Recommended Subsidies for Food

Something healthy should have a low cost in order stimulate people to buy it. Lowering costs can be done via subsidies, because part of the creation of the product is paid by government instead of the person buying it.
In the United States (and probably also elsewhere) this is not the case, as depicted in the following graph (from The Physicians Committee for Responsible Medicine):

The issue I have with the graph in the original post is, apart from the presentation aspect discussed in this post on Flowing Data, that different things are compared: percentages of subsidies to units. My alternative is expressed in % of subsidy.

I made the following assumptions and considerations:

• Nutrition recommendations are expressed in units, I converted the ‘Sugar, Oil, Salt’ category to 1 unit in order to do calculations with it.
• High subsidies should mean that government wants to stimulate eating these products.
• The ‘Federal Nutrition Recommendations’ are converted into ‘Recommended Subsidy Recommendations’ by the following statement: If we are supposed to eat 32% of vegetables or fruit out of the total intake per day, these products should be subsidized by 32% of the total budget.
• In this way, we can convert the recommended units into recommended subsidy which in turn can be compared to the actual subsidy.

I’m not particularly proud of the visualization itself (it’s quick and dirty Excel work). Perhaps the weekend will bring some time to rework the graphical part.

Using a hammer to paint the wall (part 1)

It doesn’t make sense, does it? Using a hammer to paint a wall? No, it doesn’t, because we know what a hammer looks like and we all know what it takes to paint a wall.

Similarly, we don’t run out buying a PC to know what 2 times 2 is, we don’t spend twice our yearly income for a software application that would fill out our tax papers for us, we don’t buy a mechanical crane in order to drill a small hole in the wall. And so on, and so on. You get the point…

This is the first part of a series on using tools to solve problems in real-life, especially in organizations.

The point is that human beings seem to have an obsession for tools, especially ICT tools (hardware, software). And somehow, during the development or implementation of the tool, we seem to lose sight of the reason why it is there. The development or implementation becomes the new goal, rather than the underlying reasons for selecting the tool in the first place.

More about this topic later...

Life Expectancy: Does my Insurance Company know about This?

In the previous post about life expectancy, we looked at some data of the last 20 years and extrapolated linearly. It seemed as if men and women would finally live equally long somewhere in the next decennium.

While thinking and reading about the way life expectancy is calculated, it struck me that the calculation is not fair. I started out describing the way the calculation is done and why I think it is wrong. During this, I found out that the Wikipedia page about this topic already contained the answer:

It is important to note that this statistic is usually based on past mortality experience, and assumes that the same age-specific mortality rates will continue into the future. Thus such life expectancy figures are not generally appropriate for calculating how long any given individual of a particular age is expected to live. But they are a useful statistic to summarize the current health status of a population.

(from http://en.wikipedia.org/wiki/Life_expectancy)

Basically, in the calculation, one assumes that when you are born does not influence the probability with which you will die (at a certain age). This is obviously false.

The article further describes that models exist to adjust the probabilities used in the calculations in order to correct for this systematic underestimation.

Can someone guarantee me that my insurance company uses a corrected statistic instead of the original one? I’m afraid they think I’ll die 10 years earlier than statistically expected and thus charge me too much money?!

Life Expectancy: The difference between male and female

In this post on FlowingData, some interesting statistics are shown about life expectancy in the US.

Ever wondered why insurance companies have higher rates for men than women? The main reason is that on average, women live longer than men. In other words, the risk of dying for a man of, say 50, is higher than for a woman of that age.

Why is this? I have some ideas, but no means to prove them. What can be studied from the data is the evolution of life expectancy and the consequences of this evolution. When looking at US data for the last 20 years, simple (linear) extrapolation tells us that in 2047, men and women will have the same average age. Looking at the data for Belgium, the year is 2074.

I will probably be long dead by then, but according to this linear extrapolation, my grandchildren will have children that have life expectancies of 93 years!

I agree, linear extrapolation is an approximation. But the tendency is there: men's life expectancy is getter higher at a slightly faster pace than women's.

More about this topic (including some cool graphs) later.