Productivity Dispersion and its determinants:
the role of import competition
Daniela Maggioni∗
Università Politecnica delle Marche
August 28, 2009

Preliminary and incomplete draft.
Not for quotation and circulation
Abstract
The paper, making use of a large dataset for Italy, confirms theexistence in the same sector of a great disparity in firm productivity. We shed some light on this evidence working both at sector andfirm level. First, we try to explain the determinants of the sectoral
productivity dispersion investigating the role of the international involvement, the ICT adoption and the domestic competitive context.
The technology diffusion doesn’t seem to affect the within-sector heterogeneity, while we show a significant relationship with the domestic
competition and the import penetration from low and medium income
countries. The increase in trade flows with non-developed partners isa quite recent fact in the Italian economic system that contributes toshape the industry dynamics. Then, building on these findings, weturn our attention on the firm and we look at the potential heterogeneous firm responses to the exposure to emergent countries. Our
results suggest that this tougher competition has negative effects onfirm efficiency, more deleterious effects for more productive firms close
to the frontier, and, in this regard, it helps to close the productivity
gaps across firms.

Keywords: Productivity, Dispersion, Imports, Heterogeneity
JEL codes: L25, F14, O33, O47∗
Comments are welcome. I wish to thank Giuliano Conti and Alessia Lo Turco for
useful comments. The usual disclaimers apply.

1

1

Introduction

Recent firm and plant-level works have found large and persistent differencesin productivity levels across firms even within a narrowly defined sector (Bartelsman and Doms, 2000, Haller, 2008 for Ireland, and Escribano and Stucchi,
2008 for Spain). This evidence is confirmed both for labour productivity andtotal factor productivity, thus the factor intensity is not the unique determinant behind the great disparity in firm productivity. A growing theoretical
literature has started dealing with firm heterogeneity, especially in international economics a new strand has developed on heterogeneous firm hypothesis(the pioneering work is Melitz, 2003). The availability of firm and plant level
datasets has allowed the proliferation of the empirical works in this field,
and most research has focused on manufacturing industries. The finding of
the co-existence of heterogeneous firms in the same sector also arises thequestion about the factors behind the sectoral productivity dispersion, withboth a cross-sectional and longitudinal perspective. The analysis of this issue
has received, up to now, scant attention and results shown by the existing
evidence are not conclusive.
Our contribution is to provide new evidence for Italy on the existence ofa large within-sector disparity in firm productivity and to shed some lighton the potential determinants that could affect the sectoral dispersion. We
investigate the role of the domestic market and the technological factors,
but our main focus is on the import competition. The international involvement of the sector has recently been studied as one of the main drivers
behind the within-industry firm dynamics (see for example Melitz, 2003 andBernard et al., 2003; and the debate on the trade openness and resource
reallocation among firms). The period of our analysis is very interesting inthis perspective because Italy has seen its imports grow, especially from lessdeveloped countries (e.g., Central-Eastern Europe and China) following the
EU-enlargement and the increasing involvement of these countries in international markets (due both to their industrial development and the implementation of liberalization strategies). The increased import exposure hasconcerned all sectors, and a restructuring process may have been at work.
For these reasons, it’s important to show the link between openness to tradeand industry dynamics, in terms of sectoral productivity dispersion and differences of firms’ performance. In our analysis we follow two methodologicalapproaches. First, we build indicators of sectoral productivity dispersion inorder to investigate the evolution of dispersion and shed some light on theimpact of the internationalisation and other explanatory factors. On the basis of our results, we try also to estimate a catching-up model at firm-level,
investigating the effects of import penetration that we demonstrate to be an
2

important variable in the shaping of the sectoral productivity. The processof firm entry and exit contributes to explain the evolution in productivity
dispersion, but also heterogeneous responses of firms to changes in the external environment may play an important role: firms with different efficiency
levels may display different behaviour coping with the increased competitive
pressure from foreign countries.
The work is organized as follows. The next section gives a brief overview
of the related literature. Section 3 describes the data and shows a preliminary
statistical analysis on the evolution of firm productivity, its dispersion and thesectoral exposure to imports. Section 4 presents a comprehensive framework
for an investigation of the determinants of the sectoral dispersion. Then, in
Section 5, we turn our attention on firm level and, building on the findings of
the Section 4, we test if the import competition, the main variable of interest,
has heterogeneous effects on firms’ efficiency according to their position in
the productivity distribution. A final Section gives concluding remarks.

2

Review of the related literature

Our paper relates to different strands of literature. The study of firm heterogeneity is a relatively recent research field: theoretical works rejecting therepresentative firm hypothesis date back to the end of 70s (see for example
Jovanovic, 1982), and first empirical works followed in the 1990s (Kremp andMairesse, 1991 and Oulton, 1998), nevertheless it has been in the last decadethat this topic has incurred a growing interest especially in empirical studies.
Even if research is increasing, the existing productivity dispersion and its evolution is still a puzzling topic. The investigation of the reasons for these large
disparities could give important suggestions about the productivity growth
process.
The productivity heterogeneity can be explained both by supply-side factors, like technology, firm ownership, management and human capital anddemand-side determinants, such as sectoral elasticity of substitution, institutional framework and trade exposure. One of the first empirical works aiming
the explanation of the co-existence, in the same sector, of firms with different
efficiency levels is Syverson (2004). After showing high levels of dispersion
for a cross-section of manufacturing industries in Usa, he verifies a negative
correlation between the product substitutability, that causes stronger competition, and the disparity of producer productivity levels. He finds alsothat sectors more exposed to international trade present higher productivity
dispersion. In opposite to this evidence, according to the new international
trade literature based on the firm heterogeneity hypothesis (Melitz, 2003 and3Bernard et al., 2003) trade openness should cause a resource reallocation toward more efficient firms, the exit of less productive firms and the entry of
more productive ones, as a consequence we should observe a lower withinsector dispersion following the increased international involvement. Ito andLechevelier (2008) for Japan also show some evidence about the role of internationalization on dispersion contrasting with theoretical suggestions. In
addition to trade involvement and competitive environment, they analyse
the role of technology adoption for productivity dispersion trying to verify
the conclusions of Neo-Schumpeterian models (Caselli, 1999). No significant
effect is found from the introduction of ICT, while a significant and positive impact is revealed for the sectoral internationalization and the industry
competitive level. This evidence, at odds with recent theoretical models ininternational economics, could be justified by a malfunctioning of the natural selection mechanism (hypothesized by Melitz model), or the need of along period for his correct functioning. In opposite, using data on Italianfirms for the period 1983-1999, Del Gatto et al. (2008) support the theoretical hypothesis that openness to trade contributes to lower the within-sector
dispersion, in addition to increase the productivity median level. All thesereviewed works are strictly related to the literature dealing with the Schumpeterian mechanism of ”‘creative destruction”’ in the industry dynamics andthe importance of the between-component1 for sectoral productivity growth.
Many studies have verified the existence of a within-sector reallocation process and have linked this process to market regulations (see, for instance,
Arnold et al., 2008), the presence of foreign firms (Maliranta and Nurmi,
2004), the changing of the international environment and the increasing foreign pressure from imports (Maliranta, 2005 and Eslava et al., 2009).
Our work is also related to the wide literature studying the impact of trade
openness on productivity at sector and firm level. There are many theoreticaland empirical contributions supporting the beneficial effects of the international integration and research has investigated both the easier access to foreign
market and the higher competition as main causes. Good examples of thisstrand of literature are the studies of Pavcnik (2002), Muendler (2004), Topalova (2004), Amiti and Konings (2007), Fernandes (2007) and Eslava etal. (2009) for Chile, Brazil, India, Indonesia and Colombia, respectively.
Empirical studies on developed countries are more scant and they focus on
the effects of the increased flow of imports, see for example the recent workof Dovis and Milgram-Baleix (2009) showing the positive impact of tariff
reduction and import penetration on Spanish sectoral and within-firm pro1

The between component concerns the resource reallocation process among firms, especially from less efficient firms to more productive ones.

4

ductivity. More related to our paper, for Italy, Bugamelli and Rosolia (2006)
find that competition from non developed and emergent countries has positively affected the productivity of 3-digit sectors (in large part attributable to acreative destruction process), while, moving at the firm level, Altomonte et al.
(2008) explore both horizontal and vertical (from upstream and downstream
sectors) import flows disclosing positive correlations with the firm efficiency,
even if the vertical channel seems to play a more important role. Even if
a great part of research shows that trade is usually beneficial for sectoraland firm productivity there are also models shedding light on the potential
negative effects of import competition for the firm efficiency. Rodrik (1991)
and Traga (1997) find that lower trade protection or higher import competition reduce a firm’s investments in productivity improvements, when the
incentives to invest depend on the firm’s output or market share reduced bytrade openness. Thus higher international involvement may result in either
productivity gains or losses, and empirical investigation is essential.
Finally we are especially interested in looking for heterogeneous effects
of import competition according to the firm initial productivity level. Thus
we relate to recent empirical evidence that investigates the potential asymmetrical impact of sectoral factors and external shocks on firm productivity.
Chevalier et al. (2009) analyse the firm-level convergence in France during
the period 1992-2004 as an important source of growth. They support theconvergence process among firms and investigate its potential determinants:
globalisation, ICT and competition turn out to affect positively the productivity growth, and this effect is asymmetric according to the firm position in
the productivity distribution, the gains are larger for leading firms. Griffith
et al. (2003), Sabirianova et al. (2005) and Bekes et al. (2006) analyse therole played by FDI spillovers and foreign ownership testing heterogeneous
gains for firms with different efficiency levels. Alvarez and Crespi (2007) findthat the presence of foreign firms has accelerated the catching-up processof Chilean firms. Konings and Vandenbussche (2008) display the heterogeneous response, in terms of productivity, of firms to antidumping protection.
Schor (2004) and Dimova (2008) allow for the impact of liberalization in
Brazil and Bulgaria to be heterogeneous across different firms. Both works
show that the reduction of nominal tariffs and the increased competitive
pressure have lead firms at the lower tail of productivity distribution to increase their efficiency in order to survive in the liberalized market. The same
does not happen to firms with higher productivity that don’t face with the
failure risk (Muendler, 2004). Different conclusions are presented in Iacovone (2009) that, building on the predictions of neo-Schumpeterian growththeories (Aghion et al., 2005), model and test a positive impact of the liberalization in Mexico during NAFTA, shedding light on weaker effects for plants5more distant to the production technology frontier. Only firms close to the
productive frontier increase their innovative efforts in order to prevent entryof potential foreign competitors, in opposite less efficient firms are not able
to compete successfully with foreign entrants at the frontier.

3

Sample construction and preliminary analysis

3.1

Data

We use data from a commercial database AIDA2 , the online version, producedby the private company Bureau Van Dijk. This database contains balance
sheet information of Italian unconsolidated firms and we recover data forthe period 1998-2006 for manufacturing firms. Bureau van Dijk updates
continuously the dataset, especially it keeps in the sample firms that exit orstop reporting their financial statements for four years, but after the fifth yearof non-reporting these firms are removed. In addition, through the analysed
time period, Bureau van Dijk has changed the criteria for firm inclusion: it
collects information for all firms with a turnover higher than a fixed threshold
and this threshold has lowered, in 1998 and till 2000 firms were included in
the database if they had a turnover higher than 1 million euros, in 2002 thethreshold was set to 500,000 euros and in 2004 to 100,000 euros. In orderto take into account these database characteristics we have retrieved datafor deleted firms (the exited firms) using the different releases of AIDA CDROMs for the years in our sample. Then we have dropped firms having aturnover lower than 1 millions euros3 , the 1998 threshold, in order to analyse
an uniform sample. Data tend to be more representative of larger firms,
anyway also medium and some small firms are recorded. We use the valueof operating revenues as a proxy for output; the value of firm level tangible
fixed assets as a proxy for fixed capital; and the number of employees4 andmaterial costs, as proxies for inputs. We obtain also the information about
2This database is the version for Italy of the more known AMADEUS covering different
European countries.
3
Our database presents the peculiarity that the threshold is on the total turnover andnot on the number of employees as many micro-level datasets.
4
The number of employees is in some cases missing because firms have not the duty
to declare this information to the Chambers of Commerce. Anyway we have always theinformation about the personnel costs. In order to keep the largest sample as possible wehave replaced missing data for the number of employees with the product between the firm
personnel cost of that year with the average unit labour cost of the firm in the previous
year, in the belief that the unit labour cost is quite constant in the short-term for the firm.

6the firm sector of activity at 3-digit NACE. We deflate the variables using
sectoral price indexes for output, value added, materials and capital stockfrom Istat5 . We drop observations with missing data for variables of interest(output, input variables), or with implausible figures (for example, negativevalues). After this cleaning procedure we have information about more than
30,000 firms. With AIDA database we have also calculated concentration
ratios (C10: the sectoral output share of the ten highest firms) for 3-digit
NACE industries6 , this index is used as a proxy for the sectoral degree ofdomestic competition. Then we have built import competition ratios as:
M

IM P COM Pjt = Mjt +Yjtjt−Xjt
and export openness as:X

EXP OP EN jt = Yjtjt
where j indexes a 3-digit sector, Mjt and Xjt are, respectively, the total
import and export in the year t and sector j, and Yjt is the total sectoral
output. We have measured import competition and export openness for every
3-digit sector7 also breaking between different partner countries according totheir development stage. We use the classification between high, medium
and low income countries from the World Bank. Trade data are from thedatabase WITS of World Bank, while sectoral output data are from the Firm
Economic Accounts (ISTAT)8 .
We are aware that the under-representation of small firms could prevent
us to analyse an important part of the story. This is a drawback that a lotof analysis have to cope with because it’s difficult to have reliable economic
information for small firms. Anyway we are trustful that the bias of results
is not so severe: we find that the median firm size, in terms of number of
employees, is 30 employees. In addition, to cope, at least partially, with this5

The use of sectoral deflators instead of firm level prices has become a standard method
in literature, even if it may lead to biased estimation of production function coefficients.
A recent paper by Mairesse and Jaumandreu (2005) on a panel of firms finds that whether
value added is deflated with an industry output-price index, with an individual firmoutput price index or not at all makes little difference for the estimation of the coefficientsin the production function. Anyway it’s important to keep in mind that our productivity
indicator may reflect both true efficiency and mark-up.
6
Since our dataset doesn’t cover the whole firm population, especially under-represents
small firms, we have compared C10 indices with the same indices as reported in Firm
Economic Accounts (ISTAT). We could make this comparison only for 2-digit sectors andwe find an highly significant correlation of more than 0.80.
7The last year covered by the Firm Economic Accounts is 2005, thus this is the lastyear we can construct the import competition variable.
8
For some years and sectors output values are missing because of confidentiality.

7

problem, we have also applied our analysis to a sample concerning a shortertime period, 2002-2006, and firms with a turnover at least of 500,000 euros(the threshold set in 2002).
Because of our interest in within-sector dynamics we require a firms’
sample enough large for each investigated sector, thus we have discarded
from our analysis sectors with less than 50 firms by year in order to obtainreliable measures for sectoral dispersion9 .

3.2

Descriptive statistics

Before moving to the empirical analysis it is useful to investigate the main
trends of variables. We have calculated the total factor productivity (TFP)
using a multilateral index suggested by Good et al. (1997)10 . Then we haveobtained the following sectoral dispersion indicators: the interdecile range(D1090), the interquantile range (D2575) and the standard deviation (STD)
at 3-digit NACE disaggregation. We have also estimated the productivity
using the Levinshon-Petrin (2003) approach. Anyway all results presented
concern the TFP index11 .
First of all, the estimation of firm productivity confirms that in the last
decade the firm efficiency performance has been unsatisfying12 (Figure 1).
For the whole manufacturing sector, after a little efficiency gain in 1999 theproductivity evolution has fallen down till 2003, then since 2004 firms havegone through slight improvements. However after nine years the productivity has reverted about at the initial level. This evidence found at micro-level
confirms the studies for the manufacturing sectors and disaggregated sectors
in Italy (see for example Daveri and Jona-Lasinio, 2005) and reproduce theresults presented by Altomonte et al. (2008)13 . The poor productivity evolution is a common feature of all industries. Table 1 shows the average valuesof dispersion by sectors at 2-digit level14 , we can see some differences across
9

We can’t trust in dispersion indicator calculated on too much small samples. We end
up with 68 three-digit sectors.
10
The cost of capital is computed as the user cost of capital: interest rates plus depreciation minus the variation of prices, where we assume a constant depreciation rate of 10%
and the interest rates are long-term rates from OECD.
11
Results obtained using the TFP estimated with the Levinshon-Petrin approach areavailable upon request.
12
We show the evolution of the unweighted TFP mean for the whole manufactuting
sector.
13
Even if we have found a slightly stronger productivity worsening in the period
1999/2003.
14
Dispersion indicators are calculated at three-digit level and then averaged on two-digit
sectors on the whole sample period.

8

industries, the more heterogeneous sector is “Manufacture of wearing apparel” (NACE 18), while the “Manufacture of fabricated metal products”
sector (NACE 28) presents the lower dispersion. When we turn our attention on the time evolution we don’t verify a monotonic trend in dispersion,
but it is interesting to notice that during the expansion periods, when the
average productivity grows, the within-sector heterogeneity increases, in opposite when there is a downturn in average productivity disparities widen15 .

Figure 1: TFP evolution

Focusing on the links between import penetration and domestic efficiency, it
is important to keep in mind that two different effects could be at work. The
first one is an increase of competition, while the second one is an increased
availability of imported intermediates that may be cheaper than domestic
intermediates or characterised by an higher quality. Thus, the same importflow may represent a threat for firms operating in the same sector and an opportunity for the downstream firms. Because of our interest in dealing with
15

This is consistent with the analysis of Escribano and Stucchi (2008) that shows lower
persistence and faster convergence in TFP during recessions and higher persistence andnon convergence in TFP during expansions.

9the import competition effect we have used a 3-digit sectoral disaggregation.
This disaggregation allows us to investigate the foreign pressure coming fromsectoral imports. At a fine disaggregation, we can trustfully suppose that sectoral imports represent for the firm an increase of competition because theintermediate share from the same (highly disaggregated) sector is small16 .
Looking at the exposure to imports, it is clear that, even if developed countries are always the main trade partners of Italy, the role of low and medium
income countries has increased during time and this phenomenon can beobserved in every sector. The import share from low and medium income
countries (henceforth, LMCs) differs across industries: not surprising, thelargest share of more than 27% is recorded by the sector NACE 18 (Manufacture of wearing apparel), while the lowest share (0.2%) concerns thesector NACE 22 (Publishing, printing and reproduction of recorded media).
Anyway all sectors have experienced a growing competition from LMCs (seeTable 9 in the appendix). For the whole manufacturing sector the exposure
to LMCs countries has doubled from more than 4% in 1998 to more than 8%
in 2005. This surge is in great part attributable to the industrial development and liberalization strategies of these countries, in fact it came with anincrease of their total world export share and their share in total imports ofdeveloped countries. The importance of Italian imports from industrializedcountries is, in opposite, quiet constant for the total manufacturing sector
in the period 1998/2005 (even if also in this case we can see different trends
across sectors).

4

The determinants of sectoral dispersion

In this section we present the results for a comprehensive analysis about thefactors affecting the within-industry efficiency differences across firms. Following previous empirical studies we take into account the following variables:
the competitive environment, the technology adoption and the international
involvement of the sector. First, we expect that sectors characterized by ahigh degree of competition present low dispersion. In a more contendible
market it is likely that less productive firms couldn’t survive a long time,
firms make efforts in order to improve their efficiency and stay in the market
and competitive pressures lead to the flattening of any difference. We have16

As displayed in National Input-Output tables the input narrow share, the share of
input coming from the same sector, at 2-digit level, is not so high. We find for example anaverage narrow share of 25% in the manufacturing sectors for the year 2004 and a narrow
import share of 11% when we consider 2-digit level sectors. We expect that at 3-digit level
these shares should be significantly smaller.

10Table 1: Dispersion and import competition by sector
ATECO

StdTFP

D2575TFP

D1090TFPIMPHIGH
(%)
ita

C
IMPLM
(%)
ita

C
IMPshLMhigh (%)

C
IMPshLMworld (%)

15
17
18
19
20
21
22
24
25
26
27
28
29
31
32
33
34
35
36

0.657
0.601
0.720
0.651
0.528
0.569
0.602
0.655
0.513
0.520
0.577
0.509
0.553
0.569
0.634
0.571
0.574
0.586
0.598

0.653
0.554
0.732
0.636
0.518
0.544
0.609
0.644
0.48
0.502
0.520
0.462
0.503
0.524
0.581
0.547
0.501
0.511
0.533

1.378
1.238
1.559
1.38
1.094
1.169
1.302
1.376
1.053
1.089
1.131
1.031
1.121
1.180
1.259
1.183
1.139
1.209
1.210

16.29
10.69
7.47
7.60
15.41
38.62
3.74
37.42
17.28
9.09
37.22
10.71
17.89
23.05
60.00
41.07
19.57
19.93
9.78

3.09
11.78
19.50
20.57
5.92
3.97
0.17
1.94
4.21
2.82
12.83
2.64
3.76
5.66
4.31
4.11
3.61
4.51
8.10

22.67
45.93
69.96
56.07
37.20
11.84
16.31
8.59
21.35
26.72
30.50
21.46
15.24
37.13
26.87
16.16
14.74
19.80
43.97

35.60
59.72
70.28
63.26
43.27
26.83
30.28
28.20
34.49
36.42
44.12
35.99
30.03
42.73
47.10
30.87
30.69
43.77
52.60

Total

0.589

0.555

1.216

21.20

6.50

28.55

41.38

Source: Our elaborations from AIDA, WITS and Firms Economic Accounts (ISTAT)
HIGH
LM C
IM Pitaand IM Pita
are Italian import penetration ratios from high income and LMCs countries.
C
LM C
IM P shLM
high and IM P shworld are the world and high-income countries import shares from LMCs.
All trade variables are lagged to one year.

Table 2: Dispersion and import competition by year
YEAR

StdTFP

D2575TFP

D1090TFPIMPHIGH
(%)
ita

C
IMPLM
(%)
ita

C
IMPshLMhigh (%)

C
IMPshLMworld (%)

1998
1999
2000
2001
2002
2003
2004
2005
2006

0.536
0.724
0.670
0.517
0.555
0.531
0.568
0.582
0.613

0.536
0.660
0.602
0.482
0.481
0.498
0.550
0.575
0.617

1.109
1.604
1.307
1.038
1.060
1.087
1.202
1.218
1.318

19.58
19.54
22.29
21.78
22.37
20.27
20.60
21.31
20.90

4.62
4.49
5.02
5.78
6.55
6.71
7.25
7.83
8.74

23.32
23.77
24.31
26.60
27.69
28.90
30.03
30.95
32.53

32.12
33.03
33.06
42.76
43.78
44.30
45.18
46.47
47.45

Total

0.589

0.555

1.216

20.97

6.32

27.57

40.91

Source: Our elaborations from AIDA, WITS and Firms Economic Accounts (ISTAT)
Definition of variables as in Table 1.

11

used two different variables to capture the competitive context: the numberof firms in the sector and the concentration ratio (C10). The number offirms in the sector is retrieved from the Firms Economic Accounts by Istat,while C10 is calculated using AIDA. Following Syverson (2004) we add also
a variable capturing the sunk entry cost, the amount of capital (relative tothe sectoral market size) required to build a median firm17 .
Second, we test the role of the ICT diffusion. The technology adoption
may have an ambiguous impact on dispersion according to the dominanceof innovation or knowledge spillovers. New technologies are employed only
gradually and represent an important source of heterogeneity among firms
in the same industry, thus the technology diffusion may increase the withinsector heterogeneity. As shown by Jovanovic and Lach (1997) the diffusion oftechnologies takes often long time18 , and this gradual diffusion process may
explain persistent productivity differences across firms within an industry.
In order to capture the technology effects we rely on the ratio between the
sectoral ICT capital on the total sectoral employment19 .
However, our main focus is on the sectoral international involvement. As
already said in the literature review, the new heterogeneous firms models ininternational economics suggest a reduction of the dispersion following tradeliberalisation and the increase in trade openness. We deal with both export
openness and import competition.
In this comprehensive framework, we run the following regression:
dispjt = α + βimp compj,t−1 + δexp openj,t−1 + φictj,t−1 ++ ηdom compj,t−1 + dj + dt + it(1)

where dispjt is the dispersion indicator that could be the standard deviation (ST Djt ), the interquantile range (D2575jt ) or the interdecile range(D1090jt ). imp compjt is the import competition ratio, exp openjt is theexport openness, ictjt is the ICT capital stock in the 2-digit sector anddom compjt represents the variables capturing the domestic competitive pressure in the sector (the number of firms, the C10 ratio and the sectoral sunkcosts)20 . All variables refer to 3-digit NACE sectors (with the exception of
17

Using our firm-level dataset the variable SunkCostijt is calculated as the market shareof a median-sized firm in the sector multiplied by the capital-output ratio for the sector.
18
Jovanovic and Lach (1997) show that a new technology takes, on average, 15 years togo from 10% usage to 90% usage.
19
The ICT capital represents the software, office and communication stock provided in
ISTAT National Accounts. We can calculate this indicator only at two-digit disaggregation.
20
We have analysed the pairwise correlations and we have found that only exp openjt

12

ictjt ). Every regression includes sector fixed effects and time fixed effects.
Regressors are lagged to one year both because we assume that the effects on
the industrial structure and on the within-sector dispersion take some time
before to display21 and, in addition, to ease the problem of reverse causation.
Table 3: Determinants of sectoral dispersion
VARIABLES

D2575TFP(1)

D1090TFP(2)

StdTFP(3)

D2575TFP(4)

D1090TFP(5)

StdTFP(6)

imp compj,t−1-0.003[0.098]0.039[0.096]-0.862*[0.483]0.805[0.526]-0.199*[0.116]
0.187[0.123]-0.317*[0.173]0.072[0.081]
0.058[0.111]
0.066[0.096]-0.07[0.052]-0.05[0.037]0.160**[0.062]0.033***[0.012]
1.778***[0.506]-0.999*[0.581]-0.251[0.186]0.941[0.760]0.192[0.251]
0.054[0.136]-0.185[0.118]0.202*[0.114]0.049*[0.026]2.942*[1.590]-0.494**[0.236]-0.012[0.081]0.221[0.248]0.098[0.137]-0.041[0.066]-0.035[0.039]0.111*[0.058]0.007[0.016]1.301*[0.672]

Obs
541
541
541
541
R2
0.611
0.661
0.47
0.615
Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1
Every regression controls for sector and time dummies.

541
0.661

541
0.473

exp openj,t−1
C
imp compLM
jt−1

imp compHighjt−1C
exp openLM
jt−1

exp openHigh
jt−1
ictj,t−1
nf irmj,t−1
c10j,t−1
sunkj,t−1
Const-0.072[0.052]-0.038[0.039]0.156**[0.061]0.032***[0.012]
1.691***[0.520]0.028[0.138]-0.183[0.122]0.202*[0.114]0.046*[0.026]
3.067*[1.633]-0.046[0.064]-0.023[0.037]0.108*[0.057]0.006[0.017]1.243*[0.658]

Table 3 shows the results for the Equation 1. Columns (1)-(3) present
results of the baseline specification without any distinction for exports andimports across origin/destination countries. Neither trade variables nor technology adoption seem to play a significant role in shaping the sectoral productivity distribution. Only import competition is negatively related to theinterdecile range and the standard deviation. There are, in opposite, significant effects of the competitive domestic context: sectors characterised byand imp compjt present a high correlation of 0.63, anyway this correlation significantly
drops when we split the import competition and export openness according to their origin
and destination countries.
21
Maliranta (2005), for example, finds that international trade affects the sectoral restructuring process only after some years.

13

higher competitive tensions (that is an higher number of firms, lower concentration ratios or lower sunk costs) present a lower dispersion. Because of theunsatisfying results for variables capturing the international involvement of
the sector, we try now to split the effects of imports according to the origin
countries (and the export effects according to the destination countries). We
believe that trade flows can have different effects on firm efficiency and firm
dynamics if trade partners are high income countries or low and medium
income countries. Columns (4)-(6) shows results when we split imports andexports according to the origin/destination countries. Looking at the regressions it is clear that imports from LMCs contribute to reduce differences
across firms and lower sectoral dispersion. No role is, in opposite, detected forother variables of international trade. In addition it is confirmed the significant coefficients for the domestic competition captured by the concentration
ratio and the sunk entry cost, while the ICT capital stock still remains not
significant. We use also Prais-Winsten regressions with panel-corrected standard errors. It is a weighted least squares estimator that allow us to consider
the first-order serial correlation. Results for this estimation are presented in
the appendix and reproduce in general our previous findings.
Even if some doubts about the direction of causality may rise, this analysis
sheds some light on the linkages between sectoral characteristics and thewithin-industry dynamics of firms. Especially from the investigation it resultsthat the international exposure to competition from LMCs, our main focus,
is significantly related to within-sector productivity distribution. Building on
this evidence we further study the impact of this competitive pressure looking
at the firm-level. The negative relationship we found between dispersion andimport penetration from emergent countries could be attributable to thefirm dynamics, entry of more productive firms and exit of less efficient firms(as conjectured in Melitz, 2003 and Bernard et al., 2003), but could also
disclose an heterogeneous impact of this higher competition according to thefirm initial efficiency level. We try to verify this hypothesis in the following
section.

5

The heterogeneous responses of the firms

5.1

The role of import competition

In the previous review of literature it has been shown that the documented
great efficiency heterogeneity across firms may be related to asymmetricalbehaviours at firm level in response to external shocks. We suppose thatfirms may react differently in front of an increase in the competitive tensions,14that is, in our case, the growing flow of foreign goods. In order to test thishypothesis we regress the following equation:
CLM C
tf pijt = α + βtf pij,t−1 + δimp compLM
jt−1 + ϕimp compjt−1 ∗ tf pij,t−1 ++ ηxijt−1 + di + dj + dt + it(2)where tf pijt and tf pij,t−1 are firm productivity indicators for time t andC
t − 1, imp compLM
is, as in previous section, the import competition fromjt
LMCs and xijt are firm-level controls. We add time and sectoral dummies22 .
Our interest is on the coefficient ϕ capturing the heterogeneous responses offirms to imports from LMCs23 . We want to verify if less productive firms react
differently in front of a tougher competition if compared with more efficientfirms. We use both OLS estimator and within estimator (fixed effects).
We don’t think that the endogeneity of import competition is a so severe
problem in our framework because we are analysing the effect of a sectoralvariable on the firm level efficiency. In addition the import competition islagged to one year and this further mitigates the problem. Another consideration that supports the exogeneity of the import competition from LMCs
is that the recent raise of these flows is not mainly linked to the domestic
efficiency, but it is likely to be caused by the recent economic developmentof emergent countries and their liberalisation strategies, as also suggested in
the descriptive analysis.
The presence of the lagged dependent variable among regressors, causesome difficulties in the estimation strategy. The conventional panel Least
Squares estimators potentially incur into the Nickell bias (Cameron and Trivedi, 2005). In particular OLS regression leads to an upward-biased estimation of the lagged dependent variable, while fixed effects lead to a downward
bias, thus the true value should lie in this range (Bond, 2002). In order totake into account this bias we apply the first-differenced GMM estimator
proposed by Arellano and Bond (1991). This estimator allows us also tocontrol for the potential endogeneity of the other regressors, in particular
the imports and their interaction with the lagged productivity. We instruments the first difference of regressors with their lagged levels. Then wecheck the validity of our estimation using the Hansen test of overidentifying
restrictions and a test for autocorrelation. The disturbance term in first
22

Sectoral dummies help us to correct for the correlation between the productivity inthe sector and the import competition: non developed countries could enter especially inless efficient sectors.
23
It is important to notice that when we interact the import penetration variable withthe lagged productivity we are assuming a linear relationship, and we are placing the apriori restriction that the efficiency effects of imports are monotonically increasing (ordecreasing) with the initial firm position in the productivity distribution.

15

differences should present negative and significant first-order autocorrelation
but no second-order autocorrelation. If we find second-order autocorrelationin the differenced residuals it means that instruments are not valid. Thus we
use as instruments for the equation in first differences the dependent variable
and the regressors lagged n times and more till we find no second-order autocorrelation in the disturbance term in first differences. In our analysis weverify a deep serial correlation in the productivity series and we need to takethe seventh and eight lags of TFP and explanatory variables as instruments24 .
Table 4: The firm heterogeneous responses to imports
VARIABLES

OLS

FE

GMM-DIFF

OLS

FE

GMM-DIFF

0.440***[0.006]-0.446***[0.094]-0.233***[0.075]0.019***[0.006]-0.406***[0.113]-0.243***[0.067]0.567***[0.156]-0.707[0.913]-4.946**[1.965]0.059***[0.012]

0.097***[0.006]0.011[0.007]-0.429***[0.114]-0.211***[0.067]-0.048***[0.009]-0.065***[0.004]-0.218***[0.025]0.019***[0.005]-0.051**[0.021]
0.398***[0.038]0.379**[0.150]-1.257[0.802]-4.363**[1.783]0.420***[0.118]0.12[0.163]-0.544[0.663]0.165**[0.083]-0.015[0.341]Const

0.431***[0.007]-0.415***[0.096]-0.203***[0.072]0.006***[0.002]-0.067***[0.002]0.261***[0.009]-0.034***[0.005]-0.051***[0.020]0.087***[0.017]

Obs.

156778156778129920153336a153336a126394atf pit−1
C
impLM
jt−1
C
impLM
jt−1 ∗ tf pij,t−1

sizeit−1
klit−1solvencyit−1
aver wageit−1
c10jt−1

Time Dummies
YES
YES
YES
YES
YES
Sector Dummies
YES
NO
NO
YES
NO
Firm Effects
NO
YES
NO
NO
YES
Hansen test
0.205
AR(1) test
0.000
AR(2) test
0.060
Robust standard errors in brackets are clustered by firm. *** p<0.01, ** p<0.05, * p<0.1
Even if we cluster by sector-year the variables still remain significant.
P-values for first and second order autocorrelation and for the Hansen test are reported.
a The drop in the number of observations is due to missing values for controls.

YES
NO
NO
0.083
0.000
0.606

Table 4 shows a coefficient β < 1, this confirms a catching-up process: lessproductive firms grow fastly than firms at the frontier. In addition results
seems to disclose a negative effect of the imports, especially the competition
from LMCs negatively affects more the productivity of firms with high efficiency (δ < 0 and ϕ < 0). Thus, this asymmetrical negative impact goes24

We use the 7th and 8th lags as instruments for all GMM-DIFF estimations we present.

16

together previous finding of the negative linkage between sectoral dispersion
and import competition. As Iacovone (2009) we find that the deeper international integration has significantly shaped the firm productivity distribution,
but the kind of relationship is different: a negative effect of the foreign pressure and a more harmful effect on the more productive firms. The last threecolumns of table 4 shows that our findings are also confirmed when we add
firm controls for the firm size (measured by the number of employee), therelative unit wage25 , as a proxy for the labour quality, the capital intensity,
the solvency ratio (shareholds’ funds on total assets), and a sectoral control
for the domestic competition (the concentration ratio). Capital intensity andsectoral domestic concentration are negatively related to firm productivity,
while the labour quality and the firm financial solidity present opposite signsin ols and within regressions.
A possible explanation for this counterveiling finding is that the foreigncompetition from LMCs is a threat that domestic firms have to cope with,
but it’s a particular difficult task because their rivals can benefit from lower
labour and material costs. Domestic firms may be unable to successfully
react to the growing flow of foreign goods. The foreign competitive pressure
reduce the market shares and the expected profits for domestic firms, andthis reduction may lower the opportunity and the gain from innovation andinvestments. Thus firms may decide to stop innovating and investing andthis would reduce their productivity. Especially if the high efficiency of firms
at the frontier is linked to their innovation efforts we may expect that competitive pressure impacts more on productivity of these firms. In this respect asource of the productivity differentials across firms fails because the competition discourages the innovation efforts. In opposite the negative effects forless efficient firms may be mitigated because they benefit from within-sector
spillovers. We can suppose that their productivity growth depends more on
imitation and technology adoption than innovation26 . The harmful impact offoreign exposure is stronger for more productive firms because the increased
competition affects more the innovation incentives that are at the basis of
the growth for efficient firms. In addition firms at the lower tail of the distribution may make some efforts in order to survive and stay in the market.
Due to the increased import penetration the competition is stronger and lessproductive firms might have more incentives to reduce costs of production
coping with the import flows since they are the main candidates to exit the25
The relative unit wage is the deviation of the firm unit labour cost from the year-sectormean.
26
As Bernard and Jones(1996) have introduced for convergence across countries andindustries and Griffith et al. (2006) and Nishimura et al. (2005b) have applied at firmlevel the growth rate depends in part on a catching-up component.

17market. These arguments may help to explain why the loss of productivity
seems to be smaller for less efficient firms.
An alternative explanation for the strong negative impact on more efficient firms is related to the product-switching. Firms may try to escape thecompetition thanks a restructuring process and a change in the mix of theirproducts (Bernard et al., 2006), for example moving in production of higher
quality goods in the same sector. The consequent reorganization may leadin a first period to an efficiency drop because firms are engaged in a newlearning process. Anyway we can’t test this hypothesis because we shouldhave at our disposal firm data at product-level.
Because we are interested in the sectoral dispersion, in table 5 we showthat the asymmetrical firm effects of the exposure to LMCs hold true when
we interact the import competition with an indicator of distance to frontierT f p Gapijt (as in Griffith et al., 2006 and Iacovone, 2009). Following Cameron et al. (2003)27 and Griffith et al. (2006) we allow the imports exposure
to affect the convergence process and we estimate the following model:CT f pijt = α + βT f pF rontjt + χT f p Gapijt−1 + δimp compLM
jt−1 +
C+ γimp compLM
jt−1 ∗ T f p Gapijt−1 + ηxijt−1 + di + dj + dt + ijt
(3)
where T f pF rontjt is the productivity of the sectoral frontier that we
define as the average efficiency of the top 5% firms at the highest tail of thesectoral-yearly distribution, and T f p Gapijt−1 = T f pF rontj,t−1 /T f pij,t−1 isa measure of distance to the frontier28 . Thus the coefficient γ captures theimpact of the import penetration on the firm productivity convergence to thefrontier. The estimates of this regression presented in table 5 are consistent
with the previous findings. The positive and significant coefficient γ suggeststhat the negative impact of exposure to LMCs (δ < 0) is mitigated for firms
more distant to the frontier. Thus also this analysis shows that imports
from LMCs have reduced the efficiency disparity across firms but due to aworsening of the performance of more productive ones. The results from
GMM estimation of equation 3 with firm controls (last column in table 5)
are not reliable, in fact the Hansen test rejects the validity of instruments
and AR(2) test shows the presence of second-order autocorrelation.
27

Cameron et al. (2003), analysing sectoral data, start with the ADL(1,1) model for the
non-frontier sector T F Pijt = β0 + β1 T F Pij,t−1 + β2 T F PF jt + β3 T F PF j,t−1 + ijt , where
T F PF jt is the productivity of the frontier sector, and under the assumption of long-run
homogeneity (1 − β1 = β2 + β3 ) they develop the ECM representation: ∆T F Pijt =T F P j,t−1
β0 + β2 ∆T F PF jt + (1 − β1 ) T F PFij,t−1+ ijt .
28
We set this indicator to 0 for firms at the frontier.

18

Table 5: Distance to the frontier
VARIABLES

OLS

FE

GMM-DIFF

OLS

FE

GMM-DIFF

T f pF rontjt

0.018***[0.006]-0.353***[0.005]-0.633***[0.108]0.165***[0.063]0.009[0.006]-0.028***[0.004]-0.550***[0.118]0.262***[0.059]-0.084[0.228]-0.392***[0.098]-2.895**[1.308]4.157***[1.516]0.207***[0.015]0.104***[0.009]0.010*[0.006]-0.019***[0.005]-0.551***[0.119]0.228***[0.059]-0.049***[0.008]-0.064***[0.004]-0.222***[0.025]0.012*[0.006]-0.039*[0.022]
0.394***[0.036]

0.411*[0.228]-0.046[0.117]-4.119***[1.339]5.107***[1.523]0.102[0.128]0.337**[0.146]-0.218[0.716]-0.032[0.098]0.215[0.364]Const

0.018***[0.006]-0.339***[0.005]-0.619***[0.110]0.152**[0.061]-0.011***[0.002]-0.063***[0.002]0.302***[0.011]-0.029***[0.007]0.353***[0.023]0.145***[0.020]Obs.

156778156778129920153336a153336a126394aTime Dummies
YES
YES
YES
YES
YES
Sector Dummies
YES
NO
NO
YES
NO
Firm Effects
NO
YES
NO
NO
YES
Hansen test
0.234
AR(1) test
0.000
AR(2) test
0.340
Robust standard errors in brackets are clustered by firm. *** p<0.01, ** p<0.05, * p<0.1
Even if we cluster by sector-year the variables still remain significant.
P-values for first and second order autocorrelation and for the Hansen test are reported.
a The drop in the number of observations is due to missing values for controls.

YES
NO
NO
0.003
0.000
0.003

T f pGapit−1
C
impLM
jt−1
C
impLM
jt−1 ∗ T f pGapit−1

sizeit−1
klit−1solvencyit−1
aver wageit−1
c10jt−1

19Results don’t change when we re-estimate, as a further check, the equation
2 on the relative productivity calculated as deviation from the sector-year
mean, that allows us to analyse the position of firm with respect to thesectoral distribution. These estimations are not shown but are available
upon request.

5.2

Robustness checks

An important issue we have to dealt with concerns firm exit. Because it islikely to be at work a relationship between the firm exit and its productivity(especially we expect less productive firms are likely to exit the market),failing to control for exit could lead to biased results. Thus we correct forexit both including in the regression a dummy for firms exiting in period t+1
and applying the standard Heckman (1976) selection correction. In this latter
case, we model the probability to exit assuming as additional regressors thefirm age and its investments. Table 6 confirms the heterogeneous responsesof firms to increased import competition from LMCs.
We have also applied our analysis to a sample including smaller firms(firms with a turnover at least of 500,000 euros29 ) but for a shorter time
period, 2002-2006. From table 7 we can deduce that previous findings holdtrue if we extend our attention on small firms, thus our conclusions are notbounded to large firms that may be also more productive but they seem to
be more general.
Finally as additional robustness checks, we analyse in more detail theimports trying to show the effects of an easier access of foreign intermediates
and the importance of their origin countries. The main aim of the work
has been to investigate the role of imports in terms of the increased competition and we believe that focusing on the 3-digit NACE disaggregation
the sectoral imports capture the degree of the foreign competitive pressure.
Anyway imports may affect positively domestic firms employing imported
intermediates. In order to split and identify these two effects we exploit avariable of international material outsourcing. This variable is constructedusing data drawn from the Italian input-output Tables elaborated by Giorgio
Rampa30 . The inclusion of this additional regressor doesn’t change the main
results as we can see in Table 8 and disclose that imports may have negative
effects on firm efficiency through the competition channel and a potential
beneficial impact through the access to foreign intermediates. We always
find asymmetrical effects of import competition according to the previous
29

The turnover threshold of AIDA in 2002.
When we add offshoring we loose one year because Input-Output Tables are available
till 2004.
30

20

firm productivity level. Up to now, building on findings in section 4 we haveinvestigated only the role for imports from LMCs. Anyway it is interestingalso to move the attention on the flow of goods from developed countries inorder to confirm that imports are not important per se but according to their
origin. Thus we have re-estimated the Equation 2 adding the imports fromhigh income countries and their interaction with the lagged firm productivity.
From Table 8 we can see that competitive pressure from developed countries
has no a significant coefficient. Imports from industrialised countries is nota new fact, domestic firms are used to face with these flows of goods, andthey are not displaced by this competition.
Table 6: Robustness: Control for exit
VARIABLEStf pit−1
C
impLM
jt−1
C
impLM
jt−1 ∗ tf pij,t−1

OLS

FE

OLS

FE

Heckman-MLE

0.439***[0.006]-0.454***[0.094]-0.233***[0.075]0.019***[0.006]-0.404***[0.113]-0.243***[0.067]-0.021**[0.010]0.096***[0.006]0.011[0.007]-0.426***[0.114]-0.211***[0.067]-0.048***[0.009]-0.065***[0.004]-0.219***[0.025]0.019***[0.005]-0.051**[0.021]-0.027***[0.010]0.399***[0.038]0.446***[0.009]-0.440***[0.142]-0.260***[0.087]0.021***[0.002]-0.073***[0.002]0.265***[0.012]-0.039***[0.007]-0.068**[0.027]-0.049***[0.009]0.061***[0.012]0.430***[0.007]-0.420***[0.096]-0.203***[0.072]0.006***[0.002]-0.067***[0.002]0.259***[0.009]-0.034***[0.005]-0.052***[0.020]-0.039***[0.009]0.090***[0.017]

sizeit−1
klit−1solvencyit−1
aver wageit−1
c10jt−1
exitit+1
Const

0.061***[0.022]

Obs.
156778156778153336a153336a84933bTime Dummies
YES
YES
YES
YES
YES
Sector Dummies
YES
NO
YES
NO
YES
Firm Effects
NO
YES
NO
YES
NO
Robust standard errors in brackets are clustered by firm. *** p<0.01, ** p<0.05, * p<0.1
a The drop in the number of observations is due to missing values for controls.
b We have many missing values for age and investment used in the selection equation.
Even if we cluster by sector-year the variables still remain significant.

21

Table 7: Robustness: Adding smaller firms
VARIABLEStf pit−1
C
impLM
jt−1
C
impLM
jt−1 ∗ tf pij,t−1

OLS

FE

OLS

FE

0.494***[0.007]-0.17[0.187]-0.249***[0.072]-0.114***[0.008]-0.462***[0.166]-0.271***[0.077]-0.136***[0.013]-0.091***[0.009]0.451***[0.009]-0.173[0.224]-0.263***[0.081]-0.002[0.002]-0.065***[0.002]0.264***[0.011]0.088***[0.008]0.000[0.033]0.168***[0.023]-0.136***[0.009]-0.456**[0.194]-0.298***[0.085]-0.115***[0.013]-0.045***[0.006]-0.436***[0.037]
0.066***[0.007]-0.018[0.034]
0.583***[0.057]

sizeit−1
klit−1solvencyit−1
aver wageit−1
c10jt−1
Const.

Obs.
12252412252495250a95250aTime Dummies
YES
YES
YES
YES
Sector Dummies
YES
NO
YES
NO
Firm Effects
NO
YES
NO
YES
Robust standard errors in brackets are clustered by firm. *** p<0.01, ** p<0.05, * p<0.1
a The drop in the number of observations is due to missing values for controls.

22

Table 8: Robustness: Offshoring and Imports from High Income countries
VARIABLEStf pit−1
C
impLM
jt−1
C
impLM
jt−1 ∗ tf pij,t−1

impHIGH
jt−1
impHIGH∗ tf pij,t−1
jt−1

OLS

FE

OLS

FE

OLS

FE

0.442***[0.008]-0.483***[0.096]-0.232***[0.075]0.094**[0.047]-0.013[0.032]0.021***[0.008]-0.428***[0.117]-0.242***[0.068]0.051[0.053]-0.011[0.041]0.427***[0.007]-0.414***[0.109]-0.304***[0.081]-0.005[0.006]-0.292**[0.128]-0.296***[0.072]0.376***[0.014]-0.420***[0.109]-0.296***[0.080]-0.02[0.016]-0.294**[0.128]-0.295***[0.072]0.230***[0.074]0.415***[0.078]0.425***[0.080]0.044[0.051]-0.039[0.025]136127b
YESNO
YES

of f matjt−1

Const0.043***[0.015]0.090***[0.009]0[0.022]-0.036[0.024]0.269***[0.075]0.152***[0.039]-0.011[0.022]

Obs.
Time Dummies
Sector Dummies
Firm Effects

156778YES
YESNO

156778YES
NO
YES

136127YES
YESNO

136127b
YES
NO
YES

136127b
YES
YESNO

of f matjt−1 ∗ tf pij,t−1

Robust standard errors in brackets are clustered by firm. *** p<0.01, ** p<0.05, * p<0.1
b
The drop in the number of observations is due to missing values for offshoring variable in 2005..

6

Conclusions

During the last decade, Italy experienced a rapid growth of import penetration, especially from low and medium income countries. This phenomenon
has been common to all developed countries, and it is in great part due tothe implementation of liberalization strategies by emerging and developing
countries and their industrial development, and not linked to specific domestic factors in Italy. Aware of this evidence, we analyse if this foreigncompetition contributes to shape the sectoral productivity distribution andfirm dynamics. First, we verify that, in a comprehensive framework, the exposure to LMCs is negatively related with the high productivity dispersion
at sector level. The within-sector disparity in firm productivity presents alsosignificant linkages with the concentration of the domestic market and morein general with the domestic competitive context. No role is instead detected
for the ICT adoption and other trade variables.
Second, we turn on firm level and we show that behind the negative
relationship between dispersion and import penetration from LMCs there
are asymmetrical firm responses according to their initial efficiency level.
We find that more productive firms support more deleterious effects from23the increased exposure to foreign competition. We suggest that the surge inimports from emergent countries may have reduced the incentives to innovate
and invest, but it may have also stimulated efforts of less productive firms
facing with the risk of exit. We put forward also the hypothesis of a processof product-switching. Anyway it needs more research efforts in order toclarify the heterogeneous firm behaviour. More attention should also be
posed on the within-industry dynamics and efficiency dispersion. Even if
we have shed some light on linkages between productivity heterogeneity andimportant characteristics, a large part of the dispersion remain unexplained.

References[1] Aghion P., Burgess R., Redding S. and F. Zilibotti (2005). Entry
Liberalization and Inequality in Industrial Performance. Journal of theEuropean Economic Association, 3(2-3), 291–302.
[2] Altomonte C., Barattieri A. and A. Rungi (2008). Import penetration, intermediate inputs and productivity: evidence from Italianfirms. Working paper, Bocconi University, Milan.
[3] Alvarez R. and G. Crespi (2007). Multinational firms and productivity catching-up: the case of Chilean manufacturing. International Journal of Technological Learning, Innovation and Development, 1(2), 136–
152.
[4] Amiti M. and J. Konings (2007). Trade Liberalization, Intermediate
Inputs, and Productivity: Evidence from Indonesia. American Economic
Review, 97(5), 1611–1638.
[5] Arellano M. and S. Bond (1991). Some Tests of Specification for
Panel Data: Monte Carlo Evidence and an Application to Employment
Equations. Review of Economic Studies, 58(2), 277–297.
[6] Arnold J., Nicoletti G. and S. Scarpetta (2008). Regulation,
Allocative Efficiency and Productivity in OECD Countries: Industry
and Firm-Level Evidence. OECD Economics Department Working Papers 616, OECD, Economics Department.
[7] Bartelsman E.J. and M.E. Doms (2000). Understanding Productivity: Lessons from Longitudinal Microdata. Journal of Economic Literature, 38(3), 569–594.

24

[8] Bks G., Kleinert J. and F. Toubal (2006). Spillovers from Multinationals to Heterogeneous Domestic Firms: Evidence from Hungary.
CeFiG Working Papers 1, Center for Firms in the Global Economy.
[9] Bernard A.B., Eaton J., Jensen J.B. and S. Kortum (2003).
Plants and Productivity in International Trade. American Economic Review, 93, 1268–1291.
[10] Bernard, A.B., Jensen, J.B., and P.K. Schott (2006). Survivalof the Best Fit: Exposure to Low Wage Countries and the Growth of US
Manufacturing Plants. Journal of International Economics, 68(1), 219237.
[11] Bernard A.B. and C. Jones (1996). Productivity across Industries
and Countries: Time Series Theory and Evidence. The Review of Economics and Statistics, 78(1), 135–146.
[12] Bugamelli, M. and A. Rosolia (2006). Productivity and Foreign
Competition. Bank of Italy Economic Research Paper N. 578.
[13] Cameron, G., J. Proudman and S. Redding (2005). Technological
Convergence, R&D, Trade and Productivity Growth. European Economic
Review, 49, 775–807.
[14] Cameron A.C. and P.K. Trivedi (2005). Microeconometrics: Methods and Applications. Cambridge University Press, New York.
[15] Caselli F. (1999). Technological Revolutions. American Economic Review, 89(1), 78–102.
[16] Chevalier P.A., Lecat R. and N. Oulton (2009). Convergenceof Firm-Level Productivity, Globalisation, Information Technology andCompetition: Evidence from France. CEP Discussion Paper No 916,
Centre for Economic Performance.
[17] Daveri F. and C. Jona-Lasinio (2005). Italy’s Decline: Getting
the Facts Right. Giornale degli Economisti, Giornale degli Economisti e
Annali di Economia, 64(4), 365–410.
[18] Del Gatto M., Ottaviano G.I. and M. Pagnini (2008). Openness
To Trade And Industry Cost Dispersion: Evidence From A Panel Of
Italian Firms. Journal of Regional Science, 48(1), 97–129.

25

[19] Dimova R. (2008). The impact of labour reallocation and competitive
pressure on TFP growth: firm-level evidence from crisis and transition
ridden Bulgaria. International Review of Applied Economics, 22(3), 321–
338.
[20] Dovis, M. and J. Milgram-Baleix (2009). Trade, Tariffs and Total
Factor Productivity: the case of Spanish firms. The World Economy,
32(4), 575–605.
[21] Escribano A.S. and R. Stucchi (2008). Catching up in total factor
productivity through the business cycle: evidence from Spanish manufacturing surveys. Economics Working Papers 085125, Universidad CarlosIII.
[22] Eslava M., Haltiwanger J.C., Kugler A.D. and M. Kugler(2009). Trade Reforms and Market Selection: Evidence from Manufacturing Plants in Colombia. NBER Working Papers 14935, National Bureau
of Economic Research.
[23] Fernandes A.M. (2007). Trade Policy, Trade Volumes and Plant-level
Productivity in Colombian Manufacturing Industries. Journal of International Economics, 71(1), 52–71.
[24] Good, D.H., M. Nadiri,and R. Sickles (1997). Handbook of Applied Econometrics: Micro-econometrics. Blackwell: Oxford.
[25] Griffith R., Redding S. and H. Simpson (2003). Productivity
Convergence and Foreign Ownership at the Establishment Level. CEPDiscussion Papers 0573, Centre for Economic Performance.
[26] Griffith R., Redding S. and H. Simpson (2006). Technologicalcatch-up and the role of multinationals. CEPR Discussion Paper 3765,
Centre for Economic Performance.
[27] Haller S. (2008). The Distribution of Productivity in Irish Manufacturing Between 1995 and 2004 Determinants, Changes and Implications.
The Economic and Social Review, Economic and Social Studies, 39(1),13–38.
[28] Iacovone L. (2009). The better you are the stronger it makes you: evidence on the asymmetric impact of liberalization. Policy ResearchWorking Paper Series 4930, The World Bank.

26

[29] Ito K. and S. Lechevalier (2008). The Evolution of the Productivity
Dispersion of Firms: A reevaluation of its determinants in the case ofJapan. RIETI Discussion paper Series 08-E-014.
[30] Jovanovic B. (1982). Selection and the Evolution of Industry. Econometrica, 50(3), 649–670.
[31] Jovanovic, B. and S. Lach (1997). Product innovation and the business cycle. International Economic Review, 38, 3-22.
[32] Konings, J. and H. Vandenbussche (2008). Heterogeneous responses of firms to trade protection. Journal of International Economics,
76, 371-383.
[33] Kremp E. and J. Mairesse (1991). Dispersion and Heterogeneity ofFirm Performances in Nine French Service Industries, 1984-1987. NBER
Working Papers 3665, National Bureau of Economic Research.
[34] Levinsohn J. and A. Petrin (2003). Estimating Production Functions Using Inputs to Control for Unobservables. Review of Economic
Studies, 70, 317–341.
[35] Mairesse J. and J. Jaumandreu (2005). Panel-data Estimates of
the Production Function and the Revenue Function: What Difference
Does It Make? Scandinavian Journal of Economics, 107(4), 651–672.
[36] Maliranta M. (2005). R&D, International Trade and Creative DestructionEmpirical Findings from Finnish Manufacturing Industries. Journal of Industry, Competition and Trade, 5(1), 27–58.
[37] Maliranta M. and S. Nurmi (2004). Do Foreign Players Change the
Nature of the Game Among Local Entrepreneurs?Discussion Papers 942,
The Research Institute of the Finnish Economy.
[38] Melitz M.J. (2003). The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity. Econometrica, 71(6), 1695–
1725.
[39] Muendler M.A. (2004). Trade, Technology, and Productivity: A
Study of Brazilian Manufacturers, 1986-1998. Economics Working Paper
Series 2004-06, University of California at San Diego.
[40] Nishimura K.G. and Nakajima T. and K. Kiyota (2005a). Productivity Convergence at the Firm Level. CIRJE F-Series 341, Tokyo.
27

[41] Nishimura K.G. and Nakajima T. and K. Kiyota (2005b). Innovation Versus Diffusion: Determinants of Productivity Growth AmongJapanese Firms. CIRJE F-Series 350, Tokyo.
[42] Oulton N. (1998). Competition and the Dispersion of Labour Productivity amongst UK Companies. Oxford Economic Papers, 50(1), 23–38.
[43] Pavcnik N. (2002). Trade Liberalization, Exit, and Productivity Improvement: Evidence from Chilean Plants. Review of Economic Studies,69(1), 245–276.
[44] Rodrik D. (1991). Closing the technology gap: does trade liberlaization
really help? NBER Working Papers 2654, National Bureau of Economic
Research.
[45] Sabirianova, K.P., Svejnar, J. and K. Terrel (2005). Distanceto the Efficiency Frontier and Foreign Direct Investment Spillovers. Journal of the European Economic Association, 3(2), 576-586.
[46] Schor, A. (2004). Heterogeneous Productivity Response to Tariff Reduction: Evidence from Brazilian Manufacturing Firms, Journal of Development Economics, 75(2), 373-396.
[47] Syverson C. (2004). Product Substitutability and Productivity Dispersion. The Review of Economics and Statistics, 86(2), 534–550.
[48] Topalova, P. (2004). Trade Liberalisation and Firm Productivity:
The Case of India. MF Working Paper 04/28, Washington: International
Monetary Fund.
[49] Traca D. (1997). Import-Competition, Market Power and Productivity Change. Discussion Paper FS IV 97 - 29, Wissenschaftszentrum Berlin.

28

A

TablesTable 9: Evolution of exposure to imports by sector

ATECO

IMPHIGH
ita,1998

IMPHIGH
ita,2005

C
IMPLM
ita,1998

C
IMPLM
ita,2005

C
IMPshLMhigh,1998

C
IMPshLMhigh,2005

15
17
18
19
20
21
22
24
25
26
27
28
29
31
32
33
34
35
36

16.67
10.29
6.43
6.76
17.35
39.95
3.64
32.41
17.64
9.24
36.85
9.46
17.64
23.3
52.85
37.91
17.95
14.03
8.77

12.17
10.31
9.66
8.58
13.86
36.73
3.88
41.41
16.41
8.52
33.32
11.15
16.74
24.61
61.16
37.76
18.44
22.36
9.96

3.31
7.88
14.04
14.87
5.96
3.38
0.14
1.59
3.29
1.98
10.37
1.42
2.14
3.95
2.14
2.78
2.33
3.12
5.15

1.88
16.87
27.27
24.77
6.2
5.2
0.26
2.49
5.81
3.77
17.14
4.58
6.06
8.97
7.86
6.92
5.03
5.12
9.85

21.65
39.93
65.1
51.06
33.38
8.76
12.2
7.08
17.67
21.83
26.65
16.38
11.09
31.87
19.65
13.78
11.62
18.26
37.49

24.64
52.65
75.5
61.55
41.35
15.48
20.99
10.52
25.21
31.76
36.5
27.9
20.83
42.86
36.88
19.52
19.78
24.53
51.97

Total

19.95

20.9

4.73

8.74

24.5

33.71

Source: Our elaborations from AIDA, WITS and Firms Economic Accounts (ISTAT)
Definition of variables as in table 1. Variables are in percentage (%)

29

Table 10: Determinants of sectoral dispersion: Prais-Winsten
VARIABLES

D2575TFP

D1090TFP

StdTFP

imp compj,t−1-0.003[0.081]
0.039[0.084]-0.862***[0.321]0.805***[0.251]-0.199[0.159]0.187[0.149]exp openj,t−1
C
imp compLM
jt−1

imp compHighjt−1C
exp openLM
jt−1

exp openHigh
jt−1
ictj,t−1
nf irmj,t−1
c10j,t−1
sunkj,t−1
Const-0.072[0.044]-0.038[0.042]0.156***[0.051]0.032***[0.011]1.710***[0.569]0.028[0.125]-0.183[0.114]0.202[0.164]0.046[0.038]3.232*[1.677]-0.046[0.085]-0.023[0.055]0.108*[0.061]0.006[0.015]1.18[1.020]D2575TFP

D1090TFP

StdTFP-0.317***[0.123]0.072[0.073]0.058[0.107]0.066[0.094]-0.07[0.047]-0.05[0.043]0.033***[0.010]0.160***[0.051]1.878***[0.627]-0.999**[0.430]-0.251[0.195]0.941**[0.475]0.192[0.216]0.054[0.123]-0.185*[0.103]0.049[0.037]0.202[0.161]
3.021*[1.612]-0.494***[0.155]-0.012[0.122]0.221[0.224]0.098[0.179]-0.041[0.087]-0.035[0.056]0.007[0.015]0.111*[0.061]1.301[1.139]

541
0.793

541
0.67

Obs
541
541
541
541
R2
0.826
0.793
0.668
0.827
Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1
Every regression controls for sector and time dummies.

30