A brief demographic analysis of the metropolitan statistical areas (MSA) of Savannah, Charleston, and Jacksonville was conducted, followed by analyses of the economies of Savannah, Charleston, and Jacksonville. The author then investigated statistical effect among various economic variables using one-way analysis of variance (ANOVA), concluding with an analysis of the effect estimated economic outcome from port construction and operations would have on Savannah’s overall economy, if implemented.
The study revealed through the analysis of construction data in Alabama, Florida, Georgia, North Carolina, South Carolina, and Tennessee that it is becoming increasingly apparent that these states have the potential of essentially losingthe entire construction sector in their respective economies. Where elimination of the construction industry will notoccur outright, this study suggests that the construction industry is not yet on a path of recovery, especially whenviewing the industry from an economic output perspective. For example, where the construction industry used tocontribute 6?8 percent of the output in each state, it now contributes only 3?4 percent, with these percentages set toworsen through 2015, and perhaps, beyond.
The economic output experienced during the early?to?mid?2000s was not sustainable from a statistical standpoint. Thisfact held true for GDP and most sectors contributing to GDP, such as the construction industry. GDP and theconstruction industry was positioned for collapse several years before the actual collapse. Refer to the figure below.The research literature reveals mixed findings when considering the causal effect of construction spending oneconomic output. Leveraging a sector of the economy as a stimulus thereof does not necessarily increase economicoutput; such is the case with infrastructure spending. This fact has been proven on numerous occasions in the researchliterature, and as such, caution should be exercised by those proposing such stimuli. However, the constructionindustry in each of the six states in this study did have a significant statistical effect on their respective state GDP, andas such, these findings warrant further investigation into leveraging the construction industry as a catalyst for eachstate’s economic output.
To this end, the construction industry is not in need of direct financial support, especially from the government; thegovernment has no money to spend that does not come through the private sector by some means. The constructionindustry simply needs a healthy, thriving economy under which to operate. Such will not come through moregovernment intervention, be it additional regulations or financial stimuli as it has been proven in the scientific literaturethat that government intervention most often worsens economies rather than helping. The construction industry inevery state needs the federal, state, and local governments to cut spending drastically. Nothing less will work. Until thisoccurs, the construction industry is poised to continue its downward spiral into what could be essentially defined asnonexistence.
Bacon County is not alone, however. Most communities in Georgia are struggling economically. In 2010, the unemployment rate in Coffee County was 17.6 percent, far higher than the surrounding counties of Appling, Tift, and Ware. However, all four counties, including Coffee, have experienced a significant decline in manufacturing employment since 2000. For example, the manufacturing sector in Coffee County comprised 31.1 percent of all jobs in the county in 2000; by 2010, that percentage was 14.4 percent. Worse, similar situations exist in Appling, Tift, and Ware counties. Consider Tift County, specifically. In 2000, the manufacturing sector in Tift County comprised 18.7 percent of all jobs in the county; today that percentage is an almost nonexistent 6.5 percent. In fact, the manufacturing sector in Tift County will be nonexistent altogether in approximately four years (R2=.982); given such strong explained variances, Tift County’s dismal path toward manufacturing obliteration is well established.
Full time employment is down across the state of Georgia, significantly, and perhaps more telling, over two million (22%) Georgians currently live below the federal poverty line. In 2007, nearly one half (47.9%) of the Georgia population worked full time. Today (2013), the percentage of Georgians who work is 42.5 percent, a change of 12.7 percent. Deducting for government employees, only 35.3 percent of the total population of Georgia works full time.Thus, in conclusion it was determined that too few Georgians were working such that the economy can be sustained at our preferred growth rate.
1. Projected financial cost of harbor deepening: USD812.6 million
2. Projected non-federal (local) share of harbor deepening: USD394.0 million
3. Net jobs, optimistic scenario: 3,743
4. Net jobs, conservative scenario: 1,872
5. Net business revenue, optimistic scenario: USD7.5 billion
6. Net business revenue, conservative scenario: USD3.8 billion
7. Total net benefits, optimistic scenario: USD7.8 billion
8. Total net benefits, conservative scenario: USD3.9 billion
9. Benefit-cost ratio, optimistic scenario: 9.63
10. Benefit-cost ratio, conservative scenario: 4.82
11. Payout, optimistic scenario: CY 2022
12. Payout, conservative scenario: CY 2023
To investigate the relationship between electricity generation and GDP, the researcher ran a one-way analysis of variance (ANOVA) of these data. It was determined that the effect of electricity generation on GDP was significant at the .01 alpha level and thus, explained the deviations in the dependent variable, F(1, 22)=280.63, p < .01. Further, the researcher calculated the total sum of squares (TSS), which helps explain these deviations, and R-squared (R2=.927), which estimates the percent of deviation from the mean in the dependent variable. Electricity generation had no statistically significant effect on inflation rate or unemployment.
Further, the effect that energy consumption had on GDP, inflation rate, and unemployment rate was determined. It was determined that electricity consumption had a significant effect on GDP, F(1,22)=328.13, p < .01, and on inflation rate, F(1,22)=328.13, p < .01. The explained variance between electricity consumption and GDP was .947, and the explained variance between consumption and inflation rate was .937. Electricity consumption had no statistically significant effect on unemployment rate.
For example, energy demand in Swaziland increased from 1.08 billion kWh in 2000 to 1.27 billion kWh in 2011, a 17.6 percent increase, while energy supply generated by the Swaziland Electricity Company (SEC) has remained almost constant at approximately 0.4 kWh per year over this same 11 year period. In fact, supply as a percentage of demand has decreased by 4.3 percent over this period. In general, supply fell short of demand by approximately 65.4 percent in 2011. Most of these differences are currently met by Eskom, the largest generator of electricity in eastern and southern Africa.
Implementation and subsequent operations of this project will help Swaziland better manage its waste production while simultaneously helping it more fully meet energy demand. To help meet these demands, or at least improve the supply, the kingdom considered two separate WTE systems, i.e. boiler designs, and those considerations will be addressed in the final Feasibility Study – after the Ministry reviews the Interim Report herein. However, while municipal solid waste alone will not meet our preferred generating capacity, of 50 MW or greater, there exist multiple methods for meeting and exceeding our preferred generating capacity.
Xicon Economics partnered with the Ministry of Energy in the Kingdom of Swaziland, along with Swaziland Electric company, and the Investment Promotion Authority, to develop a feasibility study for the implementation of the Waste to Energy (WTE) system throughout the kingdom.
The purpose of this study was to investigate the economic effect that electricity generation and consumption have on the world’s least-developed economies, as noted by the World Trade Organization (WTO). After calculating basic descriptive statistics, relationships among the economic and energy data were calculated using the Pearson Product moment method. To investigate effect, the researcher used analysis of variance (ANOVA) on economic and energy data from the 34 countries listed by WTO as the world’s least-developed countries, followed by the development of regression models that predict economic output using electricity generation and consumption as independent variables.
Economic output per capita ranged from approximately USD500 to USD2,000 per capita per year, with only approximately five or six countries rising above USD2,000 at any given time. On a quartile basis, per capita incomes averaged USD4,122 in the upper quartile and USD996 in the lower quartile. Further, economic output, as measured via GDP per capita (PPP) correlated significantly among several of the countries at the .01 and .05 alpha levels. For example, the relationship between economic output per capita in Angola and economic output per capita in Benin was significant at the .01 alpha level (r=.946), as well as the relationship between economic output per capita in Angola and Chad (r=.803, p < .01).
Bangladesh generated more electricity than any other country in this study, on average, at 18,103.55 MkWh per year (1990-2009), while Benin, generated the least, at approximately 82 MkWh per year. Angola consumed more electricity annually than the other countries in this study from 2000-2010, at 4,475.45 BkWh, followed by Togo, at 3,875.00 BkWh. Further, electricity generation correlated significantly among many countries in this study, including the relationship between electricity generation in Angola and electricity generation in Mozambique (r=.882, p <.01). The relationship between electricity consumption in several countries was significant. For example, electricity consumption in Angola and Bangladesh and Burkina-Faso was significant at the .01 alpha level (r=.918 and r=.942, respectively).
Analysis of variance (ANOVA) revealed that generation had a significant effect on economic output, as measured by GDP per capita (PPP), in Angola, Benin, Cambodia, Senegal, Tanzania, and Zambi (F(1,9)=103.578, p < .01; F(1,9)=8.197, p=.019; F(1,9)=5.916, p=.038; F(1,9)=6.678, p=.029; F(1,9)=56.474, p < .01; and F(1,9)=17.638, p=.002; respectively). Additionally, consumption was determined to have a significant effect at the .01 alpha level on economic output in Angola, F(1,9)=39.711; Djibouti, F(1,6)=16.991; Haiti, F(1,9)=20.768; and Zambia, F(1,9)=36.437. Additionally, consumption was determined to have a significant effect at the .05 alpha level on economic output, as measured by GDP per capita (PPP), in Mali, F(1,9)=8.428; and Solomon Islands, F(1,6)=5.991. Along with these analyses, three models were developed to forecast economic output.
Such is the case in each respective state within the U.S. All states are struggling, not for growth and prosperity per se, but for survival – where simply maintaining status quo is now considered good. This paper examines this struggle. Moreover, this study presents an economic assessment regarding the state of the Southeastern United States, with particular emphasis on the manufacturing industry in Alabama, Florida, Georgia, North Carolina, South Carolina, and Tennessee.
Among many findings, researchers at Xicon Economics concluded that the output generated by the manufacturing industry had no statistically significant effect on GDP in the Georgia and South Carolina. However, the reverse held true in the states of Alabama, Florida, North Carolina, and Tennessee, where manufacturing output did indeed have a significant effect on state GDP.
From this analysis, it was determined that corruption does adversely affect the energy sector in some countries, though no effort is made herein to estimate causation. For example, in Zimbabwe, a country historically riddled with political struggles, corruption decreased as production and consumption increased. The same held true in Mozambique; as production and consumption increased, corruption decreased. In Botswana, the relationship between consumption and corruption was also inverse, though the relationship between production and corruption was positive and moderately strong.
No significant relationship was found between energy production and consumption in Botswana or Swaziland. In addition, no significant relationships were found between energy production and corruption or consumption and corruption in Swaziland.
Additional research needs to be conducted in the energy sector of this region before definitive conclusions can be draw. However, while political corruption may be inherit in every country, adequate infrastructure is not. The lack of adequate electricity, specifically, regardless how problematic, must be overcome in Africa, and especially Sub-Sahara Africa, if these regions are to be fully developed through private investment streams.
Bivariate correlations were calculated using the Pearson product moment method to test the relationships for statistical significance. In addition, each relationship was characterized using a technique developed by Davis (1971). In general, relationships among the three construction equipment companies did not consistently parallel one another with respect to the various economic indicators. For example, the stock price of Caterpillar rarely significantly correlated with the stock price of Deere & Company, and the stock price of Manitowoc significantly correlated only occasionally, regardless of the variables investigated.
Perhaps one of the most telling findings was that no significant relationships were found between the stock prices of Caterpillar and Manitowoc and GDP. In addition, no significant relationships were found between the stock prices of Caterpillar, Manitowoc, and the construction cost index. However, there were significant relationships between the stock prices of Deere & Company, GDP, and the construction cost index. Equally as telling, Caterpillar stock price did not correlate with the US unemployment rate; that relationship should have been inverse. With no doubt, one would have expected there to be significant correlations among these relationships. Subsequently, there almost certainly exist reasons why stock prices of Caterpillar, in particular, seemed to perform with an indifference to the noted economic indicators. This finding alone leaves significant room for pondering among researchers, for while some room for discrepancies should be allowed for because Deere & Company and Manitowoc manufacture a few types of equipment not necessarily associated with the construction industry, one would think this fact would only bias a few of the findings herein, if any.
Several significant relationships were found between the variables of this study. However, as with most studies, additional relevant information was captured that was not necessarily a component of this study. It became obvious that the stock price of Deere & Company was clearly different than that of Caterpillar and Manitowoc. In general, the stock price of Caterpillar did not yield results similar to what the researcher expected even when comparing it to the tangential data collected. In fact, many of the findings associated with Caterpillar simply left the researcher wondering what differences were inherit within the stock price of Caterpillar that were not inherit in the stock prices of the other construction equipment manufacturers, especially Deere & Company.
Perhaps more importantly than the findings associated with Caterpillar, another finding of this study that was tangential to the actual purpose of the study involved a close inspection of GDP and the construction cost index. That relationship was nearly perfect (r=.989, p < .01). Subsequently, it appears that economists and researchers could have easily predicted the collapse of the US construction industry, as well as the collapse of the US economy, simply through a casual investigation of construction material and labor costs, i.e. construction cost index. However, even today, 3 to 4 years after the collapse, one hears little to nothing from economists, researchers, or mainstream media regarding realistic expectations of economic recovery. A cursory review of a simple plot of the construction cost index reveals that the US economy in all likelihood will not “recover” from this collapse soon. Conversely, the data begs the question as to whether the recent economic collapse was an economic “collapse” or simply fallout, albeit severe, from what should be seen as a predictable correction in the economic data, be it construction related or otherwise. As such, perhaps the White House Administration, US Federal Government, and US banking system should leave the economy to correct itself, e.g. banks loaning money as usual, but only to those who are capable of actually servicing that debt, without the infusion of forced capital into the economy.
It is the opinion of the researcher that the economy could have recovered to where it was in 2007, in approximately nine years. However, such a recovery mandates that the government not interfere with recovery and that banks deliberately and immediately begin loaning money to qualified entities. The increased national debt that has been cast upon taxpayers will substantially decrease the likelihood of economic recovery in the near future.