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More on the McCain Health Care Plan

I have posted a deliberately unsophisticated discussion at my other blog

After the jump, I try to add something to Tom Bozzo’s post below (which you should read first because he actually knows something about the issues).

Here I take as fact Buchmueller et al’s assertion (as summarized by Bozzo but at least I admit it) that a typical family health plan costs employers $12,000 and a similarly generous plan costs $14,000 on the individual market.

I think about how employers might respond to the McCain plan.

First I’m sure many will just keep on keeping on providing health insurance to employees with the same limited range of options (maybe one size fits all, definitely “no thanks, I’ll get mine on the individual market” is not an option). The plan will simply be a tax increase for their employees*. I think the McCain team sincerely thinks this is no big deal, because they think tax treatment is the real reason for employer provided health care, so they assume few employers will be so dumb and no one will want to work for them unless they increase wages to bear the cost of the tax and that would drive any really stubborn firm bankrupt.

Basically, I think they assume that either employers will have nothing to do with health insurance or, at least, they will give employees the option of taking the benefit as cash instead of health insurance.

I think that this is about the same thing, to a rough approximation. The advantage to insurance companies of selling insurance to employers is that the employees don’t have a choice and so the insurance companies don’t have to market or, more importantly, worry about adverse selection. I think that, quickly if not immediately, insurance formally provided by the employer but with an opt out, will vanish as the important lasting difference from the individual market will just be the $5,000 credit.

OK so why did I slip in the weasel word “lasting”. Well given how scared people are of the individual market, I think it would take a long time for the more healthy to all opt out of employer provided coverage, so the adverse selection in the employer provided pool will only slowly become as severe as that in the individual market.

So I claim an either/or either a lot of people will have employer provided insurance whether they want it or not, so the plan is a tax increase on them, or the plan will, for practical purposes, eliminate employer provided health insurance.

Now, assuming the extra $2,000 calculation is accurate, where does the money go ?

I’m sure only a small part of it goes to health insurance company profits. I would guess that the individual health insurance market is quite competitive and the profit rate, while probably higher than in group markets due to bargaining power, is nowhere near 28% of sales higher.

Some goes to increased marketing administrative and screening costs and amounts to pure inefficiency.

Some goes to the free riders who get a (partially) free ride. That is the other side of adverse selection is that some healthy people benefit by not having insurance. If they are insured they subsidize the less healthy. If they are not they get some health care without paying for it (if they go bankrupt or if the provider gives up on chasing after them to pay the bill).

If Buechmuller et al are right then, there is another either/or.

Unless the McCain plan increases the number of uninsured, it would cause the US to waste about $2,000 per family that currently has employer provided coverage. Not as huge a waste as the Iraq war but well over $100,000,000,000/year (way more than total earmarks which aren’t pure waste).

The actual waste will be lower as part of the cost to (employers + the treasury + the insured) will be an increased transfer to the increased number of free loading uninsured. It will also be lower as some people will stick (or be stuck) with employer provided insurance with no opt out and so they will just pay more in taxes.

But the plan is partly a tax increase, partly a windfall for freeloaders and partly increased waste.

Importantly, the McCain supporters argument that the plan will benefit tax payers is based on assumptions about responses such that it will reduce tax revenues. It is typical Republican logic to assume that cutting taxes gives benefits to taxpayers at no cost to anyone ever, but it is still nonsense.

Now what is the logic of serious economists who helped design the McCain plan (Douglas Holtz-Eakin is, for all I know, not the only serious economist on the McCain team, and he is serious or, at least, he was, when they were working on the plan).

The argument is that, on the individual market, people will purchase plans less generous than the one they get from their employer. That their having skin in the game will make their health care consumption choices more efficient and that this outweighs the increased costs of marketing, administering and screening. Basically I find this argument absurd, because it if it were true one would imagine that the US health care is more efficient that that of other developed countries and it obviously isn’t. But that is very crude data. To get more micro (much more micro) I suspect that underuse of health care (statins, oral anti-diabetics, insulin, anti-hypertensives, antacids that actually work and prevent ulcers, cancer screening etc etc etc) is more costly than overuse (even though the cost of accelerated morbidity is just interest on the cost of people getting deathly ill as most do before dying). Experimental evidence (the RAND study) suggests that copayments reduce demand equally for necessary and un-necessary treatment.

* and update: I was totally confused about the McCain plan. Sorry. Should have done my homework. If one still gets insurance from one’s employer, the change is pay income tax but get the $2,500 or $5,000 for a family. For most people, this is a tax cut. It is a tax increase only for people in the 33% bracket (over $200,000/yr if married filing jointly) with more costly than average insurance. The way McCain manages to cut almost everyone’s taxes while giving a lot to those with low incomes and/or without insurance is by adding 1.4 trillion More to the 10 year budget deficit.

He’s addressing the problem by throwing money at it.

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Dynamically Inconsistent Preferences and Money Demand

Emanuele Millemaci and Robert Waldmann

This paper focuses on two main issues. First, we find that, on average, households’ discount rates decline. This implies dynamically inconsistent preferences. Second, we calculate an indicator of the degree of dynamic inconsistency that may help us to understand how households overcome their self-control problems. We use a micro dataset containing households’ reports on the compensation for receiving hypothetical rewards with delays. We find that individuals with more severely dynamicly inconsistent preferences on average hold a statistically significantly lower share of their total wealth in checking accounts. A possible interpretation is that subjects use precommitment strategies to limit their temptation to consume immediately.

I was waiting for the fuss about Lehman to die down to post this abstract which has little to do with the crisis. Now I’m afraid that it would be a long long wait.

A less abstracted abstract of the paper after the jump.

Please download the whole paper (warning pdf)

OK so there is this wonderful underused dataset from CentER via Luigi Giamboni (warning pdf).

It includes a question on what return people would demand in order to wait 3 months for cash and in order to wait 12 months (no cash really changed hands so it is just a survey not an experiment). One can calculate an annual required interest rate from the answer two the wait three months question. Very often this was much higher than the required annual rate. This means that respondents had dynamically inconsistent preferences. That means (in English) that, given their stated preferences, they would like defend the interests of their 12 months later selves by preventing their 9 months later selves from spending as much as said 9 months later selves would like to spend. If one has dynamically inconsistent preferences one would like to tie one’s future hands.

A sophisticated agent who knows that he or she has dynamically inconsistent preference will find ways to restrict his or her future choices. For example, people with weight problems go to fat farms, People pay for residential drug treatment, drug addled (but not completely addled) celebrities hire dissenablers to prevent them from using drugs etc etc.

If one is worried about future consumption one’s desire for liquidity may actually be negative for some levels of liquidity. I might want to tie my money up in a non liquid asset, say a house, because otherwise I won’t be able to keep myself from spending it. Cash and the balances of checking accounts are very liquid and people with spending problems may rationally choose to hold an unusually small fraction of their wealth as checking account balances.

Why lo and behold the computer says this is true. The coefficient of a household money demand equation on the dynamic inconsistency index is negative and statistically insignificant.

Who ever would have thunk ? Well I would have for 24 years by now, but I never found the data to test the hypothesis.

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Why Doesn’t Europe Have Financial Crises ?

Kevin Drum asks

But here’s a question for one of the serious econ-bloggers out there: Have lots of big non-U.S. banks collapsed? There was Northern Rock, but anyone else? Are any European financial systems in danger of meltdown? Why not?

I like to think of myself as a serious econ-blogger, but don’t claim the title when I am writing about banks. We clearly have brilliant what’s wrong with the banks bloggers here.
I live in Europe and I have a sense about why this doesn’t happen here.

First we have to distinguish the UK and Continental Europe as the UK is much more like the USA. I will talk about Continental Europe (the easy part).

My thoughts, for what they are worth, after the jump.

Anyway serious bloggers on banking here are warmly invited to try to answer Drum’s question.

First the banks that are failing in the USA are investment banks not depository institutions. They are less tightly regulated, that is, allowed to take more risks. This is partly a relic of the Glass-Stegal act, which made it illegal for depository institutions to own stock. It is possible to set up and investment bank anywhere (no laws against it) but few pop up naturally without the protection of such a law — bank deposits are an attractive form of debt as people accept very low returns on them and they are publicly insured (note UK exception above). In particular in Continental Europe banks suck huge amounts of money out of checking and savings accounts for fees for everything and pay very low interest (my expertise is in being irritated by my bank). The commercial banking sector in Europe is much less competitive than in the US or UK. Now with European unification this might change and then there might be bankruptcies, but up until now, Continental European banks have cash cows which can keep them solvent when they make huge errors playing the market (as they do). The gap between the deposit rate (paid by banks) and the mortgage interest rate is tiny in the USA compared to most countries, so banks don’t have huge average flows of cash to keep them solvent.

Second, the sort of private bailout (Bailin in Roubini’s terms) that failed this weekend would have worked on the Continent. If the minister of the treasury or the president of the central bank asks big banks to take some losses for the good of the financial system they will. This is because good relations with the state are very valuable. What they get in return is not glaringly obvious every day, but it is worth it. For example, nothing is done about the lack of competition.

Third, aside from that, giving public money to companies at risk of bankruptcy and maximizing moral hazard is a long Continental European tradition. EU rules make it much more difficult now, but for decades, companies in Europe didn’t dramatically fail, because the treasuries bailed them out without humiliating them by making it clear what was going on.

Fourth, fierce competition among commercial banks and non-bank mortgage companies and such has implied that they loan to individuals who are not very credit worthy. This means that ordinary Americans are incredibly indebted (see Mooser’s comment stolen by me and linked by Rdan below). This is a root cause of the problem.

Fifth, US banks have designed incentive systems which make it rational for bankers to do things that will occasionally cause a crisis. Bankers and traders are rewarded for performance. This, in practice, means they are rewarded for taking risks as if the bet wins, they share the winnings, but if they lose big the worst thing that can be done to them is to fire them. In particular a collapse of the system can’t be blamed on any one person, so almost no one pays. That is it is OK to lose when everyone is losing, because not everyone can be fired.

A ‘sound’ banker, alas! is not one who foresees danger and avoids it, but one who, when he is ruined, is ruined in a conventional and orthodox way along with his fellows, so that no one can really blame him.


Of course, if you ask Keynes, he would say it is obviously because Americans are just stupid and inferior. He was wrong about that, but had a point about bankers.

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More on infant mortality: What’s with the USA

A post based on joint work with Tilman Tacke got 45 comments which is a lot for one of my posts. Many were excellent. One weak point is that people seem a bit obsessed with the USA. The post discussed cross country regressions with 71 countries (The first comment started “Why only 71 countries?”). The USA is just one data point and did not drive the result.

OK so now I will look at the USA in the context of the 71 other countries. First, as we all know, the USA has an alarmingly high reported infant mortality rate given how rich we are (see table 1).


lnim is the natural logarithm of the infant mortality rate, lnpcgdp is the log of real per capita GDP corrected for purchasing power parity, year is the year infant mortality was measured, USA is an indicator for the USA.

The coefficient on the US indicator is statistically insignificant (this is just one observation of US infant mortality and we are alarmed because the pattern persists over time). The point estimate states that the US infant mortality rate is 76.8% higher than one would expect given per capita real GDP and the year. Is is possible to learn something about what is going on with simple OLS regressions of a cross section of 71 countries ? If you want to decide the answer read on.

One possible explanation, which I can’t address, is that this happens because the US counts births as live births followed almost immediately by death when other countries count similar tragedies as still births or late miscarriages.

A very natural guess is that this has something to do with high inequality in the USA. This guess shows that the obsession is with the USA compared to Europe and Japan. Compared to the average country in our data set, the USA does not have huge inequality (the share of income going to the top quintile in the USA is 46% which is less than the average among the other countries — 47%. US inequality is anomalously high compared to other rich countries, or, in other words compared to the expected level given the Kuznets curve. In fact, controlling for income distribution by considering separately the log per capita income of different quintiles has almost no effect on the coefficient on the USA indicator (but it does explain why there are only 71 countries in the sample).

Now notice my personal obsession. The coefficient on lnq5 (log per capita income of the fifth quintile) is positive and borderline significant. This is a pattern that was found in the old data by uhm me, not found in data from the 80s and early 90s and now it’s BAAAACK ! But not as robust as it was in old data (very robust it was ah I remember).

see table 2

OK so what is it ? We have some other variables related to infant mortality. Let’s toss one in. How about female obesity ? The USA is, you know, the fattest country in the world. This causes the coefficient to drop see table 3.

How about hiv prevalence — a bit high in the USA for a super rich country. This causes a very small decrease in the coefficient (see table 4).


How about public health care spending as a percent of GDP, the USA is a bit low compared to STATA’s guess, because the level is similar to that of other rich countries and the US is richer than other rich countries (except for Luxembourg which isn’t in the sample).

That gets the coefficient down to 0.33 slightly over half the original coefficient and corresponding to an increased risk of 40% of the predicted infant mortality rate. See table 5.


So a few variables (really 2 as hiv prevalence doesn’t do much) account for about half of the mystery in an OLS sense.

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Public and Private Health Care Spending and Infant Mortality in 71 countries

by Tilman Tacke and Robert Waldmann

We don’t know if someone else has noticed this amazing fact: in a cross country regression, the ratio of public health care spending to GDP is negatively correlated with the infant mortality rate as one would expect, but the share of private health care spending in GDP is positively correlated. In a simple regression with including only log per capita GDP (corrected for PPP) as an additional explanatory variable, both coefficients have large t-statistics.

The positive coefficient on private health care spending becomes insignificant when other variables are included, but it does not become negative. The result is not due to the USA which is an extreme outlier in private health care spending over GDP.

The result is not simply due to a correlation between high public spending and low income inequality as it holds when log per capita GDP is replaced by log per capita income of each of the lower 4 quintiles (this is our original regression hence the low number of countries in the sample).

Update: I hope this version of the table is legible


update: an illegible version of the table above was deleted.

update II: Poorly labeled graphs added

OK two plots

Infant mortality and Public health care spending


the one above is of residuals of ln infant mortality on ln pc GDP, the year and private health care spending as a percent of GDP on residuals of public health care spending as a percent of GDP on those variables

The other (below) is of residuals of ln infant mortality on ln pc GDP, the year and public health care spending as a percent of GDP on residuals of private health care spending as a percent of GDP on those variables

infant mortality and Private health care spending

lnim private health care spending AV plot

Note two countries with high leverage due to very high private health care spending compared to what one would expect given the other variables. The most extreme is the USA which also has higher infant mortality than one would guess given per capita GDP, the year and public health care spending. The second most extreme is Uruguay which has the infant mortality one would expect given the other variables.

If I drop them the coefficient on private health care spending as a fraction of GDP goes up (you can see that it would in the scatter) to 0.150 from 0.134. The t-stat *increases* from 3.12 to 3.16 which is not what I expected.

dumping just the USA (which I have done ten times by now) gives a coeff of 0.117 and a t-state of 2.85.

The country with the highest infant mortality residual is South Africa. Adding HIV prevalence to the regression is one of the things that reduced the size of the strange coefficient on private health care spending. Dropping the USA and South Africa gives a coefficient of 0.0916 with a t-stat of 2.16, that is the USA and South Africa together provide a substantial part of the apparent evidence that private health care spending is bad for health.

After the jump I will report regressions done partly in response to comments. Right now I will not even try to make them easily readable (sorry) but just cut and paste STATA output with minimal explanation of variable names. Sorry. Click at your own risk

In response to comments partly.

First comment “why only 71 countries”. The answer is that we were looking at income distribution and infant mortality so we only used countries where we had estimates of quintile shares of income (q1 is share of first quintile, q2 second etc). Without consulting Tilman, I decided to replace the log per capita real income (corrected for ppp) of different quintiles with log per capita real GDP (corrected for PPP) to make a simpler table and to focus on health care expenditures. This causes the strange positive t-stat on private spending to become alarmingly large, that is, part of the positive coefficient is due to correlation of private health care spending and inequality. In my original post, I wrote that the coefficient was very non robust. This is partly because I only really think about regressions which control for inequality. Ignoring inequality (which is crazy) it is robust to many other variables with the exception of continent dummies and a combination of infant mortality related variables (each one doesn’t do it see comment thread for details).

Our original regression

reg lnim lnq1 lnq2 lnq3 lnq5 hexprivate hexpublic year

Source | SS df MS Number of obs = 71
————-+—————————— F( 7, 63) = 53.05
Model | 55.8407871 7 7.9772553 Prob > F = 0.0000
Residual | 9.47313907 63 .150367287 R-squared = 0.8550
————-+—————————— Adj R-squared = 0.8388
Total | 65.3139262 70 .933056088 Root MSE = .38777

lnim | Coef. Std. Err. t P>|t| [95% Conf. Interval]
lnq1 | .1210792 .4716871 0.26 0.798 -.8215122 1.063671
lnq2 | -.0061793 .8658731 -0.01 0.994 -1.736488 1.72413
lnq3 | -.9650523 .5634675 -1.71 0.092 -2.091052 .1609477
lnq5 | .1299795 .2405159 0.54 0.591 -.3506532 .6106123
hexprivate | .0776184 .0389142 1.99 0.050 -.0001453 .1553822
hexpublic | -.1030077 .0416674 -2.47 0.016 -.1862733 -.019742
year | -.01175 .0529402 -0.22 0.825 -.1175424 .0940425
_cons | 31.50754 105.8999 0.30 0.767 -180.1165 243.1316

lnq1 is the log of the real pc income of households in the lowest quintile. Hex stands for health care expenditures as a percent of GDP.

Now to me, the interesting question is “Is private health care spending less effective than public health care spending” and certainly not ” is it actually harmful as suggested by this silly regression”.

To answer the interesting question we (OK Tilman) ran regressions including total health care spending and just private health care spending. the coefficient on private spending should measure the difference in effectiveness private minus public.

reg lnim lnq1 lnq2 lnq3 lnq5 hextot hexprivate year

Source | SS df MS Number of obs = 71
————-+—————————— F( 7, 63) = 53.05
Model | 55.8407871 7 7.9772553 Prob > F = 0.0000
Residual | 9.47313909 63 .150367287 R-squared = 0.8550
————-+—————————— Adj R-squared = 0.8388
Total | 65.3139262 70 .933056088 Root MSE = .38777

lnim | Coef. Std. Err. t P>|t| [95% Conf. Interval]
lnq1 | .1210792 .4716871 0.26 0.798 -.8215122 1.063671
lnq2 | -.0061793 .8658731 -0.01 0.994 -1.736489 1.72413
lnq3 | -.9650522 .5634675 -1.71 0.092 -2.091052 .1609477
lnq5 | .1299795 .2405159 0.54 0.591 -.3506532 .6106122
hextot | -.1030077 .0416674 -2.47 0.016 -.1862733 -.019742
hexprivate | .1806261 .0545836 3.31 0.002 .0715495 .2897027
year | -.01175 .0529402 -0.22 0.825 -.1175424 .0940425
_cons | 31.50754 105.8999 0.30 0.767 -180.1165 243.1316


the t-state on private health care expenditures is huge 3.31 (I know it is hard to see in the table).

This stands up to inclusion of a huge number of variables including dummies for regions

tab region2

region2 | Freq. Percent Cum.
East Asia & Pacific | 5 6.85 6.85
Europe & Central Asia | 31 42.47 49.32
Latin America & Caribbean | 19 26.03 75.34
Middle East & North Africa | 3 4.11 79.45
North America | 1 1.37 80.82
South Asia | 4 5.48 86.30
Sub-Saharan Africa | 10 13.70 100.00
Total | 73 100.00
reg lnim lnq1 lnq2 lnq3 lnq4 hextot hexprivate year femlit reg2* hiv doctors saniurban
> sanirural waterurban waterrural fobesity femalesmoking

Source | SS df MS Number of obs = 65
————-+—————————— F( 22, 42) = 17.65
Model | 50.353018 22 2.28877354 Prob > F = 0.0000
Residual | 5.44750771 42 .129702565 R-squared = 0.9024
————-+—————————— Adj R-squared = 0.8512
Total | 55.8005257 64 .871883214 Root MSE = .36014

lnim | Coef. Std. Err. t P>|t| [95% Conf. Interval]
lnq1 | .0883713 .6329048 0.14 0.890 -1.188882 1.365625
lnq2 | -.0743277 1.134967 -0.07 0.948 -2.364785 2.216129
lnq3 | -.8936729 2.256105 -0.40 0.694 -5.446678 3.659332
lnq4 | .413619 1.592167 0.26 0.796 -2.799505 3.626743
hextot | -.1096067 .0445435 -2.46 0.018 -.1994991 -.0197143
hexprivate | .1511602 .0677885 2.23 0.031 .0143575 .2879629
year | -.0110253 .0642753 -0.17 0.865 -.1407381 .1186874
femlit | -.0010556 .0059176 -0.18 0.859 -.0129977 .0108866
reg21 | -.1253023 .4024909 -0.31 0.757 -.9375617 .6869572
reg22 | .289227 .4351484 0.66 0.510 -.5889381 1.167392
reg23 | .1065467 .3954925 0.27 0.789 -.6915895 .9046829
reg24 | .1840681 .4318435 0.43 0.672 -.6874274 1.055564
reg25 | .4121691 .6886778 0.60 0.553 -.977639 1.801977
reg26 | .0590227 .3634298 0.16 0.872 -.6744084 .7924538
reg27 | (dropped)
hiv | .0433275 .0307031 1.41 0.166 -.0186339 .1052888
doctors | -.0190481 .0762125 -0.25 0.804 -.1728513 .134755
saniurban | -.0039519 .0064778 -0.61 0.545 -.0170245 .0091208
sanirural | -.0092739 .0041118 -2.26 0.029 -.0175719 -.0009759
waterurban | -.0176387 .0130958 -1.35 0.185 -.0440672 .0087897
waterrural | .0052071 .0046397 1.12 0.268 -.0041561 .0145703
fobesity | .0013884 .0072406 0.19 0.849 -.0132237 .0160005
femalesmok~g | -.0019532 .0079665 -0.25 0.808 -.0180303 .0141239
_cons | 30.54749 128.6271 0.24 0.813 -229.0326 290.1275

The result is not not not due to the USA the variable reg25 is a dummy variable for the USA, since the USA is the only North American country in the sample. Dropping the USA from the regression has no effect on the results except that no coefficient on reg25 can be estimated (really zero effect I checked because I wasn’t thinking).

I would say that the evidence that private health care spending has a lesser effect on infant mortality than public health care spending is quite robust. There is no way to reliably determine the direction of causation or rule out omitted variables bias, but the robustness to inclusion of other variables convinces me that neither is the full explanation. This is relevant to reverse causation too if the newly included variable is the original cause of bad health which then causes high private health care spending. There is evidence in the data set that high HIV incidence causes high private health care spending. Including HIV incidence (the variable called hiv) does not zap the t-stat, so that’s not the whole story.

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Foreign films, Western Cultural Influence, and Divorce in Japan.

by Tilman Tacke and Robert Waldmann

Globalization has caused a decrease in cultural distinctiveness. We find indications of a link between the divorce rate in Japan after 1955 and the market share of foreign films. Foreign films in Japanese cinema, including Hollywood productions, may act as importer of Western values. The market share of foreign films has predictive power for three periods of decreasing divorce rates as well as the general convergence of the low Japanese divorce rates to higher Western levels. Using Japanese box office data since 1955 we show that a higher market share of international films is not only associated with an increase in the number of divorces, but also a decrease in the number of marriages. Both effects are especially strong before the 1980s. We explain periods of increasing and decreasing market share of foreign films and divorce rate with historical changes in cultural relations between Japan and the Western world.

pdf here


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The Silver Standard

Poblan Unico jamás será vencido

Extremely regular readers of AngryBear will have noticed that I became a guest contributor a while ago. Then I vanished. The reason is that I have become obsessed with Presidential Polling and don’t have much original to say about it. A dialog from last night between me and my 11 year old daughter

11) dad what are you thinking about
rjw) mmm
11) Obama right ?
rjw) yeah
11) why don’t you ever think about anything else
rjw) mmmm.

Now about polling. My favorite site (by far) if Very very impressive. I call it the silver standard, because, Nate Silver (aka Poblano) being a Democrat, would never support crucifying mankind on a cross of gold.

fivethirtyeight simulates elections and calculates a probability distribution for electoral votes won. He (they?) consider(s) both state level and nation wide disturbances. They note that polls tend to narrow over time. They estimate pollster reliability with data from actual voting (mostly primaries I think).

As of recently he also estimates pollster fixed effects or house effects “the tendency of certain polling firms’ numbers to tend to lean in the direction of one or another candidate”. This happens to be very important largely because the most prolific pollster — Rasmussen — has, relative to other pollsters a tendency to lean pro-McCain. They also had excellent performance in the primaries and count extra.

My one concern about is that the calculations are very complicated and not at all transparent. I would like to see some reporting on a larger set of simulations done with different assumptions. For example, the house effects correction seems to me to be conservative and I would like to see simulations with a more aggressive correction.

3) The house effect adjustment is enacted only in cases where we are at least 90% certain that there is a house effect. Even in these cases, we hedge our bets a little bit, by subtracting 166% of the standard error from the house effect coefficient.

I would rather see with/without house effects and the with house effects estimates with just the point estimates no setting to zero if not significant and no subtracting 166% of a standard error.

Silver links to and praises an article on house effects in national polls written by
Charles Franklin at (my second favorite site) . This is completely separate evidence of house effects, since fivethirtyeight’s raw data are state level polls. Rasmussen polls are significantly better for McCain than average polls.

much more after the jump.

OK here I just let go.

1. My problem with is that their trend calculations are waaaay too complicated. They use a Loess trend estimate (trend value at t estimated with weights depending on how long before/after t the poll was taken then report the fitted value for time = t). This means that new data shifts past values of the trend line which freaks me out. It also means that they say Obama is ahead in Ohio because it is about tied now and he used to be behind so they are convinced there is a significant trend. Also the initial estimates downweight outliers (not explained exactly how). I do not agree with doing this (see below). The calculations might be optimal but they are much too complicated to understand. I’d rather a point estimate based on averaging (weighted regression on a constant and no trend) and an estimate of the recent slope with a standard error reported as a number). Still, since I can get the recent simple average from I have no serious complaint (just one more url to type I do *not* hotlist pollsters)

2. What happened to the Gallup anomaly ? For years I have been reporting on the Gallup likely voter anomaly — Gallup polls are better for Republicans than other polls. In the Pollster analysis, Gallup is much less pro-McCain than is Rasmussen and is 4th best for McCain. The reason is simple. The Gallup anomaly is an aspect of the Gallup likely voter filter and the vast majority of Gallup polls reported so far are from the Gallup tracking poll of registered voters.

As Gallup explains every 4 years, their likely voter filter is not reliable long before the election. Gallup has been forced by the competition to report likely voter results earlier than they used to (I remember way back when). There was a very large huge Gallup likely voter anomaly in the poll conducted July 25-27 (click and search for USA Today in which Obama lead among registered voters and McCain lead 49 to 45 among likely voters. The likely voter pool was strongly biased against the young compared to actual votes in past presidential elections.

Gallup has an excellent record predicting elections. What this means is that the last Gallup polls before the vote are very accurate. That is, the likely voter filter works in late October. This does not mean that it works in August.

What is going on ? It is simple. Admirably, Gallup has stuck with the same method they used long long ago. This is transparent and honest (they aren’t using their success in the past with one method to justify their claims based on a new method). It is almost comprehensible how they decide who is a likely voter. The filter is based on answers to simple questions. from the Gallup FAQ

“These questions include asking whether or not the individual knows the location of his or her voting place, whether or not the individual voted in the past election, how closely the person is following the election, and so forth.”

Now obviously knowledge of the location of the voting place in August and in October will differ — some people learn where it is between August and October. It is not surprising that someone who claims he or she will certainly vote but doesn’t know where to go to vote in late October is not likely to vote. In August, the answer has, I would suspect, much more to do with how long the voter has been registered to vote at his or her current address than the probability that he or she will vote. The use of that question creates a selection against younger voters (and people who moved recently) stronger than the correlation of age and not moving and voting.

Even the assumption that eligible adults who are not currently registered will definitely not vote is dubious in August. There is still plenty of time to register.

In any case the evidence that the Gallup filter works in October tells us little about whether it works in August (as Gallup insists whenever asked and often when not asked).

There will be Gallup likely voter polls. There will be complaints about the Gallup anomaly. Democrats will be alarmed at Gallups excellent record (based on late October polls). It is all very simple and right there on the FAQ.

So what’s with Rasmussen ? Here a key feature is that they assume that partisan inclination (Dem Rep Independent) changes slowly so that differences from poll to poll in partisan inclination are mostly noise. They reweight so that the partisan inclination matches the average over the past 3 months.

Like all polling firms, Rasmussen Reports weights its data to reflect the population at large. Among other targets, Rasmussen Reports weights data by political party affiliation using a dynamic weighting process. Our baseline targets are established based upon survey interviews with a sample of adults nationwide completed during the preceding three months (a total of 45,000 interviews). For the month of August, the targets are 40.6% Democrat, 31.6% Republican, and 27.8% unaffiliated. For July, the targets were 41.4% Democrat, 31.5% Republican, and 27.1% unaffiliated (see party trends and analysis).

Now there is no need to smooth that much given the sample size (especially because Rasmussen could use data from other pollsters on party affiliation). This also shows the difference between a report which is optimal for the pollster and one which is optimal raw material for meta-analysis (here just fancy talk for averaging across pollsters). Weighting to make party affiliation fit a target reduces noise (increases precision) at the cost of introducing possible bias (if true support for the parties has shifted over time). For one poll the optimum balance may be to weight. However if one averages many polls (many Rasmussen polls or many polls across pollsters who do the same thing) the noise averages out and the bias doesn’t.

Now Rasmussen polls could be corrected by taking a more recent average (also across pollsters) of party affiliation and then using the internals (really simple like 90% of Dems for Obama and 90% of Republicans for McCain and easily available) to calculated a desmoothed Rasmussen based number.

The fact that they are making a very strong, very dubious assumption and have results which are strongly significantly different from the average pollster should give Rasmussen pause.

Finally note how quiet times are good for McCain. In quiet times the averages across pollsters are dominated by the Rasmussen and Gallup tracking polls. They both are more favorable to McCain than the average poll. Some of the alarm (among Democrats hope among Republicans) about Obama’s vacation, McCain’s celebrity campaign etc is based on this (I don’t know how much).

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Petroleum Speculation Thread N+1

I wrote something about crude oil, inventories and contango below and there were 57 comments including one, from Aaron which refered to this post in the future indicative.

A lot of the discussion is Krugman pro and con (mostly con). I think I will review my take on his arguments here as an introduction.

I had intended to write another post to clarify a particularly hand waving part of my old post (that is to report on a relative clarification in my own thoughts). I don’t think this is the focus of interest, so I will put that after the jump.

Krugman argued that the sharp increase in the price of crude oil was caused by increased demand and the fact that suppliers were already pumping just about all that they could pump (hence an almost vertical supply curve). I assume he thinks that the sharp decline is due to decreased demand. He is convinced that speculation in oil futures has not had a large impact on the spot price.

My recollection of his argument is that it was based on 4 claims (modified in part to respond to things I just learned skimming the older thread).

1) Oil is consumed and storage costs are significant. This makes analogies to housing, Nasdaq and tulips inappropriate.

2) Certainly people speculate in oil futures. The question is whether this is currently moving the spot price far from where it would be without speculators.

3) Pricing rules can determine prices, but don’t shift supply and demand curves. If spot prices move up automatically following futures prices, one would expect supply to exceed consumption — that is growth of inventories.

4) When an almost vertical supply curve meets and almost vertical demand curve, supply and demand can cause prices to move quickly huge amounts back and forth.

OK back to my “model”. Just to recall, the model assumes that the oil companies have formed a cartel and that it has become more difficult for them to keep each other in line. The driving force is low expected excess capacity (to ship and refine oil by them or to pump it out of the ground for their suppliers) makes it hard to punish a company which sells petroleum products at a price lower than the secretly agreed markup on the price of oil.

In a model, this would make them impose low inventories of crude oil and gasoline on each other and make them lower the markup increasing the price of crude and reducing the price of refined products including gasoline compared to what it would be if they could precommit to their cartel.

The really shaky part is I then claim that low inventories make them bid against each other more fiercely in the spot market so that all of the benefit from their reduced markups goes to oil exporters (and maybe then some). This is shaky, because I have forgotten the little I knew about the mechanisms of the the oil spot market and it probably isn’t the mechanism which I would need for the argument to make sense and … lots of stuff.

So I have a new way of putting it. Each Oil company can’t hold large inventories as that would give them an incentive to break their cartel (dumping the gasoline before the other companies can retaliate and benefiting from the increase in the price of crude oil). I will just assume that large inventories of crude oil are needed to keep refineries working at full capacity. If the refiners can’t hold as much crude in inventory, their suppliers will hold more. Now the price of crude includes the cost of holding that inventory essentially the oil exporters are supplying oil *and* storage. The price is higher than the price of just oil.

Now I do *not* believe that this model has anything to do with the real world. I do not think the OIL majors are colluding and I don’t think they would act like agents in game theory if they were. I don’t think their markups or storage costs are anything like large enough to fit the huge shifts in price. In fact, I agree with Krugman.

My old post was an exercise in economic theory. Fortunately commenters used it as an invitation to talk about the real world.

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Petroleum speculation without contango or growing inventories ?

As I’m sure AngryBear readers know, Paul Krugman does not believe that the spot price of petroleum shot up due to speculation. His argument is that the only way future expected prices can affect demand for crude or supply of refined products to final consumers is via inventory accumulation and inventories haven’t increased. Also he argues that speculation can only affect the spot price if there is contango: that is a futures price above the spot price.

I was convinced. Now I am not so sure. The recent decline in the price of petroleum makes it a little bit harder for me to believe in a simpl supply and demand without speculation explanation (just a little bit harder so I won’t argue the semi hemi demi point).

There are many models in which prices do funny things. One set includes customer market models — very implausible if the product is gasoline. Another set is based on implicit collusion. In this case, lets assume that the oil companies are, in fact, a cartel and enforce cooperation with threats of future retaliation. The subgame perfect semi folk theorem suggests that a continuum of equilibria are possible in this case, which sure helps if one is trying to fit the data.

I am (as always) thinking as I type. The semi model I have in mind is one in which

1) the oil companies buy crude on a thick duble auction type spot market with one worldwide price and close to perfect competition.

2) they refine crude and sell refined products (for simplicity assume that the only product is gasoline) subject to a capacity limit and, of course, demand.

3) they agree on a markup on marginal cost. Firms which sell gasoline at a lower markup are punished in the future. They choose the highest sustainable markup.

4) they agree on the highest sustainable markup and have rules restricting inventory accumulation and forward purchases of oil (key that).

The cartel will drive the price of gasoline up and the price of crude down. The extent to which it can do this depends on the costs and benefits of deviation from the agreement, that is, the gains to a firm of suddenly selling gasoline cheaper than agreed given the spot price of crude and the costs to that firm of the wrost subgame perfect punishment strategy available to the cartel.

It is very important to my story that the firms agree on a markup (let’s say a price of gasoline as a function of the price of crude) and NOT on prices for gasoline or crude or quantities of crude bought or gasoline sold.

Update: This is my second try. My first try was mathematically wrong.

The key issues are gains and costs from deviation from the agreed markup.

I will assume that following deviation, firms switch to the non-collusive oligopolly solution (make it a cournot oligopoly) with a lower price of gasoline and a higher price of crude.

If it took a long time for gasoline stations to change their posted price, an oil company with a chain of gas stations could … well this is silly.

There are gains to deviation from the agreement if the other oil companies in the cartel have limited inventories of gasoline and either limited inventories of crude limited refining capacity limiting their ability to increase their supply of gasoline. I assume that spare refining capacity is key to enforcement. The idea is that shipping refining and distributing takes a while so firms don’t do it on the sly. Spare refining capacity is key to the punishment phase but not to the deviation phase. Limited spare refining capacity implies a low markup. In particular, anticipated future refining capacity is the key (the punishment phase lasts a long time) so expectations of future demand are key.

The cost of deviation is that all firms in the future use all of their refinineries at capacity (and maybe build more).

The benefit is dumping undesired inventories of gasoline on the market and an increase in the price of crude oil which is valuable if the company owns crude oil in the ground or in tanks or has bought crude oil futures beyond their needs for crude oil to refine.

A tight incentive compatibility constraint due to limited refining capacity implies a low markup and tight restrictions on inventories (of both gasoline and crude oil) and on futures positions.

A low markup implies a high price of crude oil. Also low inventories (required to maintain collusion) and limited refining capacity imply a high price of crude. It is important that the collusive agreement allows firms complete freedom on the spot crude oil market. The idea is that the price jumps around so much that any collusive agreement would be way to complicated to work tacitly.

So low spare refining capacity implies low allowed inventories and futures purchases which implies fierce competition for crude oil (if a firm bids low it will have trouble keeping its refineries working and can’t make up later as it has limited spare capacity).

All is driven by forecasts of future demand which can bounce around as much as GNP forecasts.

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