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Research Policy 44 (2015) 999–1016

Contents lists available at ScienceDirect

Research Policy

jo ur nal ho me page: www.elsev ier .com/ locate / respol

ow does information technology improve aggregate productivity? new channel of productivity dispersion and reallocation

yunbae Chuna,∗, Jung-Wook Kimb, Jason Leec

School of Economics, Sogang University, Seoul 121-742, South KoreaCollege of Business Administration, Graduate School of Business, Seoul National University, Seoul 151-916, South KoreaSchool of Business, University of Alberta, Edmonton AB, T6G 2R6 Canada

r t i c l e i n f o

rticle history:eceived 11 June 2012eceived in revised form 5 November 2014ccepted 6 November 2014vailable online 28 November 2014

a b s t r a c t

Using U.S. firm-level data from 1971 to 2000, this paper quantifies the importance of production inputreallocation in explaining the information technology (IT) driven productivity growth. We find that cross-industry variation in input reallocation explains more than 30% of differences in the 5-year productivitygrowth rates of industries utilizing similar levels of IT. Our findings illustrate a new channel through

eywords:nformation technologyroductivity growtheallocationechnology diffusion

which IT affects the aggregate productive growth and are consistent with recent papers that empha-size the destructive nature of technology innovation and the importance of firm-level reallocation inexplaining aggregate productivity growth. Our paper implies that policy makers should focus not only onimplementing IT but also on instituting policies aimed at improving reallocation efficiency to maximizethe effect of IT on the productivity growth.

© 2014 Elsevier B.V. All rights reserved.

. Introduction

The impact of information technology (IT) on productivityrowth has been widely studied during the last decades. Mosttudies focus on how IT adoption makes firms more productiveBrynjolfsson and Hitt, 1996, 2003; Bloom et al., 2012). However,ecent empirical and theoretical researches (Hobijn and Jovanovic,001; Gârleanu et al., 2012a,b; Bartelsman, 2013; Kogan andapanikolaou, 2013, 2014) emphasize the destructive nature of IT-riven innovation. For example, the advent of digital technology inhe photography industry in the early 1990s increased the entryf new firms equipped with new technology. This new technol-gy shifted the base of technological knowledge from chemicalo digital, which challenged incumbents by destroying the valuef their accumulated knowledge and skills in the old technologiesBenner, 2007), creating a performance gap between new entrantsnd old established firms. The performance gap necessitates theeallocation of inputs from failing firms toward more productivenes, which would enhance the aggregate-level productivity in the
ong-run. This implies that IT-driven aggregate productivity growth
ay be associated with the efficiency of input reallocation. Thus,f the efficiency of input reallocation is different across industries,

∗ Corresponding author. Tel.: +82 2 705 8515; fax: +82 2 704 8599.E-mail addresses: [email protected] (H. Chun), [email protected]

J.-W. Kim), [email protected] (J. Lee).

ttp://dx.doi.org/10.1016/j.respol.2014.11.007048-7333/© 2014 Elsevier B.V. All rights reserved.

IT-driven industry-level productivity growth would also exhibit asubstantial cross-industry variation. However, there has been noempirical study which analyzes the role of reallocation efficiencyon the productivity growth associated with IT.

In this paper, using U.S. firm-level data covered in Compustatfrom 1971 to 2000, we investigate a new mechanism on how ITaffects aggregate-level productivity through the creative destruc-tion process envisioned by Schumpeter (1912). We provide robustempirical evidence of the IT-driven productivity dispersion amongfirms, highlighting the destructive nature of IT, and the resultingresource reallocation from less productive firms to more produc-tive ones. Furthermore, we show that the cross-industry variationin reallocation effect explains substantial differences in the 5-yearproductivity growth rates of industries with similar levels of IT.This finding supplements recent papers emphasizing the realloca-tion effect in explaining long-run productivity growth (Foster et al.,2001, 2006; Acemoglu et al., 2012; Kogan et al., 2012).

Information technology is an example of general purpose tech-nology (GPT).1 GPTs are introduced very infrequently but they have
a significant impact on the productivity of an economy. When aGPT is introduced, at its propagation stage, it is adopted by firmsat different rates across different firms (Bresnahan and Greenstein,
1 GPTs are technologies that change the ways in which firms conduct business.Examples include electricity, internal combustion, and most recently informationtechnology (Bresnahan and Trajtenberg, 1995; Jovanovic and Rousseau, 2005).

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996). This is because the new technology might not be compatibleith incumbents’ existing production lines or with old technolo-

ies embedded in old capital. As a result, GPTs are first adoptedy new firms and the vintage of capital plays an important role

n determining the overall productivity of firms. This also implieshat the propagation of a GPT necessarily accompanies the destruc-ion of incumbents (Hobijn and Jovanovic, 2001). Thus, while ITropagates, a performance gap might be observed between newnd young firms and old established firms (Hobijn and Jovanovic,001). In addition, even among IT adopters, the impact of IT coulde different across firms due to the unequal distribution of com-lementary assets such as management practices or the internalrganization required to deploy IT successfully (Bresnahan et al.,002). For example, some may apply IT successfully to importantusiness tasks including enterprise resource management (ERP),ustomer relationship management (CRM), and enterprise contentanagement (ECM), while others who lack the necessary com-

lementary assets may not. For example, Bloom and Van Reenen2011) and Bloom et al. (2012) emphasize that management prac-ices play an important role in explaining the different productivityffects of IT investment for U.S. and European firms.

Consistent with these studies, we find that the average pro-uctivity growth rates of top and bottom terciles of U.S. firms are2% and −23%, respectively, during our sample period and that theap is higher in IT intensive industries after controlling for otherndustry characteristics.2,3 The results are robust to the correctionf the possible endogeneity problem using 2SLS and alternativeethodologies of calculating productivity.Differing productivity growths among firms would change

he marginal productivity of inputs. Firms whose productivityncreases would have higher marginal productivity of inputs,

hereas firms whose productivity decreases would have lowerarginal productivity of inputs. In this case, the profit maximi-

ation principle implies that more productive firms should increasenputs while less productive firms should reduce inputs.4 Thismplies that if the reallocation process is more active in one industryhan in another, we should expect a higher long-run growth effectf IT in the former even though the IT intensity is similar for the twondustries. For example, when workers are released from failingrms, more active input markets would minimize the job-searcheriod for workers and allocate them to the firms with highest pro-uctivity. We propose measures that capture the degree of resourceeallocation in each industry and test whether the 5-year growthffect of IT is stronger in industries with more active input realloca-ion. We find that the reallocation effect explains more than 30% ofifferences in the 5-year productivity growth of industries utilizingimilar levels of IT. The results are robust to an alternative realloca-ion measure, the correction of the possible endogeneity problem
sing 2SLS, and differing methodologies of calculating productivity.
This study provides a new channel to explore why industriesith higher IT intensity would exhibit higher long-run productivity

2 If IT increases the productivity dispersion among firms especially at its propa-ation stage, rather than increasing the productivity of all firms, it would be difficulto have a short-run aggregate productivity growth effect from IT investment. Inhe early 1990s, researchers were puzzled by the low productivity gains observed,espite large investments in IT, which is known as the IT paradox (Brynjolfsson,993; Brynjolfsson and Hitt, 1996, 2003). The more strongly observed IT growthffect since then is known as the IT miracle.3 Section 3.4 discusses possible alternative determinants of the productivity

rowth dispersion.4 It is possible that more productive firms would hire fewer workers due to higher

roductivity. This would be especially so if the nature of the technology innovations labor-saving. However, we find that firms with higher productivity growth hire

ore workers during our sample period. Using a different measure of technologynnovation, Kogan et al. (2012) also find that more innovative firms in the U.S. hire

ore workers. See Section 2 for more discussion of this issue.

y 44 (2015) 999–1016

growths. The well-known view is that each firm may become moreproductive overtime (within-firm effect) through the efficient useof IT which requires an initial learning period, thereby increasingthe average productivity of firms in the long-run. For example,Brynjolfsson and Hitt (1996, 2003) emphasize that the contributionof IT to productivity is jointly determined by the computerizationitself and a complementary organizational investment that allowsthe efficient use of IT. According to them, even after investingin IT, it takes time for a firm to prepare an IT-friendly organi-zational structure. Eventually, the firm’s productivity increasesas IT improves timeliness, inventory control, and relationshipswith customers and suppliers. Our paper provides an additionalchannel: aggregate productivity growth could further increase viaresource reallocation from unsuccessful adopters of IT to moreproductive ones (between-firm effect). The effect of the secondchannel would be larger in countries or in industries with betterreallocation mechanisms such as more efficient input (labor andcapital) markets. Even though most IT productivity studies havefocused on the first channel, the economic importance of thesecond channel should not be overlooked. For example, the recentmacroeconomic literature highlights the importance of resourcereallocation in explaining aggregate productivity growth in the U.S.and other countries (Foster et al., 2001; Bartelsman et al., 2009).Hsieh and Klenow (2009) show that aggregate manufacturing totalfactor productivity (TFP) growth would increase approximately40% in China and about 50% in India if reallocations in China andIndia were as efficient as that in the U.S.

Chari et al. (2008) emphasize that despite accumulating evi-dence suggesting a positive impact of IT on productivity, one stillneeds to understand why and how IT affects firm- and aggregate-level productivity. We hope that our paper constitutes a stepforward in this direction. Our results also provide insights into thediffering effects of IT on productivity growths across countries. Thehigher U.S. productivity growth of the 1990s, after stagnant pro-ductivity growth in the 1970s and 1980s, is attributed primarilyto IT investments (Oliner and Sichel, 2000; Stiroh, 2002). How-ever, the IT-driven productivity miracle is not observed in Europeancountries (Colecchia and Schreyer, 2002; Basu et al., 2003; Timmerand Van Ark, 2005). Differences in available complementary assetsand organizational changes between U.S. and European firms maybe factors in this phenomenon.5 Our results show that differingreallocation efficiency can be another relevant factor. In agree-ment with our results, Dewan and Kraemer (2000) find a differingeffect of IT on productivity between developed and developingcountries. In light of our results, their findings may reflect the factthat developed countries tend to have better functioning marketsthan undeveloped ones, including developed financial markets andless regulated input markets, thus prompting more active realloca-tion.

This paper is structured as follows. Section 2 provides a litera-ture review and discusses the hypotheses. Sections 3 and 4 explainthe variable construction and empirical methodology, respectively.Sections 5 and 6 report the empirical results and robustness checks,respectively. Section 7 concludes with possible policy implications.

2. Theoretical backgrounds and hypotheses

In Section 2.1, we review recent papers on the theoretical under-
pinning for technology-driven productivity dispersion amongfirms and build our first hypothesis. In Section 2.2, we reviewrecent papers on reallocation among firms in general and the
5 Consistent with this, Basant et al. (2011) find general infrastructure to be a keycomplementary input for the efficient use of IT in developing countries.

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(2012). If cross-industry variation in the intensity ofreallocation exits, we can expect higher long-run growth inindustries with active reallocation, because those are the indus-tries in which scarce resources are used by the most efficient

H. Chun et al. / Researc

eallocation triggered by technological innovation in particular anduild our second hypothesis.

.1. IT-driven productivity dispersion among firms

Two groups of research emphasize how IT increases the pro-uctivity dispersion. The first line of research emphasizes theestructive effect of technology innovation driven by GPTs suchs IT. In this line of reasoning, productivity differs among firmsepending on whether they invest in new technology, especiallyuring the propagation stage of the new technology. The second

ine of research emphasizes that a successful IT adoption mayequire complementary assets such as organizational structure andanagement practices. For example, when two firms invest in IT,

he benefit from the investment can differ based on the differentmounts of complementary assets of the two firms.

.1.1. Destructive effect of technology innovation associated withT

A seminal paper of the first group is that of Hobijn and Jovanovic2001). Using Lucas’s (1978) asset pricing model, they show that aew GPT makes a firm’s existing physical and human capital obso-

ete, since these inputs are not suited for the new technology. As aesult, the market values of incumbent companies decrease sharplyven before younger firms better suited for the new technology areisted. As these new firms are listed, the performance gap betweenoung and old firm is realized. In a similar vein, Gârleanu et al.2012b) show that firms’ decisions about investing in GPTs or notesult in the productivity gap among firms and can generate per-istent investment-driven cycles.

Kogan and Papanikolaou (2013, 2014) analyze the impact ofnvestment-specific technology (IST) on firm value. ISTs includePTs and are those technologies in which technological advancesre embodied in new capital goods as in computers, the steamngine, and the dynamo (David, 1990; Greenwood and Jovanovic,999). Kogan and Papanikolaou (2013, 2014) find both theoreti-ally and empirically that firms’ differing sensitivity to IST shocksan explain cross-sectional differences in firm performance andtock returns as in Hobijn and Jovanovic (2001) and Gârleanu et al.2012b).

Gârleanu et al. (2012a) argue that the new technology cre-tes ‘displacement risk’ by increasing the competitive pressure onxisting firms and workers. Their model implies that old and non-nnovative firms with limited access to new technology have loweraluation ratios (such as the market-to-book ratio), while youngnd innovative firms have higher valuation ratios. Even thoughheir paper does not deal with GPT or IST, their arguments thatechnology innovation has both bright (increases in productivity)nd dark (reduced profits for inefficient firms and the erosion of theuman capital of older workers) sides are consistent with the otherodels discussed above. These papers are consistent with creative

estruction as envisioned by Schumpeter (1912) and the businesstealing hypothesis of Tirole (1988) who argues that successfulnnovators take profits away from technical laggards, increasinghe performance gap between them.

.1.2. Heterogeneous complementary assets across firmsecessary for successful IT adoption

The papers described in the previous section emphasize that GPT may be adopted at different rates by different firms, creat-ng a vintage related effect on the productivity of a firm. However,ven when firms adopt IT simultaneously, the impact of IT can
iffer across firms because of an unequal distribution of comple-entary assets such as internal organization or the human capital
equired to deploy IT successfully (Bresnahan et al., 2002). Con-istent with this, Bloom and Van Reenen (2011) and Bloom et al.

y 44 (2015) 999–1016 1001

(2012) emphasize that management practices play an importantrole in explaining the different productivity effects of IT investmentbetween U.S. and European firms. They find that U.S. multination-als operating in Europe enjoy higher returns from IT investmentthan their European counterparts mainly due to the tougher ‘peoplemanagement’ practices of U.S. multinationals.6 Another examplecan be found in the retail industry, in which IT is heavily used.The management practices of Wal-mart supported by IT-drivenefficient logistics, including a just-in-time inventory managementsystem, have allowed Wal-mart to open stores in areas neglected byits competitors including Kmart which was the dominant discountretailers in the U.S. in the 1980s. The performance gap betweenWal-mart and Kmart increased beginning in the early 1990s andeventually Kmart filed for Chapter 11 in 2002.

These previous studies suggest a testable hypothesis regardingthe dispersion in the productivity growth between top- andbottom-tercile of firms. Let us consider a hypothetical situation.Since a GPT is adopted at different rates among firms, until IT prop-agates among 50% of firms, the increase in the IT intensity of anindustry would increase productivity dispersion between adoptersand non-adopters. In addition, if complementary assets are hetero-geneous among adopters, productivity growth dispersion will beeven higher because productivity will grow at different rates evenamong adopters until the effective uses of the IT are fully sharedamong firms. This leads to the following testable hypothesis.

H1. Industries with higher IT intensity exhibit higher dispersionof productivity growth among firms, other things being equal.

2.2. IT, input reallocation, and productivity growth

Recent papers emphasize the importance of reallocation ofinputs from inefficient firms to efficient ones on the aggregateproductivity growth in the U.S. (Foster et al., 2001, 2006) andin other countries (Hsieh and Klenow, 2009; Bartelsman et al.,2009; Syverson, 2011). For example, Foster et al. (2001, 2006)and Acemoglu et al. (2012) report that reallocation, including bothamong existing firms and through entry and exit, accounts for about30–50% of U.S. manufacturing productivity growth. About half ofthis is due to reallocation between incumbent firms.

Supplementing these findings, Kogan et al. (2012) show that thereallocation is triggered by the differing levels of technology inno-vation among firms by examining the relationship between theirtechnology innovation measure and input demands using U.S. firm-level data from Compustat. They measure firm-level innovation asthe weighted average of patent where weight is defined as the stockprice reaction when each patent is granted.7 They find that inno-vative firms hire more workers, while non-innovative firms reduceworkers when there are successful innovators. Based on H1 devel-oped in the previous section and the above research on reallocation,we suggest the following hypothesis.

H2. The long-run productivity growth effect of IT is higher inindustries where inputs more actively flow away from unproduc-tive firms toward productive firms.

If IT increases the dispersion of productivity, we expecta reallocation of inputs as suggested in Kogan et al.

6 Thus, even for observationally identical workers, wage and productivity disper-sions may increase (Aghion et al., 2002; Dunne et al., 2004; Faggio et al., 2010).

7 This is to reflect the differing economic significance of a firm’s patents.

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rms. We measure the intensity of reallocation for each industrysing two different methodologies. Details on how we measureeallocation intensity are provided in Section 3.5. One caveats warranted for these measures. These two measures assumehat firms whose productivity increases require more workers.owever, if labor-saving technology innovation is prevalent, firmsith productivity increases will hire fewer workers. That said, at

east among the firms covered by Compustat during our sampleeriod, such a characteristic was not exhibited. As in Kogan et al.2012), we find that firms whose productivity increases hire moreorkers than firms whose productivity decreases.8

Fig. 1 explains our main hypotheses graphically. Fig. 1 showshat there are two channels through which IT affects the 5-yearroductivity growth. Direct growth effect of IT in the figure (upperrrow) refers to the within-firm effect discussed in the introduc-ion. The new channel highlighted in H1 and H2 are described ashe indirect growth effect of IT (lower arrows) which refers to theetween-firm effect discussed in the introduction.

. Data

We use Compustat data from 1971 to 2000 for firm-level infor-ation to construct our key variables. In constructing productivity

rowth measures and IT intensity, we use various datasets pub-ished by the Bureau of Labor Statistics (BLS) and the Bureau ofconomic Analysis (BEA). Our sample period ends in 2000, the lastear for which BEA and BLS report standard industrial classificationSIC)-based industry-level data. Our sample period is long enougho analyze the arrival and diffusion process of IT and its impactsn productivity dispersion and growth. In the case of electrifica-ion, another well-known GPT, it took about 30 years to reach aattened diffusion curve after its arrival in 1895 (Jovanovic andousseau, 2005). In the case of IT, Pastor and Veronesi (2009) sug-est that the full propagation of IT occurred around 2002 in the U.S.ased on the changing volatility patterns of stock returns.9

.1. Total factor productivity growth

We construct the TFP growth rate at the firm-level as

ln(

TFPi,j,t

)= �ln

(Yi,j,t

)− 1

2

(SL,i,j,t + SL,i,j,t−1

)�ln

(Li,j,t

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(SK,i,j,t + SK,i,j,t−1

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(Ki,j,t

)(1)

here Yi,j,t, Li,j,t, and Ki,j,t are real output, the number of employeesCompustat item 29), and real capital stock for firm i in industry jn year t.10 � denotes the difference of each variable over the twodjacent years. SL,i,j,t and SK,i,j,t are the firm’s labor and capital costhares, respectively. This TFP measure based on the cost share ofnputs allows for non-constant returns to scale (Hall, 1988; Basund Fernald, 1997).

We define firm output, Y , as real value-added.11 Real value-
i,j,tdded is nominal value-added deflated by the industry-level pricendex. Nominal value-added is defined by the sum of operat-ng income before depreciation (Compustat annual data item 13)
8 See Section 3.5 for more discussion on this issue.9 In fact, after 2000, both IT diffusion and the productivity growth dispersion

lowed down considerably in our dataset as well.10 The use of hours worked provides more precise TFP estimates. However,ompustat database does not provide firm-level average hours worked for theirmployees, and we thus use the number of employees as labor input to constructFP, as in other studies constructed firm-level TFP using Compustat (Brynjolfssonnd Hitt, 2003; Franco and Philippon, 2007).11 Construction of firm-level TFP using Compustat items mainly follows the meth-ds described by Hall (1990) and Brynjolfsson and Hitt (2003).

y 44 (2015) 999–1016

and labor and related expenses (item 42).12 We obtain two-digitindustry-level value-added deflators from the BEA gross productoriginating (GPO). For the period prior to 1977, these deflatorsare unavailable. As a result, we use gross output and intermediateinput prices from the BLS Multifactor Productivity data to constructsubstitutes.

The input factors of production are labor force and real capitalstock. Labor force, Li,j,t, is the number of employees (Compustat item29). Real capital stock, Ki,j,t, is net property, plant and equipment(PP&E) (item 8), deflated by the BEA Fixed Reproducible TangibleWealth (FRTW) industry-level deflator. The labor cost share, SL,i,j,t,is labor and related expenses (Compustat item 42) divided by thisplus capital services costs. If labor and related expenses are notavailable, we use estimated values calculated as the industry aver-age wage, from GPO data, multiplied by the firm’s workforce (item29). If employees’ benefits are excluded from labor and relatedexpenses (Compustat footnote 22), we estimate them from GPOindustry benefits to total compensation ratio data at the industry-level. The capital services costs are defined as the firm’s real capitalstock times industry j’s annual rental price of capital. As in Hall andJorgenson (1967) and BLS (1997), the rental price of capital for assetk in industry j at year t is

Wk,j,t = 1 − �k,t − utzk,t

1 − ut

(rj,t + ık − Gk,t

)qk,t (2)

with �k,t the effective rate of investment tax credit, ut the corpo-rate income tax rate, zk,t the present value of capital consumptionallowances, rj,t the nominal internal rate of return, ık the deprecia-tion rate, Gk,t the asset-specific capital gain, and qk,t the investmentdeflator. Tax variables are from the BLS. Using FRTW data on theasset composition of each industry each year, we aggregate assetrental prices using the Törnqvist method to obtain industry rentalprices of capital. Firm i’s capital cost share, SK,i,j,t, by construction,is 1 − SL,i,j,t.

3.2. Dispersion in TFP growth rate

Table 1 provides summary statistics for TFP growth by tercileand its dispersion, measured by the difference between top andbottom terciles, during our sample period of 1971–2000.13 Eachyear, we assign firms into terciles based on each firm’s TFP growthrate. Panel A of Table 1 shows that the average TFP growth rates offirms in the top and bottom terciles are 0.219 and–0.227, respec-tively, implying a substantial dispersion of 0.446. Panel B reportsthe averages for the two sub-periods of 1971–1985 and 1986–2000.The average TFP growth rate of firms in the bottom tercile decreasedfrom −0.201 to −0.253 while that in the top tercile increasedfrom 0.181 to 0.255. Therefore, the dispersion of TFP growth ratesbetween the top and bottom firms widened from 0.382 to 0.508,which is statistically significant at the 1% level. Increases in disper-sion are observed in both manufacturing and non-manufacturingsectors. Further, the magnitudes of growth rate and dispersion foreach sector are similar to those for the full sample.

The increased TFP dispersion is also shown in Fig. 2. Equallyweighted and industry value-added weighted averages show sim-ilar patterns, implying that the increased TFP dispersion is notdriven by small or particular industries. Fig. 3 shows the TFP dis-
persions for the two sub-periods of 1971–1985 and 1986–2000across industries. Industries are sorted by the dispersions in thesecond sub-period. The TFP dispersion has increased in 33 out of 41
12 We dropped observations with negative or zero value-added. However, theseobservations account for less than 1% of the sample and do not affect our results.

13 We exclude finance industries (SIC code 6000–6999) in constructing the firm-level TFP growth rates.

H. Chun et al. / Research Policy 44 (2015) 999–1016 1003

Fig. 1. Main hypothesis of the paper.

Table 1TFP growth dispersion.

Panel A. Summary statistics: 1971–2000

Mean Standard deviation Minimum Maximum

Top 0.219 0.161 −0.308 1.299Middle 0.006 0.081 −0.675 0.716Bottom −0.227 0.181 −1.973 0.605Dispersion (top minus bottom) 0.446 0.252 0.047 2.080

Panel B. Summary statistics: 1971–1985 versus 1986–2000

1971–1985 1986–2000 (1986–2000) minus (1971–1985)

All industriesTop 0.181 0.255 0.075*** (0.000)Middle 0.003 0.009 0.007 (0.154)Bottom −0.201 −0.253 −0.051*** (0.000)Dispersion 0.382 0.508 0.126*** (0.000)

ManufacturingTop 0.161 0.224 0.063*** (0.000)Middle 0.014 0.011 −0.003 (0.647)Bottom −0.157 −0.220 −0.063*** (0.000)Dispersion 0.319 0.444 0.126*** (0.000)

Non-manufacturingTop 0.201 0.285 0.085** (0.015)Middle −0.009 0.008 0.017*** (0.005)Bottom −0.246 −0.284 −0.038** (0.029)Dispersion 0.447 0.569 0.123*** (0.000)

Notes: TFP growth dispersion is defined as the difference of the average TFP growth rates between top and bottom terciles. In the third column of Panel B, numbers inparentheses are probability levels at which the null hypothesis of a zero coefficient can be rejected. The sample excludes finance (SIC 6000 to 6999) industries.*

iesmrutTds

sip

Significant at the 10% level.** Significant at the 5% level.

*** Significant at the 1% level.

ndustries. The five industries with the highest dispersions are oilxtraction, motion pictures, business services (including computeroftware), chemicals (including pharmaceuticals), and industrialachinery, all of which are high-tech industries.14 In contrast,

egulated industries (electric and gas services) and traditional man-facturing industries (transportation equipment, textile, and furni-ure) exhibit low TFP dispersion in both periods. While increases inFP dispersion are evident in almost all industries, decreases in TFP
ispersion are observed in only a few industries such as agriculturalervices, construction, and educational services.
14 High-tech industries are typically defined as industries with high R&D inten-ity. Although oil extraction and motion pictures are not R&D intensive, these twondustries intensively use high-technologies used in mineral exploration and com-uterization, respectively (Corrado et al., 2006).

3.3. Information technology

We gather industry-level IT data from the FRTW published bythe BEA. These data provide information about investments in 61different types of assets at the two-digit (1987 SIC code) industry-level.15 We define IT investment as the sum of investments in seventypes of computer hardware (mainframe computers, personal com-puters, direct access storage devices, computer printers, computerterminals, computer tape drives, and computer storage devices)
and three types of software (pre-packaged software, custom soft-ware, and own-account software). We use the Törnqvist index toaggregate these ten types of computer hardware and software into
15 A detailed description of the FRTW dataset is available in Herman (2000).

1004 H. Chun et al. / Research Policy 44 (2015) 999–1016

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

1971 1976 1981 1986 1991 1996

Top (equal)

Top(weighted)Midd le(equal)Midd le(weighted)Bottom(equal)Bottom(weighted)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1971 1976 1981 1986 1991 1996

Equal

Weighted

A

B

Fig. 2. TFP growth dispersion, 1971–2000. Panel A. TFP growth rate (Top, middle, and bottom), Panel B. TFP growth dispersion. Notes: Panel A plots the average TFP growthrate of firms in each tercile (top, middle, and bottom). TFP growth dispersion in Panel B is defined as the difference of the average TFP growth rates between the top and bottomterciles. For weighted figures in both Panels A and B (in gray color), one-year lagged industry value-added is used as the weight. The sample excludes finance industries (SIC6

Iicctds

3d

al

000 to 6999).

T investment. Then, we utilize a recursive algorithm to accumulatenvestment into IT capital stock estimates. The depreciation rate ofomputers and software is approximately 0.31.16 Analogously, weonstruct non-IT capital using data on all other asset types. Thus,otal capital is the sum of IT and non-IT capital stock. Finally, weefine the IT intensity as the ratio of IT capital to non-IT capitaltock.

.4. Other possible determinants of productivity growthispersion

Here we construct additional explanatory variables that mightlso affect the dispersion in productivity growth analyzed in theiterature.

16 See Fraumeni (1997) for asset-specific depreciation rates.

3.4.1. Research and developmentR&D expenditure is one of the key sources of innovation and a

driver of productivity growth. When firms compete based on R&D,those with successful innovations experience higher productivitygrowth, while those with unsuccessful innovations exhibit lowerproductivity. Doraszelski and Jaumandreu (2008) find that the suc-cess or failure of R&D determines the differences in productivityacross firms. In a similar vein, Comin and Mulani (2009) show thatR&D increases the heterogeneity among firms. These findings implya potential positive association between industry-level R&D inten-sity and the dispersion of productivity growths among firms in theindustry.

We construct a measure of industry-level R&D capital fromannual R&D spending (Compustat item 46) using a 20% depre-ciation rate and the GDP deflator, as in Chan et al. (2001). Each
industry’s R&D intensity is the ratio of capitalized R&D to net prop-erty, plant, and equipment (item 8). Although not tabulated, wefind that IT intensity is broadly distributed across industries, whileR&D intensity is skewed toward a few industries such as high-tech


0 0.2 0. 4 0.6 0.8 1 1.2

Furni turePersonal services

Textil eTransportation equipment

Electric & gasPape r

Stone, clay, & glassFabricate d metal

Trucking & war ehousi ngRetail

Rubber & plasticsPrinting & publishing

FoodTransportation servic es

TobaccoLumber & wood

LeatherPrimary metal

Water trans portati onAuto servic e

Educational serv icesTransportation by air

HotelsTelephone

Petr oleum product sWholes ale

Agri. serv icesOther servic es

ConstructionHealth serv ices

ApparelMiscell aneou s

ElectronicsRadio & TVInstr umen ts

Amusement servicesIndustrial machinery

Chemic alsBusiness servic es

Motion picturesOil ex trac tion

1971-198 5

1986-200 0

F –2000t 2000

itT

3

slmf

att

ig. 3. Increasing TFP growth dispersion across industries: 1971–1985 versus 1986he periods of 1971–1985 and 1986–2000, respectively. Data is sorted by the 1986–

ndustries. This bides against R&D intensity being explanatory ofhe aforementioned economy-wide elevations in the dispersion ofFP growth.

.4.2. Firm demographyProductivity dispersion can be linked to a rising proportion of

mall or young firms which are known to be more volatile thanarge and old firms (Fama and French, 2004; Davis et al., 2006). We

easure the firm age of each firm using the founding year obtainedrom the data used in Loughran and Ritter (2004).17 We use total

17 Since founding years are not available for all the firms, we also augment ournalysis by calculating a firm’s age based on the year that the firm first appears inhe CRSP database. The use of this alternative measure of age yields results similaro those reported in the paper.

. Notes: Gray and black bars are industry-level TFP growth dispersion averaged for industry average. The sample excludes finance industries (SIC 6000 to 6999).

sales of a firm to measure firm size. We then calculate the averageage and average firm size for each industry.

3.4.3. CompetitionThe degree of competition is linked to the productivity distri-

bution of firms in an industry. For example, Aghion et al. (2005)distinguish two different effects of competition on productivitydispersion among firms. The static effect is that the intense compe-tition may reduce the TFP dispersion by increasing the exit rate ofinefficient firms. In contrast to the static effect, competition mayincrease the dispersion overtime between innovators and non-innovators if some firms try to innovate to avoid neck-and-neck
competition among peers, referred to as the dynamic effect. In fact,Aghion et al. (2005) find that the dynamic effect dominates in theirsample of manufacturing firms. However, Syverson (2004b) showsthat the static effect may be stronger than the dynamic effect in

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services industries (hotels, health services, and auto services) andregulated industries (electric and gas and tobacco).


ectors where innovation activities are not intense. Thus, whetherompetition increases or decreases the dispersion in productivitys an empirical question.

Competitors are not necessarily confined to domestic firms. Forxample, Aghion et al. (2009) show that the intensified competi-ion from foreign firms may result in heterogeneous responses ofomestic firms’ innovation activities, increasing the dispersion inroductivity.

To measure the degree of competition, we estimate thendustry-level import penetration ratio to capture the competitiveressure from foreign firms. The ratio is defined as imports overross output minus exports, as in Revenga (1992) and Bertrand2004). We obtain the imports data from Feenstra et al. (2002).18

nother variable we use to control the degree of competition is theumber of firms within an industry.

Panels A and B of Table 2 show the summary statistics andorrelation coefficients of these variables. All the variables are logransformed except for the import penetration ratio.

.5. Reallocation measures

In the creative destruction process (Schumpeter, 1912), inef-cient firms will eventually be replaced by more efficientompetitors, and the economy as a whole will achieve a higher levelf efficiency through the survival of the fittest. For this process toork efficiently, underlying input markets should accommodate

smooth reallocation of scarce inputs for production. Otherwise,he long-run growth effect of IT will be limited and sluggish. Hsiehnd Klenow (2009) and Kogan et al. (2012) show that resourceisallocation can lower aggregate TFP.To estimate the intensity of reallocation, we try two different

easures. First, we run the following firm-level cross-sectionalegression for each industry each year:

ln(

Li,j,t

)= ˛j,t + RL,j,t�ln

(TFPi,j,t−1

)+ ei,j,t (3)

here �ln(

Li,j,t

)is labor input growth for firm i in industry j in

ear t and �ln(

TFPi,j,t−1

)is the one-year lagged TFP growth.19 This

egression is motivated by a similar specification used by Wurgler2000) who measures the degree of reallocation for a country by theesponsiveness of capital growth to output growth using industry-evel data. Given the results in Foster et al. (2001) and many others

ho show that massive resource reallocation among firms withinn industry is driven by TFP differences, we construct the realloca-ion measures by estimating the responsiveness of an input growtho TFP growth rather than to output growth. The coefficient RL,j,t

easures the labor input reallocation of industry j in year t. In anndustry in which resources are reallocated from inefficient firmso efficient firms more smoothly, we expect a larger RL,j,t coefficient.

One caveat is that if the technology innovation allows firms toeduce the number of workers, more productive firms may reduce,ather than increase, labor demand. However, this seems not to be
he case for U.S. firms during our sample period. Using the value-eighted patents of each firm as a technology innovation measure,ogan et al. (2012) find that firms experiencing innovations tend
o increase labor demand subsequently. For example, an increase

18 We also compute the sales-based Herfindahl–Hirschman index to capture theomestic competition for each industry. However, the Herfindahl–Hirschman indexariable is insignificant in all regressions explaining TFP growth dispersion and doesot change the results. We thus drop the index.19 Since we have 41 industries and 30-year sample period from 1971 to 2000, theotal number of industry-year observations should be 1,230. However, when westimate Eq. (3), we add the restriction that each industry should have at least 5bservations for a given year. This restriction reduces the sample size to be 1,202hich is the total number of industry-year observations in our analyses.

y 44 (2015) 999–1016

in innovation by the firm from the median to the 90th percentileleads to an increase in employment rate by 0.2% to 0.5%, comparedto the median firm-level hiring rate of 2.7%. In our sample, we havequalitatively similar results. When we divide our sample into ter-ciles based on the TFP growth of each firm, the labor growth of thefirst tercile in the following year is 0.5% on average while that ofthe third tercile is 2.1%. In estimating the reallocation effect using(3), we weigh each observation by the firm’s nominal value-addedof the prior year.20 We estimate regressions for all industries withfive or more firm-year observations. By replacing the labor inputgrowth with the capital input growth in (3), we also estimate thereallocation intensity of capital input (RK,j,t) for each industry eachyear.

Alternative specification of resource reallocation is similar insprit to (3) but focuses on input share changes of firms with differingTFP growths. We measure between-firm reallocation by measuringthe change in each firm’s input share within an industry, which ismotivated by methodologies used in Baily et al. (1992), Grilichesand Regev (1995), and Foster et al. (2001). If the input shares of firmswith higher productivity growths tend to expand while the inputshares of firms with lower productivity growths tend to shrink,inputs are flowing out of the inefficient firms toward efficient firmswithin an industry. Thus, our second measure of input reallocationmeasure is defined as follows.

RL,j,t =∑

i∈j�SLi,j,t�ln

(TFPi,j,t−1

)(4)

where �SLi,j,t is a change in the employment share of firm i in indus-try j from year t − 1 to t and �ln

(TFPi,j,t−1

)is the TFP growth of firm

i at year t − 1.21 Eq. (4) can be considered as the weighted aver-age of changes in the employment shares of firms, where weightsare firms’ TFP growths. Thus, the labor reallocation in an indus-try is higher when a firm experiencing higher productivity growthincreases its employment share within the industry.22 Panels Cand D of Table 2 show the summary statistics and correlationcoefficients of reallocation measures and other variables in 5-yearTFP growth regressions.

Fig. 4 shows a substantial cross-industry variation in the real-location measure obtained in (3) in the U.S. economy. Reallocationmeasure obtained in (4) also exhibits qualitatively similar variation.The reallocation measure used in the figure is the weighted aver-age of the labor and capital reallocation measures obtained from(3), where input cost shares are used as the weights. Each bar inFig. 4 is an industry-level input reallocation measure averaged overthe 1971–2000 sample period. Industries with high reallocationmeasures (top twenty industries) are concentrated in traditionalmanufacturing (lumber and wood, apparel, textile, and transporta-tion equipment) and high-tech industries (electronics, industrialmachinery, and business services) while industries with low real-location measures (bottom twenty industries) are concentrated in

20 Nominal value-added is used as weight to reflect the economic significanceof larger firms which on average hire more workers. However, our results remainqualitatively similar when we estimate (3) without weighing each observation bynominal value-added.

21 This measure is not obtained from a cross-sectional regression. Thus, a potentialconcern about endogeneity biases in the estimated coefficients in (3) is minimized.We thank an anonymous referee for pointing out this issue.

22 In terms of motivation, Eq. (4) is similar to (3). You could view the right hand

side of (4) as capturing the comovement between �SLi,j,tand �ln(

TFPi,j,t−1

). Thus,

(4) tends to have high values when �SLi,j,tand �ln(

TFPi,j,t−1

)tend to move in the

same direction. This will be the case if a firm with higher value of TFP growth at t−1in an industry represent higher employment share in the same industry (i.e., hirerelatively more workers than other firms) in the following year.


Table 2Summary statistics and correlation coefficients.

Panel A. Summary statistics: TFP growth dispersion regressionVariable Description Mean Standard deviation Minimum Maximum

ln(IT) Log of IT intensity: the ratio of information technology capital (computers andsoftware) to non-IT capital

−5.111 1.773 −10.022 −0.384

ln(R&D) Log of R&D intensity: the ratio of capitalized past R&D spending to property, plant andequipment (PP&E)

−4.050 2.727 −17.211 −0.294

ln(age) Log of the average age of firms in an industry 2.974 0.583 0.693 4.533ln(size) Log of the average sales of firms in an industry 6.351 1.113 3.716 10.341lmpp Import penetration ratio: the ratio of imports over industry gross output minus exports 0.189 0.295 0.003 2.419ln(Num) Log of the number of firms in an industry 4.135 1.094 1.609 6.483

Panel B. Correlation coefficients: TFP growth dispersion regression

ln(IT) ln(R&D) ln(age) ln(size) lmpp

ln(R&D) 0.336***

(0.000)ln(age) 0.024 −0.051*

(0.404) (0.079)ln(size) 0.607*** 0.074** 0.058**

(0.000) (0.011) (0.045)lmpp 0.241*** 0.259*** −0.062** 0.157***

(0.000) (0.000) (0.031) (0.000)ln(Num) 0.273*** 0.361*** 0.104*** 0.274*** 0.021

(0.000) (0.000) (0.000) (0.000) (0.478)

Panel C. Summary statistics: 5-year TFP growth regression

Variable Description Mean Standard deviation Minimum Maximum

ln(IT) Log of IT intensity: the ratio of information technology capital (computers andsoftware) to non-IT capital

−5.197 1.744 −9.955 −1.048

RL Labor reallocation: the industry average of firm-level coefficients obtained fromcross-sectional firm-level regression of the labor growth rate on the TFP growth rate

0.109 0.990 −4.711 6.470

RK Capital reallocation: similarly defined for capital input as in labor reallocation 0.208 1.736 −4.017 22.509ln(TFP) Log of the initial TFP level 2.923 0.795 −0.235 5.110

Panel D. Correlation coefficients: 5-year TFP growth regression

ln(IT) RL RK

RL 0.003(0.962)

RK −0.035 0.431***

(0.605) (0.000)ln(TFP) −0.013 0.164** 0.366***

(0.846) (0.013) (0.000)

Notes: Variables in Panels C and D are defined over six non-overlapping 5-year intervals (1971–1975, . . ., 1996–2000). In Panels B and D, numbers in parenthesis are p-values.The sample excludes finance (SIC 6000 to 6999) industries. The sample size is 1,202 for Panels A and B and 226 for Panels C and D, respectively.

4

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* Significant at the 10% level.** Significant at the 5% level.


. Methodology

.1. Panel regressions of TFP dispersion

To test the hypothesis H1 as described in Section 2.1, we employanel regressions of TFP dispersion on IT and other possible deter-inants as discussed in Section 3.4. The dependent variable is the

ifference of average TFP growth rates between top- and bottom-ercile firms in industry j at year t defined as follows.

IS(

TFPj,t

)= �ln

(TFPi∈T

i,j,t

)− �ln

(TFPi∈B

i,j,t

)(5)

here T and B denote the top and bottom terciles, respectively.sing annual industry panel data from 1971 to 2000, we run panel

egressions with both industry and time fixed effects as in the fol-owing equation

IS(

TFP)

= ̨ + ˇln(

IT)

+ CX + � + � + ε (6)
j,t j,t−1 j,t−1 j t j,t
here IT is IT intensity, X is a matrix of possible factors thatight affect firm-level productivity dispersion, and �j and �t are

he industry and time fixed effects, respectively. All explanatory

variables are lagged by one year. The positive and significantcoefficient for the IT variables indicates that the diffusion of infor-mation technology matters for the increased TFP dispersion amongfirms in the U.S. After establishing this, we examine the 5-yeargrowth effect of IT through reallocation triggered by dispersion inproductivity growth.

4.2. Panel regressions of 5-year TFP growth

To test hypothesis H2 as described in Section 2.2, we set up panelregressions of industry 5-year TFP growth on IT and reallocationmeasures. To construct the 5-year TFP growth rate, we first estimateindustry j’s TFP level, which is defined as the weighted average ofthe TFP level of each firm in the industry as defined below.

ln(

TFPj,t

)≡

∑i∈j

wi,j,t

[ln

(Yi,j,t

)− SL,i,j,t ln

(Li,j,t

)

( )]
− SK,i,j,t ln Ki,j,t (7)
where the weight wi,j,t is firm i’s nominal value-added overtotal industry value-added in industry j in year t. Since pro-ductivity improvement via the reallocation mechanism may


-0.2 -0 .1 0 0.1 0.2 0.3

Other s erv icesAmusement services

Electric & gasHotel s

TobaccoMotion pictures

Oil extracti onHealth service s

Auto servicePrimary meta l

Radio & TVTransportation by air

TelephoneFurniture

FoodPersonal services

Petr oleum productsChemical s

Ins trumen tsTransportation services

Printing & publishi ngIndustrial machinery

LeatherWater transp ort ation

Busin ess servic esConstruction

WholesaleTransportation equipment

Pape rElectronics

Fabricated m eta lEduca tional servic es

Textil eRetail

Rubber & plasticsStone, clay, & glass

ApparelMiscellan eous

Trucki ng & war ehousi ngLumbe r & wood

Fig. 4. Input reallocation measure, 1971–2000. Notes: Each bar is industry-level input reallocation measure averaged over the 1971–2000 period. The input reallocationmeasure is the weighted average of labor and capital reallocation measures where input cost shares are used as weights. The sample excludes finance industries (SIC 6000to 6999).

tAiGs(

r

ake time, we measure TFP growth over a long time horizon.s in Beck et al. (2000), we define the 5-year TFP growth of

ndustry j as the 5-year log difference in the TFP level, that is,LR

(TFPj,t

)≡ ln

(TFPj,t

)− ln

(TFPj,t−4

). Thus, in the 1971–2000

ample period, we have six non-overlapping 5-year intervals as1971–1975), . . ., (1996–2000).

We assess the hypotheses H2 by using the following panelegression:

GLR

(TFPj,t

)= ̨ + ˇ1ln

(ITj,t−4

)+ ˇ2ln

(ITj,t−4

)× RL,j,t−4

+ ˇ3RL,j,t−4 + �ln(

TFPj,t−4

)+ �j + �t + uj,t (8)

Eq. (8) includes the industry (�j) and time (�t) fixed effects,respectively. All explanatory variables (IT intensity, reallocationmeasure of labor (RL) or capital (RK), and initial TFP level) are


Table 3Panel regressions explaining TFP growth dispersion.

(1) (2) (3) (4) (5)

ln(IT) 0.026** 0.027** 0.024** 0.031** 0.028**

(0.034) (0.027) (0.047) (0.014) (0.026)ln(R&D) −0.010 −0.012 −0.016* −0.013

(0.236) (0.141) (0.054) (0.146)ln(age) −0.032*** −0.032*** −0.020*

(0.006) (0.005) (0.093)ln(size) −0.060*** −0.054*** −0.034*

(0.000) (0.002) (0.053)Impp 0.194*** 0.219***

(0.000) (0.000)ln(Num) 0.095***

(0.000)Adjusted R2 0.526 0.526 0.535 0.544 0.551Sample size 1,202 1,202 1,202 1,202 1,202

Notes: All panel regressions include both industry and time fixed effects. The dependent variable is the dispersion of TFP growth, defined as the difference in the average TFPgrowth rates between the top and bottom terciles. IT intensity, IT, is the ratio of information technology capital (computers and software) to non-IT capital. R&D intensity,R&D, is the ratio of capitalized past research and development spending to property, plant and equipment (PP&E). Age is the average age of firms. Size is the average sales offirms. lmpp is the ratio of imports over industry gross output minus exports. Num is the number of firms in an industry. All explanatory variables are lagged by one year. Thesample excludes finance industries (SIC 6000 to 6999). Numbers in parentheses are probability levels, based on t-statistics adjusted for heteroskedasticity, at which the nullhypothesis of a zero coefficient can be rejected.

mTitt

omtrcTte

oepsw

5

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5

havti

l

usage of IT reduces the amount of time required for a new firm toimplement its innovations in production processes, and this allows



easured at the beginning year of each 5-year interval. The initialFP level is included because Bernard and Jones (1996) show that its possible for TFP growth to exhibit a convergence pattern; indus-ries with higher initial TFP levels experience lower TFP growths inhe future.23

The dependent variable in (8) is 5-year TFP growth rates definedver 5-year non-overlapping intervals (between t − 4 and t). ˇ1easures the 5-year growth effect of IT. To evaluate the differen-

ial effect of IT on productivity growth according to the degree ofeallocation, we include a labor reallocation measure (RL) and theross-product of IT intensity and the reallocation measure in (8).he capital reallocation measure can be analogously included inhe regression. The growth effect of reallocation can be seen byxamining the following Eq. (9) which is derived from (8).

�GLR

(TFPj,t

)�ln

(ITj,t−4

) = ˇ1 + ˇ2 × RL,j,t−4 (9)

The coefficient for IT intensity, ˇ1, measures the direct impactf IT on the 5-year TFP growth. ˇ2 measures the incrementalffect of IT intensity via the reallocation of labor input. If ˇ2 isositive, then industries with active input reallocation exhibittronger productivity-enhancing effects from IT investment evenhen industries have similar levels of IT intensity.

. Results

We report the regression results for H1 in Section 5.1 and theesults for H2 in Section 5.2, respectively.

.1. Results for TFP growth dispersion

Table 3 reports the estimation results for (6) to test the firstypothesis (H1). The p-values based on robust standard errorsre provided in parentheses. When included alone or with other
ariables, IT intensity attracts positive and significant coefficientshroughout all five different specifications. On the contrary, R&Dntensity attracts a negative sign and is insignificant in all columns
23 There could be a possible correlation between the error term and the initial TFPevel in the regression. We deal with this issue in the robustness section.

except for the fourth. Weaker evidence for R&D relative to IT mayreflect the fact that the distribution of R&D intensity is highlyskewed toward a few large industries.24 Thus, it may not explainproductivity heterogeneity which is observed in almost all indus-tries. On the contrary, IT intensity is widely distributed across allindustries, reflecting the characteristic of a GPT. These results areconsistent with Chun et al. (2008), McAfee and Brynjolfsson (2008),and Chun et al. (2011), who argue that when a GPT propagates inan economy, it distinguishes successful adopters from unsuccessfulones, thereby increasing heterogeneity among firms.

In column (3) of Table 3, we add firm demography-relatedvariables to the regression. Industries with younger and smallerfirms may represent a large portion of extreme performers. Thus,if industries are populated more by younger and/or smaller firms,we may expect larger performance heterogeneity. Consistent withour expectation, both age and size attract negative and significantcoefficients.

In columns (4) and (5), we include the import penetrationratio (Impp) and the number of firms in each industry (Num) tocontrol the degree of competition in an industry. If an industryis very competitive, a small competitive edge in productivity caninduce a substantial change in the hierarchy of firms, amplifyingfirm heterogeneity (Aghion et al., 2005). Consistent with this, thecoefficients for the two variables are negative and significant,which implies that competition increases dispersion.

The results in Table 3 indicate that the increased TFP growthdispersion can be explained by several variables that characterizeindustries. However, Jovanovic and Rousseau (2001), Chun et al.(2008), and Chun et al. (2011) argue that IT may be a drivingforce for all these variables. For example, firm demography andthe number of firms can be affected by the arrival of a GPT, suchas IT; Jovanovic and Rousseau (2001) demonstrate that increased

the new firm to go public at a relatively young age. Further, the

24 R&D intensity is exceptionally high in industries such as chemical products(including pharmaceuticals), business services (including software), and other ser-vices (including R&D and testing services). Moreover, in 2000, R&D spending by theindustrial machinery, transportation equipment, and chemical products industriesaccounts for almost 80% of total R&D spending in the manufacturing sector.


Table 4Panel regressions of 5-year TFP growth on information technology and reallocation.

(1) (2) (3) (4) (5)

ln(IT) 0.075* 0.083* 0.081* 0.099** 0.075*

(0.085) (0.077) (0.071) (0.016) (0.089)ln(IT) × RL 0.055** 0.049***

(0.028) (0.000)RL 0.295** 0.234***

(0.035) (0.000)ln(IT) × RK 0.024 −0.002

(0.268) (0.703)RK 0.082 −0.015

(0.462) (0.666)InitialTFP

−0.369*** −0.377*** −0.386*** −0.358*** −0.368***

(0.000) (0.000) (0.000) (0.000) (0.000)Adjusted R2 0.553 0.557 0.553 0.585 0.548Sample size 226 226 226 226 226

Notes: All panel regressions include both industry and time fixed effects. The dependent variable is the industry 5-year TFP growth rate defined over six non-overlapping5-year intervals (1971–1975, . . ., 1996–2000). Initial TFP is the logarithm of TFP. IT intensity, IT, is the ratio of information technology capital (computers and software) tonon-IT capital. RL and RK are reallocation measures of labor and capital inputs, respectively. The reallocation measures in columns (2) and (3) are obtained from a firm-levelcross-sectional regression of the labor or capital growth rate of a firm in an industry on the TFP growth rate of the firm in the previous year using previous year nominalvalue-added as weights. The reallocation measures in columns (4) and (5) are calculated as the average of changes in a firm’s labor or capital share within an industry usingits previous year TFP growth rates as weights. All explanatory variables are measured at the beginning year of each 5-year interval. The sample excludes finance industries(SIC 6000 to 6999). Numbers in parentheses are probability levels, based on t-statistics adjusted for heteroskedasticity, at which the null hypothesis of a zero coefficient canb

aaeBhhmeaBoi

impbsst

5

ht(ccaIaatne

eh

e rejected.* Significant at the 10% level.

** Significant at the 5% level.*** Significant at the 1% level.

rrival of a new technology may spur competition among firms,s young firms equipped with new technology may compete withstablished firms (Hobijn and Jovanovic, 2001). In a similar vein,rynjolfsson et al. (1994) document that the widespread use of ITas increased the viability of small firms relative to large firms and,ence, has reduced the average firm size. Intensified competitionight be initiated by the introduction of new IT across firms. For

xample, the Internet can reduce buyers’ search costs in differenti-ted product markets, thus promoting competition (Bakos, 1997).rown and Goolsbee (2002) also show that the introduction of annline insurance quote system led to intensified price competitionn the life insurance industry.

Thus, the significance of other variables may highlight themportance of IT as an underlying factor. However, IT investment

ay also be affected by these variables. For example, fierce com-etition may necessitate investments in IT so that firms do not fallehind. In this case, IT investment may not be a cause of the disper-ion in productivity growth but rather its result. In the robustnessection, we address this endogeneity problem by using instrumen-al variables.

.2. Results for 5-year TFP growth

Table 4 reports estimation results for (8) to test the secondypothesis (H2). The results in columns (2) and (3) are based onhe reallocation measure as defined in (3) and columns (4) and5) are based on the reallocation measure as defined in (4). Theoefficient of the IT intensity is positive and significant in all specifi-ations, confirming the findings of existing literature (Brynjolfssonnd Hitt, 1996, 2003) on the long-run productivity growth effect ofT.25 When we include the reallocation measure of labor input (RL),s defined in (3), the coefficient for the interaction of IT intensitynd RL is positive and significant in column (2). This implies that
he impact of IT investment on the 5-year productivity growth sig-ificantly increases with the active reallocation of labor input. Wexamine the economic significance of this finding by measuring the
25 Brynjolfsson and Hitt (1996, 2003) show that the productivity effect of IT is betterxhibited with a longer time horizon of 3 to 7 years rather than with a shorter timeorizon.

effect of change in one standard deviation of the labor reallocationmeasure interacted with the IT intensity on variation in the 5-yearTFP growth as shown in the following equation:[

ˆ̌ 2 × ln(IT) × (RL)]

(GLR(TFP))(10)

where ln(IT) is the average log IT intensity and denotes standarddeviation. The reallocation measure of labor input can explain morethan 30% of the difference in 5-year TFP growths for industries withthe same IT intensity. In column (3), we examine the significance ofthe reallocation measure of capital input (RK).26 Unlike our resultsin column (2), the coefficient for the interaction of IT intensity andRK is positive but insignificant. This may reflect the fact that capitalreallocation is, generally, more difficult than labor reallocation. Ourfindings are also consistent with those of McAfee and Brynjolfsson(2008) and Bloom et al. (2012) who emphasize the importance ofhuman resource management in relation to the IT adoption.

We also try alternative reallocation measure as defined in (4).The results from column (4) of Table 4 confirm a significant roleof labor reallocation interacted with IT. The magnitudes of thecoefficients are similar regardless of whether we use (3) or (4) tomeasure the reallocation effect (0.055 and 0.049 for the interactionterm and 0.295 and 0.234 for the reallocation measure itself). Thecapital reallocation measure interacted with IT attracts a negativebut insignificant coefficient. Overall, our results in Table 4 indicatethat the reallocation effect of labor input is more important thanthat of capital input in determining the effect of IT investments on5-year productivity growth.

6. Robustness

Section 5.2 shows that our results are robust to different
methodologies for estimating reallocation effects. In this section,we provide additional robustness checks. First, we run 2SLS regres-sions to deal with possible endogeneity issues. Second, we test
26 To avoid possible multicollinearity problems, we do not include two reallocationmeasures simultaneously in regressions. Nonetheless, results with the two variablesgenerate qualitatively similar results.


Table 5Two-stage panel regressions explaining TFP growth dispersion.

(1) (2) (3) (4) (5)

ln(IT) 0.059** 0.060** 0.049* 0.094*** 0.069**

(0.029) (0.026) (0.070) (0.001) (0.026)ln(R&D) −0.011 −0.013* −0.019** −0.015*

(0.162) (0.099) (0.019) (0.080)ln(age) −0.029** −0.027** −0.017

(0.010) (0.024) (0.146)ln(size) −0.059*** −0.051*** −0.033*

(0.000) (0.003) (0.053)Impp 0.230*** 0.240***

(0.000) (0.000)ln(Num) 0.088***

(0.001)Weak instrument test 71.52 70.46 73.74 60.72 56.82Overidentificationtest

2.69 2.80* 1.55 0.21 0.79(0.101) (0.095) (0.214) (0.651) (0.373)

Adjusted R2 0.521 0.522 0.532 0.528 0.544Sample size 1,202 1,202 1,202 1,202 1,202

Notes: All panel regressions include both industry and time fixed effects. The dependent variable is the dispersion of TFP growth, defined as the difference in the average TFPgrowth rates between the top and bottom terciles. IT intensity, IT, is the ratio of information technology capital (computers and software) to non-IT capital. R&D intensity,R&D, is the ratio of capitalized past research and development spending to property, plant and equipment (PP&E). Age is the average age of firms. Size is the average sales offirms. lmpp is the ratio of imports over industry gross output minus exports. Num is the number of firms in an industry. All explanatory variables are lagged by one year. Thesample excludes finance industries (SIC 6000 to 6999). Numbers in parentheses are probability levels, based on t-statistics adjusted for heteroskedasticity, at which the nullhypothesis of a zero coefficient can be rejected. IT tax rates and the marginal cost of IT quality production are used as instrumental variables. F-statistics for the instrumentsin the first-stage regression are reported in the weak instrument test. Chi-squared statistics are reported in the overidentifying restriction test. Numbers in parentheses fort he exc

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statistics for both level of IT and its interaction with reallocationmeasure in the first-stage regressions are greater than 10 for mostcolumns, which suggests that the instruments pass weak IV tests.27

27 Some F-statistics in columns (3) and (5) are below 10. Thus, we perform addi-tional tests to check the validity of the estimated coefficients in the second stageregressions based on Anderson–Rubin statistics, which are valid whether the instru-

he overidentification test are probability levels rejecting the null hypothesis that t* Significant at the 10% level.

** Significant at the 5% level.*** Significant at the 1% level.

hether our results are concentrated in the non-manufacturingector where IT is more intensively used. Finally, we use alter-ative TFP measures to address potential mismeasurement issues

n capital and the cost share of each input. All our results remainualitatively similar to those shown in Tables 3 and 4.

.1. Endogeneity

To address a possible endogeneity problem for IT, we need toapture the exogenous variation of IT intensity which is drivenolely by the supply-side condition of IT-producing industries. Ifn exogenous change in IT is shown to increase the TFP growthispersion, we can confirm the existence of a causal chain from

T to the dispersion. Of course, this does not imply that the causalhain from the TFP growth dispersion to IT investment, which ishe demand-side driven IT investment, is impossible. Firms mayemand more IT with fierce competition reflected in greater TFProwth dispersion. In this case, higher IT intensity is a result, ratherhan a cause of the TFP growth dispersion among firms. In fact, theffect could be bi-directional in reality. What we attempt to shown this section using instrumental variable regressions is to confirmhe existence of the causal chain from the IT to the dispersion, ando demonstrate that the results reported in the previous section areot driven by possible biases in coefficients which may arise due toorrelations between the error term and IT intensity.

To address this issue, we employ two instrumental variableshat reflect the supply-side of IT as in Chun et al. (2008) and Chunt al. (2011): the tax rate on IT and the marginal cost of IT qual-ty production. The portion of IT intensity which can be explainedither by the tax rate or marginal cost of IT production reflectshanges in the purchasing price of IT and thus reflects changes inhe supply-side condition. This portion of IT intensity is not driveny the demand-side consideration. Thus, if it can explain the disper-ion in TFP growth, we can establish a causal chain from IT intensity
o the TFP growth dispersion.
To estimate the IT tax rate, we use asset-specific tax parame-ers that affect the marginal rental price of capital, which is defineds �SLi,j,t for asset k at year t, with �k,t the effective rate of the

luded instruments are valid.

investment tax credit, ut the corporate income tax rate, and zk,tthe present value of a dollar of tax depreciation allowances. Thesevariables are obtained from the BLS. Using the IT asset compositionof each industry each year, we aggregate these IT tax parametersusing the Törnqvist method. The tax parameters are not equal forall types of IT assets. For example, computer hardware qualifies foran investment tax credit, whereas software does not. Our secondinstrumental variable is the marginal cost of computer quality, asestimated in Chun and Nadiri (2008). The marginal cost of increas-ing computer quality is mainly driven by the efficacy of R&D in thecomputer-producing sector, which is exogenous to the IT demandcondition.

Table 5 reports results from 2SLS regressions to test H1. In allfive specifications, the signs and significances of the IT coefficientsremain the same as those reported in Table 3. Overidentifyingrestriction tests in all columns of the table are not significant at the5% level, thus suggesting that the instrumental variables are exoge-nous. The F-statistics in the first-stage regressions are greater than10 for all columns, which suggests that the instruments pass weakIV tests.

Table 6 reports results from 2SLS regressions to test H2. Theresults in columns (2) and (3) are based on the reallocation measureas defined in (3), while those in columns (4) and (5) are based on thereallocation measure as defined in (4). In all columns of the table,overidentifying restrictions tests are not significant at the 5% level,thus suggesting that instrumental variables are exogenous. The F-

ments are weak or even irrelevant. We also estimate the model using the limitedinformation maximum likelihood (LIML) estimation method which is robust to theweak instrument problem. The results based on Anderson–Rubin statistics and LIMLcoefficient estimates are qualitatively similar to the two-stage panel regressionresults.


Table 6Two-stage panel regressions of 5-year TFP growth.

(1) (2) (3) (4) (5)

ln(IT) 0.151* 0.198** 0.240*** 0.167** 0.171**

(0.053) (0.029) (0.006) (0.032) (0.019)ln(IT) × RL 0.148*** 0.049***

(0.004) (0.001)RL 0.776*** 0.225***

(0.003) (0.001)ln(IT) × RK 0.100** −0.005

(0.031) (0.446)RK 0.466** −0.035

(0.042) (0.462)InitialTFP

−0.367*** −0.389*** −0.418*** −0.355*** −0.367***

(0.000) (0.000) (0.000) (0.000) (0.000)Weak instrument test 11.04 11.20, 11.61 5.96, 16.19 11.56, 24.31 7.57, 16.16Overidentificationtest

0.21 0.83 2.18 3.35 2.22(0.646) (0.661) (0.337) (0.187) (0.329)

Adjusted R2 0.542 0.510 0.490 0.577 0.530Sample size 226 226 226 226 226

Notes: All panel regressions include both industry and time fixed effects. The dependent variable is the industry 5-year TFP growth rate defined over six non-overlapping5-year intervals (1971–1975, . . ., 1996–2000). Initial TFP is the logarithm of TFP. IT intensity, IT, is the ratio of information technology capital (computers and software) tonon-IT capital. RL and RK are reallocation measures of labor and capital inputs, respectively. The reallocation measures in columns (2) and (3) are obtained from a firm-levelcross-sectional regression of labor or capital growth rate of a firm in an industry on the TFP growth rate of the firm in the previous year using previous year nominal value-addedas weights. The reallocation measures in columns (4) and (5) are calculated as the average of changes in a firm’s labor or capital share within in an industry using its previousyear TFP growth rates as weights. All explanatory variables are measured at the beginning year of each 5-year interval. The IT variable and the IT variable interacted withreallocation measures are instrumented using the IT tax rate, the marginal cost of IT production, and both the tax and cost variables interacted with reallocation measures.The sample excludes finance industries (SIC 6000 to 6999). Numbers in parentheses are probability levels, based on t-statistics adjusted for heteroskedasticity, at whichthe null hypothesis of a zero coefficient can be rejected. F-statistics for the instruments in the first-stage regression are reported in the weak instrument test. Chi-squaredstatistics are reported in the overidentifying restriction test. Numbers in parentheses for the overidentification test are probability levels rejecting the null hypothesis thatthe excluded instruments are valid.



Osibcmtri

tatT

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cg

ilar results are obtained when we use a more narrowly defined

verall, our results for two-stage panel regressions are qualitativelyimilar to those in Table 4. One notable difference is that the cap-tal reallocation measure, estimated using (3), interacted with ITecomes positive and significant. However, it becomes insignifi-ant in the fifth column when we use the alternative reallocationeasure defined in (4). These results are qualitatively similar to

hose reported in Table 4 and suggest an overarching role of theeallocation effect through labor input rather than through capitalnput.

To avoid a possible correlation between the initial TFP level andhe error term in Table 6, we employ the lagged TFP levels measuredt t − 9 as an instrument for the initial TFP at t − 4.28 Although notabulated, the results are qualitatively similar to those obtained inable 6, regardless of whether the initial TFP level is instrumented.

.2. Sectoral and period differences

In Table 7, we investigate whether the positive effect of IT on theFP dispersion is concentrated on the non-manufacturing sectorhere IT is more intensively used. In order to check this possibility,e first include the IT intensity variable along with the IT variable

nteracted with a non-manufacturing dummy. If the effect of IT isoncentrated in the non-manufacturing sector, the coefficient forhe interacted variable will be significant while the coefficient for
T will be insignificant. The result is reported in the first column.he coefficient estimate of the interacted variable is insignificant,hile IT remains positive and significant.
28 The beginning year TFP level of the previous 5-year interval measured at t − 9an be an instrument for the TFP level measured at t−4 if the error terms are autore-ressive at order 1, which we confirm in our data.

Even when control variables are included, we can confirmthe positive contribution of IT in both manufacturing and non-manufacturing sectors. For example, in the model with controlsreported in column (4) of Table 7, the coefficient of the cross-product of the IT and NFG variables becomes negative andstatistically significant at the 5% level. Nonetheless, the size of thisnegative coefficient (−0.017) is relatively small compared to the ITcoefficient (0.042), which suggests that the dispersion effect of IT issubstantial in the nonmanufacturing sector as well as in the man-ufacturing sector, even though the positive coefficient is slightlysmaller in the non-manufacturing sector (0.042 vs. 0.025). This con-firms that the role of IT in firm-level productivity dispersion is notconfined to a particular sector.

In the last two columns of Table 7, we examine whether theeffect of IT on the TFP growth dispersion is stronger during theIT-driven productivity miracle period of the 1990s (Oliner andSichel, 2000)29. To regressions of columns (1) and (5) in Table 4,we add the IT intensity variable interacted with a dummy vari-able which takes the value of 1 if years in the sample lie between1991 and 2000, and zero otherwise. Column (5) of Table 7 showsthat coefficients for both IT and the interaction term are positiveand significant. However, when we add control variables in col-umn (6), even though the coefficient of IT remains positive andsignificant, the significance of interaction term disappears. Sim-

IT miracle period of between 1995 and 2000. The fact that thestronger IT effect in the 1990s in (5) are mainly explained by indus-try characteristics such as average age and size of firms in (6) can

29 We thank an anonymous referee for pointing out this issue.


Table 7TFP growth dispersion regressions: sectoral and period differences.

(1) (2) (3) (4) (5) (6)

ln(IT) 0.030** 0.033** 0.042*** 0.021* 0.027**

(0.040) (0.023) (0.002) (0.087) (0.039)ln(IT)×NFG −0.004 −0.006 −0.017**

(0.530) (0.382) (0.011)ln(IT)×D90 0.027** −0.012

(0.012) (0.147)ln(R&D) −0.010 −0.007 −0.032* −0.020*

(0.584) (0.692) (0.094) (0.092)ln(R&D)×NFG 0.001 −0.004 0.021

(0.957) (0.841) (0.325)ln(age) −0.022* −0.020*

(0.059) (0.092)ln(size) −0.034** −0.033*

(0.049) (0.058)Impp 0.204*** 0.214***

(0.000) (0.000)ln(Num) 0.115*** 0.092***

(0.000) (0.001)Adjusted R2 0.525 0.523 0.526 0.552 0.528 0.551Sample size 1,202 1,202 1,202 1,202 1,202 1.202

Notes: All panel regressions include both industry and time fixed effects. The dependent variable is the dispersion of TFP growth, defined as the difference in the average TFPgrowth rates between the top and bottom terciles. IT intensity, IT, is the ratio of information technology capital (computers and software) to non-IT capital. NFG is a dummyvariable that equals 1 if an industry belongs to the non-manufacturing sector. D90 is a dummy variable that equals 1 if the sample period is 1991–2000. R&D intensity,R&D, is the ratio of capitalized past research and development spending to property, plant and equipment (PP&E). Age is the average age of firms. Size is the average sales offirms. lmpp is the ratio of imports over industry gross output minus exports. Num is the number of firms in an industry. All explanatory variables are lagged by one year. Thesample excludes finance industries (SIC 6000 to 6999). Numbers in parentheses are probability levels, based on t-statistics adjusted for heteroskedasticity, at which the nullhypothesis of a zero coefficient can be rejected.

* Significant at the 10% level.* Significant at the 5% level.

** Significant at the 1% level.

Table 8TFP growth dispersion regressions: alternative TFP measures.

(1) (2) (3) (4)

ln(IT) 0.020* 0.021* 0.024** 0.027**

(0.099) (0.089) (0.032) (0.022)ln(R&D) −0.014 −0.014*

(0.110) (0.063)ln(age) −0.023* −0.012

(0.070) (0.328)ln(size) −0.051*** −0.027*

(0.006) (0.082)Impp 0.187*** 0.239***

(0.000) (0.000)ln(Num) 0.094*** 0.118***

(0.000) (0.000)Adjusted R2 0.577 0.600 0.546 0.578Sample size 1,202 1,202 1,202 1,202

Notes: All panel regressions include both industry and time fixed effects. The dependent variable is the dispersion of TFP growth, defined as the difference in the average TFPgrowth rates between the top and bottom terciles. The dispersion measures in columns (1)–(2) and (3)–(4) are calculated from TFP adjusted for variable capital utilizationand from TFP estimated with revenue share under the assumption of constant returns to scale, respectively. IT intensity, IT, is the ratio of information technology capital(computers and software) to non-IT capital. R&D intensity, R&D, is the ratio of capitalized past research and development spending to property, plant and equipment (PP&E).Age is the average age of firms. Size is the average sales of firms. lmpp is the ratio of imports over industry gross output minus exports. Num is the number of firms in anindustry. All explanatory variables are lagged by one year. The sample excludes finance industries (SIC 6000 to 6999). Numbers in parentheses are probability levels, basedon t-statistics adjusted for heteroskedasticity, at which the null hypothesis of a zero coefficient can be rejected.

* Significant at the 10% level.**

bRati(os

We also examine whether our results are robust to alternativemethods of calculating TFP.30 First, we address mismeasurement in

Significant at the 5% level.*** Significant at the 1% level.

e consistent with Hobijn and Jovanovic (2001) and Jovanovic andousseau (2001). They show that IT allows creative firms to be cre-ted and listed at their early stage of life-cycle, which in turn affectshe average age and size of industry as evidenced in the 1990s
n the U.S. Regardless, the fact that IT remains significant both in5) and (6) shows that the IT-driven dispersion effect is robustlybserved throughout our sample period, not confined in a specificub-period.
6.3. Alternative measures for TFP

30 We are grateful to both referees for suggesting various robustness checks forTFP measurements as discussed in this section.


Table 95-year TFP growth regressions: alternative TFP measures.

(1) (2) (3) (4)

ln(IT) 0.050 0.057* 0.048 0.060*

(0.161) (0.074) (0.228) (0.080)ln(IT) × RL 0.046** 0.041*** 0.017 0.048***

(0.027) (0.000) (0.495) (0.000)RL 0.239** 0.206*** 0.048 0.238***

(0.042) (0.001) (0.715) (0.000)InitialTFP

−0.365*** −0.357*** −0.316*** −0.319***

(0.000) (0.000) (0.004) (0.001)Adjusted R2 0.563 0.591 0.323 0.379Sample size 226 226 226 226

Notes: All panel regressions include both industry and time fixed effects. The dependent variable is the industry 5-year TFP growth rate defined over six non-overlapping5-year intervals (1971–1975, . . ., 1996–2000). The 5-year TFP growth rates in columns (1)–(2) and (3)–(4) are calculated from TFP adjusted for variable capital utilizationand from TFP estimated with revenue share under the assumption of constant returns to scale, respectively. Initial TFP is the logarithm of TFP. IT intensity, IT, is the ratioof information technology capital (computers and software) to non-IT capital. RL is reallocation measures of labor input. The reallocation measures in columns (1) and (3)are obtained from a firm-level cross-sectional regression of the labor growth rate of a firm in an industry on the TFP growth rate of the firm in the previous year usingprevious year nominal value-added as weights. The reallocation measures in columns (2) and (4) are calculated as the average of changes in a firm’s labor share within inan industry using its previous year TFP growth rates as weights. All explanatory variables are measured at the beginning year of each 5-year interval. The sample excludesfinance industries (SIC 6000 to 6999). Numbers in parentheses are probability levels, based on t-statistics adjusted for heteroskedasticity, at which the null hypothesis of azero coefficient can be rejected.

*

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Significant at the 10% level.** Significant at the 5% level.


apital, which may arise if firms vary the actual amount of capitalervices utilized in production in response to business cycle condi-ions. In this case, the capital stock does not accurately reflect theontribution of capital to output. To address this potential mismea-urement issue, we employ a TFP estimation procedure suggestedy Basu and Kimball (1997) and used in Syverson (2004a) andalasubramanian and Sivadasan (2009). Under the assumption that

firm cannot substitute materials for capital in production, thesetudies show that materials can be a proxy for capital utilizationecause materials use is proportional to capital services flows. Fol-

owing this method, we construct TFP growth and its dispersioneasures. Second, we construct TFP using revenue shares rather

han cost shares under the assumption of constant returns to scale.his approach may avoid a possible bias in TFP when the inputs areot paid by their marginal productivity (Solow, 1957).31

Table 8 reports the estimation results to test H1. The first twoolumns are based on the TFP growth measure adjusted for vari-ble capital utilization. The last two columns are based on the TFProwth measure estimated using revenue share rather than costhare and assuming constant returns to scale. All the results areualitatively similar to those in Table 3. Table 9 also reports thestimation results to test H2 for the labor reallocation measure.32

he first two columns are based on the TFP growth measuredjusted for variable capital utilization, and the last two columnsre based on the TFP growth measure estimated using revenuehare rather than cost share and assuming constant returns tocale. We use two reallocation measures in Table 9. The first andhird columns are based on the reallocation measure estimated in3), and the second and fourth columns are based on the reallo-ation measure estimated in (4). The results are stronger whene use the reallocation measure as defined in (4). In this case,

egardless of the TFP growth measurements, both the level andnteraction terms are positive and significant. However, even inhe case where we use the reallocation measure estimated in (3),
he interaction terms remain positive and significant in the firstolumn.
31 Nonetheless, the ratio of income over total costs in our sample is close to 1 (1.1),hich suggests that the bias may not be large.

32 As in Table 4, capital reallocation measures are insignificant and are not reportedere.

7. Conclusion

This paper proposes a new channel of IT-driven aggregate pro-ductivity growth, focusing on the destructive nature of IT and theresulting resources reallocation. As IT propagates across firms in aneconomy, some firms enjoy productivity growths after investing inIT, while others with capital and corporate organizational struc-tures tied to old technologies experience the destruction of theirfirm value and decreases in productivity (Hobijn and Jovanovic,2001; Gârleanu et al., 2012a,b; Kogan and Papanikolaou, 2013,2014). The resulting dispersion in productivity growths amongfirms necessitates resource reallocation from inefficient firms toefficient ones (Foster et al., 2001, 2006; Bartelsman et al., 2009;Syverson, 2011; Acemoglu et al., 2012; Kogan et al., 2012). If theefficiency of input resource reallocation differs across industries,the effect of IT on industry-level productivity growth would alsoexhibit a significant cross-industry variation. In fact, we find thatmore than 30% of the 5-year industry-level productivity growthis due to input resource reallocation in the U.S. during our sam-ple period of 1971–2000. Our paper is the first which analyzes thegrowth effect of IT in relation with the input reallocation efficiencyand supplements recent research which emphasizes the role ofreallocation in explaining productivity growth (Foster et al., 2001;Bartelsman et al., 2009; Hsieh and Klenow, 2009).

This economically significant reallocation effect might answerthe question of why European countries did not experience the IT-driven productivity miracle that the U.S. experienced in the late1990s. The difference between the two might lie not only in the dif-ferent management practices between U.S. and European firms, butalso in the different levels of efficiency in the market mechanisms(Bloom and Van Reenen, 2011; Bloom et al., 2012). For example,input markets in European countries are more regulated and lesscompetitive than their U.S. counterparts.

Our results imply that the effect of a new GPT, such as IT,on economy-wide productivity growth cannot be analyzed inde-pendently of the underlying market mechanism. If a country hasregulated labor markets and less developed financial markets, bothof which are typical characteristics of developing countries, then
the reallocation of resources may be severely hampered and thusthe growth effects of a new technology will be limited. This impliesthat policymakers in developing countries should approach theadoption of IT with a larger framework geared toward instituting

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etter infrastructure, rather than with narrowly focused programsargeted toward increasing IT investments.

cknowledgements

We thank Steven Davis, Lorin Hitt, Inmoo Lee, Randall Morck,ongwook Park, Bernard Yeung, and seminar participants at Ewhaniversity, the KAEA-KEA International Conference, KAIST Businesschool, the Korea Institute of Finance, Seoul National University,ogang University, and Yonsei University. We are also most gratefulo the editor and three anonymous referees for their particu-arly helpful comments. This work was supported by the Nationalesearch Foundation of Korea Grant funded by the Korean Govern-ent (NRF-2013S1A3A2053312), the Sogang University Researchrant of 2013 (201310066), the Institute of Finance and Banking,nd the Institute of Management Research at Seoul National Uni-ersity, respectively.

eferences

cemoglu, D., Akcigit, U., Bloom, N., Kerr, W., 2012. Innovation, reallocation andgrowth. In: Working Paper. University of Pennsylvania, Economics Department.

ghion, P., Bloom, N., Blundell, R., Griffith, T., Howitt, P., 2005. Competition andinnovation: an inverted-U relationship. Quarterly Journal of Economics 120,701–728.

ghion, P., Blundell, R., Griffith, R., Howitt, P., Prantl, S., 2009. The Effects of entry onincumbent innovation and productivity. Review of Economics and Statistics 91,20–32.

ghion, P., Howitt, P., Violante, G.L., 2002. General purpose technology and wageinequality. Journal of Economic Growth 7, 315–345.

aily, M.N., Hulten, C., Campbell, D., 1992. Productivity dynamics in manufac-turing plants. Brookings Papers on Economic Activity, Microeconomics: 1992,187–267.

akos, J.Y., 1997. Reducing buyer search costs: implications for electronic market-places. Management Science 43, 1676–1692.

alasubramanian, N., Sivadasan, J., 2009. Capital resalability, productivity disper-sion, and market structure. Review of Economics and Statistics 91, 547–557.

artelsman, E.J., 2013. ICT, reallocation and productivity. In: EuropeanEconomy–Economic Papers No. 486.

artelsman, E.J., Haltiwanger, J.C., Scarpetta, S., 2009. Cross-country differences inproductivity: the role of allocation and selection. In: NBER Working Paper No.15490.

asant, R., Commander, S., Harrison, R., Menezes-Filho, N., 2011. ICT adoption andproductivity in developing countries: new firm level evidence from Brazil andIndia. Review of Economics and Statistics 93, 528–541.

asu, S., Fernald, J.G., 1997. Returns to scale in U.S. production: estimates and impli-cations. Journal of Political Economy 105, 249–283.

asu, S., Fernald, J.G., Oulton, N., Srinivasan, S., 2003. The case of the missing pro-ductivity growth, or does information technology explain why productivityaccelerated in the United States but not in the United Kingdom? NBER Macroe-conomics Annual 18, 9–63.

asu, S., Kimball, M.S., 1997. Cyclical productivity with unobserved input variation.In: NBER Working Paper No. 5915.

eck, T., Levine, R., Loayza, N., 2000. Finance and the sources of growth. Journal ofFinancial Economics 58, 261–300.

enner, M.J., 2007. The incumbent discount: stock market categories and responseto radical technological change. Academy of Management Review 32, 703–720.

ernard, A., Jones, C., 1996. Productivity across industries and countries: time seriestheory and evidence. Review of Economics and Statistics 78, 135–146.

ertrand, M., 2004. From the invisible handshake to the invisible hand? How importcompetition changes the employment relationship. Journal of Labor Economics22, 723–766.

loom, N., Van Reenen, J., 2011. Human resource management and productivity.In: Ashenfelter, O., Card, D. (Eds.), Handbook of Labor Economics 4B. Elsevier,Amsterdam, pp. 1697–1767.

loom, N., Sadun, R., Van Reenen, J., 2012. Americans do I.T. better: US multinationalsand the productivity miracle. American Economic Review 102, 167–201.

resnahan, T.F., Brynjolfsson, E., Hitt, L.M., 2002. Information technology, workplaceorganization, and the demand for skilled labor: firm-level evidence. QuarterlyJournal of Economics 117, 339–376.

resnahan, T.F., Greenstein, S., 1996. Technical progress and co-invention in com-puting and in the uses of computers. Brookings Papers on Economic Activity,Microeconomics: 1996, 1–83.

resnahan, T.F., Trajtenberg, M., 1995. General purpose technologies ‘Engines ofgrowth’? Journal of Econometrics 65, 83–108.

rown, J.R., Goolsbee, A., 2002. Does the internet make markets more competi-tive? Evidence from the life insurance industry. Journal of Political Economy110, 481–507.

rynjolfsson, E., 1993. The productivity paradox of information technology. Com-munications of the ACM 36, 67–77.

y 44 (2015) 999–1016 1015

Brynjolfsson, E., Hitt, L., 1996. Paradox lost? Firm-level evidence on the returns toinformation systems spending. Management Science 42, 541–558.

Brynjolfsson, E., Hitt, L., 2003. Computing productivity: firm-level evidence. Reviewof Economics and Statistics 85, 793–808.

Brynjolfsson, E., Malone, T., Gurbaxani, V., Kambil, A., 1994. Does information tech-nology lead to smaller firms? Management Science 40, 1645–1662.

Bureau of Labor Statistics, 1997. BLS Handbook of Methods. Bureau of Labor Statis-tics, Washington, DC.

Chan, L.K.C., Lakonishok, J., Sougiannis, T., 2001. The stock market valua-tion of research and development expenditures. Journal of Finance 56,2431–2456.

Chari, M.D.R., Devaraj, S., David, P., 2008. The impact of information technologyinvestments and diversification strategies on firm performance. ManagementScience 54, 224–234.

Chun, H., Kim, J.W., Morck, R., 2011. Varying heterogeneity among U.S. firms: factsand implications. Review of Economics and Statistics 93, 1034–1052.

Chun, H., Kim, J.W., Morck, R., Yeung, B., 2008. Creative destruction andfirm-specific performance heterogeneity. Journal of Financial Economics 89,109–135.

Chun, H., Nadiri, M.I., 2008. Decomposing productivity growth in the U.S. computerindustry. Review of Economics and Statistics 90, 174–180.

Colecchia, A., Schreyer, P., 2002. ICT investment and economic growth in the 1990:is the United States a unique case? A comparative study of nine OECD countries.Review of Economic Dynamics 5, 408–442.

Comin, D., Mulani, S., 2009. A theory of growth and volatility at the aggregate andfirm level. Journal of Monetary Economics 56, 1023–1042.

Corrado, C.A., Hulten, C.R., Sichel, D.E., 2006. Intangible capital and economic growth.In: NBER Working Paper No. 11948.

David, P.A., 1990. The dynamo and the computer: a historical perspec-tive on the modern productivity paradox. American Economic Review 80,355–361.

Davis, S.J., Haltiwanger, J., Jarmin, R., Miranda, J., 2006. Volatility and dispersion inbusiness growth rates: publicly traded versus privately held firms. In: NBERWorking Paper No. 12354.

Dewan, S., Kraemer, K.L., 2000. Information technology and productivity: evidencefrom country-level data. Management Science 46, 548–562.

Doraszelski, U., Jaumandreu, J., 2008. R&D and productivity: estimating productionfunctions when productivity is endogenous. In: Harvard Institute of EconomicResearch Discussion Paper No. 2147.

Dunne, T., Foster, L., Haltiwanger, J., Troske, K.R., 2004. Wage and productivity disper-sion in United States manufacturing: the role of computer investment. Journalof Labor Economics 22, 397–429.

Faggio, G., Salvanes, K.G., Van Reenen, J., 2010. The evolution of inequality in pro-ductivity and wages: panel data evidence. Industrial and Corporate Change 19,1919–1951.

Fama, E.F., French, K.R., 2004. New lists: fundamentals and survival rates. Journal ofFinancial Economics 73, 229–269.

Feenstra, R.C., Romalis, J., Schott, P.K., 2002. U.S. imports, exports, and tariff data,1989–2001. In: NBER Working Paper No. 9387.

Foster, L., Haltiwanger, J., Krizan, C.J., 2001. Aggregate productivity growth: lessonsfrom microeconomic evidence. In: Hulten, C.R., Dean, E.R., Harper, M.J. (Eds.),New Developments in Productivity Analysis. University of Chicago Press,Chicago, pp. 303–372.

Foster, L., Haltiwanger, J., Krizan, C.J., 2006. Market selection, reallocation, andrestructuring in the U.S. retail trade sector in the 1990. Review of Economicsand Statistics 88, 748–758.

Franco, F., Philippon, T., 2007. Firms and aggregate dynamics. Review of Economicsand Statistics 89, 587–600.

Fraumeni, B.M., 1997. The measurement of depreciation in the U.S. National Incomeand Product Accounts. Survey of Current Business 77 (7), 7–23.

Gârleanu, N., Kogan, L., Panageas, S., 2012a. Displacement risk and asset returns.Journal of Financial Economics 105, 491–510.

Gârleanu, N., Panageas, S., Yu, J., 2012b. Technological growth and asset pricing.Journal of Finance 67, 1265–1292.

Greenwood, J., Jovanovic, B., 1999. The information-technology revolution and thestock market. American Economic Review 89, 116–122.

Griliches, Z., Regev, H., 1995. Firm productivity in Israeli industry 1979–1988. Journalof Econometrics 65, 175–203.

Hall, B.H., 1990. The manufacturing sector master file: 1959–1987. In: NBER WorkingPaper No. 3366.

Hall, R.E., 1988. The relationship between price and marginal cost in U.S. industry.Journal of Political Economy 96, 921–947.

Hall, R.E., Jorgenson, D.W., 1967. Tax policy and investment behavior. AmericanEconomic Review 57, 391–414.

Herman, S.W., 2000. Fixed assets and consumer durable goods: estimates for1925–98 and new NIPA table-changes in net stock of produced assets. Survey ofCurrent Business 80 (4), 17–30.

Hobijn, B., Jovanovic, B., 2001. The information technology revolution and the stockmarket: evidence. American Economic Review 91, 1203–1220.

Hsieh, C.T., Klenow, P.J., 2009. Misallocation and manufacturing TFP in China andIndia. Quarterly Journal of Economics 124, 1403–1448.

Jovanovic, B., Rousseau, P.L., 2001. Why wait? A century of life before IPO. AmericanEconomic Review 91, 336–341.

Jovanovic, B., Rousseau, P.L., 2005. General purpose technologies. In: Aghion, P.,Durlauf, S. (Eds.), Handbook of Economic Growth. Elsevier, Amsterdam, TheNetherlands, pp. 1181–1224.

http://refhub.elsevier.com/S0048-7333(14)00198-X/sbref0005
































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































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K

L

L

M

O

P

R


ogan, L., Papanikolaou, D., 2013. Firm characteristics and stock returns: the role ofinvestment-specific shocks. Review of Financial Studies 26, 2718–2759.

ogan, L., Papanikolaou, D., 2014. Growth opportunities, technology shocks, andasset prices. Journal of Finance 69, 675–718.

ogan, L., Papanikolaou, D., Seru, A., Stoffman, N., 2012. Technological innovation,resource allocation, and growth. In: NBER Working Paper No. 17769.

oughran, T., Ritter, J., 2004. Why has IPO underpricing changed over time? FinancialManagement 33, 5–37.

ucas, R.E., 1978. Asset prices in an exchange economy. Econometrica 46,1429–1445.

cAfee, A., Brynjolfsson, E., 2008. Investing in the IT that makes a competitivedifference. Harvard Business Review 86 (7–8), 98–107.

liner, S.D., Sichel, D.E., 2000. The resurgence of growth in the late 1990: is infor-mation technology the story? Journal of Economic Perspectives 14, 3–22.

astor, L., Veronesi, P., 2009. Technological revolutions and stock prices. AmericanEconomic Review 99, 1451–1483.

evenga, A.L., 1992. Exporting jobs? The impact of import competition on employ-ment and wages in US manufacturing. Quarterly Journal of Economics 107,255–284.

y 44 (2015) 999–1016

Schumpeter, J., 1912. Theorie der wirtschaftlichen Entwicklung. Dunker und Hum-bolt, Leipzig (The Theory of Economic Development). Harvard University Press,Cambridge, MA (Translation by Opie, R., 1934).

Solow, R.M., 1957. Technical change and the aggregate production function. Reviewof Economics and Statistics 39, 312–320.

Stiroh, K.J., 2002. Information technology and the U.S. productivity revival: what dothe industry data say? American Economic Review 92, 1559–1576.

Syverson, C., 2004a. Product substitutability and productivity dispersion. Review ofEconomics and Statistics 86, 534–550.

Syverson, C., 2004b. Market structure and productivity: a concrete example. Journalof Political Economy 112, 1181–1222.

Syverson, C., 2011. What determines productivity? Journal of Economic Literature49, 326–365.

Timmer, M.P., Van Ark, B., 2005. Does information and communication technol-
ogy drive EU–US productivity growth differentials? Oxford Economic Papers57, 693–716.
Tirole, J., 1988. The Theory of Industrial Organization. MIT Press, Cambridge, MA.Wurgler, J., 2000. Financial markets and the allocation of capital. Journal of Financial

Economics 58, 187–214.

































































































































































































































































































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