(Flawed) Data (and proof)

[Martin Taylor 2012.06.01.07.01]

[From Rick Marken (2012.05.31.1745)]

Jim Wuwert (2012.05.31.1410)–

              You mentioned in a previous post when you presented

the data written by Robert Reich and published in the
New York Times that conservatives never present data.
They just point out flaws in your data. The data
should stand on their own merits. It is valid to point
out flaws in methodology and data. The data that you
present is what is at issue not the fact that we may
or may not be able to prove it to be untrue. Or, the
fact that we may or may not present a new model.

      RM: The problem here is that pointing out "flaws" implies that

you know what the “unflawed” data are. Unless you can point to
the unflawed data that supports your point of view saying that
the data are “flawed” is just a dodge.

What could it mean to say data are flawed? Let's ask this question

from a PCT perspective.

What are 'data' within the PCT framework? I can see no answer other

than that data are inputs to one or more perceptual functions, the
outputs of which is the variables to be controlled or monitored (not
all perceptions are being controlled at any moment; those that are
not are said to be being monitored, either consciously or not).

If data are inputs to a perceptual function (let's limit the

discussion to one, for the moment), what could it mean to say they
are “flawed”?

"Flawed" means imperfect. Data can be imperfect in several ways: (1)

They misrepresent the value of an environmental property (e.g. you
tell me it is sunny and dry outside, but when I go out I get rained
on); (2) they are insufficient to allow the perceptual function to
generate a value (e.g. you are on a boat in the middle of a lake in
the fog, and you cannot determine the direction of your home dock);
(3) they correctly represent the wrong environmental property (e.g.
a mirage that correctly represents the sky but is seen as
representing water on the ground); (4) they are incomplete, and
represent the environmental property only approximately (almost all
data have this flaw).

Which sense of "flawed" is intended by those who say that the

various sets of data presented by Rick over the years are flawed?

As for proving something to be true or untrue, since all we have

access to is our own perception, “proof” in the logical sense is not
something that can be publicly communicated. Quite apart from that,
the real world appears to be rather complicated, and whereas
mathematical proof depends on a defined set of axioms, real world
“proof” depends on observations that may well omit some crucial
thing that could have been observed and was not. All we can ever do
in respect of the real world is talk about conditional
probabilities: if there are no unobserved variables that would
influence the result, and if one or other of these two (or more)
theories is true, then the data are more likely to have been
observed if the world conforms to this theory rather than to that.

In cases of dispute, one of the unobserved variables is quite often

the mode of selection of the data. That is why scientists are
usually told very early in their training that you can’t test a
theory by looking at data already gathered. Actually, you can, but
it is all too easy unconsciously to select data that support a
theory that gives results conforming to your reference values for
some of your controlled variables. The same applies to
randomization. Randomization is intended to prevent you from
unconsciously choosing the conditions so that the results you want
will be more likely to be the results you get.

"Flawed" data involving unobserved variables, particularly the

variable of data selection, can readily result in case (1) above,
the data misrepresenting the value of an environmental property.
However, in the absence of demonstrable selection of the data to
conform to one theory rather than another, it is probable that the
theory to which the data conform well is closer to the truth than
the theory to which the data conform poorly.

So I can refine my question above: Is the "flaw" in Rick's data

claimed to result from improper selection of data either by Rick or
by the sources that Rick has quoted? If so, could the problem be
resolved by providing complementary data to reduce the
incompleteness of the data available for consideration by
uncommitted readers?

Martin

[Martin Lewitt 2012 Jun 2 1643 MDT]

[Martin Taylor 2012.06.01.07.01]

[From Rick Marken (2012.05.31.1745)]

Jim Wuwert (2012.05.31.1410)–

              You mentioned in a previous post when you presented

the data written by Robert Reich and published in the
New York Times that conservatives never present data.
They just point out flaws in your data. The data
should stand on their own merits. It is valid to point
out flaws in methodology and data. The data that you
present is what is at issue not the fact that we may
or may not be able to prove it to be untrue. Or, the
fact that we may or may not present a new model.

      RM: The problem here is that pointing out "flaws" implies that

you know what the “unflawed” data are. Unless you can point to
the unflawed data that supports your point of view saying that
the data are “flawed” is just a dodge.

What could it mean to say data are flawed? Let's ask this question

from a PCT perspective.

What are 'data' within the PCT framework? I can see no answer other

than that data are inputs to one or more perceptual functions, the
outputs of which is the variables to be controlled or monitored (not
all perceptions are being controlled at any moment; those that are
not are said to be being monitored, either consciously or not).

If data are inputs to a perceptual function (let's limit the

discussion to one, for the moment), what could it mean to say they
are “flawed”?

"Flawed" means imperfect. Data can be imperfect in several ways: (1)

They misrepresent the value of an environmental property (e.g. you
tell me it is sunny and dry outside, but when I go out I get rained
on); (2) they are insufficient to allow the perceptual function to
generate a value (e.g. you are on a boat in the middle of a lake in
the fog, and you cannot determine the direction of your home dock);
(3) they correctly represent the wrong environmental property (e.g.
a mirage that correctly represents the sky but is seen as
representing water on the ground); (4) they are incomplete, and
represent the environmental property only approximately (almost all
data have this flaw).

Which sense of "flawed" is intended by those who say that the

various sets of data presented by Rick over the years are flawed?

Part of the problem was that the “data” that Rick presented wasn’t “data”, it was an interpretation and synthesis of data, and information for confirming whether they actual data supported the way that it was being used or framed wasn’t provided, and the specifics needed were difficult to find. Unless certain confounding possibilities were controlled for, the charts could well be consistent with each quintile being better off that they had been before. I also recall that productivity growth had been considered flat during the early nineties and I don’t see that in the charts, so are they using the measures of productivity and inflation that hedonically adjusted, and was in calculated the same way in all the periods being compared.

As for proving something to be true or untrue, since all we have

access to is our own perception, “proof” in the logical sense is not
something that can be publicly communicated. Quite apart from that,
the real world appears to be rather complicated, and whereas
mathematical proof depends on a defined set of axioms, real world
“proof” depends on observations that may well omit some crucial
thing that could have been observed and was not. All we can ever do
in respect of the real world is talk about conditional
probabilities: if there are no unobserved variables that would
influence the result, and if one or other of these two (or more)
theories is true, then the data are more likely to have been
observed if the world conforms to this theory rather than to that.

In cases of dispute, one of the unobserved variables is quite often

the mode of selection of the data. That is why scientists are
usually told very early in their training that you can’t test a
theory by looking at data already gathered. Actually, you can, but
it is all too easy unconsciously to select data that support a
theory that gives results conforming to your reference values for
some of your controlled variables. The same applies to
randomization. Randomization is intended to prevent you from
unconsciously choosing the conditions so that the results you want
will be more likely to be the results you get.

"Flawed" data involving unobserved variables, particularly the

variable of data selection, can readily result in case (1) above,
the data misrepresenting the value of an environmental property.
However, in the absence of demonstrable selection of the data to
conform to one theory rather than another, it is probable that the
theory to which the data conform well is closer to the truth than
the theory to which the data conform poorly.

So I can refine my question above: Is the "flaw" in Rick's data

claimed to result from improper selection of data either by Rick or
by the sources that Rick has quoted? If so, could the problem be
resolved by providing complementary data to reduce the
incompleteness of the data available for consideration by
uncommitted readers?

The data was presented to make a particular point on an editorial page, with the dividing line chosen as 1980 as if something conclusion was expected because of that date. I suspect both Rick and the sources picked charts that obscured the complexity of the data and multiplicity of interpretations to their satisfaction. I doubt Rick is truly as parochial a nationalist, as not to appreciate the great reduction in inequality and increase in wealth that has been produced by the global economy.

– Martin L

···

On 6/1/12 11:23 AM, “Martin Taylor” mmt-csg@MMTAYLOR.NET wrote:

Martin

[From Rick Marken (2012.06.03.1220)]

Martin Lewitt (2012 Jun 2 1643 MDT)

RM: Part of the problem was that the "data" that Rick presented wasn't "data",
it was an interpretation and synthesis of data

Data are measures of variables. What I presented were measurements of
variables such as productivity and average hourly wage. Productivity
is a derived variable, meaning it is based on measures of other
variables, in this case GDP and size of workforce: productivity =
GDP/(size of workforce). Average hourly wage is just the average (mean
or median), over all workers, of their hourly wage. The charts showed
these variables (data) varying over time. So your first point, that
the "data" wasn't "data" is just BS.

The only "interpretations" I saw of the data were in the second and
third chart headings. In the second chart, showing changes in wealth
discrepancy over time, it says at the top "Great wealth to the top 1%
... was reversed by policy". This is an interpretation of the data. It
is interpreting the decline in wealth discrepancy that occurred during
the 30s and 40s was a result of policy (like the New Deal policies). I
agree with that interpretation but it is only an interpretation; there
are other possible explanations for the decline. But the
interpretation is given some credibility by the fact that wealth
disparity began to increase again in around 1980, when more "free
market" policies (low taxes, less regulation, union busting) were
being implemented. The second interpretation was in the bottom chart
where he says that more women were in the work force in 2008 than in
1975 "in order to maintain household spending". The data are shown in
the graphs: in 1975, 47% of women with children under age 18 worked,
in 2008 it was 71%. That's data. Whether this data is seen because
women entered the workforce in order to maintain spending is an
interpretation

ML: Unless certain confounding possibilities were controlled for, the charts
could well be consistent with each quintile being better off that they had
been before.

This could be said about all economic time series data. This is not
experimental data -- there wasno experimental control -- so there are
confounding variables galore. So it could well be that the data would
look different if you factored out some of thee variables. But I can't
imagine what confounding variable could salvage the quintile data. I
presume that the quintiles represent the increase in income (measured
in terms of GDP) going to each quintile from 1947-1979 and from
1980-2009. So there was a 122% increase in the proportion of GDP going
to the bottom 1/5 of income earners and a 99% increase going to the
top 1/5 from 1947-1979; there was a 4% _decrease_ in the proportion of
GDP going to the bottom 1/5 of income earners and a 55% increase going
to the top 1/5 from 1980-2009. That's the data, not an interpretation.
If you can think of a confounding variable that could account for that
discrepancy -- that is, a variable that, by taking it into account,
eliminates that discrepancy and makes the gains for the top and bottom
quintile equal then I'd love to hear what it is and I'll do the
computations necessary to see if that variable will, indeed, erase the
observed discrepancy.

RM:�I also recall that productivity growth had been considered
flat during the early nineties and I don't see that in the charts

Perhaps because what you recall is the usual right wing bullshit that
has nothing to do with the data. In fact, the data I presented show
that productivity grew _faster_ in the 1990s (especially in the
Clinton years) than in the 1980s. No wonder you don't like data.

ML: , so are
they using the measures of productivity and inflation that hedonically
adjusted, and was in calculated the same way in all the periods being
compared.

So the data is "wrong" because it doesn't show what you think it
should show. Unless you can show me the appropriately "hedonically
adjusted" measures of productivity that show that it declined
precipitously in the 1990s I will assume that you are as full of BS as
you seem.

RSM

···

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

I got my RMs and MLs mixed up on this one occasionally. I’ve edited it below

···

On Sunday, June 3, 2012, Richard Marken wrote:

[From Rick Marken (2012.06.03.1220)]

Martin Lewitt (2012 Jun 2 1643 MDT)

ML: Part of the problem was that the “data” that Rick presented wasn’t “data”,

it was an interpretation and synthesis of data

RM: Data are measures of variables. What I presented were measurements of

variables such as productivity and average hourly wage. Productivity

is a derived variable, meaning it is based on measures of other

variables, in this case GDP and size of workforce: productivity =

GDP/(size of workforce). Average hourly wage is just the average (mean

or median), over all workers, of their hourly wage. The charts showed

these variables (data) varying over time. So your first point, that

the “data” wasn’t “data” is just BS.

The only “interpretations” I saw of the data were in the second and

third chart headings. In the second chart, showing changes in wealth

discrepancy over time, it says at the top "Great wealth to the top 1%

… was reversed by policy". This is an interpretation of the data. It

is interpreting the decline in wealth discrepancy that occurred during

the 30s and 40s was a result of policy (like the New Deal policies). I

agree with that interpretation but it is only an interpretation; there

are other possible explanations for the decline. But the

interpretation is given some credibility by the fact that wealth

disparity began to increase again in around 1980, when more "free

market" policies (low taxes, less regulation, union busting) were

being implemented. The second interpretation was in the bottom chart

where he says that more women were in the work force in 2008 than in

1975 “in order to maintain household spending”. The data are shown in

the graphs: in 1975, 47% of women with children under age 18 worked,

in 2008 it was 71%. That’s data. Whether this data is seen because

women entered the workforce in order to maintain spending is an

interpretation

ML: Unless certain confounding possibilities were controlled for, the charts

could well be consistent with each quintile being better off that they had

been before.

This could be said about all economic time series data. This is not

experimental data – there wasno experimental control – so there are

confounding variables galore. So it could well be that the data would

look different if you factored out some of thee variables. But I can’t

imagine what confounding variable could salvage the quintile data. I

presume that the quintiles represent the increase in income (measured

in terms of GDP) going to each quintile from 1947-1979 and from

1980-2009. So there was a 122% increase in the proportion of GDP going

to the bottom 1/5 of income earners and a 99% increase going to the

top 1/5 from 1947-1979; there was a 4% decrease in the proportion of

GDP going to the bottom 1/5 of income earners and a 55% increase going

to the top 1/5 from 1980-2009. That’s the data, not an interpretation.

If you can think of a confounding variable that could account for that

discrepancy – that is, a variable that, by taking it into account,

eliminates that discrepancy and makes the gains for the top and bottom

quintile equal then I’d love to hear what it is and I’ll do the

computations necessary to see if that variable will, indeed, erase the

observed discrepancy.

ML: I also recall that productivity growth had been considered

flat during the early nineties and I don’t see that in the charts

Perhaps because what you recall is the usual right wing bullshit that

has nothing to do with the data. In fact, the data I presented show

that productivity grew faster in the 1990s (especially in the

Clinton years) than in the 1980s. No wonder you don’t like data.

ML: , so are

they using the measures of productivity and inflation that hedonically

adjusted, and was in calculated the same way in all the periods being

compared.

So the data is “wrong” because it doesn’t show what you think it

should show. Unless you can show me the appropriately "hedonically

adjusted" measures of productivity that show that it declined

precipitously in the 1990s I will assume that you are as full of BS as

you seem.

RSM

Richard S. Marken PhD

rsmarken@gmail.com

www.mindreadings.com


Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

[Martin Lewitt 2012 Jun 3 1525 MDT]

I got my RMs and MLs mixed up on this one occasionally. I’ve edited it below

[From Rick Marken (2012.06.03.1220)]

Martin Lewitt (2012 Jun 2 1643 MDT)

ML: Part of the problem was that the “data” that Rick presented wasn’t “data”,

it was an interpretation and synthesis of data

RM: Data are measures of variables. What I presented were measurements of

variables such as productivity and average hourly wage. Productivity

is a derived variable, meaning it is based on measures of other

variables, in this case GDP and size of workforce: productivity =

GDP/(size of workforce). Average hourly wage is just the average (mean

or median), over all workers, of their hourly wage. The charts showed

these variables (data) varying over time. So your first point, that

the “data” wasn’t “data” is just BS.

Hardly. I think we can assume that the data was inflation adjusted (justified by the reference to 2009 dollars), and what inflation are productivity both are, have changed over time, most significantly by the incorporation of hedonic factors, apparently only since 1995.

Here are examples of some of the complicated factors that must be considered in analyses that you might find enlightening:

faculty-web.at.northwestern.edu/economics/gordon/334.pdf

www.nber.org/chapters/c11441.pdf

The only “interpretations” I saw of the data were in the second and

third chart headings. In the second chart, showing changes in wealth

discrepancy over time, it says at the top "Great wealth to the top 1%

… was reversed by policy". This is an interpretation of the data. It

is interpreting the decline in wealth discrepancy that occurred during

the 30s and 40s was a result of policy (like the New Deal policies).

And like total wealth decline for part of the time and like war.

I

agree with that interpretation but it is only an interpretation; there

are other possible explanations for the decline. But the

interpretation is given some credibility by the fact that wealth

disparity began to increase again in around 1980, when more "free

market" policies (low taxes, less regulation, union busting) were

being implemented.

It is more the spirit of the age that changed, since the tax reductions were minimal and the regulatory decrease, except for Clinton’s banking dereg, was only temporary , with the EPA, FDA, etc. increasing and most new deal policies were continued. As you can see from the articles, economists consider 1995 and 1972 more significant period delineators for some reason.

The second interpretation was in the bottom chart

where he says that more women were in the work force in 2008 than in

1975 “in order to maintain household spending”. The data are shown in

the graphs: in 1975, 47% of women with children under age 18 worked,

in 2008 it was 71%. That’s data. Whether this data is seen because

women entered the workforce in order to maintain spending is an

Interpretation

It was also an interpretation to focus on workers and wages, when addressing wealth disparity, given the changes in workforce and welfare structure, and the supplemental wealth for lower wage workers from foodstamps, housing subsidies and tax credits.

It was also an interpretation to be citing household debt with no obvious 1980 significance, and over a time period in which mortgage innovations made housing much more affordable, considering assets and net worth, non-wage pension accrual, etc. should be addressed in any discussion of wealth and wealth disparity.

With most of the “poor” today, having more than one auto, television, cable and cell phones, and endemic obesity, the true wealth disparity is between US poor and third world poor.

ML: Unless certain confounding possibilities were controlled for, the charts

could well be consistent with each quintile being better off that they had

been before.

This could be said about all economic time series data. This is not

experimental data – there wasno experimental control – so there are

confounding variables galore. So it could well be that the data would

look different if you factored out some of thee variables. But I can’t

imagine what confounding variable could salvage the quintile data. I

presume that the quintiles represent the increase in income (measured

in terms of GDP) going to each quintile from 1947-1979 and from

1980-2009. So there was a 122% increase in the proportion of GDP going

to the bottom 1/5 of income earners and a 99% increase going to the

top 1/5 from 1947-1979; there was a 4% decrease in the proportion of

GDP going to the bottom 1/5 of income earners and a 55% increase going

to the top 1/5 from 1980-2009. That’s the data, not an interpretation.

If you can think of a confounding variable that could account for that

discrepancy – that is, a variable that, by taking it into account,

eliminates that discrepancy and makes the gains for the top and bottom

quintile equal then I’d love to hear what it is and I’ll do the

computations necessary to see if that variable will, indeed, erase the

observed discrepancy.

I don’t see a need for the gains to be equal, just put in proper perspective.

ML: I also recall that productivity growth had been considered

flat during the early nineties and I don’t see that in the charts

Perhaps because what you recall is the usual right wing bullshit that

has nothing to do with the data. In fact, the data I presented show

that productivity grew faster in the 1990s (especially in the

Clinton years) than in the 1980s. No wonder you don’t like data.

Learn about hedonic adjustments. They only go back to 1995 on many products and services.

ML: , so are

they using the measures of productivity and inflation that hedonically

adjusted, and was in calculated the same way in all the periods being

compared.

So the data is “wrong” because it doesn’t show what you think it

should show. Unless you can show me the appropriately "hedonically

adjusted" measures of productivity that show that it declined

precipitously in the 1990s I will assume that you are as full of BS as

The data must be understood in order to assess the interpretation.

– regards, Martin L

···

On 6/3/12 2:30 PM, “Richard Marken” rsmarken@GMAIL.COM wrote:

On Sunday, June 3, 2012, Richard Marken wrote:

you seem.

RSM

Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com


Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

[From Rick Marken (2012.06.04.2215)]

Martin Lewitt (2012 Jun 3 1525 MDT)--

RM: So your first point, that the "data" wasn't "data" is just BS.

ML: Hardly. �I think we can assume that the data was inflation adjusted...

RM: Then it was data. Or do you just use the word "data" to refer to
data that is not data? If so, what do you call the data that is data?

>ML: Unless certain confounding possibilities were controlled for, the
> charts could well be consistent with each quintile being better off that they
> had been before.

RM: This could be said about all economic time series data. �This is not
experimental data -- there was no experimental control -- �so there are
confounding variables galore. So it could well be that the data would
look different if you factored out some of thee variables. But I can't
imagine what confounding variable could salvage the quintile data. I
presume that the quintiles represent the increase in income (measured
in terms of GDP) going to each quintile from 1947-1979 and from
1980-2009. So there was a 122% increase in the proportion of GDP going
to the bottom 1/5 of income earners and a 99% increase going to the
top 1/5 from 1947-1979; there was a 4% _decrease_ in the proportion of
GDP going to the bottom 1/5 of income earners and a 55% increase going
to the top 1/5 from 1980-2009. That's the data, not an interpretation.
If you can think of a confounding variable that could account for that
discrepancy -- that is, a variable that, by taking it into account,
eliminates that discrepancy and makes the gains for the top and bottom
quintile equal then I'd love to hear what it is and I'll do the
computations necessary to see if that variable will, indeed, erase the
observed discrepancy.

ML: I don't see a �need for the gains to be equal, just put in proper
perspective.

Right. The poor and middle class in the US may not have been getting
anything close to their fair share of pie since Reaganomics took over,
but at least they are doing better than those poor people in 3rd world
countries. Our poor kids aren't as hungry as theirs so stop
complaining.

> ML: , so are
> they using the measures of productivity and inflation that hedonically
> adjusted, and was in calculated the same way in all the periods being
> compared.

RM: So the data is "wrong" because it doesn't show what you think it
should show. Unless you can show me the appropriately "hedonically
adjusted" measures of productivity that show that it declined
precipitously in the 1990s I will assume that you are as full of BS as

ML: The data must be understood in order to assess the interpretation.

"Assess the interpretation"? Wow, no wonder I don't understand this
stuff. So all the data I've seen that appears to support the idea that
the US economy worked much better for everyone before 1980 is all just
my failure to assess the interpretation of that data. Or is the data
the interpretation.

It's obvious that you are able to assess anything I see as data as an
interpretation that supports your point of view. I'm pretty sure
there isn't anything that would convince you that you are as full of
it as you actually are. So why don't you just go off and assess your
interpretations while we spineless liberals watch in fascinated horror
as your Reaganomic friends continue in their implacable efforts to
destroy this once lovely country.

RSM

···

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

[From Chad Green (2012.06.05.1003)]

Imagine for a moment that in the original Star Trek television series, Data had served as captain of the USS Enterprise: http://en.wikipedia.org/wiki/Data_(Star_Trek).

How long do you think the series would have been on the air?

Best,
Chad

Chad Green, PMP
Program Analyst
Loudoun County Public Schools
21000 Education Court
Ashburn, VA 20148
Voice: 571-252-1486
Fax: 571-252-1633

"If you want sense, you'll have to make it yourself." - Norton Juster

Richard Marken <rsmarken@GMAIL.COM> 6/5/2012 1:13 AM >>>

[From Rick Marken (2012.06.04.2215)]

Martin Lewitt (2012 Jun 3 1525 MDT)--

RM: So your first point, that the "data" wasn't "data" is just BS.

ML: Hardly. I think we can assume that the data was inflation adjusted...

RM: Then it was data. Or do you just use the word "data" to refer to
data that is not data? If so, what do you call the data that is data?

>ML: Unless certain confounding possibilities were controlled for, the
> charts could well be consistent with each quintile being better off that they
> had been before.

RM: This could be said about all economic time series data. This is not
experimental data -- there was no experimental control -- so there are
confounding variables galore. So it could well be that the data would
look different if you factored out some of thee variables. But I can't
imagine what confounding variable could salvage the quintile data. I
presume that the quintiles represent the increase in income (measured
in terms of GDP) going to each quintile from 1947-1979 and from
1980-2009. So there was a 122% increase in the proportion of GDP going
to the bottom 1/5 of income earners and a 99% increase going to the
top 1/5 from 1947-1979; there was a 4% _decrease_ in the proportion of
GDP going to the bottom 1/5 of income earners and a 55% increase going
to the top 1/5 from 1980-2009. That's the data, not an interpretation.
If you can think of a confounding variable that could account for that
discrepancy -- that is, a variable that, by taking it into account,
eliminates that discrepancy and makes the gains for the top and bottom
quintile equal then I'd love to hear what it is and I'll do the
computations necessary to see if that variable will, indeed, erase the
observed discrepancy.

ML: I don't see a need for the gains to be equal, just put in proper
perspective.

Right. The poor and middle class in the US may not have been getting
anything close to their fair share of pie since Reaganomics took over,
but at least they are doing better than those poor people in 3rd world
countries. Our poor kids aren't as hungry as theirs so stop
complaining.

> ML: , so are
> they using the measures of productivity and inflation that hedonically
> adjusted, and was in calculated the same way in all the periods being
> compared.

RM: So the data is "wrong" because it doesn't show what you think it
should show. Unless you can show me the appropriately "hedonically
adjusted" measures of productivity that show that it declined
precipitously in the 1990s I will assume that you are as full of BS as

ML: The data must be understood in order to assess the interpretation.

"Assess the interpretation"? Wow, no wonder I don't understand this
stuff. So all the data I've seen that appears to support the idea that
the US economy worked much better for everyone before 1980 is all just
my failure to assess the interpretation of that data. Or is the data
the interpretation.

It's obvious that you are able to assess anything I see as data as an
interpretation that supports your point of view. I'm pretty sure
there isn't anything that would convince you that you are as full of
it as you actually are. So why don't you just go off and assess your
interpretations while we spineless liberals watch in fascinated horror
as your Reaganomic friends continue in their implacable efforts to
destroy this once lovely country.

RSM

···

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

[From Rick Marken (2012.06.05. 1645)]

Chad Green (2012.06.05.1003)--

Imagine for a moment that in the original Star Trek television series, Data had served as
captain of the USS Enterprise: http://en.wikipedia.org/wiki/Data_(Star_Trek).

How long do you think the series would have been on the air?

You're showing your age (or lack thereof). Data was not in the
original Star Trek -- that was Spock. Data was a knock-off of Spock.
But I think I know what you're getting at; people like action rather
than data. But I think the Captains of the Enterprise appreciated the
value of taking action based on information (data). Otherwise why
collect it (other than to develop the denial capabilities of right
wingers;-)?

By the way, speaking of data (observation) don't miss the transit of
Venus, which is going on right now. You can see it by putting a piece
of paper behind a telescope (if you have one, binoculars work as well
but if they are hand help the image will shake quite a bit) and
reflecting the image of the sun off the paper. The little dot on the
sum is Venus. Very cool.

Best

Rick

···

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

Spock had his chance to command the USS Enterprise in Star Trek (2009): http://en.wikipedia.org/wiki/Star_Trek_(film).

Without Kirk's intervention and assistance from Spock Prime, Earth would have been destroyed.

People like intelligent action that changes the world. Culture heroes are prime examples.

Cheers,
Chad

Chad Green, PMP
Program Analyst
Loudoun County Public Schools
21000 Education Court
Ashburn, VA 20148
Voice: 571-252-1486
Fax: 571-252-1633

"If you want sense, you'll have to make it yourself." - Norton Juster

Richard Marken <rsmarken@GMAIL.COM> 6/5/2012 7:45 PM >>>

[From Rick Marken (2012.06.05. 1645)]

Chad Green (2012.06.05.1003)--

Imagine for a moment that in the original Star Trek television series, Data had served as
captain of the USS Enterprise: http://en.wikipedia.org/wiki/Data_(Star_Trek).

How long do you think the series would have been on the air?

You're showing your age (or lack thereof). Data was not in the
original Star Trek -- that was Spock. Data was a knock-off of Spock.
But I think I know what you're getting at; people like action rather
than data. But I think the Captains of the Enterprise appreciated the
value of taking action based on information (data). Otherwise why
collect it (other than to develop the denial capabilities of right
wingers;-)?

By the way, speaking of data (observation) don't miss the transit of
Venus, which is going on right now. You can see it by putting a piece
of paper behind a telescope (if you have one, binoculars work as well
but if they are hand help the image will shake quite a bit) and
reflecting the image of the sun off the paper. The little dot on the
sum is Venus. Very cool.

Best

Rick

···

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

[Martin Lewitt 2012 Jun 11 0003 MDT]

[From Rick Marken (2012.06.04.2215)]

Martin Lewitt (2012 Jun 3 1525 MDT)--

RM: So your first point, that the "data" wasn't "data" is just BS.

ML: Hardly. I think we can assume that the data was inflation
adjusted...

RM: Then it was data. Or do you just use the word "data" to refer to
data that is not data? If so, what do you call the data that is data?

Sometimes I use "data" meaning raw data. I charts such as you put
forward, it is often difficult to tell what the data really is, how it has
been selected and massaged, because often economic and demographic data
has been subjected to different degrees of standard or controversial
processing. I guess you can look at a chart and instantly accept the
authors conclusion. I look at it more skeptically, perhaps because of my
scientific background.

>ML: Unless certain confounding possibilities were controlled for, the
> charts could well be consistent with each quintile being better off
that they
> had been before.

RM: This could be said about all economic time series data. This is not
experimental data -- there was no experimental control -- so there are
confounding variables galore. So it could well be that the data would
look different if you factored out some of thee variables. But I can't
imagine what confounding variable could salvage the quintile data. I
presume that the quintiles represent the increase in income (measured
in terms of GDP) going to each quintile from 1947-1979 and from
1980-2009. So there was a 122% increase in the proportion of GDP going
to the bottom 1/5 of income earners and a 99% increase going to the
top 1/5 from 1947-1979; there was a 4% _decrease_ in the proportion of
GDP going to the bottom 1/5 of income earners and a 55% increase going
to the top 1/5 from 1980-2009. That's the data, not an interpretation.
If you can think of a confounding variable that could account for that
discrepancy -- that is, a variable that, by taking it into account,
eliminates that discrepancy and makes the gains for the top and bottom
quintile equal then I'd love to hear what it is and I'll do the
computations necessary to see if that variable will, indeed, erase the
observed discrepancy.

ML: I don't see a need for the gains to be equal, just put in proper
perspective.

Right. The poor and middle class in the US may not have been getting
anything close to their fair share of pie since Reaganomics took over,
but at least they are doing better than those poor people in 3rd world
countries. Our poor kids aren't as hungry as theirs so stop
complaining.

Our poor kids have a problem with obesity, our poverty levels a adjusted
in proportion to nationwide income levels, and incomes reported for the
lowest quintile, sometimes include the contribution of government benefits
and sometimes don't, sometimes time series analysis track the same people
over time, and sometimes just the current population.

> ML: , so are
> they using the measures of productivity and inflation that
hedonically
> adjusted, and was in calculated the same way in all the periods being
> compared.

RM: So the data is "wrong" because it doesn't show what you think it
should show. Unless you can show me the appropriately "hedonically
adjusted" measures of productivity that show that it declined
precipitously in the 1990s I will assume that you are as full of BS as

ML: The data must be understood in order to assess the interpretation.

"Assess the interpretation"? Wow, no wonder I don't understand this
stuff. So all the data I've seen that appears to support the idea that
the US economy worked much better for everyone before 1980 is all just
my failure to assess the interpretation of that data. Or is the data
the interpretation.

If that is what the data "appears to support", then it is wrong, a good
case can be made that even the lowest quintile is better off today.
Households at the poverty level today have more than one auto, a
television, cable TV and a cell phone. Admittedly the obesity problem is
probably worse.

It's obvious that you are able to assess anything I see as data as an
interpretation that supports your point of view.

Actually, I just care to understand data that you accept without
understanding what you are accepting.

I'm pretty sure
there isn't anything that would convince you that you are as full of
it as you actually are.

If I had encountered it, I would already have changed my world model to
accommodate it.

So why don't you just go off and assess your
interpretations while we spineless liberals watch in fascinated horror
as your Reaganomic friends continue in their implacable efforts to
destroy this once lovely country.

Yes and the more progressive liberals become the more spineless they
become, look at how the Occupy protestors have to anonymize themselves
with black garb and masks to really get destructive and disruptive. Look
at how the critical race theorists have to publish in their own journals
rather than face the gauntlet of more skeptical peer review.

-- take care, Martin L

···

On 6/4/12 11:13 PM, "Richard Marken" <rsmarken@GMAIL.COM> wrote:

RSM
--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

[Martin Taylor 2012.06.11.13.31]

[Martin Lewitt 2012 Jun 11 0003 MDT]

Sometimes I use "data" meaning raw data. In charts such as you [Rick] put
forward, it is often difficult to tell what the data really is, how it has
been selected and massaged, because often economic and demographic data
has been subjected to different degrees of standard or controversial
processing. I guess you can look at a chart and instantly accept the
authors conclusion. I look at it more skeptically, perhaps because of my
scientific background.

Your scientific background ought to tell you that ALL data have problems of these kinds. An experiment in psychology (even PCT psychology) may fail to note what the subject had for breakfast, or what route was used to get to the laboratory. When the data are reported, the report always "has been subjected to different degrees of standard or controversial processing", and the importance of that processing may not be noted for centuries. There is no such thing as a replicate in science. There are data sets that are called replicates because some conditions thought to be important have the same values for every item in the data set, but there are an uncountable number of conditions that are different between any two items, not least being the age of the Universe at the time of the observation.

What I am saying is that you are setting an impossibly high bar for what you would call "data" in any science, let alone one as soft as economics. You are saying that there is no point in looking at what happens in the real world, because the data will not include whether each individual economic unit had a fight with his or her spouse each morning, whether some message arrived at an investor's inbox just before or just after the investor made a trade, ... (ad infinitum).

Rick is saying something different, at least as I read him. His argument is a balance of probabilities argument along the lines of "most often when A has been true at time T, it is observed that B increases shortly thereafter. This being the case, if there is any causal relation between A and B, it is more likely that A causes an increase in B than that A causes a decrease in B; furthermore, it is more likely that the preceding state A leads to B than that the later increase in B leads to the earlier state A; if there is no causal relation between them, there is likely to be some unreported condition that leads both to state A and to an increase in the value of B". This kind of statement seems to me to be useful, even though it falls short of logical proof.

State "A" is not defined with the exhaustive precision you demand of "data". Nor are the conditions surrounding each observation that leads to the report of the value of variable "B". It is quite possible that the mechanism linking A to B goes through a biased (but not dishonest) interpreter who sees "A" and consciously or unconsciously selects data or processing strategies so as to make "B" seem to increase, whereas a differently biased observer might be able to make a selection that had the opposite effect. Psychologists were concerned about this effect many decades ago, giving it the name of "the experimenter effect".

However, although an analogue of the experimenter effect must be kept in the back of the mind when looking at reported (always processed) data, the basic presumption must always be that the reporter is honestly trying to produce an unbiased result. To argue that the result must be wrong because it disagrees with one's prejudices or pet theories, without showing in what way the report has been biased, is simply dishonest.

Yes and the more progressive liberals become the more spineless they become, look at how the Occupy protestors have to anonymize themselves with black garb and masks to really get destructive and disruptive. Look at how the critical race theorists have to publish in their own journals rather than face the gauntlet of more skeptical peer review.

What an odd paragraph, and what a strange pairing of domains of discourse! I suppose that during the Civil Rights movement you would have called the NAACP "terrorist" because there existed small violent cells and they claimed to be violent in support of civil rights.

And you say you are opposed to peer review of scientific reports, at least peer review by unbiased reviewers? Or is your opposition only to peer review in one specific field of enquiry? Isn't it the job of a reviewer to be skeptical, and to find flaws where they are manifest in a report?

Now I begin to wonder about that claim at the top of your message, that you have a scientific background. Much of what you write seems to argue that you do not, or have forgotten its basic principles.

Martin T.

[From Rick Marken (2012.06.11.1700)]

Martin Lewitt (2012 Jun 11 0003 MDT)--

RM: So all the data I've seen that appears to support the idea that
the US economy worked much better for everyone before 1980 is all just
my failure to assess the interpretation of that data. Or is the data
the interpretation.

ML: If that is what the data "appears to support", then it is wrong a good
case can be made that even the lowest quintile is better off today.
Households at the poverty level today have more than one auto, a
television, cable TV and a cell phone. �Admittedly the obesity problem is
probably worse.

The data I showed certainly does appear to support the idea that the
US economy worked much better for _everyone_ before 1980. Look at the
data again. Note particularly the histogram that shows that from
1947-1980 income gains (in inflation adjusted dollars of course) were
nearly equal for all income quartiles: ~100% increase over that 33
year period for all quartiles. From 1980-2009 income gains went
mainly to the top fourth (+25%) and fifth (+55%) income quartiles; the
bottom quartile actually lost ground (-4%) while the second to the
bottom quartile made almost no gains (+7%).

Your "case" that "the lowest quintile is better off today" is
irrelevant to the question of whether then data support the idea that
the US economy worked much better for _everyone_ before 1980. The data
show that the lowest quintile is unquestionably better off today than
they were in 1947 (although it also shows that they are actually worse
off than they were in 1980, in terms of income gains). Thanks to
technological advances the lower quintile is probably better off after
1980 even with the decline in income. But that is all irrelevant to my
point that that data show that the US economy worked much better for
_everyone_ from 1947 to 1980 than it did from 1980 to 2009. Sure the
lowest quintile is "better off today". But that are not nearly as
much better off as they could be if it weren't for the Reagan
revolution (devolution, really).

What these data show is that, since 1980, there has been a huge
increase in the number of people in the US living in economic stress
and misery _unnecessarily_. Before 1980 the wealth was spread more
equitably. After 1980 most of the increases in wealth went to the
upper quintile (actually, the upper .01%). This is either a result of
policy changes or the sudden emergence of a small group of people --
possibly aliens from another universe -- who are so competent that
they now deserve 100s of times the compensation that the used to get
before 1980.

I'm going with the assumption that it was a policy shift and that it
was a policy shift (in the direction you prefer) that created great
misery. And a miserable population is an angry population; so right
wing demagogues have been able to get these angry people to think that
the reason they are miserable is because abortion is legal, guns are
controlled, and liberals want to take away their freedom (many of the
demagogues are jews so at least they're not blaming it on the jews
this time). So they can be fooled into voting against their best
interests. That's one reason I don't like wealth inequality; it makes
for very ugly social circumstances (eg. French Revolution).

I think Martin Taylor addressed your "scientific background" quite
nicely. I'll just say that, in general, I have found that people who
have to _say_ they have a scientific background are usually the
people who are not particularly good at science (from my perspective).

RSM

···

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

[From Chad Green (2012.06.12.1018)]

I sense another double bind situation emerging, which is inexorable given the complexity of human relations.

How do you transcend such a scenario?

Take it up a notch by challenging the assumptions of the object in question, which in this case is the scientific method and data interpretation, an empiricist epistemology. What are your views on innatism and nativism, for example?

Maybe if we keep this up we'll come to understand what Tolstoy meant when he wrote: "We can know only that we know nothing. And that is the highest degree of human wisdom" (War and Peace, Book 5, Chapter 1).

To my mind, combining the perspective above with Tolstoy's Three Questions (http://en.wikisource.org/wiki/Three_Questions) constitutes the most noble application of the scientific method. :slight_smile:

Cheers,
Chad

Chad Green, PMP
Program Analyst
Loudoun County Public Schools
21000 Education Court
Ashburn, VA 20148
Voice: 571-252-1486
Fax: 571-252-1633

"If you want sense, you'll have to make it yourself." - Norton Juster

Richard Marken <rsmarken@GMAIL.COM> 6/11/2012 8:03 PM >>>

[From Rick Marken (2012.06.11.1700)]

Martin Lewitt (2012 Jun 11 0003 MDT)--

RM: So all the data I've seen that appears to support the idea that
the US economy worked much better for everyone before 1980 is all just
my failure to assess the interpretation of that data. Or is the data
the interpretation.

ML: If that is what the data "appears to support", then it is wrong a good
case can be made that even the lowest quintile is better off today.
Households at the poverty level today have more than one auto, a
television, cable TV and a cell phone. Admittedly the obesity problem is
probably worse.

The data I showed certainly does appear to support the idea that the
US economy worked much better for _everyone_ before 1980. Look at the
data again. Note particularly the histogram that shows that from
1947-1980 income gains (in inflation adjusted dollars of course) were
nearly equal for all income quartiles: ~100% increase over that 33
year period for all quartiles. From 1980-2009 income gains went
mainly to the top fourth (+25%) and fifth (+55%) income quartiles; the
bottom quartile actually lost ground (-4%) while the second to the
bottom quartile made almost no gains (+7%).

Your "case" that "the lowest quintile is better off today" is
irrelevant to the question of whether then data support the idea that
the US economy worked much better for _everyone_ before 1980. The data
show that the lowest quintile is unquestionably better off today than
they were in 1947 (although it also shows that they are actually worse
off than they were in 1980, in terms of income gains). Thanks to
technological advances the lower quintile is probably better off after
1980 even with the decline in income. But that is all irrelevant to my
point that that data show that the US economy worked much better for
_everyone_ from 1947 to 1980 than it did from 1980 to 2009. Sure the
lowest quintile is "better off today". But that are not nearly as
much better off as they could be if it weren't for the Reagan
revolution (devolution, really).

What these data show is that, since 1980, there has been a huge
increase in the number of people in the US living in economic stress
and misery _unnecessarily_. Before 1980 the wealth was spread more
equitably. After 1980 most of the increases in wealth went to the
upper quintile (actually, the upper .01%). This is either a result of
policy changes or the sudden emergence of a small group of people --
possibly aliens from another universe -- who are so competent that
they now deserve 100s of times the compensation that the used to get
before 1980.

I'm going with the assumption that it was a policy shift and that it
was a policy shift (in the direction you prefer) that created great
misery. And a miserable population is an angry population; so right
wing demagogues have been able to get these angry people to think that
the reason they are miserable is because abortion is legal, guns are
controlled, and liberals want to take away their freedom (many of the
demagogues are jews so at least they're not blaming it on the jews
this time). So they can be fooled into voting against their best
interests. That's one reason I don't like wealth inequality; it makes
for very ugly social circumstances (eg. French Revolution).

I think Martin Taylor addressed your "scientific background" quite
nicely. I'll just say that, in general, I have found that people who
have to _say_ they have a scientific background are usually the
people who are not particularly good at science (from my perspective).

RSM

···

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

[From Rick Marken (2012.06.12.1330)]

Chad Green (2012.06.12.1018)

CG: I sense another double bind situation emerging, which is inexorable given the complexity of human relations.

RM: I assume that this is related to the discussion Martin Taylor and
I are having with Martin Lewitt about economic data but I'm not sure
what exactly your point is. What is it about the discussion that makes
you sense that a double bind is emerging? What is a double bind anyway
(isn't it just a conflict?) and how does it apply to this discussion?

Best

Rick

···

How do you transcend such a scenario?

Take it up a notch by challenging the assumptions of the object in question, which in this case is the scientific method and data interpretation, an empiricist epistemology. �What are your views on innatism and nativism, for example?

Maybe if we keep this up we'll come to understand what Tolstoy meant when he wrote: "We can know only that we know nothing. And that is the highest degree of human wisdom" (War and Peace, Book 5, Chapter 1).

To my mind, combining the perspective above with Tolstoy's Three Questions (http://en.wikisource.org/wiki/Three_Questions) constitutes the most noble application of the scientific method. :slight_smile:

Cheers,
Chad

Chad Green, PMP
Program Analyst
Loudoun County Public Schools
21000 Education Court
Ashburn, VA 20148
Voice: 571-252-1486
Fax: 571-252-1633

"If you want sense, you'll have to make it yourself." - Norton Juster

Richard Marken <rsmarken@GMAIL.COM> 6/11/2012 8:03 PM >>>

[From Rick Marken (2012.06.11.1700)]

Martin Lewitt (2012 Jun 11 0003 MDT)--

RM: So all the data I've seen that appears to support the idea that
the US economy worked much better for everyone before 1980 is all just
my failure to assess the interpretation of that data. Or is the data
the interpretation.

ML: If that is what the data "appears to support", then it is wrong a good
case can be made that even the lowest quintile is better off today.
Households at the poverty level today have more than one auto, a
television, cable TV and a cell phone. �Admittedly the obesity problem is
probably worse.

The data I showed certainly does appear to support the idea that the
US economy worked much better for _everyone_ before 1980. Look at the
data again. Note particularly the histogram that shows that from
1947-1980 income gains (in inflation adjusted dollars of course) were
nearly equal for all income quartiles: ~100% increase over that 33
year period for all quartiles. �From 1980-2009 income gains went
mainly to the top fourth (+25%) and fifth (+55%) income quartiles; the
bottom quartile actually lost ground (-4%) while the second to the
bottom quartile made almost no gains (+7%).

�Your "case" that "the lowest quintile is better off today" is
irrelevant to the question of whether then data support the idea that
the US economy worked much better for _everyone_ before 1980. The data
show that the lowest quintile is unquestionably better off today than
they were in 1947 (although it also shows that they are actually worse
off than they were in 1980, in terms of income gains). Thanks to
technological advances the lower quintile is probably better off after
1980 even with the decline in income. But that is all irrelevant to my
point that that data show that �the US economy worked much better for
_everyone_ from 1947 to 1980 than it did from 1980 to 2009. Sure the
lowest quintile is "better off today". �But that are not nearly as
much better off as they could be if it weren't for the Reagan
revolution (devolution, really).

What these data show is that, since 1980, there has been a huge
increase in the number of people in the US living in economic stress
and misery _unnecessarily_. �Before 1980 the wealth was spread more
equitably. After 1980 most of the increases in wealth went to the
upper �quintile (actually, the upper .01%). This is either a result of
policy changes or the sudden emergence of a small group of people --
possibly aliens from another universe -- who are so competent that
they now deserve 100s of times the compensation that the used to get
before 1980.

I'm going with the assumption that it was a policy shift and that it
was a policy shift (in the direction you prefer) that created great
misery. And a miserable population is an angry population; so right
wing demagogues have been able to get these angry people to think that
the reason they are miserable is because abortion is legal, guns are
controlled, and liberals want to take away their freedom (many of the
demagogues are jews so at least they're not blaming it on the jews
this time). So they can be fooled into voting against their best
interests. �That's one reason I don't like wealth inequality; it makes
for very ugly social circumstances (eg. French Revolution).

I think Martin Taylor addressed �your "scientific background" quite
nicely. I'll just say that, in general, I have found that people who
have to _say_ �they have a scientific background are usually the
people who are not particularly good at science (from my perspective).

RSM

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

[From Chad Green (2012.06.14.1726)]

Great questions, Rick.

It basically means that I am about to learn something new about myself through your help. :slight_smile:

Cheers,
Chad

Chad Green, PMP
Program Analyst
Loudoun County Public Schools
21000 Education Court
Ashburn, VA 20148
Voice: 571-252-1486
Fax: 571-252-1633

"If you want sense, you'll have to make it yourself." - Norton Juster

Richard Marken <rsmarken@GMAIL.COM> 6/12/2012 4:30 PM >>>

[From Rick Marken (2012.06.12.1330)]

Chad Green (2012.06.12.1018)

CG: I sense another double bind situation emerging, which is inexorable given the complexity of human relations.

RM: I assume that this is related to the discussion Martin Taylor and
I are having with Martin Lewitt about economic data but I'm not sure
what exactly your point is. What is it about the discussion that makes
you sense that a double bind is emerging? What is a double bind anyway
(isn't it just a conflict?) and how does it apply to this discussion?

Best

Rick

···

How do you transcend such a scenario?

Take it up a notch by challenging the assumptions of the object in question, which in this case is the scientific method and data interpretation, an empiricist epistemology. What are your views on innatism and nativism, for example?

Maybe if we keep this up we'll come to understand what Tolstoy meant when he wrote: "We can know only that we know nothing. And that is the highest degree of human wisdom" (War and Peace, Book 5, Chapter 1).

To my mind, combining the perspective above with Tolstoy's Three Questions (http://en.wikisource.org/wiki/Three_Questions) constitutes the most noble application of the scientific method. :slight_smile:

Cheers,
Chad

Chad Green, PMP
Program Analyst
Loudoun County Public Schools
21000 Education Court
Ashburn, VA 20148
Voice: 571-252-1486
Fax: 571-252-1633

"If you want sense, you'll have to make it yourself." - Norton Juster

Richard Marken <rsmarken@GMAIL.COM> 6/11/2012 8:03 PM >>>

[From Rick Marken (2012.06.11.1700)]

Martin Lewitt (2012 Jun 11 0003 MDT)--

RM: So all the data I've seen that appears to support the idea that
the US economy worked much better for everyone before 1980 is all just
my failure to assess the interpretation of that data. Or is the data
the interpretation.

ML: If that is what the data "appears to support", then it is wrong a good
case can be made that even the lowest quintile is better off today.
Households at the poverty level today have more than one auto, a
television, cable TV and a cell phone. Admittedly the obesity problem is
probably worse.

The data I showed certainly does appear to support the idea that the
US economy worked much better for _everyone_ before 1980. Look at the
data again. Note particularly the histogram that shows that from
1947-1980 income gains (in inflation adjusted dollars of course) were
nearly equal for all income quartiles: ~100% increase over that 33
year period for all quartiles. From 1980-2009 income gains went
mainly to the top fourth (+25%) and fifth (+55%) income quartiles; the
bottom quartile actually lost ground (-4%) while the second to the
bottom quartile made almost no gains (+7%).

Your "case" that "the lowest quintile is better off today" is
irrelevant to the question of whether then data support the idea that
the US economy worked much better for _everyone_ before 1980. The data
show that the lowest quintile is unquestionably better off today than
they were in 1947 (although it also shows that they are actually worse
off than they were in 1980, in terms of income gains). Thanks to
technological advances the lower quintile is probably better off after
1980 even with the decline in income. But that is all irrelevant to my
point that that data show that the US economy worked much better for
_everyone_ from 1947 to 1980 than it did from 1980 to 2009. Sure the
lowest quintile is "better off today". But that are not nearly as
much better off as they could be if it weren't for the Reagan
revolution (devolution, really).

What these data show is that, since 1980, there has been a huge
increase in the number of people in the US living in economic stress
and misery _unnecessarily_. Before 1980 the wealth was spread more
equitably. After 1980 most of the increases in wealth went to the
upper quintile (actually, the upper .01%). This is either a result of
policy changes or the sudden emergence of a small group of people --
possibly aliens from another universe -- who are so competent that
they now deserve 100s of times the compensation that the used to get
before 1980.

I'm going with the assumption that it was a policy shift and that it
was a policy shift (in the direction you prefer) that created great
misery. And a miserable population is an angry population; so right
wing demagogues have been able to get these angry people to think that
the reason they are miserable is because abortion is legal, guns are
controlled, and liberals want to take away their freedom (many of the
demagogues are jews so at least they're not blaming it on the jews
this time). So they can be fooled into voting against their best
interests. That's one reason I don't like wealth inequality; it makes
for very ugly social circumstances (eg. French Revolution).

I think Martin Taylor addressed your "scientific background" quite
nicely. I'll just say that, in general, I have found that people who
have to _say_ they have a scientific background are usually the
people who are not particularly good at science (from my perspective).

RSM

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com

--
Richard S. Marken PhD
rsmarken@gmail.com
www.mindreadings.com