Common problem. E.g., how to "aggregate" data
in astronomy? For a simple first cut answer,
work hard, get a lot more data, work hard,
have some good ideas, get a lot more data,
work hard, etc. So, for astronomy, use
spectroscopy to identify the elements in the
star. Use the brightness of the star and the
red shift. Get empirical distributions of
various characteristics of the stars and
begin to guess that stars go through stages
and where the distributions are low the
stages are short.

So, start by observing, getting a lot of data,
and being just descriptive; economics
didn't and still doesn't do nearly enough
here. Then look a little deeper
and start to make some sense of it;
economics has done next to nothing
here, necessarily so since they
didn't get a good grade in the
first grade with the descriptive work.
For something with a little promise,
e.g., Leontief's work , they don't like
that and regard it as not
acceptable in Economics Club.

So, with such work, in astronomy
get Hertzsprung–Russell diagrams.
Nice progress.

Now, continue: Take what is known about
weights in the periodic table and observe
that if press together two deuterium
atoms to make one helium 4 atom, then
lose some mass and, thus, get some energy.
Keep this up across the periodic table
and see that iron has the least such
energy. Observe that can keep
pressing light atoms together to get
heavier ones, e.g., carbon, oxygen,
..., iron and get off energy. Do a lot of
model building of the reactions in the
centers of distant stars. Check with
the data, e.g., the Hertzsprung–Russell diagrams.

Eventually come up with a well tested, apparently
quite solid, model of stars. A lot work,
bright ideas, spectroscopy, expansion rate of
the universe and red shift, nuclear physics,
etc. Didn't say it was easy.

Much the same in other fields of science that
have been successful.

So, if economics wants to copy that
methodology, then they need to get
their hands on more data. Then, say,
they need to look at the flows
that, intuitively, appear to be
generating the data. I know; I know;
too soon want a lot of curves on
propensities, and will have a tough
time there. Not guaranteed to be
easy. But neither were the other
sciences that were successful.

My view of academic economics is that they
sit in small, dark rooms with the doors
closed, hope for a single stroke
success comparable with E = mc^2 in
physics, dream up models, essentially
of imaginary economies, hope, but not
get very far. So, they don't want to
do the first, observational, low level
grunt work of data collection and
basic description that astronomy,
chemistry, biology, etc. did.

There have been suggestions that the
Princeton econ department has two
halves with one half, then with
Bernanke, the more empirical. If
so, good for the Fed and the US
since we will be less likely to be
stuck with crack pot ideas from
"defunct economists".

A danger here: So, with such work
with imaginary economies, some
to be defunct economist makes a lot
of absurd assumptions and finds some
optimal solution -- maybe he
studied some linear programming
(Kantorovich, Solow),
Kuhn-Tucker theory (Arrow), Lagrange
multiplier theory (Debreu),
or dynamic programming
(Samuelson). Then, seeing a real
economy and an opportunity for
fame, status, prestige, tenure,
lots of undergraduate co-eds for
secretaries, he takes the solution
from his imaginary economy and
says that that is what the real
economy should do. Bummer.
Good way to kill tens of thousands
of people from unemployment,
busted homes, street crime,
infant mortality, drug abuse,
suicide, etc. Incompetence
is a bad thing; incompetent economists
are really ugly things.

Your point about unemployment is an example:
Early on I looked at it and asked,
where the heck is the definition, that is,
the empirical or operational definition?
Next, one level deeper, what the heck are
we really counting? E.g., might we be
partitioning the data in some way that
would be better? So here what do we
want for better? Sure, we want something
that can help us with reductionism,
that is, clear causality. But the
actual unemployment data is so poorly
defined and so "aggregated" that our
hope for anything causal is just
smoking funny stuff, like the astronomers
calling stars just points of light and
not looking deeper with spectroscopy,
the periodic table,
nuclear physics, etc.

Finally I understood what the empirical
economists intended to do with data such
as unemployment: They just said,
it's a measurement; hopefully it is done
in a consistent way month by month;
we agree we don't have a good definition
and that the data is too aggregated and
has basic problems in measurement,
but we just take the data as it is;
with the data, we will do just empirical
statistical modeling. Then, recognized or
not, they have essentially given up
making unemployment data an input
to anything reductionist, causal,
or scientific. Suckage. Meanwhile
academic economists are driving
late model cars, and millions of
people are suffering from unemployment
likely for no very good reasons.
Suckage.