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Budget transparency and internal revenue mobilisation at sub-national government level: evidence from Nigeria
Mohammed Aminu Yaru* 
Article | Year: 2022 | Pages: 505 - 531 | Volume: 46 | Issue: 4 Received: : February 15, 2022 | Accepted: May 30, 2022 | Published online: December 7, 2022
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FULL ARTICLE
FIGURES & DATA
REFERENCES
CROSSMARK POLICY
METRICS
LICENCING
PDF
Source: Compiled from CIRDDOC, 2015, 2018 and 2020, and BudgIT database for various years.
|
Variables
|
Measurement
|
Impact on IGR
(a-priori expectation)
|
Sources
|
|
Internally Generated Revenue (IGR)
|
Share of IGR in total Revenue (SIGR)
|
|
Publications of NBS (Annual Abstract of
Statistics) and Budget publications for various years
|
|
Economic Factors
|
Population Density (Number of People per
Square Km) (PDS)
|
Positive (+)
|
Publications of NBS
|
|
Socio-economic Factors
|
Poverty Rate (POV)
|
Negative (-)
|
Publications of NBS (Annual Abstract of
Statistics for
various years)
|
|
Unemployment Rate (UR)
|
Negative (-)
|
|
Political Factors (POF)
|
Political Party Affinity with centre ((PAC)
= 0 if the Governor belongs to the ruling party at the federal level,
otherwise =1)
|
Negative (-)
|
INEC, Nigeria
|
|
TERM (= 0, if the Governor is serving
his/her
first term in office, 1 = if serving second term in office)
|
Positive (+)
|
|
Number of years in Office (YEARS)
|
Positive (+)
|
Compiled by author from INEC, Nigeria
|
|
Fiscal Institutions (BT)
|
Budget Transparency Index (BTI)
|
Positive (+)
|
CIRDDOC, Nigeria
|
|
Public Availability of Key Budget
Documents (BAI)
|
Positive (+)
|
|
Public Participation Index (PPI)
|
Positive (+)
|
|
Public Access to Public Procurement
Information (PPRI)
|
Positive (+)
|
|
Corruption
|
Control of Corruption at national level
(COR)
|
Positive (+)
|
World Governance Indicators
|
Note: NBS = National Bureau of Statistics; INEC = Independent National Electoral Commission. Source: Author’s compilation (2021).
|
Variable
|
Obs.
|
Mean
|
Std. dev.
|
Min
|
Max
|
|
SIGR
|
108
|
23.41
|
14.57
|
5.45
|
78.33
|
|
PDS
|
108
|
443.00
|
613.61
|
52.93
|
3885.70
|
|
POV
|
71
|
53.46
|
23.72
|
4.50
|
88.50
|
|
UR
|
108
|
31.88
|
15.10
|
8.37
|
64.75
|
|
PAC
|
108
|
0.39
|
0.49
|
0
|
1
|
|
BTI
|
108
|
32.41
|
18.18
|
7
|
90
|
|
BAI
|
108
|
36.00
|
21.96
|
5
|
91
|
|
PPI
|
108
|
22.64
|
20.94
|
0
|
100
|
|
PPRI
|
108
|
33.37
|
22.11
|
0
|
100
|
|
TERM
|
108
|
0.51
|
0.50
|
0
|
1
|
|
YEARS
|
108
|
3.86
|
2.24
|
1
|
10
|
|
COR
|
108
|
13.14
|
0.45
|
12.50
|
13.46
|
Source: Author’s compilation (2021).
|
SIGR
|
PDS
|
POV
|
UR
|
PAC
|
BTI
|
BAI
|
PPI
|
PPRI
|
TERM
|
YEARS
|
COR
|
|
SIGR
|
1
|
|
|
|
|
|
|
|
|
|
|
|
|
PDS
|
0.6413
|
1
|
|
|
|
|
|
|
|
|
|
|
|
POV
|
-0.5486
|
-0.4022
|
1
|
|
|
|
|
|
|
|
|
|
|
UR
|
-0.1121
|
-0.0242
|
-0.1872
|
1
|
|
|
|
|
|
|
|
|
|
PAC
|
0.0254
|
0.1022
|
-0.1192
|
0.0405
|
1
|
|
|
|
|
|
|
|
|
BTI
|
0.2250
|
0.1588
|
-0.2235
|
0.1160
|
-0.0652
|
1
|
|
|
|
|
|
|
|
BAI
|
0.1544
|
0.0720
|
-0.2478
|
0.1394
|
-0.1095
|
0.9262
|
1
|
|
|
|
|
|
|
PPI
|
0.2004
|
0.2092
|
-0.0666
|
-0.0750
|
-0.0050
|
0.742
|
0.5440
|
1
|
|
|
|
|
|
PPRI
|
0.2301
|
0.1929
|
-0.0839
|
0.1071
|
0.0185
|
0.6962
|
0.4393
|
0.4875
|
1
|
|
|
|
|
TERM
|
-0.0112
|
-0.0158
|
0.1469
|
-0.2382
|
-0.1288
|
0.0845
|
0.0424
|
0.1621
|
0.0696
|
1
|
|
|
|
YEARS
|
-0.0302
|
-0.0666
|
-0.2344
|
0.3799
|
0.1860
|
0.0613
|
0.1549
|
-0.1400
|
-0.0563
|
0.1714
|
1
|
|
|
COR
|
0.1709
|
0.0417
|
-0.4607
|
0.6722
|
0.0403
|
0.2461
|
0.3217
|
-0.0983
|
0.1199
|
-0.2226
|
0.4757
|
1
|
|
Variable
|
Obs
|
Mean
|
Std. Dev.
|
Min
|
Max
|
|
SIGR
|
51
|
30.36
|
16.91
|
8.18
|
78.33
|
|
PDS
|
51
|
737.01
|
785.35
|
172.39
|
3885.7
|
|
POV
|
34
|
38.81
|
21.55
|
4.5
|
82.9
|
|
UR
|
51
|
30.17
|
15.13
|
9.38
|
57.96
|
|
PAC
|
51
|
0.61
|
0.49
|
0
|
1
|
|
BTI
|
51
|
34.10
|
18.09
|
7
|
79
|
|
BAI
|
51
|
36.24
|
23.13
|
5
|
86
|
|
PPI
|
51
|
25.80
|
21.27
|
0
|
78
|
|
PPRI
|
51
|
36.86
|
21.47
|
2
|
100
|
|
TERM
|
51
|
0.53
|
0.50
|
0
|
1
|
|
YEARS
|
51
|
3.92
|
2.18
|
1
|
8
|
|
COR
|
51
|
13.14
|
0.46
|
12.5
|
13.46
|
|
Variable
|
Obs
|
Mean
|
Std. Dev.
|
Min
|
Max
|
|
SIGR
|
57
|
17.20
|
8.16
|
5.45
|
44.57
|
|
PDS
|
57
|
179.94
|
139.82
|
52.93
|
735.81
|
|
POV
|
37
|
66.92
|
16.72
|
20.35
|
88.5
|
|
UR
|
57
|
33.41
|
15.03
|
8.37
|
64.75
|
|
PAC
|
57
|
0.19
|
0.40
|
0
|
1
|
|
BTI
|
57
|
30.89
|
18.28
|
7
|
90
|
|
BAI
|
57
|
35.79
|
21.06
|
8
|
91
|
|
PPI
|
57
|
19.79
|
20.40
|
0
|
100
|
|
PPRI
|
57
|
30.25
|
22.38
|
0
|
93
|
|
TERM
|
57
|
0.49
|
0.50
|
0
|
1
|
|
YEARS
|
57
|
3.81
|
2.31
|
1
|
10
|
|
COR
|
57
|
13.14
|
0.46
|
12.5
|
13.46
|
|
Explanatory Variables
|
Estimates Based on 2020
Data set
|
Estimates Based on 2018
Data set
|
Estimates Based on 2015
Data set
|
|
Model IA
|
Model IIA
|
Model IB
|
Model IIB
|
Model IC
|
Model IIC
|
|
Dependent Variable =
SIGR
|
|
Constant Intercept
|
34.6379***
(7.3127)
|
37.5208***
(7.3088)
|
32.0305***
(9.5070)
|
33.0906***
(10.1008)
|
28.1200***
(8.6623)
|
28.3605***
(9.1120)
|
|
PDS
|
0.0115***
(0.0029)
|
0.0109***
(0.0029)
|
0.0127 ***
(0.0034)
|
0.0122***
(0.0036)
|
0.0161***
(0.0034)
|
0.0153***
(0.0038)
|
|
POV
|
-0.1722 **
(0 .0714)
|
-0.1960**
(0.0703)
|
|
|
|
|
|
UR
|
-0.2674 *
(0.1422)
|
-0.3058**
(0.1407)
|
-0.3740*
(0.1937)
|
-0.3873*
(0.2006)
|
-0.7740*
(0.3954)
|
-0.7755*
(0.4120)
|
|
PAC
|
1.0320
(3.9323)
|
-1.2514
(4.0456)
|
-4.3758
(4.0154)
|
-4.7116
(4.1953)
|
-1.5324
(3.8702)
|
-1.6115
(4.0812)
|
|
BTI
|
0.0765
(0.1282)
|
|
0.1185
(0.1076)
|
|
-0.0231
(0.1057)
|
|
|
BAI
|
|
-0.0785
(0.1157)
|
|
0.0162
(0.1175)
|
|
-0.0660
(0.1262)
|
|
PPI
|
|
-0.0115
(0.1345)
|
|
0.0620
(0.1229)
|
|
0.0494
(0.1177)
|
|
PPRI
|
|
0 .2064*
(0.1017)
|
|
0.0542
(0.1035)
|
|
-0.0003
(0.0978)
|
|
|
No. of Obs. = 35
Df (5,29)
R2 = 0.5624
Adj. R2 = 0.4869
F(5, 29) = 7.45
Prob > F = 0.0001
|
No. of Obs. = 35
Df (7,27)
R2 = 0.6201
Adj. R2 = 0.5216
F(7, 27) = 6.29
Prob > F = 0.0002
|
No. of Obs. = 36
Df (4,31)
R2 = 0.4820
Adj. R2 = 0.4152
F(4, 31) = 7.21
Prob > F = 0.0003
|
No. of Obs. = 36
Df (6,29)
R2 = 0.4877
Adj. R2 = 0.3817
F(6, 29) = 4.60
Prob > F = 0.0021
|
No. of Obs. = 36
Df (4,31)
R2 = 0.5503
Adj. R2 = 0.4943
F(4, 31) = 9.48
Prob > F= 0.0000
|
No. of Obs. = 36
Df (6,29)
R2 = 0.5546
Adj. R2 = 0.4624
F(6, 29) = 6.02
Prob > F = 0.0004
|
Note: Standard error in parentheses; p-values in brackets; *** p<0.01, ** p<0.05, * p<0.1. Source: Author’s computation (2021).
|
Sub-Sample of 17 Southern
States
|
Sub-Sample of 19 Northern
States
|
|
Abia
|
Adamawa
|
|
Akwa-Ibom
|
Bauchi
|
|
Anambra
|
Benue
|
|
Bayelsa
|
Borno
|
|
Cross River
|
Gombe
|
|
Delta
|
Jigawa
|
|
Ebonyi
|
Kaduna
|
|
Edo
|
Kano
|
|
Ekiti
|
Katsina
|
|
Enugu
|
Kebbi
|
|
Imo
|
Kogi
|
|
Lagos
|
Kwara
|
|
Ogun
|
Nasarawa
|
|
Ondo
|
Niger
|
|
Osun
|
Plateau
|
|
Oyo
|
Sokoto
|
|
Rivers
|
Taraba
|
|
|
Yobe
|
|
|
Zamfara
|
Abdu, M., Jibril, A. and Muhammad, T., 2020. Analysis of tax compliance in Sub-Saharan Africa: Evidence from firm-level study. Econometric Research in Finance, 5(2), pp. 119-142 [ CrossRef]
Abebe, G. and Fikre, S., 2020. Econometric analysis of the effects of corruption on government tax revenue: Evidence from panel data in developed and developing countries. European Business and Management, 6(2), pp. 28-35 [ CrossRef]
Ade, M., Rossouw, J. and Gwatidzo, T., 2018. Determinants of tax revenue performance in the Southern African Development Community. ERSA working paper, No. 762.
Ajaz, T. and Ahmad, E., 2010. The effect of corruption and governance on tax revenues. The Pakistan Development Review, 49(4), pp. 405-417 [ CrossRef]
Amusa, K., Monkam, N. and Viegi, N., 2020. Can foreign aid enhance domestic resource mobilisation in Nigeria? Journal of Contemporary African Studies, 38(2), pp. 294-309 [ CrossRef]
Andrejovskà, A. and Pulikovà, V., 2018. Tax revenues in the context of economic determinants. Motenegrin Journal of Economics, 14(1), pp. 133-144 [ CrossRef]
Bastida, F. and Benito, B., 2007. Central government budget practices and transparency: an international comparison. Public Administration, 85(3), pp. 667-716 [ CrossRef]
Bisogno, M. and Cuadrado-Ballesteros, B., 2021. Budget transparency and governance quality: a cross-country analysis. Public Management Review, 24(10), pp. 1610-1631 [ CrossRef]
Caamaño-Alegre, J. [et al.], 2013. Budget transparency in local governments: An empirical analysis. Local Government Studies, 39(2), pp. 182-207 [ CrossRef]
Carlitz, R., 2013. Improving transparency and accountability in budget process: An assessment of recent initiatives. Development Policy Review, 31(S1), pp. s49-s67 [ CrossRef]
Eiya, O. and America, D., 2018. Macroeconomic variables and tax revenue volatility in Nigeria. Journal of Taxation and Economic Development, 17(2), pp. 68-80.
Estrada, L. and Bastida, F., 2020. Effective transparency and institutional trust in Honduran municipal governments. Administration and Society, 52(6), pp. 890-926 [ CrossRef]
Ghura, D., 1998. Tax revenue in sub-Sahara Africa: Effects of economic policies and corruption. IMF Working Paper, WP/98/135 [ CrossRef]
Hausman, J. A., 1978. Specification tests in econometrics. Econometrica, 46(6), pp. 1251-1271 [ CrossRef]
Hu, Q. [et al.], 2020. Empirical study on the evaluation model of public satisfaction with local government budget transparency: A case from China. SAGE Open, 10(2) [ CrossRef]
Iniodu, P., 1999. Fiscal dependence of local governments in Nigeria's fiscal system: The case of Akwa-Ibom State. In B. E. Aigbohan, ed. 1999. Fiscal Federalism and Nigeria's Economic Development: Selected papers for 1999 Annual Conference. Ibadan: The Nigerian Economic Society, pp. 289-313.
Jahnke, B. and Weisser, R. A., 2019. How does petty corruption affect tax morale in Sub-Saharan Africa? European Journal of Political Economy, 60 (December, 2019) [ CrossRef]
Karran, T., 1985. The determinants of taxation in Britain: An empirical test. Journal of Public Policy, 5(3), pp. 365-386 [ CrossRef]
Ohiokha, F. I. and Ohiokha, G., 2018. Determinants of taxable capacity in Nigeria. Journal of Taxation and Economic Development, 17(2), 13-25.
Ortega, D., Ronconi, L. and Sanguinet, P., 2016. Reciprocity and willingness to pay taxes: Evidence from a survey experiment in Latin America. Economía, 16(2), pp. 55-87.
Sun, S. and Andrews, R., 2020. The determinants of fiscal transparency in Chinese city-level governments. Local Government Studies, 46(1), pp. 44-67 [ CrossRef]
Yaru, M. A. [et al.], 2014. Budgetary institutions and fiscal discipline in Nigeria: Empirical evidence from Kwara and Kaduna States. In: S. Tella, A. G. Garba, and M. A. Adebiyi eds. Institutions, Institutional Reforms and Economic Development: Analytical and Policy Perspectives: Selected Papers from the 2013 Annual Conference. Ibadan: The Nigeria Economic Society, pp. 163-180.
Yaru, M. A. [et al.], 2018. Public expenditure and inclusive growth in Nigeria. Ilorin Journal of Economic Policy, 5(1), pp. 46-61.
Yaru, M. A., 2015. Buhari's victory in the 2015 presidential election: The role of socio-economic factors. The Nigerian Journal of Economic and Social Studies, 57(3), pp. 499-522.
Yaru, M. A., 2020. Determinants of tax collections by the local governments: Empirical evidence from Kwara State. Journal of Taxation and Economic Development, 19(2), pp. 19-33.
Yaru, M. A., 2022. Addressing fiscal challenges through budget transparency: The case of Kwara State. In: A. M. Mainoma [et al.], eds. Taxation for Economic Development. Lagos. OGE Business School, Lagos, pp. 193-207.
Zhang, J., 2017. The illusion and the reality of Chinese budget reforms: Does budgeting influence corruption perception? Chinese Public Administration Review, 8(1), pp. 1-22 [ CrossRef]
Zvereva, T. [et al.], 2021. The impact of budget transparency on tax compliance. E3S Web of Conferences 284, 07029 (2021) TPACEE-2021 [ CrossRef]
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December, 2022
IV/2022 |
