Research question
Is there a relationship between income inequality and working hours
across different countries and years?
Initial hypotheses
Hypothesis 1:
It is expected that there is a significant relationship between
income inequality and working hours, so they are related the two
variables. First, it is posible that are related because of the way
economic resources are distributed across different income groups. If a
small part of the population of earners hold a larger share of the
income, this may create pressure for other workers to work longer hours
to maintain their standard of living or to compete for scarce job
opportunities. In this sense, income inequality may be a factor that
drives longer working hours. Additionally, some studies have found that
changes in income inequality over time are associated with changes in
working hours, suggesting that the two variables are dynamically
related, so we have to study in de database the correlation.
Hypothesis 2:
There is no statistically significant relationship between income
inequality and working hours across different countries and years. It is
possible that differences in income inequality across countries and
years are due to factors other than working hours, such as differences
in tax policies, economic growth rates, or social welfare programs.
Additionally, it is possible that working hours are influenced by
factors such as cultural norms, labor laws, or technological
advancements. This could be due to the more equal distribution of
resources and opportunities, which may reduce the need for some people
to work excessive hours.
Hypothesis 3:
The relationship between income inequality and working hours varies
across countries and years. This variation might be due to the
differences in economic and social contexts, as well as differences in
policies and regulations that impact income inequality and working
hours. For instance, some countries may have more advanced labor laws or
social welfare programs that work towards reducing income inequality
and/or limiting working hours. However, other countries may have less
advanced policies that worsen income inequality and/or permit longer
working hours
The methodology that we are going to use
To respond to our hypothesis, we will calculate the average “Average
annual working hours” for each country and the average “Top 1% Share of
Income” for each country over the years. With these data we will make
two tables with all the means. Our objective is to compare the two
averages that we have for each country to see if they coincide. In
addition, we are going to calculate the correlation coefficient between
“Average annual working hours” and “Top 1% Share of Income” for the
general mix of all countries in all years.We will create some scatter
plots to study the correlation between these two variables.
We analyze the data and present the results.
Country | Mean share of income between 1900-2000 for the top 1%
|
|
Country Name
|
Media_Top
|
|
AUS
|
8.085
|
|
CAN
|
10.325
|
|
FRA
|
9.345
|
|
GER
|
11.350
|
|
JPN
|
8.195
|
|
NET
|
10.480
|
|
SWE
|
7.210
|
|
SWI
|
9.825
|
|
UK
|
12.630
|
|
US
|
12.980
|
We observe that the mean the income of the top 1% with the highest
number is by US and UK, we can also observe that the lowest number is
held by AUS and JPN.
Country | Mean average annual working hours between 1900-2000 |
|
Country Name
|
Media_Avarage
|
|
AUS
|
1984.000
|
|
CAN
|
2042.000
|
|
FRA
|
1937.000
|
|
GER
|
2210.000
|
|
JPN
|
2095.195
|
|
NET
|
2079.000
|
|
SWE
|
1971.500
|
|
SWI
|
2053.500
|
|
UK
|
2123.000
|
|
US
|
1947.500
|
We observe that the mean the average annual working hours the
highest number is by GER and UK, we can also observe that the lowest
number is held by US and FRA.
We calculate the correlation coefficient between the two
variables
When calculating the coefficient correlation between the two
variables we get that this is 0.79. From this result and by looking at
the scattergraph we are able to verify that there is a positive
correlation as the coefficient is greater than 0. This means that as the
variable “average annual working hours” increases, the variable “top 1%
share of income” also increases.
By analysing the two tables and these two graphs which rank the
countries regarding their mean average annual working hours and their
mean top 1% share of income between the period of years which the data
provides we can appreciate that the leading country for the mean annual
working hours is GER (with a mean of 2210) where as the leading country
for the mean top 1% share of income is the US.
Finally, we have designed other types of dynamic and interactive
charts that show the evolution of both average annual work hours and the
top 1% revenue share over time.
Graphic Evolution of the Top 1% share of income
Graphic Evolution the average annual working hours
Conclusions
Yes, there is a relationship between income inequality and working
hours across different countries and years. Generally speaking,
countries with higher levels of income inequality tend to have longer
working hours, while countries with lower levels of income inequality
tend to have shorter working hours.
This relationship can be explained in a few ways. Firstly, in
countries with high levels of income inequality, those at the top of the
income distribution tend to work longer hours in order to maintain their
position and income, while those at the bottom of the income
distribution often have to work longer hours just to make ends
meet.
Secondly, in countries with lower levels of income inequality, there
tends to be more of a focus on work-life balance and quality of life,
which can result in shorter working hours. In addition, in countries
with more generous social welfare policies and labor protections,
workers may have greater bargaining power to negotiate shorter working
hours.
It’s worth noting, however, that this relationship is not
necessarily a straightforward or causal one, and there are many factors
that can influence both income inequality and working hours in a given
country.