Improving National Statistical
Systems
Rebecca M. Blank
U.S. Department of Commerce
Recent news has been bad
• International financial system came close
to collapse
• Unemployment has risen sharply
• Output has fallen
All of this was unexpected
GDP Forecasts for 2008
Forecasts as of Dec. 2007; year-over-year percent change
3
3
2
2
1
1
0
0
-1
-1
-2
-3
Average of IMF, OECD, Consensus
Canada France Germany Japan
UK
Actual
US
-2
-3
GDP Forecasts for 2009
Forecasts as of Dec. 2008; year-over-year percent change
0
0
-1
-1
-2
-2
-3
-3
-4
-4
-5
-5
-6
Average of IMF, OECD, Consensus
Canada* France Germany Japan
* Based on first three quarters.
Actual
UK
US
-6
Key question
Can we (those of us responsible for
economic data) do better?
Could we have provided better data that would
have foreshadowed these economic problems or
that would have allowed us to better understand
the crisis as we were in the midst of it?
Important Note
Data problems did not cause this recession
• The question is not:
“Could we have reported better economic
information?
• BUT…
“Could we have done a better job of reporting
economic information?”
My Focus
Five U.S. examples:
• Financial transactions
• Credit information
• Household wealth holdings
• Industry information
• Longitudinal information
Session title: “Are National
Statistical Systems Effective?”
Answer to this is an unequivocal ‘yes’
They are good and getting better (and more
coordinated across countries) all the time.
But all systems can be improved.
Example 1: Financial
Transactions
This recession was generated by near-collapse in
the global financial sector, in part due to bad debt,
much of it originating in the U.S. housing market.
This financial crisis had many causes
• Over-optimistic investors
• Under-regulation by government
• Lack of due diligence by private sector
But even once we knew there was a problem, it
was difficult to use existing data to understand it.
Flow of funds data categories too aggregated.
Within the U.S. we couldn’t tell which sectors
held the bad debt or how much of it they held.
And we couldn’t track it across national
boundaries.
Possible Improvements
• More detail on the types of instruments that are
held
• More detail on the institutions that are issuing
those instruments
• Information on new issues and retirements of
debt, as well as net flows
• Better ability to track this information across
countries
Example 2: Credit Information
Financial collapse created a credit crisis:
Banks unwilling to make new loans until greater
economic stability
Once recovery started, this led to substantial
concern about credit constraints.
But it’s been very difficult to tell whether there are
credit constraints and who they are affecting.
Need to Separate Demand and
Supply for Credit
Little information on this:
• In U.S., best data is a regular survey of senior
loan officers, asking for their judgment about
credit supply versus credit demand.
• Most of our data just shows aggregate credit
flows.
Net Borrowing by Households
Billions of dollars, annual rate
1,500
1,500
Mortgages
1,000
1,000
500
500
0
0
Consumer credit
-500
2005
2007
2009
-500
Shading indicate recessions; end of recession is assumed to be July.
Potential Improvements
• Credit applications by type of applicant
• Approvals by type of applicant
• Alternative sources of credit that are used if
formal financial institutions are not lending.
Example 3: Household Wealth
Recession led to large declines in assets and
wealth.
Potential effects:
• Later retirement and higher labor force
participation among older workers
• Less investment in children
• Reduced income among those who rely on
pension investments
Timely wealth data is limited
Annual U.S. information comes from the Flow of
Funds accounts, showing only aggregate
information on wealth holdings among all
households
Household wealth survey occurs only once every
three years. Last done in 2007.
Household Net Worth
Trillions of nominal dollars, year-end
80
80
Homeowners'
equity
Deposits
Tangible
assets
Stocks, bonds, &
other financial assets
60
60
40
40
20
20
0
0
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Federal Reserve, Flow of funds
2009 is third quarter
Possible Improvements
• Higher-frequency household-level data on
wealth collections
• Distributional information in more aggregate
data.
Example 4: Industry-level
detail
• The recession has had a big negative effect on
output, but some sectors have been hit harder
than others.
• Useful data would let us see how different
sectors are doing in a timely manner.
Manufacturing Output
Index 2000 = 100
110
110
105
105
100
100
Annual
Quarterly
95
95
90
90
85
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Shading represents recession. End of latest recession assumed to be July.
85
Potential Improvements
• Quarterly Industry GDP statistics
• BEA has this in the current budget request to
Congress.
Example 5: Longitudinal Data
Longitudinal firm-level data can:
• Look at firm births and deaths over the economic cycle
• Look at which firms contract (or grow) more than others
in a time of economic change
Longitudinal household-level data can:
• Show what happens to families when one member
experiences unemployment
• Study unemployment spells
• Investigate how changes in the housing market change
family behavior over time
Conclusions
Gaps in knowledge occur when data more
aggregated. Ways to disaggregate:
•
•
•
•
With higher frequency data
With more detailed categories of existing data
With more micro-level information
With more observations on the same unit over
time
Conclusions
Resources are limited, so this is not a plea for
“let’s do it all.” Data gaps need to be prioritized.
But let’s use this crisis:
• Seek gaps in the data we collect that would have
helped us to better understand the effects of
rapid economic change
• Use this moment to argue for the additional
resources needed to fill those gaps
Conclusions
• The purpose of the national data is to provide
information. It is exactly in times of rapid change
that the need for new and additional information
is most visible.
• May this be a moment of opportunity to improve
statistical systems so they will be even more
helpful in the next economic cycle.