Startseite Modelling Factors Affecting Internally Generated Funds of the Tamale Metropolitan Assembly of Ghana Using Multivariate Analysis Techniques
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Modelling Factors Affecting Internally Generated Funds of the Tamale Metropolitan Assembly of Ghana Using Multivariate Analysis Techniques

  • Saaka Takora , Edward Akurugu ORCID logo EMAIL logo , Salifu Katara , Gideon Mensah Engmann und Joseph Asucam
Veröffentlicht/Copyright: 1. Dezember 2023
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Abstract

The study was carried out to model factors that affect the Internally Generated Funds of the Tamale Metropolitan Assembly using Factor and Confirmatory Factor analysis. The study involved 403 categories of respondents using probability and non-probability sampling techniques. The results from the Factor analysis confirmed a four-factor structure model to accurately represent the observed data without substantial loss of information. The Factor analysis revealed that the four factors affecting Internally Generated Funds in the metropolis are a proper database on revenue mobilisation, effectiveness and efficiency of revenue collectors, revenue monitoring and utilisation, and specific tools deployed for revenue mobilisation. Further examination of the Factor analysis using Confirmatory Factor analysis revealed that some modifications to the four-factor structure model in terms of correlating the measurement errors provide a good fit to the observed data as compared to a four-factor structure model with uncorrelated measurement errors. Findings from the Sattora-Bentler parameter estimates conclude that all paths of the selected model were significant, thus confirming a relationship between all latent variables and each measured variable of Internally Generated Funds. The findings from the study suggest further research on the use of the Structural Equation Model in an analogous area to be instituted to measure and analyse relationships between measured and latent variables.


Corresponding author: Edward Akurugu, Department of Statistics, Faculty of Mathematical Sciences, University for Development Studies, Tamale, Ghana, Email:

Appendix

See Tables 7 and 8.

Table 7:

Unrotated factor loadings (pattern matrix).

Labels Measured variables Factor 1 Factor 2 Factor 3 Factor 4
V1 Number of revenue collectors 0.4190 0.4941 0.3860 0.0449
V2 Level of education of collectors 0.6587 0.4085 0.0797 0.2179
V3 Effective supervision of revenue collectors 0.6569 0.3219 −0.0192 0.3550
V4 Qualified revenue collectors 0.6647 0.2729 0.0663 0.3512
V5 Adequate logistics for collectors 0.6874 0.0395 0.1311 0.3041
V6 Use of commission collectors 0.3144 0.2511 0.5795 0.2414
V7 In-service training for collectors 0.6885 0.1788 −0.0265 0.1151
V8 Use of mobile task force 0.3753 −0.0179 0.6615 0.3396
V9 Boundary disputes 0.5437 −0.3728 0.3215 0.1979
V10 Revenue management plans 0.5939 −0.4775 −0.0027 0.0764
V11 Address maps 0.5916 −0.4963 0.0426 0.1118
V12 Availability of spatial data 0.6875 −0.4721 −0.0032 0.1570
V13 Availability of GIS equipment 0.6376 −0.4686 0.0140 0.2093
V14 Quarterly review of IGF performance 0.6637 0.1964 −0.2835 0.1251
V15 Sensitising the general public about the IGF 0.6690 0.3129 −0.3366 0.1946
V16 Monitoring of revenue 0.7295 0.0198 −0.3202 0.3216
V17 Stakeholders’ participation 0.6840 −0.2715 −0.1002 0.3799
V18 Provision of infrastructure 0.6353 0.2258 −0.3740 0.3365
Table 8:

Standardised estimates from model 2.

Measurement Coefficient SE z-statistic p-value 95 % CI
Lower Upper
V9 ← L1 0.5629 0.0453 12.43 0.0000 0.4742 0.6516
Constant 2.2421 0.0708 31.66 0.0000 2.1033 2.3809
V10 ← L1 0.6884 0.0570 12.08 0.0000 0.5767 0.8001
Constant 2.1812 0.0709 30.78 0.0000 2.0422 2.3201
V11 ← L1 0.6948 0.0455 15.27 0.0000 0.6057 0.7840
Constant 2.1862 0.0679 32.22 0.0000 2.0532 2.3192
V12 ← L1 0.8340 0.0324 25.78 0.0000 0.7706 0.8975
Constant 2.1775 0.0722 30.16 0.0000 2.0360 2.3190
V13 ← L1 0.7718 0.0365 20.98 0.0000 0.6997 0.8439
Constant 2.1290 0.0675 31.54 0.0000 1.9967 2.2613
V2 ← L2 0.7591 0.0365 20.83 0.0000 0.6877 0.8305
Constant 2.2762 0.0743 30.64 0.0000 2.1306 2.4218
V3 ← L2 0.7624 0.0323 23.58 0.0000 0.6991 0.8258
Constant 1.9044 0.0555 34.29 0.0000 1.7955 2.0132
V4 ← L2 0.7103 0.0393 18.06 0.0000 0.6332 0.7874
Constant 1.9977 0.0664 30.08 0.0000 1.8676 2.1279
V5 ← L2 0.7812 0.0543 14.39 0.0000 0.6748 0.8875
Constant 1.9557 0.0573 34.12 0.0000 1.8433 2.0680
V14 ← L3 0.7270 0.0420 17.30 0.0000 0.6446 0.8094
Constant 2.0095 0.0643 31.27 0.0000 1.8836 2.1355
V15 ← L3 0.8013 0.0376 21.29 0.0000 0.7276 0.8751
Constant 1.8316 0.0556 32.94 0.0000 1.7226 1.9406
V16 ← L3 0.7209 0.0471 15.30 0.0000 0.6285 0.8133
Constant 2.0451 0.0629 32.50 0.0000 1.9218 2.1684
V18 ← L3 0.7112 0.0403 17.66 0.0000 0.6322 0.7901
Constant 1.9119 0.0557 34.33 0.0000 1.8028 2.0211
V6 ← L4 0.5498 0.0751 7.32 0.0000 0.4027 0.6970
Constant 2.2377 0.0655 34.18 0.0000 2.1094 2.3660
V8 ← L4 0.6899 0.0820 8.42 0.0000 0.5293 0.8506
Constant 2.1737 0.0657 33.10 0.0000 2.0450 2.3024
  1. SE = standard error, CI = confidence interval, statistical significance of p-value < 0.05.

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Received: 2023-02-28
Accepted: 2023-11-15
Published Online: 2023-12-01
Published in Print: 2023-11-27

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