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The role of economic and political factors in budget forecasting errors: evidence from Turkey’s metropolitan municipalities for the period 2011-2022
Berat Kara* 
Article | Year: 2025 | Pages: 273 - 307 | Volume: 49 | Issue: 2 Received: June 14, 2024 | Accepted: September 25, 2024 | Published online: June 7, 2025
|
FULL ARTICLE
FIGURES & DATA
REFERENCES
CROSSMARK POLICY
METRICS
LICENCING
PDF
|
Variable
|
Explanation
|
Acronym
|
|
Revenue forecasting error
|
Annual budget revenue forecasting absolute error
rate
|
REV
|
|
Expenditure forecasting error
|
Annual budget expenditure forecasting absolute error
rate
|
EXP
|
|
Inflation
|
Annual local inflation rate
|
INF
|
|
Unemployment
|
Annual local unemployment rate
|
UNP
|
|
Per capita GDP
|
Annual local per capita GDP
|
GDP
|
|
Export-to-import ratio
|
Local Export-to-import ratio: (Export / Import)
|
EIR
|
|
Population growth rate
|
Annual local population growth rate
|
PGR
|
|
Mayor’s political party
|
“1” if mayor’s political party is right-wing, “0”
otherwise
|
RLP
|
|
Mayor’s re-candidacy
|
“1” if mayor re-candidate for the next election, “0”
otherwise
|
REL
|
|
Municipality election periods
|
“1” if municipality elections were held that year, “0”
otherwise
|
ELC
|
|
Budget surplus
|
“1” if budget surplus, “0” otherwise
|
BDM
|
|
Variable
|
Mean
|
Std. dev.
|
Min.
|
Max.
|
Obs.
|
|
EXP
|
overall
|
0.1887
|
0.1870
|
0.0001
|
1.1190
|
N
|
180
|
|
between
|
|
0.0693
|
0.0699
|
0.3068
|
n
|
15
|
|
within
|
|
0.1745
|
-0.0971
|
1.0452
|
T
|
12
|
|
REV
|
overall
|
0.1707
|
0.1325
|
0.0021
|
0.7005
|
N
|
180
|
|
between
|
|
0.0673
|
0.0729
|
0.3074
|
n
|
15
|
|
within
|
|
0.1154
|
-0.0332
|
0.6644
|
T
|
12
|
|
BDM
|
overall
|
0.3555
|
0.4800
|
0
|
1
|
N
|
180
|
|
between
|
|
0.2076
|
0
|
0.75
|
n
|
15
|
|
within
|
|
0.4358
|
-0.3944
|
1.1888
|
T
|
12
|
|
INF
|
overall
|
18.4078
|
18.4690
|
5.43
|
73.3
|
N
|
180
|
|
between
|
|
0.4282
|
17.6466
|
19
|
n
|
15
|
|
within
|
|
18.4643
|
4.8787
|
73.1670
|
T
|
12
|
|
UNP
|
overall
|
10.3150
|
3.3335
|
3.6
|
23.4
|
N
|
180
|
|
between
|
|
2.8017
|
6.575
|
15.6083
|
n
|
15
|
|
within
|
|
1.9351
|
1.6066
|
18.1066
|
T
|
12
|
|
GDP
|
overall
|
10,464.98
|
4,376.786
|
3,043
|
20,883
|
N
|
180
|
|
between
|
|
4,301.055
|
3,901.333
|
17,653.08
|
n
|
15
|
|
within
|
|
1,339.401
|
5,983.394
|
13,952.64
|
T
|
12
|
|
EIR
|
overall
|
1.4758
|
1.6461
|
0.3546
|
10.4561
|
N
|
180
|
|
between
|
|
1.1814
|
0.6403
|
5.3017
|
n
|
15
|
|
within
|
|
1.1831
|
-2.6785
|
8.0300
|
T
|
12
|
|
PGR
|
overall
|
14.7243
|
11.1765
|
-23.24
|
39.09
|
N
|
180
|
|
between
|
|
8.6468
|
-2.1208
|
29.8908
|
n
|
15
|
|
within
|
|
7.3986
|
-13.464
|
48.866
|
T
|
12
|
|
RLP
|
overall
|
0.7444
|
0.4373
|
0
|
1
|
N
|
180
|
|
between
|
|
0.3542
|
0
|
1
|
n
|
15
|
|
within
|
|
0.2712
|
-0.0055
|
1.3277
|
T
|
12
|
|
REL
|
overall
|
0.5888
|
0.4934
|
0
|
1
|
N
|
180
|
|
between
|
|
0.3556
|
0
|
1
|
n
|
15
|
|
within
|
|
0.3532
|
-0.3277
|
1.3388
|
T
|
12
|
|
ELC
|
overall
|
0.1666
|
0.3737
|
0
|
1
|
N
|
180
|
|
between
|
|
0
|
0.1666
|
0.1666
|
n
|
15
|
|
within
|
|
0.37375
|
0
|
1
|
T
|
12
|
Note: For all variables: N=180, n=15, T=12. Source: Author’s calculations.
|
REV
|
EXP
|
BDM
|
INF
|
UNP
|
GDP
|
EIR
|
PGR
|
RLP
|
REL
|
ELC
|
|
REV
|
1
|
|
|
|
|
|
|
|
|
|
|
|
EXP
|
0.4953
|
1
|
|
|
|
|
|
|
|
|
|
|
BDM
|
-0.1505
|
0.0248
|
1
|
|
|
|
|
|
|
|
|
|
INF
|
0.1057
|
0.4426
|
0.0511
|
1
|
|
|
|
|
|
|
|
|
UNP
|
-0.2229
|
-0.0935
|
0.232
|
0.0323
|
1
|
|
|
|
|
|
|
|
GDP
|
0.0362
|
0.3941
|
0.1327
|
0.8105
|
0.0975
|
1
|
|
|
|
|
|
|
EIR
|
0.0329
|
0.0457
|
-0.1073
|
0.0038
|
-0.1091
|
-0.1018
|
1
|
|
|
|
|
|
PGR
|
0.0476
|
-0.0175
|
0.2151
|
-0.1706
|
0.134
|
-0.0091
|
-0.133
|
1
|
|
|
|
|
RLP
|
-0.0326
|
-0.1492
|
-0.0171
|
-0.1224
|
-0.1905
|
-0.3044
|
-0.1116
|
-0.1572
|
1
|
|
|
|
REL
|
0.0497
|
-0.0384
|
-0.0398
|
-0.0607
|
-0.0893
|
0.0539
|
-0.187
|
0.1035
|
-0.1789
|
1
|
|
|
ELC
|
0.1203
|
0.1016
|
0.0415
|
-0.2056
|
0.1235
|
-0.1068
|
0.0026
|
0.0228
|
-0.0114
|
-0.0808
|
1
|
|
Municipality
|
Category
|
MPE
|
MAPE
|
NoNEP
|
NoPEP
|
|
Istanbul
|
Revenue
|
11.17
|
14.42
|
4
|
8
|
|
Expenditure
|
5.78
|
13.21
|
6
|
6
|
|
Ankara
|
Revenue
|
-4.66
|
12.62
|
8
|
4
|
|
Expenditure
|
-1.97
|
16.88
|
11
|
1
|
|
Izmir
|
Revenue
|
-1.62
|
7.29
|
8
|
4
|
|
Expenditure
|
-7.92
|
11.92
|
11
|
1
|
|
Kocaeli
|
Revenue
|
-3.09
|
13.26
|
7
|
5
|
|
Expenditure
|
-6.72
|
20.91
|
9
|
3
|
|
Bursa
|
Revenue
|
-4.10
|
10.82
|
9
|
3
|
|
Expenditure
|
3.34
|
15.68
|
7
|
5
|
|
Antalya
|
Revenue
|
-12.89
|
30.74
|
9
|
3
|
|
Expenditure
|
-20.06
|
24.61
|
10
|
2
|
|
Konya
|
Revenue
|
-24.40
|
26.35
|
11
|
1
|
|
Expenditure
|
-12.54
|
29.20
|
11
|
1
|
|
Adana
|
Revenue
|
-21.00
|
23.72
|
11
|
1
|
|
Expenditure
|
-18.51
|
18.51
|
12
|
0
|
|
Tekirdag
|
Revenue
|
-7.030
|
20.03
|
7
|
5
|
|
Expenditure
|
-24.99
|
30.68
|
11
|
1
|
|
Gaziantep
|
Revenue
|
-9.11
|
14.14
|
9
|
3
|
|
Expenditure
|
-10.38
|
10.38
|
12
|
0
|
|
Kayseri
|
Revenue
|
2.30
|
20.68
|
8
|
4
|
|
Expenditure
|
-4.95
|
26.25
|
10
|
2
|
|
Sanliurfa
|
Revenue
|
-7.79
|
11.99
|
9
|
3
|
|
Expenditure
|
-15.87
|
15.87
|
12
|
0
|
|
Samsun
|
Revenue
|
-4.95
|
9.74
|
9
|
3
|
|
Expenditure
|
1.17
|
6.99
|
7
|
5
|
|
Ordu
|
Revenue
|
-9.86
|
17.60
|
11
|
1
|
|
Expenditure
|
3.23
|
18.91
|
7
|
5
|
|
Erzurum
|
Revenue
|
-20.52
|
22.64
|
10
|
2
|
|
Expenditure
|
-0.75
|
23.11
|
10
|
2
|
Note: NoNEP: number of negative error periods; NoPEP: number of positive error periods. Source: Author’s calculations based on the Ministry of Treasury and Finance data.
|
Coef.
|
Std. err
|
z
|
P > z
|
[95% Conf. Interval]
|
|
BDM
|
-0.0405
|
0.0198
|
-2.04
|
0.042**
|
-0.0794
|
-0.0015
|
|
INF
|
0.0021
|
0.0010
|
2.08
|
0.037**
|
0.0001
|
0.0040
|
|
UNP
|
-0.0100
|
0.0038
|
-2.62
|
0.009***
|
-0.0176
|
-0.0025
|
|
GDP
|
-0.0004
|
0.0004
|
-0.99
|
0.324
|
-0.0012
|
0.0004
|
|
EIR
|
0.0014
|
0.0068
|
0.21
|
0.831
|
-0.0119
|
0.0148
|
|
PGR
|
0.0018
|
0.0010
|
1.72
|
0.085*
|
-0.0002
|
0.0039
|
|
RLP
|
-0.0265
|
0.0294
|
-0.90
|
0.367
|
-0.0842
|
0.0311
|
|
REL
|
-0.0265
|
0.0228
|
-1.16
|
0.245
|
-0.0713
|
0.0182
|
|
ELC
|
0.0671
|
0.0241
|
2.78
|
0.006***
|
0.0197
|
0.1145
|
|
_cons
|
0.2659
|
0.0614
|
4.32
|
0.000
|
0.1453
|
0.3864
|
|
R-sq
|
within
|
0.1345
|
|
Obs. per group
|
min
|
12
|
|
between
|
0.0939
|
|
avg
|
12
|
|
overall
|
0.1179
|
|
max
|
12
|
|
Number of obs
|
180
|
|
Number of groups
|
15
|
|
Wald chi2(9)
|
25.14
|
|
Prob > chi2
|
0.0028
|
|
sigma_u
|
0.0594
|
|
sigma_e
|
0.1147
|
|
rho
|
0.2114
|
|
Coef.
|
Std. err
|
z
|
P > z
|
[95% Conf. Interval]
|
|
BDM
|
-0.0023
|
0.0269
|
-0.09
|
0.929
|
-0.0551
|
0.0503
|
|
INF
|
0.0046
|
0.0012
|
3.62
|
0.000***
|
0.0021
|
0.0071
|
|
UNP
|
-0.0089
|
0.0043
|
-2.05
|
0.040**
|
-0.0173
|
-0.0004
|
|
GDP
|
0.0001
|
0.0005
|
0.22
|
0.830
|
-0.0008
|
0.0011
|
|
EIR
|
0.0017
|
0.0084
|
0.20
|
0.839
|
-0.0147
|
0.0181
|
|
PGR
|
0.0008
|
0.0012
|
0.70
|
0.482
|
-0.0015
|
0.0033
|
|
RLP
|
-0.0489
|
0.0346
|
-1.41
|
0.158
|
-0.1167
|
0.0189
|
|
REL
|
-0.0221
|
0.0280
|
-0.79
|
0.429
|
-0.0770
|
0.0327
|
|
ELC
|
0.1060
|
0.0334
|
3.17
|
0.002***
|
0.0405
|
0.1715
|
|
_cons
|
0.2063
|
0.0717
|
2.88
|
0.004
|
0.0657
|
0.3469
|
|
R-sq
|
within
|
0.2848
|
|
Obs. per group
|
min
|
12
|
|
between
|
0.1755
|
|
avg
|
12
|
|
overall
|
0.2706
|
|
max
|
12
|
|
Number of obs.
|
180
|
|
Number of groups
|
15
|
|
Wald chi2(9)
|
64.53
|
|
Prob > chi2
|
0.0000
|
|
sigma_u
|
0.0332
|
|
sigma_e
|
0.1569
|
|
rho
|
0.0429
|
|
No
|
Hypothesis
|
Status
|
|
Revenue
|
Expenditure
|
|
1
|
Budgets
are forecasted with a high degree of inaccuracy.
|
Accept
|
Accept
|
|
2
|
Forecasting
errors are mostly in the negative direction.
|
Accept
|
Accept
|
|
3
|
A
budget surplus affects budget forecasting errors.
|
Accept
|
Reject
|
|
4
|
An
increase in inflation affects the forecasting error in a positive direction.
|
Accept
|
Accept
|
|
5
|
An
increase in the unemployment rate affects revenue forecasting errors negatively
and expenditure forecasting errors positively.
|
Accept
|
Accept
|
|
6
|
An
increase in per capita GDP increases errors in a positive direction.
|
Reject
|
Reject
|
|
7
|
As
the export-to-import ratio increases, forecasting errors also increase in a
positive direction.
|
Reject
|
Reject
|
|
8
|
As
the population growth rate increases, forecasting errors also increase in a
positive direction.
|
Accept
|
Reject
|
|
9
|
Mayor’s
political party affiliations affect the forecasting error.
|
Reject
|
Reject
|
|
10
|
Mayor’s
candidacy in the next elections affects revenue forecasting errors negatively
and expenditure forecasting errors positively.
|
Reject
|
Reject
|
|
11
|
Election
periods affect revenue forecasting errors negatively and expenditure
forecasting errors positively.
|
Accept
|
Accept
|
|
(b)
|
(B)
|
(b-B)
|
sqrt(diag(V_b-V_B))
|
|
Fixed
|
Random
|
Difference
|
S.E.
|
|
BDM
|
-0.0426
|
-0.0405
|
-0.0021
|
0.0046
|
|
INF
|
0.0021
|
0.0021
|
0.0000
|
0.0004
|
|
UNP
|
-0.0107
|
-0.0100
|
-0.0006
|
0.0028
|
|
GDP
|
-0.0004
|
-0.0004
|
-0.0000
|
0.0002
|
|
EIR
|
0.0029
|
0.0014
|
0.0015
|
0.0034
|
|
PGR
|
0.0018
|
0.0018
|
-0.0000
|
0.0006
|
|
RLP
|
-0.0316
|
-0.0265
|
-0.0051
|
0.0177
|
|
REL
|
-0.0484
|
-0.0265
|
-0.0218
|
0.0112
|
|
ELC
|
0.0652
|
0.0671
|
-0.0018
|
0.0037
|
|
chi2(8)
|
7.8000
|
|
Prob>chi2
|
0.4531
|
|
(b)
|
(B)
|
(b-B)
|
sqrt(diag(V_b-V_B))
|
|
Fixed
|
Random
|
Difference
|
S.E.
|
|
BDM
|
-0.0184
|
-0.0023
|
-0.0161
|
0.0091
|
|
INF
|
0.0050
|
0.0046
|
0.0003
|
0.0008
|
|
UNP
|
-0.0062
|
-0.0089
|
0.0026
|
0.0050
|
|
GDP
|
-0.0001
|
0.0001
|
-0.0002
|
0.0004
|
|
EIR
|
-0.0010
|
0.0017
|
-0.0027
|
0.0066
|
|
PGR
|
-0.0002
|
0.0008
|
-0.0011
|
0.0011
|
|
RLP
|
-0.0625
|
-0.0489
|
-0.0135
|
0.0330
|
|
REL
|
-0.0621
|
-0.0221
|
-0.0400
|
0.0218
|
|
ELC
|
0.1001
|
0.1060
|
-0.0058
|
0.0067
|
|
chi2(8)
|
13.5000
|
|
Prob>chi2
|
0.0957
|
|
Models
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|
Revenue
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Expenditure
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Var
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sd = sqrt(Var)
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|
Var
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sd = sqrt(Var)
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|
REV
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0.0175
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0.1325
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EXP
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0.0349
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0.1870
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e
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0.0131
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0.1147
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e
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0.0246
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0.1569
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|
u
|
0.0035
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0.0594
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u
|
0.0011
|
0.0332
|
|
chibar2(01)
|
18.8800
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chibar2(01)
|
3.2800
|
|
Prob > chibar2
|
0.0000
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Prob > chibar2
|
0.0352
|
|
Models
|
|
Revenue
|
Expenditure
|
|
F (1, 14)
|
0.463
|
2.032
|
|
Prob > F
|
0.507
|
0.176
|
|
Models
|
|
Revenue
|
Expenditure
|
|
Pr
|
0.177
|
0.112
|
|
Average absolute value of the off-diagonal elements
|
0.306
|
0.418
|
Table 1Explanation and acronyms of variables DISPLAY Table
Table 2Descriptive statistics DISPLAY Table
Table 3Correlation matrix DISPLAY Table
Table 4MPE, MAPE, NoNEP and NoPEP DISPLAY Table
Table 5Panel regression results for revenue model DISPLAY Table
Table 6Panel regression results for expenditure model DISPLAY Table
Table 7Results of hypothesis DISPLAY Table
Table A1Hausman test results for revenue model DISPLAY Table
Table A2Hausman test results for expenditure model DISPLAY Table
Table A3BP-LM test results DISPLAY Table
Table A4The Lagrange Multiplier (Wooldridge Autocorrelation) test results DISPLAY Table
Table A5Pesaran CD test results DISPLAY Table
* The author would like to thank two anonymous reviewers for their valuable comments.
1 In Turkey, there are 30 metropolitan municipalities. Thirteen of these were established in 2012, and one was established in 2013. With the regulatory changes made to the Turkish financial system between 2003 and 2006, it was decided that metropolitan municipalities would publish their budget-related documents annually to the public. However, due to the time required to develop the necessary infrastructure and to train personnel on the relevant regulations, compliance with financial legislation by metropolitan municipalities took time. At this point, only a few metropolitan municipalities published information for a few years prior to 2011. The vast majority of the metropolitan municipalities covered in the study only began to publish information from 2011 onward. Indeed, the information for other metropolitan municipalities not included in the study for the years 2011 and later remains inaccessible to the public and researchers. It is believed that the reason the fifteen metropolitan municipalities covered in the study were able to achieve earlier compliance is that they are the fifteen largest metropolitan municipalities economically in Turkey. Finally, considering that the final accounts for the year 2023 have not yet been released, it was deemed appropriate to prepare the study for the period 2011-2023 for these fifteen metropolitan municipalities.
2 Since metropolitan municipalities forecast a budget balance of 0 (equal revenue and expenditure) for certain periods, the balance error could not be calculated during these periods. Therefore, a model could not be established for the budget balance forecasting errors.
3 In 2018, Turkey faced a severe currency and debt crisis that significantly impacted its economy. The crisis began with a dramatic fall in the value of the Turkish lira, which plunged against major currencies such as the US dollar and the euro. This depreciation was driven by a combination of factors including high inflation, a large current account deficit, and rising political tensions, particularly with the United States. The Turkish government’s response to the crisis included raising interest rates and seeking financial support from international institutions, but the measures had limited success in stabilizing the currency. The crisis exacerbated Turkey’s already challenging economic conditions, leading to increased borrowing costs, higher inflation rates, and a strain on both public and private sector finances. This period marked a significant economic downturn, with widespread repercussions for the Turkish economy and its financial stability.
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June, 2025
II/2025
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