Comparison of Large Lotteries (Largest Jackpots)

After comparing the payout percentage of large lotteries taking into account taxes (if any), we would like to show the largest jackpots observed in these lotteries taking into account the following:

1) Cash payment (US lotteries are marketed using annuity values)
2) Net of taxes (if any), highest personal tax rate of 35% assumed for US lotteries
3) Conversion into USD (using year-end exchange rates obtained from here)

Accordingly the values are not comparable with those seen on other sites (see here and here).

Max JP	DE-6X	EU-EJ	EU-EM	US-MM	US-PB	BR-MS	AU-OZ
	EUR	EUR	EUR	USD	USD	BRL	AUD
2012	19.1	27.0	190.0	471.0	384.7	244.8	112.0
2013	27.1	46.1	187.9	347.6	370.9	224.7	70.0
2014	26.9	61.2	190.0	230.9	242.2	263.3	30.0
Net of Tax	DE-6X	EU-EJ	EU-EM	US-MM	US-PB	BR-MS	AU-OZ
	EUR	EUR	EUR	USD	USD	BRL	AUD
2012	19.1	27.0	190.0	306.2	250.1	244.8	112.0
2013	27.1	46.1	187.9	225.9	241.1	224.7	70.0
2014	26.9	61.2	190.0	150.1	157.4	263.3	30.0
in USD	DE-6X	EU-EJ	EU-EM	US-MM	US-PB	BR-MS	AU-OZ
	USD	USD	USD	USD	USD	USD	USD
2012	25.4	35.9	253.0	306.2	250.1	119.5	112.0
2013	37.3	63.4	258.7	225.9	241.1	95.1	62.1
2014	32.7	74.4	230.9	150.1	157.4	98.0	24.5

The largest lottery jackpot in 2012 was Mega Millions (USD 656 million annuity value gross of tax), but both in 2013 and 2014 EuroMillions had the highest jackpot of USD 258.7 million and USD 230.9 million respectively.

For average jackpots, EuroMillions has the highest numbers for all three years.

Avg JP	DE-6X	EU-EJ	EU-EM	US-MM	US-PB	BR-MS	AU-OZ
	EUR	EUR	EUR	USD	USD	BRL	AUD
2012	5.3	15.5	54.5	42.6	70.4	18.4	15.3
2013	8.6	18.0	42.9	50.3	72.4	18.9	13.8
2014	7.7	23.3	49.2	55.2	61.4	27.0	9.3
Net of Tax	DE-6X	EU-EJ	EU-EM	US-MM	US-PB	BR-MS	AU-OZ
	EUR	EUR	EUR	USD	USD	BRL	AUD
2012	5.3	15.5	54.5	27.7	45.8	18.4	15.3
2013	8.6	18.0	42.9	32.7	47.1	18.9	13.8
2014	7.7	23.3	49.2	35.9	39.9	27.0	9.3
in USD	DE-6X	EU-EJ	EU-EM	US-MM	US-PB	BR-MS	AU-OZ
	USD	USD	USD	USD	USD	USD	USD
2012	7.1	20.6	72.6	27.7	45.8	9.0	15.8
2013	11.8	24.8	59.1	32.7	47.1	8.0	12.2
2014	9.3	28.3	59.8	35.9	39.9	10.1	7.6

Distribution of Federal Council Seats in Switzerland

There will be federal election in Switzerland in 2015 and one of the hot topics is already how the seven seats of the federal council will be distributed. According to Wikipedia

The Federal Council is the seven-member executive council which constitutes the federal government of Switzerland and serves as the Swiss collective head of state. While the entire council is responsible for leading the federal administration of Switzerland, each Councillor heads one of the seven federal executive departments.

By informal agreement, the so called magic formula, the seats of the of the federal council are distributed between the four largest party as follows:

In Swiss politics, the magic formula is an arithmetic formula for dividing the seven executive seats of the Swiss Federal Council between the four ruling parties. The formula was first applied in 1959. It gave the Free Democratic Party (now FDP.The Liberals), the Christian Democratic People's Party and the Social Democratic Party each two seats, while the Party of Farmers, Traders and Independents (now the Swiss People's Party) received one seat.The formula is not an official law, but rather an agreement amongst the rather large coalition of four parties. 

It has been suggested to modify the magic formula as follows:

• Allocation of two seats each to the largest three parties and one seat to the fourth largest party
• Allocation of the seats according to proportional allocation rules to all parties

In a previous post we discussed the different forms of proportional allocation rules and will no apply these to the Swiss federal council allocation. For this calculation we use the data from October 3, 2014 as published here:

The parties have been allocated to left (L), center (C) and right (R) and grossed-up to 100% (the difference to 100% is made up by a dozen of small and very small party votes):

Note than in Swiss politics the term "liberal" has the classical liberal (say Hayekian) connotation and not the US usage for left parties.

For the quota methods all four (actually three because Droop and Hagenbach-Bischoff quota are identical because of the large number of votes) yield the same allocation of seats, 2 to the left, 2 to the center and 3 to the right, details below:

For H-B and Imperiali the seats are fully allocated without the need to use the largest remainder (actually the Imperiali quota almost results on a overallocation of seats, a well known potential problem). The the Hare quota the one unallocated seat goes to the centter where the largest remainder is highest (0.79 versus 0.11 and 0.10).

For the divisor methods the calculation results as follows, also in all cases resulting in the same allocation, 3 to the right, 2 to the center and 2 to the left.

We will repeat the calculation for the individual parties in a future post, in which case there will be differences between the different methods.

It should also be noted that the magic formula is a broad agreement of the large parties for proportional representation in the federal council, but specifics have never been agreed, including

• voting threshold (four largest parties, 4% of votes, 5% of votes, none)
• apparentment to three blocks (apparentment is typical is Swiss voting systems, but generally only for the legislative)

Comparison of Large Lotteries (Net Payout)

We are comparing the payout to lottery players of six large lotteries.

EuroMillions (Europe): ticket price EUR 2.00, 50% of revenues are distributed to players, whereof 40.6% (20.3% of 100%) goes to the JP class, winnings are tax free (with the exception of Portugal, Spain and Switzerland)

Eurojackpot (Europe): ticket price EUR 2.00, 50% of revenues are distributed to players, whereof 48.0% (24% of 100%) goes to the JP class, winnings are tax free is some countries

Mega Millions (USA): ticket price USD 1.00, 50% of revenues are distributed to players, whereof 32.58% (32.58% of 100%) foes to the JP class, winnings are subject to 25% federal withholding tax and tax free in some states
(note that JP winnings are subject to the 35% highest marginal tax rate for ordinary income, but likely taxed at a lower rate for junior classes, for comparison sake we will assume 35% for the JP class and 15% for all other classes) 

Powerball (USA): ticket price USD 2.00, 50% of revenues are distributed to players, whereof 63.95% (31.98% of 100%) goes to the JP class, winnings are subject to 25% federal withholding tax and tax free in some states
(note that JP winnings are subject to the 35% highest marginal tax rate for ordinary income, but likely taxed at a lower rate for junior classes, for comparison sake we will assume 35% for the JP class and 15% for all other classes) 

Mega Sena (Brazil): ticket price BRL 2.50, 33.3% of revenues are distributed to players, whereof 62% (19.1% of 100%) goes to the JP class, tax is already incorporated as per below calculation
(note that the 33.3%  is a weighted average of 30.8% for the two senior classes and 44.0% for the junior class, 30.8% is the result obtained by calculating 32.2% divided by 104.5% and 44% is the result of the sum of 30.8% plus 13.8% divided by 104.5%, the 13.8% represents the imputed tax on the two senior classes) 

OZ Lotto (Australia): ticket price AUD 1.20, 55% of revenues are distributed to players, whereof 40.0% (24.75% of 100%) goes to the JP class, winnings are tax free
(60% represents the result of 5% multiplied with the ratio of 1.1 divided by 1.2 plus 55% multiplied with the ratio of 1.1 divided by 1.2, AUD 1.2 is the ticket price whereof  AUD 0.1 is the sales commission) 

This results in the following overview:

On an expected basis lotteries have obviously a negative value for the players, but apparently positive expected utilities. Having said this, Australian lotteries (here Oz) come in the first place followed by the European lotteries. The US and Brazilian lotteries offer poorer value, although the very high jackpots compared to other lotteries may improve the utility.

Copper Price Forecasts by Cochilco (2014 Update)

This is the third post in a series (2013, 2012) of Cochilco's copper price forecasting. Their quarterly forecasts can be found here.

As in previous years we compare
a) the deviation of the effective (average) copper price for a given year and and the spot price six months before the beginning of such year, with
b) the Cochilco forecast for the (average) copper price for a given year made six months before the beginning of such year and and the spot price six months before the beginning of such year

As can be seen in the graph there has been less volatility and Cochilco has been able to forecast the right direction for the last four years. Additionally and as typical for forecast the magnitude of the forecast is generally significantly underestimated.

Country Index-itis

Too many country indices out there. Just in the last few days I saw the following indices:

Global Peace Index (2014 version)

Good Country Index (2014, table from the Economist)

I´m questioning whether these indices have any real uses.

Separately, it seems they all end up with the same results with the Scandinavian countries, Switzerland and Canada/Australia/New Zealand in the top 10.

Patent Registration by Latin American Inventors and Applicants - Follow-up

In an earlier post, we looked at patent registrations by Latin American investors and applicants. Earlier this week there was an article in one oh Chile´s leading newspapers (El Mercurio) analying the same topic. While the results are broadly consistent, details differ substantially. Our assumption is that the main reason for the differences are the use of a different database: EPO in our case, INAPI (Instituto Nacional de Propiedad Industrial, the Chilean patent office) in the case of "El Mercurio".

We will only provide an update of the EPO numbers in the beginning of 2015.

World´s Largest Container Fleets

Alphaliner publishes a list of the largest container fleets. The top 10 fleets are are follows. The three largest fleets are from Denmark, Switzerland and France respectively. 5 of the top 10 are Asian operators, 4 European and 1 US. CSAV from Chile used to be in the top 10, but has over the years dropped to the 20th position and has just agreed to merge with Hapag-Lloyd.

Nuclear Threat Index (NTI) - 2014

The Nuclear Threat Index is published by the NTI (Nuclear Threat Initiative). The index data for 2014 can be downloaded here.

For the 25 countries with nuclear material, the results are as follows (light green highest score = Australia followed by Canada and Switzerland, red lowest score North Korea followed by Iran and India).

Correlation of Economic Growth with Population Growth

It is trivial that population growth and economic growth are (strongly) correlated. We wanted to reconfirm this relationship with actual data and also analyze the more crucial question how population growth and per capita economic growth are related.

Results significantly depend on the choice of countries and reference period (for measuring economic growth and population growth).

We decided to use the data from the Credit Suisse's research Institute, namely the Credit Suisse Global Investment Returns Yearbook 2014 (actually panning a second post to verify Piketty´s r>g claims).

Credit Suisse data is for the period 1900-2013 and itself based on Maddison (which we had previously used). Obviously there is significant uncertainty with regard to concept of economic growth in the early part of the 20th century (Kuznets' initial calculation for the US were made in 1937).

The data for the 21 countries in the data set looks as follows:

And the followoing correlation matrix confirms:
- positive correlation between population growth and GDP growth (correlation coefficient of 0.65)
- negative correlation between population growth and GDP growth per capita (correlation coefficient of -0.45)

Usual caveat: correlation is not causation and if there is causation we don't which way the causation runs

Nevertheless, the results seem to explain why elites favor population growth while the population is more ambivalent.

World´s Smartest Cities

The IESE publishes an annual ranking of the world´s smartest cities. Here are the top 10 cities with their respective ranks in 2011, 2012 and 2013. Tokyo, London and New York are top 3. Switzerland has 3, Japan 2 countries in the top 10. Santiago de Chile is only ranked #83 (although ranked first in Latin America).

Supercomputer Ranking and Density

Top 500 Supercomputer Sites publishes a cool overview of the 500 most powerful (measured in Terra Flops) worldwide.

Here are the top 10 with five computers from the US, two from Germany and one each from China, Japan and Switzerland.

Looking at the whole list (500 super computers) the country distribution is as follows with the US dominating followed by China and Japan.

An a per capita basis, the US is still leading followed by Switzerland and Norway. The relatively low density of Germany and Japan may be surprising.

Patent Registration by Latin American Inventors and Applicants

The European Patent Office allows to search patent filing by a number of criteria, including country of inventor and applicant, publication data and classification (the search is for worldwide patent offices).

The following provides ans overview of Latin American (plus Jamaica, Trinidad & Tobago, French Guiana, Guayana and Suriname) countries in comparison with Spain and Portugal. Both the absolute number of filings and the filings per capita are shown (the data is shown for 2013 and the criteria was that at least one of the applicants and one of the inventors came from the respective country):

The leading country is Uruguay followed by Chile and Trinidad & Tobago. Compared to Portugal and especially Spain, there is still significant catching-up to do in Latin America (noting the Portugal and Spain also have a relatively low patent rate compared to the leading countries). The same information is shown graphically:

For Chile the development since 1990 has been as follows (significant increase since around 2000):

In terms of categories the distribution is as follows:

Luxury Car Density (2014 Update)

The following provides an update to an analysis we made in 2012, looking at the luxury car density across countries, posted here and here.

Again we looked at the number of listings on the OOYYO Network. This time we reduced the brands included only six main brands from Italy (Ferrari, Lamborghini and Maserati) and UK (Aston Martin, Bentley and Rolls Royce).

The density calculated as the listings on the site for the referenced brands divided by the total listings looks as follows (see map and table). The observations made in 2012 are repeated:

• very high densities for Luxembourg, Switzerland and Singapore
• high rates for UK and Italy
• medium-high rates for Western Europe, Sweden and Canada
• medium-low rates for Scandinavia (ex Sweden), US, Australia and New Zealand

Outliers include Lithuania, Malaysia, Slovakia and Nigeria. Note the some of these outliers might be driven by the fact that the density relative to all offered car is shown (and not per capita).

Without further comments, the map looks as follows:

And here the data table:

Preferred Podcasts

The following provides an overview of my favourite podcast (in English and German):

• Common Sense with Dan Carlin (US)
• Dan Carlin's Hardcore History (US)
• BBC Documentaries (UK)
• SRF Echo der Zeit (Switzerland)
• Economist: Editor's Pick (UK)
• EconTalk (US)
• SRF International (Switzerland)
• BBC More or Less: Behind the Stats (UK)
• Nature Podcast (UK)
• NPR Planet Money (US)
• NPR TED Radio Hour (US)
• SFR Trend (Switzerland)
• FT World Weekly (UK)

So far I have not encountered a podcast in Italian, Spanish or French which I found worth listening too (only referring to languages I would be able to understand).