Acta Universitatis Danubius. Œconomica, Vol 13, No 2 (2017)
Implications of Fiscal
Responsibility on Economic Growth
Anca Florentina Gavriluţă (Vatamanu)^{1}
Abstract: Governmental decisions play an important role in the critical periods of the economy and usually in base of the strategy adopted, can make an effective contribution to the budget process while preserving fiscal discipline. This study tests the implications of fiscal responsibility on economic growth with the scope to analyze and find out the major issue of responsible public finances. In base of logistic regression results, the study leads to the conclusion that may be wise to reevaluate plans to cut net government revenue in future budgets and instead take a more strategic approach to nurturing growth in the EU economy.
Keywords: fiscal responsibility; fiscal rules; economic growth
JEL Classifications: G28; E62; H72
1. Introduction
Many decisions involve “temporal dilemmas”, that is conflicts between the immediate and delayed consequences of one’s actions. In the same way, governmental decision, have a direct impact on the standard of life and economic stability of entire populations, being very important that this decisions to have like support a solid strategy, a good management and a solid legal framework. We find also like argue that fiscal, or budgetary transparency has large, positive effects on fiscal performance (James, Dreyer, 2002, p. 141).
There are a lot of people who believe that fiscal responsibility, a concept who involves transparency, efficiency of public administration and care for future generations by improving sustainable development, has large and positive effects on fiscal performance. According to the IMF, “transparency in government operations is widely regarded as an important precondition for macroeconomic fiscal sustainability, good governance, and overall fiscal rectitude” (Kopits & Craig, 1998, p. 1). However, while such asserted effects are common, there is not much empirical evidence about institutional transparency and fiscal policy outcomes. Some links appear between fiscal transparency and fiscal performance in European countries, and between indirect measures of transparency and fiscal performance in Latin American countries. Many remain convinced of the importance of fiscal policy, unrealised the role of consolidate a series of principles to guide to the way of public health finance and care for future generations.
Ewijk and Casper (2006), relate that healthy public finances contribute to macroeconomic stability and support monetary policy in maintaining stable prices at low interest rates. Both effects are conducive to private investment and savings. On the other hand, by reducing public debt and the interest burden, this also creates room for a reduction in distortionary taxes and an increase in productive public spending (Wong, Christine, 2000, p. 55). The theoretical literature on the causes and consequences of fiscal, or budgetary, responsibility and transparency is not large (Rogoff, 1990). From a theoretical point of view, Shi and Svensson (2002) emphasizing that voters want more competent politicians in office, as they can provide more public goods for given levels of taxation and private consumption. In this way, besides issues related to the theory of public choice (Buchanan, Musgrave, 1999 p. 16), a theory widely treated by economists consecrated in the field, more important is the citizens' trust in the representatives of the central level. From other studies, results on deficit and debt accumulation: that transparency decreases debt accumulation, at least partly through an effect on the electoral cycle (Shi & Svensson, 2002), that increasing political polarization increases debt accumulation^{2}.
The economic and financial crisis badly weakened public finances in EU countries and significant efforts in recent years and an improved economic outlook are bearing fruit and Member States have succeeded in reducing deficits and stabilising debt levels. The purpose of this paper is to tests the implications of fiscal responsibility on economic growth with the scope to analyze and find out the major issue of responsible public finances. In base of logistic regression we want to offer an opinion on the specifics of fiscal responsibility, in order to predict GDP growth in the nature of tax rules (rules that based on own specific content, summarizes compliance with fiscal responsability of the budget).
2. Data and Methodology
The methodology used is quantitative, based on the use of logistic regression, wich in contrast to the multiple linear regression, where you can predict, based on several independent variables, a numeric dependent variable, logistic regression allows predicting a dichotomous nominal variables. Linear regression method assumes that both factorial variables and variable the result is the continuous type; by contrast, logistic regression allows working other types of variables. Logistic regression model describe the relationship between a dichotomous variable Y, which takes values 1 (Success) and 0 (failure), and k factorial variables _{ }, _{ },_{ }….._{ }. Thus, we can focused to analysis the influence of variables on GDP growth, wanting to show the implications of fiscal responsibility on administrative work and indirectly, on economic growth.
A detailed presentation of logistic regression methodology and of the issues raised by its use was performed by Amemiya, T. (1985), Balakrishnan, N. (1991), Hosmer, David W.; Stanley Lemeshow (2000), Agresti, Alan (2002) and Green, William H. (2003). In this study, the dependent variables is: GDP growth, encoded in the analysis with 1 and 0average under 2.27 = 0, over 2.27 = 1 and independent variables are: Total fiscal rules, Public Debt, Total revenue, Total fiscal pressure, Total general expenditure, GDP growth, Net lending (+)/net borrowing ().
The variables considered in the logistic regression model are:

Dependent variable (Y) – GDPgr (real GDP growth) – converted numerical variable in dummy variable categorical: it resorted to calculating the average, was established as follows: 0 = average under 2.27 over 2.27 = 1.

Independent variables (X_{j}):

X_{1 }Tfr (Total fiscal rules);

X_{2}Bl (Legal basis of fiscal rules);

X_{3}Tec (Type of economy).
By placing all variables used in the analysis of any nature whatsoever final logistic regression model used in the analysis is as follows:
3. Results and Discussions
Statistical description of the evariables used in the analysis is shown in Appendix. no 1. It can be seen that the data set is complete quantitative variables, which each have a set of 28 records. Analysis of indicators aimed at central tendency, exemplified through the media, reveals that the average value: 1.50 GDP growth is due to negative values in some countries such as Cyprus, Finland and Croatia, the tax rules 2.00, 10.70 Public debt, total revenues, 33.60, 26 fiscal pressure, total expenditure, 34.4 and deficit, surplus, 8.50 due to the preponderance of deficits values in 28 countries.
Standard deviation analysis highlights the following issues:

total sales tax rules is one less dispersed, which varies from the average level of 1.16553% positively or negatively. We can say that 68.2% of the total tax rules fall between ± σ x ̅ respectively ± 1.6553% 3.3929%;

distribution of public debt is very dispersed that vary from the average level of 39.16905% of GDP positively and negatively. We can say that 68.2% of the total public debt distribution is between ± σ x ̅ respectively 74.2643 ± 39.16905% of GDP;

total income distribution is one less dispersed, which varies from the average level of 6.62096% positively or negatively. We can say that 68.2% of the total income is between ± σ x ̅ ie 43.1679% ± 6.62096;

distribution of total fiscal pressure is one less dispersed that vary from the average level of 0.06395% positively and negatively. We can say that 68.2% of the total fiscal pressure is between ± σ x ̅ 0.3618 ± 0.006395% respectively;

distribution of government spending is one less dispersed, which varies from the average level of 6.52418% of GDP positively or negatively. We can say that 68.2% of the total of private saving is between ± σ x ̅ respectively 46.1750 ± 6.52418% of GDP;

distribution Net lending (+)/net borrowing ().is one less dispersed, which varies from the average level of 2.49549% of GDP positively or negatively. We can say that 68.2% of the total of private saving is between ± σ x ̅ respectively ± 2.49549 2.49549% of GDP;
Analysis of form distribution reveals that shape distributions for four of quantitative variables are asymmetric to the right since the coefficient of asymmetry Perason _{ } is greater than zero_{ } for all distributions respectively: 0.052 for fiscal rules, 0805 for public debt, 0.365 for total revenue, 0,283 the tax burden, 1222.
Logistic Regression Results
Table Case Processing Summary (Appendix 2.) shows that there are 28 records used in the analysis 0while Table Dependent Variables Codings highlights that are specific codes for dummy variable, with 0 being denoted countries with a growth rate of GDP less than 2.27 to 1, those average over 2.27. Appendix 3. Classification Table, shows that there are 14 countries that have a GDP growth rate of less than 2.27, another 14 have rates above 2.27. It notes that the model fails to predict a probability of 57.5%.
Table 1. Variables not in the Equation
Variables not in the Equation 


Score 
df 
Sig. 

Step 0 
Variables 
TRF 
1.287 
1 
.257 
Bazalegala 
.206 
1 
.650 

Tipuleconomiei 
2.800 
1 
.094 

Overall Statistics 
5.177 
3 
.159 
Source: own calculations using SPSS
In this table  Variables not in the Equation are presented variables that were not used in the initial stage forecasting logistic regression (Block 0), respectively: Type fiscal rules, the legal basis, the type of economy and value Sig. It shows how strongly influenced model as if it were introduced.
Table 2. Omnibus Tests of Model Coefficients
Omnibus Tests of Model Coefficients 


Chisquare 
df 
Sig. 

Step 1 
Step 
5.745 
3 
.125 
Block 
5.745 
3 
.125 

Model 
5.745 
3 
.125 
Source: own calculations using SPSS

Hypothesis testing
H_{0}: invalid model (independent variables have no influence on the dependent variable);
H_{1}: The model is valid (independent variables have influence on the dependent variable).

Significance step: α = 0.05;

Establishing the rule of decision: If sig ≥ α not reject the hypothesis H_{0} &If sig < α reject the hypothesis H_{0};

Interpretation of results.
Omnibus test, shows that Sig = 0.12> α = 0.05, so the null hypothesis is accepted, the introduction of the model variables excluded in the preliminary stage significantly altered our ability to predict GDP growth based on the critical nature fiscale. Since the critical value = 0.125 Sig I can say with a 1% risk assumed that the model is statistically significant and its results can be used in predicting the dependent variable.
Table 3. Hosmer and Lemeshow Test
Hosmer and Lemeshow Test 

Step 
Chisquare 
Df 
Sig. 
1 
5.433 
7 
.607 
Source: own calculations using SPSS

Hypothesis testing
H_{0}: There is a good connection between the model and the data recorded;
H_{1}: There isn't a good connection between the model and the data recorded.

Significance step: α = 0.05
Establishing the rule of decision
If sig ≥ α not reject the hypothesis H_{0};
If sig < α rejected the hypothesis H_{0};

Interpretation of results
Sig = 0.607 > α = 0.05 which shows that the null hypothesis is accepted. It can guarantee a 95% probability that there is a good correlation between the model and the data recorded.
Table 4. Contingency Table for Hosmer and Lemeshow Test
Contingency Table for Hosmer and Lemeshow Test 



GDP growth = under 2.27 
GDP growth = over 2.27 
Total 


Observed 
Expected 
Observed 
Expected 


Step 1 
1 
2 
2.545 
1 
.455 
3 


2 
2 
2.205 
1 
.795 
3 


3 
3 
2.123 
0 
.877 
3 


4 
0 
.516 
1 
.484 
1 


5 
3 
2.382 
2 
2.618 
5 


6 
1 
1.395 
2 
1.605 
3 


7 
1 
1.351 
2 
1.649 
3 


8 
2 
1.064 
2 
2.936 
4 


9 
0 
.420 
3 
2.580 
3 


Classification Table^{a} 


Observed 
Predicted 


GDP growth 
Percentage Correct 


Under 2.27 
Over 2.27 

Step 1 
GDP growth 
Under 2.27 
7 
7 
50.0 

Over 2.27 
3 
11 
78.6 

Overall Percentage 


64.3 

a. The cut value is .500 
Source: Own calculations using SPSS
Following the introduction of the logistic regression model of the independent variables, can be seen that the increased degree of accuracy estimation model from 50.0% (baseline when it was included only constant) to a 64% by inclusion of independent variables the legal basis, such as fiscal rules, such as the economy.
Table 5. Variables in the Equation
Variables in the Equation 


B 
S.E. 
Wald 
Df 
Sig. 
Exp(B) 

Step 1^{a} 
Tfr 
1.190 
.820 
2.107 
1 
.147 
.304 
Legal base 
.045 
.283 
.025 
1 
.873 
1.046 

Type of economy 
2.087 
1.112 
3.522 
1 
.061 
8.063 

Constant 
.848 
1.473 
.331 
1 
.565 
.428 
Source: Own calculations using SPSS
The logistic regression model equation: E (Y/X) = π (x) = _{ }.
The estimate parameter β1 is set to 1.190. The negative value of this ratio shows that an increase of 1.0% fiscal rules, lowering the chances estimated growth rate of GDP is below the average of 0.5%.
Table 6. Variables in the Equation
Variables in the Equation 


B 
S.E. 
Wald 
df 
Sig. 
Exp(B) 

Step 0 
Constant 
.000 
.378 
.000 
1 
1.000 
1.000 
Source: Own calculations using SPSS
Table Variables in the Equation are presented probabilities of Wald test (Sig = 0.1). For independent variables considered in the analysis, the value of these probabilities is greater than the materiality threshold α (0.05), thus accepting the null hypothesis (H0: βj = 0). Basically, there is a good connection between the model and the data recorded.
4. Conclusion
The way in wich the state uses the mechanisms of public finances to counteract some disturbing phenomena of the economy, is a subject of intense debate and I would say there are many studies that test the connection between the state and public finance mechanisms, but few bring into discussion the importance of fiscal responsibility and accountability of governments. In other, on the occasion of establishment of the economic crisis and an ever increasing need for efficiency in using financial resources and support sustainable development, has become a need for regulation at EU level, with implications for Member States, which led to the consolidation of certain tax rules, all this having as finality the creation of public administration efficiency.
The methodology is based on the use of logistic regression, wich in contrast to the multiple linear regression, where we can predict, based on several independent variables, a numeric dependent variable, logistic regression allows predicting a dichotomous nominal variables. Linear regression method assumes that both factorial variables and variable the result is the continuous type; by contrast, logistic regression allows working other types of variables. We had focused to analysis the influence of variables on GDP growth, and showed the implications of fiscal responsibility on administrative work and indirectly, on economic growth.
In base of study results, Sig = 0.607 > α = 0.05 which shows that the null hypothesis is accepted. It can guarantee a 95% probability that there is a good correlation between the model and the data recorded. Following the introduction of the logistic regression model of the independent variables, can be seen that the increased degree of accuracy estimation model from 50.0% (baseline when it was included only constant) to a 64% by inclusion of independent variables the legal basis, such as fiscal rules, such as type of economy economy. Wald test (Sig = 0.1). For independent variables considered in the analysis, the value of these probabilities is greater than the materiality threshold α (0.05), thus accepting the null hypothesis (H0: βj = 0). Basically, there is a good connection between the model and the data recorded.
Basically, the result of our study demonstrate that there are a lot of implications of fiscal responsibility on economic growth, because, in terms of transparency, care for future generations and a legal framework capable to assured a solid systems of public finances, responsibility, resolve the major issue of responsible public finances.
5. References
Alesina, Alberto & YESni, Rodrik. (1992). Distribution, political conflict, and economic growth: A simple theory and some empirical evidence. Political economy, growth, and business cycles, pp. 2350.
Alston, Lee; Marcus, Melo; Bernardo, Mueller & Carlos, Pereira. (2009). Presidential Power, Fiscal Responsibility Laws, and the Allocation of Spending: The Case of Brazil. In Who Decides the Budget? A Political Economy Analysis of the Budget Process in Latin America Edited by Hallerberg, Mark; Scartascini, Carlos & Stein, Ernesto, pp. 57–90. Cambridge, MA: Harvard University Press.
Alt, James E.; Lassen, David Dreyer & Skilling, David (2002). Fiscal transparency, gubernatorial approval, and the scale of government: Evidence from the states. State Politics & Policy Quarterly, 2.3, pp. 230250.
Boettke, Peter J. & YESniel, J. Smith (2015). Monetary policy and the quest for robust political economy.
Braun, Miguel & Tommasi, Mariano. (2004). Subnational Fiscal Rules: A Game Theoretical approach. In RulesBased Fiscal Policies in Emergimg Markets: Bacground, Analysis and Prospects. Edited by George Kopits, pp. 183–197. London: Palgrave McMillan.
Buchanan, James M. & Richard, A. Musgrave. (1999). Public finance and public choice: two contrasting visions of the State. Mit Press.
Corbacho, Ana & Schwartz, Gerd (2007). Fiscal Responsibility Laws. In Promoting Fiscal Discipline. Edited by Mammohan Koomar and Teresa TerMinassian, pp. 58–93. Washington, DC: International Monetary Fund.
Debrun, X. (2000). Fiscal Rules in a Monetary Union. A short Open analysis. Open Economies Review, Vol. 11, No. 4, pp. 32358.
*** (2009). Fiscal Rules: Anchoring Expectations for Sustainable Public Finances. Fiscal Affairs Department.
*** (2010). Hungary Ministry of Finance Outline of the Fiscal Responsibility Act and Fiscal Responsibility Act, disponibil lahttp://www1.pm.gov.hu/web/home.nsf/frames/English;
Kopits, G. (2004). Overview of Fiscal Policy Rules for Emerging Markets. In G. Kopits (ed.), Rules Based Fiscal Policy in Emerging Markets: Background, Analysis and Prospects, Palgrave Macmillan, New York.
Kopits, Mr. George & Craig, Mr. JD. (1998). Transparency in government operations. No. 158. International monetary fund.
Posner, Paul & BlönYESl, Jon (2012). Fiscal rules and Deficits: Prospects for Fiscal Responsibility in Democratic Nations. Governance, 25(1), pp. 11–34.
Rothbard, Murray Newton (1981). What Has Government Done to Our Money?. Ludwig von Mises Institute.
Santiso, Carlos. (2005). Budget institutions and fiscal responsibility: Parliaments and the political economy of the budget process.
Schumpeter, Joseph Alois; Salin, Edgar & Preiswerk, Suzanne (1950). Kapitalismus, sozialismus und demokratie. Vol. 2. Bern: Francke.
Van Ewijk, Casper (2006). et al. Ageing and the sustainability of Dutch public finances. The Hague: CPB Netherlands Bureau for Economic Policy Analysis.
Wong, Christine PW. (2000). Centrallocal relations revisited the 1994 taxsharing reform and public expenditure management in China. China Perspectives, pp. 5263.
Appendix
Appendix 1. Statistical description of the e variables
Descriptive Statistics 


N 
Minimum 
Maximum 
Mean 
Std. Deviation 
Skewness 
Kurtosis 

Statistic 
Statistic 
Statistic 
Statistic 
Statistic 
Statistic 
Std. Error 
Statistic 
Std. Error 

Total fiscal rules 
28 
2.00 
5.00 
3.3929 
1.16553 
.052 
.441 
1.482 
.858 
Public Debt 
28 
10.70 
179.70 
74.2643 
39.16345 
.805 
.441 
.726 
.858 
Total revenue 
28 
33.60 
56.70 
43.1679 
6.62096 
.365 
.441 
.759 
.858 
Total fiscal pressure 
28 
.26 
.48 
.3618 
.06395 
.283 
.441 
.905 
.858 
Total general expenditure 
28 
34.40 
58.10 
46.1750 
6.52418 
.023 
.441 
.773 
.858 
GDP growth 
28 
1.50 
8.50 
2.2714 
2.28081 
1.222 
.441 
2.380 
.858 
Net lending (+)/net borrowing () 
28 
8.80 
1.50 
2.9321 
2.49549 
.228 
.441 
.065 
.858 
Valid N (listwise) 
28 








Source: Own calculations using SPSS
Appendix 2. Case Processing Summary
Case Processing Summary 


Unweighted Cases^{a} 
N 
Percent 


Selected Cases 
Included in Analysis 
28 
82.4 
Dependent Variable Encoding 


Missing Cases 
6 
17.6 
Original Value 
Internal Value 


Total 
34 
100.0 
Under 2.27 
0 


Unselected Cases 
0 
.0 

Over 2.27 
1 






Total 
34 
100.0 


a. If weight is in effect, see classification table for the total number of cases. 

Appendix 3. Classification Table^{a,b}
Classification Table^{a,b} 


Observed 
Predicted 


Cresterea PIB 
Percentage Correct 


mai mic de 2.27 
peste 2.27 

Step 0 
Cresterea PIB 
mai mic de 2.27 
0 
14 
.0 
peste 2.27 
0 
14 
100.0 

Overall Percentage 


50.0 

a. Constant is included in the model. 

b. The cut value is .500 
Source: Own calculations using SPSS
1 Associate Professor, PhD, Department of Finance, Money and Public Administration, “Alexandru Ioan Cuza University of Iasi, Romania, Address: Bulevardul Carol I 11, Iasi 700506, Romania, Corresponding author: gavriluta.anca@yаhoo.com.
AUDŒ, Vol. 13, no. 2, pp. 5565
2 See, for instance, (Alesina & Tabellini, 1990).
Refbacks
 There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 4.0 International License.