Lies, Damned Lies, and Sean

Exchange rate of Indian rupees

There is signficant tech boom in India these days, fed largely by outsourcing from the US.  There is a limit to how many of India's billion people are sufficiently educated and intelligent to do the work, but it is likely in the tens of millions.  The US has millions of service, whitecolar, and factory jobs that may yet be outsourced.  So, with opportunities for cheap labor in the tens of billions of dollars, I expect an increasing number of US companies to outsource in the future, which will require paying the Indians with rupees.  What will this do to the exchange rate between dollars and rupees?  I suspect that we will witness a significant increase in the price of rupees as expressed in dollars.  Opportunities for foriegn investment in India will magnifiy the effect.  Therefore, I recomend investors buy rupees today for $0.0226912 each (i.e. 44.07 rupees/USD), and in time (a couple years?), sell them for a tidy profit.  
Graphs showing dollars to rupees over time indicate the ratio has been increasing.
I expect a similar thing will eventually happen with the Chinese yuan, but their government will keep it pegged to the dollar for some time to come.

African corporate farming

I was thinking about farming in Africa this morning after reading a Slashdot blurb, "How to Feed the World".  The problem with most farms in Africa is that they do not use modern farming techniques, which are much more efficient.  I think if I had a million dollars to invest, using it to create highly productive corporate farms in Africa would be a reasonable idea.  They have cheap labor, which would elliminate some of the need for expensive machinery, oil, and high salaries present in the US.  Other beneficial factors include cheap land, plenty of sunshine, and lots of hungry people.  I suspect charities that presently ship food over to Africa to feed the poor and hungry would be happy to instead buy it from such an African corporate farm for less money, since they wouldn't pay for shipping costs and would employ the natives to grow their own food.  Putting more money into their communities would likely help pull them out of poverty and encourage further economic growth.  

In the last few years, Zimbabwe's white farm owners have had their land seized by the government and given to blacks to avenge past racial injustices.  Unfortunately the blacks generally don't know how to run the farms, leading to severe, widespread food shortages.  The whole problem of white ownership in a predominately black country might be mitigated by the corporate farm being owned by anyone willing to buy shares in it.  Also, much of the management and workforce would be black, and they may be partly paid in shares.

Snowfall in Central Park, New York

I was chatting with Dave the other day and he mentioned he was visiting www.tradesports.com during Christmas "dinner."  I had checked it out a few months ago, but not recently, and I found they have a section for betting on the weather, which stimulated my curiosity for some reason.  Presently, it only has one sub-category, snowfall in Central Park for various months or the whole season.  I have only briefly been in New York City to change planes, so I'm not exactly an expert on snowfall there, but the folks at Noaa have a nice collection of historical data, going back over 133 years.  I imported this data into MS Excel and played with it.

The resulting spreadsheet may be downloaded.  It shows the amount of snow that fell in Central Park each month since the season of 1869-1870, the maximum months for each season, the seasonal totals, the monthly averages, etc.  There are some columns to the right with zeros (false) and ones (true), which indicate if some event happened for the given season (i.e. row), such as "Was the seasonal snowfall at least 30 inches for 2002-2003?"  Below, the probabilities of these events are estimated by averaging the zeros and ones for each column.

I do not have this years' snowfall numbers in the table.  But I read elsewhere that December of 2003 has so far been 19.8 inches, which is well above the average, 5.3", and median, 3.1".  The most obvious implication is that the total for the year will probably be in the ballpark of 19.8" plus the mean or median snowfall from Jan to Apr, i.e. 19.8"+16.8"=36.6" to 19.8"+21.9"=41.7".  I computed the raw probability that the snowfall would be least 30", 40", 50", and 60", which are ongoing wagers at tradesports.com.  The results were 38.06%, 20.90%, 9.70%, and 2.99%.  Next I computed the conditional probability that the seasonal snowfall will be at least 30", 40", 50", and 60", given that at least one month has received 19" or more snow and found the conditional probabilities to be 89.66%, 37.61%, 25.49%, and 14.29%.  The last traded values at tradesports.com, as of this writing, are 80%, 40%, 23%, and 13%, suggesting that contracts for SNOW.NYC.SEASON+30in should be bought, since there is nearly a 10% difference.

Another apparent implication of the large snowfall in December is that the expected snowfall in April is reduced.  I am not sure if this is a mere coincidence of the data or a symptom of some underlying natural phenomenon.  My estimation of the probability of April having an inch or more of snow is 12.3%, which is 8% below the normal average of 20%, and 10% below tradesports.com last value of 22%.  So I presently recommend selling SNOW.NYC.APR+1.0in.

2004-1-1 17:15  
ADDENDUM:  I reviewed online information on calculating the statistical significance of a correlation, such as the pages at Vassar by Richard Lowry.  Using the CORREL function in MS Excel, I calculated the correlation between the inches of snowfall in december and the snowfall in April to be r=-.12.  The probability that the correlation occured by chance is traditionally done by computing a "t value" from the number of samples, N, and the correlation value, r.  This t value may be computed from the equation t = r * sqrt((N-2)/(1-r*r)).  Applying this value to a t-distribution table, the probability may then be found.  A simplier approach is to use web pages instrumented with Javascipt for computing the probability, which is what I did until I realized that MS Excel had a TDIST function.  The probability that a correlation of r=-.12 for N=133 samples would randomly occur when there is actually no underlying correlation is unfortunately 16.8% -- well above the typical 5% maximum for signficance used in scientific research.  However, the correlation of the event "more than an average amount of snowfall in December" with the event "at least 1 inch of snow in April" is r=-.17, and the probability of this happening by chance is only 5%.  So don't bet the farm by selling SNOW.NYC.APR+1.0in, but there may be something there.  Other months have less correlation with December.   Of course the season total reaching 30, 40, 50, and 60 inches is strongly correlated to a higher than average snowfall in December as it is to the event that any month has at least 19 inches of snowfall.

2004-2-17 22:45  
ADDENDUM:
Even though the season is still months away from being over, there has been well over 30 inches of snow.  So if you followed my recommendation to buy SNOW.NYC.SEASON+30in, then you would have made 25 cents per dollar gambled (since at the time, the probability was at 80%, which means 4:1 odds).