Environmental Spending and Intergovernmental Revenue Growth Rate

Local governments are accountable for the way the spend public funds. The governments have to submit financial reports as required by the state law to demonstrate accountability in their usage of funds. There are differences in the spending between different types of governments. The differences could be due to their geographical location such as being a coastal or non-coastal town or whether the government is in a metro, suburban, or rural county. The analysis of the differences in the spending showed that there is a difference in the environmental spending between coastal and non-coastal counties but there is no difference in the spending between metro, suburban, and rural counties.

Literature Review

Florida is a county that is highly dependent on the environment and tourism for the growth and stability of its economy. The ocean, coasts, and different industries play a critical role in the future of Florida county. In the year 2015, Florida had about 106.3 million tourists that visited the region and they had an economic impact of approximately $90 billion (Harrington et al., 2017). The coastal economy is highly sensitive to environmental disruptions and there is a need for those economies to spend more on protecting their environment (Guo et al., 2017). According to Hiatt (2019), the average expenditures for protecting and managing the plant environment was approximately $45 million annually where 90% of the funding was provided by the state. Coastal counties are thus expected to spend a significant amount of its revenues in protecting the environment. However, non-coastal counties could experience a difference in their spending as they are not highly dependent on the environment or tourism for its economic stability but could be dependent on manufacturing and other industries (Woodruff et al., 2018). A comparison of the difference in environmental spending between the coastal and non-coastal region spending would show whether there is a difference in environmental spending.

Intergovernmental revenues consist of monies obtained from other governments. It can include revenues that are acquired from contingent advances and loans, shared taxes, and grants (Xiao, 2020). The aim of such funding is to improve the efficiency revenue sourcing (Allers & De Gree, 2018). The intergovernmental revenues growth rate identifies the rate in the growth in the revenues obtained from other governments. Different counties such as metro counties, suburban counties, and rural counties could have a difference in their spending. The theory for the spending and revenues is based on the spatial distribution and development within a region that could impact the costs of providing local services and thus the revenues (Ihlanfeldt & Willardsen, 2018). According to Jacques and Ferland (2021), the infrastructure spending in the different regions could result in a difference in the spending and revenues between rural and suburban regions.

Methods

The method used to identify the difference in the results involved gathering the data from the Florida County Government. sav data. The first data gathered was that of environmental spending between the coastal area and non-coastal area. The number of samples taken for the coastal area was 34, while the number of samples taken for the non-coastal area was 32. An independent samples t-test was conducted in SPSS to analyze the difference in the mean of the two variables. The second data was that of the intergovernmental growth rate (IGR) of the metro county, suburban county, and rural county. The samples taken for the metro county was 30, for the suburban county was 12, and the rural county was 18. SPSS was then used to conduct ANOVA tests, post-hoc tests, and ANCOVA tests. The results from the analysis and the discussion are outlined as follows.

Results

The results of the different tests were as shown in the tables and figures below.

T-Test

Group Statistics
	Coastal area or Not	N	Mean	Std. Deviation	Std. Error Mean
Average % Envir. Spending in Total spending	Coastal area	34	.14184	.063135	.010828
	Not coastal area	32	.09404	.037583	.006644

Independent Samples Test
		Levene's Test for Equality of Variances		t-test for Equality of Means
		F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
									Lower	Upper
Average % Envir. Spending in Total spending	Equal variances assumed	14.242	.000	3.708	64	.000	.047804	.012891	.022051	.073557
	Equal variances not assumed			3.763	54.329	.000	.047804	.012703	.022339	.073270

ANOVA Test

Descriptives
IGR growth Rate
	N	Mean	Std. Deviation	Std. Error	95% Confidence Interval for Mean		Minimum	Maximum
					Lower Bound	Upper Bound
Metro County	30	-.076854	.1591382	.0290545	-.136277	-.017431	-.4791	.3888
Suburban County	12	.033722	.2423555	.0699620	-.120263	.187708	-.2804	.5725
Rural County	18	.080411	.3225618	.0760285	-.079995	.240817	-.4370	.8397
Total	60	-.007559	.2419240	.0312323	-.070055	.054936	-.4791	.8397

ANOVA
IGR growth Rate
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	.304	2	.152	2.749	.072
Within Groups	3.149	57	.055
Total	3.453	59

Post-hoc Test

Multiple Comparisons
Dependent Variable: IGR growth Rate
Tukey HSD
(I) Metro, Suburban, or Rural County	(J) Metro, Suburban, or Rural County	Mean Difference (I-J)	Std. Error	Sig.	95% Confidence Interval
					Lower Bound	Upper Bound
Metro County	Suburban County	-.1105764	.0802867	.359	-.303780	.082627
	Rural County	-.1572651	.0700799	.072	-.325907	.011377
Suburban County	Metro County	.1105764	.0802867	.359	-.082627	.303780
	Rural County	-.0466887	.0875999	.855	-.257491	.164113
Rural County	Metro County	.1572651	.0700799	.072	-.011377	.325907
	Suburban County	.0466887	.0875999	.855	-.164113	.257491

Homogeneous Subsets

IGR growth Rate
Tukey HSDa,b
Metro, Suburban, or Rural County	N	Subset for alpha = 0.05
		1
Metro County	30	-.076854
Suburban County	12	.033722
Rural County	18	.080411
Sig.		.128
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 17.419.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.

ANCOVA test

Univariate Analysis of Variance

Between-Subjects Factors
		Value Label	N
Metro, Suburban, or Rural County	1.00	Metro County	30
	2.00	Suburban County	12
	3.00	Rural County	18

Tests of Between-Subjects Effects
Dependent Variable: IGR growth Rate
Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	.332a	3	.111	1.984	.127
Intercept	.016	1	.016	.289	.593
Political	.028	1	.028	.502	.482
CountyType	.328	2	.164	2.944	.061
Error	3.121	56	.056
Total	3.457	60
Corrected Total	3.453	59
a. R Squared = .096 (Adjusted R Squared = .048)

Discussion

Independent Samples t-test

The error bar plots showed that there was a spread of observations between the coastal and non-coastal counties. The coastal area had a larger spread of observations compared to the spread of observations for the non-coastal area. From the given analysis, one can estimate that the variances between the two groups are different (“SPSS tutorials: independent samples T-test”, 2021). Running the independent samples t-test would reveal that the Levene’s test as significant.

The independent samples t-test was calculated by comparing the mean score of the given regions. There was a significant difference between the means of the two groups where the value of t(2) = 3.708, and p<0.05. The mean of the of the coastal area was higher (M=0.14184, sd=.063135) than that of the mean of the non-coastal area (M = 0.09404, sd=.037583). We thus reject the null hypothesis and the results showed that there is a significant difference in the percent of total spending that is environmental spending between the coastal and non-coastal counties (Cronk, 2019).. The results from the analysis was similar to the theory that had initially shown that the spending in coastal governments would be higher than non-coastal governments as they are highly dependent on the environment and tourism.

ANOVA test

The error bar plots showed that the spread of observations was low between the metro, suburban, and rural counties. However, the graph showed that the metro county was lower, followed by the suburban county, and the rural county was the highest. From the given analysis, one can estimate that the variances between the two groups are not different. Running the ANOVA test would reveal that the Levene’s test as not being significant.

The ANOVA was not significant as the value of F (3) =2.749 and p=0.072 (showing that p>0.05). The intergovernmental revenue growth rate based on the county type did not different significantly. The metro county had a mean of -.076854 (.1591382), the suburban county had a mean of .033722 (sd=.2423555), and the rural county had a mean of .080411 (sd=.3225618). From the results, since the ANOVA test is significant, we accept the null hypothesis (“SPSS tutorials: independent samples T-test”, 2021). The test shows that there is no statistical difference in the intergovernmental revenue growth rate (IGR) based on county type of metro, suburban, and rural counties (Cronk, 2019).

Post-hoc test

The post-hoc test was undertaken using a 3 by 2 factor between the regions’ factorial ANOVA and compared for the different counties. The main effect for whether it was a metro county was not significant ( F (2,15) = .359, p > .05). The main effect for whether it was a suburban county was also not significant ( F (1,15) = .855, p > .05). Finally, the for the rural county was not significant ( F (2,15) = .072, p > .05). The results showed that there was no significant effect on the difference between the specific intergovernmental revenue growth rate (IGR) based on the metro, suburban, and rural county types. We thus accept the null hypothesis that indicates that there is no statistical difference in the intergovernmental revenue growth rate (IGR) based on county type of metro, suburban, and rural counties (Cronk, 2019).

ANCOVA test

The error bar plots showed that the spread of observations was low between the metro, suburban, and rural counties. From the given analysis, one can estimate that the variances between the two groups are not different. Running the ANCOVA test would reveal that the Levene’s test as not being significant.

The ANCOVA test showed that the test results were not significant for the county type where the value of F(2) = 2.944 and the value of P=0.061 (P>0.05). The results from the analysis showed that there was no difference in the difference in the specific intergovernmental revenue growth rate (IGR) based on the metro, suburban, and rural county types. We thus accept the null hypothesis that indicates that there is no statistical difference in the intergovernmental revenue growth rate (IGR) based on county type of metro, suburban, and rural counties (Kim, 2018).

The study showed that there is a difference in the environmental spending between the coastal regions and non-coastal regions but there was no difference in the intergovernmental growth rate between the metro, suburban, and rural counties. The difference in the environmental spending could be caused by the fact that coastal regions depend on the environment and tourism for their livelihood. The implication for the study is that non-coastal areas should strive to improve their environmental spending. The intergovernmental growth rate for the different counties was also equal showing that all the different regions were undertaking enough steps towards growth.

References

Allers, M. A., & De Greef, J. A. (2018). Intermunicipal cooperation, public spending and service levels.  Local Government Studies ,  44 (1), 127-150.

Cronk, B. C. (2019).  How to use SPSS®: A step-by-step guide to analysis and interpretation . Routledge.

Guo, Z., Robinson, D., & Hite, D. (2017). Economic impact of Mississippi and Alabama Gulf Coast tourism on the regional economy.  Ocean & coastal management ,  145 , 52-61.

Harrington, J., Chi, H., & Gray, L. P. (2017). Florida tourism.  Florida's Climate: Changes, Variations, & Impacts .

Hiatt, D., Serbesoff‐King, K., Lieurance, D., Gordon, D. R., & Flory, S. L. (2019). Allocation of invasive plant management expenditures for conservation: Lessons from Florida, USA.  Conservation Science and Practice ,  1 (7), e51.

Ihlanfeldt, K., & Willardsen, K. (2018). Local public services costs and the geography of development: Evidence from Florida counties.  Journal of Regional Science ,  58 (1), 5-37.

Jacques, O., & Ferland, B. (2021). Distributive Politics in Canada: The Case of Infrastructure Spending in Rural and Suburban Districts.  Canadian Journal of Political Science/Revue canadienne de science politique , 1-22. https://doi.org/10.1017/S0008423920000955

Kim, H. Y. (2018). Statistical notes for clinical researchers: analysis of covariance (ANCOVA).  Restorative dentistry & endodontics ,  43 (4).

“SPSS tutorials: independent samples t test”. (2021). LibGuides Kent https://libguides.library.kent.edu/spss/independentttest

SPSS tutorials: independent samples T-test. (2021). LibGuides Kent https://libguides.library.kent.edu/SPSS/OneWayANOVA

Xiao, C. (2020). Intergovernmental revenue relations, tax enforcement and tax shifting: evidence from China.  International Tax and Public Finance ,  27 (1), 128-152.

Woodruff, S., BenDor, T. K., & Strong, A. L. (2018). Fighting the inevitable: infrastructure investment and coastal community adaptation to sea level rise.  System Dynamics Review ,  34 (1-2), 48-77.