[Economic Report of the President (2008)]
[Administration of George W. Bush]
[Online through the Government Printing Office, www.gpo.gov]

 
CHAPTER 8

Improving Economic Statistics


Statistical systems have substantial value for both public
policymakers and private decisionmakers. Administration and
Congressional policymakers rely on statistics for budget
decisions and related fiscal policy choices, and the Federal
Reserve System relies on statistics for formulating sound
monetary policy. Private firms combine internal company data
with publicly provided statistics to make sales projections
and investment decisions. In addition, contracts often use
price or wage indexes to adjust payments for inflation.
Statistical systems, like physical infrastructure, become
obsolete or depreciate with time. In a dynamic market-based
economy, like that of the United States, new industries
emerge, old industries contract, and firms find new ways
of organizing and conducting their activities. The challenge
for those who manage statistical systems is to keep pace with
changes in the economy by continually evaluating the relevance
and reliability of the statistics that are produced. In
addition, it is important to maintain the continuity of
statistical time series to facilitate meaningful historical
comparisons. Up-to-date, relevant statistics are critical
to the public policy process: they help frame policy
debates by providing a sense of the size and scope of an
issue, as well as the likely benefits and costs associated
with a given policy action.
Advisory committees and researchers drawn from other parts
of the government and academia help statistical agencies
maintain the high quality of the data they collect and
publish. They provide advice and engage in academic-style
research that ensures that collected data are useful and
relevant to issues people care about. Their work also
enhances future data products by suggesting ways to
improve the statistical system.
The statistical community in government, business, and
academia recognizes that statistical agencies can improve
the quality, usefulness, and efficiency of their statistical
operations through cross-agency sharing of selected business
data. Such interagency data sharing facilitates the
synchronization of data across agencies, which in turn improves
the comparability of different datasets and makes the statistical
products of all agencies more valuable. For example, a measure
of industry input (such as labor hours) often comes from one
agency, while a measure of industry output comes from another
agency. If each agency classifies a given firm as belonging
to a different industry, then the productivity (output per input)
of both industries may be mismeasured. By sharing classification
data, agencies can reconcile these differences to ensure that the
firm is classified in a consistent manner. In addition to improving
the accuracy of government statistics, data synchronization may
reduce the reporting burden on survey respondents, thereby
improving the efficiency of the Federal statistical system.
The key points in this chapter are:
 Robust statistical systems produce products that are
important to understanding the changing state of the economy
and to formulating sound policy. But statistical systems, like
physical infrastructures, become obsolete or depreciate with
time if they are not maintained.
 Statistical measures must keep up with the changing
nature of the economy to be relevant and useful. For example,
it is important that these measures reflect new and growing
industries (such as high-technology industries or services)
and intangible capital (such as research and development).
 Disruptions in a statistical series render it much
less useful to policymakers and other data users. Thus,
continuity in statistical series
 More effective statistical use can be made of existing
data. In particular, amending relevant legislation to enable
full implementation of the Confidential Information Protection
and Statistical Efficiency Act (CIPSEA) could greatly improve
the quality of Federal statistics.

An Overview of the U.S. Statistical System
The U.S. statistical system comprises many organizations inside
and outside the U.S. government that produce statistics. Of
particular interest in this chapter are Federal statistical
agencies (whose principal function is to collect, compile,
analyze, and disseminate statistics) and associated organizations,
such as theFederal Reserve Board, that produce economic data to
inform policy decisions. As of 2007, these organizations produced 38
statistical releases designated as "principal Federal economic
indicators." These indicators include everything from agricultural
prices to new home sales, the unemployment rate, and gross domestic
product (GDP).
Among the Federal statistical agencies, the largest is the Department
of Commerce's Census Bureau, which accounted for 39 percent of spending
by principal statistical agencies in fiscal year 2007, as shown in
Chart 8-1. Spending on statistics by the Federal Reserve and many
regulatory and program agencies, as well as by nongovernmental
organizations, is excluded from this calculation. The Census Bureau's
spending expands even more during years leading up to the Decennial
Census. Although the Decennial Census receives a great deal of
attention, the Census Bureau conducts numerous other surveys much
more frequently.

The second largest Federal statistical agency, at 23 percent of
spending, is the Department of Labor's Bureau of Labor Statistics
(BLS), which produces, on a monthly and quarterly basis, the vast
majority of U.S. data on employment and prices that are used to
provide timely assessments of the current state of the economy.
A combined 20 percent of spending is accounted for by the
agencies responsible for preparing statistics on education,
agriculture, and health.
The Department of Commerce's Bureau of Economic Analysis (BEA) is
a relatively small statistical agency, with just 3 percent of spending.
Its data products rely substantially on input data collected by other
agencies and include the National Income and Product Accounts, which
are among the most comprehensive measures of the size and current
performance of the U.S. economy. Construction of the national accounts
(which includes GDP) makes the BEA a consumer of vast amounts of data
from the Census Bureau (such as import and export data) and the BLS
(such as wage and salary data), as well as many other public and
private sources.
Statistical data may be collected on a regular basis (monthly,
quarterly, or annually) or on a relatively infrequent basis
(every 5 or 10 years, for example). Chart 8-2 shows the pattern of
real spending by several statistical agencies on economic statistics
that are produced at least once per year. Examples include the monthly
employment report from the BLS; monthly data on durable goods orders
and new home sales, quarterly data on services, and official annual
estimates of income and poverty from the Census Bureau; and quarterly
GDP from the BEA. Chart 8-3 shows the pattern of real spending for
several Census Bureau programs that are produced on an infrequent
basis (the Decennial Census or the 5-year Economic Census and Census
of Governments). In both charts, expenditures on these programs were
adjusted for inflation with the Office of Management and Budget's
deflator for "all other" Federal outlays (primarily salaries and
expenses for nondefense agencies). As shown in Chart 8-2, spending
on economic statistics has largely kept up with inflation. Real
spending by the BLS has decreased slightly since 2004, after a
period of steady growth that began in 1997. The three statistical
agencies in Chart 8-2 account for about 50 percent of the total
spending on economic statistics (excluding the Decennial Census
and periodic spending by the Census Bureau). Total spending on
economic statistics by other agencies has remained level.

As shown in Chart 8-3, spending on programs with a 5- or
10-year production cycle exhibits a clear pattern: spending
climbs in preparation for the survey during the years immediately
preceding the survey, peaks during the year of the survey, and
then falls quickly upon completion. For example, real spending
(in 2007 dollars) on the Decennial Census, which measures the size
of the U.S. population, rose from about $110 million in 1997 to over
$5.3 billion in 2000, before quickly falling back. The slight upward
trend in decennial funding in the last several years was partly for
the development of the American Community Survey, discussed later in
this chapter. The 5-year budget cycle of the Economic Census, which
measures output and related statistics in the business sector, is
also apparent, though the year-to-year changes in spending are
considerably smaller. The Census of Governments-which collects data
on government organizations, finances, and employment-also picks up
every 5 years, but the annual level of spending on this program is
relatively small (less than $10 million), so the variations are less
noticeable.


Unlike the 5- and 10-year censuses, which are fairly well understood,
funding requests for other statistical initiatives, such as new products
or needed updates to existing programs, are easily misunderstood.
For example, a major redesign of an existing survey's methodology
ideally involves running two surveys concurrently (one with the old
methodology and one with the new methodology) for a brief period of
time so that the effect of the change in methodology can be isolated.
Understanding this effect is essential if results from the redesigned
survey are to be meaningfully compared to those of the survey being
replaced.

The Importance of Statistical Systems
Providing accurate information to households, firms, and policymakers
is an important role of government statistical agencies. Most
decisionmakers in private industry, in Federal, State, and local
governments, and in private households, rely in some way on data
collected by Federal agencies. Federal economic statistics are
designed to be consistent, unbiased, and reliable over time.
These statistics can prove particularly useful if their availability
and analysis allow a costly problem to be prevented or
remedied more quickly and efficiently.
Private decisionmakers benefit from high-quality statistical
systems because they improve the value of the information
upon which firms and individuals base their decisions.
For example, in formulating investment decisions, industries
may use data on final demand or on the output of other industries
that buy their output. A firm may examine a variety of labor
market data, such as wage rates and educational attainment in
the region, when deciding where to open new branches of the
company. Airport authorities may study regional economic
prospects when considering expansion decisions. Worker
organizations and employers may track inflation trends and
factor these price changes into their expectations for nominal
wage gains. Popular press accounts based on occupational
earnings may help students choose colleges, fields of study,
or other training that will have long-term implications
for their career paths.
State and local governments rely on a wide variety of statistical
data to benchmark their performance, to plan for the future,
and to readjust their allocation of resources. For example, a
State that finds its high school dropout rate rising relative
to other States may opt to devote more resources to education.
Likewise, a city that finds its crime rate rising relative to
other localities may choose to devote more resources to law
enforcement. States and cities may study data on local population
growth to assess the need for new transportation systems, schools,
and other types of physical infrastructure.
Monetary and fiscal policymakers also rely on high-quality,
publicly available data for understanding the changing state of
the economy, for formulating sound policy on a wide range of
macro- and microeconomic issues, and for economic forecasting.
For example, monetary policy depends on accurate measures of
resource utilization, current employment and unemployment
trends, productivity trends, inflation trends (including unit
labor costs), and housing market developments. If inflation
estimates are overstated, monetary policy might be unnecessarily
restrictive. Similarly, if productivity is overstated, policymakers
may think that the economy's productive capacity is expanding
quickly enough to accommodate rising output without being
inflationary, and the resulting monetary policy may not be
restrictive enough to limit the risk of inflation. Fiscal
policy depends on accurate measures of GDP growth, potential
GDP growth, labor markets, and demographic change to forecast
future government outlays and revenues. If productivity is growing
more slowly than believed, then revenue projections may be too
high, and as a result, policymakers may adopt spending plans that
are inconsistent with overall budget goals. Thus, a clear
understanding of the true trends in these variables is
critical to making sound budget projections.

Keeping Up with a Changing Economy
There are many ongoing efforts to update the statistical
infrastructure to better reflect the changing economy and to more
accurately reflect the economy as it stands now. These efforts
include maintaining the relevance of statistical classification
systems, better measuring the changing population, improving
the measurement of the service-sector output, and measuring
the contribution of investment in intangible assets
(such as research and development) to economic growth.
Statistical systems rely heavily on the classification of
activities, and over time classification structures are
changed to better reflect the economy. Sometimes the changes
are incremental, such as when an industry is split into
two more detailed industries. Other changes are more
substantial, such as the transition from the Standard
Industrial Classification (SIC) system to the North American
Industry Classification System (NAICS). Despite the benefits
of NAICS-such as better coverage of advanced technology
industries, as well as better international comparability-the
transition was nonetheless disruptive to statistical agencies
and data users. In particular, the transition to NAICS broke
the historical continuity of many data series. Further, the
official use of NAICS began in 1997 but not all data series
incorporated NAICS classification in the same year. Statistical
agencies have extended many of their statistics backward in time
on a NAICS basis, but doing so is difficult and time-consuming.
There is sometimes inadequate information to cleanly separate
SIC-reported industry data into the redefined industries and
the greater industry detail under NAICS. Many statistics
produced by the BEA and BLS, for example, have been extended
back to 1992 or 1990, respectively, and a few series go back
further.  The Federal Reserve Board extended its industrial
production and capacity utilization statistics back to 1972
based on the results of an extensive microdata reclassification
research project that was conducted with the Census Bureau's
Center for Economic Studies. Despite the improvements that
came with NAICS, it can be argued that the classification system
has yet to fully capture the character of modern economies. For
example, the shift over time from manufacturing to services is
still not fully reflected in the level of detail collected, or
even in the number of defined industries: The 2007 NAICS
recognized nearly 17 percent more private service industries than
manufacturing indus
tries (550 versus 472), even though the gross
output of private services was about 3 times larger than that of
manufacturing in 2005.
The Census Bureau recently introduced the American Community
Survey (ACS) to provide more current data on our Nation's population
and its characteristics. With a sample size of approximately 3
million addresses, the ACS collects important demographic, housing,
social, and economic information for use in the administration of
Federal programs and the distribution of Federal spending. The ACS
is the Nation's largest household survey and will eliminate the
need for the Decennial Census long form in future censuses by
providing data for the same detailed geographic locations as the
long form. Unlike the long form, however, it will provide single-year
estimates for geographic areas with populations of 65,000 or more
annually, rather than estimates every 10 years. Smaller geographic
areas will be sampled over 3- and 5-year intervals, allowing the
Census Bureau to produce estimates down to the census tract or block
group. For policymakers who need to make decisions affecting the
lives of large numbers of people, having up-to-date estimates of
population characteristics is critical to understanding a program's
likely beneficiaries and its likely costs.
Another recent improvement to the Federal statistical system has been
more accurate and timely measurement of service-sector output. In
2004, the Census Bureau introduced the new Quarterly Services Survey
(QSS), the first new principal Federal economic indicator in nearly
30 years. Prior to the introduction of this survey, the 13 private
service sectors-which together account for about 55 percent of
GDP-were measured, at most, once per year, if covered by the Service
Annual Survey. Even at the annual frequency, the available surveys
account for just 30 percent of GDP. The only comprehensive measures
of service-sector output come every 5 years during the Economic Census.
Therefore, the QSS is important because it measures service-sector
output much more frequently, which keeps the measures of service-sector
activity in the National Income and Product Accounts more current.
Even so, the QSS covers a limited portion of the service sector, which
means there is room for improvement by broadening the coverage of the
survey.
Efforts aimed at understanding the contribution to economic growth of
investment in intangible assets, such as spending on research and
development (R&D), is another example of the work being done to make
statistics better reflect the state of the economy. The BEA, with the
support of the National Science Foundation, created a R&D satellite
account of the U.S. national accounts, which treats R&D as an
investment rather than an expense. Accounting for R&D in this fashion
would have boosted the average annual change in real GDP from 1995 to
2004 by nearly one-quarter percentage point, to 3.3 percent. The BLS
has created statistical measures of business employment dynamics that
help explain the contributions to net changes in employment that come
from job losses versus job gains. As the length of the time series
increases, these employment measures will be useful for understanding
changes in employment over the business cycle. For example, a policy
response to a decrease in net employment that results from an increase
in gross job losses (i.e., greater layoffs or voluntary separations)
may be different from one that results from a decrease in gross job
gains (i.e., weaker hiring). The former might reflect transitory
industry shifts, while the latter might suggest a generally weaker
macroeconomic situation.
Other efforts to better reflect the changing economy include work at
the Federal Reserve Board, the BLS, and the BEA to improve price
measures to better represent the rapid pace of technological change
in high-technology products like computers. When adjusted for
improvements in quality, prices are estimated to fall much faster,
which raises measures of real output.
Attempts to keep up with the changing economy are complicated by
efforts to maintain consistent time series. Long time series are
valuable for making historical comparisons and inferring long-run
relationships among economic variables. When a time series is short,
it is hard to know if there is anything exceptional about a current
event. The strength of any conclusions that are drawn is a direct
function of variation in data. Short time series have too little
cyclical variation. Similarly, panel data-which follows a group of
persons, households, or firms over time-are valuable for inferring
changes over time from cross-sectional changes due, say, to different
population composition.
There are a variety of ways in which economic measures can fail to
keep up with the changing nature of the economy. Examples include:
 Firms' increased substitution of purchased services for
secondary activities previously done within the firm (such as payroll
processing) means that some statistics, such as employment, will
document this change as a shift to services. In this example, the data
accurately capture the current use of services, but the data do not
reflect the change in the use of services correctly, as the earlier
data classified all activity within the firm (including payroll
processing) by the predominant activity of the firm (i.e.,
construction, manufacturing, etc.).
 Established industries tend to receive a disproportionate
share of attention compared to new, growing industries. Industry and
product classification codes are more likely to be kept than eliminated,
while new industries and products are often poorly measured and tracked,
at least initially.
 The growth of professional employer organizations-companies
that provide employees to firms on a contractual basis-has led to
data-reporting problems and, consequently, to inaccurate employment
and wage data for industries and localities. Professional employer
organizations that report employment centrally, rather than separately
for each client, can obscure both the industry and location of the
workers and our understanding of employment change and dynamics,
negatively affecting data from BLS, the Census Bureau, the BEA,
and all derived products.
 The prices for some items may fail to fully reflect
changes in the quality of the items. Improvements in quality,
if properly accounted for, tend to boost measured real output.
The split between consumer and business spending on some products
may be updated infrequently, which can lead to misstatements about
which components of GDP are growing more rapidly. Both factors
tend to result in less reliable estimates of real s
pending by
consumers and businesses.
 Housing and geographic samples for the consumer price
index (CPI) become outdated as the population distribution shifts
(see Box 8-1).

Improving the Value of Existing Statistical Data
Federal Government statistical agencies are focusing on three ways to
improve the value of existing statistical data: Improve the detail in
publicly available data products, facilitate well-defined and secure
research on the underlying microdata, and synchronize data produced
across agencies.
Government agencies strive to improve the usefulness of their data
products by providing greater detail while protecting the
confidentiality of respondents. The Census Bureau, for example,
employs several techniques to avoid disclosing individually
identifiable data. Synthetic data, modeled on original data,
retain the needed statistical properties of the original data
but protect the confidentiality of respondents by modifying all
or selected variables. The Census Bureau creates synthetic data
to obscure the underlying demographic data used in its "On the Map"
feature. This feature creates maps showing

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Box 8-1: How to Reverse a Decline in Statistical Infrastructure:
Improving the Sample for the Consumer Price Index

The housing and geographic area samples for the Consumer Price
Index (CPI), currently based on 1990 Decennial Census data, are
overdue for an update. Each year these samples become more out of
date, in that the samples do not reflect almost 20 years of
population growth, demographic changes, and new housing construction.
Because of its widespread use to estimate price changes, the
accuracy of the CPI influences a range of economic variables in both
the public and private sectors. For example, within Federal
programs, the CPI is used to adjust Social Security payments,
civilian and military retirement payments, and individual income
tax brackets for inflation. A study by the Congressional Budget
Office found that a 1 percentage point reduction in the growth rate
of CPI estimates beginning in January 2006 would have reduced the
Federal budget deficit or surplus by $14 billion by the end of 2007
and $153 billion by 2015.

The Administration has proposed to update the 1990 Decennial
Census-based housing sample used by the BLS with data from the
Census Bureau's new, continuously conducted American Community
Survey (ACS) and/or private sector sources. With continuous
updating, the sample would never be more
than 3 years old. This
change would increase the accuracy of the CPI by creating a more
representative housing sample, reduce respondent attrition, and
reduce potential bias by more accurately reflecting new
construction. Moreover, using the ACS to update the geographic
sample on which the CPI is based would result in estimates that
more accurately reflect the geographic distribution of the
population and its characteristics.
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commuting patterns and workforce data-where people live and
work by age, earnings, and industry-for geographic areas
selected by the user. Another method used is noise
addition-the controlled introduction of variation from
reported levels to detailed data that otherwise could not
be published, with small compensating adjustments to other
data in the same series. The Census Bureau uses this method
to ensure that an individual company's data cannot be readily
inferred from published Survey of Business Owners data or other
estimates.
Government statistical agencies benefit when researchers can subject
the data and the methodology behind the statistics to academic
scrutiny in a secure research environment that maintains security
of the data, restricts access to the level of data essential for an
authorized project, and protects the confidentiality of respondents.
The analysis of underlying data by academics is an inexpensive way
for statistical agencies to improve their data products. For example,
academic researchers typically investigate relationships among
variables in a single survey, or in several surveys, that are not
examined during routine data-processing procedures. Their
nonstandardized data reviews can uncover anomalies that should be
resolved before the data are released, or provide the basis for
future improvements in standardized data-processing routines.
In addition, this third-party scrutiny adds to the credibility
of the data products. For example, the Census Bureau's Research
Data Centers (RDCs) provide secure, restricted access to Census
Bureau data for authorized researchers. Likewise, the BLS researcher
access program provides secure, restricted access to BLS data. In
both cases, researchers must undergo a strict approval process and
face significant penalties for violating the laws protecting the
confidentiality of responses to government surveys.
Previous research at the RDCs has led to new data products and
changed thinking about many important economic issues. For example,
an important strand of academic work separated net employment
flows-the published employment changes with which people are
familiar-into gross job creation and gross job destruction. The
quarterly BLS Business Employment Dynamics data release-which
reports the number and rates of gross jobs gained at opening and
expanding establishments, as well as the number and rates of
gross jobs lost at closing and contracting establishments-is an
example of a new data product that grew out of this work.
Importantly, the Business Employment Dynamics data release is
tabulated from already collected company data records, thus
creating no additional respondent burden. It is an important
example of drawing upon academic research to improve the use of
existing data in order to create new data products.
A third way that the Government can improve the value of existing
data-and the method that offers the most substantial opportunities-is
to allow the BEA, BLS, and Census Bureau to link their business data,
while maintaining confidentiality. This linking would result in
more accurate and reliable economic indicators, lower budget
costs for the agencies, and lower response burdens for survey
respondents. For example, at present, both the Census Bureau and
the BLS ask firms to break out employment and payroll data by
establishment in the Company Organization Survey and Multiple
Worksite Report, respectively. If these agencies could share
their business data, these two surveys, which are mailed to
multiunit companies, could be combined, reducing the response
burden of these firms and reducing survey costs for the
statistical agencies.
The Administration recognizes that the sharing of key business
data among Federal statistical agencies has tremendous potential
for exploiting synergies among the agencies and for improving the
quality of Federal statistics. In 2002, with Administration support,
the Congress passed the Confidential Information Protection and
Statistical Efficiency Act (CIPSEA), described in Box 8-2, whose
stated purposes were: 1) To protect the confidentiality of information
collected by Federal agencies for statistical purposes, and 2) to
improve the efficiency of the Federal statistical system by authorizing
limited sharing of business data among the Census Bureau, the BEA, and
the BLS for exclusively statistical purposes. In 2007, the Office of
Management and Budget issued implementation guidance for CIPSEA. The
first part-data protection-has been effectively implemented across
agencies, but the second part-improving statistical efficiency-cannot be
fully enabled without additional legislation. Because business tax data
(such as company name and address) are used to construct the Census
Bureau's business list, many Census Bureau data products are considered
to be comingled with tax information. Therefore, full implementation of
CIPSEA would require changes to the portion of the Internal Revenue
Service (IRS) code that authorizes the statistical use of business
tax data.
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Box 8-2: The Confidential Information Protection and Statistical
Efficiency Act (CIPSEA)

The two parts of CIPSEA are confidential information protection and
statistical efficiency.

Confidential information protection: Subtitle A establishes
standardized safeguards to protect the confidentiality of data
collected by Federal agencies for exclusively statistical purposes.
These safeguards include the assurance that information will not
be used against a respondent in any government action and that
inappropriate disclosure of confidential data will be considered a
felony and carry significant criminal penalties. In other words,
data collected for statistical purposes cannot be used for
tax, immigration, or other enforcement purposes.

Statistical efficiency: Subtitle B authorizes the sharing of
business data among the Census Bureau, the Bureau of Economic
Analysis, and the Bureau of Labor Statistics for exclusively
statistical purposes in order to:
 Reduce the paperwork burdens imposed on businesses that
provide requested information to the Federal Government;
 Improve the comparability and accuracy of Federal economic
statistics by allowing these agencies to reconcile differences in
business lists; to develop consistent classifications of businesses
into industries; and to improve coverage; and
 Increase understanding of the U.S. economy (including key
industries and regions), develop more accurate measures of the
impact of technology on productivity growth, and enhance the
reliability of the Nation's most important economic indicators,
such as the National Income and Product Accounts.
----------------------------------------------------------------------

A major goal of fully implementing CIPSEA is to better reconcile the
BLS Business Establishment List-based on State unemployment insurance
records-and the Census Bureau's Business Register-based, in part, on
IRS records. One study found that over 30 percent of single-establishment
firms had different 6-digit NAICS industry codes in the two lists, and
another study revealed large discrepancies in measures of industry-level
employment across surveys.
The failure to coordinate data across agencies can lead to noticeable
inaccuracies, especially when one needs to calculate a measure that
combines data from two agencies. For example, the implications of
discrepancies in establishment classifications are particularly acute
when measuring labor productivity, which is an important statistic for
economic policymakers, including those who project the Federal budget.
Labor productivity is the ratio of output, measured by the Census
Bureau, and hours worked, as measured by the BLS. Accurate
productivity estimates depend upon these labor and hours worked
measures being given consistent industry classifications, which is
unlikely if the underlying business lists are inconsistent.
Differences in industry classification would also result in discrepancies
in the rate of real GDP growth reported by key sectors. For example,
in the Computer and Electronic Product Manufacturing Subsector
(NAICS 334), the growth in real value added in 2002 would have
been 15.6 percent if payroll data from the Census Bureau's Economic
Census had been used. Instead, the growth in real value added was
published as 7.4 percent, a statistic based on payroll data from
the BLS. Without carefully analyzing the confidential business
lists used for the Economic Census and the BLS payroll data, it
is difficult to know which payroll measure should be used. Some
efforts to share data have proven useful in reducing inconsistencies
and reducing burden. The BLS has shared industry identifiers with
the Census Bureau since 1992 and geographic identifiers since 2002,
particularly for new and small businesses. These industry codes
covered over 3 million businesses in 2007 alone and now account
for about 30 percent of the Census Bureau's business codes.
Expanding data sharing would extend this work and further improve
consistency and accuracy of key data series.
A 2006 report noted that data sharing might highlight opportunities
for understanding data reporting that would better focus resources
on activities that would improve the measurement of national
economic activity (such as the reporting of stock options). The
National Income and Product Accounts provide two measures of
national activity, one based on total output (GDP) and one based
on total income (gross domestic income or GDI). In theory, these
measures should be equal. In practice, they differ by a measurement
error called the statistical discrepancy. The statistical discrepancy
can be persistent: From 1995-2000 real GDI grew 0.6 percentage point
faster than real GDP, on average, per year. If the growth rate of
the GDI were projected forward instead of the growth rate of GDP,
the budget implications could be substantial. An analysis of fiscal
year 2006 by the Office of Management and Budget found that if the
GDP were persistently understated by 1 percent, the projected
cumulative budget deficit would be overstated by $530 billion
over a 5-year period.
Better measures of business formation are needed to understand the
changing composition of the business sector and the factors that
contribute to business and job creation. Data synchronization would
help agencies track business formation more accurately and on a more
timely basis by reconciling the business lists from the Census Bureau
and the BLS. For example, the Census Bureau's Business Register relies
heavily on the Economic Censuses (conducted every 5 years) for
information on business structure. In the intervening years, however,
the Census Bureau makes use of its annual Company Organization Survey,
which covers all employers with more than 250 employees, but only a
sampling of smaller companies. The Census Bureau's Business Register
generally does a good job identifying ownership links among
establishments (e.g., when a single firm owns establishments in
two different States). However, the information on ownership is
weaker for smaller firms because only a subset of these
businesses is surveyed during the years between the 5-year
censuses. Firm restructuring often contributes to the difficulty
of tracking parent-subsidiary relationships. The BLS Business
Employment Dynamics accurately measures the universe of
business openings and closings on a quarterly frequency but
may not always successfully track parent-subsidiary relationships.
Combining the strengths of the Census and BLS business lists
would improve the ability to discern whether a new establishment
is an entirely new firm or a new branch of an existing firm,
and therefore improve understanding of business dynamics.
Data synchronization could also help reconcile differences between
similar statistics produced by separate agencies. For example,
the BLS publishes wages and salary data based on its Quarterly
Census of Employment and Wages business list and the Census
Bureau publishes payroll data in its County Business Patterns
series. A comparison of 2003 private wages and salaries
revealed that these two measures differed by significant amounts.
For example, the BLS measure of wages and salaries in New Mexico
was 4.2 percent higher than the Census Bureau measure, while in
Alaska, the BLS measure was 9.5 percent lower. At the national
level, BLS data were 0.6 percent (or $25.1 billion) lower than
County Business Patterns data, but they were 2 percent (or $6.7
billion) lower for New York. Understanding the sources of
these differences (such as differences in reporting and
coverage) may yield improved regional measures that would
have several implications:
 Distribution of Federal funds to the States: BEA per
capita personal income data, based largely on BLS data, are
used in the formula that  calculates  how to distribute the
Federal share of Medicaid funding to States.  Wages and
salaries and wage-related components account for two-thirds
of personal income.  In 2003, State private wage levels based
on BLS data were $2.5 billion higher in Texas and $7.1 billion
lower in Washington than levels based on the Census Bureau's
County Business Patterns.
 State tax and budget planning: The dollar difference
between  BLS and Census measures of wage and salary growth from
2001 to 2002 would result in significantly different projections
of State and local government income taxes received: a
$165 million discrepancy in New Jersey and a $193 million
discrepancy in Massachusetts. The $1.2  billion wage growth
difference in New York would yield a $173 million
discrepancy in projected State and local tax revenue.

Conclusion
The quality of public policy debates depends, in large part,
on the availability of relevant and reliable statistical data.
Consistent data series ensure that newly gathered data can be
meaningfully compared to previously collected data. At the same
time, it is also important that the statistical system maintain
the flexibility to create new data products that keep up with
the changing nature of the dynamic global economy. The
infrastructure required to develop and produce these data,
like any infrastructure, requires continuous investment to
maintain and improve the system, but not all data improvements
are costly. For example, existing economic data on businesses
could be improved through the full implementation of the
Confidential Information Protection and Statistical Efficiency
Act without increasing the reporting burden for respondents,
without compromising the confidentiality of the data collected
by the Federal statistical agencies, and without significantly
raising costs of the data collection and tabulation. Maintaining
solid statistical systems ensures that public policymakers and
private decisionmakers will have access to the information needed
to understand our dynamic economy.