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IT INVESTMENT AND FIRM PERFORMANCE IN

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

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

MACROBUTTON InsertCVP.main \* MERGEFORMAT

November, 2001

Mark Doms

Board of Governors, Federal Reserve

[emailprotected] Jarmin

Center for Economic Studies, U.S. Census Bureau

[emailprotected]

Shawn Klimek

Center for Economics Studies, U.S. Census Bureau

[emailprotected] paper analyzes productivitygrowth in the U.S. retail trade sector. We do this by examiningchanges in productivity and other measures of firm performance atthe micro-level. The primary contribution of this research is toextend a rich literature and tradition of analyzing productivitygrowth of establishments and firms in manufacturing to othersignificant portions of the economy. In particular, we examine therole of turnover, entry and exit. Also, we extend our analysis tosee how these changes are correlated with information on capitalspending and spending on information technology.

While our results are still preliminary, the patterns we see inthe data are consistent with anecdotal evidence that many areas inretail are seeing large sophisticated companies introducing newtechnologies and processes and displacing less sophisticatedretailers. However, there is more that needs to be done before wecan more fully describe this process.

Introduction

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The recent slowdown notwithstanding, the performance of the U.S.economy over the past decade has been impressive. The recent periodof strong economic and productivity growth coincided with aninvestment boom, particularly in computers and other forms ofinformation technology (IT). Many observers point to these asevidence of a new economy driven largely by improvements in, andgreater utilization, of IT. Indeed there is evidence that this incase. Aggregate level studies (Jorgenson and Stiroh 2000; Olinerand Sichel 2000; Schreyer 2000), and micro level analyses(Brynjolfsson and Hitt, 1995; Dunne et. al 1999) suggest a linkbetween IT and productivity. However, the evidence in support of anew economy link between IT and economic performance is notoverwhelming. Industry level studies (Stiroh 1998) find no link andmicro level studies are concentrated in the manufacturing sector oruse small, select samples of firms.

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Progress in this area has been hampered by the lack ofappropriate data. Many of the sectors where IT is used mostintensively are where measurement by official economic statisticsis the weakest (Bosworth and Triplett 2000; Haltiwanger and Jarmin2000). As a result, the relationship between IT and firmperformance in the trade and service sectors is poorly understood.Statistical agencies are keenly aware of the measurement challengesfacing them and that changes underway in the economy are adding tothese. The Census Bureau has taken the lead in trying to addressthe needs of data users arising from the new economy by initiatingnew measurement initiatives, adding questions to existing surveysand finding new ways to more fully utilize existing data resources(Atrostic, Gates and Jarmin 2001).

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In this paper, we take that latter path and use previouslyuntapped micro level data collected by the Census Bureau to analyzefirm performance in the retail trade sector focusing on the role ofinformation technology (IT). We extend a rich literature analyzingestablishment and firm performance with Census micro data for themanufacturing sector to other significant portions of theeconomy.

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In analyzing firm performance in the retail trade sector, weface several hurdles. First, the quantity and quality ofinformation available to measure firm or establishment productivityin the retail sector is much poorer than in manufacturing. Inparticular, measuring output is problematic and there is littleinformation collected on inputs. We dont offer much in terms ofsolving these problems and follow the standard practice ofmeasuring productivity with sales per employee. This is a simplemeasure and intuitively appealing for the retail sector.Calculating other measures of productivity, such as value added perworker or multi factor productivity, for the retail sector at thefirm or establishment level is prohibitively difficult

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An additional hurdle in examining firm performance in the tradesector arises from the fact that the data we are using arecollected in a variety of surveys using different statisticalunits. In manufacturing, the value of outputs and inputs forestablishments is collected in a single survey, the Annual Surveyof Manufacturers. Unfortunately, the variables needed to constructjust one measure of firm performance, labor productivity, for thetrade sector are scattered across different surveys with differentsampling frames and units of observation. Below we discuss how wecombined the various survey data. One of contributions of thispaper is exploring how to analyze firm performance outside of thegoods producing sectors using Census Bureau micro data.

Basic facts and hypotheses about the retail trade sector

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Retail trade accounts for a large and growing portion of U.S.economic activity. The upper panel of table 1 presents output bysector from BEAs Gross Product Originating Database--outputcorresponds to value added, so that the sum across all sectorsequals GDP. The trade sectors (both retail and wholesale) share ofoutput was about the same as that of manufacturing in 1999, about16 percent. However, the share for the trade sector has grownsignificantly faster than manufacturings since 1992. Further, thisgrowth has occurred for both the retail and wholesale sectors.

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

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

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This strong productivity performance, especially that observedin the trade sectors, was unexpected and is still not wellunderstood. What is behind the improved productivity performance ofthe retail sector? One hypothesis is that relatively productivefirms, such as Wal-Mart or Starbucks, open a large number ofestablishments, increasing the market share of these firms.Relatively inefficient firms (K-Mart and Brothers Coffee) aredriven out of the market. One factor that may make Wal-Martsuccessful is their use of information technology. Not only doesWal-Mart make substantial investments in IT, Wal-Mart knows how tomake these investments pay-off more so than other firms. In thecase of Starbucks, other factors may be at work, such as aconsistently produced product that appeals to a large set ofconsumers.

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Foster, Haltiwanger and Krizan (2001) decompose aggregateproductivity growth in the retail sector using data from theCensuses of Retail Trade. They find that most productivity growthcomes from the net entry of establishments. That is, lowproductivity establishments exit and are replaced by highproductivity new entrants. Looking more carefully at thecharacteristics of these high productivity entrants, they find thatentering plants owned by existing firms are the most productive.This finding is consistent with the Wal-Mart type stories describedabove.

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It is unlikely that a single explanation for improvedproductivity growth applies across the entire retail sector. Thereis tremendous variation within both retail and wholesale trade interms of activity. Table 1b presents the employment breakdowns bytwo-digit industry. Retail trade is especially diverse, coveringeating and drinking places, car dealers, shoe stores, departmentstores, and a wide variety of other retail establishments. Theperformance of these industries, and the firms within them, variesconsiderably. The role of IT in this performance most likely variesas well.

Data

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We use micro data from two Census Bureau programs since nosingle program collects data on all the variables we need. First,we use establishment level data from the 1992 and 1997 Censuses ofRetail Trade. The Census of Retail Trade (CRT) files at CES containinformation on the universe of retail establishments and are thesource for the measures of labor productivity we use below. Toconstruct measures of total capital and computer investment, we usethe 1992 Asset and Expenditures Survey (AES).

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

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For the reference year 1992, investment and expenditure datawere collected for the retail sector via the AES. While performedas part of the 1992 Economic Census, the sampling frame for theretail portion of the AES was the one used, at the time, for theMonthly and Annual Retail Trade Surveys. As a result, the samplingunits in the 1992 AES are substantially different from theestablishment units used in the CRT. Differences in sampling unitsand methodology across the Census and the AES make merging theinformation from them difficult. Below we describe the methods weemployed to create the matched research data set used in theanalysis. First we describe our two primary datasets in moredetail.

Census of Retail Trade

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As part of the Economic Census carried out every 5 years, theCensus Bureau collects data for the universe of retailestablishments. In an effort to reduce reporting burden on smallerbusinesses, only establishments with a specified minimum number ofpaid employees (this number varies by industry, but is generallyaround 10) are canvassed. Administrative data are used for smallemployer and non-employer establishments that are not mailed Censusforms. Primary data on payroll, employment, sales, location andindustrial classification are obtained for all retailestablishments (both the mail and non-mail segments). Additionalinformation on merchandise lines and selected other items arecollected from the mail segment. For the current analysis, we areinterested only in the base information on sales, employment and soon.

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An establishment is a single physical location where business isconducted. The frame for the CRT, and other Economic Censuses, isthe Standard Statistical Establishment List (SSEL). Sinceadministrative data from the SSEL are used directly in the CRT andbecause the CRT and SSEL share a common structure its useful tobriefly describe the SSEL.

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The SSEL has two principal components. First, the Census Bureaureceives information on taxpaying businesses from the InternalRevenue Service (IRS). This information corresponds to legal taxpaying entities and the unit corresponds with the EmployerIdentification Number (EIN). The majority of businesses, in andoutside of retail, have only one location. In these cases, the EIadministrative reporting unit the Census receives from the IRS andthe establishment are the same thing. When a new single unitestablishment EIN arrives on IRS files, Census assigns both aCensus File Number(CFN) and a Permanent Plant Number (PPN). Bothnumbers are unique to a physical establishment. However, the CFN isintended to incorporate information about the ownership of theestablishment and can change as the ownership or other legalaspects of the establishment change. The PPN remains the same aslong as the establishment remains open in the same location, evenif it changes hands.

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Second, the Census Bureau annually surveys multi-locationcompanies inquiring about the location, employment and industrialclassification of all their establishments. The CompanyOrganization Survey (COS), the Economic Censuses and other surveysare used to maintain the list of mulit-unit (those owned bymulti-location companies) establishments. Multi-unit establishmentsare also assigned CFNs and PPNs. Again, they are unique to theestablishment and the CFN contains information about the ownershipof the establishment. Unlike in the single unit case, where theyall refer to the same thing, the EI administrative reporting unit,the firm and the establishment can be very different formulti-units. This means the numeric identifiers: EIN, CFN and PPNall refer to different units. For multi-unit establishments, theCFN contains an ALPHA code which identifies the firm that owns theestablishment. An ALPHA can own many EINs, each of which can haveseveral PPNs and CFNs associated with them.

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This ID structure is mapped directly to establishments in theCRT. 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 toALPHA). This is not the case when linking the AES and the CRT asour discussion of the AES below shows.

1992 Asset and Expenditures Survey

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

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

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

Matching the AES to the CRT

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It is not possible to obtain exact unit to unit matches betweenthe AES and the CRT for all multi-unit retailers. There is not anaccurate mapping between the sampling units on the AES (identifiednumerically by AESID) and the establishments in the CRT that theAES sampling units are intended to represent. This is due to timingissues relating to drawing the ARTS/AES sample and when the CRT isdone. In addition, the ARTS is voluntary and the Census Bureaugrants companies a lot of latitude in how they report in order toobtain their participation.

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

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

Results

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

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

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Thus, we do not estimate structural production functionparameters and instead employ simple regressions with the hopedescribing the relationships of proxy measures in the data. This isalso why we chose to examine different metrics of retail firmperformance. We regress measures of retail firm performance on ameasure of IT investment intensity as well as some controls as inthe following

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

where ITj is a measure of IT investment intensity, Ij for firmj, is a measure of total investment intensity, size and IND arefirm size and 2 digit SIC dummies, respectively and is an errorterm. Performance, yj, is measured as the change between 1992 and1997 and all right hand side variables are measured in 1992.Construction of the measures we use is described in more detailbelow.

Descriptive Results: Sector Wide

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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 numberof firms in each year as well as the number of surviving, orcontinuing, firms by size class. The table shows that there isconsiderable turnover amongst retail firms, especially in thesmaller size categories. Work by Foster, Haltiwanger and Krizan(2000) suggests that net entry of establishments drives mostaggregate retail productivity growth over a similar timeperiod.

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Indeed, there is considerable turnover in retail trade at boththe establishment and firm levels. More than half of the firms inthe 0 to 9 size class in 1992 exit before 1997. We do not decomposeproductivity growth as do Foster et. al., but our results suggestthat changes to the retail sector caused by the net entry ofestablishments are dominated by large continuing firms. Results inTable 2 show that large continuing retailers contributed more thantwo thirds (26.494 of 34,980) of the increase in retailestablishments between 1992 and 1997. Even more importantlyperhaps, is the fact that large retailers contributed approximately71% of the over 2.7 million net increase in retail employment overthe 92 to 97 period! Large retailers add more retail establishmentsand jobs than do their smaller counterparts and are accounting fora larger portion of overall retail activity in the U.S. While, thisresult should seem obvious to most U.S. consumers, it is theopposite of the trends we have observed in the manufacturingsector, where large firms have reduced their employment share buthave increased the productivity gap with small firms (Baldwin,Jarmin and Tang 2001).

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

Matched AES CRT Sample

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Table 4 shows descriptive statistics for our matched sample ofAES CRT data. The AES covers most large retailers with certainty inorder to cover as much retail activity as possible, while holdingthe sample size and respondent burden to a minimum. As a result,even though our matched sample only covers 17,129 of the 1,071,737retail firms in the 1992 CRT, it covers a sizeable portion ofretail employment and sales. Productivity growth between 1992 and1997 does not vary strikingly across the size distribution, as wasthe case for retail as a whole. Firms in the matched sample dotend, however, to be larger and more productive than the typicalfirm in the entire retail universe.

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

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In table 5, we provided basic statistics on establishments,employment and productivity by capital and computer investmentintensity categories. The table shows striking differences in theproductivity performance of firms according to capital and computerintensities. Also, establishment and employment growth for thematched AES CRT sample is concentrated entirely among firms withhigh capital and/or computer intensities. The productivity growthpremium to being the high total and high computer intensitycategory is particularly interesting.

Firm Level Regression Results

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

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Before turning to the regressions, let us compare thecharacteristics of the firms in our matched subset, and used in ourregressions, to the entire retail population. Our regressions arebasically a cross sectional analyses of firms present in both 1992and 1997 using 1992 characteristics as regressors. Thus, table 6and 7 show some basic statistics on the number, size, number ofestablishments and productivity for all firms, and for our matchedsubset. Table 7 also lists statistics on capital and computerexpenditures for the matched AES-CRT subset. Characteristics aregiven by 2 digit SIC in both tables. As expected, firms in thematched subset are much larger and more productive than the generalpopulation of retailers. Interestingly, there is no obviouscorrelation between the intensity of computer investment in a2-digit industry and its productivity growth.

Productivity Growth Results

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

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Table 8 contains results from regressions looking at the impactof total and IT investment intensities on labor productivity growthbetween 1992 and 1997 using both measures of IT. The regressionscontrol for firm size, average (within firm) wage, and two digitSIC. The results show that productivity growth is lagging at verysmall retailers compared to their larger counterparts. Curiously,the results here suggest that higher wage firms enjoy lessproductivity growth. This result runs counter from what we wouldexpect to find from studies using manufacturing micro data. Thisfinding was robust to alternative specifications of the wagevariable. At this point, we are not sure what to make of thisresult. Average wages differ considerably across differ types ofretail businesses, even within two digit groups. Two digit industrycontrols are very crude and it could be that firms in industries,within 2 digit sectors, with lower average wages are those that areexperiencing higher productivity growth.

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The results from model 1 show that the productivity growthpremium for higher levels of total investment intensity isconcentrated in the highest investment intensity quartile. Therelationship between computer investment intensities andproductivity growth is monotonically increasing across thequartiles. This is true even when we control for both total andcomputer investment. Firms in the highest computer investmentquartile experienced approximately 12% higher productivity growththat those in the lowest (but still positive) quartile. Those firmsin both the highest total and computer intensity quartiles had 23%higher productivity growth that those in both of the lowestquartiles.

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

Establishment Growth Results

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

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The results in Table 9 show that only those firms in the highestcomputer and total investment intensity quartiles experience highergrowth rates in the number of establishments. While the differencesare not statistically significant, the relative magnitude of thecomputer and total investment coefficients in the third regressionsuggest that that computer investment is the more important driverhere.

Establishment Level Regression Results

(to come)

Conclusions

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The retail trade sector in the U.S. has experienced considerablegrowth over the last several years. In addition, the sector hasenjoyed substantial productivity growth over the same period. Thereasons for this impressive performance are not well understood andthere is, generally, little focus on the sector by researchers.Part of this lack of attention can be attributed to a lack of goodmicro level data with which to study the retail sector. In thispaper, we have brought different Census Bureau micro datasetstogether for the first time to examine potential explanations ofproductivity growth among firms in the retail sector.

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In particular we focus on the role played by computerinvestment. There is a sense in the popular imagination that large,technically sophisticated retailers are displacing smallerretailers. It is also widely thought that an important part of thebusiness plan of these larger sophisticated retailers is a heavyreliance on information technology. Thus, we examine therelationship between IT intensity and labor productivitygrowth.

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

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However, there is more that needs to be done before we can morefully describe this process. We are currently in the process ofincorporating data from the Annual Retail Trade Survey so that wecan analyze the relationship between computer investment and bothvalue added per employee (rather than sales per employee) andinventories. There is also, much more to do on seeing how measuresof technical sophistication like computer investment interact withentry and exit patterns of both firms and establishments to yieldimproved performance in the retail sector. Finally, we want toexpand 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 Economicl*terature, 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, AggregateProductivity Growth: Lessons from Microeconomic Evidence, NBERWorking Paper No. 6803, 1998 (also forthcoming in New Developmentsin Productivity Analysis, editors: Edward Dean, Michael Harper andCharles Hulten, University of Chicago Press)

Foster, Lucia, John Haltiwanger and C.J. Krizan, The LinkBetween Aggregate and Micro Productivity Growth: Evidence fromRetail 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 ProductivityGrowth19921993199419951996199719981999

Total (percent change from priorperiod)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 TradeIndustries

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 2,283,7561,942,613 17.6

56 Apparel and accessory stores

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

57 Home furniture, furnishings, and equipment stores 861,605702,164 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 Censusesof Retail TradeEmployment Size Class# Of Firms, 1992# of ContinuingFirms# Of Firms, 1997# of Establishments, 1992Net EntryWithin ClassContinuersCross 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 ofRetail Trade, Center for Economic Studies

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

at Firms Continuing within Size ClassNet Change from Firmstransitioning 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 and1997Employment Size ClassNumber 1992Number 1997Average LaborProductivity, 1992Average Labor Productivity, 1997AverageProductivity 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 ofRetail Trade, Center for Economic Studies

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

Employment Size ClassNumber 1992Number of Continuers 1997Numberof Estabs, 1992Number of Estabs, 1997Employment 1992Employment1997Average 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 RetailTrade and 1992 Asset and Expenditures Survey, Center for EconomicStudies. Labor productivity is the log of Sales per employee, wheresales in measured in thousands of nominal dollars.

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

Zero or Missing TotalInvestment6,32034,13627,602472,252463,4554.5594.6980.020

Low Total ; Zero or missingIT3,09922,78920,832427,943407,1414.8984.9970.037

Low Total ; LowIT4,449100,83185,5722,104,4212,119,7364.7955.0130.032

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

High Total; Zero or MissingIT7538,5068,952215,248232,3404.4264.5120.046

High Total; LowIT1,27064,62667,9422,272,2092,786,8194.1864.5020.084

High Total; HighIT79825,17226,4491,114,2631,334,7394.1274.6210.167

Source: Authors calculations using 92 and 97 Census of RetailTrade and 1992 Asset and Expenditures Survey, Center for EconomicStudies

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

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: MatchedSubset

Two Digit SICNumber of Firms, 1992Average Employment,1992Average Employment, 1997Average Number of Establishments,1992Average Number of Establishments, 1997Average LaborProductivity, 1992Average Labor Productivity, 1997Average Change inLabor 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 salesin measured in thousands of nominal dollars.

Capital expenditure included new and used equipment andbuildings but exclude land. Computer investment is for computerhardware and data processing equipment.

Table 8: Simple Labor Productivity Growth Regressions

Model 1Model 2Model 3

Variablecoefficientstandard errorcoefficientstandarderrorcoefficientstandard 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 HardwareStores0.0170.0270.0010.0310.0100.031

SIC 53: General MerchandiseStores-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 GasStations0.0480.0160.0370.0190.0370.019

SIC 56: Apparel and AccessoryStores-0.0380.019-0.0280.023-0.0310.024

SIC 57: Home Furniture and EquipmentStores0.0700.0170.0720.0210.0820.021

SIC 58: Eating and DrinkingPlaces-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 errorcoefficientstandarderrorcoefficientstandard 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 HardwareStores0.0060.0180.0060.0180.0070.018

SIC 53: General MerchandiseStores-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 GasStations0.0240.0110.0270.0110.0280.013

SIC 56: Apparel and AccessoryStores-0.0280.013-0.0290.013-0.0280.013

SIC 57: Home Furniture and EquipmentStores0.0000.0120.0000.0120.0010.012

SIC 58: Eating and DrinkingPlaces0.0480.0150.0580.0150.0530.016

SIC 59: Miscellaneous Retail ------

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

This paper reports the results of research and analysisundertaken by Census Bureau staff. It has undergone a more limitedreview by the Census Bureau than its official publications. Thisreport is released to inform interested parties and to encouragediscussion.

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

2023

_1068015980.doc

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

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 theFederal Reserve) and Ron Jarmin ad Shawn Klimek (both Center forEconomic Studies, U.S. Census Bureau). The paper represents theviews of the authors and does not necessarily reflect the opinionsof the affiliating institutions or the OECD.

Contact: Dirk PILAT: Tel: +33 1 45 24 87 49; Fax: +33 1 44 30 6258; E-mail: [emailprotected]

JT00117207

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_1067868493.xlsSheet1

1993199419951996199719981999

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

2Privateindustries.................................5,717.506,096.706,411.106,792.807,253.607,684.408,140.80

3Agriculture, forestry, andfishing...............108.3118.5109.8130.4130127.2125.4

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

5Agricultural services, forestry, andfishing...34.834.936.738.341.746.551.2

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

7Metalmining...................................4.95.66.55.85.65.15.5

8Coalmining....................................10.511.310.711.210.611.311.3

9Oil and gasextraction.........................65.764.569.386.191.977.482.8

10Nonmetallic minerals, exceptfuels.............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

13Durablegoods..................................632.8694.1729.8748.4791.2833.4877.8

14Lumber and woodproducts.....................35.739.842.339.941.241.444.1

15Furniture andfixtures.......................18.118.919.520.722.724.125.9

16Stone, clay, and glassproducts..............26.430.432.433.237.238.241

17Primary metalindustries.....................4347.65350.852.654.154.9

18Fabricated metalproducts....................73.483.287.293.197.6102.2105.5

19Industrial machinery andequipment...........113.7121132.8136.3143.2150.8158.2

20Electronic and other electricequipment......121139.3146.9153.2165.9172.8186.6

21Motor vehicles andequipment.................7895.298.292.296.5107.2114.5

22Other transportationequipment...............54.249.647.751.455.559.259.6

23Instruments and relatedproducts.............48.446.847.253.753.657.760

24Miscellaneous manufacturingindustries.......20.922.322.723.825.225.727.6

25Nondurablegoods...............................498.6529.1559.2567.6588.4602.6623.1

26Food and kindredproducts....................107.6110.2121.1118.7123.1124.8131.4

27Tobaccoproducts.............................12.313.215.114.815.416.819.9

28Textile millproducts........................25.725.624.825.325.725.425.3

29Apparel and other textileproducts...........27.728.527.32726.525.825.5

30Paper and alliedproducts....................46.950.158.955.953.855.157

31Printing andpublishing......................78.583.580.888.291.19499

32Chemicals and alliedproducts................122.7138.7150.8153.6164.8168.4176.3

33Petroleum and coalproducts..................3129.32930.231.432.928.6

34Rubber and miscellaneous plasticsproducts...41.644.946.149.752.155.155.8

35Leather and leatherproducts.................4.755.34.24.34.24.2

36Transportation and publicutilities..............573.3611.4642.6666.3688.4728779.6

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

38Railroadtransportation......................2223.323.623.42325.423.4

39Local and interurban passengertransit.......11.311.612.413.414.916.217.1

40Trucking andwarehousing.....................79.286.48992.199.4109.3116.6

41Watertransportation.........................10.711.511.612.213.114.114.4

42Transportation byair........................56.462.567.770.878.688.295

43Pipelines, except naturalgas................5.65.55.55.75.86.16.6

44Transportationservices......................20.822.623.525.727.128.530.2

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

46Telephone andtelegraph......................139148151.6163.9166.7173.9195.1

47Radio andtelevision.........................39.642.850.750.754.160.265.1

48Electric, gas, and sanitaryservices...........188.7197.4206.9208.3205.9206216

49Wholesaletrade..................................432.5479.2500.6529.6566.8610.9643.3

50Retailtrade.....................................578620.6646.8687.1740.5796.8856.4

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

52Depositoryinstitutions........................200.9200.7227.4241273.9292.7305.3

53Nondepositoryinstitutions.....................32.529.434.139.249.948.445.3

54Security and commoditybrokers.................67.677.877.7108120.8135.3152.1

55Insurancecarriers.............................99.8104.3120.2123.4146.1154.4165

56Insurance agents, brokers, andservice.........41.845.347.248.951.352.656.9

57Realestate....................................751.6791.4832.6871.6920.1969.21,034.00

58Nonfarm housingservices.....................558.1593.9628.9654.6679.1714.6756.8

59Other realestate............................193.5197.5203.7217241254.6277.2

60Holding and other investmentoffices...........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 lodgingplaces................5356.661.766.370.57683.5

63Personalservices..............................44.245.546.747.55155.458.2

64Businessservices..............................247.6273.2302342.3395.5447.1510.8

65Auto repair, services, andparking.............54.76065.168.572.880.986.8

66Miscellaneous repairservices..................19.219.320.721.822.324.525.8

67Motionpictures................................20.82022.424.626.328.829.8

68Amusem*nt and recreationservices..............45.449.253.558.364.972.278.7

69Healthservices................................394.5413.9433.1459.1472.2492.6514.2

70Legalservices.................................9394.6101.198109116.4125.1

71Educationalservices...........................49.352.655.75861.266.771.1

72Socialservices................................4144.247.449.752.657.161.3

73Membershiporganizations.......................43.446.246.749.251.65457.4

74Otherservices.................................170.6178.6194.4208.9229.7251.5272.8

75Privatehouseholds.............................10.711.111.912121411.5

76Statisticaldiscrepancy1.........................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

79Generalgovernment.............................287287.4286.8292295.4298.6309.5

80Governmententerprises.........................49.252.255.554.959.262.165.9

81State andlocal..................................588.6618647.2673.5710.1745.2783

82Generalgovernment.............................540.3567593.3616.7649.2680.7715.5

83Governmententerprises.........................48.250.953.956.960.964.467.5

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

1Gross domesticproduct.........................4,742.505,108.305,489.105,803.205,986.206,318.90

2Privateindustries.................................4,081.404,401.804,735.504,996.705,129.105,424.50

3Agriculture, forestry, andfishing...............88.989.1102108.3102.9111.7

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

5Agricultural services, forestry, andfishing...23.825.325.828.729.731.2

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

7Metalmining...................................3.755.25.25.65.6

8Coalmining....................................1312.61211.811.412

9Oil and gasextraction.........................67.473.672.187.172.262.3

10Nonmetallic minerals, exceptfuels.............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

13Durablegoods..................................516.8566.3582.7586.6575.5594

14Lumber and woodproducts.....................32.13333.832.230.332.3

15Furniture andfixtures.......................14.615.115.815.615.216.6

16Stone, clay, and glassproducts..............23.323.825.225.323.826.3

17Primary metalindustries.....................34.543.145.343.239.939.6

18Fabricated metalproducts....................62.667.468.569.467.369.5

19Industrial machinery andequipment...........95.2110.3116.9118.2109113.8

20Electronic and other electricequipment......87.696.6105105.7110.8107.7

21Motor vehicles andequipment.................58.260.652.747.345.558.8

22Other transportationequipment...............55.553.856.960.562.458.1

23Instruments and relatedproducts.............37.344.643.649.351.551.9

24Miscellaneous manufacturingindustries.......15.81819.219.819.819.6

25Nondurablegoods...............................371.8413.6434.9454468488

26Food and kindredproducts....................79.184.688.996.4103.7105.9

27Tobaccoproducts.............................10.411.111.311.912.713.8

28Textile millproducts........................20.120.6212222.425.7

29Apparel and other textileproducts...........2324.225.425.426.127.4

30Paper and alliedproducts....................37.843.845.54544.645.6

31Printing andpublishing......................62.166.571.473.17578.9

32Chemicals and alliedproducts................83.895.5103.3109.9113.9119.1

33Petroleum and coalproducts..................22.132.329.831.728.828.2

34Rubber and miscellaneous plasticsproducts...29.630.633.733.935.838.4

35Leather and leatherproducts.................3.94.44.64.74.94.9

36Transportation and publicutilities..............426.2449468.7490.9518.3538.5

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

38Railroadtransportation......................21.923.119.919.82221.6

39Local and interurban passengertransit.......8.68.99.39.110.210.9

40Trucking andwarehousing.....................64.164.567.469.470.974.5

41Watertransportation.........................8.49.19.61011.110.7

42Transportation byair........................34.342.743.945.34750.3

43Pipelines, except naturalgas................7.15.75.55.55.55.5

44Transportationservices......................14.415.416.718.219.519.9

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

46Telephone andtelegraph......................108111.6112.9119.4124128.7

47Radio andtelevision.........................17.521.124.628.731.735.2

48Electric, gas, and sanitaryservices...........141.9147159165.4176.5181.2

49Wholesaletrade..................................308.9346.6364.7376.1395.6414.6

50Retailtrade.....................................434.5461.5492.7507.8523.7551.7

51Finance, insurance, and realestate..............829.7893.7954.51,010.301,072.201,140.90

52Depositoryinstitutions........................143.9147.6157.2171.3193.9205.3

53Nondepositoryinstitutions.....................17.920.123.723.323.227.2

54Security and commoditybrokers.................41.342.245.342.340.554.5

55Insurancecarriers.............................43.656.960.564.683.382.1

56Insurance agents, brokers, andservice.........30.433.834.437.73839.4

57Realestate....................................531.4586.2630.7665.7689.1725.2

58Nonfarm housingservices.....................391.9424.3456.7488.3515.5543.4

59Other realestate............................139.5162174177.3173.6181.8

60Holding and other investmentoffices...........21.26.92.75.54.27.1

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

62Hotels and other lodgingplaces................37.140.64446.348.350.4

63Personalservices..............................3135.936.83838.840.9

64Businessservices..............................145166.9183.7203.9205.3229.4

65Auto repair, services, andparking.............40.845.346.550.351.352.1

66Miscellaneous repairservices..................13.515.416.617.71717.6

67Motionpictures................................13.714.317.917.717.918.2

68Amusem*nt and recreationservices..............26.228.83236.539.445.2

69Healthservices................................230.6253.6280.7314.4345.3377.8

70Legalservices.................................61.870.97682.785.692.7

71Educationalservices...........................31.334.237.139.643.746.5

72Socialservices................................2123.426.730.133.637.3

73Membershiporganizations.......................26.930.333.235.838.439.9

74Otherservices.................................103.3119.8135.8149.2150161.1

75Privatehouseholds.............................7.78.38.99.49.110.1

76Statisticaldiscrepancy1.........................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 ProductivityGrowth19921993199419951996199719981999

Total (percent change from priorperiod)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