Modelling Factors Affecting Internally Generated Funds of the Tamale Metropolitan Assembly of Ghana Using Multivariate Analysis Techniques
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.
See Tables 7 and 8.
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 |
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 |
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SE = standard error, CI = confidence interval, statistical significance of p-value < 0.05.
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Articles in the same Issue
- Frontmatter
- Editors Note
- Editors’ Note
- Special Issue: Political Demography; Guest Editor: Kira Renée Kurz
- Population Age Structure and the Vulnerability of States to Coups d’État
- Introducing a New Dataset: Age Representation in Parliaments on the Party-Level
- Articles
- Analysis of the Impact of Digitalization on the Quality and Availability of Public Services in Ukraine – A Comparative Approach with Insights from Estonia
- Modelling Factors Affecting Internally Generated Funds of the Tamale Metropolitan Assembly of Ghana Using Multivariate Analysis Techniques