Business

PRODUCTIVITY GROWTH HYPTHESIS

In this assignment, we will attempt to study the effects that difference

in Income Ratio (henceforth known as I.R.) between the years 1980 and 1990

have on the Productivity Growth (P.G.) during the same period of time.

The Income Ratio of one specific year can be found if we take the average

income of the richest faction of a country (the richest 20% of the

population) and divide it by that of the poorest faction (the poorest 20%).

In this assignment, the Income Ratios that were used were those of 13

different countries. The I.R.’s on both 1980 and 1990 were taken for all

these countries and, to find the difference between them, the I.R. for 1990

was divided by the I.R. for 1980, for each country. These new numbers

illustrate the change of I.R. between the two years so that we can compare

how the P.G. changes in relation to the changes in the I.R..

On this assignment, we use inductive reasoning to examine the data and

find a theory (a hypothesis) that would combine the data given in a way

that would make sense, based solely on our data. How do we know if the

“theory” that we formulate makes sense? In this case we will plot the

points (derived from the column “I.R. 1990/1980,” going on the x-axis, and

the column “Productivity Growth 79-90,” on the y-axis). According to how

the points are on the graph in relation to the Average Point (0.94,1.45)

(point that is an average of all values and which divides the graph into

four Quadrants), if 80% of these points are where they would be expected to

be to conform to the hypothesis, then there is no reason to reject this

hypothesis. If, on the other hand, the majority of the points does not

conform to our hypothesis (are not where they were predicted to be), then

it is rejected.

Another method of reasoning frequently used by Mainstream economists is

“deductive knowledge,” as opposed to “inductive,” described above. Their

theory is formulated and only then it is applied to the data. Their theory

on this subject suggests that productivity within a country grows when the

population has incentives to work harder (or to work more). When the gap

between rich and poor increases (an increase in I.R. form 1980-90,

resulting in a larger ratio on the column I.R. 1990/1980), so does the

population’s eagerness to work, therefore increasing the Productivity

Growth. Since when one variable goes up the other also goes up, there is a

positive (or direct) correlation between the two. Mainstream economists use

deductive reasoning to deduce that there exists a positive correlation

between the two factors. In short, their hypothesis is that when the Income

Ratio increases, the Productivity Growth also increases, since people are

more motivated. For this to be true, we would expect a line going up and to

the right on the graph, passing by Quadrants II and IV. Most points (80% or

more) would have to be on these two Quadrants. This, however, is not the

case (see graph), since only about 30.77% of the points plotted satisfy

these conditions.

Since the original hypothesis was rejected, we might want to see if there

is a negative correlation between the two variables (that is, as one goes

up, the other goes down). Our new hypothesis would then be “as the Income

Ratio increases, the Productivity Growth decreases.” Then, in the case of a

high I.R., people in lower classes would rationally start to feel insecure

and that their work is not being recognized by society, therefore losing

motivation and producing less. In this case, since there’s a negative

correlation, one would expect the line on the graph to go downwards, from

left to right, passing on Quadrants I and III. If this hypothesis were

valid, 80%+ of the points would have to be on these Quadrants. This is also

not the case, for only 69.32% of the points are on the appropriate

Quadrants. Like the first, this second hypothesis also has to be rejected.

After analyzing these two relationships and seeing that neither is valid,

we conclude that there is no direct relationship between the two variables

tested. That does not mean that one has no effect on the other (it probably

does), only that there may be other factors and influences involved that

have not been accounted for in this assignment and that one is not the only

factor responsible for the changes in the other.

DATA SHEET

CountryIncome Ratio1980ProductivityGrowth

1979-90Income Ratio1990Income Ratio1990 / 1980

United States9.00.411.01.2

Australia 9.60.89.61.0

New Zealand8.81.48.81.0

Switzerland8.71.08.00.91

Canada 7.01.17.01.0

Britain 6.82.07.01.03

France 6.52.46.00.92

Italy6.12.05.80.95

Germany 5.81.65.00.87

Holland 5.61.55.00.89

Belgium 4.72.23.80.81

Sweden 4.71.53.80.81

Japan4.21.03.60.85

Average Income Ratio 1990 / 1980: 0.941

Average Productivity Growth 1979-90: 1.45

No. of points conforming to first hypothesis: 4/13 = 30.77%

No. of points conforming to second hypothesis: 9/13 = 69.23%

By: Leonardo Santos