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Working Paper Series

Do Technological Improvements in the
Manufacturing Sector Raise or Lower
Employment?

WP 05-02

Yongsung Chang
Federal Reserve Bank of Richmond
Jay H. Hong
Federal Reserve Bank of Richmond

This paper can be downloaded without charge from:
http://www.richmondfed.org/publications/

Do Technological Improvements in the Manufacturing Sector Raise
or Lower Employment?
by Yongsung Chang and Jay H. Hong∗
Federal Reserve Bank of Richmond Working Paper 05-02
April 7, 2005

Abstract
We find that technology’s effect on employment varies greatly across manufacturing industries.
Some industries exhibit a temporary reduction in employment in response to a permanent
increase in TFP, whereas far more industries exhibit an employment increase in response to
a permanent TFP shock. This raises serious questions about existing work that finds that a
labor productivity shock has a strong negative effect on employment. There are tantalizing
and interesting differences between TFP and labor productivity. We argue that TFP is a more
natural measure of technology because labor productivity reflects shifts in the input mix as well
as in technology. (JEL E24, E32)

Keywords:

Technology Shocks, Hours Fluctuations, Total Factor Productivity, Sticky
Prices

∗
Chang: School of Economics, Seoul National University, Seoul 151-742, Korea, and Federal Reserve Bank of
Richmond (e-mail: yohg@snu.ac.kr); Hong: Department of Economics, University of Pennsylvania, 3718 Locust
Walk, Philadelphia, PA 19104, and Federal Reserve Bank of Philadelphia (e-mail: jayhwa@econ.upenn.edu). We
thank Mark Bils for providing us monthly price-change frequency data used in Bils and Klenow (2004) as well as
his comments. We thank Susanto Basu, Larry Christiano, Martin Eichenbaum, Jordi Galı́, John Shea, and two
anonymous referees for helpful comments. The views expressed herein are the authors’ and do not necessarily reflect
those of the Federal Reserve Bank of Richmond, the Federal Reserve Bank of Philadelphia, or the Federal Reserve
System.

I.

Introduction

Despite controversies concerning the quantitative importance of technology as a source of business
cycles, technology’s effect on employment is conventionally viewed as expansionary. Recently, a
number of studies—Jordi Galı́ (1999), Michael Kiley (1998), Neville Francis and Valerie Ramey
(2002), and Susanto Basu, John Fernald and Miles Kimball (2005)—have reported that favorable
technology shocks may reduce total hours worked in the short run. This is an important finding
because, if it is confirmed, the fluctuation induced by technological progress may violate a simple
fact of the business cycle: output and employment strongly co-move, which has been documented
at least since the work of Arthur Burns and Wesley Mitchell (1946).1
In this article, we ask whether technological improvement of an industry—identified by the
permanent components of industry’s total factor productivity (TFP)—raises or lowers employment
in U.S. manufacturing. According to our VAR analysis of 458 4-digit manufacturing industries for
the period 1958–1996, the effect of technology on employment varies vastly across industries. While
some industries exhibit a temporary reduction in employment in response to a permanent increase
in TFP, there are far more industries in which both employment and hours per worker increase
in the short run. Among 458 4-digit industries, 133 industries exhibit a statistically significant
increase of hours in response to a favorable technology shock, whereas only 25 industries exhibit a
significant decrease in hours in the short run.
Our results contrast with Kiley’s; he found a strong negative correlation between the permanent
component of labor productivity and employment in most 2-digit manufacturing industries. We
do not see these findings necessarily conflicting because we identify technology from permanent
components of TFP, while Kiley identifies it from those of labor productivity. We argue that TFP
1

In Galı́ (1999), Kiley (1998), and Francis & Ramey (2002), a technology shock is identified by a stochastic trend

of labor productivity from a structural VAR. Basu et al. construct a measure of technology change from production
functions, controlling for increasing returns to scale, utilization, and aggregation effects. In contrast, John Shea
(1998), distinctive for his use of a direct measure of technology, finds that an increase in the orthogonal components
of R&D and patents tends to increase input use, especially labor, in the short run, but reduces inputs in the long
run.

1

is a more natural measure of technology because labor productivity reflects changes in input mix
as well as improved efficiency. Disturbances affecting material-labor or capital-labor ratios (e.g.,
relative input price changes or sectoral reallocation of labor) generate a negative correlation between
labor productivity and hours along the downward sloping marginal product of labor, whereas such
changes alone do not affect the TFP. We show that significant shifts in input mix have occurred in
manufacturing and that permanent shocks to input mix are indeed associated with the short-run
reduction of hours.
The contractionary effect of technology is often interpreted in favor of the model with sticky
prices (e.g., Galı́ (1999)). We ask whether the variation across industries in the impact of technology
on employment can be accounted for by the stickiness of industry-output prices using the recent
micro data on average duration of product prices in Mark Bils and Peter Klenow (2004). For 87
manufacturing industries, we do not find a strong correlation between the industry’s employment
response and the average duration of industry-output prices.
Our findings are potentially important because (i) they undercut a growing strand of literature
that uses the short-run impact of technology on employment as evidence against the models with
flexible prices, because (ii) TFP, rather than labor productivity, is the natural measure of technol­
ogy, and because (iii) TFP and labor productivity behave quite differently at the sectoral level—in
particular, shocks that affect labor productivity in the long run do not necessarily involve changes
in TFP.
The paper is organized as follows. In Section II, we briefly describe our empirical method,
including the VAR and data, and report the estimates on technology’s effect on employment.
In Section III, we analyze the difference between the trends in TFP and labor productivity by
computing the contribution of input deepening in labor productivity. Section IV provides caveats
to our analysis. Section V is the conclusion.

2

II.

Evidence from Industry TFP and Hours

A. Data
We derive our industry data from the NBER-CES Manufacturing Industry Database by Eric Bar­
telsman, Randy Becker and Wayne Gray (2000), which includes data for 458 4-digit manufacturing
industries for 1958–1996 and largely reflects information in the Annual Survey of Manufactures
(ASM).2 The TFP growth contained in the database is based on measuring separate factor inputs
for non-energy materials, energy, labor, and capital. For TFP higher than the 4-digit level, we
aggregate 4-digit TFP growth weighted by the industry’s revenue. For hours worked, we use total
hours employed in the industry, measured by the sum of hours of production and non-production
workers. There are no data on workweeks for non-production workers. We follow the database’s
convention of setting the workweek for non-production workers equal to 40. We obtain a similar
result when we assume that hours of non-production workers are perfectly correlated with those of
production workers. The database includes only the wage and salary costs of labor. In calculating
the industry labor share, we magnify wages and salary payments to reflect the importance of fringe
payments and employer FICA payments in its corresponding 2-digit manufacturing industry. The
ratio of these other labor payments to wages and salaries in 2-digit industries, in turn, is based
on information in the National Income and Product Accounts. Industry output reflects the value
of shipments divided by the price deflator of industry output. Material expenditure includes ex­
penditure on energy as well as on non-energy materials. Capital’s share is calculated as a residual
from labor and material’s share following the database’s convention. This measurement of TFP
is correct under the assumptions of perfect competition and constant returns to scale. According
to Susanto Basu and John Fernald (1997), Craig Burnside, Martin Eichenbaum and Sérgio Rebelo
and (1995), these assumptions are reasonable descriptions of U.S. manufacturing.
2

We exclude the “Asbestos Product” industry (SIC 3292) because this time series ended in 1993.

3

B. Identifying Technology Shocks
Technology shocks are identified by the structural VAR of industry TFP and total hours worked.
Fluctuations in industry TFP and hours worked are driven by two fundamental disturbances—
technology and non-technology shocks—which are orthogonal to each other. Only technology shocks
can have a permanent effect on the level of industry productivity. Both technology and nontechnology shocks can have a permanent effect on industry hours. We do not attempt to provide
an interpretation of non-technology shocks, which can be either aggregate (e.g., monetary shocks)
or sectoral (e.g., reallocation shocks).
Let vector Δxt be [Δzt , Δlt ]� , where Δzt and Δlt denote TFP growth and labor-hours growth,
respectively. Let �t be the vector of two shocks [�zt , �lt ]� , where �tz and �tl denote the technology and
non-technology shocks, respectively. In our data, both TFP and hours are integrated of order one.
Thus, Δxt can be expressed as a (possibly infinite) distributed lag of both types of shocks:3
Δxt = C(L)�t =

�∞

j=0 Cj �t−j

(1)

with E[�t �t� ] = I, and E[�t ��s ] = 0, t =
� s.
Our identifying restriction corresponds to C 12 (1) =

�∞

12
j=0 Cj

Δxt = A(L)et =
with

= 0. The MA representation is

�∞

j=0 Aj et−j

(2)

� s,
A0 = I, E[et e�t ] = Ω, E[et e�s ] = 0, t =

where Ω = C0 C0� , et = C0 �t , and Cj = Aj C0 . The MA representation A(L) is obtained from the
VAR of
Δxt = B(L)Δxt−1 + et =

�p

j =1 Bj Δxt−j

+ et .

(3)

We estimate the VAR (3) using data aggregated to 2- and 3-digit levels, aggregating from 4-digit
data as described above. We also estimate pooled specifications on disaggregated data, restricting
some coefficients to be identical across sub-industries. The pooled data provide more observations:
i
Δxit = B(L)Δxt−1
+ eit , for i = 1, ..., N,
3

The constant terms are suppressed here for expositional convenience.

4

where N is the number of sub-industries. We assume that B(L) and Ω are the same across the
sub-industries but allow for different average growth rates in TFP and hours (constant terms in the
VAR) across sub-industries.4 Most of our discussions are based on aggregated data unless otherwise
specified. All VARs have a lag of one year.5 The standard errors are computed by bootstrapping
500 draws.
Lawrence Christiano, Martin Eichenbaum and Robert Vigfusson (2004) show that whether
hours are treated as stationary in levels or in first differences is important for the response of hours to
technology in a structural VAR. The issue of stationarity of hours worked remains controversial (e.g.,
Matthew Shapiro and Mark Watson (1988)), and the stationarity is often motivated by the so-called
balanced growth path at the aggregate level. At the industry level, however, a permanent change
in productivity may well imply a long-run change in hours worked through sectoral reallocation
of labor, and hours are, in fact, non-stationary in most industries. For example, at a 10 percent
significance level, we can reject the null hypothesis of unit root for only one out of 20 industries.
Thus, hours enter as first differences in our analysis of sectoral VARs.

C. Results from an Industry VAR
Figure 1 displays the impulse responses of TFP and hours for the aggregate manufacturing industry.
In response to a one-standard-deviation technology shock (which eventually increases the manufac­
turing TFP 1 percent), hours worked increase 0.35 percent at impact. Hours worked continue to
rise for two years, until it reaches the new steady state, 1.3 percent higher than before. In response
to a non-technology shock, TFP increases 0.7 percent initially—which indicates pro-cyclical factor
utilization—and returns to the previous level over time. Hours worked increases 3 percent and
remains high. The response based on the pooled data shows a similar pattern.
4

For aggregate manufacturing, durables and non-durables, 2-digit data are used; for a 2-digit (3-digit) industry,

3-digit (4-digit) data are used.
5

According to the Akaike information criterion (AIC), the optimal lag length is 1 in 304 industries out of 458

industries, and the Schwarz information criterion (SIC) chooses the lag length of 1 for 422 industries. Given the short
annual time series, we chose the lag length of 1.

5

Table 1 lists unconditional and conditional correlations between TFP growth and growth of
hours worked.6 Overall, growth rates of TFP and hours worked are strongly positively correlated in
aggregate manufacturing: unconditional correlation is 0.64 (with standard error of 0.09). The cor­
relation conditional on technology shocks is 0.60 (0.34): the manufacturing industry employs more
workers when the efficiency improves permanently. The conditional correlation on non-technology
shocks is also positive and significant, 0.76 (0.06): a temporary increase of TFP is associated with
longer hours of work.7
The correlation conditional on technology ranges from -0.71 in “Lumber and Wood Products
except Furniture” to 0.99 in “Apparels and Other Finished Products.” Yet the majority of 2­
digit industries show a positive correlation between TFP and hours conditional on technology
shocks; 10 industries exhibit 0.5 or higher. Among those statistically significant, eight industries
exhibit a positive correlation, whereas only one industry exhibits a statistically significant negative
conditional correlation. This pattern is robust across the level of aggregation.
In terms of the short-run response, Table 2 shows the number of industries with a negative or
positive contemporaneous response of hours to technology from the bi-variate industry VARs. The
numbers in parentheses represent the cases that are statistically significant at 10 percent. Of the 2­
digit industry estimates based on the aggregated data, 14 industries show a positive response (four
significant) whereas six industries exhibit a negative response (only one is statistically significant).
The result is similar when we use the pooled data. There are 14 (eight significant) positive and
six (one significant) negative responses. At the 3-digit level, 93 (37 significant) industries show a
positive response, and 47 (12 significant) show a negative response. Again, the estimates based on
the pooled data provide a similar pattern. Among the full sample of the 458 4-digit industries,
6

Following Galı́ (1999), we compute the conditional correlation on technology based on VAR estimates as follows:
�∞
cor(Δzt , Δlt | �i ) = �

for i = z, l, where var(Δzt | �i ) =
7

�∞

1i 2
j=0 (Cj )

j=0

Cj1i Cj2i

var(Δzt | �i ) · var(Δlt | �i )

and var(Δlt | �i ) =

�∞

2i 2
j=0 (Cj ) .

Unconditional correlation does not necessarily fall between two conditional correlations because unconditional

correlation is not necessarily a weighted average of conditional correlations. A formal proof is available from the
authors upon request.

6

320 (133 significant) industries show a positive response, whereas 138 (25 significant) industries
show a negative response. Despite considerable heterogeneity across sectors, technology’s effect
on employment does not appear strongly inconsistent with the conventional view: technological
progress increases the demand for labor. Regarding the quantitative importance of technology
shocks, for aggregate manufacturing, technology shocks account for 15 percent of the volatility of
the three-year forecast variance of hours worked according to a VAR based on the aggregated data.
A relatively small contribution of technology is consistent with previous findings from the structural
VAR based on aggregate data where technology is identified by the permanent components of
productivity (e.g., Olivier Blanchard and Danny Quah (1989)).

D. Relation to Sticky Prices
Our analysis of industry VARs reveals a considerable heterogeneity in the response of hours to
technology. A negative response is apparently inconsistent with the prediction of the baseline
flexible-price model.8 Motivated by employment’s negative short-run response to a permanent labor
productivity shock in OECD countries, Galı́ (1999) proposed a sticky-price model as a mechanism
capable of generating a negative impact of technology on employment. Intuitively, when price is
fixed, the demand for goods remains unchanged, and firms need less input, including labor, to
produce the same amount of output, thanks to the improved efficiency.9
We ask whether the industry’s response of hours (to technology shocks) is systematically cor­
related with the stickiness of industry-output prices. We take advantage of the recent study by Bils
& Klenow (2004), who compute the average price-change frequency of 350 goods and services from
the price quotes collected by the BLS for 1995-1997. For 87 manufacturing industries, we are able
8

With adjustment costs to investment, an RBC model with flexible prices can exhibit a negative response of hours

to technology (e.g., Urban Jermann (1998)).
9

Michael Dotsey (2002) and Jordi Galı́, David Lop´ez-Salido and Javier Vall´es (2003) show that technology’s effect

on employment also depends on monetary policy: employment can increase even under the sticky price model if the
monetary authority strongly accommodates technology shocks.

7

to match the SIC code with the entry-level items (ELIs).10 In matching the two data sets, each
ELI corresponds to a 4-digit SIC industry for 44 goods. For 11 goods, one ELI item corresponds to
multiple 4-digit SIC industries. In this case, we aggregate the industries’ TFP and hours. For 32
goods, multiple ELIs belong to one 3- or 4-digit SIC industry. In this case, the CPI weights from
the BLS are used to calculate the average price-change frequency of the goods. For 87 goods, the
average duration during which prices remain fixed (the inverse of average price-change frequency)
is 3.4 months.
The left panel of Figure 2 shows the relationship between the short-run response of hours to
technology (y-axis) and average duration of industry-product prices (x-axis) for 87 manufacturing
industries. Since industries may have experienced different degrees of technological change over
time, we normalize the technology shocks across industries. We consider a technology shock that
increases TFP 1 percent in the long run (instead of the conventional one-standard-deviation shock).
Under the sticky-price hypothesis we expect a negative correlation between the short-run response
of hours worked and average price duration. No systematic relationship appears; the cross-sectional
correlation between the short-run response of hours worked and average duration of prices is -0.01.
The right panel of Figure 2 shows the cross-sectional relationship between price stickiness and
the short-run response of hours to a permanent labor productivity shock (that increases the labor
productivity 1 percent in the long run in a bivariate VAR of labor-productivity growth and hours
growth, as in Galı́ (1999)) and average price duration. Again, we do not find a strong correlation
between the response of hours worked and average duration of prices.
Our evidence—a near-zero cross-sectional correlation between the employment response to
technology and average price duration—should not necessarily be viewed as evidence against the
importance of sticky prices in general.11 Rather, a low correlation suggests that price stickiness may
not be a primary reason why firms employ hours differently in the face of technological progress.
10

To calculate the Consumer Price Index, the BLS collects prices for about 71,000 non-housing goods and services

per month. These are collected from around 22,000 outlets across 44 geographic areas. The BLS divides non-housing
consumption into roughly 350 categories called “entry-level items” (ELIs).
11

Our analysis has a limited implication because the Bils-Klenow measure covers retail prices, whereas manufac­

turing output is more closely related to producers’ prices.

8

Price stickiness should generate contractionary effects of technology shocks only if there are no
inventories. If firms carry a non-negligible amount of inventories, production does not have to
equal sales. In response to a favorable cost shock, firms can expand output relative to sales and
build up inventories for future sales. Mark Bils (1998) finds that average inventory-sales ratios have
a positive and significant effect in accounting for the contemporaneous correlation between growth
rates of employment and labor productivity in manufacturing. Yongsung Chang, Andreas Hornstein
and Pierre-Daniel Sarte (2004) find that, for 98 manufacturing industries, an industry’s employment
response to technology is strongly correlated with the storability (measured by the average service
life) of industry products: an average inventory-sales ratio that is 1 percent larger (owing to the
longer average service-life of an industry’s product) results in a 0.55-percentage-point larger shortrun response of employment (with a standard deviation of 0.19), while the coefficient on the average
price duration of an industry’s output has a negative sign but is statistically insignificant.12

III.

TFP vs. Labor Productivity

Our results appear at odds with Kiley’s, which show that the permanent components of labor
productivity and employment are negatively correlated in 15 (out of 17) 2-digit manufacturing
industries for 1968:II–1995:IV. When we use labor-productivity growth (instead of TFP) in our
bivariate VAR, we also find a strong negative response of hours worked in most industries. In
Table 3, at the 2-digit level, 18 (nine significant) industries show a negative response to a permanent
increase in labor productivity, whereas only two (zero significant) industries show a positive shortrun response. A similar pattern is found across the level of aggregation and the estimation method.
We argue that TFP is a more natural measure of technology because labor productivity reflects
input mix as well as efficiency. Under constant returns to scale, labor-productivity growth Δ(y − l)t
12

By contrast, Mikael Carlsson (2003) and Domenico Marchetti and Francesco Nucci (2005) provide evidence

supporting the sticky-price hypothesis based on, respectively, Swedish and Italian manufacturing data. Both studies
use the method of Basu, Fernald & Kimball (2005) to correct for the cyclical component in the TFP and find that
the negative correlation between hours and the corrected measure of TFP is more pronounced in sectors with stickier
prices.

9

can be expressed as TFP growth and input deepening (increase of material-labor and capital-labor
ratios):
Δ(y − l)t � Δzt + αm,t Δ(m − l)t + αk,t Δ(k − l)t

(4)

where m and k denote the (logs of) material and capital input, respectively, and αm,t and αk,t
denote output elasticities (measured by revenue shares) of material and capital, respectively. Nontechnology factors, such as changes in relative input prices, affect labor productivity, whereas such
changes alone will not affect TFP. Table 4 summarizes the decomposition of the average labor
productivity growth based on (4) for 1958-1996. For aggregate manufacturing, the average annual
growth rate of labor productivity was 2.71 percent. This growth consists of a 0.9 percent increase
due to TFP, a 1.22 percent increase due to an increased material-labor ratio (αm Δ(m − l)), and a
0.46 percent increase due to an increased capital-labor ratio (αk Δ(k − l)). Changes in input mix
account for a large share of labor productivity growth across 2-digit industries.
The difference between TFP and labor productivity is dramatic in some industries. Figure 3
shows that, in “Leather and Leather Products,” TFP exhibits no apparent trend, whereas labor
productivity exhibits a strong trend because of the continuous decline in hours worked over time.
For aggregate manufacturing, we cannot reject the null-hypothesis of no co-integration between TFP
and labor productivity at a 10 percent significance level. At the 2-digit level, the null hypothesis
of no co-integration cannot be rejected for 17 industries at a 10 percent significance level.
If permanent shocks to labor productivity reduce hours, but permanent shocks to TFP do not,
then some permanent shocks to inputs must reduce hours in the short run. Consider a bivariate
VAR of the growth rate of non-labor input per hour (Δ(n−l)t ) and the growth rate of hours worked
(Δlt ): [Δ(n − l)t , Δlt ]� = C(L)εt . The non-labor input growth is the weighted (by their cost shares)
sum of material and capital growth. The long-run restriction, C 12 (1) = 0, distinguishes between
the shocks that increase non-labor input per hour in the long run and those that do not. The first
row of Figure 4 shows the responses of (n − l)t and lt to permanent shocks to non-labor input per
hour. Hours worked indeed decrease in the short run following a shock that increases non-labor
input per hour permanently. A similar bivariate VAR is estimated for the material per hour and
hours worked (i.e., [Δ(m − l)t , Δlt ]� = C(L)εt ) as well as for capital per hour and hours worked
(i.e., [Δ(k − l)t , Δlt ]� = C(L)εt ). The second row of Figure 4 shows the response of material per
10

hour and hours worked to a shock that increases the material-labor ratio in the long run. Likewise,
the third row shows the response of capital per hour and hours worked to a shock that increases
the capital-labor ratio in the long run. While both permanent shocks (to the material-labor and
capital-labor ratios) reduce hours in the short run, permanent shocks to the material-labor ratio
generate a more pronounced negative response of hours worked.
In sum, we find that TFP and labor productivity behave quite differently at the sectoral level—
in particular, there are shocks that affect labor productivity in the long run that do not involve
changes in TFP. While the studies based on aggregate data emphasize the technological progress
in the form of improved efficiency, the shift in input mix is also important for understanding
labor-productivity growth at the sectoral level. For example, increased outsourcing of intermediate
products and business services may account for the substitution of material input for labor in man­
ufacturing. (See Almas Heshmati (2003) for a survey on outsourcing’s effect on the measurement
of productivity.)

IV.

Some Caveats

We provide some caveats regarding the identification of technology from measured TFP. We are
concerned with mismeasurement due to increasing returns to scale, factor utilization, and imperfect
competition, as well as potential specification errors in the VAR due to omitted variables.

A. Comparison with Basu et al.
Basu et al. (2005) propose a method to correct measured TFP for increasing returns and factor
utilization. The key equation to estimate is the sectoral production function:
Δyt = γΔxt + βΔht + Δzt

(5)

where Δxt = αm Δmt + αk Δkt + αl Δ(et + ht ), and Δyt , Δzt , Δet and Δht are growth rates of,
respectively, output, technology, employment, and hours per worker. The basic insight of Basu
et al. is that increases in observed inputs (hours per worker) can be a proxy for unobserved
changes in utilization (capacity utilization and labor effort). Following Basu et al., we estimate the
11

system of Equation (5) (separately for durable and non-durable industries) based on 2-digit data
using the 3SLS. The coefficient for utilization is restricted to be common across sub-industries.
We use the instruments suggested by Basu et al. (2005): the growth rates of oil prices and real
military government spending (current and one-period lagged values) and monetary policy shocks
(one-period lagged values).13 According to Table 5 the median estimate for the returns to scale,
γ, is 1.15. The factor utilization parameter, β, is 0.17 and 0.76 for durables and non-durables,
respectively. The estimates are not identical to those in Basu et al. because of the differences in
the data set (KLEM in Basu et al. vs. NBER database in ours).14 The residuals from the estimated
production functions are aggregated to obtain the aggregate technology of manufacturing. We call
this measure of technology Basu-TFP. We obtain four types of Basu-TFP based on 2- and 4-digit
production functions as well as on gross and value-added output.15
Given these productivity measures, we estimate a structural VAR of productivity and hours
with the same long-run restriction: only technology has a long-run effect on productivity. Figure
5 shows the response of hours from the bivariate VARs with eight different productivity measures:
uncorrected TFP (1st row), 2-digit Basu-TFP (2nd row), 4-digit Basu-TFP (3rd row), and labor
productivity (4th row), each measure based on gross output (1st column) and value-added output
(2nd column). When the TFP is corrected for the returns to scale and factor utilization based on
2-digit production functions, hours worked decreases in a significant and persistent way as in Basu
et al. When 4-digit production functions are used, the hours worked still decrease in the short run
but not in a significant way.
Table 6 shows the number of industries with a positive or negative short-run response of hours
from bivariate VARs of Basu-TFP and hours worked. When TFP is corrected for returns to scale
and factor utilization, there are more industries with a negative response of hours in the short
run. While Basu et al.’s work is an important contribution that constructs the technology measure
from a micro production structure, we interpret the negative impact of technology with caution.
13

We thank John Fernald for providing the instruments used in Basu et al.

14

For example, Basu et al.’s estimates for β are 1.34 and 2.13 for durables and non-durables, respectively.

15

The value-added-based TFP growth is obtained by Δz� =

estimated residual from the gross production function (5).

12

Δz
,
1−αm

where Δz is the gross-output-based TFP, the

First, we found that the estimates of production functions are somewhat sensitive to the choice
of instruments (for example, to whether the current values of instruments are included or not).
Second, most explanatory power of the instruments stems from the oil-price changes which tend
to be more transitory than a typical business cycle. As Bils (1998) points out, we would expect a
greater use of increased factor utilization for more transitory shocks (which may result in a greater
degree of correction in TFP). Finally, we note that despite a negative impact in the aggregate,
the short-run response of hours from the industry VAR using the Basu-TFP still shows no crosssectional correlation (-0.02) with the Bils-Klenow measure of prices stickiness.

B. Markup
When the TFP measure is constructed in the NBER database, the capital share is computed as a
residual revenue share (αk,t = 1 − αm,t − αl,t ). This implicitly assumes that the price-cost markup
is 1. If the true markup is greater than 1, input and TFP may be spuriously correlated. When the
true markup is µ, the measured TFP growth (incorrectly assuming a markup of 1) is:
Δzt = Δzt∗ + (µ − 1)[αm,t (Δmt − Δkt ) + αl,t (Δlt − Δkt )]

(6)

where Δzt∗ denotes the true TFP growth. Table 7 reports the short- and long-run responses of
hours to technology from the bivariate VAR of Δzt∗ and Δlt assuming, respectively, µ = 1.05 and
µ = 1.1 in (6).16 As the markup ratio increases, the response of hours worked tend to decrease in
the short run as well as in the long run. In fact, the estimated short-run response of hours decreases
to -0.27 (with standard error of 0.78) when the markup is 1.1 and the value-added TFP is used.
Nevertheless, given the small profit rates reported in manufacturing over the years (e.g., Basu &
Fernald (1997)), the average markup of 1.1 appears high.

C. VAR Specification
John Shea’s study (as well as ours) estimates the dynamic response of hours to technology from
the structural VAR. Shea makes use of direct measures such as R&D and patent applications.
16

We thank Jordi Galı́ for suggesting this exercise.

13

Confronted with an identification problem, he imposes a restriction on the contemporaneous ef­
fects (whereas we use the long-run restriction).17 While the identification based on the long-run
restriction is widely used and consistent with a large class of macro models, it has shortcomings,
too. First, it requires no trend in the intensity of factor utilization.18 The workweek of production
workers has declined persistently over decades. If this trend affected the intensity of labor effort,
the long-run movement of TFP may also reflect such changes. Second, recent studies report that an
estimated dynamics identified by the long-run restrictions is sensitive to the medium-run movement
(Jon Faust and Eric Leeper (1997)) and omitted variables in the VAR (Christopher Erceg and Luca
Guerrieri and Christopher Gust (2003)).
To address potential specification errors due to a small scale VAR, we compare the short-run re­
sponses of hours from our bivariate VAR to those from the alternative (larger scale) VARs. The first
alternative specification we consider includes aggregate TFP. For each 4-digit industry, we estimate
a tri-variate VAR of [ΔAgg. T F Pt , ΔT F Pt , Δlt ]� = C(L)�t where the innovation vector �t consists
of aggregate technology shock, sectoral technology shock, and non-technology shock. We distin­
guish three fundamental shocks based on a long-run restriction. Neither the sectoral technology
shock nor the non-technology shock affects aggregate TFP in the long run: C 12 (1) = C 13 (1) = 0.
The non-technology shock does not affect the sectoral TFP in the long run: C 23 (1) = 0. We then
compute the short-run response of hours to sectoral technology by the contemporaneous effect of
the sectoral technology shock on hours worked: C032 . The sectoral technology we identify reflects
the sectoral technology that has no impact on aggregate TFP in the long run. This restriction may
be justifiable at the 4-digit industry level where the sector is too small for a sectoral TFP to affect
the level of aggregate TFP in the long run in a significant way. The second alternative specification
includes other input variables ([ΔT F Pt , Δlt , Δkt , Δmt ]� = C(L)εt ). The same long-run restriction
is used to identify the technology shock of the sector: C 12 (1) = C 13 (1) = C 14 (1) = 0.
17

In Shea, the technology variable is placed last in the VAR. Empirically, innovations to industry output are posi­

tively correlated with innovations to R&D and patent applications. Placing technology last highlights an accelerator
mechanism running from industry activity to technology—i.e., R&D is encouraged by the output demand, but an
instantaneous impact of technology shocks on output is not allowed.
18

This assumption is also required for the Basu et al. method to identify technology from the measured TFP.

14

The first graph in Figure 6 plots the short-run responses of hours worked from tri-variate
VARs against those from our benchmark bi-variate VARs. Inclusion of aggregate TFP has a nonnegligible impact on the estimate of the short-run response of hours to technology. The magnitude
of the responses of hours worked increases (in absolute value) overall. This makes sense because the
sectoral technology shock has a small (or zero) income effect in labor supply. Yet the ordering and
the signs are similar to those from the bivariate sectoral VAR and the cross-sectional correlation
between two estimates across 458 4-digit industries is 0.82. The second graph of Figure 6 shows
that inclusion of other input variables does not have a very significant effect on the estimates of the
short-run response of hours: the cross-sectional correlation between two estimates is 0.85. In sum,
our conclusion based on the short-run employment effect of technology from the bi-variate VARs
does not seem significantly sensitive to the omission of aggregate TFP or other input variables.

D. Aggregate Economy
We showed that there is a tantalizing difference in the response of hours to stochastic trends
in TFP and labor productivity in manufacturing. While our analysis focuses on manufacturing
industries because the reliable data on capital are available at the detailed disaggregate level, many
previous empirical works concern the aggregate economy.19 In Figure 7 we compare the short-run
responses of hours, respectively, to permanent TFP and labor-productivity shocks for the aggregate
non-farm business economy. At the aggregate level, the difference is not as striking as that in the
disaggregate data. Nevertheless, there is an important difference. According to the bivariate VARs,
following a permanent TFP shock, hours worked slightly decreases (statistically not significant)
in the short run, gradually increases, and remains high in the long run—a positive but delayed
response; however, hours worked declines significantly following a permanent labor-productivity
19

Galı́’s (1999) empirical work has recently been disputed on the grounds of mis-specifications along two dimensions.

Altig, Christiano, Eichenbaum & Linde (2002) argue that Galı́’s results are subject to omitted variable bias while
Christiano, Eichenbaum & Vigfusson (2004) point out that whether hours are treated as stationary or not matters in
a structural VAR. V.V. Chari, Patrick Kehoe and Ellen McGrattan (2004) argue that the long-run identification in
a structural VAR may not be consistent with the data-generating process of a standard dynamic stochastic general
equilibrium model. Yet Francis & Ramey (2002) find evidence in support of Galı́.

15

shock.

V.

Conclusion

We find that technological improvement raises employment in many U.S. manufacturing industries.
This finding substantially differs from those of previous studies based on labor productivity, which
found a negative correlation between the permanent component of labor productivity and employ­
ment in manufacturing. We argue that TFP is the natural measure for technology because labor
productivity reflects the input mix as well as technology. We show that TFP and labor productivity
behave quite differently at the sectoral level and that permanent shocks to input mix are indeed
associated with the short-run reduction of hours. Using micro data on average price duration, we
ask whether the variation in employment’s response to a technology shock across industries is cor­
related with the average duration of industry-output prices. Among 87 manufacturing industries,
we do not find strong evidence of this relationship.
Our findings are potentially important because they undercut a growing strand of literature
that uses the short-run impact of technology on employment as evidence against flexible-price
business cycle models and because some shocks affecting labor productivity in the long run do
not necessarily involve changes in the level of TFP. Given the considerable heterogeneity in the
employment effect of technology, more research on micro and historic data—such as Michael Gort
and Steven Klepper (1982), Zvi Grilliches and Fuden Lichtenberg (1984), Samuel Kortum (1993),
Shea (1998), and Basu et al. (2005)—is necessary to better understand what technology shocks are
and what they do.

References
Altig, D., Christiano, L., Eichenbaum, M. & Linde, J. (2002). Technology shocks and aggregate
fluctuations. Unpublished Manuscript, Northwestern University.
Bartelsman, E., Becker, R. & Gray, W. (2000). NBER-CES Manufacturing Industry Database,
NBER.

16

Basu, S. & Fernald, J. (1997). Returns to scale in u.s. production: Estimates and implications,
Journal of Political Economy 105(2): 249–283.
Basu, S., Fernald, J. & Kimball, M. (2005). Are technology improvements contractionary? Forth­
coming, American Economic Review.
Bils, M. (1998). Discussion, in J. C. Fuhrer & S. Schuh (eds), Beyond Shocks: What Causes
Business Cycles?, Vol. 42 of Conference Series, Federal Reserve Bank of Boston, pp. 256–263.
Bils, M. & Klenow, P. (2004). Some evidence on the importance of sticky prices, Journal of Political
Economy 112(5): 947–985.
Blanchard, O. J. & Quah, D. (1989). The dynamic effects of aggregate demand and supply distur­
bances, American Economic Review 79(1): 1146–1164.
Burns, A. & Mitchell, W. (1946). Measuring business cycles, National Bureau of Economic Re­
search.
Burnside, C., Eichenbaum, M. & Rebelo, S. (1995). Capital utilization and returns to scale, in
S. Fischer & J. J. Rotemberg (eds), NBER Macroeconomics Annual, Vol. 10, MIT–Press,
pp. 67–123.
Carlsson, M. (2003). Measures of technology and the short-run responses to technology shocks,
Scandinavian Journal of Economics 105(4): 555–579.
Chang, Y., Hornstein, A. & Sarte, P.-D. (2004). Productivity, employment, and inventory: Smooth­
ing over sticky prices. Unpublished Manuscript, Federal Reserve Bank of Richmond.
Chari, V., Kehoe, P. & McGrattan, E. (2004). Are structural VARs useful guides for developing
business cycle theories? Unpublished Manuscript, Federal Reserve Bank of Minneapolis.
Christiano, L., Eichenbaum, M. & Vigfusson, R. (2004). What happens after a technology shock?
Unpublished Manuscript, Northwestern University.
Dotsey, M. (2002). Structure from shocks, Federal Reserve Bank of Richmond Economic Quarterly
88(4): 37–47.

17

Erceg, C., Guerrieri, L. & Gust, C. (2003). Can long-run restrictions identify technology shocks?
Unpublished Manuscript, Board of Governors Federal Reserve System.
Faust, J. & Leeper, E. (1997). When do long-run identifying restrictions give reliable results?,
Journal of Business & Economic Statistics 15(3): 345–353.
Francis, N. & Ramey, V. (2002). Is the technology-driven real business cycle hypothesis dead?
NBER Working Paper, No. 8726.
Galı́, J. (1999). Technology, employment, and the business cycle: Do technology shocks explain
aggregate fluctuations?, American Economic Review 89(1): 249–271.
Galı́, J., Lopéz-Salido, D. & Vallés, J. (2003). Technology shocks and monetary policy: Assessing
the fed’s performance, Journal of Monetary Economics 50(4): 723–743.
Gort, M. & Klepper, S. (1982). Time paths in the diffusion of production process, Economic Journal
92(367): 630–653.
Griliches, Z. & Lichtenberg, F. (1984). R&d and productivity growth at the industry level: Is
there still a relationship?, in Z. Griliches (ed.), R&D, Patents, and Productivity, University of
Chicago Press, pp. 466–501.
Heshmati, A. (2003). Productivity growth, efficiency and outsourcing in manufacturing and services,
Journal of Economic Surveys 17(1): 79–112.
Jermann, U. (1998). Asset pricing in production economies, Journal of Monetary Economics
41(2): 257–275.
Kiley, M. (1998). Labor productivity in u.s. manufacturing: Does sectoral comovement reflect
technology shocks? Unpublished Manuscript, Board of Governors Federal Reserve System.
Kortum, S. (1993). Equilibrium r&d and patent r&d ratio: Us evidence, American Economic
Review 83(2): 450–457.
Marchetti, D. & Nucci, F. (2005). Price stickiness and the contractionary effect of technology
shocks, European Economic Review 49(5): 1137–1163.

18

Shapiro, M. & Watson, M. W. (1988). Sources of business cycle fluctuations, in S. Fischer (ed.),
NBER Macroeconomics Annual, Vol. 3, MIT–Press, pp. 111–148.
Shea, J. (1998). What do technology shocks do?, in B. S. Bernanke & J. J. Rotemberg (eds), NBER
Macroeconomics Annual, Vol. 13, MIT–Press, pp. 275–310.

19

Table 1: Unconditional and Conditional Correlations in Manufacturing for 1958–1996

SIC

Industry

cor(Δz, Δl)

cor(Δz, Δl | �z )

cor(Δz, Δl | �l )

Aggregate Manufacturing

0.638∗∗
(0.086)

0.595∗
(0.340)

0.762∗∗
(0.060)

Nondurables

0.478∗∗
(0.118)

0.229
(0.564)

0.801∗∗
(0.113)

0.203
(0.136)
0.259∗
(0.152)
0.256∗∗
(0.111)
0.315∗∗
(0.149)
0.476∗∗
(0.198)
0.323∗∗
(0.146)
0.207∗
(0.124)
0.085
(0.155)
0.614∗∗
(0.088)
0.054
(0.164)

0.446
(0.503)
0.996
(0.633)
0.519∗
(0.288)
0.995∗∗
(0.421)
0.487
(0.704)
−0.270
(0.693)
−0.258
(0.396)
−0.473
(0.591)
0.784∗∗
(0.205)
−0.355
(0.602)

0.510
(0.498)
0.759
(0.630)
−0.689
(0.690)
−0.558
(0.561)
0.755∗∗
(0.115)
0.736∗∗
(0.277)
0.582∗∗
(0.116)
0.793∗
(0.453)
0.721∗∗
(0.302)
0.512
(0.406)

0.658∗∗
(0.078)

0.712∗∗
(0.205)

0.760∗∗
(0.055)

−0.101
(0.134)
0.748∗∗
(0.060)
0.675∗∗
(0.085)
0.444∗∗
(0.123)
0.675∗∗
(0.069)
0.528∗∗
(0.113)
0.464∗∗
(0.134)
0.506∗∗
(0.119)
0.147
(0.170)
0.405∗∗
(0.126)

−0.710∗∗
(0.221)
0.848∗∗
(0.106)
0.745∗∗
(0.224)
0.566
(0.528)
0.863∗∗
(0.135)
0.399
(0.477)
0.152
(0.519)
0.820∗∗
(0.401)
0.119
(0.587)
0.891∗∗
(0.421)

0.508∗∗
(0.180)
0.868∗∗
(0.110)
0.796∗∗
(0.054)
0.667∗∗
(0.230)
0.690∗∗
(0.286)
0.733∗∗
(0.123)
0.863∗∗
(0.056)
0.658∗∗
(0.311)
0.549
(0.465)
0.565
(0.527)

20

Food And Kindred Products

21

Tobacco Products

22

Textile Mill Products

23

Apparel And
Other Finished Products
Paper And Allied Products

26
27
28
29
30
31

Printing, Publishing,
And Allied Industries
Chemicals And Allied Products
Petroleum Refining
And Related Industries
Rubber And
Miscellaneous Plastics Products
Leather And Leather Products

Durables
24
25
32

Lumber And Wood Products,
Except Furniture
Furniture And Fixtures

33

Stone, Clay, Glass,
And Concrete Products
Primary Metal Industries

34

Fabricated Metal Products

35

Industrial, Commercial Machinery
And Computer Equipment
Electronic Equipment,
Except Computer Equipment
Transportation Equipment

36
37
38
39

Measuring, Analyzing,
And Controlling Instruments
Miscellaneous
Manufacturing Industries

Note: The numbers in parentheses are standard errors. Those with double asterisks are
statistically significant at 5 percent.

Table 2: Short-Run Response of Hours to TFP shock

Number of Industries
Positive
Negative

Data
2-digit

aggregated
pooled

14 (4)
14 (8)

6 (1)
6 (1)

3-digit

aggregated
pooled

93 (37)
107 (47)

47 (12)
33 (5)

320 (133)

138 (25)

4-digit

Note: The number of industries with a positive or negative short-run response of
hours to a technology shock from industry VARs. Those in parentheses are the
number of industries whose estimates are statistically significant at 10 percent.

Table 3: Short-Run Response of Hours to Labor Productivity Shock

Number of Industries
Positive
Negative

Data
2-digit

aggregated
pooled

2 (0)
2 (1)

18 (9)
18 (15)

3-digit

aggregated

25 (6)

115 (60)

pooled

17 (2)

123 (72)

107 (17)

351 (174)

4-digit

See Note in Table 2.

21

Table 4: Decomposition of Labor Productivity Growth
Δ(y − l)

ΔT F P

αm Δ(m − l)

αk Δ(k − l)

Manufacturing

2.71

0.90

1.22

0.46

Nondurables

2.34

0.55

1.17

0.57

20
21
22
23
26
27
28
29
30
31

2.32
3.00
3.31
2.51
2.42
1.01
3.11
2.88
2.42
1.95

0.45
-1.05
1.16
0.72
0.49
-0.07
0.92
0.44
1.38
0.11

1.34
0.92
1.94
1.18
1.18
0.43
1.19
2.17
0.83
1.12

0.45
2.64
0.36
0.67
0.72
0.57
0.83
0.26
0.36
0.78

Durables

3.00

1.20

1.28

0.38

24
25
32
33
34
35
36
37
38
39

1.71
1.64
1.88
1.84
1.51
3.71
5.70
2.85
3.46
2.12

0.46
0.33
0.80
0.54
0.60
2.07
2.92
0.80
0.93
0.66

1.09
0.82
0.72
1.19
0.62
1.64
1.61
1.92
1.23
0.90

0.14
0.41
0.30
0.14
0.42
0.60
0.92
0.37
1.21
0.59

:
:
:
:
:
:
:
:
:
:

:
:
:
:
:
:
:
:
:
:

Note: The long-run decomposition is based on Equation (4).

22

Table 5: Parameter Estimates based on Basu et al. Method
Returns to Scale(γ)
Durables
Lumber, Wood Products (24)
Furniture (25)
Stone, Clay, Glass (32)
Primary Metal (33)
Fabricated Metal (34)
Non-Electronic (35)
Electronic Equipment (36)
Transportation Equipment (37)
Measuring, Analyzing (38)
Miscellaneous (39)

0.92
1.18
1.36
1.29
1.29
1.67
1.53
1.12
0.97
1.41

(0.11)
(0.08)
(0.07)
(0.09)
(0.09)
(0.15)
(0.21)
(0.07)
(0.09)
(0.18)

Non-Durables
Food (20)
Tobacco (21)
Textile Mill (22)
Apparel (23)
Paper Products (26)
Printing, Publishing (27)
Chemicals (28)
Petroleum Refining (29)
Rubber, Plastics (30)
Leather (31)

0.38
1.08
0.86
1.24
1.48
1.49
1.52
0.53
1.15
0.39

Utilization(β)
Durables
0.17 (0.25)

Non-Durables
0.76 (0.37)

Note: The estimates are based on 3SLS (separately for durables and non-durables).

Table 6: Short-Run Response of Hours to Basu-TFP Shock

Number of Industries
Positive
Negative

Data
2-digit

aggregated
pooled

4 (0)
7 (2)

16 (5)
13 (7)

3-digit

aggregated
pooled

38 (12)
43 (13)

102 (32)
97 (34)

161 (43)

297 (100)

4-digit

See Note in Table 2.

23

(0.41)
(0.96)
(0.16)
(0.16)
(0.21)
(0.26)
(0.22)
(0.14)
(0.08)
(0.40)

Table 7: Imperfect Competition
Productivity
Measure

Gross Output
Short Run
Long Run

Value Added
Short Run
Long Run

TFP
(µ = 1)

0.35
(0.73)

1.35∗
(0.69)

0.49
(0.75)

1.52∗∗
(0.73)

TFP
(µ = 1.05)

0.25
(0.74)

1.25
(0.77)

0.28
(0.77)

1.27
(0.79)

TFP
(µ = 1.1)

0.09
(0.73)

1.09
(0.77)

-0.26
(0.78)

0.38
(0.96)

Labor
Productivity

−1.77∗∗
(0.47)

0.53
(0.72)

−1.58∗∗
(0.54)

0.83
(0.71)

Note: The numbers represent the short-run and long-run responses of hours (in percent) to
a permanent TFP or labor productivity shock. Those in parentheses are standard errors.
The aggregate economy reflects the non-farm business sector.

24

Figure 1: Impulse Responses of TFP and Hours – Aggregate Manufacturing

TFP Response to Technology Shock
1.5

Hours Response to Technology Shock
3
2
1

percent

percent

1

0

0.5
−1
0
0

1

2

3

4

−2
0

5

1

2

3

4

5

year

year

TFP Response to Non−technology Shock
1

Hours Response to Non−technology Shock
5
4
percent

percent

0.5
3
2

0
1
−0.5
0

1

2

3

4

0
0

5

year

1

2

3

4

5

year

Note: The shaded area represents the 90-percent confidence intervals based on bootstrapping
500 draws.

25

Figure 2: Price Duration and Hours Response to Technology Shock

TFP Shock

Labor Productivity Shock

2
cor(srr,duration) = −0.01

1.5

1

short−run response

short−run response

1.5

2

0.5
0
−0.5

1
0.5
0
−0.5

−1

−1

−1.5

−1.5

−2
−1

0

1
2
log(duration)

3

−2
−1

4

cor(srr,duration) = 0.08

0

1
2
log(duration)

3

Note: The x-axis is the (log of) average monthly duration of industry output prices. The
y-axis is the short-run response of hours to a shock that increases industry TFP (or labor
productivity in the right panel) by one percent in the long run.

26

4

Figure 3: TFP, Labor Productivity, and Hours – Leather and Leather Products
2.2
2
1.8

TFP
Labor Productivity
Hours Worked

1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
1955

1960

1965

1970

1975 1980
year

1985

1990

1995

2000

Note: All variables are relative to the 1958 value. Labor productivity is value added divided
by total hours worked.

27

Figure 4: Response of Hours to Input Mix Shocks
Hours
1

2.5

0

2

−1

percent

percent

Non−Labor Input per Hour
3

1.5

−2

1

−3

0.5

−4

0
0

1

2

3

4

−5
0

5

1

2

year

3

4

5

4

5

4

5

year

Material per Hour

Hours

4

2

3.5

1
0

2.5

percent

percent

3

2
1.5

−1
−2

1
−3

0.5
0
0

1

2

3

4

−4
0

5

1

2

year

3
year

Capital per Hour

Hours

4

1

3

0

percent

percent

2
1

−1
−2

0
−3

−1
−2
0

1

2

3

4

−4
0

5

year

1

2

3
year

Note: The first row represents the responses of non-labor input per hour (n − l) and hours
(l), respectively, to a one-standard-deviation permanent shock to non-labor input per hour.
The second row represents the responses of material per hour (m−l) and hours, respectively,
to a one-standard-deviation permanent shock to material per hour. The third row represents
the responses of capital per hour (k − l) and hours, respectively, to a one-standard-deviation
permanent shock to capital per hour.

28

Figure 5: Hours Response to Various Measures of Productivity
Hours response to gross TFP

percent

3

Hours response to value−added TFP
3

2

2

1

1

0

0

−1

−1

−2

−2

percent

−3
0
1
2
3
4
5
Hours response to gross Basu−TFP (2)
3
2

2

1

1

0

0

−1

−1

−2

−2

percent

−3
0
1
2
3
4
5
Hours response to gross Basu−TFP (4)
3

−3
0
1
2
3
4
5
Hours response to value−added Basu−TFP (4)
3

2

2

1

1

0

0

−1

−1

−2

−2

−3
0

1
2
3
4
Hours response to gross LP

5

3

percent

−3
0
1
2
3
4
5
Hours response to value−added Basu−TFP (2)
3

−3
0
1
2
3
4
5
Hours response to value−added LP
3

2

2

1

1

0

0

−1

−1

−2

−2

−3
0

1

2

3
year

4

5

−3
0

1

2

3

4

5

year

Note: The figures in the left column use productivity measures based on gross output. Those
in the right column use productivity measures based on value-added output. The first row
shows the hours responses when TFP is used. The second row shows the responses when
the Basu-corrected TFP is aggregated from the 2-digit industry production functions. The
third row shows the responses when the Basu-corrected TFP is aggregated from the 4-digit
industry production functions. The last row shows the responses when labor productivity
is used.
29

Figure 6: Robustness to a VAR Specification

Tri−variate VAR with aggregate TFP

8
0.82

6
4
2
0
−2
−4
−6
−8
−4

−2

0
Bivariate VAR

2

4

4
3
0.85

4−variate VAR

2
1
0
−1
−2
−3
−4

−2

0
Bivariate VAR

2

4

Note: x-axis: short-run responses of hours to permanent TFP shocks from the 4-digit
bivariate VARs. y-axis: short-run responses of hours to industry TFP shocks from tri­
variate VARs where the third variable is aggregate TFP growth (top graph) and those from
the 4-variate VARs where the 3rd and 4th variables are material and capital input growth
(bottom graph).

30

Figure 7: Aggregate Economy: TFP vs. Labor Productivity Shocks
TFP

Hours

2.5

2
1.5

2
1.5

percent

percent

1

1

0.5
0
−0.5

0.5
−1
0
0

1

2

3

4

−1.5
0

5

1

2

year

3

4

5

4

5

year

Labor Productivity

Hours

2.5

1.5
1

2
1.5

percent

percent

0.5

1

0
−0.5
−1

0.5
−1.5
0
0

1

2

3

4

−2
0

5

year

1

2

3
year

Note: The first (second) row represents the responses of the aggregate non-farm business
economy to a one-standard-deviation permanent TFP (labor productivity) shock.

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