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Improving Business Statistics Through Interagency Data Sharing: Summary of a Workshop
7
The Importance of Data Sharing to Consistent Macroeconomic Statistics
Dennis Fixler and J. Steven Landefeld
Bureau of Economic Analysis
The Bureau of Economic Analysis (BEA) has a unique position in the decentralized U.S. statistical system. BEA produces the national income and product accounts (NIPAs), a comprehensive and consistent double-entry set of accounts for the economy. BEA uses a myriad of data collected from public and private data sources to construct these accounts. In this role, BEA often confronts major inconsistencies in piecing these data together that are not evident from the perspective of the agencies collecting the individual pieces of the economic puzzle. BEA has been described as the canary in the mineshaft for the U.S. statistical system.
The U.S. statistical system has evolved over time in such policy agencies as the U.S. Department of Commerce, Labor, the Treasury, Agriculture, and Energy to provide data and answer questions relevant to the agencies’ missions. Surveys and the legislation supporting them have evolved independently. The result is a diverse set of data using different business registers, different industry classifications for establishments, different concepts and definitions, different timing, and different collection methods.
These differences in survey frames and procedures produce significant quantitative differences in what would appear to be the same measures of economic activity. For example, employment in individual industries as reported by the Labor Department’s Bureau of Labor Statistics (BLS) can differ markedly from that reported by the Department of Commerce’s Census Bureau. Differences exist for wages and salaries across industries, across states, and in the aggregate (see Tables 7-2 and 7-6).
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Improving Business Statistics Through Interagency Data Sharing: Summary of a Workshop
WHY IT MATTERS
The implications of these differences in estimates is illustrated in Table 7-1, which summarizes the various data sources used by BEA in constructing one of its sets of accounts. Gross domestic product (GDP) is mainly estimated using data collected by the Census Bureau, while gross domestic income (GDI) is mainly estimated using data collected by BLS, the Census Bureau, and the Statistics of Income (SOI, part of the Internal Revenue Service, IRS). In concept, GDP should equal GDI because all final expenditures should end up as income to households, business, or government. However, because of the differences in the source data used in estimating GDP and GDI, often they are not equal, and the result is the statistical discrepancy.
Such discrepancies between GDP and GDI can have large impacts on fiscal and monetary policy. During the latter half of the 1990s, a large and persistent discrepancy arose, with real GDI growing 0.6 percent faster than real GDP (1995-2000). This was important for budget planning because real trend GDP growth is used as the baseline for estimating near-term trend growth in 5-year budget forecasts made by the Office of Management and Budget (OMB) and the Congressional Budget Office. To illus-
TABLE 7-1 BEA Summary Account 1—Primary Data Sources (billions of dollars)
Primary Data Source
2004
Income side
Labor compensation
BLS
$6,693.4
Corporate profits & gov’t enterprises
Census Bureau, SOI
973.6
Proprietors’ income and rental income
Census Bureau, SOI
1023.8
Interest on assets, taxes, & misc. payments
SOI, FRB
1,531.3
Depreciation
Census Bureau
1,435.3
GROSS DOMESTIC INCOME
$11,657.5
Statistical discrepancy
76.8
GROSS DOMESTIC PRODUCT
$11,734.3
Expenditure side
Personal consumption expenditures
Census Bureau
$8,214.3
Gross private domestic investment
Census Bureau
1,928.1
Gov’t consumption exp. & gross invest.
Gov’t, Census Bureau
2,215.9
Net exports of goods and services
Census Bureau, BEA
–624.0
GROSS DOMESTIC PRODUCT
$11,734.3
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Improving Business Statistics Through Interagency Data Sharing: Summary of a Workshop
trate the impact, according to OMB’s FY 2006 analysis of the “sensitivity of the budget to economic assumptions,” a persistent understatement of real GDP growth by 1.0 percent would result in an overstatement of the projected deficit of $530 billion. Similarly, a persistent understatement of real trend GDP growth could lower the Federal Reserve’s estimate of non-inflationary sustainable growth and signal the need for a tighter monetary policy than necessary.
One possible answer to the source of this discrepancy could lie in the recording of stock options, bonuses, and fringe benefits in employee compensation. While there are many sources of the difference between BLS and the Census Bureau payroll data, it is interesting that during the latter half of the 1990s, when stock options and bonuses were growing rapidly, the Census Bureau data rose at a 7.8 percent average annual rate, whereas the BLS data rose at a 7.5 percent average annual rate (1995-2000). Part of this may reflect the recording of stock options. For example, in Washington State—a state with significant stock option activity—the Census Bureau data grew nearly twice as fast (11.5 percent) as the BLS payroll data (6.2 percent) for 2000. If it turned out that stock options were under-reported in the BLS data, it would suggest that the growth rate of GDI might be even higher, thereby focusing additional effort on improving the reporting of final expenditures on services and other less-well-measured components of GDP.
Another example of the importance of BEA accuracy is illustrated by its regional data, which are used in the geographic allocation of nearly $200 billion in federal funds. These data are also used by virtually every state for its tax and planning purposes. BEA uses BLS data for these state and local estimates, which are taken from quarterly employment and unemployment tax forms.
The differences between the two sets of payroll data across states vary from the BLS set’s being 4.2 percent higher in New Mexico to 9.5 lower in Alaska than what is reported by the Census Bureau. These differences could have a significant impact on the allocation of state Medicaid funds, which uses BEA per capita state personal income to determine the federal share of payments for each state. Differences in growth rates can also have an important impact on state tax projections and spending plans. For example, in New York the $1.2 billion difference in growth in wages and salaries between 2001 and 2002 between BLS and the Census Bureau series would amount to about a $173 million difference in projected income taxes.
These are but a few of the examples of the implications for government and business decision makers. In the sections below, the implications for users of estimates ranging from profits and productivity to inflation and offshoring are explored.
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Improving Business Statistics Through Interagency Data Sharing: Summary of a Workshop
DATA SHARING
Over the years, numerous proposals have been put forth to resolve the problems associated with a decentralized system. One has been the creation of a central statistical office such as those that exist in Canada, Australia, and other countries. The creation of such an entity has not proved popular for various reasons. Consolidation would require extensive budget negotiation and resources to coordinate and implement a process that ensures there is little to no disruption in data production. Furthermore, the current system allows for the specialization that has arguably led to many of the innovations produced by U.S. statistical agencies.
Given these difficulties, a practical way to achieve many of the benefits of a central statistical agency without the costs is to permit the sharing of business data among the three general-purpose statistical agencies—BEA, BLS, and the Census Bureau—that produce the bulk of the nation’s economic data. All three agencies have an excellent record of protecting confidential data, have access to various types of tax data, and share various types of data that could be significantly improved by data sharing.
A major step forward in allowing data sharing was the passage of the Confidential Information Protection and Statistical Efficiency Act of 2002 (CIPSEA). Section 521 stipulated that business data can be shared for statistical purposes among BEA, the Census Bureau, and BLS. At the time CIPSEA was formulated, it was understood that for data sharing to be completely operational, there would have to be some changes in Section 6103, paragraph (j), of Title 26 (Internal Revenue Code) and the accompanying regulations that govern access to federal tax information (FTI). These changes are necessitated by the facts that much of the Census Bureau information is commingled with FTI and neither BEA nor BLS has the Census Bureau level of access to use such data. Although there have been discussions concerning the formulation of a bill to submit to Congress to bring about the necessary changes in Title 26, to date no bill has been written for submission.
The absence of fully implemented data sharing especially affects BEA because it collects few data of its own and relies primarily on the Census Bureau for its data. Data sharing, however, does not just affect the ability of BEA to access Census Bureau data; the inability of BLS and the Census Bureau to share data greatly affects the quality of the data that BEA receives from both agencies. In this chapter we provide examples of how the absence of data sharing affects BEA estimates.
The limited access to business tax data has enormous effects on BEA’s ability to access Census data that are commingled with tax data. The Cen-
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sus Bureau sample frames are constructed from IRS data, and, under current rules, name, address, and employer identification numbers are generally considered tax data. Although in principle BEA has access to corporate tax records in the SOI sample, the Census Bureau does not know the identification of those firms and so BEA has generally not been allowed access to Census records. Without going into the arcane detail, whether BEA has access to corporate Census records that are commingled with tax data is determined by the extent to which the Census Bureau claims that data are based on their own collection and not IRS records. Such a claim is generally made by the Census Bureau in the case of multiunit establishments. Thus BEA cannot access Census Bureau records from single-unit establishments. Finally, because legislation limits BEA access to corporate tax records, BEA cannot access partnership and sole proprietor Census Bureau records, which are collected from tax data–based sample frames.
The limited access to tax data also impedes BEA’s use of the Census records to construct sample frames for its international surveys. The impediment is especially problematic in the services area, because many of these providers are not multiunit establishments. In a joint effort by BEA, Census, and the National Science Foundation (NSF) regarding identifying international research and development expenditures, it was discovered that there was considerable difference between Census and BEA sample frames. In this case, BEA had identified many firms that were not in the Census Bureau sample.
Below we provide some detailed illustrations of how the absence of data sharing affects BEA estimates. We also discuss how the effect on BEA estimates would affect policy decisions that are based on those estimates.
Industry Employment Differences
BLS, the Census Bureau, and SOI are the main sources of wage and salary data in the U.S. economy. Figures 7-1 and 7-2 show that the levels and growth of total payroll according to these sources are broadly consistent, but that there are significant differences in magnitudes. Below we focus on the BLS and the Census Bureau data, as those are the two main sources used by BEA.1
BLS prepares comprehensive wage and salary data in its Quarterly
1
The SOI data are composed from a sample of tax returns and therefore are not as comprehensive as either BLS or the Census Bureau data. Furthermore these data are released with a lag.
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Improving Business Statistics Through Interagency Data Sharing: Summary of a Workshop
FIGURE 7-1 Payroll data comparison—the Census Bureau, BLS, and SOI levels.
FIGURE 7-2 Payroll data comparison—the Census Bureau, BLS, and SOI growth.
Census of Employment and Wages Program (QCEW).2 These data are widely used in BEA and are the basis for the wage and salary component of personal income. The Census Bureau also prepares payroll data as part of its Quinquennial Economic Census and Annual Survey programs. These Census data are considered to be less timely than BLS data, but in some areas, such as educational services, membership organizations, and nonprofits, they are considered to be more complete than the QCEW data.
2
These data are commonly referred to as the ES-202 data, the former name of the program.
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In addition, Census data on wages are generally recognized as providing a better industry distribution of aggregate wages, and incorporating these data into BEA estimates offers a unique opportunity to create greater consistency among the BEA industry accounts’ measures of gross output, intermediate inputs, and value added. The decision, however, to choose one set of data over the other has implications for the measurement of value added in the industry accounts, which can be traced out through examining the estimates prepared as part of the annual industry accounts.
For some industries, the differences in the level of employment are significant. Table 7-2 identifies the differences in levels between BLS and the Census wage and salary data for 2002, an economic census year.3 The primary explanations for the differences are that the Census Bureau and BLS have different sample frames of establishments and that establishments in both frames are not always classified in the same industry. The Census Bureau and BLS are currently engaged in a project that seeks to study this source of difference and explore other sources of differences. Before this project could be undertaken, approval from the IRS had to be obtained. The presentation at the workshop by James Spletzer (BLS) and Paul Hanczaryk (Census Bureau) provided details of the study (see Chapter 2 of this volume).
As shown in Table 7-2, there are many relatively large differences among industries in which estimates are available from both BLS and the Census Bureau. In the case of oil and gas extraction, the Census payroll estimate is about 50 percent lower than the BLS estimate. In addition, the Census Bureau estimate for all of manufacturing is about 15 percent—or roughly $100 billion—lower than the BLS estimate. In contrast, Census payroll estimate for management of companies and enterprises is about 63 percent—or over $70 billion—higher than the BLS estimate.4
Because employment and wage data are used in several places in the national accounts, we will now show how BEA estimates would be different if the Census data were used instead of the currently used BLS data for manufacturing and a few other industries in the computation of value-added. Although the current-dollar growth rate could change by as much as 2.0 percentage points (e.g., computers and electronic products), Table 7-3 shows the relative rankings for the selected industries tended to be relatively stable.
3
The Census payroll data used are from the U.S. Census Bureau web site as of April 1, 2005. BLS wage data are consistent with the 2004 annual revision to the national income and product accounts and the 2004 annual revision to the annual industry accounts.
4
This pattern may suggest a different classification treatment of head company offices by the Census Bureau and BLS.
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TABLE 7-2 Differences in BLS Wages and the Census Bureau Payroll by NAICS Industry, 2002 (in thousands of dollars)
1997 NAICS Codes
Industry Name
BEA Wagesa
All Industries
4,968,131,000
Private industries
4,119,730,000
11
Agriculture, forestry, fishing, and hunting
31,815,000
111, 112
Crop and animal production (“Farms”)
17,685,000
113, 114, 115
Forestry, fishing, and related activities
14,130,000
21
Mining
30,788,000
211
Oil and gas extraction
11,455,000
212
Mining, except oil and gas
10,470,000
213
Support activities for mining
8,863,000
22
Utilities
40,094,000
23
Construction
272,418,000
31, 32, 33
Manufacturing
675,523,000
33, 321, 327
Durable goods
441,182,000
321
Wood product manufacturing
17,585,000
327
Nonmetallic mineral product manufacturing
20,674,000
331
Primary metal manufacturing
23,209,000
332
Fabricated metal product manufacturing
59,742,000
333
Machinery manufacturing
57,050,000
334
Computer and electronic product manufacturing
98,359,000
335
Electrical equipment and appliance manufacturing
20,630,000
3361, 3362, 3363
Motor vehicle, body, trailer, and parts manufacturing
58,705,000
3364, 3365, 3366, 3369
Other transportation equipment manufacturing
38,954,000
337
Furniture and related product manufacturing
18,232,000
339
Miscellaneous manufacturing
28,042,000
31, 32 (excluding 321 and 327)
Nondurable goods
234,341,000
311 ,312
Food product manufacturing
60,356,000
313, 314
Textile and textile product mills
14,525,000
315,316
Apparel manufacturing
10,751,000
322
Paper manufacturing
25,611,000
323
Printing and related support activities
27,061,000
324
Petroleum and coal products manufacturing
7,632,000
325
Chemical manufacturing
57,293,000
326
Plastics and rubber products manufacturing
31,112,000
42
Wholesale trade
280,745,000
44, 45
Retail trade
360,341,000
48, 49
Transportation and warehousing, excluding postal service
162,206,000
481
Air transportation
30,550,000
482
Rail transportation
11,824,000
483
Water transportation
2,888,000
484
Truck transportation
47,917,000
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Improving Business Statistics Through Interagency Data Sharing: Summary of a Workshop
Percent Differenced
BLS Wagesb
Census Payrollc
BEA and Census
BLS and Census
BLS and Census Differencee
—
—
3,923,090,541
—
—
24,146,183
—
—
15,862,753
—
—
8,283,429
—
—
30,557,227
—
—
11,269,829
5,564,811
–51.4
–50.6
–5,705,018
10,321,353
8,987,397
–14.2
–12.9
–1,333,956
8,966,044
6,707,242
–24.3
–25.2
–2,258,802
39,895,551
43,493,804
8.5
9.0
3,598,253
260,841,814
254,000,182
–6.8
–2.6
670,676,772
573,401,510
–15.1
–14.5
–97,275,262
437,547,486
370,407,941
–16.0
–15.3
–67,139,545
16,952,331
15,909,908
–9.5
–6.1
–1,042,423
20,543,618
17,933,376
–13.3
–12.7
–2,610,242
23,246,080
21,508,667
–7.3
–7.5
–1,737,413
59,352,280
57,361,374
–4.0
–3.4
–1,990,906
56,689,509
49,470,768
–13.3
–12.7
–7,218,741
98,045,569
64,314,150
–34.6
–34.4
–33,731,419
20,479,516
17,957,015
–13.0
–12.3
–2,522,501
58,579,129
50,331,680
–14.3
–14.1
–8,247,449
38,446,534
31,231,174
–19.8
–18.8
–7,215,360
18,107,133
17,364,837
–4.8
–4.1
–742,296
27,105,787
27,024,992
–3.6
–0.3
–80,795
233,129,286
202,993,569
–13.4
–12.9
–30,135,717
59,649,421
52,334,562
–13.3
–12.3
–7,314,859
14,501,506
12,333,814
–15.1
–14.9
–2,167,692
10,360,588
8,567,969
–20.3
–17.3
–1,792,619
25,744,232
21,336,257
–16.7
–17.1
–4,407,975
26,457,610
25,738,613
–4.9
–2.7
–718,997
7,891,082
6,202,508
–18.7
–21.4
–1,688,574
57,322,150
44,032,801
–23.1
–23.2
–13,289,349
31,202,697
32,447,045
4.3
4.0
1,244,348
276,607,852
249,986,560
–11.0
–9.6
–26,621,292
348,909,029
296,215,722
–17.8
–15.1
–52,693,307
146,810,674
—
—
–146,810,674
30,180,386
—
—
–30,180,386
10,869
—
—
–10,869
2,793,556
3,031,880
5.0
8.5
238,324
46,824,531
47,833,730
–0.2
2.2
1,009,199
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1997 NAICS Codes
Industry Name
BEA Wagesa
485
Transit and ground passenger transportation
8,996,000
486
Pipeline transportation
3,272,000
487, 488, 492
Other transportation and support activities
39,802,000
493
Warehousing and storage
16,957,000
51
Information
189,736,000
511
Publishing including software
58,394,000
512
Motion picture and sound recording industries
18,258,000
513
Broadcasting and telecommunications
84,838,000
514
Information and data processing services
28,246,000
52
Finance and insurance
370,088,000
521, 522
Federal Reserve banks, credit intermediation and related services
132,010,000
523
Securities, commodity contracts, investments
112,344,000
524
Insurance carriers and related activities
119,830,000
525
Funds, trusts, and other financial vehicles
5,904,000
53
Real estate, rental, and leasing
71,785,000
531
Real estate
51,015,000
532,533
Rental and leasing services and lessors of intangible assets
20,770,000
54
Professional and technical services
415,422,000
5411
Legal services
80,297,000
5415
Computer systems design and related services
84,251,000
5412-5414, 5416-5419
Other professional, scientific and technical services
250,874,000
55
Management of companies and enterprises
117,147,000
56
Administrative and waste services
193,525,000
561
Administrative and support services
180,230,000
562
Waste management and remediation services
13,295,000
61
Educational services
74,446,000
62
Health care and social assistance
472,214,000
621
Ambulatory health care services
209,724,000
622, 623
Hospitals and nursing and residential care facilities
217,119,000
624
Social assistance
45,371,000
71
Arts, entertainment, and recreation
51,526,000
711, 712
Performing arts, museums, and related activities
24,724,000
713
Amusements, gambling, and recreation
26,802,000
72
Accommodation and food services
153,922,000
721
Accommodation
40,764,000
722
Food services and drinking places
113,158,000
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Percent Differenced
BLS and Census Differencee
BLS Wagesb
Census Payrollc
BEA and Census
BLS and Census
7,787,298
7,575,497
–15.8
–2.7
–211,801
3,277,932
3,082,558
–5.8
–6.0
–195,374
39,101,445
34,489,516
–13.3
–11.8
–4,611,929
16,834,658
18,689,122
10.2
11.0
1,854,464
188,758,526
—
—
–188,758,526
58,307,089
64,712,028
10.8
11.0
6,404,939
17,879,785
12,516,040
–31.4
–30.0
–5,363,745
84,664,461
88,624,463
4.5
4.7
3,960,002
27,907,191
27,686,444
–2.0
–0.8
–220,747
356,371,058
—
—
–356,371,058
131,188,066
124,076,870
–6.0
–5.4
–7,111,196
108,325,327
101,285,387
–9.8
–6.5
–7,039,940
110,965,984
120,683,183
0.7
8.8
9,717,199
5,891,681
—
—
–5,891,681
68,801,129
—
—
–68,801,129
48,110,832
41,911,444
–17.8
–12.9
–6,199,388
20,690,296
18,706,319
–9.9
–9.6
–1,983,977
390,450,138
—
—
–390,450,138
69,875,728
69,939,404
–12.9
0.1
63,676
83,897,952
72,168,495
–14.3
–14.0
–11,729,457
236,676,458
—
—
–236,676,458
117,462,176
190,807,531
62.9
62.4
73,345,355
191,825,310
—
—
–191,825,310
178,563,429
195,425,035
8.4
9.4
16,861,606
13,261,881
12,178,484
–8.4
–8.2
–1,083,397
64,700,545
—
—
–64,700,545
456,030,369
—
—
–456,030,369
204,320,753
203,716,200
–2.9
–0.3
–604,553
215,390,850
212,480,514
–2.1
–1.4
–2,910,336
36,318,766
36,090,970
–20.5
–0.6
–227,796
47,050,671
—
—
–47,050,671
24,652,961
24,057,801
–2.7
–2.4
–595,160
22,397,710
21,069,716
–21.4
–5.9
–1,327,994
142,208,429
—
—
–142,208,429
36,805,629
34,874,261
–14.4
–5.2
–1,931,368
105,402,801
92,632,794
–18.1
–12.1
–12,770,007
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NAICS
2000
Code
Industry Code Description
CBP
QCEW
$ dif.
% dif.
Total
3,879.4
3,889.0
9.6
0.2
11
Forestry, fishing, hunting, and agric. support
4.7
23.2
18.5
395.7
21
Mining
22.1
29.7
7.6
34.3
22
Utilities
40.7
37.9
–2.7
–6.7
23
Construction
239.9
245.8
5.9
2.4
31
Manufacturing
644.0
743.8
99.8
15.5
42
Wholesale trade
270.1
276.8
6.7
2.5
44
Retail trade
302.6
335.8
33.2
11.0
48
Transportation and warehousing
125.6
147.2
21.6
17.2
51
Information
209.4
212.3
2.9
1.4
52
Finance and insurance
346.8
333.8
–13.0
–3.7
53
Real estate, rental, and leasing
59.2
64.4
5.2
8.8
54
Professional, scientific, andtechnical services
362.0
395.4
33.4
9.2
55
Management of companies and enterprises
211.4
124.0
–87.4
–41.4
56
Admin, support, waste management, remediation services
210.3
185.4
–24.9
–11.8
61
Educational services
61.9
55.6
–6.4
–10.3
62
Health care and social assistance
431.4
394.7
–36.8
–8.5
71
Arts, entertainment and recreation
43.2
45.1
1.9
4.4
72
Accommodation and food services
125.6
134.2
8.6
6.9
81
Other services (except public administration) Mean (excluding Total and 11)
109.9
92.7
–17.2
–15.6 0.9
NOTE: BLS QCEW data prior to 2001 have been backcasted to NAICS 2002 using NAICS reports from employers in the first quarter of 2001. Data for 2001 and 2002 also use NAICS 2002. CBP data are based on NAICS 1997.
data. This project was conducted under the authority of the International Investment and Trade Act and CIPSEA. No Title 26 data were used in the linking operation or subsequent tabulations or reports for this study; neither BEA nor the Census Bureau data sets used for the project contained such data, as all original FTI were replaced by respondent data for the Census Bureau surveys being linked. The Census Bureau informed the IRS of the project to alleviate any questions or concerns the IRS might have.
The project demonstrated that it is feasible to link the Census Bureau and BEA survey data, and that by linking the data an integrated data set on the domestic and international dimensions of R&D can be created. Table 7-10 compares the NSF data with the BEA data for U.S. parent companies and therefore examines only a subset of U.S. firms—U.S. affiliates of foreign companies were not included. Despite the smaller BEA uni-
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2001
2002
CBP
QCEW
$ dif.
% dif.
CBP
QCEW
$ dif.
% dif.
3,989.1
3,952.2
–36.9
–0.9
3,943.2
3,930.8
–12.4
–0.3
4.8
23.6
18.8
393.2
5.0
24.1
19.2
385.0
25.0
31.9
6.9
27.7
24.0
30.6
6.6
27.5
41.9
39.3
–2.6
–6.2
41.8
39.9
–1.9
–4.7
247.2
260.2
13.0
5.2
247.3
260.8
13.5
5.5
617.7
704.1
86.4
14.0
580.4
670.7
90.3
15.6
275.9
279.6
3.7
1.3
262.5
276.6
14.1
5.4
314.8
344.1
29.3
9.3
320.7
348.9
28.2
8.8
129.5
149.8
20.3
15.6
127.3
146.9
19.6
15.4
207.1
205.8
–1.3
–0.7
188.1
188.8
0.7
0.4
373.6
359.4
–14.2
–3.8
372.7
356.4
–16.3
–4.4
64.0
66.9
2.9
4.6
65.2
68.8
3.6
5.5
374.4
403.7
29.3
7.8
368.8
390.5
21.7
5.9
213.1
118.4
–94.8
–44.5
204.8
117.5
–87.3
–42.6
221.4
189.1
–32.3
–14.6
212.2
191.8
–20.4
–9.6
67.1
60.4
–6.7
–10.0
72.0
64.7
–7.3
–10.1
465.7
425.5
–40.3
–8.6
499.2
456.0
–43.1
–8.6
46.1
45.2
–0.9
–2.0
47.7
47.1
–0.7
–1.4
128.6
138.1
9.5
7.4
131.1
142.2
11.1
8.5
115.2
97.7
–17.5
–15.2 –0.7
118.9
101.0
–17.9
–15.1 0.1
SOURCES: Census Web site (5/23/05): http://censtats. census.gov/cgi-bin/cbpnaic/cbpsel.pl; and BLS Web site (5/24/05): ftp://ftp.bls.gov/pub/special.requests/cew/.
verse, the table shows that in some industries BEA data indicate a far higher level of R&D expenditures than that for all U.S. firms— pharmaceuticals and medicines, for example. At the micro level, there were 11 cases in which the BEA and Census data for total R&D spending for the matched U.S. parent companies differed by more than $500 million. There is a substantial difference in the collected data for manufacturing and nonmanufacturing. The substantially lower number for non-manufacturing may result from the fact that R&D expenditures for nonmanufacturing firms are relatively more difficult to define and identify; so this area is more likely to be affected by differences in treatment.
The study also demonstrated some of the main benefits of data sharing—in the improvement of sample frames and the quality of reported data. For example, as a result of the project, the Census Bureau added over 500 companies to the sample for the Survey of Industrial Research
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TABLE 7-8 Private Annual Payroll Data—Census Bureau (CBP) and BLS (QCEW): Growth
Growth Rate (%)
1999
NAICS
CBP
QCEW
Diff.
Ab. Diff.
Code
Industry Code Description
Total
7.4
7.7
0.3
0.3
11
Forestry, fishing, hunting, and agric. support
3.4
3.8
0.4
0.4
21
Mining
–4.4
–4.7
–0.3
0.3
22
Utilities
3.3
2.8
–0.5
0.5
23
Construction
10.4
10.5
0.1
0.1
31
Manufacturing
3.0
3.5
0.5
0.5
42
Wholesale trade
6.9
7.2
0.3
0.3
44
Retail trade
8.3
6.8
–1.5
1.5
48
Transportation and warehousing
7.5
6.9
–0.5
0.5
51
Information
16.0
16.3
0.4
0.4
52
Finance and insurance
8.0
8.5
0.5
0.5
53
Real estate, rental, and leasing
8.4
6.5
–2.0
2.0
54
Professional, scientific, and technical services
12.1
17.3
5.2
5.2
55
Management of companies and enterprises
9.5
7.5
–2.0
2.0
56
Admin, support, waste management, remediation services
11.9
9.8
–2.0
2.0
61
Educational services
8.8
7.4
–1.4
1.4
62
Health care and social assistance
3.5
3.9
0.4
0.4
71
Arts, entertainment, and recreation
9.4
8.2
–1.1
1.1
72
Accommodation and food services
6.7
6.9
0.2
0.2
81
Other services (except public administration)
6.2
5.5
–0.7
0.7
Minimum
–2.0
0.1
Mean
–0.2
1.0
Maximum
5.2
5.2
and Development. For more information, see a report on the findings of the project—“Research and Development Link Project: Final Report” at www.bea.gov/bea/di/FinalReportpublic.pdf.
How Data Sharing Could Help
A large part of BEA’s job is adjusting the various data for differences in timing, concepts, and definitions. However, this is often difficult because, for the most part, BEA does not have access to the underlying
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2000
CBP
QCEW
Diff.
Ab. Diff.
9.1
8.2
–0.9
0.9
–2.7
4.0
6.7
6.7
5.3
5.7
0.4
0.4
3.3
6.9
3.6
3.6
9.5
9.6
0.1
0.1
2.9
5.9
2.9
2.9
8.0
8.3
0.2
0.2
7.3
6.8
–0.5
0.5
7.6
6.9
–0.7
0.7
23.0
14.0
–9.0
9.0
10.7
10.2
–0.5
0.5
9.5
8.4
–1.1
1.1
16.3
9.1
–7.2
7.2
9.9
9.4
–0.4
0.4
14.8
11.0
–3.9
3.9
8.9
8.5
–0.3
0.3
5.4
6.1
0.6
0.6
9.6
10.1
0.4
0.4
7.4
6.8
–0.6
0.6
7.8
6.7
–1.0
1.0
–9.0
0.1
–0.6
2.1
6.7
9.0
microdata. If armed with full data-sharing capability, BEA, BLS, and the Census Bureau could explore and resolve differences in the activities of major companies or in their classification to various industries and regions. The agencies could also compare data to investigate and resolve persistent differences, such as the reporting of bonuses and stock options, the capitalization of computer investment, the impact of differences in timing, and the differences in company practices with respect to the writing-down of inventories or to the treatment of pensions and other fringe benefits.
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Growth Rate (%)
NAICS
2001
CBP
QCEW
Diff.
Ab. Diff.
Code
Industry Code Description
Total
2.8
1.6
–1.2
1.2
11
Forestry, fishing, hunting, and agric. support
2.3
1.8
–0.5
0.5
21
Mining
13.2
7.7
–5.6
5.6
22
Utilities
3.2
3.7
0.5
0.5
23
Construction
3.0
5.9
2.8
2.8
31
Manufacturing
–4.1
–5.3
–1.3
1.3
42
Wholesale trade
2.1
1.0
–1.1
1.1
44
Retail trade
4.0
2.5
–1.6
1.6
48
Transportation and warehousing
3.1
1.8
–1.3
1.3
-51
Information
1.1
–3.1
–2.0
2.0
52
Finance and insurance
7.7
7.7
–0.1
0.1
53
Real estate, rental, and leasing
8.1
3.9
–4.2
4.2
54
Professional, scientific, and technical services
3.4
2.1
–1.3
1.3
55
Management of companies and enterprises
0.8
–4.5
–5.4
5.4
56
Admin, support, waste management, remediation services
5.3
2.0
–3.3
3.3
61
Educational services
8.4
8.7
0.4
0.4
62
Health care and social assistance
7.9
7.8
–0.1
0.1
71
Arts, entertainment, and recreation
6.8
0.3
–6.5
6.5
72
Accommodation and food services
2.4
2.9
0.5
0.5
81
Other services (except public administration)
4.9
5.3
0.5
0.5
Minimum
–6.5
0.1
Mean
–1.5
2.0
Maximum
2.8
6.5
NOTE: BLS QCEW data prior to 2001 have been backcasted to NAICS 2002 using NAICS reports from employers in the first quarter of 2001. Data for 2001 and 2002 also use NAICS 2002. CBP data are based on NAICS 1997.
The limited access also affects BEA’s ability to study observed anomalies in the Census Bureau data. The following are some examples of observations that BEA would like to study.
There are substantial differences in the reported payrolls from the Census Bureau and BLS, by area. For example, in Washington state, between 1999 and 2000, the Census Bureau reports an 11.5 percent increase (more than $9 billion) while BLS reports a 6.2 percent increase ($5 billion). There are many possible reasons for the discrepancy, and data sharing
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2002
1998-2002
CBP
QCEW
Diff.
Ab. Diff.
CBP
QCEW
Diff.
Ab. Diff.
–1.2
–0.5
0.6
0.6
4.5
4.2
–0.3
0.3
3.9
2.2
–1.7
1.7
1.7
2.9
1.2
1.2
–4.2
–4.3
–0.1
0.1
2.2
0.9
–1.3
1.3
–0.2
1.4
1.7
1.7
2.4
3.7
1.3
1.3
0.0
0.3
0.2
0.2
5.6
6.5
0.8
0.8
–6.0
–4.7
1.3
1.3
–1.1
–0.3
0.8
0.8
–4.8
–1.1
3.8
3.8
2.9
3.8
0.8
0.8
1.9
1.4
–0.5
0.5
5.4
4.3
–1.0
1.0
–1.7
–1.9
–0.2
0.2
4.0
3.4
–0.7
0.7
–9.2
–8.3
0.9
0.9
6.4
4.2
–2.2
2.2
–0.2
–0.8
–0.6
0.6
6.5
6.3
–0.2
0.2
1.9
2.8
0.9
0.9
6.9
5.4
–1.6
1.6
–1.5
–3.3
–1.8
1.8
7.4
6.0
–1.3
1.3
–3.9
–0.8
3.2
3.2
3.9
2.7
–1.2
1.2
–4.2
1.4
5.6
5.6
6.7
6.0
–0.7
0.7
7.2
7.1
–0.1
0.1
8.3
7.9
–0.4
0.4
7.2
7.2
0.0
0.0
6.0
6.2
0.2
0.2
3.4
4.0
0.6
0.6
7.3
5.6
–1.7
1.7
1.9
3.0
1.1
1.1
4.6
4.9
0.3
0.3
3.2
3.4
0.2
0.2
5.5
5.2
–0.3
0.3
–1.8
0.0
–2.2
0.2
0.7
1.2
–0.4
0.9
5.6
5.6
1.3
2.2
SOURCES: Census Bureau web site (5/23/05): http://censtats. census.gov/cgi-bin/cbpnaic/cbpsel.pl and BLS Web site (5/24/05): ftp://ftp.bls.gov/pub/special.requests/cew/.
with access to tax data would help get at the cause. For example, one source of the difference could be differences in the recording of stock options. By knowing the companies in the state, it would be possible to check with firm reports about reported stock options and thereby reconcile any difference. Such huge differences in the payroll numbers affect the estimation of GDI.
BEA obtains monthly data from the Census Bureau for the manufacturing sector based on the M3 (Manufacturers’ Shipments, Inventories and Orders) survey. However, because participation in this survey is vol-
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TABLE 7-9 Comparison of the Census Bureau and BLS Data for Foreign-Owned Establishments, 1992
Census Bureau
BLS
BLS Value as Percentage of Census Bureau Value
Number of Establishments
Number of Employees
Number of Reporting Units
Number of Employees
Number of Establishments
Number of Employees
All industries
102,958
4,944,157
106,041
4,747,637
103
96
Agricultural services, forestry, and fishinga
139
5,814
139
4,265
100
73
Mining
120,782
1,640
102,814
102
85
Construction
3,322
2,305
90,866
189
97
Manufacturing
81
2,004,947
13,076
1,930,135
102
96
Transportation and public utilitiesb
231,638
3,792
222,999
97
96
Wholesale trade
91
513,012
34,999
491,578
186
96
Retail trade
3,190
26,756
853,158
71
100
Finance, insurance, and real estate
401,018
9,558
360,287
83
90
Services
2,775
12,899
676,091
85
94
Otherc
9
1,116
569
3,137
n.m.
n.m.
NOTE: n.m. = not meaningful.
aExcludes agricultural production of crops and livestock.
bThe Census Bureau data exclude railroad transportation.
cFor the Census Bureau: consists of private education and noncommerical establishments; for BLS: consists of nonclassifiable est ablishments.
SOURCES: The Census Bureau data: Foreign Direct Investment in the United States: Establishment Data for 1992 available on BEA’s Web site at http://www.bea.gov/bea/ai1.htm#BEACENS. BLS data: BLS news release: “Employment and Wages in Foreign-Owned Businesses in the United States, Fourth Quarter 1992,” October 1996.
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TABLE 7-10 Comparison of NSF R&D Expenditures by All U.S. Companies with BEA R&D Expenditures by U.S. Parent Companies, 2001 (millions of dollars or percentage)
NSF: All U.S. Companies
BEA: U.S. Parent Companies
Parent Companies as Percentage of all U.S. Companies
All industries
198,505
143,017
72
Manufacturing
120,705
115,118
95
Food
1,819
914
50
Beverage and tobacco products
152
469
309
Textiles, apparel, and leather
(D)
125
n.a.
Wood products
182
(D)
n.a.
Paper, printing and support activities
(D)
(D)
n.a.
Petroleum and coal products
(D)
1,002
n.a.
Chemicals
17,892
31,927
178
Basic chemicals
1,876
1,742
93
Resin, synthetic rubber, fibers, and filament
(D)
2,972
n.a.
Pharmaceuticals and medicines
10,137
23,169
229
Other chemicals
(D)
4,045
n.a.
Plastics and rubber products
(D)
929
n.a.
Nonmetallic mineral products
990
339
34
Primary metals
485
484
100
Fabricated metal products
1,599
554
35
Machinery
6,404
8,561
134
Computer and electronic products
47,079
38,356
81
Computers and peripheral equipment
(D)
7,727
n.a.
Communications equipment
15,507
14,526
94
Semiconductor and other electronic components
14,358
11,114
77
Navigational, measuring, electromedical, and control instruments
12,947
4,158
32
Other computer and electronic products
(D)
832
n.a.
Electrical equipment, appliances, and components
4,980
2,008
40
Transportation equipment
25,965
25,147
97
Motor vehicles, trailers, and parts
(D)
18,183
n.a.
Other
(D)
6,964
n.a.
Furniture and related products
301
128
43
Miscellaneous manufacturing
6,606
2,570
39
Nonmanufacturing
77,799
27,899
36
Mining, extraction, and support activities
(D)
411
n.a.
Utilities
133
59
44
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NSF: All U.S. Companies
BEA: U.S. Parent Companies
Parent Companies as Percentage of all U.S. Companies
Construction
320
(D)
n.a.
Trade
24,372
(D)
n.a.
Transportation and warehousing
1,848
12
1
Information
(D)
9,514
n.a.
Publishing
13,760
6,452
47
Newspaper, periodical, book, and database
649
(D)
n.a.
Software
13,111
(D)
n.a.
Broadcasting and telecommunications
(D)
796
n.a.
Telecommunications
(D)
782
n.a.
Other
(D)
14
n.a.
Other information
(D)
2,266
n.a.
Finance, insurance, and real estate
(D)
624
n.a.
Professional, scientific, and technical services
27,704
10,348
37
Architectural, engineering, and related services
3,386
18
1
Computer systems design and related services
9,154
8,929
98
Other
15,164
1,401
9
NOTES: (D) = suppressed to avoid disclosure of data of individual companies; n.a. = not available.
SOURCES: R&D spending by all U.S. companies: Research and Development in Industry: 2001 available on the NSF web site at http://www.nsf.gov/statistics/nsf05305/htmstart.htm; R&D spending by U.S. parent companies: U.S. Direct Investment Abroad: Operations of U.S. Parent Companies and Their Foreign Affiliates, Revised 2001 Estimates available on the BEA’s web site at http://www.bea.gov/bea/ai/iidguide.htm#link12b.
untary and some firms decide not to participate, BEA does not know the extent of participation. A recent example is the decision by a major producer of semiconductors to terminate its participation, which represented a huge erosion in the representativeness of the surveys. The Annual Survey of Manufacturers, however, is mandatory. Thus BEA must wait until the annual data are available before it can check the estimates based on the monthly data. If BEA had access to the M3 data, then it could identify the firms responsible for missing data and possibly estimate the missing information from publicly available sources such as company reports. Publicly available sales data from company reports could aid in the estimation of missing shipment data from a company that did not provide
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such information in the M3 survey. The ability to estimate such missing information would serve to reduce revisions to GDP.
Relatedly, the M3 surveys provide inventories data to BEA, and BEA does not always know whether a company has reported an inventory adjustment to the Census Bureau in the same way that the company has entered it on its financial accounts. A few years ago the press reported a major write-down of inventory by a major producer of information technology equipment. Without knowing how the firm entered the write-down in its Census Bureau report, BEA had no way of checking if the inventory adjustment was accurately reflected both on the product side of the national accounts (inventories is a component of investment) and on the income side (the valuation of inventories affects corporate profits). In fact, there was a large adjustment to inventories.
How Data Sharing Would Help Cope with Disasters
The massive destruction wrought by Hurricane Katrina is having a significant impact on the ability of the statistical agencies to collect economic data in the affected regions. As a result of the disappearance of sample units, estimates of retail trade, construction, employment and wages, and other components of the principal economic indicators will contain many imputations. Data sharing would allow a combining of data that would enable the statistical agencies to better impute missing values. For example, in the absence of complete business list reconciliation between the Census Bureau and BLS, data sharing would allow one of the agencies to find alternative establishments that might serve as proxies for missing establishments and thereby provide a straightforward imputation. In addition, the Census Bureau sales values might be used by BLS to impute prices for its price indexes. The ability to share data would also enable the statistical agencies to examine each other’s establishment-level imputations to see if they suit an agency’s needs. For example, the BEA regional program would have access to the BLS establishment imputations for QCEW to see if their needs are met. In short, data sharing would allow the statistical agencies to economize resources to efficiently handle disruptions to the usual production of economic statistics.
REFERENCES
Morisi, T. 2003 Recent changes in the National Current Employment Statistics Survey. Monthly Labor Review June.
Moyer, B.C., M.A. Planting, M. Fahim-Nader, and S.K.S. Lum 2004 Preview of the comprehensive revision of the annual industry accounts. Survey of Current Business 84(March):38-51.
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National Science Foundation, Division of Science Resources Statistics 2005 Research and Development in Industry: 2001. (NSF #05-305.) Arlington, VA: National Science Foundation. Available: http://nsf.gov/statistics/nsf05305/front.htm [accessed May 2006].
Strassner, E.H., G.W. Medeiros, and G.M. Smith 2005 Annual industry accounts: Introducing KLEMS input estimates for 1997-2003. Survey of Current Business 85(September):34.
U.S. Bureau of Economic Analysis, International Economic Accounts 2005a Foreign Direct Investment in the United States: Establishment Data for 1992. (Census data.) Available: http://www.bea.gov/bea/ai1.htm#BEACENS [accessed May 2006].
2005b U.S. Direct Investment Abroad: Operations of U.S. Parent Companies and Their Foreign Affiliates, Revised 2001. Available: http://www.bea.gov/bea/ai/iidguide.htm#link12b [accessed May 2006].
U.S. Bureau of Economic Analysis and U.S. Census Bureau 1992 Foreign Direct Investment in the United States: Establishment Data for 1987. Washington, DC: U.S. Government Printing Office.
U.S. Census Bureau 2003 County Business Patterns, NAICS. Available: http://censtats.census.gov/cgi-bin/cbpnaic/cbpsel.pl [accessed May 2006].
U.S. Census Bureau, Bureau of Economic Analysis, and National Science Foundation Division of Science Resources Statistics 2005 Statistics Research and Development Data Link Project: Final Report. Available: http://www.bea.gov/bea/di/FinalReportpublic.pdf [accessed May 2006].
U.S. Department of Labor, Bureau of Labor Statistics 1996 Employment and Wages in Foreign-Owned Businesses in the United States, Fourth Quarter 1992. (News release.) Washington, DC: U.S. Department of Labor, Bureau of Labor Statistics.
2002 Quarterly Census of Employment and Wages Program Data. Available: ftp://ftp.bls.gov/pub/special.requests/cew/ [accessed May 2006].
Representative terms from entire chapter:
data sharing
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