Thriving amidst uncertainty: a financial blueprint for the public budget

Lulaj, Enkeleda

doi:10.3326/pse.48.4.5.

946
Views

54
Downloads

Thriving amidst uncertainty: a financial blueprint for the public budget

Enkeleda Lulaj*

Download citation

https://doi.org/10.3326/pse.48.4.5

FULL ARTICLE

FIGURES & DATA

REFERENCES

CROSSMARK POLICY

METRICS

LICENCING

PDF

Table 1
Definition and description of the study variables

Item	Construct	Source
Factor 1 Budgetary resilience (BR)
BR1	Uncertainty is a major challenge for the public budget	Upadhaya et al. (2020) Farhana and Siti-Nabiha (2023) Agyemang et al. (2023)
BR2	A sustainable public budget protects the economy from negative effects
BR3	A well-prepared public budget contributes to economic development
BR4	A well-prepared public budget can increase public investment
BR5	A well-prepared public budget improves the quality of public services
Factor 2 Budgetary stability (BS)
BS1	A well-prepared public budget contributes to financial stability	Mauro, Cinquini and Sinervo (2019) Lulaj (2024)
BS2	A well-prepared budget based on a clear financial plan increases citizen confidence
BS3	A well-prepared public budget helps to manage financial crises
Factor 3 Budgetary sustainability (BSu)
BSu1	A well-prepared budget plan minimizes financial risks	Giosi et al. (2014)
BSu2	A well-prepared public budget helps to reduce public debt
BSu3	A well-prepared public budget contributes to poverty reduction
Factor 4 Budgetary empowerment (BE)
BE1	Employment opportunities are enhanced by a well-prepared public budget	Reddick (2004)
BE2	Social sustainability can be achieved through a well-prepared public budget
BE3	A well-prepared public budget improves the transparency of public finances
Factor 5 Budgetary preparedness (BP)
BE1	A clear financial plan is useful in managing the public budget	Agyemang et al. (2023)
BE2	A well-prepared financial plan can mitigate the effects of budget uncertainty	Agyemang et al. (2023)
Factor 6 Budgetary governance (BG)
BG1	A well-prepared public budget helps to reduce corruption	Lulaj (2019a) Kasperskaya and Xifré (2020) Drew (2017)
BG2	A well-prepared public budget increases the financial accountability of public institutions
BG3	A well-prepared public budget increases accountability to citizens
BG4	A well-prepared public budget helps to reduce wealth inequality
BG5	A well-prepared public budget promotes environmental sustainability
BG6	A well-prepared public budget increases citizen participation in financial decision-making
BG7	A well-prepared public budget promotes social justice
BG8	A well-prepared public budget reduces income inequality
BG9	Mechanisms for monitoring and evaluating the implementation of the public budget are necessary
Factor 7 Budgetary inclusion priorities (BIP)
BIP1	Necessity of public budget allocation for programs promoting gender equality	Looney (1987)
BIP2	The belief that public investment should prioritize long-term economic development
BIP3	Public consultation plays a crucial role in the process of public budgeting
Factor 8 Budgetary agility (BA)
BA1	Satisfaction with the frequency of updates on the implementation of the public budget	Barbera, Guarini and Steccolini (2020) Lappi and Aaltonen, (2017) Palsodkar, Yadav and Nagare (2023)
BA2	Satisfaction with the inclusiveness of the public budget in addressing diverse community needs
BA3	Satisfaction with government responsiveness to public input during the budget process
BA4	Information about services and programs funded by the public budget is easily accessible
BA5	The government effectively communicates budget decisions to the public
BA6	The government can meet future fiscal challenges

Source: Author’s own calculations.

Table 2
Confirmatory factorial analysis (CFA)

Observed variable	Latent variable	Standardized regression weights	Estimate	S.E.	C.R.	p-value	Confidence level
BR1	BR	0.597***	1.000				Statistically significant
BR2	BR	0.561***	0.914	0.061	15.104	p < 0.001	Statistically significant
BR3	BR	0.734***	1.335	0.074	17.972	p < 0.001	Statistically significant
BR4	BR	0.569***	1.005	0.066	15.266	p < 0.001	Statistically significant
BR5	BR	0.509***	0.945	0.067	14.027	p < 0.001	Statistically significant
BS1	BS	0.707***	1.000				Statistically significant
BS2	BS	0.714***	1.436	0.075	19.082	p < 0.001	Statistically significant
BS3	BS	0.633***	0.869	0.049	17.680	p < 0.001	Statistically significant
BSu1	BSu	0.580***	1.000				Statistically significant
BSu2	BSu	0.649***	1.021	0.063	16.238	p < 0.001	Statistically significant
BSu3	BSu	0.618***	1.071	0.068	15.749	p < 0.001	Statistically significant
BE1	BE	0.641***	1.000				Statistically significant
BE2	BE	0.604***	0.969	0.062	15.533	p < 0.001	Statistically significant
BE3	BE	0.503***	0.782	0.058	13.576	p < 0.001	Statistically significant
BP1	BP	0.559***	1.000				Statistically significant
BP2	BP	0.548***	0.921	0.068	13.586	p < 0.001	Statistically significant
BG1	BG	0.632***	1.000				Statistically significant
BG2	BG	0.500***	0.658	0.045	14.748	p < 0.001	Statistically significant
BG3	BG	0.579***	0.939	0.056	16.670	p < 0.001	Statistically significant
BG4	BG	0.580***	0.834	0.050	16.683	p < 0.001	Statistically significant
BG5	BG	0.658***	0.945	0.051	18.431	p < 0.001	Statistically significant
BG6	BG	0.624***	1.071	0.061	17.695	p < 0.001	Statistically significant
BG7	BG	0.555***	0.765	0.047	16.099	p < 0.001	Statistically significant
BG8	BG	0.619***	1.122	0.064	17.581	p < 0.001	Statistically significant
BG9	BG	0.672***	0.934	0.050	18.728	p < 0.001	Statistically significant
BIP1	BIP	0.541***	1.000				Statistically significant
BIP2	BIP	0.543***	1.016	0.079	12.792	p < 0.001	Statistically significant
BIP3	BIP	0.614***	1.115	0.081	13.678	p < 0.001	Statistically significant
BA1	BA	0.581***	1.000				Statistically significant
BA2	BA	0.514***	0.896	0.065	13.799	p < 0.001	Statistically significant
BA3	BA	0.587***	1.048	0.069	15.173	p < 0.001	Statistically significant
BA4	BA	0.630***	1.070	0.067	15.902	p < 0.001	Statistically significant
BA5	BA	0.514***	0.878	0.064	13.814	p < 0.001	Statistically significant
BA6	BA	0.592***	1.011	0.066	15.273	p < 0.001	Statistically significant

Note: Standard error (S.E.), Critical ratios (C.R.), *** p<0.001 indicates statistical significance. The confidence interval is set at 99.9% (CI).

Source: Author’s own calculations.

Table 3
Standardized total effects – two tailed significance

Variable	BA	BSu	BG	BP	BIP	BE	BS	BR
BA6	0.019*
BA5	0.010**
BA4	0.003**
BA3	0.006**
BA2	0.005**
BA1	0.020*
BSu3		0.009**
BSu2		0.016*
BSu1		0.018*
BG9			0.009**
BG8			0.007**
BG7			0.010**
BG6			0.008**
BG5			0.003**
BG4			0.007**
BG3			0.012*
BG2			0.007**
BG1			0.006**
BP1				0.012*
BP2				0.006**
BIP3					0.011*
BIP2					0.010**
BIP1					0.013*
BE3						0.003**
BE2						0.005**
BE1						0.021*
BS3							0.013*
BS2							0.012*
BS1							0.012*
BR5								0.011*
BR4								0.015**
BR3								0.008**
BR2								0.007**
BR1								0.003**

Note: *** p<0.001, ** p<0.01, * p<0.05.
Source: Author's own calculations.

Table 4
Model fit summary

Tests/Parameters	Default model	Tests clarification & equations	Threshold values	Interpretation
CMIN
$CMIN (χ^{2})$ α=.05	71.862	(N – 1)_FML where _FML is the value of the statistical criterion (fit function) minimized in ML estimation and (N – 1) Minimum discrepancy function by degrees of freedom divided (Steiger and Lind, 1980) $χ^{2} - {χ'}^{2} = \sum_{i = 1}^{k} \frac{{χ_{i}}^{2}}{m_{i}} - \sum_{i = 1}^{k} \frac{χ^{2}}{m_{i}}$
$d_{f}_{M} (\frac{X^{2}}{df})$	28	Degrees of freedom are important for understanding model fit, ≤2 = acceptable fit (Tabachnick and Fidell, 2007)	n/a	n/a
*χ2_M*	0.000	p-value (Joreskog and Surbom, 1996)	<.05	Significant
CMIN/DF	2.567	Chi-square divided by degree of freedom (Kline, 1998)	Between 1 and 3	Excellent fit
RMR, GFI
RMR	0.010	Root mean square residual ≤0.05 = acceptable fit (Diamantopoulos and Siguaw, 2000)	The smaller the RMR value the better	Perfect fit
GFI	0.989	Goodness of fit index A value ≥ 0.9 indicates a reasonable fit (Hu and Bentler, 1998) A value of ≥ 0.95 is considered an excellent fit $GFI = 1 - \frac{C_{res}}{C_{tot}}$ where C_res and C_tot , the residual and total variability in the sample covariance matrix	≤ 1 > 0.80	Good fit
AGFI	0.975	Adjusted goodness of fit index	>0.80	Good fit
PGFI	0.420	Parsimony goodness of fit index	n/a	n/a
Baseline Comparisons
NFI	0.974	Normed fit index also referred to as delta 1 A value of 1 shows a perfect fit while models valued < 0.9 can be usually improved substantially (Bentler and Bonett, 1980)	>0.80	Good fit
RFI	0.949	Relative fit index	>0.70	Good fit
IFI	0.984	Incremental fit index	>0.90	Perfect fit
TLI	0.981	Tucker-Lewis coefficient	0 to 1 >0.90	Perfect fit
CFI	0.984	Comparative fit index A CFI value of ≥ 0.95 is considered an excellent fit for the model $CFI = 1 = \frac{{χ_{M}}^{2} - {df}_{M}}{{χ_{B}}^{2} - {df}_{B}}$	>0.95	Excellent fit
Parsimony-Adjusted Measures
PRATIO	0.509	Parsimony ratio	0 to 1 >0.50	Good fit
PNFI	0.496	Parsimony normed fixed index expressing the result of parsimony adjustment (Mulaik and Brett, 1982) to the Normed fixed index (NFI)	0 to 1 >0.50	Good fit
PCFI	0.501	Parsimony comparative fit index	0 to 1 >0.50	Good fit
NCP
NCP	43.862	Non-centrality parameter	17.3 – 106.1 CI 90%	Good fit
LO 90	22.582	Lower boundary	17.3 – 106.1 CI 90%	Good fit
HI 90	72.817	Upper boundary	17.3 – 106.1 CI 90%	Good fit
FMIN
FMIN	0.060	Index of model fit	.08 – .53 CI 90%	Good Fit
F0	0.037	Confidence interval	.08 – .53 CI 90%	Good Fit
LO 90	0.019	Lower boundary	.08 – .53 CI 90%	Good Fit
HI 90	0.061	Upper boundary	.08 – .53 CI 90%	Good Fit
RMSEA
RMSEA	0.036	Root mean square error of approximation Values ≤ 0.05 are considered excellent (MacCallum, Browne and Sugawara, 1996) $RMSEA = \sqrt{\frac{{χ_{M}}^{2} - {df}_{M}}{{df}_{M} (N - 1)}}$	<0.06	Excellent fit
LO 90	0.026	Lower boundary	CI 90%	Excellent fit
HI 90	0.047	Upper boundary	CI 90%	Excellent fit
PClose	0.987	Close fit hypothesis (Browne and Cudeck, 1993)	>0.05	Excellent fit

Note: PClose > 0.05, CFI > 0.95.
Source: Author's own calculations.

Table 5
Hypothesis testing results

Test type	Description	Results
Hypothesis (H1)	There is a statistically significant and positive relationship among the budgetary factors	Accepted
Model fit tests
CFA	Confirmatory factor analysis	Significant results
EFA	Exploratory factor analysis	Significant results
C.I	Confidence interval	≈ 99.9%
α	Cronbach alpha	0.60 ≥ α
λ	Lambda	0.05 ≥ λ
Significance levels
p < 0.001		***
p < 0.01		**
p < 0.05		*
RMSEA	Root mean square error of approximation	90% CI, p = 0.049
$χ 2$	Chi-squared	$χ 2$ , p = 0.000
CFI	Comparative fit index	CFI = 96%
Relationships
BR ↔ BE	Budgetary resilience ↔ Budgetary empowerment	Accepted
BR ↔ BP	Budgetary resilience ↔ Budgetary preparedness	Accepted
BR ↔ BG	Budgetary resilience ↔ Budgetary governance	Accepted
BR ↔ BSu	Budgetary resilience ↔ Budgetary sustainability	Accepted
BS ↔ BE	Budgetary stability ↔ Budgetary empowerment	Accepted
BS ↔ BIP	Budgetary stability ↔ Budgetary inclusion priorities	Accepted
BS ↔ BP	Budgetary stability ↔ Budgetary preparedness	Accepted
BS ↔ BSu	Budgetary stability ↔ Budgetary sustainability	Accepted
BE ↔ BIP	Budgetary empowerment ↔ Budgetary inclusion priorities	Accepted
BE ↔ BP	Budgetary empowerment ↔ Budgetary preparedness	Accepted
BE ↔ BG	Budgetary empowerment ↔ Budgetary governance	Accepted
BE ↔ BSu	Budgetary empowerment ↔ Budgetary sustainability	Accepted
BE ↔ BA	Budgetary empowerment ↔ Budgetary agility	Accepted
BIP ↔ BP	Budgetary inclusion priorities ↔ Budgetary preparedness	Accepted
BIP ↔ BG	Budgetary inclusion priorities ↔ Budgetary governance	Accepted
BIP ↔ BSu	Budgetary inclusion priorities ↔ Budgetary sustainability	Accepted
BP ↔ BG	Budgetary preparedness ↔ Budgetary governance	Partially accepted
BP ↔ BSu	Budgetary preparedness ↔ Budgetary sustainability	Accepted
BP ↔ BA	Budgetary preparedness ↔ Budgetary agility	Accepted
BG ↔ BSu	Budgetary governance ↔ Budgetary sustainability	Partially accepted
BG ↔ BA	Budgetary governance ↔ Budgetary agility	Accepted
BSu ↔ BA	Budgetary sustainability ↔ Budgetary agility	Accepted
BR ↔ BS	Budgetary resilience ↔ Budgetary stability	Accepted
BS ↔ BA	Budgetary stability ↔ Budgetary agility	Accepted
BR ↔ BIP	Budgetary resilience ↔ Budgetary inclusion priorities	Accepted
BIP ↔ BA	Budgetary inclusion priorities ↔ Budgetary agility	Accepted
BS ↔ BG	Budgetary stability ↔ Budgetary governance	Accepted

Note: PClose > 0.05, CFI > 0.95.
Source: Author's own calculations.

Table 6
Robustness checks and sensitivity analyses

Parameter estimates (tests of model effects)
Factors	Parameter	B	Std. error	95% Wald confidence interval		Hypothesis test (H2)
Factors	Parameter	B	Std. error	Lower	Upper	Wald Chi-Square	df	Sig.
BR	(Intercept)	21.820	.0626	21.697	21.943	121364.364	1	0.000
BR	(Scale)	4.708^a	.1922	4.346	5.100	121364.364	1	0.000
BS	(Intercept)	12.820	.0512	12.720	12.920	62658.178	1	0.000
BS	(Scale)	3.148^a	.1285	2.906	3.410	62658.178	1	0.000
BSu	(Intercept)	13.140	.0403	13.061	13.219	106412.134	1	0.000
BSu	(Scale)	1.947^a	.0795	1.797	2.109	106412.134	1	0.000
BE	(Intercept)	12.927	.0395	12.849	13.004	107346.480	1	0.000
BE	(Scale)	1.868^a	.0763	1.724	2.024	107346.480	1	0.000
BP	(Intercept)	8.840	.0265	8.788	8.892	111494.990	1	0.000
BP	(Scale)	.841^a	.0343	.776	.911	111494.990	1	0.000
BG	(Intercept)	38.580	.1160	38.353	38.807	110592.581	1	0.000
BG	(Scale)	16.150^a	.6593	14.908	17.496	110592.581	1	0.000
BIP	(Intercept)	13.073	.0379	12.999	13.148	119151.674	1	0.000
BIP	(Scale)	1.721^a	.0703	1.589	1.865	119151.674	1	0.000
BA	(Intercept)	25.933	.0716	25.793	26.074	131108.448	1	0.000
BA	(Scale)	6.156^a	.2513	5.682	6.668	131108.448	1	0.000
Hypothesis2Model	(Intercept)	147.133	.2618	146.620	147.646	315844.526	1	0.000
Hypothesis2Model	(Scale)	82.249^a	3.3578	75.924	89.101	315844.526	1	0.000

Note: Dependent variable: BR (budgetary resilience), BS (budgetary stability), BSu (budgetary sustainability), BE (budgetary empowerment), BP (budgetary preparedness), BG (budgetary governance), BIP (budgetary inclusion priorities), BA (budgetary agility); Model: (Intercept); ^a Maximum likelihood estimate, Standard error (S.E.), Intercept (Int.), Scale parameter (Scale Param.), Wald Chi-square value: Wald χ².
Source: Author's own calculations.

Table A1
Descriptive statistics of variables

Items	Minimum statistic	Maximum statistic	Items	Minimum statistic	Maximum statistic
Nonsig	3.00	5.00	BG1	2.00	5.00
BP1	2.00	5.00	BG2	2.00	5.00
BR1	3.00	5.00	BG3	2.00	5.00
BP2	3.00	5.00	BG4	2.00	5.00
BS1	3.00	5.00	BG5	2.00	5.00
Nonsig	2.00	5.00	Nonsig.	2.00	5.00
BSu1	3.00	5.00	BG6	2.00	5.00
BR2	3.00	5.00	BG7	2.00	5.00
BS2	1.00	5.00	BG8	1.00	6.00
BS3	3.00	5.00	BG9	2.00	5.00
BR3	3.00	5.00	Nonsig.	1.00	7.00
BR4	3.00	5.00	Nonsig.	1.00	3.00
BSu2	3.00	5.00	BIP2	3.00	5.00
BE3	3.00	5.00	BIP3	2.00	5.00
Nonsig.	3.00	5.00	BA1	3.00	5.00
BE1	3.00	5.00	BA2	3.00	5.00
BSu3	3.00	5.00	BIP1	3.00	5.00
BE2	3.00	5.00	Nonsig.	2.00	5.00
BR5	3.00	5.00	BA3	3.00	5.00
Nonsig.	3.00	5.00	BA4	3.00	5.00
			BA5	3.00	5.00
			BA6	3.00	5.00

Note: Nonsig. – non significant variable. N = 1,200.
Source: Author's own calculations.

Table A2
Exploratory factorial analysis (EFA) reliability analysis (Cronbach’s Alpha)

Item	Construct	Factor loading λ	KMO and Bartlett’s Test	Variance explained (VE) Cronbach’s Alpha	Interpretation
Factor 1: Budgetary resilience (BR)
BR1	Uncertainty is a major challenge for the public budget	0.701	KMO=0.794 χ²=1079.483 df=10 Sig.=0.000	VE=58.2% α=0.729	Valid results
BR2	A sustainable public budget protects the economy from negative effects	0.682
BR3	A well-prepared public budget contributes to economic development	0.785
BR4	A well-prepared public budget can increase public investment	0.673
BR5	A well-prepared public budget improves the quality of public services	0.619
Factor 2: Budgetary stability (BS)
BS1	A well-prepared public budget contributes to financial stability	0.783	KMO=0.763 χ²=747.742 df=3 Sig.=0.000	VE=64.5% α=0.724	Valid results
BS2	A well-prepared budget based on a clear financial plan increases citizen confidence	0.846
BS3	A well-prepared public budget helps to manage financial crises	0.778
Factor 3: Budgetary sustainability (BSu)
BSu1	A well-prepared budget plan minimizes financial risks	0.798	KMO=0.749 χ²=485.093 df=3 Sig.=0.000	VE=58.9% α=0.750	Valid results
BSu2	A well-prepared public budget helps to reduce public debt	0.767
BSu3	A well-prepared public budget contributes to poverty reduction	0.736
Factor 4: Budgetary empowerment (BE)
BE1	Employment opportunities are enhanced by a well-prepared public budget	0.783	KMO=0.725 χ²=379.184 df=3 Sig.=0.000	VE=55.7% α=0.800	Valid results
BE2	Social sustainability can be achieved through a well-prepared public budget	0.767
BE3	A well-prepared public budget improves the transparency of public finances	0.684
Factor 5: Budgetary preparedness (BP)
BP1	A clear financial plan is effective in managing the public budget	0.808	KMO=0.700 χ²=118.201 df=3 Sig.=0.000	VE=65.3% α=0.769	Valid results
BP2	A well-prepared financial plan can mitigate the effects of budget uncertainty	0.821	KMO=0.700 χ²=118.201 df=3 Sig.=0.000	VE=65.3% α=0.769	Valid results
Factor 6: Budgetary governance (BG)
BG1	A well-prepared public budget helps to reduce corruption	0.683	KMO=0.859 χ²=3092.409 df=36 Sig.=0.000	VE=55.3% α=0.837	Valid results
BG2	A well-prepared public budget increases the financial accountability of public institutions	0.566
BG3	A well-prepared public budget increases accountability to citizens	0.645
BG4	A well-prepared public budget helps to reduce wealth inequality	0.646
BG5	A well-prepared public budget promotes environmental sustainability	0.703
BG6	A well-prepared public budget increases citizen participation in financial decision-making	0.678
BG7	A well-prepared public budget promotes social justice	0.622
BG8	A well-prepared public budget reduces income inequality	0.666
BG9	Mechanisms for monitoring and evaluating the implementation of the public budget are necessary	0.714
Factor 7: Budgetary inclusion priorities (BIP)
BIP1	Necessity of public budget allocation for programs promoting gender equality	0.753	KMO=0.733 χ²=335.591 df=3 Sig.=0.000	VE=54.7% α=0.786	Valid results
BIP2	The belief that public investment should prioritize long-term economic development	0.715
BIP3	Public consultation plays a crucial role in the process of public budgeting	0.752
Factor 8: Budgetary agile (BA)
BA1	Satisfaction with the frequency of updates on the implementation of the public budget	0.669	KMO=0.823 χ²=1254.973 df=15 Sig.=0.000	VE=53.8% α=0.742	Valid results
BA2	Satisfaction with the inclusiveness of the public budget in addressing diverse community needs	0.580
BA3	Satisfaction with government responsiveness to public input during the budget process	0.657
BA4	Information about services and programs funded by the public budget is easily accessible	0.711
BA5	The government effectively communicates budget decisions to the public	0.658
BA6	The government can meet future fiscal challenges	0.688

Note: KMO = Kaiser-Meyer-Olkin, χ² = Chi-Square, df = degrees of freedom, *** p < 0.001, α = Cronbach’s Alpha.

Source: Author’s own calculations.

Table A3
Descriptive analysis for respondents

		Frequency	Percent
Education	High school	22	1.8
	Basic studies – faculty	382	31.8
	Post-graduate studies – master	732	61.0
	Other (Ph.D.)	64	5.3
	Total	1,200	100.0
Gender	Male	391	32.6
	Female	782	65.2
	Prefer not to answer	27	2.3
	Total	1,200	100.0
Age	15-35 years old	851	70.9
	36-55 years old	266	22.2
	Over 55 years old	83	6.9
	Total	1,200	100.0

Source: Author's own calculations.

Table A4
Covariances and correlations

Path variables	Covariances	S.E.	C.R.	P value	Correlation	Estimate	Interpretation
BR <--> BE	0.102***	0.008	12.041	***	p < 0.001	0.733	Cov (BR, BS, BSu, BE, BP, BG, BIP, and BA) Cor (BR, BS, BSu, BE, BP, BG, BIP, and BA) Positive and significant relationship The covariance’s of the factors: BP<--> BG and BG <--> BSu are not statistically significant at the 5% level.
BR <--> BP	0.070***	0.007	9.855	***	p < 0.001	0.602
BR <--> BG	0.016**	0.006	2.738	.006	p < 0.01	0.101
BR <--> BSu	0.099***	0.008	11.927	***	p < 0.001	0.764
BS <--> BE	0.104***	0.009	11.121	***	p < 0.001	0.589
BS <--> BIP	0.036***	0.007	5.324	***	p < 0.001	0.249
BS <--> BP	0.107***	0.009	11.415	***	p < 0.001	0.720
BS <--> BSu	0.116***	0.009	12.047	***	p < 0.001	0.701
BE <--> BIP	0.057***	0.007	8.126	***	p < 0.001	0.456
BE <--> BP	0.097***	0.009	11.105	***	p < 0.001	0.763
BE <--> BG	0.020**	0.007	2.872	.004	p < 0.01	0.117
BE <--> BSu	0.094***	0.008	11.082	***	p < 0.001	0.670
BE <--> BA	0.052***	0.007	7.662	***	p < 0.001	0.369
BIP <--> BP	0.033***	0.006	5.381	***	p < 0.001	0.316
BIP <--> BG	0.019**	0.007	3.174	.002	p < 0.01	0.133
BIP <--> BSu	0.038***	0.006	6.405	***	p < 0.001	0.332
BP <--> BG	0.010	0.007	1.446	.148	nonsig.	0.067
BP <--> BSu	0.098***	0.009	11.380	***	p < 0.001	0.827
BP <--> BA	0.061***	0.007	8.839	***	p < 0.001	0.518
BG <--> BSu	0.010	0.006	1.549	.121	nonsig.	0.061
BG <--> BA	0.023***	0.006	3.793	***	p < 0.001	0.142
BSu <--> BA	0.053***	0.006	8.240	***	p < 0.001	0.405
BR <--> BS	0.059***	0.007	8.159	***	p < 0.001	0.361
BS <--> BA	0.040***	0.007	5.852	***	p < 0.001	0.244
BR <--> BIP	0.048***	0.006	8.025	***	p < 0.001	0.417
BIP <--> BA	0.097***	0.008	11.466	***	p < 0.001	0.831
BS <--> BG	0.036***	0.008	4.628	***	p < 0.001	0.176
BR <--> BA	0.053***	0.006	8.644	***	p < 0.001	0.405

Note: *** p<0.001, ** p<0.01, Standard error (S.E.), Critical ratios (C.R.), Covariance’s (Cov), Correlations (Cor), C.I. = 95%, nonsig. – not significantly different from zero at the 0.05 level (two-tailed).

Source: Author’s own calculations.

Views

946

Downloads

Visits refer to users (not robots) and are expressed cumulatively .

Article visits include visits to the article web page and visits to all full text files regarding this article.

To obtain a more realistic insight into the actual visits and reduce the possibility of manipulation (manually or automated) the following records were filtered out:

detected robot visits
consecutive access to the same page or full text file from the same IP addres

December, 2024
IV/2024

Petar Sorić, Mirjana Čižmešija
Assessing uncertainty and its effects on the public sector: Editors’ introduction to the thematic issue of Public Sector Economics

Marija Hruška, Mirjana Čižmešija
Traditional or social media: which capture employment better?

Tihana Škrinjarić
Macroprudential policy stance assessment: the case of Croatia

Marijana Andrijić
Uncertainty, populism and foreign direct investment: the state of play in economic research

Enkeleda Lulaj
Thriving amidst uncertainty: a financial blueprint for the public budget

Gent Sejko, Altin Tanku
Central bank balance sheet and inflation in a euroised small open economy: a cointegrated SVAR analysis