Technical Paper Series

Congressional Budget Office

Washington, DC

INTERNATIONAL TRADE AND U.S. PRODUCTIVITY

Juann Hung

Macroeconomic Analysis Division

Congressional Budget Office

Matt Salomon 

Joint Economic Committee

U.S. Congress

Stacia Sowerby

Department of Economics

Harvard University

June 2003

2003-5

We thank colleagues at the Congressional Budget Office, especially Bob Arnold,

David Brauer, Bob Dennis, Kim Kowalewski, Frank Russek, and Angelo

Mascaro for valuable comments. The views expressed in this paper are those of

the authors alone and should not be interpreted as those of the Congressional

Budget Office or of the Joint Economic Committee.
Abstract

This paper examines the effect of international trade on U.S. productivity. We

argue that trade can affect domestic productivity through economies-of-scale

effects, competition effects, reallocation effects, and spillover effects. We then

estimate the net impact of these effects. The results of both panel and time-series

regressions of manufacturing data indicate that (1) a decrease in the import price

has a positive competition effect on manufacturing productivity growth after one

to two years, and this impact is bigger when import penetration is bigger; and (2)

exporting activity by itself does not seem to promote productivity. Our results

suggest that the competition effect and/or the reallocation effect are the most

powerful among the four channels. The simulation results indicate that import

competition accounted for about 32 percent of labor productivity growth in

manufacturing during 1996-2001. 
1. Some economists—for example, Gordon (1999, 2000a)—maintain that the rise in

productivity growth is mainly due to cyclical forces, and is likely to fizzle away as the 1990s

boom gives way to slow growth. However, others—for example, Jorgenson and Stiroh

(2000), Oliner and Sichel (2000), and Whelan (2000)—argue that both the production and

the use of information technology (IT) have contributed substantially to the productivity

resurgence of the late 1990s.

2. Gordon argues that “the closing of American labor markets to immigration, and of goods

markets to trade, between the 1920s and 1960s gave a boost to real wages which, in turn,

made labor expensive and promoted productivity growth. The post-1972 slowdown in

productivity growth coincided with a reopening of labor markets to immigration and of

goods markets to foreign trade.” 

1

1. Introduction

The strong revival of US productivity growth since the end of 1995 has

generated much debate, and has spurred voluminous research, on what caused the

productivity acceleration and whether that acceleration is sustainable.1 The

intensity of the debate underscores the importance of productivity growth in the

overall economic performance as well as the inadequacy of our understanding of

what drives productivity growth. 

Against this background, and in light of continuing controversy over

globalization, whether international trade helps promote productivity growth is an

area of research that is gaining in importance. Some researchers have indeed

found some, if not overwhelming, support for the view that increasing openness

has a positive impact on productivity. For example, Mann (1998) finds that

increases in the import share of consumption are associated with increases in

trend productivity growth in the manufacturing sector. Bernard and Jensen

(1999) find that, mainly through reallocation of resources from less efficient to

more efficient plants, manufacturing exporters within the same industry do grow

faster than non-exporters. Lawrence (2000) finds that trade with developing

countries boost total-factor productivity (TFP) growth in manufacturing industries

with a relatively large share of imports from developing countries. Coe and

Helpman (1995) find that a country’s TFP depends on not only its own R&D

capital stock but also on the R&D stock of its trade partners. Most recently Keller

and Yeaple (2003) find that there is some evidence for imports-related spillover

of technology. Nevertheless, neither theory nor empirical evidence on this

subject is definitive so far. In one of his papers prompted by the recent

productivity debate, Gordon (2000b) even argues that openness to trade has an

adverse effect on productivity.2

 

This paper looks into the role of international trade in driving U.S.

productivity growth. We first discuss and estimate the relationship between

international trade and manufacturing productivity. We then use the estimation
3. For example, Gullickson and Harper (1999) note that manufacturing TFP more or less

accounts for all of the measured TFP changes in private business since 1979. Gordon (1999)

also notes that all of the true structural productivity growth acceleration in nonfarm private

business is explained by structural acceleration in durable manufacturing, leaving nothing

for the 87% of the economy outside of durable manufacturing. 

2

results to impute the impact of international trade on nonfarm business

productivity to gauge whether the impact is of macroeconomic significance. The

reason for focusing on manufacturing productivity in the empirical analysis is

threefold. First is the well documented fact that almost all of the gain in

measured productivity growth in the nonfarm business sector has taken place in

the manufacturing sector.3

 The second is that the U.S. manufacturing sector has

seen a much greater rise international exposure than the nonfarm business sector. 

The third is that the proxy variables for international exposure in nonfarm

business are subject to more serious measurement errors than those in

manufacturing. 

This paper identifies four theoretical channels through which increases

in international trade affect domestic productivity: (1) economies-of-scale effects,

(2) competition effects, (3) reallocation effects, and (4) spillover effects. While

an increase in imports has an adverse effect on import competitors’ productivity

growth through the economies-of-scale channel, increases in trade volumes are

expected to have positive effects on productivity growth of domestic industries

through the other three channels.

 We then estimate the effect of trade on labor productivity (LP) and TFP

in the manufacturing sector. At the industry level, we conduct panel-data

regressions to estimate the relationship between productivity and various

measures of international competition or exposure—such as import prices, import

penetrations, and export shares—across manufacturing industries at the 3-digit

SIC level. At the aggregate level, we use time-series regressions to estimate the

impacts of international exposure and competition on aggregate productivity

growth in the manufacturing sector. 

Three major findings are identified from the panel regression results. 

First, falling import prices indeed help to boost both LP and TFP growth after one

to two years. Moreover, this impact is more pronounced when import

penetration—i.e., the import share of domestic market—is bigger. Second, an

increase in import penetration has a positive impact on LP growth with a two-year

lag, even though it is negatively correlated with LP growth contemporaneously. 

This suggests that the positive effects of international trade outweigh the negative

economies-of-scale after sufficient time lapses, even though the negative effect 

prevails in the short run. Third, even though export shares are positively

correlated with LP contemporaneously, that correlation fades after one year. This
4. Labor productivity measures output per hour of labor, or overall production efficiency. Total

factor productivity measures output per combined unit of labor and capital input, or

technical progress. Section 3 gives a more elaborate account of these two measures of

productivity. 

3

suggests that, while higher-productivity firms are more likely to export because

they are more confident of recovering the initial sunk costs incurred to enter

foreign markets, exporting activity by itself does not promote productivity.

 The results of time-series regressions of LP growth are similar to those

of panel regressions in terms of import prices and export shares, though not in

terms of import penetration. Simulations based on time-series regressions show

that the combination of changes in the import prices and in import penetration has

contributed as much as 32 percent of average LP growth in manufacturing, and

about 10 percent of LP growth in nonfarm business, over the 1996-2001 period. 

These results, while subject to simplifying assumptions, suggest that import

competition does play an important role in driving U.S. productivity growth.

The rest of the paper is organized as follows. Section 2 discusses the

theoretical channels through which international trade affects domestic

productivity. Section 3 gives an overview of the data and concept used in the

empirical analysis of this paper. Section 4 presents the estimation and results of

pane regressions. Section 5 presents the estimation and results of time-series

regressions. Section 6 conducts simulation to gauge the aggregate impact of

international trade on productivity growth in the economy. Section 7 concludes.

2. Theoretical Channels: How Does Trade Affect Productivity?

We identify four channels through which international trade can affect

productivity growth of domestic industries: (1) economies-of-scale effects, (2)

competition effects, (3) reallocation effects, and (4) spillover effects. The first

two channels directly affect productivity growth at the firm level, while the latter

two affect productivity growth at the aggregate level. The first three channels

affect labor productivity both directly and through their impacts on TFP, while

the last channel affects labor productivity mainly through its impact on TFP.4

Economies-of-Scale Effects

International trade could affect a firm’s productivity through economiesof-scale effects in two ways. The first is by moving output to a lower-cost point

in the average cost curve: assuming variable costs do not rise too quickly as

output increases, an increase in a firm’s output decreases its average unit costs by

reducing the share of average fixed costs in the unit costs of output. Through this
5. Baily and Gersbach (1995) argued that “productivity is improved by competition and that it

is improved most when the competition is global.” They argue that “Increasing the

geographic range of competition increases the opportunities for absorbing a new and more

productive production process, especially if the range of competition includes best-practice

companies. As long as US auto companies, for example, did not compete against Japanese

companies, they were unwilling to risk making radical changes in their production methods. 

Barriers to the adoption of best practice production methods can thus explain why regional

competition is not enough to ensure best-practiced productivity.” 

6. If cheaper foreign imports are inputs into the production process rather than competing final

goods, this could lower firms’ costs, improve their profit margins, and perhaps dissuade

them from adopting best-practices. However, the import share of total inputs is still small. 

Campa and Goldberg (1997) find that about 4% of inputs in US manufacturing industries

were imported in 1975, and about 8% were imported in 1995.

4

along-the-cost-curve effect, an exporting firm’s labor productivity rises when

exports lead to an increase in its output, while an import-competing firm’s labor

productivity falls when imports crowd out its market share and lead to a drop in

its output. 

The second type of economies-of-scale effect results in shifting the

average cost curve downward: the expectation of higher output through exporting

offers incentive for exporting firms to undertake fixed-cost investment, including

R&D, thereby enhancing their potential productivity. This “dynamic”

economies-of-scale effect thus helps boost TFP through the added incentive for

exporters to pursue “true technical progress.” Arguably, the dynamic effect is

likely to matter more for exporting firms than for import-competitors, as

technology is more likely to progress along with (exporters’) R&D investment

than it is to regress along with (import-competitors’) R&D divestment. 

Competition Effects

 International competition in the form of lower-priced foreign goods may

play a key role in the productivity growth of import-competing firms. As

competition intensifies—whether foreign or domestic—manufacturing firms will

be forced to improve productivity if they want to stay competitive without cutting

profit margins. However, as Baily and Gersbach (1995) documented for the US

auto industry, domestic competition alone may not provide sufficient incentives to

adopt best-practice technologies; only increases in the geographic range of

competition will ensure most efficient production.5 Under competitive pressures,

domestic firms can raise TFP in a number of ways—by investing in R&D, by

corporate restructuring, by learning from foreign competitors through the reverseengineering of their products, or by imitating foreign competitors’ production

processes.6
7. See, for example, Rivera-Batiz and Romer (1991), Ben-David and Loewy (1998), and

Feeney (1999).

5

Reallocation Effects

 Increases in international trade could boost aggregate productivity

growth through three types of reallocation effects:

(1) As the more efficient of import-competing firms survive while the less

efficient are forced to exit, the average productivity growth at the industry

level will rise.

(2) Higher-productivity plants are more likely to enter foreign markets

because they are more confident that their initial entry costs will be

recovered. After entry, the increase in their markets allows higherproductivity exporting firms to expand faster than in a closed economy. 

Increases in international trade thus could help augment industry-wide 

productivity growth by allowing higher-productivity exporting firms to

represent a larger share of their industries.

(3) Inexpensive imports will displace domestic production in lowerproductivity industries. Domestic resources in these industries are

released and reallocated to industries further up the ladder of technological

sophistication, raising average productivity growth in the manufacturing

sector as a whole.

Spillover effects

 Romer (1986) pointed out the possibility that an individual firm’s R&D

efforts could spill over to affect the stock of knowledge available to all firms. A

firms face constant returns to scale to all private inputs, but its level of technology

depends on the aggregate stock of all firm’s knowledge, so that the production

function of firm i is characterized as Yi

= A(R) F(Ki, Li, Ri), where Yi , Ki,, Li, Ri are

respectively output, capital input, labor input, and the stock of knowledge of firm

i, while R is the aggregate stock of knowledge in the economy. From this

perspective, an increase in international exposure could raise overall productivity

growth through two types of spillover effects:

(1) The increase in R&D by domestic firms in response to international

exposure—for example, through import-competing firms’ responses to

international competition and exporting firms’ incentive to exploit the

economies-of-scale effect through exports—will increase the aggregate

stock of knowledge, thereby raising aggregate productivity.7
8. See Coe and Helpman (1995).

6

(2) Domestic firms—both import-competitors and exporters—could upgrade

their technology by learning from and adopting the best-practice

technologies of foreign competitors. The aggregate stock of knowledge

available to domestic firms thus could increase as their exposure to

foreign firms and foreign stocks of knowledge increases, thereby boosting

aggregate productivity.8

Through these four channels, an increase in international trade is likely to

have a net positive impact on domestic productivity growth. To be sure, some

import-competitors’ productivity growth will be adversely affected by

international exposure through the economies-of-scale channel. However, it is

questionable that the negative economies-of-scale effect will dominate the

positive effects for all import-competing firms. Moreover, even as some firms’

productivity growth are being eroded by international exposure, aggregate

productivity growth will still benefit from international trade through the

reallocation channel as productivity-losing firms become a smaller share, while

productivity-gaining firms become a bigger share, of their industries.

3. Overview of Data and Related Issues 

Before empirically testing whether the theoretical trade channels have a

net positive impact on manufacturing productivity growth, we give an overview

of data on productivity and international trade and issues related to these data.

3.1 Productivity Growth

The relationship between LP growth and TFP growth can be derived from

the growth accounting first suggested by Solow (1957). Under the assumption of

constant returns to scale and competitive markets, the rate of output growth is the

weighted sum of output growth accrue to labor, capital, or technical progress:

(1) gy = a gn + (1-a) gk + q

where gy, gn and gk are the growth rates of output, labor hours and capital,

respectively, a is the share of labor in output, and q is the Solow residual or TFP. 

TFP thus constitutes the part of output growth unexplained by growth in

production input and is attributed to technical progress. Subtracting gn from both

sides of (1), one can see that labor productivity growth (gy - gn) is a combination

of capital deepening (gk - gn) and TFP growth:
9. For example, a one-dollar increase in the stock of computer equipment provides more

productive services per period than a one-dollar increase in the stock of office buildings. 

Likewise, one- hour labor of a high-skilled worker provides more productive services than

one-hour labor of a low-skilled worker. Clearly, failure to account for quality improvements

in either labor or capital would overstate historical TFP growth.

The Bureau of Labor Statistics (BLS) takes into account changes in labor and capital quality

in its measurements of TFP. BLS’ computation of labor input growth includes both the

increase in raw hours worked and the change in the skill composition (as measured by

education and work experience) of the work force. Likewise, BLS constructs real capital

stocks as vintage aggregates of historical investments according to a service flow concept. 

See BLS Handbook of Methods (1997), Chapter 10.

7

(1') gy - gn = (1-a)(gk - gn) + q.

It should be noted that reported TFP is subject to a number of

measurement errors, and thus may not reflect the change in productivity due to

“true technical progress.” Chief among the measurement errors is the

heterogeneity in the quality or “service flow” of capital and labor inputs.9

 In

addition, measured productivity growth not attributable to “true technological

progress”—such as that derived from cyclical fluctuations, economies of scale,

and reallocation effects—may also be lumped into measured TFP. 

Reported productivity growth in the U.S. nonfarm business sector

accelerated strongly after 1995 from its prolonged stagnation. However, the

acceleration appears merely to have returned the average rate of productivity

growth to the rate that prevailed during 1960-1974 (Table 1). This pattern

pertains to both LP and TFP. LP growth in nonfarm business, after averaging

nearly 3 percent per year during 1960-1973, dropped to a meager 1.4 percent

annual rate during 1974-1995; it has since revived but not resumed its pre-1974

rate. Likewise, the average rate of TFP growth has recovered since 1995, after

a sharp slowdown over 1974-1995, but it is still considerably below its 2 percent

rate over the 1960-1973 period. 

Productivity growth in manufacturing—growth in both LP and TFP—is

considerably more upbeat than that in nonfarm business. To begin with,

productivity growth in manufacturing did not suffer as much of a slowdown as

did nonfarm business. Moreover, the growth acceleration in manufacturing after

1995 has brought the average post-1995 growth rates of both LP and TFP beyond

their average rates during 1974-1995. LP growth rates averaged 3 percent during

1960-1973, dipped to 2.7 percent during 1974-1995, then surged to 4.4 percent

during 1996-2000. Likewise, TFP growth rates averaged 1.7 percent during

1960-1973, dropped to 0.7 percent during 1974-1995, and then soared to 2.1

percent during 1996-2000. 
10. The literature offers three main theories on why productivity is procyclical. First, business

cycles may be driven mainly by exogenous changes in production technology. Second,

productivity may be procyclical because of economies of scale: the economy endogenously

becomes more efficient by moving to higher levels of activity. Third, reported factor (labor

and capital) input tends to overstate the true amount of input in down times, giving rise to

the procyclicality in measured (as opposed to true) productivity. See Basu (1996).

11. For example, see Krugman (1994, 1995) 

8

The above overview indicates that LP growth and TFP growth have

roughly moved in tandem. This comovement could be due to the fact that both

growth rates are procyclical.10 But it also indicates that LP growth has been

driven more by changes in the rate of technical progress than by capital

deepening. The overview also confirms what most analysts have agreed: even

after allowing for the fact that measured productivity growth in service sectors

may be biased downward, most of the productivity acceleration in nonfarm

business in the past decade is driven by that in the manufacturing sector. As

shown in Figure 1, LP growth in manufacturing has increasingly surpassed that in

nonfarm business since the early 1970s, and that wedge has become even more

pronounced since 1994. The wedge between TFP growth in manufacturing and

that in nonfarm business, which did not emerge until the early 1980s, has also

widened since the early 1990s (Figure 2). It appears that manufacturing

industries have enhanced productivity through labor-saving investment and

technical progress much more than non-manufacturing industries since the early

1990s. 

3.2 Openness to International Trade

A casual observation of the U.S. trade numbers tends to suggest that the

U.S. economy is still ‘effectively insulated’ from foreign competition.11 Despite

the sustained rise in imports and exports since the early 1970s, the imports/GDP

ratio was only about 14 percent, and the exports/GDP ratio about 10 percent in

2002. Such trade ratios are not much larger than their values in the early 1980s 

and suggest that U.S. economic growth and fluctuations are mainly driven by

domestic demand. 

The picture is quite different, however, if one looks at the rise in the

international exposure of the manufacturing sector. The import share of

manufactured shipments has tripled from 8 percent in 1981 to 24 percent in 2001. 

The export share of manufactured shipment rose from 9 percent to 16 percent

over the same period. Indeed, as indicated in Figures 3 and 4, the increase in

manufacturing’s exposure to international competition since 1970 is much higher

than that in either the nonfarm business sector or in the economy as a whole. It is

interesting to note that the manufacturing sector—which was less exposed to
12. The import share of nonfarm business output rose from 12 percent to 16 percent, while the

export share rose from 10 percent to a mere 12 percent, from 1981 to 2001. The import share

in the nonfarm business sector is constructed as goods and services imports/ nonfarm output.

The export share is constructed as nonagricultural goods and services exports/nonfarm

output. 

9

international competition than nonfarm business before 1985—became much

more exposed to international competition than the nonfarm business sector after

1985,12 the year that productivity growth—both LP and TFP—in manufacturing

began to outstrip productivity growth in nonfarm business unambiguously. That

phenomenon supports the hypothesis that international exposure exerts a net

positive impact on productivity growth.

The rise in the manufacturing sector’s exposure to international

competition is even more vividly portrayed by the increase in the number of

industries facing greater import penetration since 1970. As Table 2 shows, the

share of manufacturing industries (at 4-digit SIC level) exposed to import

penetration greater than 20 percent was only 6 percent in 1970. That share

jumped to 13 percent in 1980, then surged to 32 percent in 1990, and reached 40

percent in 1996. In 1970, manufacturing industries exposed to import penetration

greater than 50 percent represented only 1 percent of the about 400 manufacturing

industries in the sample. By 1996, that share already reached 12 percent.

While it is true that the share of manufacturing output in the economy has

been shrinking, manufacturing industries continue to play an important role in the

aggregate economy. It is well established that productivity growth in the

manufacturing sector has been mainly responsible for the productivity resurgence

after 1995. Clearly, an accurate indicator of the degree with which the U.S.

economy interacts with the rest of the world needs to take into account of the U.S.

manufacturing sector’s exposure to international trade.

4. Panel Data Analysis

We use panel data from 40 manufacturing industries at the 3-digit SIC

level to estimate several variants of equation (2):

() ( ) 2 , , ,, , , ,

11 1 1

∆Γ ∆ ∆ ∆ ∆ ∆ jt j i

i

n

jt i

i

n

i jt i jt i i

i

n

jt i i

i

n

=+ + + + + + RP MP RP MP X CU jt i t jt

=

−

=

− −

=

−

=

αβ γ φ δ θ μ ∑∑ ∑ ∑ −

where,

αj is the constant representing the trend growth in industry j;
10

∆Γj,t is the percent change in labor productivity, or TFP, in industry j at

time t;

∆RPj,t is the percent change in the relative import price—import price

relative to PPI—in industry j at time t;

∆MPj,t is the first difference in the import penetration ratio (=

import/(shipment-imports+exports)) for industry j at time t;

∆Xj,t is the first difference in the export share (exports/shipment) in

industry j at time t;

∆CUj,t is the first difference in capacity utilization, in industry j at time t.

The constant term αj

 is included to control for average trend productivity

growth in industry j. ∆CUjt is included to account for the procyclicality of

productivity growth in industry j; consequently, θ is expected to be positive. By

including αj

 and ∆CUjt in the panel regression, we aim to estimate the effects of 

international exposure on productivity growth beyond and above what can be

accounted for by the trend growth and cyclical fluctuations. 

A negative β and γ would suggest that decreases in import prices, by

intensifying competitive pressure, help to promote US manufacturing

productivity. In this way, a negative and significant β and γ would lend support

to the competition effects described in Section 2. By the same token, we would

expect a positive φ, since the competition effects, reallocation effects, and

spillover effects all suggest that a rise in import penetration would help promote

productivity growth. On the other hand, a negative φ would indicate the less

probable scenario that the negative economies-of-scale effect dominates the other 

positive channels. Finally, a positive δ would indicate that, all else equal,

productivity growth tends to increase when exports become a larger share of total

shipments. This would be consistent with the economies-of-scale channel, the

reallocation channel, and the spillover channel.

To find out the dynamics of the trade-productivity relationship, we

estimate equation (2) in three ways: (1) i = 0, n = 0; (2) i = 1, n = 1; 

(3) i = 1, n = 2. 

4.1 Panel Data

Annual data at the 3-digit SIC industry level, from 1989 through 1996, are

used for the regression analysis. Due to the limitation imposed by industry

productivity data, the sample used for LP regressions consists of just 40

manufacturing industries, while that used for TFP regressions consists of 35

industries. This gives 320 (= 40 x 8 years) cross-sectional time series

observations for each variable in the LP regressions, and 280 (=35 x 8 years)

observations for each variable in the TFP regressions. Table 3 gives a detailed

report of the characteristics of these industries. 
13. Capacity utilization data are from http://www.federalreserve.gov/releases/G17/ipdisk/utl.sa.

(For data before 1986, see http://www.federalreserve.gov/releases/G17/iphist/utlhist.sa.) For

industries where capacity utilization data are not available at 3-digit SIC level, we use the

closest 2-digit SIC level as a proxy.

14. Both fixed-effects and random-effects regressions allow intercepts to vary, while keeping

other coefficients fixed, across industries. The fixed-effects method does so by using

industry dummies for the constant. The random-effects method makes specific assumptions

about the distribution of the intercepts. Consequently, if the restrictive distributional

assumption of the random-effects method is correct, using that additional information leads

to a more efficient estimator. 

15. When both MP*∆RPand ∆RP are included in the regressions, the coefficient estimates on 

∆RP become statistically insignificant, but the point estimates on MP*∆RP become bigger

than those reported in Tables 4, 4a, 5, and 5a.

11

Industry data on labor productivity, total factor productivity, import

prices, and domestic prices are obtained from the Bureau of Labor Statistics

(BLS). Industry data on import value, export value, and shipment are from the

U.S. Census Bureau. Capacity utilization by industry is from the Board of

Governors of the Federal Reserve System.13

4.2 Panel Results

 For both LP and TFP regressions, the basic specification in equation (2) is

estimated with fixed-effects and random-effects regressions.14 The results on LP

growth regressions are reported in Tables 4 & 4a. To avoid the problem arising

from the multicollinearity between MP*∆RP and ∆RP, Table 4 reports results of

regressions that exclude MP*∆RP as an explanatory variable, while Table 4a

reports results of regression that exclude ∆RP as an explanatory variable. In the

same fashion, the results on TFP regressions are reported in Tables 5 & 5a.15

In all four tables, the coefficient estimate on ∆CU is statistically

significant and positive. This finding confirms the well-known procyclicality of

both LP and TFP growth. More important, they confirm that the coefficient

estimates on international variables adequately controlled for cyclical impacts on

productivity growth.

Labor Productivity Growth

Overall, the results of fixed-effects and random-effects regressions are

compatible qualitatively and quantitatively, with both indicating that imports and

import-price competition has a net positive impact on LP growth in

manufacturing industries. Four main observations emerge from Tables 4 and 4a:
12

(1) A sustained decrease in the relative import price (RP) has a positive and

significant impact—both quantitatively and statistically—on LP growth

after one to two years. This is seen in Table 4 where the coefficient

estimate on ∆RPt-1 is negative and statistically significant in the randomeffects regression (-0.118) , and the sum of coefficient estimates on ∆RPt-1

and ∆RPt-2 is negative and significant in both types of regressions ( -0.253

in fixed-effects and -0.303 in random-effects). The results suggest that a

sustained one percent drop in the average RP results in about 0.3

percentage-points increase in LP growth over two years. 

(2) In the short run, the negative economies-of-scale effects of an increase in

import penetration (MP) unambiguously outweigh its productivityenhancing effects— competition effects, reallocation effects, spillover

effects— for import-competing industries. Over time, however, the

productivity-enhancing effects become increasingly dominant, yielding a

net positive impact on LP growth. This pattern is seen most clearly in

Table 4a: ∆MP has a negative and significant correlation with LP growth

contemporaneously, but this correlation turns insignificant after one year

and positive and significant after two years. The pattern can also be

detected in Table 4, albeit with a lower level of statistical confidences.

(3) The competition impact of a given degree of change in the relative import

price (∆RP) is bigger when and where import penetration is bigger. This

result is seen in Table 4a where the coefficient estimate on the interactive

term (MPj,t-1

*∆RPj,t-1 ) is significant and negative in the random-effect

regression (-0.006), and the sum of coefficient estimates on MPj,t-1

*∆RPj,t-1

and MPj,t-2*∆RPj,t-2 is significant and negative in both types of regressions

(-0.013 in fixed effects and -0.016 in random effects). These results

suggest that the impact of a 1 sustained percent decrease in RPj will

increase labor productivity in industry j by about (1.5*MPj) percent over

two years.

(4) An increase in export shares (∆X) is positively correlated with LP growth

contemporaneously, but does not have a significant or positive effect on

LP growth after one year. This suggests that the contemporaneous

correlation is due to the fact that higher-productivity firms are more likely

to export because they are more confident that the initial sunk costs

incurred to enter foreign markets will be recovered and that exporting

activity per se does not promote productivity. 
13

Total Factor Productivity Growth

As in LP regressions, the results of fixed-effects and random-effects

regressions of TFP are compatible quantitatively and qualitatively, with both

indicating that imports and import-price competition will have a net positive

impact on TFP growth in manufacturing industries. Four notable findings emerge

from Tables 5 and 5a:

(1) A sustained decrease in the relative import price (RP) begins to boost

TFP growth after one year, and that positive impact builds up as time

goes on. In Table 5, the coefficient estimate on ∆RPt-1 is statistically

significant and negative in both types of regression (- 0.089 in fixed

effects and - 0.090 in random effects), as is the sum of coefficients on

∆RPt-1 and ∆RPt-2 (-0.27 in fixed effects and -0.22 in random effects). This

suggests that a sustained one-percentage decrease in ∆RP increases TFP

growth cumulatively by about 0.25 percentage points over two years. 

(2) In the short run, the negative economies-of-scale effect of an increase in

import penetration outweighs the productivity-enhancing effects in

import-competing industries. Over time, however, the positive effects

grow sufficiently to offset the negative economies-of-scale effect. This

pattern is shown in both Tables 5 and 5a: in both types of regression, 

∆MP is negatively correlated with TFP growth contemporaneously, but

the sum of coefficient estimates on lagged ∆MP become positive and

statistically insignificant.

(3) The impact of a decrease in the relative import price on TFP growth is

greater when import penetration is greater, and that impact builds up over

time. This pattern is exhibited in Table 5a: the coefficient estimate on

MPt-1*∆RPt-1 is negative and significant (-0.007 in both types of

regressions), and the sum of coefficient estimates on MPt-1*∆RPt-1 and 

MPt-2*∆RPt-2 becomes even more negative (-0.015 in fixed effects, and -

0.011 in random effects) and more significant.

(4) An increase in export share (∆X) is not significantly correlated with TFP

either contemporaneously or with lags, suggesting that exporting activity

by itself does not boost productivity growth.

 The results of LP and TFP panel regressions suggest that international

trade spurs manufacturing productivity growth mostly through imports rather

than exports. Import competition boosts not only LP growth but also TFP growth

in manufacturing industries. The coefficient estimates on the international

variables in TFP regressions are smaller in magnitude, and are estimated with a

lower level of confidence, than those in the LP regressions. This suggests that

improvement in LP growth stems from capital deepening as well as TFP growth.
16. The Bureau of Labor Statistics measures LP as output per hour of labor for major

sectors—private business and private nonfarm business—as well as manufacturing, but it

measures TFP differently for major sectors than for manufacturing. For major sectors, TFP

is measured as the value-added per combined unit of labor and capital input. For aggregate

manufacturing and manufacturing industries, TFP is measured as output per combined unit

of capital, labor, energy, materials, and purchased business services inputs. 

14

() ( ) 3 11 1 1

∆Γ ∆ ∆ ∆ ∆ ∆ t i

i

n

t i

i

n

i ti ti i

i

n

ti i

i

n

=+ + + + + + RP MP RP MP X CU ti t t

=

−

=

− −

=

−

=

αβ γ φ δ θ μ ∑∑ ∑ ∑ −

5. Time-Series Analysis

In this section, we use OLS regressions and time-series data to estimate

the impact of international exposure on productivity growth in the manufacturing

sector. We estimate equation (3): 

where, 

α is the constant term representing the trend growth rate of LP or TFP in the

manufacturing sector;

∆Γ is the first difference of the log of either LP or TFP in manufacturing;

∆RP is the first difference of the log of the relative import prices (price of

imported goods/producer prices);

∆MP is the first difference of import penetration in manufacturing; 

∆X is the first difference of export share (exports/shipment) in manufacturing;

∆CU is the first difference of capacity utilization in the manufacturing sector.

The expectation and interpretation of the parameters in equation (3) are

similar to those in equation (2). 

5.2. Time-Series Data

For LP regressions, because quarterly data on manufactured exports and

imports are not available before 1978, we use quarterly data from 1978:Q1 to

2001:Q4. For TFP regressions, because TFP data are available only annually, we

use annual data from 1970 to 2000. To capture the dynamics of tradeproductivity relationship, we set n = 4 or 8 quarters in LP regressions and n = 1 or

2 years in TFP regressions.

Labor productivity (indexed at 1992=100) and total factor productivity

(indexed at 1996=100) are obtained from the Bureau of Labor Statistics.16 Prices
17. For documentation of SITC classifications, see the web site of the Statistical Division of

United Nations: http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=14 .

18. When both MP*∆RP and ∆RP are included in the regressions, the coefficient estimates on 

∆RP become statistically insignificant, but the point estimates on MP*∆RP become bigger

than those reported in Table 6.

15

of goods imported and the producer price index (PPI) are obtained from the

Bureau of Economic Analysis. Annual data on manufactured exports and

imports, available beginning in 1970, are obtained from The Statistical Abstract

of the United States 2002. Quarterly manufactured exports (imports) are

obtained by adding up exports (imports) of manufactured goods in four SITC

categories: SITC05, SITC06, SITC07, and SITC08.17 Data on manufactured

exports, imports, and shipments are all from the U.S. Census Bureau. 

5.2 Time-Series Results

The estimation results of equation (3) are reported in Table 6. As in the

panel regressions, to avoid the problem arising from the multicollinearity between

MP*∆RP and ∆RP, columns (a) and (b) report results that exclude MP*∆RP

as a regressor, while columns (c) and (d) report results that exclude ∆RP as a

regressor.18 Table 6 shows that both the constant and the coefficient estimate on

∆CU are positive and significant in LP as well as in TFP regressions, indicating

that the estimations on international variables are generated after properly

controlling for the trend growth rate and the effect of business cycles.

The results of time-series regressions are not exactly the same as those of

panel regressions. This should not come as a surprise since only up to 40

manufacturing industries, whose combined shipment represents about 32 percent

of total manufacturing shipment, are studied in the panel regressions. 

For LP regressions, the coefficient estimates on ∆X, ∆RP, and MP*∆RP

are similar to those in the panel regressions, though those on ∆MP are not. The

sum of coefficient estimates on lagged ∆X is insignificant, suggesting that

exporting activity by itself does not boost productivity. A decrease in the relative

import price has a positive impact on LP growth: the sum of coefficient estimates

on ∆RPt-i is -0.18 when i = 1 to 4 quarters and grows to -0.21 when i = 1 to 8

quarters. In comparison, the sum of coefficient estimates on ∆RPt-i in the panel

regressions, based on the random-effect results reported in Table 4, is -0.12

when i = 1 year and grows to -0.30 when i = 1 to 2 years. It is reassuring that

the quantitative effect of ∆RP on LP growth estimated in the time-series

regression does not differ much from that estimated in the panel regressions, even
19. This difference in sensitivity could also be due to the fact that the average import penetration

of the 40 industries included the panel regressions (31% during 1989-1996) is bigger than

that in the manufacturing sector (15% during 1989-1996). 

16

though the effect appears to take place somewhat faster in aggregate than in the

40 industries studied in the panel analysis. Of course, this result could also

suggest that most of the impact of international trade on LP growth occurs at the

industry level.

As in the panel regressions, the time-series regressions also find that the

impact of ∆RP on LP growth grow in tandem with import penetration—that is, the

coefficient estimates on MP*∆RP are negative and significant. The sum of

coefficient estimates on MPt-i

*∆RPt-i is -0.018 when i = 1 to 4 quarters and

grows to -0.023 when i = 1 to 8 quarters. In comparison, the sum of coefficient

estimates on MPt-i

*∆RPt-i in the panel regression, based on the random-effect

results in Table 4a, is -0.006 when i = 1 year and -0.016 when i = 1 to 2 years. 

The coefficients on MP*∆RP are somewhat greater here than those in panel

regressions, but not to a puzzling degree. Many factors could have contributed to

the difference in the coefficient estimates. For example, when inexpensive

imports displace domestic production in lower-productivity industries and release 

resources to produce higher-value products, aggregate productivity growth will

increase but industry productivity growth may not. Alternatively, this result

could also suggest that the impact of import-price competition on productivity is

more sensitive to the size of import penetration for the industries excluded from

the panel study than for those 40 industries that are included.19

Unlike in the panel results, an increase in import penetration has neither a

negative and significant correlation with LP growth contemporaneously, nor a

positive and significant impact on LP growth over time. This result could be due

to the fact that the power of test in estimating the coefficient on ∆MP is greater in

the panel analysis than in the time-series analysis, as the former uses a sample of

40 industries with a high variation in MP distribution while the latter’s sample

lacks that cross-industry variation. 

For TFP regressions, the estimation results are less similar to those of

panel regressions than what we found for LP regressions. While coefficient

estimates on ∆X and ∆MP are qualitatively similar to those in the panel

regressions, those on MP*∆RP and ∆RP are not. In other words, we find that

exports share does not boost TFP growth while an increase in import penetration

does help to increase TFP growth with a two-year lag. However, changes in the

relative import price do not have a statistically significant impact on TFP growth,

even if we allow that impact to vary with import penetration. Again, this

difference could stem from the fact that panel regressions have a greater power of

test than time-series regressions.
17

From the viewpoint of growth accounting, the time-series finding that an

increase in import penetration helps to boost TFP growth—after sufficient time

has lapsed—but not LP growth appears irreconcilable, since LP growth is the sum

of capital deepening and TFP growth. Indeed, this results are the opposite of

what we find in the panel analysis. Which set of results is more credible? 

It appears that both economic intuition and the statistical power of test

demand that we give more credit to panel results than time-series results. As we

already argued, the power of test in the time-series estimations is not as great as in

the panel estimations. Moreover, the economic implication of the time-series

results—that the negative economies-of-scale effect of an increase in MP

completely offset all its positive effects (i.e., competition effect, reallocation

effect, and spillover effect)—are less plausible than the opposite which is implied

by the panel results. The choice is obviously in favor of the panel results, and that

in turn suggests that we should give more credibility to LP results, rather than

TFP results, of time-series regressions. Finally, the fact that LP

regressions—which use quarterly data—enjoy a higher degree of freedom than

the TFP regressions—which use annual data—also help lend credibility to LP

regression results.

6. International Trade’s Contribution to Aggregate Productivity Growth

This subsection estimates the contribution of import competition to labor

productivity growth in the manufacturing sector and in the nonfarm business

sector over the 1981-2001 period. We first use results of time-series regressions

to conduct simulations to find out the contribution of international trade to

manufacturing LP growth over the 1981-2001 period. We then use these

simulated results to impute the contribution of import competition to LP growth

in nonfarm business.

6.1 Contribution to Productivity Growth in Manufacturing 

To assess the contribution of international trade to LP growth in the

manufacturing sector over 1981-2001, we first use the coefficient estimates

reported in Table 6 to simulate what the growth rates of labor productivity in

manufacturing would have been if import prices had not changed since 1978. 

That is, we first use the coefficient estimates to project the in-sample forecast of

growth rates of manufacturing labor productivity. We then use the coefficient

estimates to project what the growth rates of manufacturing labor productivity

would have been if ∆RP = 0. (Given that the coefficient estimates on ∆X and

∆MP are statistically insignificant, the simulation allows both ∆X and ∆MP to be

what they were historically.) The contribution of ∆RP to manufacturing LP
20. Figure 5 shows that our sample period can be roughly divided into three sub-periods by the

behavior of the dollar exchange rate—the 1978-1985 period of dollar appreciation, the

1986-1995 period of dollar depreciation, and the 1996-2001 period of dollar appreciation. 

However, because ∆RP and MP*∆RP are estimated to take one-to-two years to affect

productivity and because we are keen in assessing international trade’s contribution to the

post-1995 productivity growth revival, Table 7 reports their contributions in three subperiods that do not exactly synchronize with the dollar’s swings. 

18

growth rates is then obtained by subtracting hypothetical LP growth rates from

the in-sample forecast, or fitted value, of growth rates. 

Table 7 reports the contribution of ∆RP and MP*∆RP to manufacturing

LP growth in three sub-periods (1981-1987, 1988-1995, and 1996-2001) that 

roughly follow the pattern of the dollar exchange rate’s swings (see Figure 5).20

The upper panel in Table 7 shows that, although rising and falling in tandem with

∆RP, the contribution of import competition to manufacturing LP growth has

been quantitatively significant. The influence of the dollar exchange rate on

manufacturing productivity growth is also quite palpable. If we take the

simulation results seriously, MP*∆RP contributed 0.41 percentage-points to the

3.39 percentage-point increase in the average growth rate of manufacturing labor

productivity during 1981-1987, roughly a 12 percent contribution. The

contribution then fell to 7 percent (=0.19/2.68) during 1988-1995, then rose to 32

percent(=1.20/3.76) during 1996-2001. Note that, because import penetration

has been rising steadily and the depreciation of the dollar during 1986-1995 was

not sufficiently passed through to increase the average relative import price, the

net contribution of MP*∆RP to manufacturing LP growth during 1988-1995 was

still positive, though lower than in 1981-1987 or 1996-2001.

6.2 Contribution to Productivity Growth in Nonfarm Business

To impute the impact of international trade on labor productivity in the

nonfarm private business (NFPB) sector, it is useful to think of the impact on

NFPB productivity growth as the sum of the impacts on two main subsectors of

NFPB: 

 (4) ∆Γt

NFPB = ω1t ∆ΓtM + ω2t ∆ΓtNM

where ∆Γt

NFPB , ∆ΓtM , and ∆ΓtNM is respectively the impact of international trade

on labor productivity growth in nonfarm business, in manufacturing, and in

nonmanufacturing at time t; ω1t is the share of manufacturing output, and ω2t is

the share of nonmanufacturing output, in the nonfarm business sector at time t. 

We already have obtained an estimate of ∆Γt

M in the previous section. 

The question now is: what is a reasonable estimate of ∆Γt

NM? It is quite

conceivable that international trade would have direct effects on output in
19

nonmanufacturing industries such as transportation, banking and other financial

service industries, thereby enhancing productivity growth through the economiesof-scale effect, if nothing else. However, it is hard to see how those effects would

show up in the official productivity data, as service-sector productivity tends to

depend on payroll data inordinately, a tendency that may bias measures of LP

growth toward zero and may understate growth in TFP. For these reasons, we opt

for assuming that ∆Γt

NM is zero, and that ∆ΓtNFPB = ω1t ∆ΓtM. 

With this simplifying assumption, our computation indicates that that 

MP*∆RP contributed 0.10 percentage-points to the 1.71 percentage-point increase

in the average growth rate of NFPB labor productivity during 1981-1987, roughly

a 6 percent contribution (see the lower panel in Table 7). The contribution then

fell to 3 percent (=0.04/1.35) during 1988-1995, then rose to 10 percent during

1996-2001. 

7. Conclusion

This paper argues that increasing international trade could affect domestic

productivity through competition effects, economies-of-scale effects, reallocation

effects, and spillover effects. The paper then estimates the empirical relationship

between international exposures and productivity growth in manufacturing

industries (through panel regressions) and in the manufacturing sector (through

time-series regressions). 

 The results of both panel and time-series regressions indicate that

increases in import competition indeed have a positive and significant impact on

manufacturing productivity growth, even though increases in exports do not. The

strong and unambiguous effect of changes in import prices on productivity growth

suggest that the competition channel and the reallocation channel operate quite

powerfully, even if the evidence is less clear for the economies-of-scale effect

and the spill-over effect. The results also indicate that the relationship between

international exposure and productivity growth is dynamic rather than static,

evolving over time. 

Our simulation results indicate that import competition’s contribution to

aggregate productivity growth has become quite pronounced during 1996-2001, a

period of a sustained dollar appreciation. Together, the increase in import

penetration and the decrease in import prices have contributed to 32 percent of

labor productivity growth in manufacturing, and to 10 percent of labor

productivity growth in nonfarm business, during 1996-2001. These results

suggest that import competition has unmistakably helped boost U.S. productivity

growth. 
20

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22

60 65 70 75 80 85 90 95 00

(%)8

6

4

2

0

-2

-4

Figure 1. U.S. Labor Productivity Growth 

(Annual Growth, 1960-2002)

Nonfarm

Manufacturing

60 65 70 75 80 85 90 95 00

(%)6

4

2

0

-2

-4

-6

Figure 2. U.S. Total Factor Productivity Growth 

(Annual Growth, 1960-2001)

Manufacturing

NonFarm
23

70 75 80 85 90 95 00

(%)25

20

15

10

5

0

Figure 3. U.S. Imports Share by Sector

(1970-2001)

Manufacturing

Nonfarm business

US economy

70 75 80 85 90 95 00

(%)16

14

12

10

8

6

4

Figure 4. U.S. Exports Share by Sector

(1970-2001)

Manufacturing

Nonfarm business

US economy

73 78 83 88 93 98

150

140

130

120

110

100

90

80

70

Source: Federal Reserve Board of Governors 

Figure 5. Trade-Weighted Dollar Exchange Rate vs. Major Currencies

(1973=100)

Real

Nominal

 

 

 

 

 

 

 

 
24

Table 1. Productivity Growth in U.S. Manufacturing and Nonfarm Business 

(Average annual growth rate, percent)

1960-1973 1974-1995 1996-2000 2001 2002

Labor Productivity

Nonfarm Business 2.9 1.4 2.5 1.1 4.7 

Manufacturing 3.0 2.7 4.4 0.8 4.6 

Total Factor Productivity

 Nonfarm Business 2.0 0.4 1.2 NA NA

Manufacturing 1.7 0.7 2.1 NA NA

Table 2. The Distribution of Manufacturing Industries by Import Penetration over Time 

Import Penetration (IP)

(Percent) 1958 1970 1980 1990 1994 1996

 0 # MP # 10 92 84 70 46 43 42

10 < MP # 20 4 10 17 22 20 18

20 < MP # 30 2 4 6 13 14 15

30 < MP # 40 01 3 6 8 9

40 < MP # 50 01 2 5 6 6

50 < MP # 60 01 1 3 4 3

60 < MP # 70 00 0 2 2 3

70 < MP # 80 00 0 1 2 2

80 < MP # 90 00 0 1 1 2

90 < MP # 100 00 0 1 1 2

 0 # MP # 100 100 100 100 100 100 100

Total Number of Industries 431 431 431 346 398 398

NOTES:

1. This table is adopted from Gamber and Hung (2001). 

2. MPi = Mi/(Si-Xi+Mi), where Mi is imports, Si is shipments, Xi is exports, in industry i.

3. The distribution is in terms of the percent share of 4-SIC industries by in each range of import

penetration.
25

Table 3. Average Shipment Share, Import Penetration, and Export Share for a Sample of 40

Manufacturing Industries

SIC # Industry

 Shipment

Share

1987-96

Import 

Penetration

1989-96

Export

Share 

1989-96 

201 * Meat Products 6.5 4.4 9.9

202 Dairy Products 4.8 1.6 1.6

203 Preserved Fruits & Vegetables 3.8 6.8 6.0

206 Sugar & Confectionery Products 0.6 34.4 28.0

207 Fats & Oils 2.0 6.5 14.5

208 Beverages 5.5 6.9 2.7

209 Miscellaneous Food & Kindred Products 2.2 9.2 6.9

221 Broad woven Fabric Mills, Cotton 0.6 20.7 10.8

222 Broad woven Fabric Mills, Manmade Fiber & Silk 0.9 15.5 10.8

229 Miscellaneous Textile Goods 0.9 12.5 14.0

232 Mens' & Boys' Furnishings 0.8 57.8 21.0

238 Miscellaneous Apparel & Accessories 0.2 62.9 19.9

242 Sawmills & Planing Mills 2.4 16.6 10.0

243 Millwork, Plywood & Structural Members 2.5 5.2 4.1

259 Miscellaneous Furniture & Fixtures 0.4 71.2 52.8

261 * Pulp Mills 0.7 52.9 50.9

262 Paper Mills 3.4 19.8 13.4

281 * Industrial Inorganic Chemicals 2.2 20.2 26.6

289 Miscellaneous Chemical Products 2.0 6.1 14.0

301 Tires & Inner Tubes 1.2 19.5 11.9

314 Footwear, Except Rubber 0.4 67.7 10.6

317 Handbags & Personal Leather Goods 0.1 64.9 8.9

326 Pottery & Related Products 0.3 41.5 15.4

331 Blast Furnace & Basic Steel Products 5.4 18.9 7.1

333 Primary Nonferrous Metals 1.4 52.9 49.2

335 Nonferrous Rolling & Drawing 3.8 11.4 14.3

345 * Screw Machine Products, Bolts, etc 0.6 22.7 14.9

353 Construction & Related Machinery 2.9 21.2 38.0

355 Special Industry Machinery 2.0 29.4 31.0

362 Electrical Industrial Apparatus 1.2 34.0 31.8

363 Household Appliances 1.8 20.0 14.3

364 Electric Lighting & Wiring Equipment 1.5 28.9 23.6

366 Communications Equipment 2.0 47.2 44.2

369 Miscellaneous Electrical Equipment & Supplies 1.8 42.3 43.0

371 Motor Vehicles & Equipment 25.1 28.3 15.3

382 Measuring & Controlling Devices 2.4 29.1 43.8

386 Photographic Equipment & Supplies 1.9 28.2 21.6

391 Jewelry, Silverware & Plated Ware 0.6 74.7 53.3

394 Toys & Sporting Goods 1.1 48.6 20.9

396 * Costume Jewelry & Notions 0.2 41.7 17.2

Source: Import and Export data are from the U.S. Census Bureau. Shipments data are from Annual Survey

of Manufacturers, published by the U.S. Census Bureau.

Notes: (1) Industries with an asterisk are not included in the TFP regressions. 

(2)Shipment share is an industry’s shipment relative to total shipment of all 40 listed industries.

The 40 industries together make up about 32% of total manufacturing shipment each other.
26

Table 4. Panel Regressions of Labor Productivity Growth in Manufacturing

Fixed Effects Random Effects

(a)

No lag

(b)

1- year lag

(c)

2- year lag

(a)

No lag

(b)

1- year lag

(c)

2- year lag

∆RP 0.064 -0.078 -0.253** 0.007 -0.118* -0.303***

(1.01) (1.10) (2.02) (0.12) (1.88) (3.23)

∆MP -0.542*** 0.105 0.528 -0.506*** 0.111 0.303

(3.56) (0.57) (1.59) (3.64) (0.69) (1.24)

∆X 0.297*** -0.184 -0.338* 0.281*** -0.159 -0.201

(2.83) (1.53) (1.75) (2.88) (1.50) (1.25)

∆CU 0.461*** 0.405*** 0.359** 0.542*** 0.514*** 0.517***

(3.47) (2.73) (2.04) (4.60) (4.12) (3.55)

Adjusted R2 0.181 0.123 0.153 0.133 0.092 0.068

Table 4a. Panel Regressions of Labor Productivity Growth in Manufacturing

Fixed Effects Random Effects

(a)

No lag

(b)

1- year lag

(c)

2- year lag

(a)

No lag

(b)

1- year lag

(c)

2- year lag

MP*∆RP 0.004 -0.003 -0.013** 0.001 -0.006** -0.016***

(1.64) (1.15) (2.51) (0.35) (2.17) (4.12)

∆MP -0.583*** 0.112 0.645** -0.517*** 0.140 0.494**

(3.77) (0.61) (2.03) (3.64) (0.87) (1.96)

∆X 0.304*** -0.183 -0.318* 0.284*** -0.165 -0.239

(2.90) (1.53) (1.65) (2.91) (1.55) (1.49)

∆CU 0.472*** 0.404*** 0.357** 0.543*** 0.506*** 0.531***

(3.57) (2.73) (2.04) (4.59) (4.07) (3.69)

Adjusted R2 0.187 0.124 0.167 0.137 0.096 0.110

NOTES: (1). Contemporaneous coefficient estimates on ∆CU for all columns. In column (b), coefficient estimates

are for 1-year lag. In column (c), coefficients are the sum of coefficient estimates over 2 lagged years. 

(2). *** denotes 1% significance level; ** denotes 5%, and * 10%, significance level. 

(3) Numbers in parentheses are absolute values of t-statistics.

VARIABLES: RP = relative import price, MP = import penetration, X = exports share, CU = capacity utilization.
27

Table 5. Panel Regressions of TFP Growth in Manufacturing

Fixed Effects Random Effects

(a)

No lag

(b)

1- year lag

(c)

2- year lag

(a)

No lag

(b)

1- year lag

(c)

2- year lag

∆RP 0.039 -0.089* -0.273*** 0.044 -0.090* -0.221***

(0.78) (1.62) (2.72) (0.95) (1.80) (2.89)

∆MP -0.248** 0.050 0.138 -0.214* 0.065 0.026

(1.94) (0.33) (0.47) (1.77) (0.49) (0.12)

∆X 0.117 -0.069 -0.135 0.073 -0.096 -0.090

(1.38) (0.72) (0.80) (0.91) (1.15) (0.65)

∆CU 0.235** 0.229** 0.205 0.411*** 0.495*** 0.359***

(2.25) (1.93) (1.39) (4.74) (6.08) (3.12)

Adjusted R2 0.231 0.179 0.104 0.308 0.230 0.163

Table 5a. Panel Regressions of TFP Growth in Manufacturing

Fixed Effects Random Effects

(a)

No lag

(b)

1- year lag

(c)

2- year lag

(a)

No lag

(b)

1- year lag

(c)

2- year lag

MP*∆RP 0.002 -0.007*** -0.015*** 0.002 -0.007*** -0.011***

(0.78) (2.75) (3.39) (0.81) (2.83) (3.26)

∆MP -0.257** 0.078 0.193 -0.221* 0.083 0.056

(1.98) (0.52) (0.66) (1.81) (0.62) (0.25)

∆X 0.117 -0.064 -0.072 0.074 -0.084 -0.069

(1.39) (0.67) (0.42) (0.92) (1.00) (0.50)

∆CU 0.238** 0.220* 0.240* 0.412*** 0.420*** 0.378***

(2.27) (1.88) (1.64) (4.73) (4.69) (3.20)

Adjusted R2 0.231 0.202 0.125 0.308 0.271 0.137

NOTES: (1). Contemporaneous coefficient estimates on ∆CU for all columns. In column (b), coefficient estimates

are for 1-year lag. In column (c), coefficients are the sum of coefficient estimates over 2 lagged years. 

(2). *** denotes 1% significance level; ** denotes 5%, and * 10%, significance level. 

(3) Numbers in parentheses are absolute values of t-statistics.

VARIABLES:RP = relative import price, MP= import penetration, X = exports share, CU = capacity utilization.
28

Table 6. Time-Series Regressions of Manufacturing Productivity

(Numbers in parentheses are absolute values of t-statistics.)

Dependent Variable: ∆ln(LP) Dependent Variable: ∆ln(TFP)

(a)

1 year

(b)

2 years

(c)

1 year

(d)

2 year 

(a)

1 year

(b)

2 year

(c)

1 year

(d)

2 year

CONSTANT

0.006***

(5.27)

0.005***

(2.95)

0.006***

(4.97)

0.005***

(2.95)

0.007

(1.39)

-0.005

(0.84)

0.006

(1.27)

-0.005

(0.87)

∆RP

-0.18**

(2.40)

-0.21**

(1.95) NA NA

-0.06

(1.05)

-0.01

(007) NA NA

MP*∆RP NA NA

-0.018**

(2.63)

-0.023**

(2.54) NA NA

-0.011

(1.55)

-0.005 

(0.55)

∆MP

0.64

(1.28)

0.67

(0.80)

0.67

(1.35)

0.52

(0.62)

0.74

(1.14)

2.36**

(2.72)

0.68

(1.07)

2.28**

(2.55)

∆X

0.00

(0.00)

0.82

(1.22)

-0.11

(0.24)

0.59

(0.93)

-0.30

(0.51)

0.62

(0.94)

-0.27

(0.47)

0.67

(1.05)

∆CU

0.21***

(3.36)

0.24***

(3.59)

0.22***

(3.62)

0.23**

(3.52)

 0.30***

(3.08)

 0.31***

(3.32)

0.31***

(3.33)

0.31***

(3.54)

Adjusted R2 0.12 0.19 0.14 0.19 0.46 0.57 0.49 0.58

Notes:

1. Labor productivity (LP) regressions use quarterly data from 1978:Q1 to 2001:Q4. Coefficients on ∆CU are estimates

on the contemporaneous quarter. Coefficients on all other variables are the sum of coefficient estimates over 4 lagged

quarters for columns (a) and (c), and that over 8 lagged quarters for columns (b) and (d).

2. TFP regressions use annual data from 1970 to 2001. Coefficients on ∆CU are estimates on the contemporaneous year.

Coefficients on all other variables are the estimates on one-year lag for columns (a) and (c), and the sum of estimates

on one-year and two-year lags for columns (b) and (d).

3. VARIABLES: RP = relative import price, MP = import penetration, X = export share, CU = capacity utilization. 

4. *** denotes 1% , ** 5%, and * 10%, significance level.
29

Table 7. Net Impact of Import Competition on US Labor Productivity Growth

(Simulation results based on Table 6)

Yearly Average, Percent 

 1981-1987 1988-1995 1996-2001

 Manufacturing LP growth 3.39 2.68 3.76

LP growth contributed by ∆RP 

(based on column b) 0.50 0.13 0.65

LP growth contributed by MP*∆RP 

(based on column d) 0.41 0.19 1.20

 Nonfarm Business LP growth 1.71 1.35 2.25

LP growth contributed by ∆RP

(based on column b) 0.12 0.03 0.12

LP growth contributed by MP*∆RP

(based on column d) 0.10 0.04 0.23

 Memo:

∆RP (change in the relative import price) -2.2 -1.5 -2.8

MP (Import Penetration) 9.8 14.1 18.6

MP*∆RP -15.1 20.4 -50.7

Manufacturing GDP/Nonfarm Business GDP ratio 23.7 21.6 19.0