The interpretation should be obvious: Higher economic growth = More votes for politicians currently in power.
Read the paper here:
http://www.douglas-hibbs.com/HibbsArticles/QJPS_%202007.pdf
Saturday, December 22, 2007
The Economics of Voting and Governance
In a recent paper (2007), Economist Douglas A. Hibbs Jr. ran an Econometrics analysis and plotted this graph:
The interpretation should be obvious: Higher economic growth = More votes for politicians currently in power.
Read the paper here:
http://www.douglas-hibbs.com/HibbsArticles/QJPS_%202007.pdf
The interpretation should be obvious: Higher economic growth = More votes for politicians currently in power.
Read the paper here:
http://www.douglas-hibbs.com/HibbsArticles/QJPS_%202007.pdf
Tuesday, November 27, 2007
Do the rich get richer?
Source: http://www.realclearpolitics.com/articles/2007/11/the_transient_income_classes.html
Books that purport to teach people the "secrets of the rich" should take a course in Econometrics (Economic Statistics).
Quote:
Americans in the top one percent, like Americans in most income brackets, are not there permanently, despite being talked about and written about as if they are an enduring "class" -- especially by those who have overdosed on the magic formula of "race, class and gender," which has replaced thought in many intellectual circles.
At the highest income levels, people are especially likely to be transient at that level. Recent data from the Internal Revenue Service show that more than half the people who were in the top one percent in 1996 were no longer there in 2005.
Among the top one-hundredth of one percent, three-quarters of them were no longer there at the end of the decade.
These are not permanent classes but mostly people at current income levels reached by spikes in income that don't last.
Note:
Warren Buffett and Bill Gates seem to be up there for a very long time.
Books that purport to teach people the "secrets of the rich" should take a course in Econometrics (Economic Statistics).
Quote:
Americans in the top one percent, like Americans in most income brackets, are not there permanently, despite being talked about and written about as if they are an enduring "class" -- especially by those who have overdosed on the magic formula of "race, class and gender," which has replaced thought in many intellectual circles.
At the highest income levels, people are especially likely to be transient at that level. Recent data from the Internal Revenue Service show that more than half the people who were in the top one percent in 1996 were no longer there in 2005.
Among the top one-hundredth of one percent, three-quarters of them were no longer there at the end of the decade.
These are not permanent classes but mostly people at current income levels reached by spikes in income that don't last.
Note:
Warren Buffett and Bill Gates seem to be up there for a very long time.
Thursday, November 22, 2007
The Tragedy of the Commons
Based on an article on Real Clear Politics: LINK
Isn't sharing wonderful?
The first European settlers in America certainly felt so. Their farm economy was set up according to the principle of sharing everything equally: everyone worked on public land and split the harvest equally. With such grand visions of teamwork and kinship there has to be only one outcome...
They nearly all starved.
One of the principles of Economics is that people are driven by incentive. If people get the same reward from putting in a small amount of work as with a large amount, they will chose to put in a small amount of work. This is exactly what happened to the settlers as they faked illness, make little effort and even stole. Total production felt below what was required and a famine resulted.
The first Americans certainly learned their lessons in Economics: Once farmland were divided so that each person had to grow his own crops, they ended up with a huge surplus.
Hence, America held its first Thanksgiving.
Isn't sharing wonderful?
The first European settlers in America certainly felt so. Their farm economy was set up according to the principle of sharing everything equally: everyone worked on public land and split the harvest equally. With such grand visions of teamwork and kinship there has to be only one outcome...
They nearly all starved.
One of the principles of Economics is that people are driven by incentive. If people get the same reward from putting in a small amount of work as with a large amount, they will chose to put in a small amount of work. This is exactly what happened to the settlers as they faked illness, make little effort and even stole. Total production felt below what was required and a famine resulted.
The first Americans certainly learned their lessons in Economics: Once farmland were divided so that each person had to grow his own crops, they ended up with a huge surplus.
Hence, America held its first Thanksgiving.
Saturday, November 3, 2007
The Economics of Gender
"Violence, learning, and the gender divide" By Edward L. Glaeser (Professor of Economics, Harvard University)
In the United States:
Men are more than 8 times as likely as women to commit murder.
Men are more than 50 times as likely to engage in a gang-related killing.
84 percent of all violent crimes are committed by Men.
Young men are 4 times more likely than Women to carry a gun.
94 percent of suicide bombers are Male.
Men are 5 times more likely to commit suicide than Women.
90 percent of those incarcerated for crimes are Men.
"Principles of Criminology," notes that crime rates for men greatly exceed those for women "for all nations, for all communities within a nation, for all age groups, for all periods of history for which reliable statistics are available, and for all types of crime except those peculiar to females."
Sunday, October 14, 2007
The Economics of Marriage
The Economist ran an rather interesting article on marriage:
http://www.economist.com/world/na/displaystory.cfm?story_id=9218127
MORE EDUCATION LESS DIVORCE
The first finding was that education is negatively correlated with failure in marriage. That is, the higher your education level, the less likely you will divorce your partner.
Based on my many years of experience (translated: zero experience), my hypothesis is that people who obtained higher education levels tend to spend more time in school and thus marry later than those who achieved lower levels of education. Hence, they are more likely to know what they want in a partner and have less tendency to marry on impulse. I am sure it is generally agreed that people are more likely to know what they want at the age of 24 (undergraduate/postgraduate) than at 18 (high school).
Note that the data for PhDs is missing. :)
MORE EDUCATION, LESS SINGLE-PARENTHOOD
The second finding was that children raised by single mothers tend to have mothers of lower education levels. That is, the lower the education of the female, the more likely that she will become a single parent.
This is probably an implication of the first finding.
http://www.economist.com/world/na/displaystory.cfm?story_id=9218127
MORE EDUCATION LESS DIVORCE
The first finding was that education is negatively correlated with failure in marriage. That is, the higher your education level, the less likely you will divorce your partner.
Based on my many years of experience (translated: zero experience), my hypothesis is that people who obtained higher education levels tend to spend more time in school and thus marry later than those who achieved lower levels of education. Hence, they are more likely to know what they want in a partner and have less tendency to marry on impulse. I am sure it is generally agreed that people are more likely to know what they want at the age of 24 (undergraduate/postgraduate) than at 18 (high school).
Note that the data for PhDs is missing. :)
MORE EDUCATION, LESS SINGLE-PARENTHOOD
The second finding was that children raised by single mothers tend to have mothers of lower education levels. That is, the lower the education of the female, the more likely that she will become a single parent.
This is probably an implication of the first finding.
Tuesday, October 2, 2007
Do we suck at picking investing strategies?
Or why is it so hard to prove that winning methods works and losing methods sucks.
This article serve to highlight what i think is the fundamental problem of investing. It is probably also the source of the biggest fallacy currently circulating around the investing world. (the fallacy that past performance is a definitive indicator of future performance)
The problem can be stated simply as such:
Failure to take into account the existence of probability.
THE CULPRIT
"It made money, therefore it works."
Shockingly, I have heard this being used as a justification for investment decisions. The issue with this is: past results tells us nothing about future performance. To see why, imagine an asset that has performed well in the past. To many investors, this will be deemed as a "good investment" simply because of its history. However, if we stop to think for a moment we might realize that there might have been two possible scenarios:
1) The asset is good and therefore past performance is good.
2) The asset is bad but performed well in the past by pure chance.
How can we tell which one is true?
Even if there are "good reasons/fundamentals" for the asset to perform well, how do we know if these reasons and/or fundamentals are not painted on after the fact? What if a good asset, despite having good fundamental attributes, continue to perform poorly due to chance? Is it a failure of our fundamental criterion or is the asset "bad" to start with?
Can we ever really tell?
STATISTICS 101
To illustrate how probability distorts investing decisions in the real world, lets suppose we have a very lousy mutual fund manager. Lets call him Bob.
Bob really sucked at fund management: 70% of the time, he end up losing money overall for the year. 30% of the time, he beats the market for the year. Would I invest any money in him? No way!
However, Bob beats the market 30% of the time. This means that he has a 0.81% chance of making money 4 years in a row. Out of 125 fund managers like Bob, there is likely to be 1 who shows a stellar track record of 4 consecutive winning years.
Investors might then see advertisements that say:
Make your fortune with Bob's Fund, the premium investment vehicle with an excellent track record of 4 consecutive market beating winning years!
Will investors who placed their bets on Bob necessarily lose in the coming year by virtue of him being a bad fund manager? It need not be so. Bob still have a 30% chance of making a profit.
And then advertisements will say: "5 years of excellence in fund management".
This article serve to highlight what i think is the fundamental problem of investing. It is probably also the source of the biggest fallacy currently circulating around the investing world. (the fallacy that past performance is a definitive indicator of future performance)
The problem can be stated simply as such:
Failure to take into account the existence of probability.
THE CULPRIT
"It made money, therefore it works."
Shockingly, I have heard this being used as a justification for investment decisions. The issue with this is: past results tells us nothing about future performance. To see why, imagine an asset that has performed well in the past. To many investors, this will be deemed as a "good investment" simply because of its history. However, if we stop to think for a moment we might realize that there might have been two possible scenarios:
1) The asset is good and therefore past performance is good.
2) The asset is bad but performed well in the past by pure chance.
How can we tell which one is true?
Even if there are "good reasons/fundamentals" for the asset to perform well, how do we know if these reasons and/or fundamentals are not painted on after the fact? What if a good asset, despite having good fundamental attributes, continue to perform poorly due to chance? Is it a failure of our fundamental criterion or is the asset "bad" to start with?
Can we ever really tell?
STATISTICS 101
To illustrate how probability distorts investing decisions in the real world, lets suppose we have a very lousy mutual fund manager. Lets call him Bob.
Bob really sucked at fund management: 70% of the time, he end up losing money overall for the year. 30% of the time, he beats the market for the year. Would I invest any money in him? No way!
However, Bob beats the market 30% of the time. This means that he has a 0.81% chance of making money 4 years in a row. Out of 125 fund managers like Bob, there is likely to be 1 who shows a stellar track record of 4 consecutive winning years.
Investors might then see advertisements that say:
Make your fortune with Bob's Fund, the premium investment vehicle with an excellent track record of 4 consecutive market beating winning years!
Will investors who placed their bets on Bob necessarily lose in the coming year by virtue of him being a bad fund manager? It need not be so. Bob still have a 30% chance of making a profit.
And then advertisements will say: "5 years of excellence in fund management".
Thursday, September 20, 2007
Are stock prices unpredictable?
"Surely, one of the best-established facts in economics is that changes in stock prices are essentially unpredictable. And just as surely, this fact is one of the least believed and most disliked."
- "Macroeconomics" by Dornbusch, Fischer and Startz.
I shall try to explain one such piece of evidence.
"TIME SERIES ECONOMETRICS"
Regression analysis is a popular statistical method used by many scientists to detect relationships between phenomenas. (Like the link between smoking and lung cancer for example.)
This is a chart showing the relationship between values of the S&P 500 index one month ago and its current values:
(Source: "Macroeconomics" by Dornbusch, Fischer & Startz)
A straight line can be drawn roughly joining all the points on the chart. In fact, all the data points are clustered around this line so much that they look like a smear of black ink blots. Statisticians call lines like that "very good fit on the data". But what does this mean? It means that there is a very powerful link between the past values of the S&P and the current value.
THE EQUATION
An equation can be written to show this relationship mathematically:
"P(t+1)" stands for current stock prices. It can be explained by three factors: "a" is the expected return to holding stocks. "P(t)" is last time period's price. "e" represents the unpredictable random errors that resulted in the "ink blot smear" we observed in the previous diagram. (without the random error, all the data points would have formed up in a smooth line by themselves)
The equation shows a process known to statisticians as a "random walk" or more precisely, a "random walk with drift".
Implications of the equation:
P(t+1) - P(t) = Change in Stock Price = a + e
Hence, other than the very small "a" component, the changes in stock price can be attributed to the unpredictable "e". (Which is relatively small, as seen by how close the data points are from the line.)
Also, changes in stock prices are independent over time: If stocks did well last month, they are no more likely to either do well or do poorly this month than at any other time.
TO BE OR NOT TO BE
The conclusion I formulated from reading such practical evidence is that while stock picking might still be possible, it is unlikely to be an easy source of profit for the common man.
Readers are advised to come to the same conclusion.
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