The beginning of the 1980's saw a sharp increase in productivity and cooperate profits. One would logically think, as many did, that higher productivity would translate into higher wages. Oddly enough though, worker wages remained stagnant. Economist across the country charged that productivity profits were going into corporate pockets rather than worker's wages. Many workers believed that growing global competition and increased job insecurity were undercutting the fruits of their hard work. Around the middle half of the 1900's a strange phenomenon began that has today's

While there are many similarities between the data we collected the national levels, we must account for a level of disparity due to biases and differences in the nature of the two studies. Whereas the Consumer Price Index and other measure of inflation rates take into account such statistics as price, unemployment levels and productivity, our local study only looked at prices. Although the factors of productivity, and unemployment have eventual effects on the local products we surveyed, their effect is not nearly as great as it is on the national level. In addition, we looked at a narrow scope of production that was limited to predominately grocery items. In order to produce a more valid survey we would have needed to look at a wider spectrum of products. Product industries such as automobile, technology, and health care had little effect on our survey, while they are major factors in the calculation of the national rate of inflation.

Another basis between our data and national date is that our samples had a skewed depiction of wages. Since we are college student whatever income we calculated for the use of this project would be disproportionate to national incomes. The bias occurs in the fact that inflation effects real income, and through this exercise we were unable to incorporate adjustments that would affect our income, thus we are not able to connect how income relates to inflation.

Regional implications also contribute to biases in our data. Items such as fruits and vegetables will tend to experience price fluctuation at a higher degree in a region such as the mid-west. The prices reflect not necessarily i

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