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IT INVESTMENT AND FIRM PERFORMANCE IN

DSTI/EAS/IND/SWP/AH(2001)16


MACROBUTTON InsertCVP.main \* MERGEFORMAT

November, 2001

Mark Doms

Board of Governors, Federal Reserve

[email protected] Jarmin

Center for Economic Studies, U.S. Census Bureau

[email protected]

Shawn Klimek

Center for Economics Studies, U.S. Census Bureau

[email protected] paper analyzes productivity growth in the U.S. retail trade sector. We do this by examining changes in productivity and other measures of firm performance at the micro-level. The primary contribution of this research is to extend a rich literature and tradition of analyzing productivity growth of establishments and firms in manufacturing to other significant portions of the economy. In particular, we examine the role of turnover, entry and exit. Also, we extend our analysis to see how these changes are correlated with information on capital spending and spending on information technology.

While our results are still preliminary, the patterns we see in the data are consistent with anecdotal evidence that many areas in retail are seeing large sophisticated companies introducing new technologies and processes and displacing less sophisticated retailers. However, there is more that needs to be done before we can more fully describe this process.

Introduction

1 MACROBUTTON NUMBERING .

The recent slowdown notwithstanding, the performance of the U.S. economy over the past decade has been impressive. The recent period of strong economic and productivity growth coincided with an investment boom, particularly in computers and other forms of information technology (IT). Many observers point to these as evidence of a new economy driven largely by improvements in, and greater utilization, of IT. Indeed there is evidence that this in case. Aggregate level studies (Jorgenson and Stiroh 2000; Oliner and Sichel 2000; Schreyer 2000), and micro level analyses (Brynjolfsson and Hitt, 1995; Dunne et. al 1999) suggest a link between IT and productivity. However, the evidence in support of a new economy link between IT and economic performance is not overwhelming. Industry level studies (Stiroh 1998) find no link and micro level studies are concentrated in the manufacturing sector or use small, select samples of firms.


Progress in this area has been hampered by the lack of appropriate data. Many of the sectors where IT is used most intensively are where measurement by official economic statistics is the weakest (Bosworth and Triplett 2000; Haltiwanger and Jarmin 2000). As a result, the relationship between IT and firm performance in the trade and service sectors is poorly understood. Statistical agencies are keenly aware of the measurement challenges facing them and that changes underway in the economy are adding to these. The Census Bureau has taken the lead in trying to address the needs of data users arising from the new economy by initiating new measurement initiatives, adding questions to existing surveys and finding new ways to more fully utilize existing data resources (Atrostic, Gates and Jarmin 2001).


In this paper, we take that latter path and use previously untapped micro level data collected by the Census Bureau to analyze firm performance in the retail trade sector focusing on the role of information technology (IT). We extend a rich literature analyzing establishment and firm performance with Census micro data for the manufacturing sector to other significant portions of the economy.


In analyzing firm performance in the retail trade sector, we face several hurdles. First, the quantity and quality of information available to measure firm or establishment productivity in the retail sector is much poorer than in manufacturing. In particular, measuring output is problematic and there is little information collected on inputs. We dont offer much in terms of solving these problems and follow the standard practice of measuring productivity with sales per employee. This is a simple measure and intuitively appealing for the retail sector. Calculating other measures of productivity, such as value added per worker or multi factor productivity, for the retail sector at the firm or establishment level is prohibitively difficult


An additional hurdle in examining firm performance in the trade sector arises from the fact that the data we are using are collected in a variety of surveys using different statistical units. In manufacturing, the value of outputs and inputs for establishments is collected in a single survey, the Annual Survey of Manufacturers. Unfortunately, the variables needed to construct just one measure of firm performance, labor productivity, for the trade sector are scattered across different surveys with different sampling frames and units of observation. Below we discuss how we combined the various survey data. One of contributions of this paper is exploring how to analyze firm performance outside of the goods producing sectors using Census Bureau micro data.

Basic facts and hypotheses about the retail trade sector


Retail trade accounts for a large and growing portion of U.S. economic activity. The upper panel of table 1 presents output by sector from BEAs Gross Product Originating Database--output corresponds to value added, so that the sum across all sectors equals GDP. The trade sectors (both retail and wholesale) share of output was about the same as that of manufacturing in 1999, about 16 percent. However, the share for the trade sector has grown significantly faster than manufacturings since 1992. Further, this growth has occurred for both the retail and wholesale sectors.


The second panel in table 1 shows employment by industry. Trade sector employment was about 60 percent greater than manufacturing employment in 1999. As in output, the growth in employment has been greater in the trade sector than in manufacturing, especially in retail.


Figure 1 and the third panel in table 1 compare a crude measure of labor productivity--output per employee (a better measure would be to use hours worked, but the qualitative results remain the same)across the sectors. Since 1992, productivity growth in the trade sectors and in manufacturing averaged a bit more than 4 percent per year, greater than the average for the entire economy. Given the great interest surrounding the rebound in aggregate productivity growth since 1995, it is interesting that the retail sectors productivity growth also picked up.


This strong productivity performance, especially that observed in the trade sectors, was unexpected and is still not well understood. What is behind the improved productivity performance of the retail sector? One hypothesis is that relatively productive firms, such as Wal-Mart or Starbucks, open a large number of establishments, increasing the market share of these firms. Relatively inefficient firms (K-Mart and Brothers Coffee) are driven out of the market. One factor that may make Wal-Mart successful is their use of information technology. Not only does Wal-Mart make substantial investments in IT, Wal-Mart knows how to make these investments pay-off more so than other firms. In the case of Starbucks, other factors may be at work, such as a consistently produced product that appeals to a large set of consumers.


Foster, Haltiwanger and Krizan (2001) decompose aggregate productivity growth in the retail sector using data from the Censuses of Retail Trade. They find that most productivity growth comes from the net entry of establishments. That is, low productivity establishments exit and are replaced by high productivity new entrants. Looking more carefully at the characteristics of these high productivity entrants, they find that entering plants owned by existing firms are the most productive. This finding is consistent with the Wal-Mart type stories described above.


It is unlikely that a single explanation for improved productivity growth applies across the entire retail sector. There is tremendous variation within both retail and wholesale trade in terms of activity. Table 1b presents the employment breakdowns by two-digit industry. Retail trade is especially diverse, covering eating and drinking places, car dealers, shoe stores, department stores, and a wide variety of other retail establishments. The performance of these industries, and the firms within them, varies considerably. The role of IT in this performance most likely varies as well.

Data


We use micro data from two Census Bureau programs since no single program collects data on all the variables we need. First, we use establishment level data from the 1992 and 1997 Censuses of Retail Trade. The Census of Retail Trade (CRT) files at CES contain information on the universe of retail establishments and are the source for the measures of labor productivity we use below. To construct measures of total capital and computer investment, we use the 1992 Asset and Expenditures Survey (AES).


For the manufacturing sector, it is possible to match production and investment data at the establishment level. This is not the case in retail, however. Detailed (by type of equipment) annual investment data are not available for retail establishments from any Census Bureau survey. In 1998, the Annual Capital Expenditure Survey (ACES) asked firms to break out capital expenditures by equipment type for their companies three primary industries. In addition, most capital expenditure items were taken off the 1997 version of the AES, which is now known as the Business Expenditure Survey (BES), so as not to duplicate inquiries in the ACES.


For the reference year 1992, investment and expenditure data were collected for the retail sector via the AES. While performed as part of the 1992 Economic Census, the sampling frame for the retail portion of the AES was the one used, at the time, for the Monthly and Annual Retail Trade Surveys. As a result, the sampling units in the 1992 AES are substantially different from the establishment units used in the CRT. Differences in sampling units and methodology across the Census and the AES make merging the information from them difficult. Below we describe the methods we employed to create the matched research data set used in the analysis. First we describe our two primary datasets in more detail.

Census of Retail Trade


As part of the Economic Census carried out every 5 years, the Census Bureau collects data for the universe of retail establishments. In an effort to reduce reporting burden on smaller businesses, only establishments with a specified minimum number of paid employees (this number varies by industry, but is generally around 10) are canvassed. Administrative data are used for small employer and non-employer establishments that are not mailed Census forms. Primary data on payroll, employment, sales, location and industrial classification are obtained for all retail establishments (both the mail and non-mail segments). Additional information on merchandise lines and selected other items are collected from the mail segment. For the current analysis, we are interested only in the base information on sales, employment and so on.


An establishment is a single physical location where business is conducted. The frame for the CRT, and other Economic Censuses, is the Standard Statistical Establishment List (SSEL). Since administrative data from the SSEL are used directly in the CRT and because the CRT and SSEL share a common structure its useful to briefly describe the SSEL.


The SSEL has two principal components. First, the Census Bureau receives information on taxpaying businesses from the Internal Revenue Service (IRS). This information corresponds to legal tax paying entities and the unit corresponds with the Employer Identification Number (EIN). The majority of businesses, in and outside of retail, have only one location. In these cases, the EI administrative reporting unit the Census receives from the IRS and the establishment are the same thing. When a new single unit establishment EIN arrives on IRS files, Census assigns both a Census File Number(CFN) and a Permanent Plant Number (PPN). Both numbers are unique to a physical establishment. However, the CFN is intended to incorporate information about the ownership of the establishment and can change as the ownership or other legal aspects of the establishment change. The PPN remains the same as long as the establishment remains open in the same location, even if it changes hands.


Second, the Census Bureau annually surveys multi-location companies inquiring about the location, employment and industrial classification of all their establishments. The Company Organization Survey (COS), the Economic Censuses and other surveys are used to maintain the list of mulit-unit (those owned by multi-location companies) establishments. Multi-unit establishments are also assigned CFNs and PPNs. Again, they are unique to the establishment and the CFN contains information about the ownership of the establishment. Unlike in the single unit case, where they all refer to the same thing, the EI administrative reporting unit, the firm and the establishment can be very different for multi-units. This means the numeric identifiers: EIN, CFN and PPN all refer to different units. For multi-unit establishments, the CFN contains an ALPHA code which identifies the firm that owns the establishment. An ALPHA can own many EINs, each of which can have several PPNs and CFNs associated with them.


This ID structure is mapped directly to establishments in the CRT. These IDs are how researchers at CES can link establishments, firms and firm segments across different surveys. In most cases, these links are between like units (e.g., PPN to PPN or ALPHA to ALPHA). This is not the case when linking the AES and the CRT as our discussion of the AES below shows.

1992 Asset and Expenditures Survey


Data on total capital expenditures and computer investment for the retail sector in 1992 are available from the 1992 Asset and Expenditure Survey (AES), done as part of the 1992 Economic Census. As mentioned above, the sampling frame for the1992 AES was that for Annual and Monthly Retail Trade Surveys. These surveys use significantly different sampling units than the establishments used in the CRT. The 1992 AES, following the sampling methodology of the Annual Retail Trade Survey (ARTS) was comprised of a list sample and an area sample. We do not use any of the data from the area sample, so we wont discuss it here (see U.S. Census Bureau, 1996 for discussion on the area sample). The list sample has two sub-lists for different types of records, EI and ALPHA records.


Large multi-location retailers identified from the 1989 COS make up the first (ALPHA) list. Their establishments (and their corresponding EINs) were removed from the SSEL before drawing the EI list sample. The remaining establishments and their corresponding EINs make up the EI list. Most of the units in the ALPHA list are large multi-unit retailers that were selected in to the ARTS and, thus, the AES with certainty. These units typically correspond to an entire large retail company, but some larger retailers can have more that one reporting unit where the units are separated by major kind of business, and still others may have kinds of business that are out of scope for the CRT (e.g., wholesale or manufacturing establishments).


Smaller multi-unit and single unit retailers are contained in the EI sub-list. The ARTS chooses three rotating probability samples from this list and the AES uses two of the three. For all businesses in the EI list, the EIN is the sampling unit. Therefore, it is possible for a multi-unit EI list company (an ALPHA) with more than one EI to be represented in the AES more than once, but for distinct segments of the firm.

Matching the AES to the CRT


It is not possible to obtain exact unit to unit matches between the AES and the CRT for all multi-unit retailers. There is not an accurate mapping between the sampling units on the AES (identified numerically by AESID) and the establishments in the CRT that the AES sampling units are intended to represent. This is due to timing issues relating to drawing the ARTS/AES sample and when the CRT is done. In addition, the ARTS is voluntary and the Census Bureau grants companies a lot of latitude in how they report in order to obtain their participation.


Matching the AES to the CRT is not too problematic for EI cases since the EI sampling unit in the AES is intended to cover all establishments (usually only one) operating under a given EIN. The ALPHA cases, which account for a large amount of retail activity, are more difficult. For matching purposes, the unit of analysis in these can be thought of as an ALPHA - kind of business combination. That is the sampling unit is intended to describe the activities of a company within a given industrial, geographic or other classification. We match at the ALPHA two digit SIC (kind of business) level.


The 1992 AES contained 20,355 EI units and 2810 ALPHA units. The ALPHA units collapse to 2024 ALPHA two digit SIC combinations. We matched 15,498 of the 20,355 EI units to the CRT. These EIs corresponded to 32,731 establishments. We matched 1631 of the 2024 ALPHA two digit SIC units (and 2385 of the 2819 ALPHA units) to the CRT. These companies had 228,982 establishments in the 1992 CRT. The result is a matched dataset with 17,129 firms. Note that what we are calling a firm, does not always match the legal definition of many large enterprises.

Results


Our goal is to better understand the processes generating productivity growth and improved firm performance in the retail trade sector. The matched AES CRT dataset we constructed allows us to exploit cross sectional variation in the intensity of computer and total capital investment and in labor productivity growth to see if firms that invested heavily in 1992 enjoyed more productivity growth over the 1992 to 1997 period. In the retail sector, perhaps more so that other sectors, increases in sales and the number of establishments a firm operates are good signals of firm success. We examine these below as well. We employ both firm and plant level regressions.


Our empirical framework is straightforward. Our preference would be to estimate production functions. However, the quality and quantity of the data available prevents us from doing so. The only input we observe is total employment. We can not measure the capital stock, only investment for one period. Further, sales is a crude measure of output and we do not have firm specific deflators, which are important in a sector with large quality differentials between firm operating inside well-defined industries.


Thus, we do not estimate structural production function parameters and instead employ simple regressions with the hope describing the relationships of proxy measures in the data. This is also why we chose to examine different metrics of retail firm performance. We regress measures of retail firm performance on a measure of IT investment intensity as well as some controls as in the following

yj = f(ITj, Ij, sizej, INDj, wagej, j)

where ITj is a measure of IT investment intensity, Ij for firm j, is a measure of total investment intensity, size and IND are firm size and 2 digit SIC dummies, respectively and is an error term. Performance, yj, is measured as the change between 1992 and 1997 and all right hand side variables are measured in 1992. Construction of the measures we use is described in more detail below.

Descriptive Results: Sector Wide


Tables 2 and 3 contain descriptive statistics for the "quasi-firm" units we constructed from the CRT. All establishments, in both the 1992 and 1997 CRTs, are represented. We list the number of firms in each year as well as the number of surviving, or continuing, firms by size class. The table shows that there is considerable turnover amongst retail firms, especially in the smaller size categories. Work by Foster, Haltiwanger and Krizan (2000) suggests that net entry of establishments drives most aggregate retail productivity growth over a similar time period.


Indeed, there is considerable turnover in retail trade at both the establishment and firm levels. More than half of the firms in the 0 to 9 size class in 1992 exit before 1997. We do not decompose productivity growth as do Foster et. al., but our results suggest that changes to the retail sector caused by the net entry of establishments are dominated by large continuing firms. Results in Table 2 show that large continuing retailers contributed more than two thirds (26.494 of 34,980) of the increase in retail establishments between 1992 and 1997. Even more importantly perhaps, is the fact that large retailers contributed approximately 71% of the over 2.7 million net increase in retail employment over the 92 to 97 period! Large retailers add more retail establishments and jobs than do their smaller counterparts and are accounting for a larger portion of overall retail activity in the U.S. While, this result should seem obvious to most U.S. consumers, it is the opposite of the trends we have observed in the manufacturing sector, where large firms have reduced their employment share but have increased the productivity gap with small firms (Baldwin, Jarmin and Tang 2001).


Table 3 gives some basic statistics for labor productivity (sales per worker) for 1992 and 1997 and gives the average firm level change in productivity. All productivity calculations are nominal, at this point. The results suggest that the productivity performance of large retailers is rather similar to all but the smallest firms.

Matched AES CRT Sample


Table 4 shows descriptive statistics for our matched sample of AES CRT data. The AES covers most large retailers with certainty in order to cover as much retail activity as possible, while holding the sample size and respondent burden to a minimum. As a result, even though our matched sample only covers 17,129 of the 1,071,737 retail firms in the 1992 CRT, it covers a sizeable portion of retail employment and sales. Productivity growth between 1992 and 1997 does not vary strikingly across the size distribution, as was the case for retail as a whole. Firms in the matched sample do tend, however, to be larger and more productive than the typical firm in the entire retail universe.


The matched data allow us to look at the relationship between capital intensity and firm performance. The AES asks for total capital expenditures and for expenditures on selected types of equipment, such as computers. It does not include questions on stocks and we dont have time series data available at the firm level to construct capital stock measures. However, we are interested only in the cross sectional variation in capital and computer intensity. Previous work (Adams 19??) indicates that the patterns of cross sectional variation in investment and capital stocks are very similar. Therefore, we proxy total capital and computer intensities with, respectively, total and computer investment per dollar of sales.


In table 5, we provided basic statistics on establishments, employment and productivity by capital and computer investment intensity categories. The table shows striking differences in the productivity performance of firms according to capital and computer intensities. Also, establishment and employment growth for the matched AES CRT sample is concentrated entirely among firms with high capital and/or computer intensities. The productivity growth premium to being the high total and high computer intensity category is particularly interesting.

Firm Level Regression Results


To get a better handle on the role that investments in IT have in firm performance, we turn now to some simple regressions. We use two dependent variables in our analysis: labor productivity and establishment growth between 1992 and 1997. The construction of these measures means our analysis focuses on those firms that were active in both years. This could be a problem in light of the findings of Foster, Haltiwanger and Krizan (2000) who show that net entry accounts for a large portion of aggregate productivity growth in the retail sector. However, recall their results are based on the net entry of establishments. We are looking at firms here and, as table 2 shows, continuing firms (especially large ones) account for a substantial portion of net establishment entry.


Before turning to the regressions, let us compare the characteristics of the firms in our matched subset, and used in our regressions, to the entire retail population. Our regressions are basically a cross sectional analyses of firms present in both 1992 and 1997 using 1992 characteristics as regressors. Thus, table 6 and 7 show some basic statistics on the number, size, number of establishments and productivity for all firms, and for our matched subset. Table 7 also lists statistics on capital and computer expenditures for the matched AES-CRT subset. Characteristics are given by 2 digit SIC in both tables. As expected, firms in the matched subset are much larger and more productive than the general population of retailers. Interestingly, there is no obvious correlation between the intensity of computer investment in a 2-digit industry and its productivity growth.

Productivity Growth Results


We are interested in seeing whether retail firms that use more capital, both IT and total, experience more productivity growth and are more likely to expand their operations through increased sales or by increasing the number of retail establishments. We use two measures of IT in the regressions. First, we group firms reporting non-zero investment into total and IT investment intensity (investment/sales) quartiles. In the AES, most respondents had either zero or missing responses to the question on IT spending and over a third had zero or missing total capital expenditures. Therefore, we also include dummies for zero or missing responses to both the total and IT investment variables. The other specification for IT investment is to enter IT as the share of total investment (IT/I).


Table 8 contains results from regressions looking at the impact of total and IT investment intensities on labor productivity growth between 1992 and 1997 using both measures of IT. The regressions control for firm size, average (within firm) wage, and two digit SIC. The results show that productivity growth is lagging at very small retailers compared to their larger counterparts. Curiously, the results here suggest that higher wage firms enjoy less productivity growth. This result runs counter from what we would expect to find from studies using manufacturing micro data. This finding was robust to alternative specifications of the wage variable. At this point, we are not sure what to make of this result. Average wages differ considerably across differ types of retail businesses, even within two digit groups. Two digit industry controls are very crude and it could be that firms in industries, within 2 digit sectors, with lower average wages are those that are experiencing higher productivity growth.


The results from model 1 show that the productivity growth premium for higher levels of total investment intensity is concentrated in the highest investment intensity quartile. The relationship between computer investment intensities and productivity growth is monotonically increasing across the quartiles. This is true even when we control for both total and computer investment. Firms in the highest computer investment quartile experienced approximately 12% higher productivity growth that those in the lowest (but still positive) quartile. Those firms in both the highest total and computer intensity quartiles had 23% higher productivity growth that those in both of the lowest quartiles.


Models 2 and 3 use the IT share of total investment measure. While the coefficient on the IT share variable is positive it is only marginally significant (at the 6% level) when a measure of total investment spending is included. However, we will see below that the IT share of investment (measured at the firm level) has a strong positive relationship with productivity growth measured at the establishment level.

Establishment Growth Results


Table 9 show results from similar regressions where the dependent variable is log change in the number of establishment at retail firms. This is good measure of overall firm performance in retail. Even with the Internet and catalogue shopping, most retail markets are local. A firms participation is a given market is indicated by the presence of one its establishments in that market. Firms that are successful expand into additional markets.


The results in Table 9 show that only those firms in the highest computer and total investment intensity quartiles experience higher growth rates in the number of establishments. While the differences are not statistically significant, the relative magnitude of the computer and total investment coefficients in the third regression suggest that that computer investment is the more important driver here.

Establishment Level Regression Results

(to come)

Conclusions


The retail trade sector in the U.S. has experienced considerable growth over the last several years. In addition, the sector has enjoyed substantial productivity growth over the same period. The reasons for this impressive performance are not well understood and there is, generally, little focus on the sector by researchers. Part of this lack of attention can be attributed to a lack of good micro level data with which to study the retail sector. In this paper, we have brought different Census Bureau micro datasets together for the first time to examine potential explanations of productivity growth among firms in the retail sector.


In particular we focus on the role played by computer investment. There is a sense in the popular imagination that large, technically sophisticated retailers are displacing smaller retailers. It is also widely thought that an important part of the business plan of these larger sophisticated retailers is a heavy reliance on information technology. Thus, we examine the relationship between IT intensity and labor productivity growth.


Our results are still preliminary, so we hesitate drawing too much from them. However, the patterns we see in the data are consistent with anecdotal evidence that many areas in retail are seeing large sophisticated companies introducing new technologies and processes and displacing less sophisticated retailers.


However, there is more that needs to be done before we can more fully describe this process. We are currently in the process of incorporating data from the Annual Retail Trade Survey so that we can analyze the relationship between computer investment and both value added per employee (rather than sales per employee) and inventories. There is also, much more to do on seeing how measures of technical sophistication like computer investment interact with entry and exit patterns of both firms and establishments to yield improved performance in the retail sector. Finally, we want to expand our analysis to cover the entire Trade Sector.

References

Atrostic, Gates, Jarmin 2001

Bartelsman, Eric and Mark Doms, Understanding Productivity: Lessons from Longitudinal Microdata, Journal of Economic Literature, September 2000.

Bosworth Triplett 2000

Byrnolfson and Hitt 1995

Dumas, Mark, Productivity Trends in Two Retail Trade Industries, 1987-1995, Monthly Labor Review, July 1997, 35-39.

Dunne et. al. 1999

Foster, Lucia, John Haltiwanger and C.J. Krizan, Aggregate Productivity Growth: Lessons from Microeconomic Evidence, NBER Working Paper No. 6803, 1998 (also forthcoming in New Developments in Productivity Analysis, editors: Edward Dean, Michael Harper and Charles Hulten, University of Chicago Press)

Foster, Lucia, John Haltiwanger and C.J. Krizan, The Link Between Aggregate and Micro Productivity Growth: Evidence from Retail Trade, working paper, November 2000.

Haltiwanger and Jarmin 2000

Oliner and Sichel 2000

Schreyer (200).

Table 1: Basic Facts for Retail and Wholesale Trade

Output by Industry (billions, $1992)19921993199419951996199719981999

Total (GDP)6,318.96,642.37,054.37,400.57,813.28,318.48,790.29,299.2

Trade

966.31,010.51,099.81,147.41,216.71,307.31,407.71,499.7

Retail

551.7578.0620.6646.8687.1740.5796.8856.4

Wholesale414.6432.5479.2500.6529.6566.8610.9643.3

Manufacturing1,082.001,131.41,223.21,289.11,316.01,379.61,436.01,500.8

Source: BEA, Gross Product by Industry

Employment

19921993199419951996199719981999

Total Nonfarm Employees (1000s)108,591110,692114,135117,188119,597122,677125,845128,772

Trade

25,35225,75326,66427,56428,07828,61429,09529,712

Retail

19,35519,77220,50121,18721,59621,96622,29522,788

Wholesale5,9975,9826,1636,3776,4826,6486,8006,924

Manufacturing18,10618,07618,32318,52618,49618,67518,80618,543

Source: BLS

Crude Labor Productivity19921993199419951996199719981999

Total (1000s $1992/employee)58.260.061.863.265.367.869.872.2

Trade

38.139.241.241.643.345.748.450.5

Retail

28.529.230.330.531.833.735.737.6

Wholesale69.172.377.878.581.785.389.892.9

Manufacturing59.862.666.869.671.173.976.480.9

Crude Labor Productivity Growth19921993199419951996199719981999

Total (percent change from prior period)3.13.02.23.43.83.03.4

Trade

2.95.10.94.15.45.94.3

Retail

2.63.60.84.26.06.05.2

Wholesale

4.67.50.94.14.45.43.4

Manufacturing4.76.74.22.23.83.46.0

Table 1b: Components of the Retail and Wholesale Trade Industries

Paid employees

19971992% Change

Retail Trade 21,265,862 18,407,453 15.5

52 Building materials, hardware, garden supply

and mobile home dealers

830,357 665,747 24.7

53 General Merchandise stores

-- 2,078,530

54 Food stores

3,109,336 2,969,317 4.7

55 Automotive dealers and gasoline service stations 2,283,756 1,942,613 17.6

56 Apparel and accessory stores

1,116,140 1,144,587 -2.5

57 Home furniture, furnishings, and equipment stores 861,605 702,164 22.7

58 Eating and drinking places

-- 6,547,908

59 Miscellaneous Retail

2,795,472 2,356,587 18.6

Wholesale Trade 6,509,333 5,791,264 12.4

50 Durable goods

3,887,371 3,349,064 16.1

51 Nondurable goods

2,621,962 2,442,200 7.4

Table 2: Descriptive Statistics for the 1992 and 1997 Censuses of Retail TradeEmployment Size Class# Of Firms, 1992# of Continuing Firms# Of Firms, 1997# of Establishments, 1992Net EntryWithin Class ContinuersCross Class Continuers# of Establishments, 1997

0 9814,902370,866806,329824,9142020-1367-5335813,492

10 - 19137,23679,615144,137157,3011491-955-4314159,847

20 - 4984,54553,07392,374119,4555115-1046-3688125,096

50 - 9922,40215,18125,50750,6612999-48-38554,254

100 - 49910,7947,94112,43781,634-16602755292582,480

500 +1,8581,4632,071292,250-40272649614004326,026

Total1,071,737528,1391,082,8551,526,21559382583532071,561,195

Source: Authors calculations using 1992 and 1997 Census of Retail Trade, Center for Economic Studies

Employment Size ClassEmployment, 1992Net Change in Employment from Net Entry of FirmsNet Change in Employment

at Firms Continuing within Size ClassNet Change from Firms transitioning into and out of the size classEmployment, 1997

0 - 92,558,086-4311491528-371842,569,316

10 - 191,829,7302975812710546691,925,867

20 - 492,528,8839662738330998202,763,660

50 - 991,502,2679657923244909401,713,030

100 - 4991,991,90411872696250747642,281,644

500 +7,997,583-17334019495211385819,912,345

Total18,408,453125236221158342159021,165,862

Table 3: Descriptive Statistics for Firms: All Retail - 1992 and 1997Employment Size ClassNumber 1992Number 1997Average Labor Productivity, 1992Average Labor Productivity, 1997Average Productivity Growth

0 9814,902806,3294.2674.345-0.057

10 19137,236144,1373.9404.0430.092

20 4984,54592,3743.9053.9820.110

50 9922,40225,5074.0844.2330.133

100 - 49910,79412,4374.1264.3190.152

500 +1,8582,0714.3094.3580.100

EntrantsNA554,716NA4.182NA

Exiters543,5984.016nana

Source: Authors calculations using 1992 and 1997 Census of Retail Trade, Center for Economic Studies

Labor productivity is the log of Sales per employee, where sales in measured in thousands of nominal dollars.

Employment Size ClassNumber 1992Number of Continuers 1997Number of Estabs, 1992Number of Estabs, 1997Employment 1992Employment 1997Average Labor Productivity, 1992Average Labor Productivity, 1997Average Productivity Growth

0 97,9804,4918,9634,96933,17219,5944.533 / 4.6364.671-0.054

10 192,9261,8464,2882,55439,58725,3594.557 / 4.6974.7050.073

20 492,6301,7955,6833,71182,26256,2944.692 / 4.8624.8940.098

50 991,2561,0414,6003,78386,77472,8344.988 / 5.0745.1360.109

100 4991,4161,21120,28615,446303,068258,4564.891 / 5.1205.1900.110

500 +921874217,893211,9906,173,2957,014,3294.678 / 4.7414.7990.090

Total17,12911,258261,713242,4536,718,1587,446,866

Source: Authors calculations using 92 and 97 Census of Retail Trade and 1992 Asset and Expenditures Survey, Center for Economic Studies. Labor productivity is the log of Sales per employee, where sales in measured in thousands of nominal dollars.

Table 5: Descriptive Statistics for Firms: Matched Subset - 1992 and 1997Investment Intensity CategoryNumber Number of Estabs, 1992Number of Estabs, 1997Employment 1992Employment 1997Average Labor Productivity, 1992Average Labor Productivity, 1997Average Productivity Growth

Zero or Missing Total Investment6,32034,13627,602472,252463,4554.5594.6980.020

Low Total ; Zero or missing IT3,09922,78920,832427,943407,1414.8984.9970.037

Low Total ; Low IT4,449100,83185,5722,104,4212,119,7364.7955.0130.032

Low Total: High IT4405,6535,104111,832102,6364.7324.8840.050

High Total; Zero or Missing IT7538,5068,952215,248232,3404.4264.5120.046

High Total; Low IT1,27064,62667,9422,272,2092,786,8194.1864.5020.084

High Total; High IT79825,17226,4491,114,2631,334,7394.1274.6210.167

Source: Authors calculations using 92 and 97 Census of Retail Trade and 1992 Asset and Expenditures Survey, Center for Economic Studies

Two Digit SICNumber of Firms, 1992Average Employment, 1992Average Survivor Employment, 1997Average Number of Establishments, 1992Average Number of Establishments at survivors, 1997Average Labor Productivity, 1992Average Survivor Labor Productivity, 1997Average Change in Labor Productivity at Survivors

5255,19912121.2580.7684.5954.7371.8%

5310,2642032353.3712.7544.2834.375-7.3%

54127,57523211.4150.7924.4284.563-3.4%

55142,25614121.4170.9545.0945.2863.8%

5663,02018152.3081.3674.1624.316-5.4%

5779,610981.3820.8174.5224.636-1.4%

58331,48820151.3080.7573.4023.493-2.4%

59262,325981.3360.8204.3024.4741.7%

Table 7. Descriptive Statistics By Two-Digit Industry: Matched Subset

Two Digit SICNumber of Firms, 1992Average Employment, 1992Average Employment, 1997Average Number of Establishments, 1992Average Number of Establishments, 1997Average Labor Productivity, 1992Average Labor Productivity, 1997Average Change in Labor Productivity

527962283189.8699.8654.8264.9993.3%

536642,8963,48929.48628.9444.3394.409-6.2%

541,3041,0531,11221.71420.3394.5774.673-0.008%

553,4221111228.9718.6845.4215.6128.0%

562,49123521523.89819.0564.3344.5071.0%

572,89873887.0306.7164.7674.9237.2%

581,52989689328.36927.0633.4623.5730.4%

594,02517321212.91412.6284.5134.6803.2%

Table 7, Continued.

Two Digit SICNumber of Firms, 1992Capital Expenditures, 1992Computer Expenditures, 1992Average Capital Expenditures as a % of Sales, 1992Average Computer Expenditures as a % of Sales, 1992

527961,060,403109,7364.8%0.4%

5366414,661,4951,190,8862.0%0.1%

541,3042,955,922107,1872.0%0.0.6%

553,422336,73818,9471.5%0.07%

562,491314,66331,0872.2%0.2%

572,898201,68921,3821.8%0.2%

581,5291,344,70736,5304.9%1.9%

594,025476,19148,8912.6%0.3%

Labor productivity is the log of Sales per employee, where sales in measured in thousands of nominal dollars.

Capital expenditure included new and used equipment and buildings but exclude land. Computer investment is for computer hardware and data processing equipment.

Table 8: Simple Labor Productivity Growth Regressions

Model 1Model 2Model 3

Variablecoefficientstandard errorcoefficientstandard errorcoefficientstandard error

Constant

1.3120.0741.0650.0801.1880.082

Employment Size Class0 - 9-0.1940.023-0.1930.023-0.1850.023

10 -19-0.0520.025-0.0850.025-0.0660.025

20 - 50-0.0110.025-0.0230.024-0.0230.024

50 - 1000.0210.0270.0010.0270.0010.028

100 -5000.0240.0260.0070.0250.0070.026

500 +------

log(wage)-0.1110.007-0.0980.008-0.0980.008

IT Share

0.0360.0290.0560.029

Capital Investment Intensity Quartile1st-0.0920.021

-0.1330.019

2nd-0.0710.020

-0.1000.019

3rd-0.0950.020

-0.1120.019

4th--

IT Investment Intensity Quartile1st-0.1190.027

2nd-0.0680.026

3rd-0.0410.025

4th--

Zero Reported Capital Investment-0.0510.020

Zero Reported IT Investment -0.0590.022

SIC 52: Building Materials and Hardware Stores0.0170.0270.0010.0310.0100.031

SIC 53: General Merchandise Stores-0.1520.031-0.1490.036-0.0750.036

SIC 54: Food Stores-0.0890.022-0.0800.025-0.0580.025

SIC 55: Automotive Dealers and Gas Stations0.0480.0160.0370.0190.0370.019

SIC 56: Apparel and Accessory Stores-0.0380.019-0.0280.023-0.0310.024

SIC 57: Home Furniture and Equipment Stores0.0700.0170.0720.0210.0820.021

SIC 58: Eating and Drinking Places-0.1210.023-0.1100.027-0.1360.027

SIC 59: Miscellaneous Retail

N / R210919 / 0.0457173 / 0.0407173 / 0.048

Table 9: Establishment Growth Regressions

Model 1Model 2Model 3

Variablecoefficientstandard errorcoefficientstandard errorcoefficientstandard error

Constant

-0.3260.049-0.3050.050-0.2870.050

Employment Size Class0 - 90.0650.0160.0610.0160.0680.016

10 -190.0320.0170.0290.0170.0350.017

20 - 500.0070.0170.0060.0170.0100.017

50 - 1000.0300.0190.0290.0190.0320.019

100 -500-0.0110.018-0.0120.018-0.0100.018

500 +------

log(wage)0.0320.0050.0320.0050.0310.005

Capital Investment Intensity Quartile1st-0.0510.013

-0.0340.015

2nd-0.0270.013

-0.0120.014

3rd-0.0510.013

-0.0370.014

4th------

IT Investment Intensity Quartile1st

-0.0610.017-0.0460.019

2nd

-0.0830.017-0.0730.018

3rd

-0.0650.017-0.0540.017

4th

----

Capital Investment zero or missing-0.0650.012

-0.0450.014

IT Investment zero or missing

-0.0800.013-0.0580.015

SIC 52: Building Materials and Hardware Stores0.0060.0180.0060.0180.0070.018

SIC 53: General Merchandise Stores-0.0390.021-0.0370.021-0.0380.021

SIC 54: Food Stores-0.0050.0150.0080.0150.0060.015

SIC 55: Automotive Dealers and Gas Stations0.0240.0110.0270.0110.0280.013

SIC 56: Apparel and Accessory Stores-0.0280.013-0.0290.013-0.0280.013

SIC 57: Home Furniture and Equipment Stores0.0000.0120.0000.0120.0010.012

SIC 58: Eating and Drinking Places0.0480.0150.0580.0150.0530.016

SIC 59: Miscellaneous Retail ------

Any findings, opinions or conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the Board of Governors of the Federal Reserve.

This paper reports the results of research and analysis undertaken by Census Bureau staff. It has undergone a more limited review by the Census Bureau than its official publications. This report is released to inform interested parties and to encourage discussion.

Foster, Haltiwanger and Krizan (1998) and Bartlesman and Doms (2000) both discuss the usefulness of using micro data in understanding a variety of issues including aggregate productivity growth.

2023

_1068015980.doc

For Official Use


Organisation de Coopration et de Dveloppement Economiques

Organisation for Economic Co-operation and Development

23-Nov-2001

________________________________________________________________________________________________________

English text only

DIRECTORATE FOR SCIENCE, TECHNOLOGY AND INDUSTRY

COMMITTEE ON INDUSTRY AND BUSINESS ENVIRONMENT

Working Party on Statistics

IT INVESTMENT AND FIRM PERFORMANCE IN U.S. RETAIL TRADE

WORKSHOP ON FIRM-LEVEL STATISTICS, 26-27 NOVEMBER 2001

Session 5: Examining the Drivers of Growth at the Firm Level

This paper was prepard by Mark Doms (Board of Governors of the Federal Reserve) and Ron Jarmin ad Shawn Klimek (both Center for Economic Studies, U.S. Census Bureau). The paper represents the views of the authors and does not necessarily reflect the opinions of the affiliating institutions or the OECD.

Contact: Dirk PILAT: Tel: +33 1 45 24 87 49; Fax: +33 1 44 30 62 58; E-mail: [email protected]

JT00117207

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Complete document available on OLIS in its original format

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_1032077760.doc

_1067868493.xlsSheet1

1993199419951996199719981999

1Gross domestic product.........................6,642.307,054.307,400.507,813.208,318.408,790.209,299.20

2Private industries.................................5,717.506,096.706,411.106,792.807,253.607,684.408,140.80

3Agriculture, forestry, and fishing...............108.3118.5109.8130.4130127.2125.4

4Farms..........................................73.683.673.292.288.380.874.2

5Agricultural services, forestry, and fishing...34.834.936.738.341.746.551.2

6Mining...........................................88.490.295.7113118.9105.6111.8

7Metal mining...................................4.95.66.55.85.65.15.5

8Coal mining....................................10.511.310.711.210.611.311.3

9Oil and gas extraction.........................65.764.569.386.191.977.482.8

10Nonmetallic minerals, except fuels.............7.38.99.19.910.811.812.3

11Construction.....................................248.9275.3290.3316.4338.2378.1416.4

12Manufacturing....................................1,131.401,223.201,289.101,316.001,379.601,436.001,500.80

13Durable goods..................................632.8694.1729.8748.4791.2833.4877.8

14Lumber and wood products.....................35.739.842.339.941.241.444.1

15Furniture and fixtures.......................18.118.919.520.722.724.125.9

16Stone, clay, and glass products..............26.430.432.433.237.238.241

17Primary metal industries.....................4347.65350.852.654.154.9

18Fabricated metal products....................73.483.287.293.197.6102.2105.5

19Industrial machinery and equipment...........113.7121132.8136.3143.2150.8158.2

20Electronic and other electric equipment......121139.3146.9153.2165.9172.8186.6

21Motor vehicles and equipment.................7895.298.292.296.5107.2114.5

22Other transportation equipment...............54.249.647.751.455.559.259.6

23Instruments and related products.............48.446.847.253.753.657.760

24Miscellaneous manufacturing industries.......20.922.322.723.825.225.727.6

25Nondurable goods...............................498.6529.1559.2567.6588.4602.6623.1

26Food and kindred products....................107.6110.2121.1118.7123.1124.8131.4

27Tobacco products.............................12.313.215.114.815.416.819.9

28Textile mill products........................25.725.624.825.325.725.425.3

29Apparel and other textile products...........27.728.527.32726.525.825.5

30Paper and allied products....................46.950.158.955.953.855.157

31Printing and publishing......................78.583.580.888.291.19499

32Chemicals and allied products................122.7138.7150.8153.6164.8168.4176.3

33Petroleum and coal products..................3129.32930.231.432.928.6

34Rubber and miscellaneous plastics products...41.644.946.149.752.155.155.8

35Leather and leather products.................4.755.34.24.34.24.2

36Transportation and public utilities..............573.3611.4642.6666.3688.4728779.6

37Transportation.................................206223.2233.4243.4261.8287.8303.4

38Railroad transportation......................2223.323.623.42325.423.4

39Local and interurban passenger transit.......11.311.612.413.414.916.217.1

40Trucking and warehousing.....................79.286.48992.199.4109.3116.6

41Water transportation.........................10.711.511.612.213.114.114.4

42Transportation by air........................56.462.567.770.878.688.295

43Pipelines, except natural gas................5.65.55.55.75.86.16.6

44Transportation services......................20.822.623.525.727.128.530.2

45Communications.................................178.6190.7202.3214.7220.8234.1260.2

46Telephone and telegraph......................139148151.6163.9166.7173.9195.1

47Radio and television.........................39.642.850.750.754.160.265.1

48Electric, gas, and sanitary services...........188.7197.4206.9208.3205.9206216

49Wholesale trade..................................432.5479.2500.6529.6566.8610.9643.3

50Retail trade.....................................578620.6646.8687.1740.5796.8856.4

51Finance, insurance, and real estate..............1,205.301,254.801,347.201,436.801,569.901,689.501,792.10

52Depository institutions........................200.9200.7227.4241273.9292.7305.3

53Nondepository institutions.....................32.529.434.139.249.948.445.3

54Security and commodity brokers.................67.677.877.7108120.8135.3152.1

55Insurance carriers.............................99.8104.3120.2123.4146.1154.4165

56Insurance agents, brokers, and service.........41.845.347.248.951.352.656.9

57Real estate....................................751.6791.4832.6871.6920.1969.21,034.00

58Nonfarm housing services.....................558.1593.9628.9654.6679.1714.6756.8

59Other real estate............................193.5197.5203.7217241254.6277.2

60Holding and other investment offices...........115.884.67.736.833.5

61Services.........................................1,287.701,365.001,462.401,564.201,691.501,837.101,986.90

62Hotels and other lodging places................5356.661.766.370.57683.5

63Personal services..............................44.245.546.747.55155.458.2

64Business services..............................247.6273.2302342.3395.5447.1510.8

65Auto repair, services, and parking.............54.76065.168.572.880.986.8

66Miscellaneous repair services..................19.219.320.721.822.324.525.8

67Motion pictures................................20.82022.424.626.328.829.8

68Amusement and recreation services..............45.449.253.558.364.972.278.7

69Health services................................394.5413.9433.1459.1472.2492.6514.2

70Legal services.................................9394.6101.198109116.4125.1

71Educational services...........................49.352.655.75861.266.771.1

72Social services................................4144.247.449.752.657.161.3

73Membership organizations.......................43.446.246.749.251.65457.4

74Other services.................................170.6178.6194.4208.9229.7251.5272.8

75Private households.............................10.711.111.912121411.5

76Statistical discrepancy1.........................63.858.526.532.829.7-24.8-71.9

77Government............................................924.8957.6989.51,020.401,064.801,105.801,158.40

78Federal..........................................336.2339.6342.3346.9354.7360.7375.4

79General government.............................287287.4286.8292295.4298.6309.5

80Government enterprises.........................49.252.255.554.959.262.165.9

81State and local..................................588.6618647.2673.5710.1745.2783

82General government.............................540.3567593.3616.7649.2680.7715.5

83Government enterprises.........................48.250.953.956.960.964.467.5

1 Equals GDP measured as the sum of expenditures less gross domestic income.

1Gross domestic product.........................4,742.505,108.305,489.105,803.205,986.206,318.90

2Private industries.................................4,081.404,401.804,735.504,996.705,129.105,424.50

3Agriculture, forestry, and fishing...............88.989.1102108.3102.9111.7

4Farms..........................................65.163.876.279.673.280.5

5Agricultural services, forestry, and fishing...23.825.325.828.729.731.2

6Mining...........................................92.299.297.1111.996.787.6

7Metal mining...................................3.755.25.25.65.6

8Coal mining....................................1312.61211.811.412

9Oil and gas extraction.........................67.473.672.187.172.262.3

10Nonmetallic minerals, except fuels.............8.187.87.87.57.7

11Construction.....................................219.3237.2245.8248.7232.7234.4

12Manufacturing....................................888.6979.91,017.701,040.601,043.501,082.00

13Durable goods..................................516.8566.3582.7586.6575.5594

14Lumber and wood products.....................32.13333.832.230.332.3

15Furniture and fixtures.......................14.615.115.815.615.216.6

16Stone, clay, and glass products..............23.323.825.225.323.826.3

17Primary metal industries.....................34.543.145.343.239.939.6

18Fabricated metal products....................62.667.468.569.467.369.5

19Industrial machinery and equipment...........95.2110.3116.9118.2109113.8

20Electronic and other electric equipment......87.696.6105105.7110.8107.7

21Motor vehicles and equipment.................58.260.652.747.345.558.8

22Other transportation equipment...............55.553.856.960.562.458.1

23Instruments and related products.............37.344.643.649.351.551.9

24Miscellaneous manufacturing industries.......15.81819.219.819.819.6

25Nondurable goods...............................371.8413.6434.9454468488

26Food and kindred products....................79.184.688.996.4103.7105.9

27Tobacco products.............................10.411.111.311.912.713.8

28Textile mill products........................20.120.6212222.425.7

29Apparel and other textile products...........2324.225.425.426.127.4

30Paper and allied products....................37.843.845.54544.645.6

31Printing and publishing......................62.166.571.473.17578.9

32Chemicals and allied products................83.895.5103.3109.9113.9119.1

33Petroleum and coal products..................22.132.329.831.728.828.2

34Rubber and miscellaneous plastics products...29.630.633.733.935.838.4

35Leather and leather products.................3.94.44.64.74.94.9

36Transportation and public utilities..............426.2449468.7490.9518.3538.5

37Transportation.................................158.8169.2172.2177.4186.1193.4

38Railroad transportation......................21.923.119.919.82221.6

39Local and interurban passenger transit.......8.68.99.39.110.210.9

40Trucking and warehousing.....................64.164.567.469.470.974.5

41Water transportation.........................8.49.19.61011.110.7

42Transportation by air........................34.342.743.945.34750.3

43Pipelines, except natural gas................7.15.75.55.55.55.5

44Transportation services......................14.415.416.718.219.519.9

45Communications.................................125.5132.8137.4148.1155.7163.9

46Telephone and telegraph......................108111.6112.9119.4124128.7

47Radio and television.........................17.521.124.628.731.735.2

48Electric, gas, and sanitary services...........141.9147159165.4176.5181.2

49Wholesale trade..................................308.9346.6364.7376.1395.6414.6

50Retail trade.....................................434.5461.5492.7507.8523.7551.7

51Finance, insurance, and real estate..............829.7893.7954.51,010.301,072.201,140.90

52Depository institutions........................143.9147.6157.2171.3193.9205.3

53Nondepository institutions.....................17.920.123.723.323.227.2

54Security and commodity brokers.................41.342.245.342.340.554.5

55Insurance carriers.............................43.656.960.564.683.382.1

56Insurance agents, brokers, and service.........30.433.834.437.73839.4

57Real estate....................................531.4586.2630.7665.7689.1725.2

58Nonfarm housing services.....................391.9424.3456.7488.3515.5543.4

59Other real estate............................139.5162174177.3173.6181.8

60Holding and other investment offices...........21.26.92.75.54.27.1

61Services.........................................789.9887.99761,071.501,123.801,219.40

62Hotels and other lodging places................37.140.64446.348.350.4

63Personal services..............................3135.936.83838.840.9

64Business services..............................145166.9183.7203.9205.3229.4

65Auto repair, services, and parking.............40.845.346.550.351.352.1

66Miscellaneous repair services..................13.515.416.617.71717.6

67Motion pictures................................13.714.317.917.717.918.2

68Amusement and recreation services..............26.228.83236.539.445.2

69Health services................................230.6253.6280.7314.4345.3377.8

70Legal services.................................61.870.97682.785.692.7

71Educational services...........................31.334.237.139.643.746.5

72Social services................................2123.426.730.133.637.3

73Membership organizations.......................26.930.333.235.838.439.9

74Other services.................................103.3119.8135.8149.2150161.1

75Private households.............................7.78.38.99.49.110.1

76Statistical discrepancy1.........................3.3-42.216.330.619.643.7

Sheet2

Nonfarm

YearJanFebMarAprMayJunJulAugSepOctNovDecAnn

1991108759108500108330108145108107108200108131108215108223108209108115108121108255

1992108084108077108119108301108495108541108595108741108807108941109119109266108591

1993109502109816109749110055110398110539110744110957111204111525111780112034110692

1994112302112532112982113350113697113980114333114673114980115235115641115918114135

1995116235116523116679116864116830117024117138117444117664117789117946118118117188

1996118049118538118774118949119293119557119753120031120182120430120696120913119597

1997121116121411121758122052122311122537122833122904123335123653123945124269122677

1998124559124752124934125178125531125748125847126225126469126677126939127286125845

1999127463127883128054128282128377128630128898129057129265129523129788130038128772

Wholesale

YearJanFebMarAprMayJunJulAugSepOctNovDecAnn

19916121610660936084607760786081607060766069606060556080.8

19926045603860366024601660055985597559645965596559485997.2

19935957595159445954597459685984598259986010602260365981.7

19946054606760886109613061456161619562206240625862856162.7

19956310633663546363636763836388639664006404641064176377.3

19966420642364316440645664746476650065186542655165576482.3

19976565658266006613662466366656667066826706671567266647.9

19986755676167746786679767996798680968216821683368506800.3

19996847687068776892689869056927694669626973698970026924.0

Retail

199119423193621933619271192491927519254192651925619237192231923319282

199219245192531925319319193621935319362193731938019423194711946319355

199319509195761953419640197111974419790198531989319967199882005619772

199420088201652026620347203932045420535206042068020743208452089420501

199521012210672104521118211192118521214212532128421279213172134721187

199621345213982144121448215322157221625216582168721774218062186121596

199721842218332189221905219112193721938219902202222061221192214621966

199822145221402215422163222402226122306223452239122414224662250922295

199922560226622270222744227632281022833228412284422863228932293622788

Manufacturing

199218151181251810318133181401813218123180971807418064180601806918106

199318098181041809318072180671804918030180441806618081180971811218076

199418155181671819818234182641830518333183831840618437184851851318323

199518549185521855518568185411853118505185211851418491184771850218526

199618464184911845318466184831849118493185131851118523185301853718496

199718548185681859718608186231865418667187081872218764188081883718675

199818868188691888018881188741885818688188061880118753187071868918806

199918667186261860218574185401851518552185031849418484184841847918543

Chart1

3.12238043442.94382064182.55747268364.58772698434.7381975923

2.99865450625.12104411093.55008673527.54351518126.6549829404

2.1749438180.92098368970.84960057130.94936609454.2374263294

3.44983795414.09887186044.21917514034.07942617782.2481672913

3.7930562355.43244224135.95281122984.35845907253.8275535501

3.01126025215.90131257556.01909564635.36482477463.3640067555

3.38665402124.32403077295.15424273943.42286591715.9938962014

Total

Trade

Retail

Wholesale

Manufacturing

Percent change

Chart 1: Labor Productivity Growth by Sector

Sheet3

Table 1: Basic Facts for Retail and Wholesale Trade

Output by Industry (billions, $1992)19921993199419951996199719981999

Total (GDP)6,318.96,642.37,054.37,400.57,813.28,318.48,790.29,299.2

Trade966.31,010.51,099.81,147.41,216.71,307.31,407.71,499.7

Retail551.7578.0620.6646.8687.1740.5796.8856.4

Wholesale414.6432.5479.2500.6529.6566.8610.9643.3

Manufacturing1,082.001,131.41,223.21,289.11,316.01,379.61,436.01,500.8

Source: BEA, Gross Product by Industry

Employment19921993199419951996199719981999

Total Nonfarm Employees (1000s)108,591110,692114,135117,188119,597122,677125,845128,772

Trade25,35225,75326,66427,56428,07828,61429,09529,71260.2278446881

Retail19,35519,77220,50121,18721,59621,96622,29522,788

Wholesale5,9975,9826,1636,3776,4826,6486,8006,924

Manufacturing18,10618,07618,32318,52618,49618,67518,80618,543

Source: BLS

Crude Labor Productivity19921993199419951996199719981999

Total (1000s $1992/employee)58.260.061.863.265.367.869.872.2

Trade38.139.241.241.643.345.748.450.5

Retail28.529.230.330.531.833.735.737.6

Wholesale69.172.377.878.581.785.389.892.9

Manufacturing59.862.666.869.671.173.976.480.9

Crude Labor Productivity Growth19921993199419951996199719981999

Total (percent change from prior period)3.13.02.23.43.83.03.4

Trade2.95.10.94.15.45.94.3

Retail2.63.60.84.26.06.05.2

Wholesale4.67.50.94.14.45.43.4

Manufacturing4.76.74.22.23.83.46.0

yes bank 004

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