Factors that Influence GDP
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The United States experienced the longest uninterrupted period of economic growth during the 60s. Computer industry and housing overpowered chemicals, automobiles, and other electrically powered gadgets. The efficiency and productivity improved significantly.
In 1960-61, recession started. Kennedy wanted the economic expansion at an annual rate of 4-6 percent and unemployment rate at 4 percent. Kennedy brought some aid including an increase in unemployment payment plus augmented aid to children of jobless workers, increase social security assistances to a bigger pool of people, emergency relief for farmers, vocational training for emigrant workers, area redevelopment, and federal subsidy for home construction and slum abolition until the inflation became stable in 1963. To ensure inflation rate, the government purchases short-term bonds to lower the interest rates in the market. However, when monetary policy is closed to zero, bonds can no longer aid the government in maintaining inflation status. According to the Keynesian Theory, if the poorer sections of society are provided sums of money, they will probably spend it, instead of saving it, therefore encouraging economic growth. By the end of the 1970, the average Americans income had improved by 50 percent. Median family earnings increased from $8,540 in 1963 to $10,770 by 1969.
Gross domestic product GDP is considered as one the chief indicators being utilized to assess the status of a country`s economy. It characterizes the over-all dollar value of the entire products and services manufactured over a certain time period. As an individual can visualize economic manufacture and development, what GDP exemplifies has a great effect on almost every person within that economy. When GDP is at its best performance, there is lesser number of unemployed individuals and wages increase. On the other hand, when GDP is low, there is greater number of unemployed workers and the wages become stagnant.
It is not difficult to realize why a poor economy generally signifies lesser revenues for companies, which in turn signifies lower stock values. A vital transformation in GDP, either up or down, typically has a substantial impact on the stock market. Stockholders are really anxious with regards to negative GDP progress, which is one of the aspects that economists utilize to identify whether a country`s economy is in a recession. GDP per capita is every so often deliberated as a sign of a country`s average living.
As reported by the Bureau of Labor and Statistics, unemployment rate in the United States has decreased to 7.30 percent in August of 2013 from 7.40 percent in July of 2013 (tradingeconomics.com, 2013). Based on last year`s survey, over 368,000 people were unemployed. Consumer spending, on the other hand, has increased in December. While there was an increased in the consumer spending last December, unemployment benefits claims also escalated in January. In addition, 165,000 individuals were able to get a job. The data implies that the economy is thriving despite some hindrances such as the reduction in the GDP last quarter caused by `temporary` factors. The consumption function tells us that as income rises, consumption rises more slowly than income. Induced consumption expenditures are influenced primarily by income. The United States have been consuming too much and saving too little. During a very bad depression both net investment and gross investment can be negative.
In gauging the empirical relationship between GDP per capita of the 51 states of the United States, it can be seen that GDP has a positive impact on GDP per capita while the population has a negative impact on it i.e. it acts as a deterrent for the GDP per capita. Since GDP has shown to have a positive influence on GDP per capita that means if a state wants to increase the standard of living of its population then it should target to increase the GDP per capita which can be achieved by increasing the GDP of the state. In doing this, new policies have to be formulated in order to trigger the GDP growth. Since the population has shown to have a negative influence on GDP per capita that means even if a state grows in term of GDP, its benefit will not be fully utilized and the standard of living for the population residing in that state will not increase substantially if it has a high population.
The state has to formulate some policies to keep a tab on its population and that could be achieved through measures like adopting some way to preach about birth control among its population and also a state has to find a way out to cut the number of immigrants into it. Otherwise even if a state has high GDP, a high population can eat it out. Factors which affect GDP per capita of a state are as follows: demography of a state, its proximity to industry, the kind of industries that are established inside the state, the literacy level of the state, the sex ratio of the state, the rate of inflation for each state, the unemployed percentage of population for each state and there could be more other reasons which can affect the GDP per capita of the states.
Correlation Coefficient
Regression Analysis
Regression equation is: OUPUT = – 100 + 0.72 UNEMPLOYMENT + 0.16 CONSUMER SPENDING + 0.09 POPULATION IN MILLION
The concentration of this analysis is on the effect of %unemployment on GDP. As evident above, the unemployment coefficient of .7163 fell into the rejection region, indicating positive autocorrelation. Autocorrelation happens when a pattern exists between the error terms due to a missing variable from the analysis. In this regression, the coefficients of the independent variables are predisposed to an unidentified extent and are not dependable or reportable in an academic paper, journal, or report.
The R[2] value of 77.6% suggests that the independent variables account for 77.6% of the variation of the result. Another significant consideration when regressing an equation is the existence of multicollinearity which in this case was not present.
As presented above, the bivariate regressions yield R[2] values which are less than the value of the whole regression. Multicollinearity exists when two more of the predictors are in a linear relationship. When this happens, the statistical software package could not distinguish which variable to provide the coefficient to.
Data.bls.gov (2013). Notice: Data not available: U.S. Bureau of Labor Statistics. [online] Retrieved from: http://data.bls.gov/pdq/SurveyOutputServlet [Accessed: 23 Sep 2013].
Data.bls.gov (2013). Bureau of Labor Statistics Data. [online] Retrieved from: http://data.bls.gov/timeseries/LNS14000000 [Accessed: 23 Sep 2013].

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