The Contribution of Multinational Corporations to U.S. Productivity Growth, 1977-2000
Keywords: productivity, multinational corporations, nonfinancial corporations
Abstract:
In this paper, we decompose aggregate labor productivity growth in order to gauge the relative importance of multinational corporations (MNCs) to the economic performance of the United States in the 1990s. As we define it, the MNC sector refers to the U.S. activities of multinational corporations operating in the United States. We develop productivity estimates for MNCs using (1) published and unpublished industry-level data from two surveys conducted by the Bureau of Economic Analysis and (2) productivity data for industries and major sectors from the FRB productivity system (Bartelsman and Beaulieu 2003, 2004). The resulting MNC sector accounted for about 40 percent of the gross product of all nonfinancial corporations and all of the pickup in nonfinancial corporate labor productivity in the late 1990s. Accordingly, the MNC sector accounted for more than half of the acceleration in labor productivity growth of all U.S. nonfarm private businesses.
Introduction and Background
Concomitant with the surge in productivity growth in the United States since 1995 has been a surge in research on productivity. Before the productivity step-up had become fully evident, Corrado and Slifman (1999) focused attention on productivity by major sector as well as problems in measuring productivity and their implications for the performance of productivity in the mid-1990s.1 Later, others began to concentrate on the role of information technology (IT) - examining the productivity of the producers of IT equipment as well as the users of IT equipment. This research often uses growth accounting as the organizing principle for analysis, and it is conducted using both detailed industry-level data (Jorgenson and Stiroh 2000) and macroeconomic time-series data at only the broadest levels of disaggregation (Oliner and Sichel 2000).
But IT is not the only important economic force that has been influencing productivity growth in recent years. In particular, many companies reportedly have been able to achieve significant efficiencies by re-organizing the way they conduct their operations. Meanwhile, business has become increasingly global in its nature, with globalization arguably a significant part of the enhanced organizational efficiencies.2
Many studies that have examined the link between globalization and productivity have looked at the productivity of multinational corporations (MNCs). The emphasis in this literature is on foreign-owned MNCs in the host country. Using microeconomic data, two questions often addressed are whether the host-country operations of foreign-owned firms are more productive than the operations of domestically-owned firms in the host-country and whether the higher productivity creates favorable spillovers in the host country (see Keller 2004 for a review of the recent literature).3 Doms and Jensen (1998a and 1998b) broadened the scope of this research strain to look at both foreign-owned and domestically-owned MNCs and to inquire whether country of ownership matters.4 Their results, which are based on microeconomic data, suggest that for productivity growth country of ownership does not matter: "It is not the fact that the plants are foreign owned that is important.... rather, it is the fact that the plants are owned by multinational corporations that seems important."5
In this paper, we attempt to merge these research strains by measuring the contribution of MNCs to the aggregate productivity record of the United States. While we cannot examine the causal linkages between specific characteristics of MNCs and their higher productivity as carefully as most micro-level studies, we can move beyond
such studies--which typically focus on the manufacturing sector--to assess the
importance of MNCs in the macroeconomy. Towards this end, we first we develop a consistent database of information from 1977 to 2000 on the activities of foreign-owned operations in the United States and the domestic activities of U.S. firms that have foreign operations. Then we integrate that database with a more standard productivity database covering all establishments of all industries operating in the U.S. (Bartelsman and Beaulieu 2003, 2004) and examine the contribution of the MNC sector to overall labor productivity growth in the United States. We look at labor productivity growth because, even though studies of MNC performance based on microeconomic data have tended to identify effects on the level of productivity, if these underlying productivity-enhancing effects are spreading and/or filtering in over time, productivity aggregates will be affected in terms of growth rates (as well as levels).
Although our final analysis is relatively straightforward--indeed, most of the hard work of this study involved the integration of the various data sets--we nevertheless believe our findings are quite striking. Specifically, although the MNC sector accounts for only 40 percent of the output of nonfinancial corporations (NFCs) between 1977 and 2000, MNCs appear to have accounted for more than three-fourths of the increase in NFC labor productivity over this period. Moreover, MNCs account for all of the NFC sector's pickup in labor productivity growth in the late 1990s; accordingly, they account for more than half of the much-studied acceleration in aggregate productivity.6 And, while MNCs involved in the production of IT contributed significantly towards this acceleration, MNCs in other manufacturing and non-manufacturing industries contributed significantly as well.
Why might MNCs have better productivity performance than other firms?
Although the aggregate nature of our analysis does not allow for an examination of the specific sources of the MNC productivity advantage, there has recently been a great deal of micro-level research on the link between "global engagement" and firm productivity. Such work has focused mostly on two main factors - characteristics of the plants and cross-border integration of operations.
In terms of plant characteristics, MNCs tend to be larger than domestic plants, they are more capital intensive, and they use more advanced technology (Doms and Jensen). All else equal, these characteristics tend to be associated with higher labor productivity - in part because of the greater amount of capital per worker and in part because size and technology can enhance the organizational efficiency of a plant.7 Several recent general equilibrium models propose that global engagement--either through trade or as an MNC--is a consequence rather than a cause of higher productivity. In these models, heterogeneity in firm productivity is exogenously determined (Melitz 2003; Helpman, Melitz, and Yeaple 2004). As such, only the most highly productive firms can afford the costs of becoming a multinational by establishing a foreign affiliate.
Alternatively, MNCs may be able to enhance their organizational efficiency through their ability to integrate their operations across borders. Indeed, intra-MNC trade by U.S.-owned MNCs has risen steadily over time, accounting for 22 percent of total U.S. exports in 2002, and 16 percent of total imports (Mataloni, 2004).8 Such vertical integration between parents and affiliates allows MNCs to take advantage of international factor price differentials as a means of holding down unit costs of production.9 In addition, outsourcing to foreign affiliates may also allow the parent to organize overall production processes more efficiently (Hanson, Mataloni, and Slaughter, 2001).
Finally, internationalized production by MNCs may serves as a conduit for the transfer of knowledge between parents and affiliates, thereby contributing to higher productivity.10 For instance, Criscuolo, Haskel, and Slaughter (2005) find that MNCs generate more ideas than their purely domestic counterparts, not only because they use more researchers, but also because they draw on a larger stock of ideas through their "intra-firm worldwide pool of information." More generally, cross-border integration enables firms to spread firm-specific intangible assets (R&D, for example) across geographical boundaries (Lipsey, et. al. make this point).11 This spreading of intangible assets, input production, and final processing across borders occurs prominently, for example, in industries that manufacture electronic and electrical equipment.
The Data
The primary data on U.S. multinational companies come from two surveys conducted by the Bureau of Economic Analysis (BEA). The survey of U.S. Direct Investment Abroad (USDIA) provides information on the operations of U.S.-headquartered multinational companies (parents), while the survey of Foreign Direct Investment in the United States (FDIUS) provides information on operations of foreign companies operating in the United States (affiliates). The surveys contain much data on the domestic activities of parents and affiliates--data such as total sales, gross product (value added), capital spending, R&D spending, compensation of employees, and employment. The BEA tabulates the data by industry of the parent or affiliate. Periodically, BEA also shows the sales and employment of parents (or affiliates) by industry of sales.
One major advantage of the data from these surveys is that they are designed to yield measures aligned with National Income and Product Account (NIPA) concepts. For example, the published figures for the gross product of nonbank parents of U.S. multinational companies are conceptually consistent with the NIPA figures for the gross product, or value added, of all businesses.12 Because of the conceptual consistency, therefore, these data can be integrated with other relevant productivity data in order to conduct growth accounting exercises.
Creating a Multinational Corporate (MNC) sector. Corrado and Slifman highlighted the value of looking at the economy not only by industry but also by sector - for example, corporate and non-corporate, financial and nonfinancial. In particular, they focused their analysis on productivity trends in the nonfinancial corporate (NFC) sector. This paper carries that approach one step further by dividing the nonfinancial corporate sector into two distinct sectors: MNCs and domestically oriented firms. These sectoral data are then disaggregated into key industry sub-divisions. Each survey's results were therefore first adjusted to be conceptually consistent with this general approach. Results for nonbank finance and insurance MNCs were excluded to obtain data on nonfinancial activities, and results for real estate were excluded to approximate results for corporations.13
Because we are interested creating an MNC sector and studying its contribution to overall U.S. productivity growth, the published BEA survey data need further development, and they need to be integrated with broader aggregates to perform growth accounting for the overall U.S. economy. Fortunately, a tool exists to readily carry out the development and integration: the Federal Reserve Board Productivity Data System (Bartelsman and Beaulieu 2003). This is a general system that contains all the aggregate and industry-level data typically used by productivity researchers organized within a highly structured database. The system also contains specialized tools to manipulate and analyze the data. After adding the relevant USDIA and FDIUS data issued by BEA to the productivity data system, we used many of its tools to help carry out such tasks as balancing, concording, deflation, and aggregation.14 The routines in the system also facilitate the calculation of capital stocks and capital services although we do not create such measures for the MNC sector in this study.
Before the USDIA and FDIUS data could be combined and used for productivity analysis, we had to deal with several important measurement issues. The appendix describes the methods we used in full. Here we present a brief overview.
Survey overlap. As we define it, the MNC sector refers to the U.S. activities of multinational corporations operating in the United States. Accordingly, we need to combine data on the activities of parents from the USDIA survey with data on activities of U.S. affiliates from the FDIA survey. In the spirit of the Doms and Jensen results, the combined data from the USDIA and FDIUS surveys provide information on the activities of MNCs in the United States regardless of country of ownership
However, some firms that are technically U.S. parents are actually under the control of a foreign parent company. Accordingly, some firms in the USDIA data are also captured in the FDIUS survey. The overlap of firms in the two surveys prevents us from simply adding together the results of the two surveys. Because we want to combine the data from both surveys, we need to adjust for the overlap.
The overlap arises because some U.S. affiliates of foreign companies engage in foreign direct investment that is attributed to U.S. affiliates. For survey purposes this makes some U.S. affiliates both a U.S. "parent" and a U.S. "affiliate;" accordingly, the company is counted in both the FDIUS survey (as a U.S. affiliate of a foreign company) and in the USDIA survey (as a U.S. parent of a foreign affiliate.) As an example, suppose a Japanese automaker sets up a foreign affiliate in the United States. That U.S. affiliate then sets up a parts-producing subsidiary in Canada that only serves the U.S. affiliate. The Canadian parts-producing facility is considered to be foreign direct investment by a U.S. entity, which, by definition, makes the U.S. affiliate of the Japanese company a "U.S. parent" of the Canadian affiliate. As a result, the U.S. affiliate will be counted in both surveys: as a U.S. affiliate of a Japanese parent in the FDIUS survey, and as a U.S. parent of a Canadian affiliate in the USDIA survey.
How big is the overlap? As it turns out, a substantial number of foreign affiliates operating in the U.S. have their own foreign affiliates. According to BEA, when measured in terms of gross product, about 45 percent of the activities of U.S. affiliates during 2000 took place at companies that had their own foreign affiliates. These "U.S. parent" foreign affiliates, however, represent only a small part of the overall number of U.S. parents. Again using gross product as the metric, the activities of "U.S. parent" foreign affiliates were only 11 percent of the gross product of all U.S. parents.15 Moreover, these ratios have been relatively unchanged over time (see appendix table A3).
In order to adjust for the overlap, we obtained from the BEA special tabulations of the activities of those U.S. parents that are also affiliates of foreign companies and, hence, counted in both surveys. Because of concerns at the BEA regarding the disclosure of information about individual survey respondents, the data on overlap firms are only available for all non-bank industries and all manufacturing industries, and only for 1990 on. However, the BEA also provided us with industry-level information on the number of U.S. parent companies that are also foreign affiliates. As described in the appendix, we used the information from these special tabulations and the concording and balancing tools of the FRB productivity system to create industry-level overlap data so that U.S. parent-foreign affiliates are only counted once when we combine the results of the two surveys.
Level of consolidation. Another issue with these data is that they are collected at the overall company level. For many multinational corporations, the company level is a very aggregate level of consolidation by industry. Most industry-level data used for productivity analysis is collected at the establishment (or plant) level. Thus, the activities of a company that produces in more than one industry (say, home appliances and jet engines) will have the activities of its individual plants allocated to the relevant industry. In contrast, data for the MNC surveys are collected for a group of enterprises under common control (referred to as "a consolidated business enterprise"). This can lead to serious problems in classifying the data by industry, because in most tabulations, all of the operations of a given U.S. parent or foreign affiliate are assigned to one primary industry, even if the parent or affiliate has secondary activities in other industries. In order to get around this problem, we constructed our own establishment estimates from the consolidated MNC data. The method is described in detail in the appendix. Essentially, however, we use the periodic information provided by BEA on sales and employment of affiliates or parents (as appropriate) by industry of sales. As noted by Zeile (1999), "these data ... approximate the disaggregation of the data for all U.S. businesses by industry of establishment." We apply the employment/sales shares to the consolidated data to create establishment estimates.
Industry Classification. BEA's USDIA and FDIUS survey data for recent years use the North American Industry Classification System (NAICS) to group results by industry, whereas data for earlier periods apply various issues of the Standard Industrial Classification (SIC) system. We converted the more recently published NAICS-based data to the SIC system, which (as of the initial writing of this paper) BEA still used for its U.S. industry-level data on gross product and gross product prices.
Deflators. The data in the two MNC surveys are collected in current dollars (except, of course, employment). However, for productivity analysis it is necessary to have data measured in real terms, i.e., adjusted to remove the effects of price changes. Mataloni (1997) describes one method for deflating current dollar figures that relies on producer prices indexes (PPIs) by industry. However, PPIs alone are imperfect as deflators for industry gross product; PPIs are appropriate for gross output, but a gross product price should represent an implicit price for gross output less intermediate inputs. As an alternative, therefore, we used the deflators published by the BEA for gross product originating by industry. Real GDP by industry is computed using the double-deflation method in which separate estimates of real gross output and intermediate inputs are combined in a Fisher chain-type quantity-index-number formula (Yuskavage 1996). These deflators are for all establishments in an industry, not just those owned by MNCs. By applying these deflators to the data from the MNC surveys, we are assuming that within a given industry establishments owned by MNCs and non-MNCs had the same product composition, input composition, and price behavior over time.
Method of analysis
Much of the recent literature on the post-1995 pickup in US productivity growth disaggregates the data into IT-producing and IT-using sectors. This paper adds a new dimension: specifically, we consider the role of MNCs. As indicated previously, we do this by looking separately at the role of U.S. parents and foreign affiliates. Then, in the spirit of the findings in Doms and Jensen, we combine the data to create a single MNC sector for the U.S. economy. As far as we know, this is the first time the data have been combined consistently to create time series for a single MNC sector.
Following the approach of Corrado and Slifman, we disaggregate the overall U.S. economy into an economically meaningful group of sectors and sub-sectors. We do this to examine the contribution of individual sectors to overall productivity growth. The ratios of each sector's gross product to the gross product of all U.S. nonfarm private businesses--the sector's contribution to the total (unduplicated) value of production by business--help unravel the role of each sector in the productivity decomposition. As may be seen in Table 1, we estimate that the MNC sector accounts for about 25 percent of U.S. nonfarm private business (NFPB) gross product (or value added). Although the MNC share fell off a bit in the early 1990s, it subsequently rebounded and, all told, has been relatively stable for the period shown.
The relative stability in the MNC share masks important developments within both the MNC and corporate sectors, however. As may be seen, the value added by financial corporations has been rising steadily over the period, whereas the share of overall value added accounted for by nonfinancial corporations has fallen off. The drop is in the domestically-oriented share: It was 45 percent in 1977 but was under 40 percent by 2002, with much of the drop occurring after 1995. Within the MNC sector, the share of value added accounted for by U.S. parents has declined, while the share attributed to foreign affiliates increased from 2 percent in 1977 to 6-1/2 percent in 2002. All told, the MNC sector currently is about 40 percent of the nonfinancial corporate sector.
Table 2 looks deeper within the nonfinancial corporate and MNC sectors. As may be seen, 43 percent of MNC gross product in 2000 originated in manufacturing. This is nearly 20 percentage points below the share observed in 1977, with the decline being offset by rising MNC concentration in services industries and in wholesale and retail trade. While the proportion of output originating in manufacturing is roughly equivalent for U.S. parents and affiliates of foreign companies, it appears that U.S. parents maintain a somewhat larger presence in IT equipment. In non-manufacturing, however, a larger proportion of the output of foreign affiliates is concentrated in wholesale and retail trade, while the proportion of output originating in the transportation, communications, and public utilities group is larger for U.S. parents.
Results for Labor Productivity
Our results for the sectoral decomposition of labor productivity are shown in tables 3 through 6. Labor productivity estimates were calculated as follows. In each year, sectoral labor productivity levels 
were defined as real value added (
per total hours worked of all persons (
: 
. Aggregate labor productivity growth can therefore be decomposed as follows:
where
is the two-period average of each industry's share of nominal gross product. The first term on the right hand side measures the direct contributions to aggregate labor
productivity, i.e. the share weighted sum of the labor productivity growth rates for individual industries and sectors. The second term on the right hand side captures an indirect contribution owing to the reallocation of hours across sectors. This contribution is positive when, on balance, the
change in hours is positive for sectors where gross product shares exceed hours shares (Stiroh, 2002).
As may be seen on table 3, the rate of change in NFPB output per hour averaged 1.5 percent per year from 1977 to 2000 in the United States.16 We estimate that the growth of output per hour in the MNC sector averaged 3.2 percent per year during the same period, or more than twice the NFPB average. As indicated in table 4, this accounted for more than half of the overall gain.
The sectoral decomposition by sub-period also reveals interesting developments: From 1977 to 1989 and, to a lesser extent, from 1989 to 1995, gains in MNC sector productivity accounted for a goodly portion of the overall increase in output per hour. The pickup in productivity in the late 1990s,
however, was generally widespread across the individual sectors shown. Even so, according to our sectoral hierarchy and as can be seen by comparing the two right-hand columns, the MNC sector contributed significantly (about
percentage point) to the 1.2 percentage point pickup in NFPB output per hour during the late
1990s.
Because output per hour varies by industry, part of the MNC productivity story in the late 1990s could be explained by differences between the industry mix of the MNC sector compared with that of all nonfinancial corporations or total nonfarm businesses. As is well known, the production of IT equipment was a major source of the rapid gains in U.S. productivity in the late 1990s (see Jorgensen and Stiroh 2000, Oliner and Sichel 2000, among others), and the IT equipment-producing sector has a relatively large MNC share.
Tables 5 and 6 present a broad industry cut of the productivity results for nonfinancial corporations. As may be seen, this decomposition is consistent with the extraordinary productivity change in the production of IT equipment accounting for part of the story for the pickup in MNC and nonfinancial corporate labor productivity in the late 1990s. The decomposition also shows, however, that the pickup in MNC productivity was based more broadly in other manufacturing and non-manufacturing industries. Meanwhile, the aggregate domestically-oriented sector did not contribute to the pickup in nonfinancial corporate labor productivity in the late 1990s, a result driven mainly by the poor performance of its manufacturing component.17 Moreover, while there is some evidence that reallocation of hours contributed to the pickup, its contribution is nevertheless quite small.
Of course, some of the MNC contribution to the productivity pickup could be due to the reallocation of value added among MNC components rather than a faster rate of productivity growth for the underlying MNC subsectors and industries. As shown in table 1 and table 2, the MNC share of nonfinancial corporate value added rose during the late 1990s owing to the ongoing expansion of MNCs into non-manufacturing industries. Table 7 shows a standard decomposition of the pickup in nonfinancial corporate labor productivity during this period into "within" and "between" effects. The "within" effect measures how much of the pickup in labor productivity growth can be attributed to faster productivity growth for individual sectors when their weights are held fixed at the average for the two periods, while the "between" effect measures how much of the pickup can be attributed to rising weights for sectors with above-average labor productivity growth in both periods.18
As may be seen, about half of the contribution of non-manufacturing MNCs to the productivity acceleration in the late 1990s can be attributed to their rising weight (the "between" effect).19 The absolute size of this effect, however, is quite small and suggests that the reallocation of value added is not a big part of the MNC productivity story.
To summarize, between 1977 and 2000, labor productivity growth in the MNC sector consistently outpaced that of the nonfinancial corporate sector as a whole, with the gap widening noticeably during the second half of the 1990s. A final question, therefore, is whether the pickup in MNC productivity growth has continued more recently. Unfortunately, at this stage it is not possible to know for sure. Although more recent, consistent data for both U.S. parents and foreign affiliates are available, methodologically consistent industry-level estimates only extend through 2001.20 As such, only "back-of-the-envelope" estimates can currently be made based on an extrapolation of the output and hours series for major sectors (i.e. nonfinancial corporations and manufacturing) using published estimates from the BLS and making an assumption about the growth of our deflators.
With this caveat in mind, MNCs appear to have been disproportionately affected by the onset of the 2001 recession. Indeed, we estimate that output per hour in the MNC sector fell at an annual rate of 1.4 percent between 2000 and 2002, even while productivity for the nonfinancial corporate sector as a whole continued to rise.
Interestingly, the weakness in the MNC sector appears to have been driven entirely by U.S. parents. Indeed, labor productivity growth for foreign affiliates accelerated further between 2000 and 2002. The productivity declines for U.S. parents probably reflected the particular circumstances in a number of industries where they have a significant presence. This includes the cyclically-sensitive durable goods manufacturing industries--like motor vehicles and high-tech--as well as telecommunications services. In contrast, the activities of foreign affiliates are more highly concentrated in less cyclical industries such as retail and wholesale trade. However, in light of the rapid growth of overall productivity in 2003 and 2004, the productivity declines for U.S. parents in all likelihood were temporary.
In this paper we have begun to investigate the role played by the U.S. operations of multinational corporations in the overall performance of the U.S. economy, especially in the late 1990s. We identify these corporations as a separate segment of the economy--we call it the MNC sector--and we develop labor productivity estimates for this sector.
While progress has been made regarding the contribution of MNCs to aggregate trade flows and employment growth, much less is known about the significance of MNCs for overall productivity growth. This omission from the literature seems particularly glaring when one considers the substantial body of micro-level research on the link between global engagement and productivity at the firm level. We therefore hope that the results in this paper will complement this micro-level work by placing the superior performance of MNCs into a broader perspective.
Using the tools and procedures in the FRB productivity data system, the new productivity estimates were developed by integrating information from BEA's surveys of multinational operations with conventional productivity data in a consistent fashion.
The resulting data set permits the decomposition of labor productivity along MNC/nonMNC, legal form of organization, and major industry lines for the period 1977 to 2000. The results clearly slice the U.S. aggregate productivity data in a novel way and, we hope, confirm the utility of our approach.
The results, which were foreshadowed by the Doms and Jensen findings, confirmed the important role played by multinational corporations in the aggregate productivity record of the U.S. economy. The sector (as we define it) accounts for more than 25 percent of the gross product of all nonfarm private businesses and about 40 percent of nonfinancial corporate gross product. Nonetheless, the sector accounted for all of the increase in the labor productivity of nonfinancial corporations in the late 1990s and more than half of the increase for all nonfarm private businesses.
Of course, our estimates may be sensitive to some of the assumptions we were forced to make when constructing our integrated dataset. For example, by applying the industry-level deflators published by the BEA to both MNCs and domestically oriented firms, we are implicitly assuming that, within a given industry, establishments owned by MNCs and non-MNCs had the same product composition, input composition, and price behavior over time. If, instead, value added deflators actually rose less rapidly for MNCs, then clearly our estimate of real output growth for MNCs would be too low, meaning their contribution to productivity growth could be even larger. Given the literature on the organizational efficiencies afforded by the integration of MNC operations across borders, such a scenario certainly seems plausible.
Another issue that merits further investigation is the extent to which transfer pricing may influence BEA's measures of value added and thereby the interpretation of our results.21 Transfer pricing is not supposed to distort official statistics because tax regulations generally require that intra-firm transactions be valued at "arms-length" prices. Nevertheless, inter-country differences in tax rates almost certainly create incentives to deviate from this standard. Moreover, intra-MNC trade in intermediates accelerated in the second half of the 1990s, suggesting the possibility of at least some role for distortions due to transfer pricing. However, Mataloni (2000) finds little evidence that transfer pricing has unduly impacted BEA's industry-level profits data for MNCs.22 Although Mataloni's results are not dispositive on the issue, we do not think that our results are being systematically biased by transfer pricing.23
In sum, our work establishes new stylized facts about the contribution of multinational corporations to the growth of aggregate labor productivity. We have yet to address one of the issues laid out in the introduction of this paper, namely, what are the respective roles for total factor productivity and IT capital use for the MNC sector compared with other sectors? That and further work to pinpoint the source of the MNC productivity advantage are topics for future research.
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| Category | 1977 | 1989 | 1995 | 2000 | 2002 |
|---|---|---|---|---|---|
| Nonfinancial Corporations | 70.5 | 68.8 | 67.7 | 66.7 | 65.6 |
| MNC Sector** | 25.5 | 24.2 | 24.7 | 28.6 | 26.2 |
| Parents** | 23.5 | 19.3 | 19.4 | 22.1 | 19.7 |
| Affiliates of Foreign Companies | 2.0 | 4.9 | 5.3 | 6.6 | 6.5 |
| Domestically Oriented | 45.0 | 44.6 | 43.0 | 38.1 | 39.3 |
| Financial Corporations | 4.6 | 6.3 | 7.4 | 9.0 | 9.2 |
| Noncorporate Business | 25.0 | 24.9 | 24.9 | 24.3 | 25.3 |
*Calculated using gross domestic income, excludes government enterprises.
**Excludes U.S. parent companies that are also affiliates of foreign companies
| Category | MNCs: Parents | MNCs: Foreign Affl. | MNCs: Total | Domestically: Oriented | Total |
|---|---|---|---|---|---|
| 2000 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
| 2000: Manufacturing | 42.5 | 44.9 | 43.0 | 15.5 | 19.2 |
| 2000: High Tech | 5.7 | 3.1 | 5.1 | 0.6 | 1.7 |
| 2000: Manfuacturing, except High Tech | 36.8 | 41.7 | 38.0 | 14.9 | 17.5 |
| 2000: Non-Manufacturing | 57.5 | 55.1 | 57.0 | 84.5 | 80.8 |
| 2000: Wholesale & Retail Trade | 13.6 | 24.5 | 16.1 | 34.4 | 20.0 |
| 2000: Services | 15.9 | 13.3 | 15.3 | 26.9 | 21.4 |
| 2000: Transportation, Comm, and PU | 18.9 | 9.5 | 16.8 | 10.1 | 10.2 |
| 2000: Other | 9.1 | 7.8 | 8.8 | 13.1 | 29.3 |
| 1995 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
| 1995: Manufacturing | 49.9 | 49.8 | 49.8 | 20.9 | 22.2 |
| 1995: High Tech | 5.4 | 3.8 | 5.0 | 1.7 | 2.0 |
| 1995: Manfuacturing, except High Tech | 44.5 | 46.0 | 44.8 | 19.2 | 20.2 |
| 1995: Non-Manufacturing | 50.1 | 50.2 | 50.2 | 79.1 | 77.8 |
| 1995: Wholesale & Retail Trade | 11.4 | 22.5 | 13.8 | 32.5 | 19.8 |
| 1995: Services | 12.0 | 9.2 | 11.4 | 23.8 | 19.6 |
| 1995: Transportation, Comm, and PU | 19.8 | 8.7 | 17.4 | 12.3 | 11.0 |
| 1995: Other | 7.0 | 9.8 | 7.6 | 10.6 | 27.4 |
| 1989 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
| 1989: Manufacturing | 53.7 | 52.4 | 53.5 | 22.6 | 23.8 |
| 1989: High Tech | 5.9 | 4.2 | 5.6 | 1.3 | 1.9 |
| 1989: Manfuacturing, except High Tech | 47.8 | 48.2 | 47.9 | 21.3 | 21.8 |
| 1989: Non-Manufacturing | 46.3 | 47.6 | 46.5 | 77.4 | 76.2 |
| 1989: Wholesale & Retail Trade | 9.5 | 21.8 | 12.0 | 32.4 | 20.0 |
| 1989: Services | 9.5 | 7.3 | 9.0 | 19.8 | 17.9 |
| 1989: Transportation, Comm, and PU | 19.5 | 4.8 | 16.5 | 12.9 | 10.9 |
| 1989: Other | 7.8 | 13.7 | 9.0 | 12.3 | 27.4 |
| 1977 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
| 1977: Manufacturing | 61.1 | 59.6 | 61.0 | 28.5 | 29.1 |
| 1977: High Tech | 3.4 | 5.5 | 3.5 | 0.9 | 1.3 |
| 1977: Manfuacturing, except High Tech | 57.7 | 54.1 | 57.5 | 27.6 | 27.8 |
| 1977: Non-Manufacturing | 38.9 | 40.4 | 39.0 | 71.5 | 70.9 |
| 1977: Wholesale & Retail Trade | 10.4 | 26.3 | 11.6 | 31.0 | 21.0 |
| 1977: Services | 4.4 | 2.6 | 4.3 | 12.7 | 12.3 |
| 1977: Transportation, Comm, and PU | 15.7 | 3.7 | 14.8 | 14.4 | 11.3 |
| 1977: Other | 8.3 | 7.9 | 8.3 | 13.4 | 26.3 |
*Excludes Corporate Farms
| Category | 1977-2000 | 1977-1989 | 1989-1995 | 1995-2000 |
|---|---|---|---|---|
| Nonfarm Private Business | 1.5 | 0.9 | 1.6 | 2.8 |
| Nonfinancial Corporations | 1.6 | 1.2 | 1.6 | 2.6 |
| MNCs | 3.2 | 2.5 | 2.7 | 5.6 |
| Parents | 3.5 | 2.8 | 2.8 | 6 |
| Affiliates of foreign companies | 1.9 | 0.6 | 2.4 | 4.5 |
| Domestically oriented | 0.7 | 0.6 | 1.0 | 0.5 |
| Financial Corporations | 0.1 | -0.0 | 0.3 | 0.4 |
| Nonfarm noncorporate businesses | 0.3 | 0.1 | 0.4 | 0.7 |
Note. Nonfarm private business output is calculated using gross domestic income.
| Category | 1977-2000 | 1977-1989 | 1989-1995 | 1995-2000 |
|---|---|---|---|---|
| Nonfarm Private Business | 1.5 | 0.9 | 1.6 | 2.8 |
| Nonfinancial Corporations | 1.1 | 0.9 | 1.1 | 1.8 |
| MNCs | 0.9 | 0.6 | 0.7 | 1.5 |
| Parents | 0.8 | 0.6 | 0.5 | 1.2 |
| Affiliates of foreign companies | 0.1 | -0.0 | 0.1 | 0.3 |
| Domestically Oriented | 0.3 | 0.3 | 0.4 | 0.2 |
| Financial Corporations | 0.1 | -0.0 | 0.3 | 0.4 |
| Nonfarm noncorporate businesses | 0.3 | 0.1 | 0.4 | 0.7 |
Memo: Reallocation of Hours
Note. Nonfarm private business output is calculated using gross domestic income.
| Category | 1977-2000 | 1977-1989 | 1989-1995 | 1995-2000 |
|---|---|---|---|---|
| Nonfinancial Corporations | 1.6 | 1.2 | 1.6 | 2.6 |
| MNCs | 3.2 | 2.5 | 2.7 | 5.6 |
| Manufacturing | 4.1 | 3.3 | 2.5 | 7.8 |
| IT equipment | 25.0 | 20.0 | 19.5 | 45.3 |
| Other manufacturing | 2 | 1.8 | 0.8 | 3.9 |
| Non-manufacturing | 2.3 | 1.4 | 2.9 | 3.6 |
| Domestically Oriented | 0.7 | 0.6 | 1 | 0.5 |
| Manufacturing | 1 | 1.6 | 2.6 | -2.3 |
| Non-manufacturing | 0.3 | 0.5 | 1.1 |
| Category | 1977-2000 | 1977-1989 | 1989-1995 | 1995-2000 |
|---|---|---|---|---|
| Nonfinancial Corporations | 1.6 | 1.2 | 1.6 | 2.6 |
| MNCs | 1.3 | 0.9 | 1.0 | 2.2 |
| Manufacturing | 0.8 | 0.7 | 0.5 | 1.4 |
| IT equipment | 0.4 | 0.3 | 0.4 | 0.9 |
| Other manufacturing | 0.4 | 0.3 | 0.1 | 0.6 |
| Non-manufacturing | 0.4 | 0.2 | 0.5 | 0.8 |
| Domestically Oriented | 0.4 | 0.4 | 0.6 | 0.3 |
| Manufacturing | 0.1 | 0.3 | 0.4 | -0.3 |
| Non-manufacturing | 0.2 | 0.0 | 0.2 | 0.5 |
Memo: Reallocation of Hours
| Acceleration | Within Effect | Between Effect | |
|---|---|---|---|
| Nonfinancial Corporations | 1.05 | 1.05 | 0.00 |
| MNCs | 1.26 | 1.10 | 0.16 |
| Manufacturing | 0.96 | 0.97 | -0.01 |
| IT equipment | 0.54 | 0.50 | 0.04 |
| Other mfg. | 0.50 | 0.50 | -0.01 |
| Non-manufacturing | 0.28 | 0.15 | 0.13 |
| Domestically oriented | -0.30 | -0.27 | -0.03 |
| Manufacturing | -0.61 | -0.60 | 0.00 |
| Non-manufacturing | 0.29 | 0.30 | -0.01 |
| Variable & Year | Source for U.S. Parents (USDIA Survey) | Source for Foreign Affiliates (FDIUS Survey) |
|---|---|---|
| Gross Product: 1977, 1982, 1989 | Survey of Current Business, Feb. 1994 | Survey of Current Business, June 1990 |
| Gross Product: 1994-2000 | BEA website | BEA website |
| Employment: 1977 | U.S. Direct Investment Abroad, 1977 | BEA website |
| Employment: 1982 | U.S. Direct Investment Abroad: 1982 Benchmark Survey | BEA website |
| Employment: 1989, 1994-2000 | BEA website | BEA website |
| Sales: 1977 | U.S. Direct Investment Abroad, 1977 | BEA website |
| Sales: 1982 | U.S. Direct Investment Abroad: 1982 Benchmark Survey | BEA website |
| Sales: 1989, 1994-2000 | BEA website | BEA website |
| Compensation: 1977 | U.S. Direct Investment Abroad, 1977 | BEA website |
| Compensation: 1982 | U.S. Direct Investment Abroad: 1982 Benchmark Survey | BEA website |
| Compensation: 1989, 1994-2000 | BEA website | BEA website |
| sales and employment by industry of sales: 1980 | none | BEA website |
| sales and employment by industry of sales: 1982 | U.S. Direct Investment Abroad: 1982 Benchmark Survey | none |
| sales and employment by industry of sales: 1989 | U.S. Direct Investment Abroad: 1989 Benchmark Survey | BEA website |
| sales and employment by industry of sales: 1992 | none | BEA website |
| sales and employment by industry of sales: 1993 | none | BEA website |
| sales and employment by industry of sales: 1994 | U.S. Direct Investment Abroad: 1994 Benchmark Survey | BEA website |
| sales and employment by industry of sales: 1995 | none | BEA website |
| sales and employment by industry of sales: 1996 | none | BEA website |
| sales and employment by industry of sales: 1997 | none | BEA website |
| sales and employment by industry of sales: 1998 | none | BEA website |
| sales and employment by industry of sales: 1999 | U.S. Direct Investment Abroad: 1999 Benchmark Survey | BEA website |
| sales and employment by industry of sales: 2000 | none | BEA website |
| Level 1 Code & Description | Level 2 | Level 3 | Level 4 | Level 5 |
|---|---|---|---|---|
| E10 Metal mining | Mining | xxx | Non-Mfg. | NFPB |
| E12 Coal mining | Mining | xxx | Non-Mfg. | NFPB |
| E13 Oil and gas extraction | Mining | xxx | Non-Mfg. | NFPB |
| E14 Nonmetallic minerals, except fuels | Mining | xxx | Non-Mfg. | NFPB |
| E24 Lumber and wood products | Lumber, wood, & furniture | Mfg. excl. High Tech | Mfg. | NFPB |
| E25 Furniture and fixtures | Lumber, wood, & furniture | Mfg. excl. High Tech | Mfg. | NFPB |
| E32 Stone, clay, and glass products | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E33 Primary metal industries | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E34 Fabricated metal products | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E35X Other machinery | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E36X Other electrical machinery | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E371 Motor vehicles and equipment | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E372T9 Other transportation equipment | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E38 Instruments and related products | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E39 Miscellaneous manufacturing industries | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E20 Food and kindred products | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E21 Tobacco products | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E22 Textile mill products | Textile and Apparel | Mfg. excl. High Tech | Mfg. | NFPB |
| E23 Apparel and other textile products | Textile and Apparel | Mfg. excl. High Tech | Mfg. | NFPB |
| E26 Paper and allied products | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E27 Printing and publishing | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E28 Chemicals and allied products | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E29 Petroleum and coal products | xxx | Mfg. excl. High Tech | Mfg. | NFPB |
| E30 Rubber and miscellaneous plastics products | Rubber and Leather | Mfg. excl. High Tech | Mfg. | NFPB |
| E31 Leather and leather products | Rubber and Leather | Mfg. excl. High Tech | Mfg. | NFPB |
| E15T7 Construction | xxx | xxx | Non-Mfg. | NFPB |
| E49 Electric, gas, and sanitary services | Transportation & Communications | xxx | Non-Mfg. | NFPB |
| E40 Railroad transportation | Transportation & Communications | xxx | Non-Mfg. | NFPB |
| E41 Local and interurban passenger transit | Transportation & Communications | xxx | Non-Mfg. | NFPB |
| E42 Trucking and warehousing | Transportation & Communications | xxx | Non-Mfg. | NFPB |
| E44 Water transportation | Transportation & Communications | xxx | Non-Mfg. | NFPB |
| E45 Transportation by air | Transportation & Communications | xxx | Non-Mfg. | NFPB |
| E46 Pipelines, except natural gas | Transportation & Communications | xxx | Non-Mfg. | NFPB |
| E47 Transportation services | Transportation & Communications | xxx | Non-Mfg. | NFPB |
| E481A2A9 Telephone and telegraph | Transportation & Communications | xxx | Non-Mfg. | NFPB |
| E483A4 Radio and television | Transportation & Communications | xxx | Non-Mfg. | NFPB |
| E50A1 Wholesale trade | xxx | Trade | Non-Mfg. | NFPB |
| E52T9 Retail trade | xxx | Trade | Non-Mfg. | NFPB |
| E60 Depository institutions | Finance | FIRE | Non-Mfg. | NFPB |
| E61 Nondepository institutions | Finance | FIRE | Non-Mfg. | NFPB |
| E62 Security and commodity brokers | Finance | FIRE | Non-Mfg. | NFPB |
| E63 Insurance carriers | xxx | FIRE | Non-Mfg. | NFPB |
| E64 Insurance agents, brokers, and service | xxx | FIRE | Non-Mfg. | NFPB |
| E65hs Nonfarm housing services | Real estate | FIRE | Non-Mfg. | NFPB |
| E65re Other real estate | Real estate | FIRE | Non-Mfg. | NFPB |
| E67 Holding and other investment offices | xxx | FIRE | Non-Mfg. | NFPB |
| E70 Hotels and other lodging places | Services | xxx | Non-Mfg. | NFPB |
| E72 Personal services | Services | xxx | Non-Mfg. | NFPB |
| E73 Other business services | Services | xxx | Non-Mfg. | NFPB |
| E75 Auto repair, services, and parking | Services | xxx | Non-Mfg. | NFPB |
| E76 Miscellaneous repair services | Services | xxx | Non-Mfg. | NFPB |
| E78 Motion pictures | Services | xxx | Non-Mfg. | NFPB |
| E79 Amusement and recreation services | Services | xxx | Non-Mfg. | NFPB |
| E80 Health services | Services | xxx | Non-Mfg. | NFPB |
| E81 Legal services | Services | xxx | Non-Mfg. | NFPB |
| E82 Educational services | Services | xxx | Non-Mfg. | NFPB |
| E83 Social services | Services | xxx | Non-Mfg. | NFPB |
| E86 Membership organizations | Services | xxx | Non-Mfg. | NFPB |
| E84A7A9 Other services | Services | xxx | Non-Mfg. | NFPB |
| E357 Computers and related equipment | High Technology | xxx | Mfg. | NFPB |
| E366 Communications equipment | High Technology | xxx | Mfg. | NFPB |
| E367 Semiconductors | High Technology | xxx | Mfg. | NFPB |
| E91b Federal government enterprises | Govt. enterprises | xxx | Non-Mfg. | NFPB |
| E92b State and local government enterprises | Govt. enterprises | xxx | Non-Mfg. | NFPB |
| Cetegory | Sales | Capital expenditures | R&D expenditures | Gross product | Employee compensation | Employment |
|---|---|---|---|---|---|---|
| All industries: 1990 | 13.0 | 15.8 | 12.8 | ... | 10.4 | 9.9 |
| All industries: 1991 | 12.9 | 14.1 | 11.3 | ... | 10.6 | 10.2 |
| All industries: 1992 | 13.0 | 14.4 | 11.5 | ... | 10.7 | 9.9 |
| All industries: 1993 | 12.7 | 13.7 | 11.7 | ... | 10.2 | 9.1 |
| All industries: 1994 | 13.8 | 12.9 | 10.7 | 10.3 | 10.6 | 9.1 |
| All industries: 1995 | 13.3 | 12.7 | 9.9 | 10.0 | 10.2 | 9.1 |
| All industries: 1996 | 13.6 | 13.3 | 9.8 | 10.2 | 10.4 | 9.1 |
| All industries: 1997 | 13.5 | 13.0 | 10.1 | 10.3 | 10.5 | 8.9 |
| All industries: 1998 | 14.7 | 17.3 | 11.8 | 10.9 | 11.6 | 9.8 |
| All industries: 1999 | 14.8 | 18.5 | 13.9 | 10.8 | 11.8 | 9.8 |
| All industries: 2000 | 15.4 | 17.1 | 14.8 | 11.3 | 13.3 | 10.6 |
| Manufacturing: 1990 | 17.1 | 24.3 | 13.9 | ... | 13.5 | 13.4 |
| Manufacturing: 1991 | 16.9 | 21.1 | ... | ... | 13.6 | 13.6 |
| Manufacturing: 1992 | 16.4 | 19.5 | ... | ... | 13.5 | 13.4 |
| Manufacturing: 1993 | 15.8 | 17.2 | 12.4 | ... | 13.1 | 12.3 |
| Manufacturing: 1994 | 15.9 | 16.2 | 11.5 | 13.7 | 13.0 | 12.3 |
| Manufacturing: 1995 | 14.7 | 14.8 | 10.5 | 12.8 | 12.1 | 11.5 |
| Manufacturing: 1996 | 15.4 | 14.5 | 9.7 | 12.6 | 11.8 | 11.4 |
| Manufacturing: 1997 | 15.1 | 15.8 | 10.2 | 13.0 | 12.1 | 11.9 |
| Manufacturing: 1998 | 17.9 | 26.2 | 12.2 | 15.1 | 14.7 | 13.8 |
| Manufacturing: 1999 | 18.4 | 26.0 | 15.1 | 14.4 | 14.9 | 14.4 |
| Manufacturing: 2000 | 16.8 | 18.2 | 16.2 | 12.1 | 15.3 | 14.5 |
| Non-Manufacturing: 1990 | 9.1 | 9.1 | 5.9 | ... | 6.0 | 6.0 |
| Non-Manufacturing: 1991 | 9.3 | 8.8 | ... | ... | 6.5 | 6.4 |
| Non-Manufacturing: 1992 | 9.7 | 10.7 | ... | ... | 6.7 | 6.0 |
| Non-Manufacturing: 1993 | 9.7 | 11.0 | 6.0 | ... | 6.4 | 5.6 |
| Non-Manufacturing: 1994 | 12.0 | 10.4 | 6.0 | 6.5 | 7.5 | 6.0 |
| Non-Manufacturing: 1995 | 12.1 | 10.9 | 5.4 | 6.9 | 7.8 | 6.7 |
| Non-Manufacturing: 1996 | 11.9 | 12.4 | 10.1 | 7.6 | 8.6 | 7.0 |
| Non-Manufacturing: 1997 | 12.2 | 11.3 | 8.8 | 7.7 | 8.6 | 6.6 |
| Non-Manufacturing: 1998 | 12.0 | 11.4 | 8.6 | 6.9 | 8.4 | 6.7 |
| Non-Manufacturing: 1999 | 11.8 | 13.1 | 7.8 | 7.4 | 9.0 | 6.8 |
| Non-Manufacturing: 2000 | 14.2 | 16.3 | 9.0 | 10.5 | 11.4 | 8.0 |
"..." indicates Not applicable.
| Item | PARENTS | AFFILIATES | MNC | NMNC | NFC | FC | COR | XCOR | BUS |
|---|---|---|---|---|---|---|---|---|---|
| 1977 - Employee Hours: Gross domestic product | 30,863 | 2,230 | 33,093 | 56,479 | 89,572 | 4,010 | 93,582 | 29,672 | 123,254 |
| 1977 - Employee Hours: Nonfarm Business | 30,863 | 2,230 | 33,093 | 56,479 | 89,572 | 4,010 | 93,582 | 24,438 | 118,020 |
| 1977 - Employee Hours: Manufacturing | 18,871 | 1,475 | 20,346 | 19,492 | 39,838 | 39,838 | 1,283 | 41,121 | |
| 1977 - Employee Hours: High Technology Industries | 1,011 | 110 | 1,121 | 539 | 1,660 | 1,660 | 20 | 1,679 | |
| 1977 - Employee Hours: Manufacturing, except High Tech | 17,860 | 1,365 | 19,225 | 18,953 | 38,178 | 38,178 | 1,264 | 39,442 | |
| 1977 - Employee Hours: Non-Manufacturing | 11,992 | 755 | 12,748 | 36,987 | 49,734 | 4,010 | 53,745 | 23,154 | 76,899 |
| 1977 - Real Gross Product: Gross domestic product | 685,616 | 60,335 | 747,995 | 1,457,904 | 2,210,918 | 305,990 | 2,499,951 | 1,065,389 | 3,553,215 |
| 1977 - Real Gross Product: Nonfarm Business | 685,616 | 60,335 | 747,995 | 1,457,904 | 2,210,918 | 305,990 | 2,499,951 | 985,165 | 3,473,079 |
| 1977 - Real Gross Product: Manufacturing | 368,429 | 34,716 | 405,980 | 366,936 | 772,022 | 772,022 | 16,960 | 788,267 | |
| 1977 - Real Gross Product: High Technology Industries | 1,030 | 633 | 1,389 | 991 | 2,309 | 2,309 | 1 | 2,236 | |
| 1977 - Real Gross Product: Manufacturing, except High Tech | 473,076 | 36,367 | 507,024 | 416,161 | 922,311 | 922,311 | 20,369 | 941,145 | |
| 1977 - Real Gross Product: Non-Manufacturing | 315,840 | 25,596 | 340,649 | 1,085,459 | 1,428,398 | 305,990 | 1,712,391 | 964,091 | 2,668,746 |
| 2000 - Employee Hours: Gross domestic product | 36,032 | 12,028 | 48,060 | 95,276 | 143,336 | 5,481 | 148,817 | 38,249 | 187,066 |
| 2000 - Employee Hours: Nonfarm Business | 36,032 | 12,028 | 48,060 | 95,276 | 143,336 | 5,481 | 148,817 | 33,220 | 182,036 |
| 2000 - Employee Hours: Manufacturing | 15,171 | 5,643 | 20,814 | 19,617 | 40,431 | 40,431 | 1,395 | 41,826 | |
| 2000 - Employee Hours: High Technology Industries | 1,385 | 382 | 1,767 | 655 | 2,422 | 2,422 | 62 | 2,484 | |
| 2000 - Employee Hours: Manufacturing, except High Tech | 13,786 | 5,260 | 19,047 | 18,962 | 38,009 | 38,009 | 1,333 | 39,342 | |
| 2000 - Employee Hours: Non-Manufacturing | 20,861 | 6,385 | 27,246 | 75,658 | 102,905 | 5,481 | 108,386 | 31,824 | 140,210 |
| 2000 - Real Gross Product: Gross domestic product | 1,752,905 | 503,859 | 2,256,787 | 2,871,566 | 5,121,125 | 637,192 | 5,761,391 | 1,846,462 | 7,605,677 |
| 2000 - Real Gross Product: Nonfarm Business | 1,752,905 | 503,859 | 2,256,787 | 2,871,566 | 5,121,125 | 637,192 | 5,761,391 | 1,754,303 | 7,513,077 |
| 2000 - Real Gross Product: Manufacturing | 807,780 | 228,800 | 1,035,219 | 464,266 | 1,493,420 | 1,493,420 | 72,202 | 1,566,247 | |
| 2000 - Real Gross Product: High Technology Industries | 333,765 | 36,626 | 368,978 | 60,177 | 429,452 | 429,452 | 11,111 | 440,516 | |
| 2000 - Real Gross Product: Manufacturing, except High Tech | 593,233 | 201,627 | 794,918 | 407,271 | 1,200,331 | 1,220,331 | 64,395 | 1,264,933 | |
| 2000 - Real Gross Product: Non-Manufacturing | 943,762 | 274,797 | 1,218,581 | 2,405,737 | 3,625,490 | 637,192 | 4,264,410 | 1,683,942 | 5,946,905 |
Note. Employee hours reported in thousands; real gross product reported in thousands of 1996 dollars.
NMNC = Domestically Oriented; NFC = Nonfinancial Corporations; FC = Financial Corporations
COR = Corporate Business; XCOR = Nonfarm Corporate Business; BUS = Nonfarm Private Business
I. Overview and Data Sources
As described in the text, the data on U.S. multinationals come from two surveys conducted annually by the Bureau of Economic Analysis (BEA). The survey of U.S. Direct Investment Abroad (USDIA) provides information on the operations of U.S.-headquartered multinationals (parents), while the survey of Foreign Direct Investment in the United States (FDIUS) provides information on the operations of U.S.-based affiliates of foreign-owned multinationals (affiliates). Throughout our analysis, a foreign affiliate is defined as a U.S. business with 10 percent or more foreign ownership. Information on majority-owned foreign affiliates is also available in more recent BEA publications but does not appear in the earlier surveys. See Mataloni (2002) and Zeile (1999) for detailed descriptions of the methodologies for the two surveys.
We used the following variables in our analysis: gross product (value added), employment, compensation, and sales. Hours worked by employees are not measured in either survey and had to be estimated (see Section 5 below). Table A1 presents the source for each of these variables in each survey and in each year. As shown in the table, while most of these data can be downloaded directly from the BEA website, several older series are only available as tables in selected BEA publications; a subset of these are only available in paper format and therefore had to be scanned into the FRB Productivity Data System.
Our analysis was performed for the period of 1977-2000. An annual time series is available for 1994-2000. Prior to this, the variables of interest are only available for both surveys in 1977, 1982, and 1989. Although data now exist for both surveys through 2004, the Bartelsman and Beaulieu database with which we integrate the MNC surveys ends in 2001.24 Because 2001 is a recession year, we chose not to include it in our analysis.
II. Industrial Classification and Concordances
The industrial classification of both surveys varies over time, complicating efforts to combine them into a consistent time-series. For example, the FDIUS survey switched away from the 1987 Standard Industrial Classification system (SIC87) to the 1997 North American Industry Classification System (NAICS) beginning with its 1997 Benchmark Survey. The USDIA survey transitioned to NAICS in its 1999 Benchmark Survey. In addition, the level of industry detail varies over time, across variables, and across surveys.
Because of these classification issues, considerable effort was spent concording the data to a level of detail common to both surveys in all years under consideration. The standard which we ultimately chose is based upon the BEA's SIC87-based Gross Product Originating (GPO) industry data. These data also formed the basis of the work by Bartelsman and Beaulieu. In that work, the authors broke out computers (SIC 357), communications equipment (SIC 366), and semiconductors (SIC 367) from Industrial Machinery and Equipment (SIC 35) and Electronic and Other Electric Equipment (SIC 36) in order to permit an improved focus on the high-tech sector. We adopted the resulting industrial hierarchy, which they called the "GPO87HT" hierarchy, and which is shown in Table A2. The 64 industries in the first column are the "atoms," or finest level of detail, available in the GPO87HT hierarchy. The tools of the FRB Productivity Data System permit values associated with these atoms (for instance, gross product or employment) to be aggregated to higher level sub-aggregates (columns 2-5) as well as the total for the entire nonfarm private business sector (column 6).
Using the tools of the FRB Productivity Data System, we created numerous industrial hierarchies, called "metadata," to analyze the MNC surveys and ultimately concord all variables of interest to industries contained within the GPO87HT hierarchy. Often this was accomplished by first concording variables to an intermediate industrial hierarchy common to a subset of years or surveys.25
Unfortunately, while the level of detail we created for the manufacturing sector is typically at the two digit level, we could not carve out a correspondingly fine level of detail for the services, mining, or transportation and communications industries. As such, the atom-level industries in our final MNC database do not always correspond to those in the GPO87HT hierarchy. Rather, the 29 shaded industries in Table A2 denote the MNC-level atoms which ultimately fed into our analysis.
II. Sectoral Classification
Corrado and Slifman (1999) highlighted the importance of studying productivity not only by industry but also by legal-form of organization, specifically along noncorporate, nonfinancial corporate, and financial corporate lines. Bartelsman and Beaulieu adopted this "sectoral" approach as well but implemented it for each industry in the GPO data. In this paper, we make the additional step of breaking out the nonfinancial corporate sector into two distinct parts: an MNC sector and a "domestically oriented" sector. MNCs are further divided into parents and foreign affiliates. The figure below shows the sectoral hierarchy that we developed for each industry in the nonfarm private business sector:
Figure: Sectoral Hierarchy
Data on nonbank finance and insurance companies were excluded from our MNC database so that we could focus on the nonfinancial activities of multinationals. The real estate industry was also excluded in order to focus more directly on multinational corporations. The number of non-corporate multinationals is small but concentrated in this industry.
III. Constructing a Database for U.S. Parents
As noted in the text, the 1999 and 2000 USDIA surveys are classified on a NAICS97 basis, meaning it was necessary to concord these data to an SIC87 basis in order to make them time-series compatible with the older surveys. Before doing this, however, a few additional steps were necessary. First, beginning with the release of the revised 1999 survey, BEA began including U.S. parents with very small affiliates abroad, i.e. affiliates with assets, sales, and net income less than $7 million (Mataloni, 2002). These new parents represented 3.8 percent of gross product, 6.1 percent of the employment, and 2.7 percent of the capital expenditures in 1999. We rescaled the industry level data in 1999 to remove the published aggregate contribution of small parents. These level adjusted values were then extrapolated forward to 2000 based on the growth rate of the unadjusted (i.e. officially published) estimates. In doing this, we implicitly assumed that small parents grew at the same rate as the larger parents.
Second, we corrected an apparent reclassification of an unnamed firm (or firms) from the computers and peripheral equipment manufacturing industry (N334) to the computer systems design and related services industry (N5415). Recall that the BEA assigns all of the operations of a U.S. parent to a primary industry based upon a breakdown of the parent's sales. It appears that the primary industry designation of a large company (or several companies) with sales in both N334 and N5415 changed between the initial release for 1999 and when the 1999 data were revised as part of the 2000 release.26
Finally, we addressed the overlap issue. As noted in the text, the BEA provided us with special tabulations for 1990-2000 of the activities of those U.S. parents that are also affiliates of foreign companies and thus counted in both surveys. Because of concerns about the disclosure of information about individual survey respondents, these tabulations were made at a highly aggregate level, specifically all non-bank industries, manufacturing, and non-manufacturing.
Table A3 presents these tabulations expressed as a percent of the published values for the USDIA survey. For example, in 2000 the activities of foreign affiliates that are also counted as U.S. parents accounted for 11 percent of the gross product and 13 percent of the employee compensation in the USDIA survey. The BEA also provided us with more detailed industry-level information on the number of U.S. parent firms in 2000 that were also foreign affiliates. After reviewing these data, we made a few additional adjustments, roughly doubling the overall manufacturing share for motor vehicles and parts, chemicals, petroleum refining, and stone, clay, and glass; and halving the overall manufacturing share for semiconductors, miscellaneous manufacturing, and furniture. We then made overlap adjustments for 1977, 1982, 1989, and 1994-1998 using the same special tabulations. Overlap adjustments for 1977, 1982, and 1989 were based on the tabulations for 1994.
IV. Constructing a Database on Foreign Affiliates
For 1977, 1982, and 1989, all key variables except for gross product were concorded to the GPO87HT hierarchy. Gross product data for this period are organized according to a different industrial hierarchy, which in turn is different from the one used for all variables from 1992-1996. Moreover, the level of industry detail for 1977-1986 is limited (16 categories) compared to 1987-1989 (77 categories). We therefore used the detailed industry shares for 1987 to fill in the gaps in 1986, and then repeated this process back to 1977. All data were then concorded to the GPO87HT hierarchy.
For 1992-1996, data for all key variables were published at a slightly more disaggregate level than the corresponding USDIA estimates for 1994-1998. This necessitated an additional concordance in order to ultimately convert them to the GPO87HT hierarchy.
Data for 1997-2000 were published on a NAICS basis, meaning it was necessary to concord them to an SIC87 basis in order to make them time-series compatible with the pre-1997 FDIUS surveys. We used the same time-invariant concordance that was applied to the USDIA surveys in 1999 and 2000. The data were then concorded to the GPO87HT hierarchy.
V. Establishment-level Estimates for U.S. Parents and Foreign Affiliates
We constructed our establishment-level estimates using periodic information from the BEA on sales and employment of affiliates or parents broken out by industry of sales. As shown in Table A1, for the USDIA survey, these data are only available in the benchmark surveys years for 1982 forward. For the FDIUS survey, the data are available annually for 1987-2000 but are not available in any previous years except for 1980.27
Unfortunately, unlike the firm-level data, the data on sales- and employment -by-industry-of-sales include information on banking, meaning the total values in the two types of files do not match. In addition, two categories--central administrative offices and a residual, "not specified" industry--only exist for the sales- and employment-by-industry-of-sales variables. We therefore implemented an iterative bi-proportional fitting or "RASing" procedure to adjust these values and ensure that they matched the totals implied by the firm-based data. Ratio variables were then constructed of employment (or sales) in the industry of sales to employment (or sales) at the firm level.
Because data for sales- and employment-by-industry-of-sales were published on a NAICS97 basis in 1999 for the USDIA and in 1997-2000 for the FDIUS, we first had to remove the contributions of the additional parents that began to appear in the USDIA survey in this year, following the same approach described above before concording them to a GPO87HT basis.
Finally, we applied the establishment-to-firm ratios to the firm-level, overlap adjusted estimates in order to generate our establishment-level estimates. For the USDIA data, because these ratios only exist for 1982, 1989, and 1999, we applied the 1982 ratio to the 1977 firm-level data, the 1994 ratio to the 1995 and 1996 firm-level data, and the 1999 ratio to firm-level data to 1997-2001. For the FDIUS data, because these ratios do not exist in 1977 and 1982, we applied the 1980 ratios to both years.
VI. Combining the Parent and Affiliate Databases
Having concorded both surveys to a single, time-series-consistent industrial hierarchy, addressed the overlap problem in the USDIA survey, and generated estimates on an establishment basis, we combined the data from the two surveys into a consolidated MNC database. We then merged this dataset with the Bartlesman and Beaulieu industry-level estimates for the nonfinancial corporate sector. Thus, for each industry, the resulting dataset contained values for parents, affiliates, and the entire nonfinancial corporate sector. We estimated hours worked for parents and affiliates as the product of their employment and the average workweek in the corresponding industry for the nonfinancial corporate sector as a whole.28 Values for the entire MNC sector in each industry are simply the sum of the corresponding parent and affiliate values. Values for domestically-oriented nonfinancial corporations were calculated residually.29
As discussed in the text, we applied the gross product deflators generated by Bartlesman and Beaulieu for industries in the nonfinancial corporate sector to the atom-level parent, affiliate, and domestically-oriented industries in our MNC database (i.e. the 29 shaded industries in Table A2). Thus, in our analysis, chain aggregation of these atom-level deflators to higher-level sub-aggregates like high tech, manufacturing excluding high tech, and nonmanufacturing provides the sole source of price variation across parents, affiliates, and domestically-oriented firms in any given industry in the nonfinancisal sector.
Because the deflators are Fisher indexes, chain aggregation requires values for both prices and quantities in adjoining years. This posed a problem because, prior to 1994, we only have nominal gross product data for parents and affiliates at infrequent intervals. It was therefore necessary to estimate nominal gross product in years adjacent to 1977, 1982, 1989. To do so, we implemented an iterative proportional fitting procedure that ensured these estimates summed to known totals (i.e. nonfinancial corporate gross product in each atom-level industry) and were consistent with the various accounting identities in our sectoral hierarchy (i.e. MNC = Parent + Affiliate; Nonfinancial Corporate = MNC + domestically-oriented). We exploited the availability of nonfinancial corporate gross product and gross product deflators in the adjacent years and used values for parents and affiliates in 1977, 1982, and 1989 as starting values. Finally, we combined all relevant data on MNCs and nonfinancial corporations with data on the noncorporate, financial corporate, and government sectors to complete our analysis dataset.
Table A4 presents our sectoral estimates of employee hours and real gross product in both 1977 and 2000 for selected aggregates and sub-aggregates. Estimates for all other years and variables as well as for atom-level industries are available on request.
Footnotes
The views expressed in this paper are those of the authors and should not be attributed to the Board of Governors of the Federal Reserve System or other members of its staff.
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