)/GDPN (–1)  0.086  –1.321 
(0.587)  (1.191)  
SOC (–2)/GDPN (–2)  0.095  1.789 
(0.368)  (0.746)  
DLOG (GDP (–1))  –0.160  –0.360 
(0.123)  (0.250)  
DLOG (GDP (–2))  –0.021  0.026 
(0.116)  (0.236)  
C  0.097  –0.019 
(0.049)  (0.100)  
DLOG (URALS)  –0.018  0.139 
(0.012)  (0.024)  
R ^{2}  0.786  0.925 
Fstatistic  2.208  7.437 
Loglikelihood  34.185  27.813 
Akaike criterion  –6.263  –4.847 
Schwarz criterion  –6.132  –4.716 
Loglikelihood  62.657  
Akaike information criterion  –11.257  
Schwarz information criterion  –10.994 
SVAR model for social policy expenditures as a percentage of GDP.
Sample: 2008–2017  
Coefficient  Standard error  zstatistic  Pvalue  
C(1)  0.009  0.002  4.243  0.000 
C(2)  –0.007  0.006  –1.147  0.251 
C(3)  0.018  0.004  4.243  0.000 
Loglikelihood  52.769  
Estimated matrix B:  
0.009  0.000  
–0.007  0.018 
In model (2), as in model (1), the highest positive effect among productive expenditures is associated with expenditures on the national economy: an increase of 1% of the GDP while keeping total spending constant results in an increase in GDP growth rate by 1.1 p.p. (Tables
VAR model for expenditures on the national economy as a percentage of GDP.
Sample: 2006–2017  
ECO/GDPN  LOG (GDP)  
ECO (–1)/GDPN (–1)  0.025  –0.542 
(0.169)  (0.361)  
LOG (GDP (–1))  0.042  0.670 
(0.019)  (0.040)  
C  –0.450  3.526 
(0.202)  (0.434)  
LOG (URALS)  0.008  0.034 
(0.004)  (0.009)  
D09  0.021  –0.093 
(0.005)  (0.011)  
R ^{2}  0.833  0.986 
Fstatistic  7.479  105.419 
Loglikelihood  47.168  38.782 
Akaike criterion  –7.667  –6.142 
Schwarz criterion  –7.486  –5.961 
Loglikelihood  87.519  
Akaike information criterion  –14.094  
Schwarz information criterion  –13.733 
SVAR model for expenditures on the national economy as a percentage of GDP.
Sample: 2006–2017  
Coefficient  Standard error  zstatistic  Pvalue  
C (1)  0.004  0.001  4.690  0.000 
C (2)  0.005  0.003  1.765  0.077 
C (3)  0.008  0.002  4.690  0.000 
Loglikelihood  80.851  
Estimated matrix B:  
0.004  0.000  
0.005  0.008 
At the same time, the results for healthcare and education expenditures in model (2) differ from the corresponding results in model (1). In model (2), healthcare expenditures have the lowest positive impact on economic growth: the effect from their increase is estimated at a 0.1 p.p. increase in GDP growth rate (Tables
VAR model for healthcare expenditures as a percentage of GDP.
Sample: 2007–2017  
HEA/GDPN  DLOG (GDP)  
HEA (–1)/GDPN (–1)  –0.160  –5.106 
(0.455)  (6.917)  
DLOG (GDP (–1))  0.006  –0.063 
(0.016)  (0.248)  
C  0.039  0.195 
(0.016)  (0.236)  
DLOG (URALS)  –0.003  0.135 
(0.003)  (0.038)  
R ^{2}  0.328  0.714 
Fstatistic  0.978  4.986 
Loglikelihood  50.516  23.310 
Akaike criterion  –9.303  –3.862 
Schwarz criterion  –9.182  –3.741 
Loglikelihood  73.827  
Akaike information criterion  –13.165  
Schwarz information criterion  –12.923 
SVAR model for healthcare expenditures as a percentage of GDP.
Sample: 2007–2017  
Coefficient  Standard error  zstatistic  Pvalue  
C (1)  0.002  0.000  4.472  0.000 
C (2)  0.000  0.010  0.021  0.983 
C (3)  0.030  0.007  4.472  0.000 
Loglikelihood  68.719  
Estimated matrix B:  
0.002  0.000  
0.000  0.030 
Education expenditures have the second highest effect in model (2). Increasing these expenditures by 1% of the GDP while keeping total spending constant produces additional GDP growth of 0.8 p.p. (Tables
VAR model for education expenditures as a percentage of GDP.
Sample: 2008–2017  
EDU/GDPN  DLOG (GDP)  
EDU (–1)/GDPN (–1)  0.497  –3.311 
(0.693)  (0.668)  
EDU (–2)/GDPN (–2)  –0.574  10.840 
(1.558)  (1.502)  
DLOG (GDP (–1))  0.016  –0.060 
(0.024)  (0.023)  
DLOG (GDP (–2))  –0.018  0.562 
(0.070)  (0.068)  
C  0.041  –0.281 
(0.050)  (0.048)  
DLOG (URALS)  –0.001  0.109 
(0.005)  (0.004)  
D09  0.005  –0.078 
(0.003)  (0.003)  
R ^{2}  0.737  0.999 
Fstatistic  0.936  583.179 
Loglikelihood  49.411  49.741 
Akaike criterion  –9.425  –9.498 
Schwarz criterion  –9.271  –9.345 
Loglikelihood  103.889  
Akaike information criterion  –19.975  
Schwarz information criterion  –19.669 
SVAR model for education expenditures as a percentage of GDP.
Sample: 2008–2017  
Coefficient  Standard error  zstatistic  Pvalue  
C (1)  0.002  0.000  4.243  0.000 
C (2)  0.002  0.001  2.947  0.003 
C (3)  0.001  0.000  4.243  0.000 
Loglikelihood  90.352  
Estimated matrix B:  
0.002  0.000  
0.002  0.001 
Table
Sensitivity of GDP growth rate to shocks in various expenditure categories as a percentage of GDP.
Category of expenditures  Estimated b in type (2) models  Estimated b in type (1) models 
National defense expenditures  –2.110  –0.795 
Social policy expenditures  –0.749  –0.339 
National economy expenditures  1.068  0.224 
Healthcare expenditures  0.103  2.706 
Education expenditures  0.778  0.323 
It should be noted that the signs for b estimates obtained with model (2) coincide with the signs of corresponding estimates of this parameter from model (1). This also speaks in favor of the hypothesis that increasing the share of productive expenditures has a positive impact on GDP growth rate, while increasing the share of unproductive expenditures has a negative impact. In model (2), as in model (1), we find that the highest positive effect on GDP growth is produced by expenditures on the national economy, while the highest negative effect is produced by defense expenditures.
We obtained a rather high estimate for the effect of expenditures on the national economy on Russia’s GDP growth rate, which is attributable to three causes. First, a considerable portion of this effect originates from increased expenditures on infrastructure and developing various industries (the fuel and energy complex, agriculture, water and forestry, transportation, roads, and communications) from 2005 to 2008, driven by high oil prices. During those years, the economy grew at a high rate, in particular thanks to fiscal stimulus through increased spending on the national economy. Second, expenditures on the national economy include government subsidies, which were also allocated in large volumes to companies in the real sector during the 2009 crisis (a corresponding peak of expenditures on the national economy is apparent in Fig. 1). These were countercyclical fiscal measures which eased the GDP reduction in 2009. Third, the second peak of expenditures on the national economy was in 2014, when considerable resources were allocated to support and develop the Crimean economy. In 2014, the Russian economy decelerated relative to 2013. However, this deceleration would have been more pronounced if not for the additional expenditures to support the new Russian region.
We could also ask which specific subsections of expenditures on the national economy made the greatest contribution to the high value of their corresponding GDP expenditure multiplier. To answer this question, we need to build similar type (1) and (2) models for all subsections of expenditures on the national economy and calculate their respective multipliers. This paper is not concerned with this task; however, it appears to be a promising direction for further research.
The estimated response of economic growth to changes in the spending allocation appears to be more adequate from an economic point of view for models where expenditures are represented as percentages of GDP, than for models where expenditures are represented as shares of total spending. Moreover, the values of the information criteria presented in the tables above also speak in favor of type (2) models with expenditures as percentages of GDP.
This study estimated the effects of changes in the allocation of general government spending on economic growth in Russia. Our econometric analysis shows the following.
Increasing the share of productive expenditures (national economy, education, healthcare) has a positive effect on economic growth rate, whereas increasing the share of unproductive expenditures (national defense and social policy) has an adverse impact on GDP growth rate.
The impact of particular categories of government spending on economic growth in Russia has previously been studied only through a multiplier indicator, i.e. without keeping total spending constant. Nevertheless, the results obtained in this paper are generally consistent with the results of previous empirical papers based on multipliers, and based on Russian data. More specifically, productive expenditures — and first of all expenditures on the national economy (including government investments) — have a positive effect on GDP growth rate, whereas unproductive expenditures have a negative effect, with the highest negative effect being produced by national defense expenditures. The results obtained are also generally similar to the results of empirical works based on data from other countries and international data.
The highest positive effect among productive expenditures is produced by expenditures on the national economy: increasing them by 1% of the GDP while keeping total spending constant increases GDP growth rate by 1.1 p.p. The next highest effect is produced by education expenditures. Increasing these expenditures by 1% of the GDP while keeping total spending constant produces additional GDP growth of 0.8 p.p. Healthcare expenditures have the lowest positive impact on growth: the effect of increasing them is estimated at a 0.1 p.p. increase in GDP growth rate. For defense and social expenditures, the effect is negative: –2.1 p.p. and –0.7 p.p. respectively.
If expenditures are measured as percent shares of total general government spending rather than as percentages of GDP, the results produced are slightly different, although similar in essence. Healthcare expenditures have the most notable positive impact, as increasing their share of total general government spending by 1 p.p. adds 2.7 p.p. to GDP growth rate. An equal increase in the share of education expenditures results in a 0.3 p.p. increase in GDP growth rate. For expenditures on the national economy, the corresponding effect is 0.2 p.p. For defense and social expenditures, the effect is negative: –0.8 p.p. and –0.3 p.p. respectively.
Given the statistical properties of our models, we prioritize the estimates with expenditures expressed as percentages of the GDP.
The correlation analysis has shown that Russia is characterized by a stable, codirectional change in expenditures on the national economy, healthcare, and education. At the same time, these expenditures in real terms are found to be in a stable positive correlation with the real GDP and oil prices. Thus, productive government spending on physical and human capital is procyclical. At the same time, according to the analysis, unproductive expenditures on defense in Russia are independent of the business cycle phase.
The allocation of Russia’s government spending is currently characterized by a high proportion of unproductive expenditures, which accounted for 70% of total spending in 2017. However, as shown in previous empirical papers and in this paper, it is productive budget expenditures (investments in human and physical capital) that encourage economic growth in Russia. Throughout the past 10 years, the level of productive budget expenditures has consistently been as low as 10.5% to 11.0% of the GDP (or 28% to 30% of total spending). Thus, in order to accelerate Russia’s economic growth, the allocation of government spending should be shifted in favor of productive expenditures by optimizing unproductive expenditures.