Italian manufacturing firms, offshoring to highand low income countries and the labour

demand.

Alessia Lo Turco and Daniela Maggioni∗

Universita Politecnica delle Marche

November 30, 2009

First draft. Please do not quote

Abstract

Making use of an original data set on a panel of Italian manufac-turing firms we estimate the effects of offshoring on the demand forlabour. We estimate two alternative versions of a dynamic labour de-mand by means of System GMM thus allowing for the endogeneity ofour right hand side regressor, especially our offshoring measure. Ourresults bear a negative employment effect which is corroborated by anumber of robustness checks. This negative elasticity is attributableexclusively to imports of intermediates from low income trading part-ners. No significant effect is estimated for imports from high incomecountries.

JEL:F14, J23, L23Keywords: Offshoring, Employment, dynamic model

∗Comments are welcome. We wish to thank Giuliano Conti for useful discussions.

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1 Introduction

The advances in information and communications technologies and the gro-wing international integration of low-income countries have fostered the in-volvement of firms in international markets. The consequent restructuring ofthe production process has made the offshoring practices one of the most dis-puted phenomena, both in the economic literature and in the public debate.The term offshoring usually refers to the purchase of foreign inputs from fo-reign affiliates - international insourcing - and the supply of intermediatesfrom independent foreign firms at arm’s length - international outsourcing -.These two aspects have usually been studied in conjunction and measuredfrom the national Input-Output Tables in the empirical literature. The latter,in particular, has investigated the labour market consequences of offshoringdue to its potential role in explaining the rising wage inequality betweenhigh and low skilled workers. The evidence, so far, has been focused on thedisentangling of the effects of trade and technological change on the relativedemand for the high skilled workers in advanced countries. Nevertheless, forthe same labour markets, the role of imports from low labor cost countriescan also be related to the general reduction of manufacturing employment.

A wide number of studies provide evidence based on industry-level studiesand supports a positive, although often small, effect of offshoring on the re-lative demand for skilled workers. Little attention, instead, has been paid tothe overall employment effects of offshoring in manufacturing. A few contri-butions deal with this topic at the sector level (Guscina, 2006; Hjzen andSwain, 2007; Cadarso et al., 2008), some further works combine micro-leveldata on employees with sector level measures of offshoring (Gorg and Hanley,2005; Munch, 2005; Gescheicker, 2008), while very scant is the evidence onoffshoring and labour demand at the firm level (Moser et al. 2009).

A firm-level perspective can shed light on the adjustment costs followingoffshoring, and could help in understanding the equilibrium level of outputand employment.

Within this framework, we provide firm-level evidence on offshoring andemployment, with a focus on the labour demand by Italian manufacturingfirms. Making use of an original data set on firms between 2000 and 2004,we estimate the labour demand as function of the offshoring intensity in thefirm, testing alternative specifications and distinguishing between importsof intermediates from high and low income countries. Then, apart fromconveying new results on the offshoring effects for an advanced country, weshed some light on the differential impacts of trade across origin countries.Previous studies usually do not take into account the existence of heteroge-neous effects across partner countries, but this can be potentially misleadingbecause the reasons and the effects of intermediate material imports maydiffer in terms of employment and productivity. Furthermore, the use of theSystem GMM estimator allows us to interpret our results as causal effects.Our work is structured as follows. The next Section presents a review of the

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literature, Section 3 describes the empirical model of the offshoring effectson employment. Section 4 shows the dataset and some descriptive statistics.Results and robustness checks follow in Section 5 and 6 Section 7 concludes.

2 Review of the related literature

As noticed in Grossman and Rossi-Hansberg (2006) international trade ismoving from goods to single tasks. The production can be considered as amultistage process, where each stage is a bundled task that may be repla-ced with intermediate goods outsourced to other firms. Firms may decideto relocate part of their activities abroad in order to take advantage of lo-wer labour costs, the higher quality and high-technology content of foreigninputs. Thus, offshoring involves an international division of labour thatmay drive important effects on employment and also on productivity andsector/firm performance. The measurement of offshoring involves some diffi-culties and indicators have been improved over time. First works combinedInput-Output (henceforth, IO) tables and trade data to proxy imported in-puts by sector (Feenstra and Hanson, 1996 and 1999; Egger and Egger, 2003and 2005; and Strauss-Kahn, 2004; Amiti and Wei, 2005), anyway morerecent works have taken advantage of the use matrix from the IO tables thatcontains the information of the intermediate import shares (see for exampleFalk and Koebel, 2002; Cadarso et al, 2008; Hijzen et al, 2005; Ekholm andHakkala, 2008). Finally the new micro-level dataset may further contributeto a better measurement of the phenomenon and to its deeper understanding.

Different theoretical contributions (to cite only a few, Feenstra and Han-son, 1996a, Deardorff, 2002, Kohler, 2004 and Grossman and Rossi-Hansberg,2006), have dealt with the employment consequences of offshoring, especiallyfocusing on the skill composition. However, the predictions are not clear andconclusive and empirical evidence is needed to shed some light on the issues.

Different channels have to be taken into account in order to explain thelabour consequences of offshoring. First of all, there is a direct impact ofthe use of foreign inputs on employment. If foreign intermediates substi-tute previous internal productions, offshoring results in lay-offs of workers.This is a technology effect, direct consequence of the relocation, reducing thelabour-intensity of production. In opposite, if imported inputs replace domes-tic intermediates previously bought from domestic suppliers, offshoring doesnot cause any reduction of employment for offshorer firm but a disruptionof the domestic supply relationships 1. Anyway, the story is not complete,because an indirect channel may affect the offshorer labour demand. Thanksto a strong focus on production stages where firm has comparative advan-tage and thanks to the higher quality or cheaper inputs, firms may obtain

1In this case there should be some negative employment effects on domestic suppliers,because offshoring of downstream firms means a contraction of demand for domestic ups-tream firms. See for example Bertoli (2008) and Costa and Ferri (2007) for Italy.

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productivity improvements, and gain more flexibility and an higher compe-titiveness, increasing their market shares, their production and finally theiremployment (that is the scale effects). In this context, we may expect lay-offsand unemployment in the short-term because of the substitution effect, whilein the long term we expect a positive impact on employment. These long-runpredictions are presented also in Grossman and Rossi-Hansberg (2006) thattreat international outsourcing as a factor increasing technological progress.Literature has estimated a conditional labour-demand in order to capturethe short-term technology effects, and an unconditional labour demand cap-turing the long-run scale effects, and in the great part of works offshoringenter the labour demand function as a shift parameter.

The bulk of the empirical literature has focused on the impact of offsho-ring on the skill composition of employment and on the wage gap betweenworkers with different skills. Even if conclusions are not clear a review ofthe literature seem to suggest a small negative impact on unskilled ratioand wages of low-skilled workers. First studies have estimated the sign andmagnitude of the employment impact of offshoring with industry-level data.Feenstra and Hanson (1996 and 1999) for USA analyse the way trade affectsthe relative demand for skilled labour by estimating a relative labour costequation augmented by an offshoring measure. In their first work (1996) theyconclude that the purchase of foreign inputs increase the high-skill labourshare even if this effect is found only for a subperiod of their sample. In thesecond one (1999) they perform a similar analysis taking into account also theeffects of technological change and showing that narrow offshoring, if compa-red with broad offshoring, has stronger sectoral consequences. Many papershave followed the seminal approach of Feenstra and Hanson and displayed, ingeneral, the same findings. Hijzen et al (2005) for the UK estimate a systemof four factor demand functions and conclude that offshoring moves labourdemand away from workers with low skill levels to workers with high skill le-vels. While Strauss-Kahn (2004) for France analyse the role of internationaloutsourcing and technological progress drawing the same conclusion for theskill ratio. However there are also some opposing results: Falk and Koebel(2002), dealing with Germany industries, show no evidence that labor withthe lowest educational attainment is substituted for imported materials andservices, while they disclose a complementarity between semi-skilled workersand offshored materials.

All the reviewed works have focused on the effect of outsourcing on re-lative labour demand, anyway part of the literature has also dealt with theconsequence for the total employment level. Falk and Wolfmayr (2005) high-light the offshoring role for a group of seven EU countries in the period 1995-2000. They estimate a labour demand using 2-digit manufacturing data andfocusing on a narrow offshoring indicator from low-wage countries. Theyfind a reduction of 0.25 percentage points in employment per year drivenby offshoring, and show that this negative impact is significant only for lowskill intensity industries but not in skill intensive ones. Cadarso et al (2008)

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for Spain, estimating a dynamic labour demand, find heterogeneous effectsaccording to the technological level of sectors and the origin countries ofinputs: significant and negative impact are disclosed only when narrow off-shoring concerns medium and high-tech industries and inputs come fromCentral and Eastern European countries. No significant effect is found forlow-tech industries and other origin economies. Anyway they don’t deal withthe problem of endogeneity of offshoring. The two latter papers show alsothe importance of the origin countries in order to detect the employment ef-fects. As already said in the introduction, this is an important dimension toinvestigate. Behind the foreign input flows there could be different reasonsaccording to the origin countries and, as a consequence, different effects maybe displayed. Literature has usually given great importance to the relocationof parts of the production process from relatively skill-abundant countries tounskilled-abundant countries. Anyway, as we also will show in our analysis,most input imports to high-income countries probably come from other ad-vanced countries2. In this case the reasons for an increase of the skill shareof labour are not at work because it is likely that the offshored activities willnot be unskill intensive. In order to examine the importance of the origincountries it is essential to utilize microdata, including detailed informationon offshoring, because at sectoral data we can only infer the share of inputcoming from different origins, merging the use matrix of IO Tables with na-tional trade data. When papers focus on the origin countries they usuallyfind a significant effect only for offshoring to non developed countries as do-cumented in the works presented above and also in Geishecker (2006) andEkholm and Hakkala (2008). Geishecker (2006) shows a reduction in Ger-man sectoral relative demand for manual workers due to international (broad)outsourcing to Central Eastern Europe and no role for input imports fromEU15. Ekholm and Hakkala (2008), for Sweden, distinguish between threedifferent skill groups based on educational attainment (lower secondary, up-per secondary and tertiary education) and estimate a seemingly unrelatedregression (SUR) of two labour share equations. Their results confirm nosignificance for offshoring to high-income countries while a reduction of thelower educated workers driven by imports from low-wage economies.

In the same framework some papers have focused their attention espe-cially on service offshoring. This is the case of Amiti and Wei (2005 and2006). In the first paper (2005) they show for USA that service outsourcinghas small negative effects on employment when they analyse highly disaggre-gated sectors, but when they move on more aggregated sectors no significantrelation is detected. Also in Amiti and Wei (2006) for UK the sectoral jobgrowth is not found to be negatively related to service outsourcing. Recently,

2Falk and Wolfmayr (2005) argue that for seven EU advanced members outsourcing toindustrialised countries is dominant and cover 80% of their imported materials. Anyway,even if intermediate imports from low-wage countries present low levels, they have sub-stantially grown in last years. This evidence has to be considered with caution because itis found combining the use matrix of IO tables with trade data.

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for service offshoring, Crin (2009) has shown that in USA foreign service im-ports drive to the increase of sectoral employment of high-skilled workers andthe reduction of medium and low skilled workers.

A strand of literature has then studied the impact on wages. Hijzen(2007) shows for UK that the wage gap between high and low skilled laborincreases significantly when international outsourcing takes place even if itis the technological change the predominant factors. Similiar conclusions areobtained in Broccolini et al (2008) that disclose an increasing wage inequalitybetween blue and white collars both in traditional and innovative industries.

The recent availability of firm and employee level dataset has pushed tore-examine these issues focusing on the microeconomic behaviors. Micro-level studies allow to control for individual heterogeneity and to capture theoffshoring implications for micro units that may be hidden in the aggre-gate dynamics. Gorg and Hanley (2005) study the dynamic labour demandon a plant level database for the Irish Electronics sector. They find a re-duction of the total employment level in the short-run and stronger effectsseem to come from material than service outsourcing. Even if their analy-sis concerns firm performance the offshoring is calculated at sectoral level.Munch (2005), taking advantage of an employee dataset, investigate the im-pact of international outsourcing on the job separation risks in Denmarkfinding an increase of the risk to become unemployed for low-skilled workers,and an higher probability to change job for high-skilled workers. A similaranalysis is implemented by Geishecker (2008) who combines household paneldata and industry-level offshoring indicators. He shows a negative impact ofnarrow offshoring on the probability of workers to preserve their workplace,but this effect is not heterogeneous across workers with different skill levels.This is an interesting point to highlight because is at odds with sectoral stu-dies and a great part of previous findings showing a decrease in the relativelow-skilled labour demand. Thus it emerges the hypothesis that the sectoralreduction of low-skill employment may be a consequence of the lower pro-bability of low-skilled workers to find a new job after the lay-off. Using thesame database, Geishecker and Gorg (2008) show that offshoring has dif-ferent effect on employee wages according to the skill level, increasing wagesfor skilled workers and reducing ones of unskilled workers. To this strand ofliterature belongs also the paper of Egger et al (2007) that shows for Austriathe role of international offshoring in shaping the employment turnover anddecreasing the probability of staying in the manufacturing sector, especiallyfor comparative disadvantage industries.

The most reviewed works have a short-run focus and analyse only thedirect effects of offshoring, estimating mainly a conditional labour demand.Anyway some papers present a more comprehensive framework, trying tocapture direct and also indirect effects that work through productivity gains3

3In last few years the issue of the offshoring impact on productivity is receiving greatattention. Up to now empirical evidence has not highlighted clear patterns (for a reviewsee Olsen, 2006) and results often differ according to the level of analysis (sector or firm

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and competitiveness improvements. Hijzen and Swaim (2007) and Moser etal (2009) are two good examples of this strand. Hijzen and Swaim (2007) ana-lyse both technology and scale effects of offshoring using industry-level datafor 17 high income OECD countries. According their results, narrow offsho-ring reduces the labour-intensity of production, but has no significant impacton the overall employment. Focusing instead on an indicator of broader off-shoring (inter-sectoral offshoring) they show no changes in labour-intensityand positive effects on overall employment. Moser et al (2009) applying adifferent empirical strategy to a German plant dataset obtain similar conclu-sions. They use a difference-in-difference analysis for a matched sample andfind an increase of employment level caused by offshoring. In opposite someloss of jobs may happen when offshoring is combined with a restructuringprocess. These findings suggest that it is also important to take into accountthe scale effects driven by efficiency improvements if we want to show a longterm effect, even if short term effects still are informative of the adjustmentcosts of the offshoring process.

For Italy studies on employment effect of offshoring are driven mainly atsetoral level and focus on the skill composition. Helg and Tajoli (2004) in-vestigate the effect of the international fragmentation of sectoral productionin Italy and Germany and show a positive impact of international integra-tion in Italy, but no significant effect in Germany. Their work is limitedbecause they exploit data on outward processing trade flows, but offshoringis a broader phenomenon and outward processing trade statistics record onlya part of the story. Falzoni anf Tajoli (2009) use a more comprehensiveoffshoring indicator built on the National IO tables; they show no signifi-cant employment reduction following the increase in offshoring, but presentsome heterogeneous effects on skill composition according to the type of sec-tors: skill ratio is affected positively only in skill intensive industries. Bertoli(2008) tries to analyse the consequences of offshoring on sectoral employmentinvestigating not only the intra-sectoral effects but also intersectoral effects,building a measure of offshoring of downstream sectors. The idea behindthis analysis is that offshoring may impact on employment because it candisrupt the domestic sub-contracting relationships. A similar aim is followedin Costa and Ferri (2008) who present a study at firm level focusing both ondirect effects of offshoring and on effects for subcontracting firms. They findsimilar results to Bertoli (2008): no reduction of employment is caused bydirect offshoring, while subcontracting clusters characterised by high levelsof offshoring present lower employment than low-offshoring clusters.

At best of our knowledge there is a single work at firm level on these topicsfor Italy4. Exploiting a panel of 1995-2003 Antonioli and Antonietti (2007)

level) the characteristics of firms and sectors, and the type of input involved (materials orservices). Using firm level database efficiency gains are usually find, see for example Gorget al (2008) and Hijzen, Inui and Todo (2007).

4Previous papers, Barba-Navaretti and Castellani (2004) and Castellani et al (2009),deal with the employment consequences of FDI, but not take into account the process of

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apply a difference in difference propensity score matching estimator and finda positive weak offshoring effect on the firm skill ratio, anyway this effectis significant only for a subperiod of the sample and it seems to be drivenmainly by a decrease of the unskilled employment. Anyway their resultsconcern a very small firm sample. In front of this scant firm level evidence,both for Italy and other advanced countries, we think to give some originalcontributions to the existing literature. First of all, we have at our disposala large dataset that covers a large part of the Italian manufacturing outputand employment and that allow us to present some descriptive statistics thatmay disclose important features of the offshoring phenomenon in Italy5. Inaddition we are the firsts to estimate for Italy a dynamic labour demandmodel at firm level in order to detect the offshoring effects. Finally, we showthe importance of the input origin countries that drive an heterogeneity ofeffects.

3 Modeling the effects of offshoring

Transposing the usual skilled/unskilled labor analytical framework to thecapital/labor dichotomy, offshoring is modeled as to reduce the relative de-mand for labor exactly in the same way labor saving technological changedoes and it should enter any empirical specification of the labour demandas a demand shifter. Assuming a production technology with two factor in-puts - labour, L and capital, K- we follow Hamermesh (1993)6 and derivethe log linear conditional demand for labour from a constant elasticity ofsubstitution (CES) cost function as follows

lnLijt = −σlnwijt + lnYijt + σlnAijt(OFFSHORING) (1)

L is the number of workers in firm i, industry j at time t, w measures theaverage wage paid in the firm, Y measures the firm’s output as real valueadded and σ = 1

1−ρ is the elasticity of substitution. Finally, A in equation

1 refers to technical change and, following the suggestions from the theory(Feenstra and Hanson, 1996, 1999; Feenstra, 2004) and previous empiricalwork (e.g. Amiti and Wei, 2006), it is modeled as a function of offshoring:

Aijt = BjeδOFFSHORINGijt+τt (2)

with Bj representing an industry specific scale factor and τt representingcommon yearly macro shocks affecting the level of A. Then substituting 2

international outsourcing.5The database doesn’t include the whole population of firms and neither is constructed

with a representative sampling, but it is composed by all company surviving in the period2000-2004 and .

6See page 30.

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into 1, adding an idiosyncratic disturbance term, εijt, and taking capital, K,as fixed in the short-run, we get the following empirical model

lnLijt = α0 + α1lnwijt + α2lnYijt + δOFFSHORING+ ηj + τt + εijt (3)

The previous model, however, assumes that the elasticity of substitutionbetween the two inputs is constant regardless the level of the factor inputsand/or prices. Then we relax this assumption and estimate the conditionaldemand for labour originating by a translog cost function with no a priorirestriction on the substitutability of factors. From Shephard’s lemma, takingthe partial derivative of the log cost function with respect to the log of wagewe get

∂lnC

∂lnw=w

C∗ ∂C∂w

=w ∗ LC

= SL (4)

Considering the real turnover as a function of capital, labour, materialand services and not imposing any restriction on the way intermediate goodsaffect production and the demand for labour we obtain the following equationto estimate7

SLijt= βl+βlllnwijt+

∑q=m,s

βlqlnpqijt+βlklnKijt+βlylnYijt+ηj+τt+εijt (5)

Thus, SLijtis a function of its own price, on the price of the other variable

inputs, pqijt with q = m, s and on the level of fixed inputs, output and othershift parameters represented by capital K, real turnover Y and the offshoringintensity OFFSHORING.

Finally, we consider a long-run alternative where the capital stock is sub-stituted by its price8

SLijt= βl + βlllnwijt +

∑q=m,s,k

βlqlnpqijt + βlylnYijt + ηj + τt + εijt (6)

This specification has the advantage of representing the relationship bet-ween offshoring and domestic labour in a twofold perspective: firstly as thesubstitutability/complementarity between two factors of the same productionprocess and, secondly, as the non-neutral effect of the offshoring intensity ontotal factor productivity. In this respect we would follow the suggestions ofpart of the theoretical literature which stresses the endogeneity of offshoring(Kohler, 2004, 2008) in the form of demand for imported intermediates.

7See Feenstra page 119.8We have followed the procedure used in the EU-KLEMS Project in order to calculate

the user capital cost and we have recovered the data from the relative EU-KLEMS data-base. Especially the user cost capital is obtained with the formula pK

jt = rjtpIj,t−1 + δjtp

Ij,t

where j labels the sector, rjt is the internal rate of return, δjt is the sectoral depreciationrate and pI

j,t is the gross fixed capital formation price index.

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3.1 Estimation issues

The above empirical specifications coming from the theory of the firm implyan immediate adjustment of labour to its equilibrium level. A preliminary in-vestigation of data revealed us that the static specification poorly fits our dataso we preferred a dynamic panel data model in the form of a ARDL(1,1)9.Then, the empirical specifications take the general following form

LABOURt = αLABOURt−1 + Γ′Xt + Λ′Xt−1 + εt + εt−1 (7)

where firm and industry subscripts are omitted for the ease of presentation.The same dynamic model is estimated for the labour cost share estimation.The introduction of the lag of the dependent variable represents a source ofendogeneity for our estimates and, as standard in the literature, we solvethis issue by means of the System GMM estimator which, preserving boththe cross-section and time-varying dimension of the data, conveys the furtheradvantage of allowing for the endogeneity of the remaining regressors and,in particular, of our variable of interest, OFFSHORING. The estimatorfurthermore represents a useful tool to overcome the lack of information onthe firm’s location in our data.

According to the theoretical predictions and previous studies, we expectthat offshoring has a negative impact on the firm level of employment, es-pecially if foreign inputs are bought from low-income countries. Offshoringshare enter as a shift parameter in the labour demand equation and in the la-bour cost share equation, and all regressions are run both for the total offsho-ring share and for the breakdown between offshoring to developed countriesand offshoring to non developed countries.

4 Data and preliminary analysis

4.1 Data

The main source of this work is an Italian firm level panel covering a 5-yearperiod from 2000 to 2004. It is a balanced panel constructed by the Na-tional Statistical Institute (Istat)10. After a cleaning procedure11 we remain

9The presence of a AR(1) in the disturbances led us to test the common factor restric-tions which were rejected so we identified a dynamic specification with the inclusion of theregressor and right hand side variables lagged to one period.

10The dataset has been constructed by Istat for some analysis published in the IstatAnnual Relation. The panel concerns only limited companies and covers, on average,54% of all limited companies in manufacturing, 64% of their employment and 40% of thetotal employment in manufacturing. The panel has been created by the merge betweendata from trade statistics and data from firm economic accounts. Researchers of Istathave labeled as offshoring the firm import flows of intermediates from all sectors and theimports of finished goods from the firm’s sector.

11We drop firms in NACE sectors 16 and 23. We also delete firms which are consideredas outliers for at least one year in the sample period, we consider as outliers observations

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with information on more than 40,000 firms. We have at our disposal dataon firm output and inputs, labour costs, tangible and intangible fixed assets,exports, imports and offshoring activity and the firm activity sector at 3-digitNACE. Offshoring shares have been also split according to the partner coun-tries, developed and non-developed economies. As in literature, the definitionof offshoring includes both international outsourcing, the firm purchases ofinputs from independent foreign suppliers, and inhouse-offshoring, the relo-cation abroad of parts of production process that give rise to good flows fromforeign affiliates. We are not able to distinguish between these two pheno-mena. Our offshoring indicator is constructed following the broad definition,thus it includes all material imports concerning the firm activity sector andother sectors and, in addition, it includes also the imports of finished goodfrom the same activity sector of the firm. These latter flows are part of thedelocalisation process and it is important to take them into account. Whenfirms decide to move some parts of their production process abroad they candecide also to delocalise the final stages of production. This phenomenonis not captured by the sectoral indicators constructed with IO Tables thatonly record intermediate flows. In opposite our indicator doesn’t include theservice imports, thus our analysis is limited to material offshoring.

We adopt the standard measure of offshoring intensity calculated as thetotal material imports on the firm total material intermediates.

In the Appendix we provide detailed information on the construction ofvariables and data sources.

4.2 Sector and firm-level evidence on offshoring andemployment

We start to give a preliminary evidence of offshoring and employment atsectoral level building on National IO Tables and National Accounts. Table1 displays the phenomena’s evolution by 2-digit sector. Although a clear-cut pattern is not immediately readable in the figures we can see that formost of the traditional activities the offshoring intensity increases. For moreadvanced sectors offshoring increases in some cases and is reduced in others.In general, however, in most cases the direction of employment growth isopposite with respect to the change of offshoring.

The reason usually advanced to explain international outsourcing is theexistence of a labour cost gap between countries for unskilled work. Any-way this cannot be the single motivation behind the growing flows of foreigninputs if we look at the data. Previous studies for different countries showthat offshoring from high-income countries represents the great part of fo-reign sourcing (Geishecker, 2006). For Italy, we can hypothesize that thesame consideration holds. In fact from our firm sample, we can distinguish

from the bottom and top 0.5 percent of distribution of some main ratio.

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Table 1: Sectoral Evolution of Offshoring and Employment

c Offshoring: Employment:c 2000 c Growth 2000-2004 c Growth 2000-2004

15 0.1 0.10 -17 0.2 0.11 -0.1618 0.2 0.10 -0.0819 0.2 -0.01 0.0320 0.2 0.05 0.0321 0.3 -0.14 0.0522 0.2 -0.08 -0.2024 0.4 0.08 0.0425 0.3 -0.05 -0.1126 0.1 -0.15 -0.0327 0.3 0.13 -0.1328 0.2 0.15 -0.1529 0.2 0.01 -0.0330 0.7 -0.19 0.1431 0.2 -0.16 0.0532 0.5 -0.13 0.0833 0.3 0.07 0.2834 0.3 -0.11 0.0735 0.3 -0.07 -0.1036 0.2 0.00 -0.01Source: Our elaborations from National IO Tables and Firm Economic Accounts.

between the total offshoring share to high and to low income countries12.Building on sectoral data, we can show the correlation between the total off-shoring indicator, constructed with the National Input-Output Tables, andthe import penetration from high and low income countries at 2-digit sec-tors. An high significant correlation of 0.77 exists between offshoring andimports from high income countries, while the correlation between offshoringand imports from low income countries is only 0.15.

This evidence would suggest that the great part of offshoring involveshigh income countries as trade partners. This fact is also confirmed when weconstruct from our firm level dataset the sectoral total inputs imported fromhigh and low income countries.

Table 2: Offshoring by origin

In every sector, with the exception of NACE 18 (Manufacture of wearingapparel, dressing and dyeing of fur) and NACE 19 (Manufacture of leatherand leather products), the amount of foreign materials from advanced coun-tries is higher than total inputs from low-wage ones (Table ??). Anywayit is likely that the role of foreign sourcing from less developed countries is

12The classification between developed and non developed countries has been performedby the Italian National Statistical Office.

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increased in few last years and will grow in the future following the liberalisa-tion strategies of these countries, their greater international involvement andtheir economic development. This growing involvement with less developedcountries is confirmed in our dataset (Table ??). In opposite the offshoringshare to industrialized economies results to be quite constant across sampletime. The disclosure that a large part of input imports are from high-incomecountries is in contrast with the models that usually theorize a home coun-try more developed than the foreign country where activities are offshored,and explain offshoring with the existence of a cost labour differential. Fromthe dataset we also find out that firm offshoring to low-wage countries andoffshoring to developed countries are poorly related, the correlation is lessthan 8%. This is an interesting fact that supports our empirical strategyto study the two offshoring intensity (to developed and non developed eco-nomies) separately because they are capturing two different process. Firmsthat offshore to a country group often don’t offshore to the other countriesand also the import shares are different. The dataset is composed of ?? off-shorer firms and ?? non offshorers. The average offshoring share to advancedcountries for offshorers is ??, while to low income countries is ??.

Table ?? shows also the descriptive statistics for all the other main va-riables used in the analysis and Table ?? displays the correlation ratios.

Table 3: Descriptive Statistics

5 Results

As said in Section 3 we start to show results for the estimation of the dyna-mic version of the labour demand function specified in equation 3. Becausethe lagged dependent variable (Lit−1) is correlated with the error term, anendogeneity problem rises. In order to solve this problem Arellano and Bond(1991) have proposed to use the General Moment Method applied to thedifferenced equation and to use lagged levels of the variables as instruments(GMM-DIFF). Anyway it has been proved that GMM-DIFF is less informa-tive and is characterized by weak instruments if there is a strong persistencein the analysed phenomenon, that is the autoregressive parameter is neara unit root, and if the cross-section variability dominates time variability.Arellano and Bover (1995) and Blundell and Bond (1998) deal with the ins-trument weakness and suggest to combine the differenced equation with the

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equation in levels in a system estimation13. Thanks to the availability of a5-year panel we can apply a GMM-SYS estimation to our dynamic model. Inorder to very the goodness of our estimates we report also the results for OLSand FE regressions. We know that in this framework FE drives to a down-ward biased estimation of the lagged dependent variable, while OLS drives toan upward bias, thus GMM results should lie in this range. GMM-SYS alsoallows us to deal with the problem of the endogeneity in our explanatoryvariables, especially offshoring (the variable of interest), and interpret ourresults as causal relationship. In all regressions we use one-step GMM withrobust standard errors. Following Blundell and Bond (1998) the second (anddeeper) lags of the variables in levels should be used as instruments in thedifferenced equation. Anyway in our case, the second order correlation testand the Hansen test of overidentifying restrictions reject the validity of lag-ged levels dated (t− 2). This is consistent with the presence of measurementerrors as also shown in Bond(2002). In opposite instruments dated (t − 3)(and (t− 4)) are not rejected and we will use these instruments in every ourestimation14. The Hansen tests support, in general, the validity of our esti-mations and we comment this test only when it detects some problems withinstruments. Finally we include in all regressions time and 3-digit industrydummies.

Table?? display the results for the labour demand and we can noticethat, for the main variables, they reply results in literature. The coefficientof the lagged dependent variable lies in the range between FE coefficientand OLS coefficient, especially it seems to well capture the high persistenceof the firm employment. Thus, we are trustful about the goodness of ourestimates. Output is positive and significant with an elasticity of ? whilewage is significant and negative. The capital stock is not significant in all thespecifications of the labour demand. Concerning the variable of interest, thetotal offshoring intensity presents a negative but not significant coefficient. Inopposite when we move to the specification broken by origin country groups, anegative and significant effect is detected for the firm material imports fromlow income countries, while a positive and no significant impact is shownfor the offshoring to developed countries. In the Appendix it is also shown

13In this case, the lagged levels of variables (second and deeper lags) are always usedto instrument the differenced equation, instead lagged differences of variables (first lag)become instruments for the level equation.

14We have collapsed the instruments, as in Calderon, Chong, and Loayza (2002), Beckand Levine (2004) and Carkovic and Levine (2005), because this allow us to improvethe validity of instruments and anyway preserve the information contained in originalvariables. For more details see Roodman (2009). In Tables we report both m1 and m2that are tests for first-order and second-order serial correlation. Anyway in our case weallow the existence of the second-order serial correlation because we have verified that . Inopposite we should test the third-order correlation because we are using the third lag (andfourth) of the dependent variables as instruments, but the short panel at our disposal,covering 5 years, prevents us to apply the third order correlation test that is defined onlyfor T ≥ 5, because it involves differenced residuals. In any case the Hansen test supportsthe validity of the instruments.

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the estimation of the ECM representation of the dynamic demand labourequation that allow an ease of reading. These results also hold when we usedthe lags of the intensity offshoring (total and split by origin country groups)instead of the current level.

Table 4: Results I - model 3

Offshoring to high-income countries has no effect on the employment le-vel, and this may be due to the fact that this type of input procurement isnot related to a relocation abroad of the labor intensive activities in order toexploit the labor cost differentials. While, consistently with the literature,input flows from low wages economies seem to substitute domestic employ-ment.

Now, we move to present results for the labour demand originating froma translog cost function as specified in 615. Table ?? concerns the cost shareequation that treat capital stock as a quasi-fixed factor, while Table ?? re-places the capital stock with its price (in a long-term view). The most impor-tant finding is that the effects detected for offshoring confirms what we havepresented for the labour demand. Total offshoring is negative, as before, butthis time is also significant. Splitting the intensity by country group we againobtain a negative and significant impact for inputs from low-income coun-tries and no significant effects for other countries. The price of other inputs(material and capital stocks) present a negative and significant coefficient.

Table 5: Results II - model 6

Table 6: Results III - model ??

15When we estimate the labour cost share we use relative factor prices, that is wages,materials and capital, expressed in terms of service price. Material, Service and Capitalprices are defined at sectoral level because we have not at our disposal firm level prices.

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6 Robustness checks

To check the robustness of our findings we have performed the followingcontrols.

• sub-sample estimates: we have estimated the labour demand equationsplitting the sample according to the technological level of sectors. Thisbreakdown follows Pavitt classification: traditional sectors are supplier-dominated sectors in the Pavitt classification, while non traditionalsectors include science-based, specialized-supplier and scale intensiveindustries. This distinction may display some important features ofthe offshoring process because we can suppose that the reasons for in-ternational production fragmentation are different between traditionaland high-tech sectors. Especially in non traditional sectors it is likelythat offshoring is driven by the high technology and quality of foreigninputs and the motivation of the input imports is not the exploitationof low labour costs of foreign countries. Table ?? compares results forthe two sector groups. We can see that the significant and negativeeffect of the offshoring to low-income countries is driven especially bythe traditional sectors. We don’t detect any significant effect of offsho-ring in non traditional sectors, even if these latter results for this sectorgroup have to considered with caution because the Hansen test reveals aproblem with the instrument validity. Our findings are consistent withresults shown at sectoral level by Falk and Wolfmayr (2005). It seemsthat most adjustment costs driven by offshoring concern non-high techindustries, where the labour cost saving reasons are more important.

Table 7: Robustness I - model 3 by activity

• introduction of sector and firm level controls: we test if our results holdwhen we control for different firm and sectoral level variables. Tables?? and ??, respectively display these robustness checks for the twodynamic versions of the models 3 and ??. We include the sectoral broadoffshoring indicator, the sectoral ICT capital stock, the sectoral skillratio (at 3-digit level), the firm export share and the firm intangiblecapital stock. Sectoral controls are treated as exogenous, while firmlevel controls are instrumented with their lags.

The sectoral ICT presents a positive sign in both Tables even if wecould expect that technological change and innovation drive to a labourreduction. In this case it is important to notice that ICT indicator ishighly aggregated and it is not available a firm level indicator, thus itis likely that this indicator simply distinguish between high-tech and

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Table 8: Robustness II - model 3- Sectoral and firm controls

Table 9: Robustness II - model ??- Sectoral and firm controls

low-tech sectors. The sectoral broad offshoring and the firm intangiblecapital stock result to be non significant. Results diverge between thetwo Tables for the sectoral skill ratio and the firm export share. Whilethe coefficient of the skill ratio is not significant in the first one andit becomes positive and significant in the second one. For the exportshare a negative impact is detected in both specifications even if it issignificant only for second one. Our main findings about the interestvariables still remain: the offshoring to high-income countries is notsignificant, while there is a negative and significant impact of importsfrom non developed economies.

7 Conclusion

In opposite to a large part of the literature dealing with the skill upgradingeffects of offshoring, in this paper we focus on the firm total level of employ-ment. First of all, we show for Italy that most of the intermediate importsare from industrialized countries and that the most dynamic part of importscomes from low income countries. As a consequence, the firm level insighton the origin of intermediate imports helps us to enrich the comprehensionof the labour market effects of international competition. In particular, withthe data at hand, we have been able to identify the effect of offshoring fromlow income countries on the firm labour demand. Although the evidence onthe skill unskilled labor ratio in Italian manufacturing is not as clear-cut asfor other industrial countries, our firm-level evidence show a negative impacton the overall employment. So regardless of the skill composition it seemsthat the international fragmentation of production has actually affected theequilibrium employment especially in the traditional sectors. Our resultsare robust to alternative specifications of the labour demand and to severalchecks. Further work should be devoted in identifying the labor effects ofoffshoring induced by the scale increase following offhsoring. In particularit would be important to highlight the role of offshoring decisions in downs-tream manufacturing customers. This research line is in our agenda.

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A Data and Variables

All firm level data are from the Istat database constructed merging tradestatistics and data of firm economic accounts (from administrative sources).Output and input variables are deflated with the relative 2-digit price indexretrieved by the Italian National Accounts. Firm capital stock is proxiedwith the tangible fixed assets deflated with the capital price index (alwaysretrieved from the Italian National Accounts). The firm unit wage has beendeflated both using the GDP deflator and a 3-digit producer price index(Istat). The user capital cost is constructed using EU-KLEMS data with thefollowing equation: pKjt = rjtp

Ij,t−1 + δjtp

Ij,t where j labels the sector, rjt is the

internal rate of return, δjt is the sectoral depreciation rate and pIj,t is the grossfixed capital formation price index. Sectoral broad offshoring is calculatedfrom the use matrix of National IO Tables at 2-digit NACE. Sectoral skillratio, that is the share of white collars on the sectoral total employment,comes from Firm Economic Accounts (Istat) and it is constructed at 3-digitNACE. The sectoral real ICT capital stock is constructed as the sum of soft-ware, office and communication real capital stock on the total employmentand it is constructed for ATECO sub-sections (a slightly higher aggregationthan 2-digit NACE); data are retrieved from Istat National Accounts. Realvariables assume 2000 as base year.

B ECM representation of the dynamic labourdemand

Table ?? shows results for the following ECM equation:

∆Lit = φLit−1 + µ∆Xit + ηXit−1 + εt (8)

where the vector X = w,K, Y,OFF .

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