the key factors behind such a bproductivity gapQ has long been recognized in the lack ofability of the Italian labour force to adapt to the everchanging needs of the global market.

Labour Economics 12 (2005) 557576

www.elsevier.com/locate/econbase1. Introduction

Between 1995 and 2002, the annual growth rate of hourly labour productivity in

manufacturing has been 4.5% in the US, 4.6% in France, 2.4% in Germany and only 0.9%

in Italy (OECD, 2002). The Governor of the Bank of Italy, Antonio Fazio, in his bFinalConsiderationsQ of this years Report to the annual General Meeting, has urged immediatepolicy responses to stop the loss of competitiveness suffered by the Italian system. One ofTraining, productivity and wages in Italy

Gabriella Conti*

Department of Economics and Institute for Social and Economic Research, University of Essex,

Wivenhoe Park, Colchester CO43SQ, United Kingdom

Abstract

This paper presents for the first time panel evidence on the productivity and wage effects of

training in Italy. It is based on an original dataset which has been created aggregating individual-level

data on training with firm-level data on productivity and wages into an industry panel covering all

sectors of the Italian economy for the years 19961999. I use several modelling specifications and a

variety of panel data techniques to argue that training significantly boosts productivity. However, no

such effect is uncovered for wages. This seems to suggest that firms do actually reap more of the

returns.

D 2005 Elsevier B.V. All rights reserved.

JEL classification: C23; J24; J31

Keywords: Training; Productivity; Wages0927-5371/$ -

doi:10.1016/j.

* Tel.: +44 1

E-mail add206 874875.see front matter D 2005 Elsevier B.V. All rights reserved.

labeco.2005.05.007

ress: gconti@essex.ac.uk.

However, notwithstanding the complete redesign of the training system carried out in the

recent years, Italys performance appears highly unsatisfactory also on this ground. The

available evidence clearly shows that Italy has one of the lowest values of training

incidence in the European Union, together with other Mediterranean countries, such as

Portugal and Greece (Brunello, 2002).

These facts appear rather puzzling if analysed in the light of rigorous and sound

economic theory. The recent approach to training in imperfect labour markets (Stevens,

1994a,b,c and 1996; Acemoglu and Pischke, 1998 and 1999a,b) points to some forms of

labour market imperfections as driving a wedge between increases in wages and increases

in productivity, allowing the firms to recoup some of the costs of training, and fostering

their incentives to invest in it. This is consistent with some empirical findings which show

a lower level of on-the-job training in the US compared to Germany and Japan (Lynch,

1994). However, this is not true for Italy. Italy is on the top of ranking of regulated labour

markets, with a strong role of unions in wage bargaining, and high hiring and firing costs.

However, in sharp contrasts with the predictions of the theory, Italy is trapped in a low-

training equilibrium.

The research presented in this paper is an attempt to shed some light on this puzzle,

testing for training effects on labour productivity and wages. Many studies have tried to

establish this link in an international context. However, no such work has been done for

Italy. Moreover, the available literature has achieved controversial results, which seem

to depend strongly on the training measure used, the modelling specifications and the

estimation techniques adopted, and the controls included in the empirical model.

Overall, the majority of the studies have found a positive impact of training on

productivity, although often not significant. In addition, some forms of training (general

training and off-the-job training) seem to have a greater effect. This study takes stock of

the available knowledge, and tries to overcome the limitations of the previous studies in

several ways.

First of all, due to the lack of longitudinal data, many studies have failed to control

for unobserved heterogeneity (Black and Lynch, 1995 and 1996), and potential

endogeneity of training (Bartel, 1994; Bishop, 1994; Barrett and OConnell, 2001);

longitudinal data with repeated training information have become available only recently

(Black and Lynch, 2001; Dearden et al., 2000; Ballot et al., 2001; Zwick, 2002). This

paper deals with both the issues of unobserved heterogeneity and endogeneity of

training, by using a variety of panel data techniques on an original dataset which

contains longitudinal information on training and measures of corporate productivity

covering all sectors of the Italian economy for the years 19961999. Coherently with the

previous literature, I show that failing to take into account these issues leads to severe

biases in the estimates.

Secondly, most of the available studies have used a flow measure of training, due to

the lack of an appropriate measure of the stock of human capital. However, measuring

training participation only over a relatively short period of time fails to take into account

the role played by skills accumulated during the working life. The richness of the

database allows me to overcome also this limitation. So, I construct a stock measure of

G. Conti / Labour Economics 12 (2005) 557576558training, using a question which has been consistently asked over time in the Italian

Labour Force Survey.

used for the empirical estimation. A simple empirical framework to analyze the impact oftraining on productivity and wages is specified in Section 3. Section 4 is devoted to the

presentation and the discussion of the results. A section of concluding remarks and

directions for future research closes the paper.

2. The data

The empirical analysis is based on a original panel which has been created merging two

different complementary datasets. In doing so, I have followed the methodology adopted

by Dearden et al. (2000). I have used the 19961999 waves of the Italian Labour Force

Survey (April quarter), assembled with accounting data on firms for the corresponding

years, drawn from the AIDA Database. The reason for this choice relies on the fact that no

one Italian dataset contains the information on training and measures of corporate

performance required for this kind of analysis.

The first database used is the Italian Labour Force Survey. This is a household-level

survey carried out with a detailed questionnaire every quarter (in the months of January,

April, July and October) since 1959. Around 75,000 households are interviewed every

three months, for a total of approximately 200,000 individuals. From the LFS I gather

information on training, personal characteristics of the individuals (sex, age, region of

residence), measures of their skills (educational qualification and occupation), job

characteristics (hours of work) and workplace characteristics (industry sector). The

sample includes all men and women aged between 15 and 64 inclusive who were

employed at the time of the survey (including the self-employed).Thirdly, many studies have used very parsimonious specifications both in terms of the

modelling strategy adopted and in terms of the controls for firms and workers

characteristics included in the empirical model. In this paper, I use several different

specifications of the baseline model (an augmented Cobb-Douglas production function), in

order to relax the assumption of constant returns to scale and to evaluate the effect of

training on the growth of labour productivity and wages. In addition, the richness of the

database allows me to control for a plethora of firms and workers characteristics,

including another important intangible investment of the firm, namely R&D, and other

measures of workers skills, such as education. I show that the results are sensitive to the

modelling specification adopted, and that the inclusion of several controls significantly

reduces the magnitude and the level of significance of the estimated returns.

Finally, following Dearden et al. (2000), the production function estimates are explicitly

compared with the wage equations. This is important to examine how the benefits from

training are shared between the firms and the workers. The recent models of training in

imperfect labour markets predict that the benefits from training do not fully accrue to the

workers. I show that firms do indeed reap most of the returns: the main finding I obtain is

that training significantly boosts productivity, while no significant effect is uncovered for

wages. This result is robust to alternative specifications.

The outline of the paper is as follows. In Section 2 I describe the data and the variables

G. Conti / Labour Economics 12 (2005) 557576 559Two main training questions are asked in the LFS. The first refers to courses undertaken

by the individual in the month before the interview, and has changed slightly in 1998;

however, it was possible to build up a consistent series because the basic structure of the

question was unchanged. The question in 19961997 was: bDuring the month before theinterview, have you attended any of the following courses?Q1; then, in 19981999 it wasrephrased as: bDuring the month before the reference week, have you attended any of thefollowing courses or have you taken part in any of the following on-the-job training

activities?Q.2 The main drawback with this training measure is that it is a flow variable. Inorder to derive a measure of the workers stock of post-schooling human capital, I have

used the answer to the second major question available in the LFS, which refers to training

received by the individual during his whole life. In 19961997 the question was: bWhat isthe highest level of vocational training achieved during your life?Q; in 19981999 it wasrephrased as: bDuring your life have you concluded a vocational training course or haveyou taken part in an on-the job training activity?Q. I have used this question, combinedwith the previous information on current training, to calculate the stock of post-schooling

human capital in each year.3

Most of the literature uses the current level of training to measure the stock of human

G. Conti / Labour Economics 12 (2005) 557576560capital in the firm. However, this procedure is correct only if one assumes fully

depreciation of the skills after one period. Moreover, it is not consistent with the

underlying theory.4 Henceforth, in this paper I have followed a different procedure.

Following Boon and van der Eijken (1998), I have constructed the stock of human capital

as the sum of the proportion of workers trained at time t in industry i (the flow) and the

stock of the previous year,5 taking into account depreciation, according to the Perpetual

Inventory Method:

TRAINit FLOWit 1 d STOCKi;t1 1

where y measures the depreciation rate of the human capital. Since an exact value fory is essentially unknown, I have experimented with various rates of depreciation. Theresults were not sensitive to different values, within a plausible range 5%35%, so I

1 The information collected in this question refers exclusively to courses which are connected with the current

job, or relevant for a job that the interviewed might be able to do in the future.2 In the 19961997 questionnaire, a list of 10 courses was available; in 1998, this number was increased to 14

courses, and in 1999 two more options were added. These additions have been necessary in order to take into

account the greater range of options available, following the introduction of the reform in the training system after

the Treu Law 175/98.3 Along with the type of courses attended, the LFS also contains additional information related to training

undertaken in the month preceding the interview, such as the scope of the course, its overall duration, the number

of weekly hours of training (only available for the years 19961997). However, the focus on the stock of

accumulated post-schooling human capital, combined with the great amount of non-response, hampered the use

of these additional questions.4 Some papers calculate the stock of training by cumulating past flows: however, this methodology suffers from

the weakness of not having suitable initial values.5 The availability of LFS data for 1995 (April quarter) allowed me to obtain a measure of the stock of human

capital with reliable starting values also for 1996. Moreover, I have used the answer to the question at t1 inorder to avoid double-counting: although the question refers to the training already concluded, and not stillongoing, there is a small chance that the worker would have completed the course in the beginning of the

preceding month, hence referring to the same episode in both replies.

have chosen a value of y=0.15, taking the estimates derived in Groot (1998) as abenchmark.6 Moreover, I have controlled for turnover in all the estimated specifications, in

order to take into account the loss in human capital arising from separations. It is crucial to

account for reallocation of workers across industries, since the stock measure is derived

from information on past courses, but the exact reference period is not known. Henceforth,

it could be well the case that the training course was attended while the worker was

G. Conti / Labour Economics 12 (2005) 557576 561employed in a different sector. Since I dont follow the same workers over time, I cannot

derive a proper measure of turnover at individual level; however, including controls for

inflow and outflow rates across different sectors will control for much of this problem.

Moreover, each year at least 40% of the workers interviewed reported that they had

completed a formal course in a school with duration of at least one year and release of final

certificate: henceforth, it is reasonable to believe that the training undertaken mostly

provides portable skills, which are likely to have a long-lasting impact on productivity.

Finally, given that we are most likely to see workers moving across sectors in case of wage

and productivity gains, we should see the effect of the initial training even if this training is

not directly relevant in the current job.

The second source used in this paper is AIDA (Analisi Informatizzata delle Aziende).

This is a private database,7 which provides accounting information from the balance sheets

of all Italian companies with an annual turnover higher than one million Euros.8 The

original sample contained 189,059 firms, covering a ten-year period from 1993 to 2002.

However, an accurate work of data cleaning has reduced the sample size. In the first place,

observations for years before 1996 have been excluded, due to severe reduction in the

number of firms with reported information compared to the following years: preserving

those observations would have severely affected the representativeness of the sample, due

to the fact that only information for a smaller subset of firms is available for the early

years. Secondly, the years subsequent to 1999 have been excluded, due to the impossibility

of deriving a consistent series for the training variable in the Labour Force Survey. Then,

40,141 firms (332,936 observations) have been excluded due to incomplete balance sheets,

and further 16,543 firms (45,673 observations) for lack of consistency between specific

budget items. Henceforth, the final sample is an unbalanced panel of 132,039 firms,

covering all sectors of the Italian economy for the years 19961999. From the AIDA

database I have derived information on value added, wages, capital stocks, R&D

expenditure and employment. Real values have been obtained by deflating the nominal

measures with two digit producer price indices for the different years provided by ISTAT

(the Central Statistics Institute).

The data drawn from the two datasets have then been aggregated into proportions

(for the variables training, male, age, education and occupation, taken from the LFS)

6 Groot (1998) develops a model to estimate the rate of depreciation of human capital. He estimates the model

on data for Great Britain and the Netherlands, and finds that the rate of depreciation of education is 1117% per

year. Since there is no reason to believe that economic and technological changes happen at a faster rate in Italy

than in UK or the Netherlands, I have used an average value of 15% in my estimations.7 It is provided by Bureau van Dijk.8 It is important to note that the absence of any dimensional limit constitutes one of the main strenghts of thedatabase used, given the structural composition of the Italian industry, mainly formed of small and medium

enterprises.

Table 1

Training incidence by sector

Rank ATECO2002 Description Flow(%) Stock(%)

1 11 Education, Health and related Social Services 10.74 43.38

2 8 Finance, Banking and Real Estate 8.12 37.29

3 2 Energy, Mining and Quarrying 5.96 35.46

4 9 Business Services and other Professional Activities 5.79 32.39

5 12 Community, Social and Personal Services 4.44 27.26

6 7 Transports and Communication 3.72 23.49

G. Conti / Labour Economics 12 (2005) 557576562and averages (for the variables value added, wages, capital stock, R&D expenditure,

hours worked and employees) at industry9 level, and then merged. The rationale behind

this choice relies on the different level of aggregation available in the two datasets: while

the AIDA database contains data disaggregated at the firm level (5digit ATECO2002),

the Labour Force Survey only provides divisional information at a higher level of

aggregation (12 sectors). Aggregating the data also at regional level has, henceforth,

two advantages: on the one side, it increases the level of disaggregation in a

geographical dimension, on the other, it allows to take into account the high

productivity differentials and the marked disparities in industry agglomeration and

labour market outcomes existing in the Italian regions. As argued in Dearden et al.

(2000) aggregation allows to capture the within-industry spillovers that would be left

out in case of a firm- or individual-level analysis,10 although the advantages arising

from this methodology have to be weighed against the possible problems due to

aggregation bias (see Grunfeld and Griliches, 1960).

Ideally, the aggregation process would have left me with data on 228 industries over 4

7 3 Manufacturing 2.65 22.15

8 5 Wholesale and Retail Trade 2.09 17.96

9 6 Hotels and Restaurants 1.98 19.77

10 4 Construction 1.79 17.97

11 1 Agriculture 1.54 11.91years, for a total of 912 data points. After cleaning the AIDA database, I was left with 228

industries, for a total of 866 observations. However, I was worried about the quality of the

data in some cells with a very low number of firms. The majority of these industries were

operating in the public sector, and a closer inspection of the data revealed that the series

were quite unreliable,11 so I decided, somewhat reluctantly, to drop the cells containing

less than 20 firms, otherwise the measurement error in the micro data would have been

9 Here industry is defined as a cluster of firms located in the same region and operating in the same sector of

activity. The sectors are coded according to the ATECO2002 classification, using a 2digit level of disaggregation

into 12 sectos. The number of regions amounts to 19, due to the lack of information on one of the smaller regions

in the North of Italy (Val dAosta) in the Labour Force Survey. Dropping it corresponded to a loss of only 318

observations in the AIDA database; moreover, further 18 observations were excluded as consisting of firms

located outside Italy.10 The new growth theory (see Aghion and Howitt, 1998) has stressed the role played by human capital

externalities in fostering long-term economic performance.11 Measuring productivity and wages in the public sector is a well-known difficult problem.

Table 2

Summary statistics (pooled sample)

Variable Mean Std. Dev. Min. Max.

Proportions

Stock of training 0.243 0.115 0.028 0.680

Flow of training 0.039 0.036 0 0.214

G. Conti / Labour Economics 12 (2005) 557576 563Male employees 0.665 0.181 0.251 1

1524 0.091 0.044 0.005 0.267

2534 0.280 0.068 0.054 0.567

3544 0.290 0.051 0.156 0.517

4554 0.234 0.057 0.097 0.500

5564 0.114 0.058 0 0.468

Degree/postdegree 0.096 0.117 0 0.468

Upper-secondary 0.284 0.135 0.041 0.711

Vocational 0.072 0.046 0 0.285

Compulsory education 0.548 0.220 0.089 0.941

Levels

Log real value added per employee 10.701 0.367 9.152 14.219exacerbated and worsened attenuation bias.12 The final sample consists of 176 industry

groupings observed over a maximum period of 4 years, for a total of 633 data points used

in the empirical estimates.

The basic characteristics of the matched sample are described in the following two

tables. Table 1 illustrates the incidence of training across sectors,13 ranking each of them

both by its propensity to train,14 and by the stock of accumulated post-schooling human

capital of their workforce.15 It can be readily seen that high-training industries are those

providing services of different nature; this seems pretty obvious, given that this kind of

businesses crucially rely on the role of human resources. The high ranking of Finance,

Banking and Real Estate also comes at no surprise, given the intensive use of computers

12 These accounted for 26.9% of the original sample, but only for 16.9% of the total employment.13 The absence of sector 10, Public Administration, Defence and Social Insurance, is a consequence of the

sample selection procedure outlined above.14 Here I refer to propensity to train as the proportion of workers who have attended a course in the four weeks

preceding the interview, according to the question asked in the LFS.15 The second variable is the derived measure of training, obtained following the procedure outlined above.

Log real wage per employee 9.894 0.272 8.527 13.742

Log capital-labour ratio 10.823 0.712 8.435 14.208

Log R&D expenditure per employee 5.063 1.447 0 9.054

Average hours worked 40.5 3.5 29.4 48.9

Average firm size 138.9 626.1 7.6 8137.9

Growth rates

Labour productivity 0.028 0.177 1.474 1.218Wages 0.033 0.153 1.495 1.249Inflow rate 0.061 0.163 0 1.351

Outflow rate 0.109 0.203 0 1.638

Table 3

Summary statistics (pooled sample)

Variable High training Low training

Proportions

Stock of training 0.331 0.155

Flow of training 0.061 0.016

G. Conti / Labour Economics 12 (2005) 557576564Male employees 0.609 0.721

Age 1524 0.083 0.098

Age 2534 0.289 0.271

Age 3544 0.306 0.274

Age 4554 0.236 0.233

Age 5564 0.085 0.124

Degree/postdegree 0.155 0.036

Upper-secondary 0.347 0.222

Vocational 0.084 0.060

Compulsory education 0.413 0.682

Levels

Log real value added per employee 10.771 10.632and IT equipments in these sectors. By the same token, also the high training incidence

observed in the Energy, Mining and Quarrying sector is to be expected, since these

industries use specialized equipment and require stringent safety measures. Finally, the

low ranking of industries in Transport and Communication and in Manufacturing sectors

are not surprising, if we take into account the peculiar industrial structure in Italy, mainly

characterized by small and medium enterprises (SMEs), specialized in products with a low

technological content (such as clothing, furnishing and electrical appliances), and

employing low-skilled labour. The skill content of the workforce also holds as an

explanation for the low ranking of firms in the Trade, Tourism, Construction and

Agricultural Sector.16

Log real wage per employee 9.949 9.838

Log capital-labour ratio 10.809 10.836

Log R&D expenditure per employee 4.976 5.150

Average hours worked 38.71 42.37

Average firm size 226.74 49.33

Growth rates

Labour productivity 0.043 0.012

Wages 0.042 0.024

Inflow rate 0.068 0.053

Outflow rate 0.099 0.121

16 Note that the ranking of the sectors does not change according to whether we consider the flow or the stock

measure of training. However, in case of the Trade sector, only 19.81% of the workforce has already attended a

training course during the lifetime, while the proportion is 21.68% for the Tourism sector. The differences

between the two measures, however, can be easily reconciled if one thinks that usually those employed in hotels

and restaurant are specifically trained for this job at the beginning of the holiday season, so it is understandable to

find a lower proportion of these workers being trained in March, which is the month the question in the LFS refers

to.

Summary statistics for the variables used in this paper are provided in Table 2. It is

worth noting that, after having accounted for depreciation, there is quite enough

variation in the stock of training of the workforce across the industries, ranging from a

minimum of 2.8% to a maximum of 68%. On the contrary, 2917 industries report 0

training propensity: the lack of sufficient variation in this measure hinders the possibility

of using it to identify the effect of training. The table also shows that the sample consists

mainly of middle-aged male employees working on average 40.5 hours a week in a

medium-sized firm,18 among whom more than a half has attained only a compulsory level

of education.

Finally, in Table 3 I split the sample into dhigh-trainingT and dlow-trainingTindustries, according to the stock of human capital embodied in their workforce.19 High

training industries are mainly composed by larger firms, who employ more middle-aged

female workers with a higher level of education, who work fewer hours, are more

productive and get paid higher wages as expected. Moreover, they also experience a higher

it is assumed that the production function for the economy is represented by a standard

Cobb-Douglas:

G. Conti / Labour Economics 12 (2005) 557576 565Q ALaKb 2where Q is value added,23 L is effective labour, K is capital, and A is a Hicks-neutral

technology parameter. Following Dearden et al. (2000), under the assumption that

17 This amounts to 16.5% of the sample.18 It is worth stressing that the dimensionality of the firm in the database is representative of the Italian economy,

mainly formed by SMEs.19 Here the criterion is the median training stock, which amounts to 0.217. However, very similar numbers and

the same relative proportions are obtained if the sample is split according to the median training intensity.20 It is also worth noting that productivity and wages are closely linked in high-training industries, whereas

workers in low-training industries experience greater increases in wages.21 These corresponds to the mean inter-industry inflow and outflow rates, and have been calculated as the

absolute change in industry-level employment between t and t1 divided by the average employment in the twoperiods. They provide a measure of workers reallocation across sectors. In order to derive them, data on

employment in 1995 have been used; given the relevant amount of missing values for those years, inflow and

outflow dummies have been included in all the estimations in order to preserve the sample size.22 Since some industries in the sample do not invest in R&D at all, in order not to furtherly reduce the sample

size, I have used a small value (1 euro) for their R&D stock. Hence, a dummy variable is added in al the estimated

models, which equals 1 for those industries not engaging in R&D activities, and 0 otherwise.23rate of labour productivity and wage growth,20 and have a higher inflow and a lower

outflow rate.21 The fact that high-training industries are less capital intensive and engage

less in R&D22 can be easily explained by noting that the majority of these industries

operates in the service sector.

3. The model

Following a modelling strategy consolidated in the literature (see Dearden et al., 2000),Griliches and Ringstad (1971) list numerous justifications for the value-added specification of the production

function.

G. Conti / Labour Economics 12 (2005) 557576566training has a positive effect on workers productivity, effective labour can be written

as:

L NU cNT 3

where NU are untrained workers and NT are trained workers (and we expect gN1).Substitution of Eq. (3) into (2) yields:

Q A NU cNT aKb 4which, after some manipulations, can be rewritten as:

Q A 1 c 1 TRAIN aNaKb 5

where TRAIN =NT/N represents the proportion of trained workers in an industry. The

production function can be rewritten in logarithmic form as:24

lnQ lnA a c 1 TRAIN alnN blnK 6

Finally, under the assumption of constant returns to scale, Eq. (6) can be respecified in

per-capita terms as:

lnQ

N

lnA 1 b c 1 TRAIN bln K

N

7

where the dependent variable, labour productivity, is measured as the natural logarithm of

real value added per employee from the balance sheets, TRAIN is the proportion of trained

workers in an industry, and ln KN

is measured as the natural logarithm of the real value of

tangible fixed assets from the balance sheets (plant and machinery, land and buildings,

tools and equipment).

Following Dearden et al. (2000), I have firstly estimated the above production function,

in order to assess the effect of training on the average level of productivity for the

economy in the years 19961999; in a second step, I have estimated a wage equation

keeping the same explanatory variables, in order to compare the gains from training

accruing to firms and to workers. Both equations can be expressed in terms of the

following general specification:

yit a b1TRAIN b2Xit eit 8

where yit is the outcome of interest (labour productivity or wages), Xit the vector of

explanatory variables, and qit = fi+uit, i.e. the error term is composed of a time-invariantindustry-specific effect, and a time-varying white noise.

Eq. (8), however, may suffer from the major weakness that some of the regressors could

be correlated with the error term due to the presence of industry-specific time-invariant24 Here I use the approximation ln(1+x)=x, assuming (g1)TRAIN is small.

factors.25 To deal with this potential source of bias, a first-difference version of Eq. (8) has

been estimated:

yi;t yi;t1 b1 TRAINit TRAINi;t1 b2 Xit Xi;t1 eit ei;t1 9

This equation relates productivity growth to the change in the proportion of trained

workers. As argued in Barrett et al. (2001), the assumption underlying this model points to

the change in the stock of human capital, rather than the flow, as the main factor fostering

G. Conti / Labour Economics 12 (2005) 557576 567long-term economic performance.

In addition, in order to avoid omitted variable bias (and hence overestimate the true

returns to training), in the empirical estimation I have included several controls, taking into

account observed heterogeneity both in the workers dimension (by adding proxies for

human capital such as age and education), and in the firms dimension (by including per-

capita expenditure in R&D as a proxy for the rate of innovation); I have also controlled for

gender, working hours, and inflow and outflow rates. Time dummies have been included

to control for time-varying effects, such as the impact of technological progress or some

other unobserved factor linked to the business cycle. Finally, several estimation techniques

have been applied: firstly, the model has been estimated using standard linear techniques

and the within-group estimator. However, for short panels the consistency of the latter

estimator requires the regressors to be strictly exogenous, which is not a suitable

assumption in the present case, because transitory shocks on productivity could be

correlated with training26 (as well as the other inputs), resulting in an underestimation of

the true returns. Hence, I have drawn on more recent advances in the Generalized Method

of Moments techniques to deal with this limitations. The GMM handles not only

unobserved heterogeneity, but also potential endogeneity of training. In the original First-

Difference GMM estimator developed by Arellano and Bond (1991), and then extended by

Arellano and Bover (1995), the variables are first-differenced, in order to eliminate time-

invariant industry-specific effects, and the predetermined and endogenous variables in first

differences are instrumented with suitable lags of their own levels, in order to correct for

simultaneity. However, it is well known that the original Arellano-Bond estimator has poor

finite sample properties when the lagged levels of a series are weak instruments for the

first differences, especially for variables which are close to a random walk.27 Blundell and

Bond (1998 and 2000) described how to increase efficiency by taking into account

additional nonlinear moment conditions, which corresponds to adding T2 equations in

levels to the system,28 in which pre-determined and endogenous variables in levels are

instrumented with suitable lags of their own differences. The so-called extended System-

GMM estimator, as any valid instrumental variable strategy, handles not only endogeneity,

25 For example, technological change may occur at a faster rate in some industries, having an impact on both the

regressors and the dependent variable: in this case, cross-section estimates are inconsistent.26 For example, firms may choose to train the workforce in periods in which the demand is low.27 Griliches and Mairesse (1997) have noted that this a severe problem especially in the context of the

production functions. If the variables evolve in a random walk like fashion, the past levels have no power as

instruments for the current growth rates, unless one assumes the existence of lags in adjustments to shocks, in

which case, nonentheless, the power of the internal instruments is rather low.28 The additional equations come from the moment restriction: E (qitDqi,t1)=0, where i indexes the industry,and t =3, 4,. . .,T is the total number of periods in which the industry is present.

but it should also correct for bias arising from transitory measurement error both in the

dependent variable and the regressors. I have implemented the two-step version of the

heterogeneity both in an observed and in an unobserved dimension. However, endogeneity

G. Conti / Labour Economics 12 (2005) 557576568and serial correlation must be taken into account in the context of production functions:

shocks in productivity might be well correlated with training, since firms can adjust their

29 In Monte Carlo simulations it has often been found that the asymptotic standard errors of the efficient two-stepextended GMM-SYS estimator using the finite-sample correction for the two-step

covariance matrix developed by Windmeijer (2000).29

4. The results

Table 4 presents the results for the productivity regressions. In Model 1, training is

measured in levels, as the proportion of workers who have accumulated post-schooling

skills during their working life, using the TRAIN variable derived following the

methodology outlined above. Firstly I have estimated the OLS as a reference. Training

has a positive and significant effect on labour productivity in the basic specification which

includes only capital, R&D and hours worked as controls; however, this impact is clearly

overstated, since the coefficient becomes negative and significant after controlling for

inflow and outflow rates, workers observed characteristics (sex and age) and skills

(education). This could be well a signal of endogeneity. In fact, fixed effect estimates

recover a positive impact of training on labour productivity, which remains significant

with a high point estimate also after conditioning upon the full set of controls. Turning to

the other variables, the coefficient on the capital-labor ratio is highly significant, and its

magnitude confirms the existence of constant returns to scale, since the share of the wage

bill in value added is about 0.46. Productivity appears to fall in hours worked, but the

effect is well determined only in the baseline model. Lastly, R&D expenditure has a strong

and significant impact on productivity only in the simplest specification. However, the

negative sign of the coefficient seems to be mainly driven by endogeneity: when

reestimating all the equations using the lagged value, the coefficient reverts to a positive

sign, and the effect of training is reinforced (the coefficient is 0.449 with a level of

significance of 1%). The estimation results for the first-difference version of this model

(the last four columns in Table 4) confirm the robustness of the main finding: the change in

the stock of accumulated human capital has a positive effect on labour productivity

growth, with a level of significance stable at 5% across all different specifications. This

confirms the fact that training has also a long-lasting effect on industry productivity.

Turning to the other variables, the capital-labour ratio maintains the high level of

significance achieved in the equations in levels, while R&D and hours worked are poorly

estimated. On the other side, both measures of turnover exhibit positive and highly

significant coefficients: this seems to suggest that a higher speed of reallocation of workers

across sectors leads to a better match, which fosters productivity.

Until now the estimation techniques adopted have taken into account industriesGMM estimator are severely downward biased in small samples. Windmeijer (2000) has developed a variance

correction to increase the accuracy of the inference in two-step GMM estimations, and overcome this limitation.

Table 4

Production function estimates

Model 1 OLS(1) OLS(2) FE(1) FE(2) Model 2 OLS(1) OLS(2) FE(1) FE(2)

Train(%) 0.234 (0.130) 0.424 (0.178) 0.322 (0.167) 0.349 (0.172) DTrain(%) 0.314 (0.132) 0.343 (0.144) 0.383 (0.159) 0.376 (0.159)Ln(K/N) 0.333 (0.017) 0.330 (0.018) 0.431 (0.027) 0.431 (0.028) DLn(K/N) 0.314 (0.025) 0.317 (0.026) 0.399 (0.032) 0.392 (0.032)Ln(R&D/N) 0.029 (0.009) 0.015 (0.009) 0.016 (0.009) 0.017 (0.010) DLn(R&D/N) 0.006 (0.008) 0.005 (0.008) 0.003 (0.009) 0.002 (0.009)Ln(Hours/N) 0.919 (0.169) 0.365 (0.318) 0.236 (0.346) 0.315 (0.364) DLn(Hours/N) 0.124 (0.281) 0.102 (0.299) 0.451 (0.338) 0.354 (0.343)Inflow(%) 0.044 (0.073) 0.045 (0.059) 0.018 (0.047) 0.007 (0.048) DInflow(%) 0.079 (0.034) 0.087 (0.035) 0.133 (0.038) 0.127 (0.038)Outflow(%) 0.078 (0.062) 0.066 (0.049) 0.027 (0.039) 0.024 (0.039) DOutflow(%) 0.040 (0.031) 0.045 (0.031) 0.071 (0.032) 0.065 (0.035)Male 0.381 (0.158) 0.152 (0.251) DMale 0.441 (0.202) 0.558 (0.224)Age 2534 0.171 (0.408) 0.393 (0.389) DAge 2534 0.005 (0.318) 0.028 (0.363)Age 3544 0.404 (0.396) 0.404 (0.381) DAge 3544 0.343 (0.303) 0.337 (0.329)Age 4554 0.470 (0.048) 0.127 (0.396) DAge 4554 0.202 (0.318) 0.374 (0.351)Age 5564 0.193 (0.429) 0.395 (0.458) DAge 5564 0.176 (0.363) 0.270 (0.413)Degree/post 0.391 (0.327) 0.022 (0.433) DDegree/post 0.527 (0.344) 1.013 (0.393)Uppersec 0.238 (0.217) 0.294 (0.238) DUppersec 0.466 (0.195) 0.588 (0.217)Vocational 0.168 (0.435) 0.060 (0.408) DVocational 0.515 (0.329) 0.495 (0.372)R2 0.428 0.669 0.407 0.413 R2 0.283 0.292 0.411 0.451

NT 633 633 633 633 NT 456 456 456 456

Dependent variable: log(value added per worker) in Model 1, change in log(value added per worker) in Model 2.

All models include year dummies, R&D dummies, inflow and outflow dummies. Models OLS(2) also include region and sector dummies.

G.Conti/LabourEconomics

12(2005)557576

569

inputs to changes in demand, as outlined above. Henceforth, the GMM-estimator has been

G. Conti / Labour Economics 12 (2005) 557576570implemented to overcome the limitations of the techniques previously adopted. The results

are presented in Table 5. Each regression includes all the variables used in the previous

estimations, but only the results on the variables more closely related to the production

process are reported.30 In the first column, the First-Difference GMM is firstly estimated

as a benchmark. The coefficient on training is positive, with a magnitude that resembles

the one obtained in the previous specification, but fails to achieve significance at a

conventional level. Furthermore, the coefficient on capital is unreasonably low: this is in

line with what shown by Blundell and Bond (1998): the lagged levels of a series provide

weak instruments for the first differences, and produce implausibly low estimates

because measurement error in the explanatory variables bias the coefficients towards

zero. Consequently, I have implemented the full two-step GMM system estimator, using

the finite-sample correction for the two-step covariance matrix proposed by Windmeijer

(2000). Training recovers significance at a conventional level, and the coefficient on

capital is doubled. In the next four columns, I have respectively added a lag for the

dependent variable, the training variable, and for capital, hours worked, R&D and the

turnover rates. The coefficient on training varies somehow across the different

specifications, but it is always strongly significant and with a higher point estimate

than the one estimated when treating it as exogenous. The other variables included also

exhibit highly significant values, and they have the expected sign; in particular, it should

be noted that, while the current levels of R&D fail to achieve significance, its lagged

levels are positive and significant at 10%. All the specifications easily pass all the

diagnostics tests.

In order to check the robustness of the results obtained in the dynamic specification, I

included employment (and eventually its lag) to test for non-constant returns: only in one

case the coefficient achieved a level of significance of 10%, but in that case the diagnostics

exhibited clear evidence of misspecification; furthermore, when included in the full

dynamic model (last column), employment and its lag were jointly insignificant with a p-

value of 0.24.

Now Ill turn to discuss the wage equation results. In order to ease comparability, I have

used exactly the same models as in the productivity regression. There is no significant

effect of training on wages in the static model of Table 6; in particular, when controlling

for skills using linear estimation techniques, the coefficient becomes negative and

significant, suggesting some form of endogenous effect similar to the one known as

dAshenfelters dipT. All the other variables are conventionally signed. Turning to theresults for the model in first differences, a positive and significant impact of the increase in

the stock of trained workforce on wage growth is uncovered, which seems suggestive of

the existence of a long-run effect of accumulated skills on the earnings of the individuals.

Finally, Table 7 presents the estimation results for the GMM estimation of the effect of

training on wages. When taking endogeneity into account, the estimated coefficients fail to

achieve significance at a conventional level in all the specifications adopted. Turning to

the effect of the other variables, industries with a high capital-labour ratio and engaging30 Full results are available from the author upon request.

Table 5

Production function estimates

Model 1 GMM-FD GMM-SYS(1) GMM-SYS(2) GMM-SYS(3) GMM-SYS(4) GMM-SYS(5)

Ln(vadd/N)t1 0.516 (0.088) 0.505 (0.084) 0.527 (0.082) 0.502 (0.079)Train(%) 0.313 (0.210) 0.334 (0.203) 0.317 (0.168) 0.383 (0.147) 0.451 (0.177) 0.408 (0.183)

Train(%)t1 0.412 (0.279) 0.431 (0.293) 0.349 (0.282)Ln(K/N) 0.153 (0.068) 0.317 (0.056) 0.246 (0.050) 0.254 (0.044) 0.348 (0.057) 0.336 (0.055)

Ln(K/N)t1 0.134 (0.057) 0.131 (0.055)Ln(R&D/N) 0.006 (0.029) 0.006 (0.023) 0.013 (0.018) 0.013 (0.016) 0.026 (0.016) 0.021 (0.017)Ln(R&D/N)t1 0.037 (0.015) 0.035 (0.014)Ln(Hours/N) 0.359 (0.583) 0.942 (0.558) 0.432 (0.398) 0.526 (0.420) 0.199 (0.424) 0.293 (0.398)Ln(Hours/N)t1 0.710 (0.455) 0.732 (0.464)Inflow(%) 0.062 (0.055) 0.006 (0.087) 0.166 (0.134) 0.166 (0.145) 0.139 (0.137) 0.132 (0.135)Inflow(%)t1 0.018 (0.075) 0.039 (0.085)Outflow(%) 0.095 (0.061) 0.060 (0.065) 0.359 (0.133) 0.344 (0.121) 0.303 (0.117) 0.299 (0.113)Outflow(%)t1 0.059 (0.047) 0.054 (0.040)Hansen test 0.315 0.306 0.292 0.250 0.236 0.166

AR(1) test 0.001 0.001 0.003 0.003 0.004 0.005

AR(2) test 0.441 0.955

NT 456 456 456 456 456 456

Dependent variable: log(value added per worker).

All models include year dummies, R&D dummies, inflow and outflow dummies.

All the variables are treated as endogenous (except the dummies).

Model GMM-SYS(5) includes lags for all the variables.

p-values are reported for AR(1), AR(2) and Hansen tests. A full stop in the AR(2) test box indicates that no output has been reported: the residuals and the L(2) residuals

have no obs in common, so the AR(2) is trivially zero.

G.Conti/LabourEconomics

12(2005)557576

571

Table 6

Wage equation estimates

Model 1 OLS(1) OLS(2) FE(1) FE(2) Model 2 OLS(1) OLS(2) FE(1) FE(2)

Train(%) 0.059 (0.112) 0.459 (0.167) 0.203 (0.172) 0.215 (0.176) DTrain(%) 0.253 (0.123) 0.305 (0.134) 0.319 (0.157) 0.287 (0.156)Ln(K/N) 0.171 (0.014) 0.177 (0.017) 0.342 (0.028) 0.339 (0.029) DLn(K/N) 0.184 (0.023) 0.187 (0.024) 0.234 (0.032) 0.225 (0.031)Ln(R&D/N) 0.017 (0.008) 0.001 (0.008) 0.003 (0.010) 0.003 (0.010) DLn(R&D/N) 0.005 (0.008) 0.004 (0.008) 0.003 (0.009) 0.004 (0.009)Ln(Hours/N) 0.959 (0.145) 0.537 (0.299) 0.245 (0.355) 0.273 (0.373) DLn(Hours/N) 0.164 (0.262) 0.136 (0.280) 0.435 (0.334) 0.343 (0.336)Inflow(%) 0.021 (0.062) 0.080 (0.056) 0.085 (0.048) 0.073 (0.135) DInflow(%) 0.030 (0.032) 0.025 (0.033) 0.004 (0.037) 0.001 (0.037)Outflow(%) 0.070 (0.053) 0.033 (0.047) 0.017 (0.040) 0.010 (0.041) DOutflow(%) 0.044 (0.029) 0.049 (0.029) 0.066 (0.034) 0.071 (0.034)Male 0.499 (0.148) 0.030 (0.258) DMale 0.341 (0.189) 0.480 (0.219)Age 2534 0.061 (0.383) 0.429 (0.399) DAge 2534 0.018 (0.297) 0.169 (0.356)Age 3544 0.676 (0.372) 0.549 (0.391) DAge 3544 0.386 (0.283) 0.363 (0.323)Age 4554 0.570 (0.383) 0.615 (0.406) DAge 4554 0.497 (0.297) 0.567 (0.344)Age 5564 0.261 (0.403) 0.351 (0.469) DAge 5564 0.126 (0.339) 0.006 (0.405)Degree/post 0.329 (0.307) 0.001 (0.444) DDegree/post 0.522 (0.322) 0.845 (0.386)Uppersec 0.194 (0.204) 0.207 (0.245) DUppersec 0.514 (0.182) 0.697 (0.213)Vocational 0.352 (0.408) 0.179 (0.418) DVocational 0.709 (0.308) 0.787 (0.365)R2 0.236 0.468 0.331 0.337 R2 0.168 0.175 0.246 0.306

NT 633 633 633 633 NT 456 456 456 456

Dependent variable: log(wage per worker) in Model 1, change in log(wage per worker) in Model 2.

All models include year dummies, R&D dummies, inflow and outflow dummies. Models OLS(2) also include region and sector dummies.

G.Conti/LabourEconomics

12(2005)557576

572

Table 7

Wage equation estimates

Model 1 GMM-FD GMM-SYS(1) GMM-SYS(2) GMM-SYS(3) GMM-SYS(4) GMM-SYS(5)

Ln(W/N)t1 0.331 (0.118) 0.341 (0.108) 0.299 (0.117) 0.314 (0.111)Train(%) 0.199 (0.197) 0.021 (0.167) 0.091 (0.128) 0.158 (0.124) 0.181 (0.154) 0.123 (0.160)

Train(%)t1 0.434 (0.206) 0.375 (0.228) 0.412 (0.260)Ln(K/N) 0.092 (0.061) 0.154 (0.043) 0.153 (0.045) 0.166 (0.041) 0.194 (0.052) 0.185 (0.060)

Ln(K/N)t1 0.053 (0.029) 0.046 (0.030)Ln(R&D/N) 0.031 (0.027) 0.002 (0.019) 0.005 (0.017) 0.009 (0.015) 0.019 (0.021) 0.017 (0.022)Ln(R&D/N)t1 0.031 (0.011) 0.023 (0.010)Ln(Hours/N) 0.144 (0.562) 1.022 (0.409) 0.738 (0.297) 0.825 (0.286) 0.588 (0.315) 0.607 (0.454)Ln(Hours/N)t1 0.664 (0.369) 0.685 (0.408)Inflow(%) 0.092 (0.053) 0.053 (0.101) 0.022 (0.134) 0.007 (0.116) 0.032 (0.134) 0.016 (0.123)Inflow(%)t1 0.003 (0.053) 0.008 (0.056)Outflow(%) 0.062 (0.063) 0.027 (0.067) 0.248 (0.114) 0.244 (0.113) 0.248 (0.100) 0.264 (0.112)Outflow(%)t1 0.012 (0.029) 0.016 (0.031)Hansen test 0.373 0.259 0.463 0.638 0.484 0.382

AR(1) test 0.002 0.001 0.004 0.004 0.006 0.007

AR(2) test 0.814 0.609 . . . .

NT 456 456 456 456 456 456

Dependent variable: log(wage per worker).

All models include year dummies, R&D dummies, inflow and outflow dummies.

All the variables are treated as endogenous (except the dummies).

Model GMM-SYS(5) includes lags for all the variables.

p-values are reported for AR(1), AR(2) and Hansen tests. A full stop in the AR(2) test box indicates that no output has been reported: the residuals and the L(2) residuals

have no obs in common, so the AR(2) is trivially zero.

G.Conti/LabourEconomics

12(2005)557576

573

more in R&D pay higher wages, while longer working hours are associated with a lower

pay. Finally, a higher degree of workers reallocation across industries is associated with

higher wages, which seems to be suggestive of the fact that turnover leads to a better

matching.

To summarize the results, training seems to have a positive and strongly significant

effect on productivity. This effect disappears only when controlling for observed

heterogeneity with the standard linear techniques, but the sign and the size of the

greater than the effect on wages (for example, in the full-dynamic specification, the

G. Conti / Labour Economics 12 (2005) 557576574coefficient is 0.408 in the productivity regression and 0.123 in the wage equation): this is

consistent with a human capital model in which some of the costs of training are borne

by the employees. Using the results obtained in the full-dynamic model, this implies that

raising the stock of trained workers in an industry by one percentage point leads to a

0.4% increase in productivity and to a 0.1% increase in wages. This effect is much

smaller if compared to the results obtained in other papers in this literature.32

Nonetheless, it is quite substantial on its own. There could be several reasons behind

this: on the one hand, estimation at aggregate level captures the within-industry spillovers

arising from training externalities, which are missed in case of a firm-level analysis.33 On

the other, it fails to account for non-random selection of workers in the training pool. On

a more general ground, it could reflect the existence of other unobserved industry-specific

factors that have not been controlled for (such as the existence of other human resource

management practices, as argued in Ichniowski et al., 1997). Above all, also allowing for

these caveats, the key qualitative result still holds: firms do seem to actually reap more of

the returns.

5. Concluding remarks

This paper has examined for the first time the productivity and wage effects of training

in Italy. It is based on an original dataset, which has been created aggregating individual-

level data on training from the Labour Force Survey with firm-level data on productivity

31 This finding also emerges in a recent paper by Arulampalam et al. (2004). They show that training has no

significant effect on wages at all the quantiles of the conditional wage distribution.32 For example, Dearden et al. (2000) found that increasing the proportion of workers being trained in an

industry by 5% leads to a 4% increase in productivity and to a 1.5% increase in wages: this amounts to,

respectively, a 2% increase in productivity and to a 0.6% increase in wages in the Italian case.33 Under this respect, the availability of a linked employer-employee database with training information, stillcoefficient clearly reflects endogenous effects. Most importantly, it persists when

unobserved heterogeneity is taken into account, and it is robust to the first-difference

specification and to the GMM estimation. On the other side, the effect of training on

wages is much less robust.31 It achieves significance at a conventional level only in the

first-difference specification, but it does not pass the GMM estimation. Moreover,

whatever specification is considered, the estimated impact on productivity is alwayslacking for Italy, will provide useful results in terms of comparability between private and social returns to post-

schooling human capital.

I am deeply indebted to my supervisor, Amanda Gosling, for her generous and

Acemoglu, D., Pischke, J.-S., 1998. Why do firms train? Theory and evidence. Quarterly Journal of Economics113 (1), 79119 (also available as NBER Working Paper, n.5605).

Acemoglu, D., Pischke, J.-S., 1999a. Beyond Becker: Training in imperfect labour markets. Economic Journal

109 (453), F112F142.

Acemoglu, D., Pischke, J.-S., 1999b. The structure of wages and investment in general training. Journal of

Political Economy 107 (3), 539572 (also available as NBER Working Paper, n.6357).

Aghion, P., Howitt, P., 1998. Endogenous Growth Theory. MIT Press, Cambridge, Mass.

Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application

to employment equations. Review of Economic Studies 58 (2), 277297.

Arellano, M., Bover, O., 1995. Another look at the instrumental-variable estimation of error-components model.

Journal of Econometrics 68 (1), 2952.

Arulampalam, W., Booth, A.L. Bryan, M.L., 2004. Training in Europe. ISER working paper 0401.

Ballot, G., Fakhfakh, F., Taymaz, E., 2001. Firms human capital, R&D and performance: a study on French and

Swedish Firms. Labour Economics 8, 443462.

Barrett, A., OConnell, P., 2001. Does training generally work? The returns to in-company training. Industrial and

Labour Relations Review 54 (3), 647662 (also available as IZA Discussion Paper, n.51).invaluable support and guidance throughout the realisation of this research. I am also

grateful to two anonymous referees and to seminar participants at the 16th annual

conference of the European Association of Labour Economists 2004, for helpful

comments and suggestions which greatly improved the paper. Many people have helped

me in accessing the data. I owe special thanks to Sergio Destefanis, Tullio Jappelli and

Mario Padula for supplying the AIDA data, and to Marco Musella and Francesco Pastore

for supplying the Labour Force Survey data. Financial support from the Economic and

Social Research Council, award no. PTA030200300812, is gratefully acknowledged.

The usual disclaimer applies.

Referencesand wages from AIDA into an industry-level panel, covering the years from 1996 to 1999.

Given the availability of longitudinal information I have been able to control for

unobserved heterogeneity and potential endogeneity of training. The richness of the

database has also allowed me to construct a stock measure of training, and to control for

several workers and firms characteristics. I have then allowed for flexibility in the

modelling specification, and estimated all the models in the dual form of a production

function and a wage equation, in order to assess how the benefits from training are shared

between the firms and the workers.

The main finding is that training has a positive and significant effect on productivity.

This finding is robust to several estimation strategies, including System-GMM. However,

the effect uncovered for wages is much less robust, and smaller in size. This proves clear

evidence of the fact that firms do actually reap more of the returns.

Acknowledgements

G. Conti / Labour Economics 12 (2005) 557576 575Bartel, A.P., 1994. Productivity gains from the implementation of employee training programs. Industrial

Relations 33 (4), 411425 (also available as NBER Working Paper, n.3893).

Bishop, J.H., 1994. The impact of previous training on productivityand wages. In: Lynch, L.M. (Ed.), Training

and the Private Sector, International Comparisons, NBER Series in Comparative Labour Markets. University

of Chicago Press, Chicago, pp. 161199.

Black, S.E., Lynch, L.M., 1995. Beyond the incidence of training: evidence from a national employers survey,

NBER Working Paper, n.5321.

Black, S.E., Lynch, L.M., 1996. Human capital investments and productivity. American Economic Review

(Papers and Proceedings) 86 (2), 263267.

G. Conti / Labour Economics 12 (2005) 557576576Black, S.E., Lynch, L.M., 2001. How to compete: the impact of workplace practices and information technology

on productivity. The Review of Economics and Statistics 83, 434445.

Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of

Econometrics 87 (1), 115145.

Blundell, R., Bond, S., 2000. GMM estimation with persistent panel data: an application to production function.

Econometric Reviews 19 (3), 321340.

Boon, M., van der Eijken, B., 1998. Employee training and productivity in Dutch manufacturing firms. Statistics

Netherlands 13.

Brunello, G., 2002. Is training more frequent when wage compression is higher? Evidence form 11 European

countries, CESifo Working Paper, n.637 (4).

Dearden, L., Reed, H., Van Reenen, J., 2000. Who gains when workers train? Training and corporate productivity

in a panel of british industries, IFS Working Paper, n.00/01.

Griliches, Z., Mairesse, J., 1997. Production functions: the search for identification. In: Strom, S. (Ed.), Essays in

Honour of Rayner Frisch, Econometric Society Monograph Series. Cambridge University Press.

Griliches, Z., Ringstad, V., 1971. Economies of scale and the form of the production function. North Holland,

Amsterdam.

Groot, W., 1998. Empirical estimates of the rate of depreciation of education. Applied Economics Letters 5 (8),

535538.

Grunfeld, D., Griliches, Z., 1960. Is aggregation necessarily bad? The Review of Economics and Statistics XLII

(1), 113.

Ichniowski, C., Shaw, K., Prennushi, P., 1997. The effects of human resource management practices on

productivity: a study of steel finishinglines. American Economic Review 87 (3), 291313.

Lynch, L.M., 1994. Introduction. In: Lynch, L.M. (Ed.), Training and the Private Sector, International

Comparisons, NBER Series in Comparative Labour Markets. University of Chicago Press, Chicago, pp. 1

24.

OECD, 2002. Economic Outlook, Paris.

Stevens, M., 1994a. Labour contracts and efficiency in on-the-job training. Economic Journal 10 (423), 408419.

Stevens, M., 1994b. A theoretical model of on-the-job training with imperfect competition. Oxford Economic

Papers 46 (4), 537562.

Stevens, M., 1994c. An investment model for the supply of training by employers. Economic Journal 104 (424),

556570.

Stevens, M., 1996. Transferable training and poaching externality. In: Booth, A.L., Snower, D.J. (Eds.), Acquiring

Skills, Market Failures, Their Symptoms and Policy Responses. Cambridge University Press, Cambridge.

Windmeijer, F., 2000. A finite sample correction for the variance of linear two-step GMM estimators, IFS

Working Paper, n.00/01.

Zwick, T., 2002. Continuous training and firm productivity in Germany, ZEW Discussion Paper, n.0250.

Training, productivity and wages in ItalyIntroductionThe dataThe modelThe resultsConcluding remarksAcknowledgementsReferences