In this study three possible determinants of the increased demand for skilled workers are tested using a panel of 412 Italian manufacturing firms over the period 1989-1997. The results suggest the statistical significance of the impact of organisational change, while they tend to exclude the roles of R&D spending and foreign direct investment.

Marco Vivarelli

Mariacristina Piva

English

Research Papers in Economics

The skill bias in Italy: a first report 
Mariacristina Piva Marco Vivarelli
UCSC UCSC
Abstract
In this study three possible determinants of the increased demand for skilled workers are
tested using a panel of 412 Italian manufacturing firms over the period 1989−1997. The
results suggest the statistical significance of the impact of organisational change, while they
tend to exclude the roles of RDspending and foreign direct investment.
Financial support from MURST is gratefully acknowledged.
Citation: Piva, Mariacristina and Marco Vivarelli, (2001) "The skill bias in Italy: a first report." Economics Bulletin, Vol. 15,
No. 2 pp. 1−8
Submitted: November 12, 2001.  Accepted: November 12, 2001.
URL: http://www.economicsbulletin.com/2001/volume15/EB−01O30002A.pdf1. Introduction 
 
The debate regarding the skill bias developed fully in the United States in the early '90s. 
At first, researchers tended to identify the spread of new technologies as the main cause of the 
increase in the ratio between skilled and unskilled workers. The hypothesis is that R&D 
expenditure is complementary to the skilled workers, while it tends to make the less skilled 
redundant. The skill biased technological change (SBTC) hypothesis has been supported by 
many empirical tests regarding the manufacturing sectors in the US and the UK (see Berman et 
al. 1994, Betts 1997, Autor et al. 1998, Machin and Van Reenen 1998, and Adams 1999). 
  In recent years - partly due to less unequivocal empirical results (see Doms et al. 1997 
and Mairesse et al. 2001) - two further possible explanations for the skill bias have been put 
forward. The first is based on the fact that trade flows and foreign direct investment (FDI) result 
in a relocating of production which is less intensive in terms of skilled labour to the less 
developed countries, and a specialisation by industrialised countries in sectors which are more 
intensive in terms of skilled labour (see Wood 1994, Feenstra and Hanson 1996, and Markusen 
and Venables 1996). The second tends to underline the importance of firm's organisational 
change (sometimes, but not always, linked to technological change), which results in a reduction 
in unskilled labour and an increase in skilled labour (see Aghion et al. 1999, Bresnahan et al. 
2001, and Caroli and Van Reenen 2001). 
  Our contribution to the understanding of the problem – based on data and methodology 
presented in Section 2 – is an attempt to answer the following question. In a technologically-
intermediate country such as Italy, is the hypothesis which identifies R&D as the main cause for 
skill upgrading still valid? In Section 3 we shall discuss the results of the empirical analysis 
carried out, and in the final section we shall attempt to provide some preliminary conclusions. 
 
2.  Data and methodology 
 
The data used come from the investment bank Mediocredito Centrale (MCC) and are 
based on the results of three questionnaire waves (1989-91; 1992-94; 1995-97) carried out over a 
representative sample of Italian manufacturing firms; the panel comprises 412 firms, which 
represent approx. 160,000 employees in 1997. 
Following Berman et al. (1994) and Machin and Van Reenen (1998), the method used is 
based on the estimation of a restricted function of total variable cost which is given only by the 
cost of labour (the only variable factors of production are the two categories of workers) while 
capital and technology are assumed to be quasi fixed factors for firm i: 
 
( ) i ij i i i D & R , w , K , Y f LC =                            (1) 
where: LC = labour cost; f  = translog functional form; Y = output; K = capital; wj= wage for the 
jth category of workers (in our case j = 2: WC = white collars, i.e. non manuals and BC = blue 
collars, i.e. manuals); R&D = research and development. 
  Taken all the variables in logarithms, we make use of the minimisation of costs and the 
implementation of Shephard's lemma and at time t we obtain: 
) D & R ln( ) w / w ln( ) K ln( ) Y ln(
w ln
LC ln
s i BC , i WC , i i i i
WC , i
i
i δ γ β α ϕ + + + + =
∂
∂
=               (2) 
  1where si represents the share of labour cost of white collars. 
 
  To eliminate the ϕi, fixed effects, we proceed to differencing (∆) with a random error 
term (u) and the following stochastic form is obtained: 
 
i i BC , i WC , i i i i u ) D & R ln( ) w / w ln( ) K ln( ) Y ln( s + + + + = ∆ δ ∆ γ ∆ β ∆ α ∆                  (3) 
  In this specification, the relative wage variable entails a risk of endogeneity - due to its 
collinearity with the dependent variable - and  it is generally eliminated or instrumented (see 
Machin and Van Reenen 1998). However, the specification proposed in (3) can be translated in 
terms of employment and in this case the dependent variable can be measured either as the ratio 
of white collars (WC) to total workers or the ratio of white collars (WC) to blue collars (BC) 
(Hamermesh 1993, and Berman et al. 1994). One of the advantages of this alternative 
specification is that it is not necessary to instrument the wage variable. 
  Starting from the general specification (3) with WC/BC as dependent variable, we used a 
Seemingly Unrelated Regression (SUR) method testing jointly two equations: one for the white 
collars and one for the blue collars. This method is based on the assumption that the right-hand 
part of the equation is independent of the error term, that the errors are crossed and that the 
method therefore guarantees greater efficiency compared with an OLS estimation of the single 
equations (see Betts 1997 and Adams 1999). Both the equations are expressed in long 
differences, in order to avoid any further problem of endogeneity (a possible problem of 
endogeneity is based on the consideration that only the firms which already have skilled workers 
see the potential for innovative investment). The SUR estimate is thus based on the following 
system (4): 
 
 



+ + + + + + + =
+ + + + + + + =
− − −
− − −
it 1 t , i 1 t , i 1 t , i it BC it it it
it 1 t , i 1 t , i 1 t , i it WC it it it
v ORG FDI D & R ) w ln( ) K ln( ) Y ln( C BC ln
u ORG FDI D & R ) w ln( ) K ln( ) Y ln( C WC ln
ϑ λ δ ∆ γ ∆ β ∆ α ∆
ϑ λ δ ∆ γ ∆ β ∆ α ∆
                              ( 4 )  
where: C = constant; t = 1991-97 interval; t-1= 1989-91 interval; ∆ represents the long difference 
(1991-97) for the dependent variables WC (white collar employees) and BC (blue collar 
employees), for turnover Y, for capital K and for wages w, while R&D, FDI and ORG represent 
respectively the dummies of technological innovation, foreign direct investment and 
reorganisation during the period 1989-91
1. 
  Once the adopted specification has been defined, we can now return to the empirical 
definition of the variables used. WC and BC are the number of non manual and manual 
employees respectively. All the variables in values have been deflated and expressed according 
to 1990 prices. Capital, K, has been derived from the balance sheet item "net technical assets", 
                                                 
1 The choice of 1991 as the first year of the second period is due to increased amount and reliability of data 
compared with corresponding data for 1992; in fact, the first survey was carried out in 1991, while 1992 is 
retrospective in the second survey - carried out in 1994 - and thus there is more data missing and the available data 
are less reliable (however, the presence of a year's overlap in long differences estimates is fairly common, see Caroli 
and Van Reenen 2001). 
  2
Note that the lack of quantitative data for the main determinants R&D, FDI and ORG preclude the possibility to 
carry out different kinds of panel analysis such as GMM estimates. while production, Y, has been quantified in terms of turnover. The average annual wages for WC 
and BC workers, not being provided by the MCC database, have been proxied by using the INPS 
(Italian National Institute for Social Security) database
2. The three possible determinants of the 
skill bias are represented in the estimates by three dummies which indicate the presence or 
absence – during the first three years of the examined period – of R&D expenditure, firm's 
organisational changes (ORG), and FDI. 
 
3. Results 
 
In all the proposed estimates three sector fixed effects have been included (sectors with 
high, medium and low technology, according to OECD 1998), as have 5 size fixed effects (11-
20; 21-50; 51-250; 251-500; >500 employees) and 2 fixed effects related to the declared 
takeovers or breakups (TB) in the period 1991-97
3. 
Table 1 gives the results of three SUR estimates in which first all the three possible 
determinants of skill bias are given, and then the less-statistically significant ones are dropped. 
The third column of each estimate shows the tests for significance of the difference in the two 
estimated coefficients, while the last row gives the result of the independence test for the two 
equations which make up the system (in all cases the test rejects the independence hypothesis, 
confirming the benefits of using the SUR method). 
Not surprisingly, turnover, capital and wages have the expected signs and are statistically 
significant (note the greater disincentive effect of wage increases on the labour demand for 
skilled workers). However, what is more interesting for our aims is the confirmation of the 
fundamental role of the organisational variable, while the supposed roles of R&D and FDI in 
determining the skill bias do not seem to be confirmed. 
  More precisely, the ORG variable has a positive (but not significant) impact on the use of 
skilled labour, and a higher, very significant negative impact on the use of unskilled labour. As 
further proof of the robust nature of this result, the test of the difference of the two coefficients is 
statistically significant in all the three cases. 
  If with reference to the R&D variable no indication of its possible role in determining 
skill bias emerges, the case is different for the FDI variable. The relative coefficient for this 
latter determinant is positive (and barely significant) in the equations regarding white collar 
                                                 
2 The calculation of wages was rather complex, because it was impossible to find a common firm-identifying code 
in the two databases. The INPS database was used to single out two categories of workers (WC and BC),  the 
average wage for each category and the wage dispersion index; this index has been applied to the MCC firms in the 
same time/size/area/sector cells.  
  3
3 Takeovers and breakups may imply obvious discontinuities in employment trends; in order to get rid of unreliable 
data, the following procedure has been applied. Firms subject to breakup and characterised by the following 
employment trend:  . and firms subject to takeover and characterised by the following 
trend: 
Dev . Std
1989 T
occu
occu
T
1989 t
t
T −
−
<
∑
=
. Dev . Std
1989 T
occu
occu
T
1989 t
t
T +
−
>
∑
=  were dropped from the original MCC database; occu indicates employment, 
while T indicates the final year of the three years during which the takeover or breakup was declared. workers and negative (but not significant) in the equations regarding blue collar workers. This 
result– though of limited statistical significance – encourages further research in this area. 
 
4. Conclusions 
 
From the evidence gathered, the supposed link between R&D expenditure and the skill 
bias does not seem to be confirmed. This result does not mean that Italian manufacturing is 
immune to the SBTC; however, this probably does not take place mainly through internal 
channels – i.e. in-house innovation resulting from R&D expenditure – but through organisational 
changes connected with the purchase and use of new technology. It is interesting to note how 
this link is based not so much on the complementary nature of reorganisation and skilled labour, 
but rather on savings in unskilled labour obtained through firm's reorganisation. 
To summarise, an interpretative hypothesis could be proposed that distinguishes between 
the leader countries committed to internal innovation (especially product innovation) with 
intensive use of skilled labour, and the technologically-intermediate countries more committed to 
embodied technical change (especially process innovation) linked with organisational changes 
aimed at saving on unskilled labour. 
  4References 
 
Adams, J. (1999) "The Structure of Firm R&D, the Factor intensity of production and skill bias" 
Review of Economics and Statistics 81, 499-510. 
 
Aghion, P., E. Caroli, and C. García-Peñalosa (1999) "Inequality and Economic Growth: the 
Perspective of the New Growth Theories" Journal of Economic Literature 37, 1615-1660. 
 
Autor, D., L. Katz, and A. Krueger (1998) "Computing Inequality: Have Computers Changed the 
Labor Market?" Quarterly Journal of Economics 113, 1169-1214. 
 
Berman, E., J. Bound, and Z. Griliches (1994) "Changes in the Demand for Skilled Labor Within 
U.S. Manufacturing Industries: Evidence from the Annual Survey of Manufacturing" Quarterly 
Journal of Economics 109, 367-397. 
 
Betts, J. (1997) "The Skill Bias of Technological Change in Canadian Manufacturing Industries" 
Review of Economics and Statistics 79, 146-150. 
 
Bresnahan, T.F., E. Brynjolfsson, and L.M. Hitt (2001) "Information Technology, Workplace 
Organization and the Demand for Skilled Labor: Firm-level Evidence" Quarterly Journal of 
Economics, forthcoming. 
 
Caroli, E., and J. Van Reenen (2001) "Skill biased Organizational Change? Evidence from a 
Panel of British and French Establishments" Quarterly Journal of Economics, forthcoming. 
 
Doms, M., T. Dunne, and K. Troske (1997) "Workers, Wages and Technology" Quarterly 
Journal of Economics 112, 253-290. 
 
Feenstra, R., and G. Hanson (1996) "Globalization, Outsourcing, and Wage Inequality" 
American Economic Review Paper and Proceedings 86, 240-245. 
 
Hamermesh, D. (1993) Labor Demand, Princeton University Press: Princeton. 
 
Machin, S., and J. Van Reenen (1998) "Technology and Changes in the Skill Structure: Evidence 
from Seven OECD Countries" Quarterly Journal of Economics 113, 1215-1244. 
 
Mairesse, J., N. Greenan, and A. Topiol-Bensaïd (2001) "Information Technology and Research 
and Developments Impacts on Productivity and Skills: Looking for Correlations on French Firm 
Level Data" NBER working paper 8075. 
 
Markusen, J.R., and A.J. Venables (1996) "Multinational Production, Skilled Labor, and Real 
Wages" NBER working paper 5483. 
 
 
  5OECD (1998) "OECD Data on Skills: Employment by Industry and Occupation" STI working 
paper 1998/4. 
 
Wood, A. (1994) North-south Trade, Employment, and Inequality: Changing Fortunes in a Skill-
driven World, Clarendon Press: Oxford. 
 
  6Table 1: SUR estimates of the variation in the two employment components (WC and BC) between 1997 and 1991 
       (1)      (2)       χ2(1)     (3)     (4)        χ2(1)     (5)     (6)            χ2(1) 
              dlWC    dlBC    coef. dif.  dlWC    dlBC     coef. dif.   dlWC   dlBC          coef. dif.    
                   
Constant  -0.17   0.20    -0.11   0.21    -0.09   0.21 
  (1.64)   (2.18)**   (1.19)   (2.21)**   (1.13)   (2.20)** 
Turnover  0.40    0.42       0.04  0.40    0.42       0.03  0.40    0.42    0.04 
    (6.32)*** (6.66)***   (6.29)*** (6.67)***     (6.31)***  (6.67)*** 
Capital   0.08    0.11       0.14  0.08    0.11       0.15  0.08    0.11    0.13 
  (2.13)**  (2.74)***   (2.14)**  (2.74)***   (2.16)**  (2.74)*** 
Wages   -0.67    -0.31       7.46***  -0.67    -0.31       7.52***  -0.69    -0.31    8.41*** 
    (8.69)*** (3.89)***   (8.71)*** (3.89)***   (8.94)*** (3.88)*** 
R&D    -0.03    -0.002      0.13     
  (0.64)   (0.03)      
FDI    0.14    -0.03       1.76  0.136    -0.03       1.67             
  (1.84)*   (0.42)    (1.78)*   (0.43)    
ORG    0.05    -0.12      4.55**  0.05    -0.12        4.44**  0.06    -0.12    4.97** 
  (1.10)   (2.52)**   (1.03)   (2.54)**   (1.19)   (2.59)***    
 
Fixed effects: 
TB    (test  F)    5.63*   0.38    5.31*   0.39    5.23*   0.39   
Sector  (test  F)    0.36   0.71    0.69   0.71    0.82   0.67 
  Size  (test  F)  13.38***  8.00**    14.84***  8.03**    13.14***  8.29**
 
R2   0.28   0.18    0.28   0.18    0.27   0.18 
Observations  412   412    412   412    412   412 
Breusch-Pagan’s 
independence test             χ2(1) = 81.6***                            χ2(1) = 81.2***                χ2(1) = 81.3*** 
 
Notes: 1) Absolute t-statistics in brackets; 2)* significant at 10%; ** significant at 5%; *** significant at 1%; 3) R&D, FDI and ORG refer to the three-year 
period 1989-91; WC, BC, turnover, capital and wages are all expressed as difference of the logarithm (1997-91) 4) With reference to the fixed effects (TB = 
takeovers and breakups; Sector = 3 technological categories; Size = 5 size categories) the result of test F is given, regarding the joint significance of the relative 
dummies. 
  7

The skill bias in Italy: a first report

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The skill bias in Italy: a first report

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