Offshoring, Hicks-Neutral Productivity and
Skill-Biased Technological Change
Zhanar Akhmetova∗
Department of Economics, UNSW Business School, University of New South Wales
Sydney, NSW 2052, Australia
Shon Ferguson
Research Institute of Industrial Economics (IFN)
Grevgatan 34, SE-102 15 Stockholm, Sweden
April 9, 2015
Abstract. The paper answers two questions simultaneously. What is the effect of
offshoring on firms’ Hicks-neutral productivity? What is the effect of offshoring on
skill-biased technological change? We estimate a model of firm production that allows for the effect of offshoring on both Hicks-neutral productivity and skill-biased
productivity, and for spillovers between the two. The model is fitted to Swedish firmlevel data between 2001-2011. We find significant positive effects of offshoring on
Hicks-neutral productivity in several manufacturing industries, and significant effects
(positive and negative) of offshoring on skill-biased productivity in production and
non-production activities in some industries. Strong support for the interdependency
between Hicks-neutral productivity, skill-biased productivity in production activities
∗
Corresponding author. School of Economics, UNSW Business School, University of New South
Wales, Sydney 2052 Australia. Email: [email protected].
1
and skill-biased productivity in non-production activities emerges.
Keywords: Offshoring, intermediate inputs, Hicks-neutral productivity, skill-biased
technological change, relative skilled labor demand.
JEL classification: D24, F14, F16.
1
Introduction
Offshoring, that is subcontracting the production of some tasks and inputs by firms
to overseas companies or subsidiaries, has intensified since the late 1980s. It has
attracted considerable attention both in the media and in the academic literature,
due to the observed or perceived effects of offshoring on wages and employment in
both the origin and destination countries. In the media, offshoring has been blamed
for the loss of jobs in developed countries and rising wage inequality, measured as the
ratio of skilled to unskilled labor wages. In the academic literature, no consensus on
the effects of offshoring has been reached so far (Gorg, 2011).
Some theoretical models suggest that offshoring leads to lower employment of
unskilled labor and rising wage inequality in developed countries (Feenstra and Hanson, 1995). Still others point to the possibility of enhanced firm productivity as a
result of offshoring, and hence expansion in output and higher employment of all
factors, as well as declining wage inequality in developed countries (Grossman and
Rossi-Hansberg, 2008). To be more precise, there are at least two potential effects
of offshoring. Industrialised countries offshore unskilled-labor-intensive parts of production, while skilled-labor- and capital-intensive tasks are kept at home. As a direct
effect of this movement, demand for unskilled labor goes down, while the demand
for skilled labor and capital increases. This can be called the relocation effect. It
2
also creates an upward pressure on the wages of skilled labor relative to unskilled
labor. There is another possible consequence of offshoring, however, which is the rise
in the productivity of offshoring firms, due to access to higher quality inputs and the
reorganisation of their production process. As a result, total output of these firms
may expand, leading to higher employment of both skilled and unskilled labor. This
is the so-called scale effect. Moreover, if unskilled labor productivity increases the
most due to offshoring, the wage of skilled labor relative to unskilled labor may fall,
i.e. wage inequality may decrease.
In this paper we will concern ourselves with a very specific definition of offshoring,
namely, importing intermediate inputs from abroad. This measure is convenient as it
is easily computed from firm-level production and trade data, and it has been widely
used in the literature (Gorg et al. (2008), Yasar and Morison Paul (2006), Andersson
et al. (2008), among others). The questions that we tackle are two-fold. First, we
would like to study the effect of offshoring on firms’ Hicks-neutral productivity. Higher
Hicks-neutral productivity will translate into higher production and total employment
levels, and is beneficial for everyone’s welfare. Second, we are interested in the effect of
offshoring on skill-biased technological change. If offshoring leads to improvements in
(relative) skill-specific productivity, then it also implies an increase in relative skilled
labour demand, which contributes to higher wage inequality.
Many papers have addressed either of these issues individually. Particularly, Kasahara and Rodrigue (2008) study the effect of offshoring on Hicks-neutral productivity
using Chilean data, and Kasahara et al. (2013) investigate the effect of offshoring on
relative skilled labour demand using Indonesian data. To the best of our knowledge,
we are the first to consider these two issues in a single framework. Estimating one of
the two types of productivity (Hicks-neutral and skilled-labor-specific) is impossible
without estimating the other. At the same time, correctly estimating either in the
presence of potential effects of offshoring requires incorporating this hypothetical link
into the estimation procedure. Moreover, we claim that changes in one can lead to
3
changes in another, and this should be taken into account when estimating these
productivities, as well.
In what follows, we consider a model that allows for all of the above interdependencies, and fit it to Swedish firm-level data between 2001-2011. We find significant positive effects of offshoring on Hicks-neutral productivity in three out of
16 manufacturing industries (Basic Metals, Machinery and equipment n.e.c., and
Other transport equipment (transport equipment other than motor vehicles, trailers
and semi-trailers)). Offshoring leads to unskilled labour biased technological change
in production activities in one sector (Food products, beverages & tobacco), and
to skilled labour biased technological change in production activities in one industry (Printing and reproduction of recorded media). Offshoring leads to skill-biased
technological improvements in non-production activities in three sectors (Wood and
products of wood and cork, Rubber and plastics products, and Motor vehicles, trailers
and semi-trailers).
Moreover, we find significant spillovers between Hicks-neutral productivity, skillbiased productivity in production activities and skill-biased productivity in nonproduction activities. This emphasises the importance of estimating these jointly
and allowing for these links in the estimation procedure.
The rest of the paper is organised as follows. Section 2 lays out the theoretical
model, Section 3 discusses the data, and Section 4 presents the results. Section 5
concludes.
2
Theoretical Framework
We carry out TFP estimation for each industry separately. We extend the theoretical framework of Kasahara et al. (2013), who consider the following Cobb-Douglas
production function with embedded CES aggregator functions:
Yit = Qit euit ,
4
α
p
n
Qit = Lp,it
Lαn,it
Kitαk eωit ,
where i indexes firms in a given industry, and t indexes time (years in our case), Y
is the value added, that is, gross output net of intermediate components and raw
materials, Lp , Ln , K are inputs - production labor, non-production labor, and capital,
respectively, ωit is an unobserved Hicks-neutral productivity coefficient, uit is an error
term, representing shocks to production or productivity that are not observable (or
predictable) by firms before making their input decisions at t. We deal with a value
added production function, which requires an implicit assumption that the intermediate inputs are a fixed proportion of the gross physical output (M = mY , where M
is the intermediate inputs quantity, Y is gross physical output, and m is a positive
real number).
Moreover, production and non-production labor are composites of skilled and
unskilled labor units:
Lp,it = ((Ap,it Lsp,it )
Ln,it = ((An,it Lsn,it )
σp −1
σp
+ (Lup,it )
σn −1
σn
σp −1
σp
+ (Lun,it )
σp
) σp −1 ,
σn −1
σn
σn
) σn −1 ,
where Lsp and Lup is the number of units of skilled and unskilled labor employed in
production, respectively, and Lsn and Lun is the number of units of skilled and unskilled
labor employed in non-production activities, respectively. The constants Ap,it ∈ R+
and An,it ∈ R+ are the skilled-labor-biased productivity coefficients in production and
non-production activities of firm i in year t, respectively, and an increase in Ap (An )
reflects skill-biased technological progress in production (non-production) activities.
The first-order conditions of the firm’s profit maximisation problem with respect
to Lsj,it , Luj,it , j = p, n, are given by
u u
Wj,t
Lj,it
= αj
Qit
s s
Wj,t
Lj,it
= αj
Qit
(Luj,it )
(Aj,it Lsj,it )
σj −1
σj
σj −1
σj
+
(Aj,it Lsj,it )
(Aj,it Lsj,it )
5
σj −1
σj
+
(Luj,it )
σj −1
σj
,
σj −1
σj
(Luj,it )
σj −1
σj
,
which gives us
σ −1
j
s
Luj,it 1
Wj,t
σ
( s ) σj Aj,itj = u ,
Lj,it
Wj,t
for
j = p, n,
(1)
s
u
where Wp,t
and Wp,t
are the wages of skilled and unskilled labor in year t in production,
u
s
are the wages of skilled and unskilled labor in year t
and Wn,t
respectively, and Wn,t
in non-production activities, respectively.
Substituting (1) into the expressions for Lp,it and Ln,it , we obtain
−σ
Lj,it = Xj,it
σj
j −1
Luj,it ,
where
Xj,it ≡
u u
Wj,t
Lj,it
,
s s
u u
Wj,t Lj,it + Wj,t
Lj,it
for
j = p, n.
Substituting these equations into the production function and taking the logarithm
results in
u
u
yit = αk kit + αp lp,it
+ βp xp,it + αn ln,it
+ βn xn,it + ωit + uit ,
(2)
where the lower case letters denote the logarithms of the upper case variables (e.g.
σ α
yit ≡ ln Yit ), and βj ≡ − σjj−1j , for j = p, n.
Taking the logarithm of equation (1), we can also get
rj,it = σj sj,t − (σj − 1) ln Aj,it ,
Lu
(3)
Ws
where rj,it ≡ ln( Lsj,it ), and sj,t ≡ ln Wjtu .
j,it
jt
We propose the following dynamics for the Hicks-neutral productivity term ω:
2
3
ωit = ξt + γ1 ωi(t−1) + γ2 ωi(t−1)
+ γ3 ωi(t−1)
+ γ4 of fi(t−1) + γ5 expi(t−1) + γ6 ln Ap,i(t−1) + γ7 ln An,i(t−1) + νit ,
(4)
Mf
where ξt is an industry-specific Hicks-neutral productivity shock, of fit ≡ ln(1 + Mitit ),
offshoring intensity of firm i in year t, where Mitf is the quantity of intermediate inputs
imported from abroad, and Mit is the total quantity of intermediate inputs used in
production, expit is a dummy equal to 1 if firm i exports in year t, and 0 otherwise,
and νit is a firm-specific zero-mean shock to ω in year t, which is unforeseen before
year t and is independent of ξt .
6
We also propose the following dynamics for the skill-biased technological terms
Ap,it and An,it :
ln Ap,it = ζp,t + θp,1 ln Ap,i(t−1) + θp,2 ln A2p,i(t−1) + θp,3 ln A3p,i(t−1)
+ θp,4 of fi(t−1) + θp,5 expi(t−1) + θp,6 ωi(t−1) + θp,7 ln An,i(t−1) + ηp,it ,
(5)
ln An,it = ζn,t + θn,1 ln An,i(t−1) + θn,2 ln A2n,i(t−1) + θn,3 ln A3n,i(t−1)
+ θn,4 of fi(t−1) + θn,5 expi(t−1) + θn,6 ωi(t−1) + θn,7 ln Ap,i(t−1) + ηn,it ,
(6)
where ζp,t and ζn,t are industry-specific skill-biased productivity shocks, ηp,it and ηn,it
are firm-specific zero-mean shocks to ln Ap,it and ln An,it , respectively, which are unforeseen before year t and are independent of ζp,t and ζn,t , respectively.
We assume therefore that changes in Hicks-neutral productivity can affect future
direction of skill-biased technological progress in production and non-production activities, and that skill-biased technological progress in production and non-production
activities can affect future values of Hicks-neutral productivity of the firm.
This dynamic interaction necessitates joint estimation of ω and ln Ap , ln An . We
do this by jointly fitting equations (2) and (3), as well as (4) and (5), (6) to the data.
We incorporate the ACF (Ackerberg, Caves and Frazer (2006)) critique of the
Levinsohn and Petrin (2003) and Olley and Pakes (1996) estimation approaches, and
estimate all production coefficients in the second stage of our estimation.
The first stage consists of relying on the equation for the optimal choice of intermediate inputs (in logs)
mit = mt (ωit , kit ),
to invert:
ωit ≡ ψt (mit , kit ),
assuming monotonicity in the function mt (.). Current capital input kit is set at t − 1,
as it depends on past capital ki(t−1) and investment at time t − 1. Inserting this into
7
the expression for value added:
u
u
yit = αk kit + αp lp,it
+ βp xp,it + αn ln,it
+ βn xn,it + ψt (mit , kit ) + uit ,
It is evident from the above that the coefficient αk is not identified, since kit
appears in ψ(mit , kit ), and the coefficients αp , αn are not identified, since the optimal
u
u
choice of lp,it
and ln,it
likely depends on ωit ≡ ψt (mit , kit ).
We can however estimate the residual uit by fitting the regression equation
u
u
yit ≡ αp lp,it
+ βp xp,it + αn ln,it
+ βn xn,it + Ψt (mit , kit ) + uit ,
where Ψt (mit , kit ) is a polynomial in capital and intermediate inputs, one for each
year:
Ψt (mit , kit ) ≡
T
X
b0t Dt +
t=1
+
+
+
T
X
t=1
T
X
t=1
T
X
T
X
b1mt Dt mit +
t=1
T
X
b1kt Dt kit
t=1
b2mkt Dt kit mit +
T
X
b2mmt Dt m2it +
t=1
T
X
b3kkmt Dt kit2 mit +
T
X
b2kkt Dt kit2
t=1
b3kmmt Dt kit m2it
t=1
b3kkkt Dt kit3 +
t=1
T
X
b3mmmt Dt m3it ,
t=1
where Dt are year dummies. Using the obtained estimates, we can purge yit of the
error term uit :
yˆit ≡ yit − uˆit .
In the second stage we apply GMM to estimate αk , αp , αn , σp , σn . For given values
σ α
of αk , αp , αn , σp , σn , one can calculate βj ≡ − σjj−1j , for j = p, n. Next, calculate ωit
from
u
u
ωit = yˆit − αk kit − αp lp,it
− βp xp,it − αn ln,it
− βn xn,it ,
and
ln Aj,it =
σj
1
sj,t −
rj,it ,
σj − 1
σj − 1
8
from (3).
We would like to estimate the residual νit by running the regression (4). However,
this equation is subject to a selection bias, since we only observe firms with high
enough productivity to stay active. That is, a firm i produces in year t if and only
if ωit ≥ ω t (kt ), where ω t (kt ) is a threshold below which firms exit in year t, which
depends on the (log-)capital stock of the firm in year t and industry-level demand
and cost considerations (hence the subscript t). We rely on the Heckman correction
to tackle this issue.
Since ωit can be predicted using ωi(t−1) (from (4)), which we expressed as
ω(i−1)t ≡ ψt−1 (mi(t−1) , ki(t−1) ),
and kt is predetermined by ki(t−1) and investment in year t, it(i−1) , we can employ
these variables to predict firm survival probabilities. We also use lagged offshoring
intensity of fi(t−1) , since we assume that it affects the firm’s current productivity ωit :
P rob(Sit = 1) =
T
X
2
δt Dt + δk ki(t−1) + δm mi(t−1) + δi ii(t−1) + δkk ki(t−1)
+ δmm m2i(t−1)
t=1
+ δii i2i(t−1) + δmk mi(t−1) ki(t−1) + δik ii(t−1) ki(t−1) + δof f of fi(t−1)
≡ Xδ,
(7)
where Sit is a dummy variable equal to 1 if the firm survives in year t and 0 otherwise,
and Dt are year dummies. In the data we treat all the years between the first time and
the last time the firm is observed producing as the years when it survives, and the year
after the last year the firm is observed producing as the year it exits production. X ≡
2
[D1 , ..., DT , ki(t−1) , mi(t−1) , ii(t−1) , ki(t−1)
, m2i(t−1) , i2i(t−1) , mi(t−1) ki(t−1) , ii(t−1) ki(t−1) , of fi(t−1) ],
and δ ≡ [δ1 , ..., δT , δk , δm , δi , δkk , δmm , δii , δmk , δik , δof f ]0 .
Once we fit equation (7), we can calculate the non-selection hazard ratio, or the
inverse Mills ratio, for each observation point, as
ˆ =
nsh(X δ)
9
ˆ
φ(X δ)
,
ˆ
Φ(X δ)
where φ denotes the probability density function of the standard normal distribution,
and Φ - its cumulative distribution function. We then include the non-selection hazard
ratio as an additional explanatory variable in the regression for ω:
2
3
ωit = ξt + γ1 ωi(t−1) + γ2 ωi(t−1)
+ γ3 ωi(t−1)
ˆ + νit ,
+ γ4 of fi(t−1) + γ5 expi(t−1) + γ6 ln Ap,i(t−1) + γ7 ln An,i(t−1) + γ8 nsh(X δ)
(8)
Estimate the residual νit by running the regression (8) and setting
2
3
νit = ωit − ξˆt − γˆ1 ωi(t−1) − γˆ2 ωi(t−1)
− γˆ3 ωi(t−1)
ˆ
− γˆ4 of fi(t−1) − γˆ5 expi(t−1) − γˆ6 ln Ap,i(t−1) − γˆ7 ln An,i(t−1) − γˆ8 nsh(X δ),
and estimate the residuals ηp,it , ηn,it by running the regressions (5), (6) and setting
ηp,it = ln Ap,it − ζˆp,t − θˆp,1 ln Ap,i(t−1) − θˆp,2 ln A2p,i(t−1) − θˆp,3 ln A3p,i(t−1)
− θˆp,4 of fi(t−1) − θˆp,5 expi(t−1) − θˆp,6 ωi(t−1) − θˆp,7 ln An,i(t−1) ,
ηn,it = ln An,it − ζˆn,t − θˆn,1 ln An,i(t−1) − θˆn,2 ln A2n,i(t−1) − θˆn,3 ln A3n,i(t−1)
− θˆn,4 of fi(t−1) − θˆn,5 expi(t−1) − θˆn,6 ωi(t−1) − θˆn,7 ln Ap,i(t−1) .
Use the moments
E[νit (αk , αp , αn , σp , σn )kit ] = 0,
u
E[νit (αk , αp , αn , σp , σn )lp,i(t−1)
] = 0,
E[νit (αk , αp , αn , σp , σn )xp,i(t−1) ] = 0,
u
E[νit (αk , αp , αn , σp , σn )ln,i(t−1)
] = 0,
E[νit (αk , αp , αn , σp , σn )xn,i(t−1) ] = 0,
E[ηp,it (αk , αp , αn , σp , σn )rp,i(t−1) ] = 0,
E[ηn,it (αk , αp , αn , σp , σn )rn,i(t−1) ] = 0,
10
to identify the parameters αk , αp , αn , σp , σn . Since we have 7 moments to estimate 5
parameters, we conduct the test for over-identifying restrictions, which allows us to
check whether the moment conditions match the data well or not.
α
ˆp ˆ
α
ˆn
Given estimates α
ˆk , α
ˆp, α
ˆn, σ
ˆp , σ
ˆn , and βˆp ≡ − σˆσˆpp−1
, βn ≡ − σσˆˆnn−1
, the estimate of
Hicks-neutral productivity ω, is given by
u
u
ω
cit = yˆit − α
ˆ k kit − α
ˆ p lp,it
− βˆp xp,it − α
ˆ n ln,it
− βˆn xn,it ,
and the estimates of skill-biased productivity terms ln Ap and ln An are given by
lnd
Aj,it =
σ
ˆj
1
sj,t −
rj,it .
σ
ˆj − 1
σ
ˆj − 1
We can then investigate the relationship between offshoring and firm productivity
through the regressions
2
3
ω
cit = ξt + γ1 ωd
i(t−1) + γ2 ωd
i(t−1) + γ3 ωd
i(t−1)
d
ˆ
+ γ4 of fi(t−1) + γ5 expi(t−1) + γ6 ln Ad
p,i(t−1) + γ7 ln An,i(t−1) + γ8 nsh(X δ) + νit , (9)
and
d 2
d 3
lnd
Ap,it = ζp,t + θp,1 ln Ad
p,i(t−1) + θp,2 ln Ap,i(t−1) + θp,3 ln Ap,i(t−1)
d
+ θp,4 of fi(t−1) + θp,5 expi(t−1) + θp,6 ωd
i(t−1) + θp,7 ln An,i(t−1) + ηp,it ,
(10)
d 2
d 3
lnd
An,it = ζn,t + θn,1 ln Ad
n,i(t−1) + θn,2 ln An,i(t−1) + θn,3 ln An,i(t−1)
d
+ θn,4 of fi(t−1) + θn,5 expi(t−1) + θn,6 ωd
i(t−1) + θn,7 ln Ap,i(t−1) + ηn,it .
(11)
Studying the effect of offshoring on the skill-biased productivity terms ln Ap and
ln An directly gives us the effect of offshoring on relative skilled-labor demand, since
rp,it
Lup,it
≡ ln( s ) = σp sp,t − (σp − 1) ln Ap,it ,
Lp,it
rn,it
Lun,it
≡ ln( s ) = σn sn,t − (σn − 1) ln An,it ,
Ln,it
11
from (3). Hence, if offshoring has a positive effect on ln Ap (ln An ), this implies
that it has a negative effect on relative unskilled-labor demand, and a positive effect
on relative skilled-labor demand in production (non-production) activities, ceteris
paribus.
3
Data and Descriptive Statistics
The data is obtained from the Swedish Survey of Manufacturers conducted by Statistics Sweden, the Swedish government’s statistical agency. The survey covers all firms
within manufacturing (2-digit NACE Rev.2 codes 15-32) with 10 or more employees. We use data for the period 2001-2011, which is when data on the occupation
of workers employed by Swedish firms is available. The survey contains information
on value-added, intermediate inputs, capital stock, investment and the number of
employees at the firm level. We merge the firm-level data with the employee data.
The employee data contains information on their level of education and their occupation. We define workers as ‘high-skilled’ if they have some high-school education,
and ‘low-skilled’ otherwise. Occupation is defined according to the Swedish Standard
Classification of Occupations (SSYK). We define ‘non-production’ workers as workers
with occupation codes 1-5 (managers, professionals, technicians, clerks and service
workers). We define ‘production’ workers as workers with occupation codes 6-9 (agricultural and fishery workers, craft and related trades workers, plant and machine
operators and assemblers and elementary occupations).
We define intermediate inputs as inputs that are transformed by the firm. These
include raw materials1 . Our measure of intermediate inputs thus does not include
goods that are sold onward without any modification. We define capital as tangible
assets, which includes buildings, land and equipment. We calculate capital using
1
In the dataset, we observe a variable that incorporates both intermediate components and raw
materials, and we do not observe these separately.
12
the perpetual inventory method. Capital for year t equals the capital in year t − 1
plus investment in year t, depreciating capital using Hulten and Wykoff’s (1981)
depreciation rates for buildings (0.0361) and equipment (0.1179).
We deflate value-added, capital and intermediate inputs using data available from
Statistics Sweden and follow the EUKLEMS methodology to construct the deflators.
2
. The level of industry aggregation for the deflators appears in Table 1. The deflators
are available at the 2-digit industry level for most industries, but in some cases 2-digit
industries are aggregated due to a lack of observations. We calculate TFP using the
level of industry aggregation given in Table 1.3
We merge the firm-level data with customs data on imports. Our measure of
offshoring is offshoring intensity - the ratio of the quantity of imported intermediate
Mf
inputs to total intermediate inputs, or more precisely, ln of fit ≡ ln(1 + Mitit ).4 We define imports as intermediate inputs if they correspond to ‘Industrial supplies not elsewhere specified’ or ‘Fuels and lubricants’, using the Classification by Broad Economic
Categories (BEC), revision 4. We match our trade data (Combined Nomenclature)
to the BEC classification using a concordance provided by Eurostat.
The descriptive statistics in Table 2 indicate a large degree of heterogeneity among
Swedish firms in terms of value-added, capital, employment and materials.
2
3
EUKLEMS does not report Swedish NACE rev.2 deflators for materials and capital.
We omit ‘Coke and refined petroleum products’ (NACE rev.2 19) from the analysis due to a
lack of observations.
4
We introduce the 1 in the definition to allow firms with no offshoring to enter our dataset.
13
Table 1: Industry classification
Industry
NACE Revision 2 Description
10-12
Food products, beverages and tobacco
13-15
Textiles, wearing apparel, leather and related products
16
Wood and products of wood and cork
17
Paper and paper products
18
Printing and reproduction of recorded media
20-21
Chemicals and chemical products
22
Rubber and plastics products
23
Other non-metallic mineral products
24
Basic metals
25
Fabricated metal products, except machinery and equip.
26
Computer, electronic and optical equip.
27
Electrical equip.
28
Machinery and equip. n.e.c.
29
Motor vehicles, trailers and semi-trailers
30
Other transport equip.
31-32
Furniture, other manufacturing
Table 2: Descriptive statistics
Variable
N
mean
std. dev.
min
max
Value-added (SEK thousands)
52,141
3.579e+07
2.324e+08
707.4
2.549e+10
Capital (SEK thousands)
52,141
306,404
1.557e+06
0.497
5.645e+07
Low-skilled labour (employees)
52,141
48.16
204.0
0.00145
18,611
High-skilled labour (employees)
52,141
11.12
78.75
7.64e-05
6,595
Materials (SEK thousands)
52,141
5.656e+07
3.054e+08
1,994
1.722e+10
Non-production labour (employees)
39,156
19.79
112.6
0
7,928
Production labour (employees)
39,156
37.93
201.0
0
18,200
14
4
Results
In Table (3) we present the estimates of the parameters αk , αp , αn , σp , σn . All sectors, other than ‘Furniture, other manufacturing’ satisfy the test for over-identifying
restrictions at 5% confidence level, while the sector ‘Furniture, other manufacturing’
satisfies this test at 10% confidence level.
In Table (4) we test the crucial assumption that intermediate inputs are a monotonic function of firm Hicks-neutral productivity by regressing mit on ωit and kit ,
over all firms and all years. Since we are dealing with estimates of ωit , we rely on
bootstrapped standard errors for significance tests. We include industry-year fixed
effects in the regression to control for industry-level time-varying variables. Both ωit
and kit have significant positive coefficients, and a 1% increase in ωit is associated
with a 1.72% increase in intermediate inputs use.
We also regress offshoring intensity on ωit , kit , ln Ap,it and ln An,it , over all firms
and all years, in Table (5). Both ωit and kit have significant positive coefficients,
while ln Ap,it has a statistically significant, but economically insignificant, negative
coefficient. The R2 of this regression is also quite small, at only 0.171, which suggests
some explanatory variables are missing.
In what follows we present the results of regressions (9), (10), and (11). We
normalise ωit , ln Ap,it and ln An,it by subtracting the log of the industry-wide mean
of the firm-averages of ωit , ln Ap,it and ln An,it , respectively. For each of these three
regressions, we first present the results for a pooled regression over all industries,
with industry-year fixed effects. Next, results for industry-specific regressions, with
year-fixed effects, are shown. All standard errors are bootstrapped.
15
Table 3: Estimated parameters.
Industry
αk
αp
αn
σp
σn
Food products, beverages & tobacco
.2609581
.4911126
.391406
1.128439
1.67804
Textiles, wearing apparel, leather & r.p.
.3260372
.3791375
.1519337
1.165775
2.259352
Wood & products of wood and cork
.2366288
.3939885
.3372445
1.08367
1.359775
Paper and paper products
.3244185
.24625
.470887
1.159046
2.346776
Printing and reprod. of recorded media
.1203349
.4893677
.6032252
1.076156
1.373085
Chemicals and chemical products
.3495868
.2032533
.5338477
2.277436
6.447295
Rubber and plastics products
.2385715
.3752728
.337668
1.206993
2.019283
Other non-metallic mineral products
.3438172
.2690409
.4669266
1.104144
1.902186
Basic metals
.2469986
.3840903
.3812921
1.153433
2.358303
Fab. metal products, exc. mach. & equip.
.14636
.3903397
.3625153
1.153083
1.515414
Computer, electronic and optical equip.
.1084342
.2893059
.6455436
1.764767
4.783109
Electrical equip.
.1938643
.272957
.5750953
1.218866
4.2536
Machinery and equip. n.e.c.
.1977321
.4608776
.4556512
1.12464
2.229365
Motor vehicles, trailers & semi-trailers
.1055057
.3356779
.5791256
1.062893
3.656644
Other transport equip.
.0431014
.6570168
.4448475
3.743433
2.935108
Furniture, other manufacturing
.2605653
.3520406
.4561782
1.126869
1.799166
16
Table 4: The regression for intermediate inputs
Dependent variable: firm-level intermediate inputs mit
ωit
1.720
(0.039)***
kit
0.839
(0.004)***
Constant
3.246
(0.071)***
Observations
24896
R2
0.716
Industry-year fixed effects are included
Bootstrapped standard errors in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
17
Table 5: The regression for offshoring intensity
Dependent variable: firm-level offshoring intensity ln of fit
ωit
0.009
(0.001)***
kit
0.005
(0.000)***
ln Ap,it
-0.000
(0.000)***
ln An,it
0.000
(0.000)
Constant
-0.078
(0.004)***
Observations
24321
R2
0.171
Industry-year fixed effects are included
Bootstrapped standard errors in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
18
4.1
The effect of offshoring on Hicks-neutral productivity
In Table (6), we investigate the relationship between offshoring and firms’ Hicksneutral productivity, by estimating equation (9) over all industries jointly. Neither
lagged offshoring intensity nor the lagged exporting dummy have significant coefficients. The lagged linear, quadratic and cubic ω terms have significant coefficients,
supporting the hypothesised AR(1) structure of the dynamics of ω. None of the
lagged skill-biased productivity terms in production and non-production activities
have significant coefficients.
Next, we estimate equation (9) for each industry individually, and show the results
in Table (7). Lagged offshoring intensity has a positive effect in three industries: Basic
Metals, Machinery and equipment n.e.c., and Other transport equipment (transport
equipment other than motor vehicles, trailers and semi-trailers). It has the largest
effect in the Other transport equip industry, where a 5% increase in the measure of
offshoring intensity we introduced (1 +
f
Mit
)
Mit
results in a nearly 7% increase in the
Hicks-neutral productivity.
The lagged exporting dummy has a positive significant coefficient in three industries, confirming the hypothesis in the international trade literature that exporting
has a favourable effect on firms’ productivity.
The lagged skill-biased productivity terms in production and non-production activities, ln Ap,i(t−1) and ln An,i(t−1) have significant positive coefficients in some industries, and significant negative coefficients in some others. Notably, they both have
negative coefficients in two sectors, Food products, beverages & tobacco and Machinery and equip. n.e.c., and positive or insignificant coefficients in all other sectors.
This finding (a negative effect of lagged skill-biased technological change on Hicksneutral productivity) has to be explained. It could be the case that resources are
diverted from innovation that contributes to Hicks-neutral efficiency to innovation
that contributes to skill-biased technological advances. The positive effect of lagged
skill-biased progress is probably explained by spillovers between the innovation pro19
cesses within the firm, and by higher productivity of engineers and innovators within
the firm as a result of skill-biased technological improvement, which in turn translates
into higher Hicks-neutral productivity.
4.2
The effect of offshoring on skill-biased productivity in
production activities
In Table (8), the results of estimating equation (10) over all industries jointly are
presented. Neither lagged offshoring intensity nor exporting seem to have a significant effect on skill-biased productivity in production activities. The lagged linear,
quadratic and cubic ln Ap terms have significant coefficients. Both lagged Hicksneutral productivity and lagged skill-biased productivity in non-production activities
have positive significant coefficients. A 1% increase in Hicks-neutral productivity is
associated with a 0.189% percent increase in skill-biased productivity in production,
while a 1% increase in skill-biased productivity in non-production activities - with a
0.101% increase.
Next, we study equation (10) for each industry individually. In Food products,
beverages & tobacco, lagged offshoring intensity has a negative significant coefficient.
This suggests that offshoring induces unskilled-labour-biased technological improvements in this industry, which translates into rising relative unskilled labor demand.5
5
Note that the model can be seen as isomorphic to the following model.
α
˜ pL
˜ αn αk ω˜ it ,
Qit = L
p,it n,it Kit e
Yit = Qit euit ,
s
˜ p,it = ((Bp,it
L
Lsp,it )
σp −1
σp
u
+(Bp,it
Lup,it )
σp −1
σp
σp
s
˜ n,it = ((Bn,it
L
Lsn,it )
) σp −1 ,
σn −1
σn
u
+(Bn,it
Lun,it )
σn −1
σn
σn
) σn −1 ,
s
s
where the constants Bp,it
∈ R+ and Bn,it
∈ R+ are the skilled-labor-specific productivity coefficients
in production and non-production activities of firm i in year t, respectively, and the constants
u
u
Bp,it
∈ R+ and Bn,it
∈ R+ are the unskilled-labor-specific productivity coefficients in production
and non-production activities of firm i in year t, respectively. Transform the above two equations:
u
˜ p,it = Bp,it
L
((Ap,it Lsp,it )
σp −1
σp
+(Lup,it )
σp −1
σp
σp
u
˜ n,it = Bn,it
L
((An,it Lsn,it )
) σp −1 ,
20
σn −1
σn
+(Lun,it )
σn −1
σn
σn
) σn −1 ,
In the ‘Printing and reproduction of recorded media’ industry, offshoring has a
significant positive coefficient, and a 1% increase in lagged offshoring intensity there is
associated with a 17.332% percent increase in skill-biased productivity in production.
This translates into rising skilled-labor demand in production activities.
Lagged exporting dummy has a significant coefficient in only one industry. In
Motor vehicles, trailers and semi-trailers exporting seems to have a positive effect on
unskilled-labour-biased productivity.
Lagged Hicks-neutral productivity has a positive significant coefficient in Textiles,
wearing apparel, leather and related products.
Lagged skill-biased productivity in non-production activities has positive significant coefficients in Food products, beverages and tobacco, Wood and products of
wood and cork, Paper and paper products, Chemicals and chemical products, Rubber
and plastics products, Fabricated metal products, except machinery and equipment,
Computer, electronic and optical equipment, Machinery and equip. n.e.c., and Furniture and other manufacturing. This suggests spillovers in skill-biased innovation
processes from non-production to production activities in these sectors.
4.3
The effect of offshoring on skill-biased productivity in
non-production activities
In Table (10), the results of estimating equation (11) over all industries jointly are
presented. Lagged offshoring intensity has a strongly significant positive coefficient,
where a 1% increase in offshoring intensity results in a 0.094% percent increase in
where Ap,it ≡
s
Bp,it
,
u
Bp,it
An,it ≡
s
Bn,it
u
Bn,it
are the skill-biased productivity terms. Then
α
αk ωit
p
n
Qit = Lp,it
Lα
,
n,it Kit e
where Lp,it ≡
˜ p,it
L
,
u
Bp,it
Ln,it ≡
˜ n,it
L
u
Bn,it
u
u
and eωit ≡ eω˜ it (Bp,it
)αp (Bn,it
)αn . We cannot identify separately
u
u
ω
˜ it , Bp,it
or Bn,it
, and can only identify ωit , and Ap,it , An,it . Increases in Ap,it , An,it signal increases
in (relative) skill-biased productivity terms
s
Bp,it
Bs
, Bn,it
,
u
u
Bp,it
n,it
21
that is, skill-biased technological change.
skill-biased productivity in non-production activities. Lagged exporting dummy also
has a positive significant coefficient. The lagged linear and quadratic ln An terms
have significant coefficients. Both lagged Hicks-neutral productivity and lagged skillbiased productivity in production activities have positive significant coefficients. A
1% increase in Hicks-neutral productivity is associated with a 0.107% percent increase in skill-biased productivity in production, while a 1% increase in skill-biased
productivity in production activities - with a 0.008% increase.
Next, we investigate equation (11) for each industry individually. Lagged offshoring intensity has significant positive coefficients in Wood and products of wood
and cork, Rubber and plastics products, and Motor vehicles, trailers and semi-trailers.
These coefficients are also economically significant, with the percentage effects of a
1% increase in offshoring intensity ranging from 0.330 to 0.815.
Lagged exporting dummy has a significant positive coefficient in 12 out of 14
industries, suggesting a statistically significant effect of exporting on relative skilled
labour demand in non-production activities in these sectors.
Lagged Hicks-neutral productivity seems to have a significant effect on current
skill-biased productivity in non-production occupations in three industries: Wood
and products of wood and cork, Fabricated metal products, except machinery and
equipment, and Computer, electronic and optical equipment.
Lagged skill-biased productivity in production has a positive effect on skill-biased
productivity in non-production activities in 13 out of 16 sectors. This confirms the
strong spillovers in skill-biased technological progress between production and nonproduction activities.
22
Table 6: Estimating equation (9)
Dependent variable: firm-level Hicks-neutral productivity ωit
ln of fi(t−1)
0.024
(0.019)
expi(t−1)
-0.003
(0.003)
ωi(t−1)
0.800
(0.009)***
ωi(t−1) 2
0.036
(0.020)*
ωi(t−1) 3
-0.092
(0.018)***
ln Ap,i(t−1)
-0.000
(0.000)
ln An,i(t−1)
-0.000
(0.001)
nshit
0.053
(0.035)
Constant
0.025
(0.004)***
Observations
17577
R2
0.757
Industry-year fixed effects are included
Bootstrapped standard errors in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
23
Table 7: The effect of offshoring on Hicks-neutral productivity, 2001-2011
Panel A
Industry
ln of fi(t−1)
expi(t−1)
ωi(t−1)
2
ωi(t−1)
3
ωi(t−1)
ln Ap,i(t−1)
ln An,i(t−1)
nsh
Constant
10-12
13-15
16
17
18
20-21
22
23
Food
Textiles
Wood
Paper
Print.
Chem.
Rubber
Nonmet.
0.177
-0.096
0.033
-0.015
0.329
0.007
0.045
-0.066
(0.395)
(0.654)
(0.068)
(0.091)
(0.810)
(0.027)
(0.156)
(0.041)
-0.012
0.069
0.025
0.003
-0.006
0.106
-0.002
-0.033
(0.008)
(0.038)*
(0.011)**
(0.044)
(0.020)
(0.045)**
(0.021)
(0.023)
0.819
0.896
0.601
0.801
0.803
0.696
0.667
0.732
(0.026)***
(0.059)***
(0.051)***
(0.067)***
(0.050)***
(0.043)***
(0.049)***
(0.069)***
-0.066
-0.148
0.199
-0.122
-0.033
-0.103
0.151
-0.153
(0.052)
(0.069)**
(0.118)*
(0.072)*
(0.059)
(0.133)
(0.064)**
(0.147)
-0.198
-0.078
-0.661
-0.393
0.039
-0.273
-0.052
-0.087
(0.050)***
(0.195)
(0.336)**
(0.173)**
(0.055)
(0.237)
(0.068)
(0.297)
-0.004
-0.000
0.001
-0.001
0.003
0.003
0.002
-0.001
(0.001)***
(0.002)
(0.001)*
(0.002)
(0.001)***
(0.012)
(0.001)
(0.002)
-0.008
-0.002
0.001
0.005
0.003
0.070
-0.005
0.018
(0.004)**
(0.016)
(0.002)
(0.010)
(0.004)
(0.035)**
(0.005)
(0.011)
0.229
-0.097
-0.173
0.654
0.023
0.031
-0.960
-0.053
(0.139)*
(0.182)
(0.182)
(0.141)***
(0.117)
(0.092)
(0.237)***
(0.189)
-0.087
0.009
-0.004
0.016
0.234
-0.087
0.088
0.015
(0.021)***
(0.049)
(0.024)
(0.048)
(0.040)***
(0.047)*
(0.033)***
(0.043)
Observations
1709
295
1139
732
406
714
1294
398
R2
0.744
0.756
0.358
0.718
0.863
0.617
0.667
0.644
Panel B
Industry
ln of fi(t−1)
expi(t−1)
ωi(t−1)
2
ωi(t−1)
3
ωi(t−1)
ln Ap,i(t−1)
ln An,i(t−1)
nsh
Constant
Observations
R2
24
25
26
27
28
29
30
31-32
BasicMet.
FabMet.
Comp.
Electrical
Machines
Vehicles
Transport
Furniture
0.232
0.120
4.333
0.338
0.354
0.108
6.986
0.032
(0.074)***
(0.141)
(7.832)
(0.743)
(0.148)**
(0.203)
(4.115)*
(0.087)
0.010
-0.001
-0.037
-0.005
-0.004
-0.011
0.072
0.008
(0.046)
(0.003)
(0.044)
(0.016)
(0.008)
(0.021)
(0.079)
(0.011)
0.776
0.845
0.483
0.652
0.722
0.698
0.711
0.856
(0.055)***
(0.019)***
(0.109)***
(0.060)***
(0.025)***
(0.043)***
(0.202)***
(0.050)***
0.111
0.214
0.033
0.028
0.011
-0.005
0.064
0.149
(0.113)
(0.079)***
(0.045)
(0.136)
(0.060)
(0.068)
(0.151)
(0.079)*
-0.126
-0.289
0.038
-0.124
-0.065
0.001
-0.208
-0.548
(0.135)
(0.131)**
(0.056)
(0.296)
(0.133)
(0.072)
(0.292)
(0.287)*
0.003
0.001
0.035
-0.001
-0.003
-0.000
0.198
0.001
(0.002)
(0.000)*
(0.010)***
(0.001)
(0.000)***
(0.001)
(0.170)
(0.001)***
-0.012
-0.000
0.055
-0.021
-0.016
-0.003
0.135
-0.004
(0.011)
(0.001)
(0.043)
(0.021)
(0.003)***
(0.016)
(0.056)**
(0.003)
-0.008
-0.326
-0.111
-0.125
0.630
0.308
0.705
-0.126
(0.104)
(0.076)***
(0.168)
(0.190)
(0.121)***
(0.153)**
(0.487)
(0.118)
0.013
0.042
0.144
0.039
(0.050)
(0.007)***
(0.049)***
(0.022)*
24
-0.082
0.028
-0.125
0.048
(0.015)***
(0.030)
(0.088)
(0.015)***
438
3982
462
747
2778
958
79
1446
0.608
0.831
0.907
0.688
0.745
0.750
0.765
0.793
Dependent variable: firm-level Hicks-neutral productivity at time t. Year fixed effects included in all specifications.
Bootstrapped standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Table 8: Estimating equation (10)
Dependent variable: firm-level skill-biased productivity
in production activities, ln Ap,it
ln of fi(t−1)
-0.103
(0.184)
expi(t−1)
-0.083
(0.061)
ln Ap,i(t−1)
0.917
(0.006)***
ln Ap,i(t−1) 2
-0.001
(0.001)**
ln Ap,i(t−1) 3
-0.000
(0.000)**
ωi(t−1)
0.189
(0.091)**
ln An,i(t−1)
0.101
(0.015)***
Constant
0.283
(0.056)***
Observations
21652
R2
0.832
Industry-year fixed effects are included
Bootstrapped standard errors in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
25
Table 9: The effect of offshoring on skill-biased productivity in production activities
Panel A
Industry
ln of fi(t−1)
expi(t−1)
ln Ap,i(t−1)
ln A2
p,i(t−1)
ln A3
p,i(t−1)
ωi(t−1)
ln An,i(t−1)
Constant
10-12
13-15
16
17
18
20-21
22
23
Food
Textiles
Wood
Paper
Print.
Chem.
Rubber
Nonmet.
-8.346
11.754
-0.814
-0.802
17.332
0.023
-0.556
0.256
(4.718)*
(7.967)
(1.060)
(0.607)
(6.250)***
(0.039)
(0.911)
(0.403)
-0.215
-0.140
-0.137
-0.182
-0.300
0.025
-0.016
0.270
(0.133)
(0.576)
(0.270)
(0.374)
(0.331)
(0.166)
(0.271)
(0.489)
0.705
0.579
0.668
0.731
1.171
0.920
0.786
0.546
(0.111)***
(0.087)***
(0.138)***
(0.085)***
(0.168)***
(0.026)***
(0.054)***
(0.265)**
-0.015
-0.037
-0.009
-0.022
0.012
-0.002
-0.027
-0.019
(0.008)*
(0.011)***
(0.006)
(0.011)**
(0.006)*
(0.025)
(0.008)***
(0.014)
-0.000
-0.001
-0.000
-0.001
0.000
-0.026
-0.001
-0.000
(0.000)**
(0.000)***
(0.000)
(0.000)
(0.000)**
(0.020)
(0.000)***
(0.000)
0.328
1.050
0.316
0.068
0.268
0.094
0.016
0.685
(0.352)
(0.586)*
(0.428)
(0.250)
(0.455)
(0.062)
(0.194)
(0.748)
0.286
0.189
0.074
0.385
0.088
0.168
0.118
0.171
(0.070)***
(0.212)
(0.034)**
(0.136)***
(0.057)
(0.063)***
(0.051)**
(0.169)
-1.695
-1.454
-3.691
-0.507
-0.267
-0.018
-0.479
-3.691
(0.567)***
(0.588)**
(1.146)***
(0.412)
(1.482)
(0.165)
(0.281)*
(1.645)**
Observations
1981
428
1592
911
836
900
1504
539
R2
0.723
0.794
0.849
0.883
0.853
0.835
0.799
0.807
Panel B
Industry
ln of fi(t−1)
expi(t−1)
ln Ap,i(t−1)
ln A2
p,i(t−1)
ln A3
p,i(t−1)
ωi(t−1)
ln An,i(t−1)
Constant
Observations
R2
24
25
26
27
28
29
30
31-32
BasicMet.
FabMet.
Comp.
Electrical
Machines
Vehicles
Transport
Furniture
-0.239
0.139
2.425
1.920
-2.016
-2.906
0.647
-1.117
(0.630)
(0.818)
(7.700)
(4.949)
(4.995)
(4.703)
(0.945)
(1.165)
-0.114
-0.136
0.084
-0.029
0.119
-1.028
0.016
0.117
(0.378)
(0.089)
(0.080)
(0.202)
(0.154)
(0.597)*
(0.030)
(0.257)
0.899
0.729
0.870
0.903
0.821
0.522
0.909
0.717
(0.062)***
(0.041)***
(0.034)***
(0.050)***
(0.053)***
(0.159)***
(0.060)***
(0.065)***
-0.008
-0.013
-0.043
-0.004
-0.007
-0.013
-0.102
-0.016
(0.013)
(0.004)***
(0.027)
(0.008)
(0.004)*
(0.006)**
(0.205)
(0.005)***
-0.001
-0.000
-0.011
-0.000
-0.000
-0.000
-0.191
-0.000
(0.001)
(0.000)**
(0.008)
(0.000)
(0.000)*
(0.000)*
(0.288)
(0.000)***
0.073
-0.049
0.085
0.108
0.141
-0.387
0.000
0.397
(0.242)
(0.192)
(0.083)
(0.284)
(0.256)
(0.836)
(0.028)
(0.379)
0.033
0.059
0.162
0.238
0.262
0.870
0.018
0.179
(0.097)
(0.020)***
(0.066)**
(0.226)
(0.065)***
(0.537)
(0.017)
(0.065)***
-0.029
-1.289
-0.092
-0.374
-1.197
-4.684
-0.009
-1.476
(0.408)
(0.167)***
(0.080)
(0.234)
(0.264)***
(1.538)***
(0.029)
(0.385)***
548
4646
620
855
3196
1196
221
1679
0.869
0.831
0.841
0.869
0.834
0.842
0.838
0.853
Dependent variable: firm-level skill-biased productivity in non-production activities at time t.
Year fixed effects included in all specifications.
26
Bootstrapped standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Table 10: Estimating equation (11)
Dependent variable: firm-level skill-biased productivity
in non-production activities, ln An,it
ln of fi(t−1)
0.094
(0.036)***
expi(t−1)
0.108
(0.016)***
ln An,i(t−1)
0.893
(0.007)***
ln A2n,i(t−1)
0.005
(0.002)**
ln A3n,i(t−1)
0.000
(0.001)
ωi(t−1)
0.107
(0.020)***
ln Ap,i(t−1)
0.008
(0.001)***
Constant
-0.045
(0.015)***
Observations
20732
R2
0.822
Industry-year fixed effects are included
Bootstrapped standard errors in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
27
Table 11: The effect of offshoring on skill-biased productivity in non-production activities
Panel A
Industry
ln of fi(t−1)
expi(t−1)
ln An,i(t−1)
ln A2
n,i(t−1)
ln A3
n,i(t−1)
ωi(t−1)
ln Ap,i(t−1)
Constant
10-12
13-15
16
17
18
20-21
22
23
Food
Textiles
Wood
Paper
Print.
Chem.
Rubber
Nonmet.
2.430
0.910
0.815
0.039
3.425
0.014
0.583
-0.035
(1.743)
(0.565)
(0.297)***
(0.078)
(3.621)
(0.013)
(0.273)**
(0.054)
0.195
0.073
-0.042
0.087
0.190
0.031
0.140
0.125
(0.037)***
(0.060)
(0.086)
(0.056)
(0.110)*
(0.030)
(0.062)**
(0.055)**
0.837
0.850
0.939
0.896
0.853
0.931
0.877
0.872
(0.021)***
(0.041)***
(0.020)***
(0.025)***
(0.081)***
(0.030)***
(0.021)***
(0.036)***
-0.018
-0.007
0.008
0.007
-0.006
0.036
-0.022
-0.023
(0.012)
(0.034)
(0.008)
(0.028)
(0.026)
(0.065)
(0.018)
(0.018)
-0.007
0.000
0.000
-0.011
-0.000
-0.521
-0.007
-0.000
(0.004)*
(0.034)
(0.001)
(0.021)
(0.002)
(0.369)
(0.009)
(0.010)
-0.023
0.057
0.450
0.041
-0.030
-0.002
0.063
0.035
(0.075)
(0.064)
(0.145)***
(0.035)
(0.134)
(0.011)
(0.061)
(0.101)
0.015
0.001
0.009
0.010
0.009
0.013
0.014
0.011
(0.004)***
(0.003)
(0.004)**
(0.003)***
(0.005)*
(0.004)***
(0.004)***
(0.004)***
0.075
-0.068
0.084
0.018
-0.296
-0.031
-0.051
0.114
(0.061)
(0.063)
(0.124)
(0.058)
(0.172)*
(0.030)
(0.067)
(0.089)
Observations
1897
417
1478
886
792
884
1438
508
R2
0.762
0.780
0.833
0.847
0.782
0.850
0.806
0.862
Panel B
Industry
ln of fi(t−1)
expi(t−1)
ln An,i(t−1)
ln A2
n,i(t−1)
ln A3
n,i(t−1)
ωi(t−1)
ln Ap,i(t−1)
Constant
Observations
R2
24
25
26
27
28
29
30
31-32
BasicMet.
FabMet.
Comp.
Electrical
Machines
Vehicles
Transport
Furniture
0.109
0.037
3.017
-0.353
0.115
0.330
1.233
0.185
(0.137)
(0.166)
(1.914)
(0.594)
(0.263)
(0.177)*
(1.245)
(0.448)
0.123
0.095
0.042
0.040
0.077
0.043
0.137
0.089
(0.071)*
(0.033)***
(0.020)**
(0.015)***
(0.022)***
(0.021)**
(0.044)***
(0.044)**
0.971
0.922
0.933
0.941
0.948
0.853
0.972
0.940
(0.028)***
(0.010)***
(0.026)***
(0.030)***
(0.012)***
(0.022)***
(0.047)***
(0.017)***
0.005
0.008
0.087
-0.097
-0.020
0.009
0.055
0.003
(0.016)
(0.004)**
(0.047)*
(0.061)
(0.010)**
(0.041)
(0.032)*
(0.008)
-0.028
-0.000
-0.130
-0.274
-0.019
0.073
-0.043
-0.005
(0.014)**
(0.001)
(0.114)
(0.144)*
(0.006)***
(0.060)
(0.036)
(0.003)*
0.030
0.488
0.031
-0.004
0.001
-0.035
-0.024
0.095
(0.037)
(0.074)***
(0.015)**
(0.024)
(0.031)
(0.024)
(0.037)
(0.071)
0.005
0.008
0.015
0.003
0.004
0.001
0.037
0.006
(0.004)
(0.002)***
(0.004)***
(0.001)***
(0.001)***
(0.000)**
(0.058)
(0.002)***
-0.082
-0.061
-0.032
-0.005
0.005
-0.009
-0.134
-0.030
(0.077)
(0.038)
(0.021)
(0.017)
(0.025)
(0.024)
(0.042)***
(0.054)
531
4395
616
844
3080
1163
207
1596
0.904
0.821
0.895
0.825
0.877
0.799
0.901
0.859
Dependent variable: firm-level skill-biased productivity in non-production activities at time t.
Year fixed effects included in all specifications.
28
Bootstrapped standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
5
Conclusion
TO BE COMPLETED
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30